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Climate Change and Variability edited by Suzanne W. Simard and Mary E. Austin

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Climate Change and Variability

Edited by Suzanne W. Simard and Mary E. Austin Published by Sciyo Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2010 Sciyo All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by Sciyo, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Jelena Marusic Technical Editor Sonja Mujacic Cover Designer Martina Sirotic Image Copyright kkaplin, 2010. Used under license from Shutterstock.com First published September 2010 Printed in India A free online edition of this book is available at www.sciyo.com Additional hard copies can be obtained from [emailprotected]

Climate Change and Variability, Edited by Suzanne W. Simard and Mary E. Austin p. cm. ISBN 978-953-307-144-2

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Contents Preface IX Chapter 1 A regional approach to the Medieval Warm Period and the Little Ice Age 1 Fredrik Charpentier Ljungqvist Chapter 2 Multi-months cycles observed in climatic data 27 Samuel Nicolay, Georges Mabille, Xavier Fettweis and M. Erpicum Chapter 3 Summer-Time Rainfall Variability in the Tropical Atlantic 45 Guojun Gu Chapter 4 Tropical cyclones, oceanic circulation and climate 65 Lingling Liu Chapter 5 Possible impacts of global warming on typhoon activity in the vicinity of Taiwan 79 Chia Chou, Jien-Yi Tu and Pao-Shin Chu Chapter 6 Influence of climate variability on reactive nitrogen deposition in temperate and Arctic climate 97 Lars R. Hole Chapter 7 Climate change: impacts on fisheries and aquaculture 119 Bimal P Mohanty, Sasmita Mohanty, Jyanendra K Sahoo and Anil P Sharma Chapter 8 Community ecological effects of climate change 139 Csaba Sipkay, Ágota Drégelyi-Kiss, Levente Horváth, Ágnes Garamvölgyi, Keve Tihamér Kiss and Levente Hufnagel Chapter 9 Pelagic ecosystem response to climate variability in the Pacific Ocean off Baja California 163 Gilberto Gaxiola-Castro, Bertha E. Lavaniegos, Antonio Martínez, Rubén Castro, T. Leticia Espinosa-Carreón Chapter 10 Climate change and resilience value of mussel farming for the baltic sea 183 Ing-Marie Gren

Chapter 11 Temperate forests and climate change in mexico: from modelling to adaptation strategies 195 Gómez-Mendoza, Leticia and Galicia, Leopoldo Chapter 12 The influence of climate change on tree species distribution in west part of south-east europe 211 J. Vukelić, S. Vojniković, D. Ugarković, D. Bakšić and S. Mikac Chapter 13 Climate change impact on vegetation: lessons from an exceptionally hot and dry decade in south-eastern France 225 Vennetier Michel and Ripert Christian Chapter 14 Climate change, forest fires and air quality in Portugal in the 21st century 243 Anabela Carvalho Chapter 15 The role of mycorrhizas in forest soil stability with climate change 275 Simard, Suzanne W. and Austin, Mary E. Chapter 16 Impact of temperature increase and precipitation alteration at climate change on forest productivity and soil carbon in boreal forest ecosystems in Canada and Russia: simulation approach with the EFIMOD model 303 Oleg Chertov, Jagtar S. Bhatti and Alexander Komarov Chapter 17 Simulating peatland methane dynamics coupled to a mechanistic model of biogeochemistry, hydrology, and energy: Implications to climate change 327 Takeshi Ise, Allison L. Dunn, Steven C. Wofsy and Paul R. Moorcroft Chapter 18 Towards a New Agriculture for the Climate Change Era in West Asia, Iran 337 Farzin Shahbazi and Diego de la Rosa Chapter 19 Simulated potato crop yield as an indicator of climate variability and changes in Estonia 365 Triin Saue and Jüri Kadaja Chapter 20 Determining the relationship between climate variations and wine quality: the WEBSOM approach 389 Subana Shanmuganathan and Philip Sallis Chapter 21 Ecological modernisation and the politics of (un)sustainability in the Finnish climate policy debate 409 Tuula Teräväinen

VII

Chapter 22 Transportation and climate change 427 Yuri Yevdokimov Chapter 23 Cost-optimal technology and fuel choices In the transport sector under a stringent climate stabilization target 439 Takayuki Takesh*ta Chapter 24 Impact of climate change on health and disease in Latin America 463 Alfonso J. Rodríguez-Morales, Alejandro Risquez and Luis Echezuria

Preface Global change, including climate change, ecosystem shifts and biodiversity loss as a result of explosive human population growth and consumption, is emerging as one of the most important issues of our time (Vitousek, 1994). Climate change in particular appears to be altering the function, structure and stability of the Earth’s ecosystems (Lovelock, 2009). It has been marked by an 80% increase in atmospheric CO2 level and a 0.74 °C increase in average global near-surface temperature over the period 1906–2005, with average temperature projected to increase by an additional 1 to 6oC by 2100 (Intergovernmental Panel on Climate Change [IPCC], 2007). The spring months in 2010, in particular, have been the warmest on record (National Atmospheric and Oceanic Administration, 2010). Warming is expected to continue for centuries, even if greenhouse gas emissions are stabilized, owing to time lags associated with climate processes and feedbacks. There is also evidence that precipitation patterns have changed along with temperature, with average annual increases up to 20% in high-latitude regions but decreases up to 20% in mid- and low-latitudinal regions. The changes in temperature and precipitation have resulted in higher sea levels, reduced extent of snow and ice, earlier timing of species spring events, upward and pole-ward shifts in species ranges, increased and earlier spring run-offs, and increased forest disturbances by fires, insects and diseases (Parmesan, 2006). They have already contributed to increased human morbidity and mortality in some regions of the world (Patz et al., 2005; McMicheal et al., 2006). Global change is clearly having profound cascading effects on ecosystems and society, but they are poorly understood (Vitousek, 1994; Parmesan, 2006; Fischhoff, 2007). As a result, scientists have mobilized world-wide to more effectively research the impacts of climate change as well as the options for mitigating and adapting to these impacts (IPCC, 2007). New ideas, methods and theories have emerged for investigating and understanding the effects of global change, and for managing our socio-ecological systems sustainably as complex adaptive systems (Root et al., 1995; Folke et al., 2004; Levin, 2005; Puettmann et al., 2009). In spite of these efforts, large gaps in understanding and prediction remain. These gaps have hindered development of strategies for mitigation of global change effects and for improvement of adaptive capacity of ecosystems and society (Lemmen et al, 2008). Moreover, there has been little meaningful action from governments or the public aimed at slowing the pace of climate change (Friedman, 2010), which is apparent in our inability to meet Kyoto Protocol goals, despite the compelling evidence summarized by the IPCC. In this collection of 24 chapters, we present a cross-section of some of the most challenging issues related to oceans, lakes, forests, and agricultural systems under a changing climate. The authors passionately present evidence for changes and variability in climatic and atmospheric conditions, investigate some the impacts that climate change is having on Earth’s ecological and social systems, and provide novel ideas, advances and applications for mitigation and adaptation of our socio-economic systems to climate change. Difficult

questions are asked. What are the impacts of climate change on some of our most productive and precious ecosystems? How do we manage for resilient socio-ecological systems? How do we predict the future? What are relevant climatic and management scenarios? How can we shape management regimes to increase our adaptive capacity? These themes are visited across broad spatial and temporal scales, touch on important and relevant ecological sociopolitical patterns and processes, and represent broad geographic regions, from the tropics, to temperate and boreal regions, to the Arctic. It is the wish of the authors that this information will be used to improve predictions and inform policy for reducing threats and facilitating adaptation and management in the face of our changing climate. The book is divided into six sections. The first section examines evidence for changes and variability in the Earth’s climate and the atmosphere. The first chapter, “A regional approach to the medieval warm period and the little ice age”, presents an interesting analysis of temperature variability in the late Holocene. The second chapter, “Multi-months cycles observed in climatic data”, uses the wavelet-based methodology to detect and describe cycles in air-surface temperatures, and the authors discuss some mechanisms that may underlie these cycles. Chapter 3, “Summer-time rainfall variability in the Tropical Atlantic”, explores summer-time rainfall variations within the tropical Atlantic basin, with a focus on local effects of sea surface temperature and the El Niño-Southern Oscillation. The fourth chapter, “Tropical cyclones, oceanic circulation and climate”, examines the role of tropical cyclones in regulating the general oceanic circulation and climate, as well as the effects of the ocean on tropical cyclones. Chapter 5, entitled “Some of the possible impacts of global warming on typhoon activity in the vicinity of Taiwan”, presents data indicating an abrupt shift in the typhoon track near Taiwan, discusses its association with global warming, and presents potentially large-scale environmental changes in response to the shift. The final chapter in this section (Chapter 6) examines the influence of climate variability on reactive nitrogen deposition in Norway, and presents evidence that future reductions in nitrogen deposition due to emission reductions in Europe could be partly offset due to increasing precipitation in some regions. The second section of the book, which includes four chapters, examines some impacts of climate change on aquatic systems and aquaculture. Chapter 7, “Climate change: impacts on fisheries and aquaculture”, provides a general review of the impacts of climate change on fisheries and aquaculture. It reaches further to suggest possible mitigation options, and provides guidance on developing suitable monitoring tools. This is followed by Chapter 8, “Community ecological effects of climate change”, which is a case study of the effects of warming on phytoplankton activity in the Danube River of Hungary. The authors contrast strategic and tactical models of productivity with other approaches, such as the “geographical analogy” method. Chapter 9, “Pelagic ecosystem response to climate variability in the Pacific Ocean off Baja California”, is a second case study examining the associations between largescale temporal climate physical forcing and the plankton variability off of Baja California. The final chapter in this section, Chapter 10, “Climate change and resilience value of mussel farming for the Baltic Sea”, estimates the impacts of global change (specifically nutrient loading) on the resilience values of mussel farming in the Baltic Sea. The authors conclude that mussel farming shows promise for buffering against variable nutrient loads in waters that are becoming increasingly eutrophic with climate change.

The third section of the book examines climate change effects on forests and plant communities. One of the studies was conducted in Mexico and the remaining three chapters report on studies in Europe. In Mexico, the authors of Chapter 11 conducted a modeling study to predict the effects of climate change on temperate forests. They go on to suggest adaptation strategies for dealing with climate change in these forest types. Chapter 12, entitled “The influence of climate change on tree species distribution in west part of south-east Europe”, used ecological niche modelling to project climate change scenarios on the distribution of the dominant tree species in south-eastern Europe. Chapter 13 describes a study in France, entitled “Climate change vs vegetation track race: what did we learn from a ten years long anticipated occurrence of 2040 climate in South-eastern France”. The authors measured 10-year plant composition changes in a permanent plot network, modeled the potential floristic turnover, and discussed the relationships between observed changes and local site conditions. They found a 14% turnover in species, mainly biased against water-demanding species, and caution that current reserve networks in France may be inadequate to ensure long-term species persistence. The last chapter in this section, Chapter 14, “Climate change, forest fires and air quality in Portugal in the 21st century”, focuses on climate change effects on fire disturbance. It evaluates the impacts of the IPCC SRES A2 climatic scenario on forest fire activity and air quality over Portugal. The analysis predicts an increase the fire weather index components and fire severity in all Portuguese districts. The fourth section examines the some of the effects of climate change on carbon cycling and soil systems, and includes three chapters. Chapter 15, entitled “The role of mycorrhizas in forest soil stability with climate change”, reviews the role of mycorrhizas and mycorrhizal networks in the stability of forest ecosystems and forest soils as climate changes. It begins with a review of the role mycorrhizal fungi play in soil carbon flux dynamics, examines the effects of climate change factors on plants and mycorrhizal fungi, and ends with a review of the role of mycorrhizas and mycorrhizal networks in helping mitigate the effects of climate change in on forest ecosystems. Chapter 16 uses a modeling approach to examine potential impacts of climate (temperature and precipitation) changes on forest productivity and soil carbon in boreal forest ecosystems in Canada and Russia. The authors found that all climate change scenarios predicted similar increasing trends of net primary production (NPP) and stand productivity; that disturbances led to a strong decrease in NPP, stand productivity, soil organic matter and nitrogen pools; but that they also led to an increase in CO2 emission to the atmosphere. Chapter 17, “Simulating peatland methane dynamics coupled to a mechanistic model of biogeochemistry, hydrology, and energy: Implications to climate change”, also employed simulation modeling. The authors predicted that a 4°C in warming would result in a short term increase in CH4 emission due to a transient increase in microbial activity, followed by a significant long-term decline in CH4 emission caused by a loss in substrate and an increasing prevalence of aerobic conditions. The fifth set of chapters discusses some of the implications of climate change for agricultural systems. There are three chapters in this section. Chapter 18 is entitled “Towards a New Agriculture for the Climate Change Era in West Asia, Iran”. It presents a land evaluation decision support system that can be used for sustainable agriculture planning and management in west Asia under climate change conditions. Chapter 19, entitled “Simulated potato crop yield as an indicator of climate variability and changes in Estonia”, applies the meteorologically possible yield (MPY) – the maximum yields under given meteorological

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conditions (in this case, for potatoes) - to derive qualitatively new information about climate variability. The probable range of temperature and precipitation in years 2050 and 2100 is applied to construct possible distribution of MPY in those years, and in the future. The authors predict that variability of potato yields will decrease slightly due to climate change, but strongly caution that further study, including estimates of meteorological variability, is sorely needed. In Chapter 20, “Determining the relationship between climate variations and wine quality: the WEBSOM approach”, the authors first review the literature on potential influence of climate and environmental variation on viticulture. They then use a modeling approach to predict wine quality from grapevine phenology in New Zealand. They conclude with concrete suggestions for future modeling research to predict climate change effects on the world’s major wine regions in the southern hemisphere. We close our book with four studies of some of the socio-economic implications of climate change. Chapter 21 is entitled “Ecological modernisation and the politics of (un)sustainability in the Finnish climate policy debate”. This chapter examines the political side of technology of the climate change policy debate in Finland. It provides interesting perspectives at the national and international levels. In Chapter 22, “Transportation and climate change”, the authors analyse the contribution of transportation to climate change as well as the impacts of climate change on transportation in Atlantic Canada. Chapter 23 is entitled “Cost-optimal technology and fuel choices in the transport sector under a stringent climate stabilization target”. This chapter examines the cost-optimal choice of propulsion systems and fuels for different transport modes over the 21st century under a global mean temperature rise of 2-2.4oC. It also presents the results of a sensitivity analysis with respect to: (1) climate stabilization; (2) technology advances, and (3) demand for supersonic air travel. Our final chapter, Chapter 24, is on the “Impact of climate change on health and disease in Latin America”. It explores the implications of climate change on specific communicable and non-communicable diseases in Latin America. In conclusion, this book presents a broad cross-section of reviews, surveys, experiments, model predictions and discussions focused on climate change and climate variability, the realized and potential impacts of climate change on the Earth’s ecological and social systems, and potential mitigation and adaptation strategies for coping with climate change. It is our hope that these studies will provide the basis for future research, policy changes and action for improving the future ecological and human conditions of our home, the planet Earth. Editor: Suzanne W. Simard and Mary E. Austin The University of British Columbia, Vancouver Canada References: Fischhoff, B. 2007. Nonpersuasive communication about matters of greatest urgency: climate change. Environmental Science & Technology 41: 7205-7208. Folke, F.C., Carpenter, B., Walker, M., Scheffer, M., Elmqvist, T., Gunderson, L., Holling, C.S. 2004. Regime shifts, resilience, and biodiversity in ecosystem management. Annual Review of Ecology and Evolution 35: 557-581. Friedman, T. 2010. We’re gonna be sorry. The New York Times. http://www.nytimes. com/2010/07/25/opinion/25friedman.html?_r=1&ref=thomaslfriedman

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IPCC, Climate Change 2007: The Physical Science Basis. Fourth Assessment Report. Eds., S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller. Cambridge University Press, New York. Lemmen, D.S., Warren, F.J., Lacroix, J., Bush, E. (eds). 2008. From Impacts to Adaptation: Canada in a Changing Climate 2007; Government of Canada, Ottawa, ON, 448p. Levin, S.A. 2005. Self-organization and the emergence of complexity in ecological systems. Bioscience 55: 1075-1079. Lovelock, J. 2009. The vanishing face of Gaia. Basic Books, New York. McMichael, A.J., Woodruff, R.E., Hales, S. 2006. Climate change and human health: present and future risks. The Lancet 367: 859-869. National Oceanic and Atmospheric Administration. 2010. State of the Climate, June 2010 Global Analysis Report. National Climatic Data Centre, US Department of Commerce. http://www.ncdc.noaa.gov/sotc/index.php Parmesan, C. 2006. Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution and Systematics 37: 637–69. Patz, P.A., Campbell-Lendrum, D., Holloway, T., Foley, J.A. 2005. Impact of regional climate change on human health. Nature 438: 310-317. Puettmann, K.J., Coates, K.D., Messier, C. 2009. A Critique of Silviculture: Managing for Complexity. Island Press, Washington. Root, T.L., Schneider, S.H. 1995. Ecology and climate: research strategies and implications. Science 269: 334-341. Vitousek, P.M. 1994. Beyond global warming: ecology and global change. Ecology 75: 18611876.

A regional approach to the Medieval Warm Period and the Little Ice Age

11 A regional approach to the medieval warm period and the little ice age Fredrik Charpentier Ljungqvist

Stockholm University Sweden

1. Introduction In order to gain knowledge of the temperature variability prior to the establishment of a widespread network of instrumental measurements c. AD 1850, we have to draw information from proxy data sensitive to temperature variations. Such data can be extracted from various natural recorders of climate variability, such as corals, fossil pollen, ice-cores, lake and marine sediments, speleothems, and tree-ring width and density, as well as from historical records (for a review, see IPCC 2007; Jones et al. 2009; NRC 2006). Considerable effort has been made during the last decade to reconstruct global or northern hemispheric temperatures for the past 1000 to 2000 years in order to place the observed 20th century warming in a long-term perspective (e.g., Briffa, 2000; Cook et al., 2004; Crowley and Lowery, 2000; D’Arrigo, 2006; Esper et al., 2002; Hegerl et al., 2007; Jones et al., 1998; Jones and Mann, 2004; Juckes et al., 2007; Ljungqvist, 2010; Loehle, 2007; Mann et al., 1999; Mann et al., 2008; Mann et al., 2009; Mann and Jones, 2003; Moberg et al., 2005; Osborn and Briffa, 2006). Less effort has been put into investigating the key question of to what extent earlier warm periods have been as hom*ogeneous in timing and amplitude in different geographical regions as the present warming. It has been suggested that late-Holocene long-term temperature variations, such as the Medieval Warm Period (MWP) and the Little Ice Age (LIA), have been restricted to the circum-North Atlantic region (including Europe) and have not occurred synchronic in time with warm and cold periods respectively in other regions (Hughes and Diaz, 1994; Mann et al., 1999; Mann and Jones, 2003). This view has, however, been increasingly challenged through the ever growing amount of evidence of a global (or at least northern hemispheric) extent of the MWP and the LIA that have become available (see, for example, Esper and Frank, 2009; Ljungqvist, 2009, 2010; Moberg et al., 2005; Wanner et al., 2008). A main obstacle in large-scale temperature reconstructions continues to be the limited and unevenly distributed number of quantitative palaeotemperature records extending back a millennium or more. The limited number of records have rendered it impossible to be very selective in the choice of data. Palaeotemperature records used in a large-scale temperature reconstruction should preferably be accurately dated, have a high sample resolution and have a high correlation with the local instrumental temperature record in the calibration period (see the discussion in Jones et al., 2009). The number of long quantitative

Climate Change and Variability

palaeotemperature records from across the globe, of which a majority are well suited for being used in large-scale temperature reconstructions, have been rapidly increasing in recent years (Ljungqvist, 2009). Thus, it has now become possible to make regional temperature reconstructions for many regions that can help us to assess the spatio-temporal pattern and the MWP and LIA. Only by a regional approach can we truly gain an understanding of the temperature variability in the past 1–2 millennia and assess the possible occurrence of globally coherent warm and cold periods. Presently, only four regional multi-proxy temperature reconstructions exist: two for eastern Asia (Yang et al., 2002; Ge et al., 2010), one for the Arctic (Kaufman et al., 2009), and one for South America (Neukom et al., 2010). Six new quantitative regional multi-proxy temperature reconstructions will here be presented in order to improve our understanding of the regional patterns of past temperature variability.

Fig. 1. Comparison of three recent millenium-long multi-proxy Northern Hemisphere temperature reconstructions: decadal means of Moberg et al. (2005), the ‘error-in-variables’ (EIV) regression method variant of Mann et al. (2008), and the extra-tropical Northern Hemisphere reconstruction by Ljungqvist (2010).

2. New regional temperature reconstructions Only for limited parts of the Northern Hemisphere is the data coverage sufficient for making quantitative regional temperature reconstructions in order to assess the regional pattern of temperature changes during the last 12 centuries. This has been done for: 1) warm season temperatures of Scandinavia north of 60°N, 2) warm season temperatures for northern Siberia, 3) annual mean temperatures for Greenland, 4) warm season temperatures for the Alp region of Central Europe, 5) annual mean temperatures for China, and 6) annual mean temperatures for the whole of the North American continent. The reconstructions follow the simple but robust method outlined in the new multi-proxy temperature reconstruction for the extra-tropical (90°–30°N) Northern Hemisphere by Ljungqvist (2010). Only proxy records with reasonably high resolution (multi-decadal or better) were utilised

A regional approach to the Medieval Warm Period and the Little Ice Age

and records with lower resolution were instead used for the purpose of verifying the reconstructed low-frequency trends of the reconstructions. We use the common “composite-plus-scale” method (Lee et al. 2008; von Storch et al. 2004) for creating the different regional temperature reconstructions. First, all records with less than annual resolution were linearly interpolated to have annual resolution and then calculated 10-year-mean values of each record. All the 10-year-mean values were normalized to zero mean and unit standard deviation fitting the decadal mean and variance AD 1000–1899. The arithmetic mean of all the normalized records included in each regional reconstruction was then calculated. Each regional reconstruction was scaled to fit the decadal mean and variance over the longest possible time period in the variance adjusted CRUTEM3 instrumental temperature record (Brohan et al., 2006) and adjusted to have a zero equaling the 1961–1990 mean of this instrumental record. A 2 standard deviation error bar for each regional reconstruction was calculated from the decadal correlation between proxy and instrumental temperature in the calibration period. Proxy location

Latitude

Longitude

Sample resolution Annual

Season bias Summer

Reference

25.00

Proxy type* TRW

1. Finnish Lapland 2. Lake Tsuolbmajavri 3. Torneträsk 4. Jämtland

69.00 68.45

22.05

Summer

68.31 63.10

19.80 13.30

MXD TRW

Multidecadal Annual Annual

Summer Summer

5. Severnaja

81.00

106.00

Decadal

Summer

6. Taimyr peninsula 7. Indigirka 8. Yamal 9. Polar Urals 10. Lower Murray Lake 11. GISP

73.00

105.00

TRW

Annual

Summer

70.00

149.00

66.83 81.21

65.75 –69.32

TRW TRW MXD V

Annual Annual Annual Annual

Summer Summer Summer Summer

Korhola et al. 2000 Grudd 2008 Linderholm and Gunnarson 2005 Solomina and Alverson 2004 Naurzbaev et al. 2002 Moberg et al. 2005 Briffa 2000 Esper et al. 2002a Cook et al. 2009

72.60

–38.50

Annual

12. GRIP

72.35

–37.38

Annual

13. Crête

71.12

–37.32

Annual

14. Nansen Fjord

68.25

–29.60

Decadal

Summer

15. Igaliku Fjord 16. Lake Silvaplana 17. The Alps

60.40 46.45

–46.00 9.48

Sd Lf

Summer Summer

46.30

8.00

MXD

Decadal Annual to decadal Annual

Summer

18. Mongolia

48.30

98.93

TRW

Annual

Summer

19. Shihua Cave 20. Dulan

39.47 36.00

115.56 98.00

Sp TRW

Annual Annual

Summer Annual

Helama et al. 2009

Johnsen et al. 2001 Grootes and Stuiver 1997 Clausen, et al. 1988 Jennings and Weiner 1996 Jensen et al. 2004 Larocque et al. 2009 Büntgen et al. 2006 D’Arrigo et al. 2001 Tan et al. 2003 Zhang et al. 2003

Climate Change and Variability

21. E. China 22. E. China 23. Wanxiang 24. Zhang Delta 25. Hesheng 26. Devon Island

35.00 35.00 33.19 32.00 19.41 75.33

114.00 114.00 105.00 121.00 109.36 –82.50

27. Iceberg Lake, Alaska 28. Gulf of Alaska 29. Canadian Rockies 30. Chesapeake Bay 31. Bermuda Rise

60.78

32. Nicoa Cave 33. Punta Laguna

Decadal Decadal Decadal Decadal Decadal 5 years

Annual Winter Annual Annual Annual Annual

Yang et al. 2002 Ge et al. 2003 Zhang et al. 2008 Zhang et al. 2008 Hu et al. 2008 Fisher et al. 1983

–142.95

D D Sp D Sp Icecore Lf

Annual

Summer

Loso 2009

60.00

–145.00

MXD

Annual

Summer

52.15

–117.15

MXD

Annual

Summer

39.00

–76.40

Spring

33.72

–57.63

Annual

Keigwin 1996

10.20 20.63

–85.30 –87.50

Sp Lf

Multidecadal Multidecadal Decadal Decadal

D’Arrigo et al. 2006 Luckman and Wilson 2005 Cronin et al. 2003

Annual Annual

Mann et al. 2008 Curtis et al. 1996

* D, documentary; Lf, lake/river fossils and sediments; MXD, tree-ring maximum latewood density; Md, marine sediments; Sp, speleothem isotopic analysis; TRW, tree-ring width; V, varved thickness sediments. Table 1. All temperature proxy records used in the regional temperature reconstructions with information regarding geographical location, latitude and longitude, type of proxy, sample resolution, season bias, and reference to the original publication. The geographical locations of the records are shown on the map in Fig. 2. Proxy location

Latitude

Longitude 17.75

Proxy type* Po

Sample resolution Centennial

Season bias Summer

A. Lake Gammelheimenvatnet B. Lake Sjuodjijaure

68.47 67.37

18.07

Centennial

Summer

C. Korallgrottan

64.89

14.16

Annual

D. Taimyr pollen

70.77

99.13

Multidecadal Centennial

Summer

E. Indigirka pollen

70.00

149.00

Centennial

Summer

F. Yamal tree-line

67.00

69.00

Centennial

Summer

G. GRIP

72.35

–37.38

Centennial

Annual

H. Dye-3

65.11

–43.49

Centennial

Annual

I. Qipisarqo Lake

61.01

–47.75

Centennial

Summer

J. Lake Neuchatel

46.80

6.70

Centennial

Summer

K. Aletsch Glacier

46.38

7.75

Centennial

Summer

Reference Bjune et al. 2009 Rosén et al. 2003 Sundqvist et al. 2010 Andreev et al. 2002 Velichko et al. 1997 Solomina and Alverson 2004 Dahl-Jensen et al. 1998 Dahl-Jensen et al. 1998 Kaplan et al. 2002 Filippi et al. 1999 Holzhauser et

A regional approach to the Medieval Warm Period and the Little Ice Age

L. Groner Glacier

46.05

7.62

Centennial

Summer

M. Lake Qinghai

37.00

100.00

Annual

N. Hongyuan

32.46

102.3

Multidecadal Centennial

Annual

O. Jiaming Lake

25.01

121.3

Centennial

Annual

P. Farewell Lake Q. Tebenkof Glacier

62.55 60.75

–153.63 –148.45

Lf O

Centennial Centennial

Summer Summer

R. North pollen stack

70–30

–50–170

Centennial

Summer

America

al. 2005 Holzhauser et al. 2005 Liu et al. 2006 Yang et al. 2002 Yang et al. 2002 Hu et al. 2001 Barclay et al. 2009 Viau et al. 2006

* B, borehole; Lf, lake/river fossils and sediments; Md, marine sediments; Sp, speleothem isotopic analysis; O, other types of proxies; Po, pollen. Table 2. All temperature proxy records used for verifying the low-frequency trends in the regional temperature reconstruction with information regarding geographical location, latitude and longitude, type of proxy, sample resolution, season bias, and reference to the original publication. The geographical locations of the records are shown on the map in Fig. 2. 2.1 Scandinavia Scandinavia is probably the most data rich region in the world when it comes to palaeotemperature proxy data. Climate and environmental research has a long history in Scandinavia and numerous studies of past climate and vegetation have been conducted, especially in the far north of Scandinavia, for several decades. Many different kinds of archives have been utilised including, but not limited to, tree-ring width and density data, pollen profiles, chironomid records, diatom records, annually-laminated sediments, radiocarbon-dated megafossil tree-remains, and speleothem δ18O records. Most of this data are, unfortunately, not available from databases and the majority of the records do not, moreover, possess such a high resolution that they are suited for being used in calibrated temperature reconstructions of the last 1200 years. Almost all palaeotemperature proxy data from Scandinavia, especially northern Scandinavia, primarily capture growing season temperatures and are hence biased towards the warm part of the year. In southern Scandinavia the growing season lasts approximately five months (May to September), whereas the growing season in northern Scandinavia lasts three months (June to August) or less on high elevations. We still have a limited possibility to reconstruct cold season or annual mean temperatures for Scandinavia despite the fact that the region is so comparatively rich in data. Presently, only four records possess such quality that we can use them here for reconstructing the warm season temperature variability in Scandinavia for the last 1200 years: 1) the Torneträsk tree-ring maximum latewood density record (Grudd, 2008), 2) the Finnish Lapland tree-ring width record (Helama et al., 2009), and 3) the Jämtland tree-ring width record (Linderholm and Gunnarson, 2005). The sediment records from Lake Tsuolbmajavri in northernmost Finland (Korhola et al., 2000) also possess such a relatively high resolution and dating control that they can be useful to include in a Scandinavian

Climate Change and Variability

warm season temperature reconstruction. Other records usually have too low a temporal resolution.

Fig. 2. Map with the geographical location of the proxy records listed in Table 1 and 2. The Scandinavian warm season temperature reconstruction, consisting of the abovementioned four records, was calibrated against the May to September mean temperature from the 60–70°N × 5–30°E CRUTEM3 grid cells (Fig. 3). This area represents the central and northern parts of Scandinavia. We have a long and good network of instrumental temperature measurements for Scandinavia and we can thus calibrate the reconstruction over the whole period AD 1850–1999. The correlation coefficient over the calibration period amounts to 0.88 (r2 = 0.77). Late 20th century warm season temperatures in Scandinavia do not seem to be remarkably warm in a long-term perspective. During the MWP, occurring here c. AD 900–1100, Scandinavian warm season temperatures seem to have exceeded those

A regional approach to the Medieval Warm Period and the Little Ice Age

of recent decades. Temperatures also equalled or exceeded the 1961–1990 reference level in the early 15th century. A LIA cooling is clearly seen approximately AD 1560–1720 and low temperatures are also prevailing c. AD 1350 and c. AD 1900. The total reconstructed decadal temperature variability of the last 12 decades is about 2.5°C, with a centennial variability of as much as 1.5°C.

Fig. 3. May to September temperature reconstruction for Scandinavia north of 60°N (blue line) calibrated to instrumental temperatures for the same region (red line) with 2 standard deviation error bars (grey shading).

Fig. 4. Three normalised temperature records from Scandinavia shown relative to their AD 1000–1899 mean. The Korallgrottan record has been smoothed with a 100-year cubic spline filter. As discussed above, there exist large numbers of quantitative temperature reconstructions with lower temporal resolution that can be used to compare and verify the reconstructed low-frequency trend. We have used the Lake Gammelheimenvatnet pollen-based reconstruction (Bjune et al., 2009) and the Lake Sjuodjijaure chironomid-based

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reconstruction (Rosén et al., 2003) from the northern tree-line area for this purpose together with the speleothem δ18O record from Korallgrottan in Jämtland in Central Scandinavia (Sundqvist et al., 2010) (Fig. 4). Lake Gammelheimenvatnet and Lake Sjuodjijaure show high summer temperature variability whereas Korallgrottan probably reflects annual mean temperature variability. The general multi-centennial trends of the reconstruction are also seen in the three low-resolution records, and although they do not agree when the maximum LIA cooling occurred, they are consistent with regard to the occurrence of a clear MWP in Scandinavia. 2.2 Northern Siberia Russia has a long tradition of palaeoclimatology and although much of it is still only available in the Russian language, considerable efforts were made already in the first half of the 1980s in order to incorporate Russian (then Soviet) palaeoclimatology with the Western research community (Velichko, 1984). However, the Russian palaeoclimatology has primarily been focused on long Pleistocene and Holocene perspectives and less focused on the climate variability of the last one or two millennia (Velichko et al., 1997). Siberian, as well as European Russian, pollen-based temperature reconstructions clearly show the occurrence of a MWP and a LIA but they have such a crude resolution that only multicentennial variations can be detected (Andreev et al., 2000, 2001, 2003, 2004, 2005). Five records, all primarily reflecting warm season temperatures, with temporal resolution high enough to be used here were found: 1) the Severnaja lake sediment record (Solomina and Alverson, 2004), 2) the Taimyr tree-ring width record (Naurzbaev et al., 2002), 3) the Indigirka tree-ring width record (Moberg et al., 2006), 4) the Yamal tree-ring width record (Briffa, 2000), and 5) the Polar Urals tree-ring maximum latewood density record (Esper et al., 2002). Regular instrumental temperature measurements were started relatively late in Siberia. Our calibration period is therefore limited to AD 1890–1989 and thus we have a limited degree of freedom. The northern Siberia warm season temperature reconstruction has been calibrated against the May to September mean temperature from the 60–80°N × 60– 180°E CRUTEM3 grid cells (Fig. 5). During this 10 decade long calibration period, the correlation coefficient amounts to 0.70 (r2 = 0.48). The relationship between the proxy composite and instrumental temperatures is thus relatively weak although the general trends are in quite good agreement. The reconstructed Siberian warm season temperatures show that temperatures exceeded the 1961–1990 reference level c. AD 950–1150 and mostly equalled that level from c. AD 800–950 and c. AD 1150–1540. A quite clear LIA is seen c. AD 1540–1920 with temperatures about 0.5°C below the 1961–1990 reference level. Three especially distinct cold spells are seen during the LIA: c. AD 1280, a long cold period c. AD 1600–1750, and c. 1820. The overall amplitude of the reconstructed decadal variability the last 12 centuries well exceeds 1°C. The reconstructed low-frequency temperature trend agrees well with the normalised values of three warm season temperature reconstructions with lower temporal resolution: the Yamal tree-line record (Solomina and Alverson, 2004), the Indigirka pollen-based temperature reconstruction (Velichko et al., 1997), and the Taimyr pollen-based temperature reconstruction (Andreev et al., 2002) (Fig. 6). The MWP, the LIA, and the modern warming are clearly visible in the low-resolution records, although they are less clear in the Yamal tree-line record. However, maximum LIA cooling seems to occur somewhat earlier in the

A regional approach to the Medieval Warm Period and the Little Ice Age

three low-resolution records than in the quantitative, calibrated temperature reconstruction (Fig. 5).

Fig. 5. May to September temperature reconstruction for northern Siberia (blue line) calibrated to instrumental temperatures for the same region (red line) with 2 standard deviation error bars (grey shading).

Fig. 6. Three normalised temperature records from northern Siberia shown relative to their AD 1000–1899 mean. 2.3 Greenland Greenland has been the subject of palaeoclimatological research efforts for several decades, resulting among other things in a number of well-known δ18O ice-core records (Andersen et al., 2006; Dansgaard et al., 1975). In the last decades, marine sediment cores from Greenland’s extensive coastlines as well as lake sediment cores from the ice-free coastal areas have also become available (Andresen et al., 2004; Cremer et al., 2001; Moros et al. 2006; Møller et al., 2006; Roncaglia and Kuijpers, 2004; Seidenkrantz et al., 2007; Wagner and Melles, 2001). Most of the sediment records unfortunately have too low a temporal resolution and/or too large uncertainties in the dating to be useful in a quantitative multiproxy temperature reconstruction aimed at being calibrated to temperature values. Other

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potentially useful records end too early to be calibrated to instrumental temperatures (e.g. Alley, 2000). Further records are unsuitable to use in order to reconstruct Greenland’s temperature since they either do not have a significant correlation to temperature (as the NorthGRIP δ18O ice-core record) or stronger correlation to Icelandic rather than Greenlandic temperatures (as the Renland δ18O ice-core record) (Vinther et al., 2010). Thus, all in all, we find six records useful for our purpose: 1) the Crête δ18O ice-core record, 2) the GISP2 δ18O ice-core record, 3) the GRIP δ18O ice-core record, 4) the Nansen Fjord benthic foraminifera sea floor sediment record (Jennings and Weiner, 1996), 5) the Igaliku Fjord biostratigraphic diatom sea floor sediment record (Jensen et al., 2004), and 6) the annually varved Lower Murray Lake sediment record (Cook et al., 2009). The last record, from Lower Murray Lake, actually comes from Ellesmere Island in the northernmost Canadian Arctic Archipelago but can be used to represent climate conditions in nearby northern Greenland. The Greenland temperature reconstruction has been calibrated against the annual mean temperature from the 85–60°N × 15–70°W CRUTEM3 grid cells (Fig. 7). The correlation coefficient over the calibration period 1850–1969 amounts to 0.74 (r2 = 0.55). The reconstruction shows a peak value in the 10th century c. 3°C above the 1961–1990 reference level and c. 1.5°C above the mid-20th century maximum. Temperatures then gradually declined but essentially remained above the 1961–1990 reference level until about AD 1300. We can thus conclude with reasonable safety that the MWP in Greenland well exceeded the observed 20th century warming, although this did not necessarily apply to the Arctic region as a whole (Kaufman et al., 2009). Such a strong regional warming of Greenland is actually well in agreement with global temperature field reconstructions indicating a post-1990 warming exceeding the medieval one on a global scale but not on Greenland (Mann et al., 2009). A pronounced cold period occurred in the mid to late 14th century, probably marking the onset of the LIA, although temperatures then rose again and hovered around the 1961– 1990 reference level until the main phase of the LIA commenced in the late 17th century and lasted until c. AD 1920. It may be noted that no late 20th century warming is visible in either the reconstructed or the instrumental temperatures since the recent warming in Greenland only started in the late 1990s and still has not exceeded the level of the mid-20th century Greenland warming (Chylek et al., 2006).

Fig. 7. Annual mean temperature reconstruction for Greenland (blue line) calibrated to instrumental temperatures for the west coast of Greenland (red line) with 2 standard deviation error bars (grey shading).

A regional approach to the Medieval Warm Period and the Little Ice Age

Fig. 8. Three normalised temperature records from Greenland shown relative to their AD 1000–1899 mean. The reconstructed low-frequency temperature trend agrees well with the normalised values of the Dye-3 and GRIP deep borehole temperature profiles from the Greenland inland ice sheet (Dahl-Jensen et al., 1998) as well as with the normalised values of the biogenic silica sediment record from Qipisarqo Lake (Kaplan et al., 2002) (Fig. 8). Moreover, based on the analysis of the oxygen isotope composition of human tooth enamel (δ18Op) in teeth from Norse and Inuit skeletons, Fricke et al. (1995) found a similar amplitude in the annual mean temperature variability as we have reconstructed here. 2.4 Central Europe Considerable palaeoclimatological research has been conducted in the Alp region of Central Europe, aimed at different time perspectives but few useful quantitative temperature reconstructions have so far been produced for the last one or two millennia, although several are under development in the Millennium project founded by the European Union (Gagen et al., 2006). Several of the records from this region that are now available are less suited for being used in a quantitative calibrated temperature reconstruction. The siliceous algae-based Oberer Landschitzsee temperature reconstructions (Schmidt et al., 2007) have a crude resolution and likely contain much non-temperature related information. The Lake Anterne sediment record (Millet et al., 2009) is influenced by anthropogenic activities during the recent centuries, which makes it difficult to compare the level of the medieval warmth to the modern one. The temperature reconstruction from Central Europe by Glaser and Riemann (2009) based on historical documentary sources first starts in the early 1000s and probably underestimates the low-frequency variability in medieval times when the data coverage is sparse. The Spannagel Cave speleothem δ18O record (Mangini et al., 2005) would be a potentially good record but it unfortunately ends already in the 1930s and thus has too short a calibration period for being really useful. This leaves us with only two records, both reflecting summer temperatures, 1) the varved Lake Silvaplana chironomid-based record

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(Larocque et al., 2009), and the tree-ring maximum latewood density record from the Alps by Büntgen et al. (2006).

Fig. 9. May to September temperature reconstruction for Central Europe (blue line) calibrated to instrumental temperatures for the Alp region (red line) with 2 standard deviation error bars (grey shading).

Fig. 10. Three normalised temperature records from Central Europe shown relative to their AD 1000–1899 mean. Long, hom*ogenised instrumental records are available from Central Europe, going back to ~1760 (Auer et al., 2007; Böhm et al., 2010). Even if we thus have longer instrumental temperature records from Central Europe, we will, for a matter of consistency with the other regional reconstructions, use the May to September 50–45°N × 5–15°E CRUTEM3 grid cells. The correlation coefficient over the calibration period 1850–1999 amounts to 0.84 (r2 = 0.70). During most of the past 12 centuries, the May to September temperatures of Central Europe have been below the 1961–1990 reference level with the exception for parts of the MWP. In the 10th century, and partly in the 12th century, temperatures seem generally to have been at

A regional approach to the Medieval Warm Period and the Little Ice Age

or above this reference level. A very clear, albeit somewhat variable, LIA is seen from c. AD 1250 until the mid-19th century with a maximum cooling in the 17th century. The reconstructed decadal temperature variability of the last 12 decades is about 2°C, with probably the warmest temperatures observed at the end of the 20th century, although this is within the uncertainty level of the medieval warmth (Fig. 9). A medieval warming c. AD 800–1280 is seen, peaking approximately AD 1000, but is interrupted by at least three major cool events: c. AD 1050 and c. AD 1170, and c. 1250. A reconstruction based on only two records cannot be regarded as particularly robust but the low-frequency trend of the reconstruction agrees very well with the with three low-resoled temperature reconstructions from the Alp region (Fig. 10): the glacier length records from the Great Aletsch Glacier and Groner Glacier (Holzhauser et al., 2005) and the pollen-based summer temperature reconstruction from Lake Neuchatel (Filippi et al., 1999) (Fig. 10). 2.5 China Whereas the coverage of proxy data is very sparse for most of Asia, the coverage for China is rather good. In fact, China is, together with parts of North America, probably the region outside of Europe, with the best palaeoclimatological and palaeoenvironmental records. Previously, much of the research has been published in Chinese, and thus not been easily accessible to Western scholars, but in recent years most Chinese research has been published in English and consequently well incorporated in the international field of palaeoclimatology. The problem remains, however, that relatively few of the reconstructions are available in digital form from public databases. China has the world’s longest continuous written historical records that can be used to reconstruct past climate variability. Therefore, much valuable research in the field of historical climatology has been done in China. Many syntheses on past climate in China, using different natural and/or historical archives, have been written and two of them are of relevance to us. Yang et al. (2002) published the first highly resolved annual mean quantitative temperature reconstruction for China covering the last two millennia and Ge et al. (2010) updated this reconstruction and also made reconstructions for different parts of China.

Fig. 11. Annual mean temperature reconstruction for China (blue line) calibrated to instrumental temperatures for China (red line) with 2 standard deviation error bars (grey shading).

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Fig. 12. Three normalised temperature records from China shown relative to their AD 1000– 1899 mean. The records have been smoothed with a 100-year cubic spline filter. Eight records were deemed suitable for reconstructing decadal China temperature variability of the last 12 centuries: 1) the Mongolia tree-ring width record (D’Arrigo et al., 2001), 2) the ShiHua Cave speleothem δ18O record (Tan et al., 2003), 3) a composite of East China documentary records of annual mean temperature (Yang et al., 2002), 4) a composite of documentary East China records of cold season temperature (Ge et al., 2003), 5) the Dulan, northeast Qinghai-Tibetan Plateau, tree-ring width record (Zhang et al., 2003), 6) the Wanxiang Cave speleothem δ18O record (Zhang et al., 2008b), 7) a composite of Zangtze Delta region documentary records of annual mean temperature (Zhang et al., 2008a), and 8) the Heshang Cave speleothem δ18O record (Hu et al., 2008). The China annual temperature reconstruction has been calibrated against the annual mean temperature from the 55–20°N × 70–135°E CRUTEM3 grid cells. Unfortunately, the meteorological network of instrumental temperature measurements is much shorter and sparser back in time in China than in Europe or most of North America. We can therefore not start the calibration period of the Chinese temperature reconstruction until AD 1880 and the lack of proxy data in the 1990s forces us to end the calibration period AD 1989. Thus, our calibration period for China is just 11 decades. The correlation coefficient over the calibration period amounts to 0.83 (r2 = 0.68) and thus shows, although the degrees of freedom are limited, a strong relationship between the proxy composite and the instrumental temperatures. The multi-decadal annual mean temperature variability in China seems to have been slightly less than 1°C and thus smaller then in most other regions (Fig. 11). During most of the past 12 centuries, the temperatures have been well below the 1961–1990 reference level. However, during the second half of the 10th century temperatures equalled or exceeded the 1961–1990 mean. This warm event probably represents the peak of the MWP in China. Warm conditions, similar to those of the 20th century, are also seen in the first half of the 13th century. Five major cooling events during the LIA are visible, centred c. AD 1300, AD 1450, AD 1600, AD 1675 and AD 1850. According to the instrumental record, late 20th century

A regional approach to the Medieval Warm Period and the Little Ice Age

temperatures may be the highest in the last 12 centuries but this is not seen in the proxy reconstruction itself. Overall, both the shape and amplitude of the low-frequency temperature variability are well in line with the pollen-inferred temperature reconstructions by He et al. (2004). There also exist some other quantitative temperature reconstructions with lower temporal resolution that can be used to compare and verify the reconstructed low-frequency trend. We have chosen to use the Jiaming Lake (Yang et al., 2002), the Hongyuan Lake (Yang et al., 2002), and the Lake Qinghai (Liu et al., 2006) sediment records for this purpose. They show a quite similar picture of China low-frequency temperature variability, especially the existence of a MWP c. AD 850–1300 and a clear LIA cooling c. AD 1400–1700 (Fig. 12). 2.6 North America Relatively much data, supposedly reflecting past temperature variability, are available from North America but most of this data consist of tree-ring records. A significant problem is that many of the tree-rings records, especially those from lower latitudes, apparently are more sensitive to precipitation than temperature. All tree-ring width records from highelevation semi-arid sites in the North American Southwest can be said to possess an ambiguous temperature signal at best, although they have indeed been used in many previous multi-proxy reconstructions (e.g., Cook et al., 2004; Crowley and Lowery, 2000; Esper et al., 2002; Jones and Mann, 2004; Mann et al., 1999; Mann et al., 2008; Mann et al., 2009; Mann and Jones, 2003; Osborn and Briffa, 2006). The potential problems with tree-ring records from semi-arid, high-elevation regions in the North American Southwest have been highlighted in D’Arrigo et al. (2006) and Loehle (2009). Moreover, most of those chronologies have a low replication value back in medieval times. Excluding the tree-ring width records from lower latitudes leaves us with a relatively limited number of palaeotemperature proxy records with adequate resolution and time control to use in a calibrated temperature reconstruction.

Fig. 13. Annual mean temperature reconstruction for North America (blue line) calibrated to instrumental temperatures for this continent (red line) with 2 standard deviation error bars (grey shading). Eight records were deemed suitable for reconstructing decadal North American temperature variability of the last 12 centuries: 1) the Devon Island, Canadian Arctic

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Archipelago, ice-core δ18O record (Fisher et al., 1983), 2) the Gulf of Alaska tree-ring width record (D’Arrigo et al., 2006), 3) the Iceberg Lake varved sediment record (Loso, 2009), 4) the Canadian Rockies tree-ring width record (Luckman and Wilson, 2005), 5) the Chesapeake Bay sea sediment record (Cronin et al., 2003), 6) the Bermuda Rise sea sediment record (Keigwin, 1996), 7) the Punta Laguna lake sediment record (Curtis et al., 1996), and 8) the Nicoa Cave speleothem δ18O record (Mann et al., 2008). The North America annual temperature reconstruction has been calibrated against the annual mean temperature from the 85–5°N × 50–170°W CRUTEM3 grid cells. The correlation coefficient over the calibration period 1860–1989 amounts to 0.78 (r2 = 0.61).

Fig. 14. Three normalised temperature records from North America shown relative to their AD 1000–1899 mean. The Lake Farwell record has been smoothed with a 100-year cubic spline filter. The reconstructions show that annual mean temperatures have been below the 1961–1990 reference level prior to the 20th century except during the MWP (Fig. 13). A very sharp warm peak during the MWP occur c. AD 960 with temperature levels equalling those of the last two decades. A general cooling, consistent with the LIA, is seen in the North American reconstruction c. AD 1400–1850 with a markedly cold spell also c. AD 1900. The maximum cooling occurs around AD 1500 and AD 1700. The reconstructed decadal variability of the last 12 centuries is slightly more than 1°C.The reconstructed low-frequency trend could be compared to a number of different proxy records with lower temporal resolution. We have chosen to use three that, admittedly, primarily reflect warm season temperatures: The Tebenkof Glacier length record from southern Alaska (Barclay et al., 2009), the Farewell Lake sediment record from central Alaska (Hu et al., 2001), and the entire North American pollen-based temperature reconstruction by Viau et al., 2006. The reconstructed long-term changes are well reflected in the low-resolution records (Fig. 14). The existence of a distinct MWP in North America is verified and also the existence of a LIA, although the three lowresolution records are less consistent when it was coldest.

A regional approach to the Medieval Warm Period and the Little Ice Age

3. Discussion The North Atlantic region – from Greenland to Europe – has the best data coverage together with China and the coastal areas of North America. Data are lacking to make meaningful regional reconstructions for Africa, the Middle East or the interior of Asia as well as for all of the Southern Hemisphere (except for, perhaps, South America, see Neukom et al., 2010). Even in relative data rich regions (e.g., Central Europe and Scandinavia) the seasonal bias in the data limits our possibility to make annual mean temperature reconstructions. Most high latitude data, except for the Greenland borehole and δ18O ice-core records, have a clear bias towards warm season temperatures. We have been able to present regional reconstructions for 1) warm season temperatures of Scandinavia north of 60°N, 2) warm season temperatures for northern Siberia, 3) annual mean temperatures for Greenland, 4) warm season temperatures for the Alp region of Central Europe, 5) annual mean temperatures for China, and 6) annual mean temperatures for the whole of the North American continent. In all the six reconstructions we can see evidence for the MWP, the LIA and the modern warming. Moreover, in all the six regional reconstructions the medieval warming is peaking in the 10th century. Maximum LIA cooling, however, seems to occur at somewhat different times in the different reconstructions – from the 16th to the 19th centuries – but the 17th century is very cold in all reconstructions. All reconstructions, except the one from central Europe, also agree that the mid to late 19th century was unusually cold, which is of special interest since it is during this period that widespread instrumental temperature measurements start. Late 20th century temperatures are lower than those of the MWP in the Scandinavia, Siberia and especially in the Greenland temperature reconstruction and equal to the medieval warming in the North America reconstruction. However, in the China and the Central Europe reconstructions late 20th century warming exceeds the medieval one, although this is not clear from the proxy reconstructions themselves but only from the instrumental temperature data spliced to the reconstructions. The trends visible in palaeotemperature proxy records with low (multi-decadal to centennial) resolution, which thus cannot be robustly calibrated to the instrumental record, are generally in good agreement with the trends of the calibrated reconstructions from the same regions. This implies that the calibrated regional reconstructions preserve a good degree of the low-frequency variability although the amplitude of the reconstructed changes probably is too small due to the statistical problems of calibrating noisy data to instrumental data (see, e.g., von Storch et al., 2004; Lee et al., 2008). Although, from the regional reconstructions presented here, it seems doubtful if the late 20th century warming exceeded the medieval one in the Northern Hemisphere, much more data are needed to draw firm conclusions in this matter. In order to truly assess the spatiotemporal pattern of past temperature variability we also need to be able to make reconstructions from other regions as Africa, the Middle East and Central Asia. Much more research in the field is therefore needed to develop new proxy records from different natural recorders of climate variability, such as fossil pollen, ice-cores, lake and marine sediments, speleothems, and tree-ring width and density, as well as from historical records. It is also very important that all new palaeotemperature records are archived, and thus easily accessible, in public databases as the World Data Center for Paleoclimatology (http://www.ncdc.noaa.gov/paleo/paleo.html). Although it is outside the scope of this article, we can briefly discuss the possible influence of variations in solar and volcanic forcing on the reconstructed temperatures. All the six

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regional temperature reconstructions show some agreement with the assumed lowfrequency variability in solar forcing of the last 12 centuries (Bard et al., 2000). The medieval period, with high temperatures, had a general high solar activity, whereas the cold LIA was dominated by lower solar activity (Ammann et al., 2007). The warming in the 20th century coincides with an increase in solar forcing, although the warming trend has probably also been amplified in the last decades by anthropogenic greenhouse gas emissions (IPCC, 2007).

4. Conclusion The presently available palaeotemperature proxy data records do not support the assumption that late 20th century temperatures exceeded those of the MWP in most regions, although it is clear that the temperatures of the last few decades exceed those of any multidecadal period in the last 700–800 years. Previous conclusions (e.g., IPCC, 2007) in the opposite direction have either been based on too few proxy records or been based on instrumental temperatures spliced to the proxy reconstructions. It is also clear that temperature changes, on centennial time-scales, occurred rather coherently in all the investigated regions – Scandinavia, Siberia, Greenland, Central Europe, China, and North America – with data coverage to enable regional reconstructions. Large-scale patterns as the MWP, the LIA and the 20th century warming occur quite coherently in all the regional reconstructions presented here but both their relative and absolute amplitude are not always the same. Exceptional warming in the 10th century is seen in all six regional reconstructions. Assumptions that, in particular, the MWP was restricted to the North Atlantic region can be rejected. Generally, temperature changes during the past 12 centuries in the high latitudes are larger than those in the lower latitudes and changes in annual temperatures also seem to be larger than those of warm season temperatures. In order to truly assess the possible global or hemispheric significance of the observed pattern, we need much more data. The unevenly distributed palaeotemperature data coverage still seriously restricts our possibility to set the observed 20th century warming in a global long-term perspective and investigate the relative importance of natural and anthropogenic forcings behind the modern warming.

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Climate Change and Variability

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Multi-months cycles observed in climatic data

2 0 Multi-months cycles observed in climatic data Samuel Nicolay, Georges Mabille, Xavier Fettweis and M. Erpicum University of Liège Belgium

1. Introduction Climatic variations happen at all time scales and since the origins of these variations are usually of very complex nature, climatic signals are indeed chaotic data. The identification of the cycles induced by the natural climatic variability is therefore a knotty problem, yet the knowing of these cycles is crucial to better understand and explain the climate (with interests for weather forecasting and climate change projections). Due to the non-stationary nature of the climatic time series, the simplest Fourier-based methods are inefficient for such applications (see e.g. Titchmarsh (1948)). This maybe explains why so few systematic spectral studies have been performed on the numerous datasets allowing to describe some aspects of the climate variability (e.g. climatic indices, temperature data). However, some recent studies (e.g. Matyasovszky (2009); Paluš & Novotná (2006)) show the existence of multi-year cycles in some specific climatic data. This shows that the emergence of new tools issued from signal analysis allows to extract sharper information from time series. Here, we use a wavelet-based methodology to detect cycles in air-surface temperatures obtained from worldwide weather stations, NCEP/NCAR reanalysis data, climatic indices and some paleoclimatic data. This technique reveals the existence of universal rhythms associated with the periods of 30 and 43 months. However, these cycles do not affect the temperature of the globe uniformly. The regions under the influence of the AO/NAO indices are influenced by a 30 months period cycle, while the areas related to the ENSO index are affected by a 43 months period cycle; as expected, the corresponding indices display the same cycle. We next show that the observed periods are statistically relevant. Finally, we consider some mechanisms that could induce such cycles. This chapter is based on the results obtained in Mabille & Nicolay (2009); Nicolay et al. (2009; 2010).

2. Data 2.1 GISS temperature data

The Goddard Institute for Space Studies (GISS) provides several types of data. The GISS temperature data (Hansen et al. (1999)) are made of temperatures measured in weather stations coming from several sources: the National Climatic Data Center, the United States Historical Climatology Network and the Scientific Committee on Antarctic Research. These data are then reconstructed and “corrected” to give the GISS temperature data. The temperatures from the Global Historical Climatology Network are also used to build temperature anomalies on a 2◦ × 2◦ grid-box basis. These data are then gathered and “corrected” to obtain hemispherical temperature data (HN-T for the Northern Hemisphere and HS-T for the Southern Hemisphere) and global temperature data (GLB-T).

Climate Change and Variability

2.2 CRU global temperature data

The Climate Research Unit of the East Anglia University (CRU) provides several time series related to hemispherical and global temperature data (Jones et al (2001)). All these time series are obtained from a 5◦ × 5◦ gridded dataset: CRUTEM3 gives the land air temperature anomalies (CRUTEM3v is a variance-adjusted version of CRUTEM3), HadSST2 gives the seasurface temperature (SST) anomalies and HadCRUT3 combines land and marine temperature anomalies (a variance-adjusted version of these signals is available as well). For each time series, a Northern Hemispheric mean, a Southern Hemispheric mean and a global mean exist. 2.3 NCEP/NCAR reanalysis

The National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) cooperate to collect climatic data: data obtained from weather stations, buoys, ships, aircrafts, rawinsondes and satellite sounders are used as an input for a model that leads to 2.5◦ × 2.5◦ datasets (humidity, windspeed, temperature,...), with 28 vertical levels (Kalnay et al. (1996)). Only the near-surface air temperature data were selected in this study. 2.4 Indices

The Arctic oscillation (AO) is an index obtained from sea-level pressure variations poleward of 20N. Roughly speaking, the AO index is related to the strength of the Westerlies. There are two different, yet similar, definitions of the AO index : the AO CPC (Zhou et al. (2001)) and the AO JISAO. The North Atlantic Oscillation (NAO) is constructed from pressure differences between the Azores and Iceland (NAO CRU, Hurrel (1995)) or from the 500mb height anomalies over the Northern Hemisphere (NAO CPC, Barnston & Livezey (1987)). This index also characterizes the strength of the Westerlies for the North Atlantic region (Western Europe and Eastern America). The El Niño/Southern Oscillation (ENSO) is obtained from sea-surface temperature anomalies in the equatorial zone (global-SST ENSO) or is constructed using six different variables, namely the sea-level pressure, the east-west and north-south components of the surface winds, the sea-surface temperature, the surface air temperature and the total amount of cloudiness (Multivariate ENSO Index, MEI, Wolter & Timlin (1993; 1998)). This index is used to explain sea-surface temperature anomalies in the equatorial regions. The Southern Oscillation Index (SOI, Schwing et al. (2002)) is computed using the difference between the monthly mean sea level pressure anomalies at Tahiti and Darwin. The extratropical-based Northern Oscillation index (NOI) and the extratropical-based Southern Oscillation index (SOI*) are characterized from sea level pressure anomalies of the North Pacific (NOI) or the South Pacific (SOI*). They reflect the variability in equatorial and extratropical teleconnections (Schwing et al. (2002)). The Pacific/North American (PNA, Barnston & Livezey (1987)) an North Pacific (NP, Trenberth & Hurrell (1994)) indices reflect the air mass flows over the north pacific. The PNA index is defined over the whole Northern Hemisphere, while the NP index only takes into account the region 30N–65N, 160E–140W. The Pacific Decadal Oscillation (PDO, Mantua et al (1997)) is derived from the leading principal component of the monthly sea-surface temperature anomalies in the North Pacific Ocean, poleward 20N.

Multi-months cycles observed in climatic data

3. Method 3.1 The continuous wavelet transform

The wavelet analysis has been developed (in its final version) by J. Morlet and A. Grossman (see Goupillaud et al. (1984); Kroland-Martinet et al. (1987)) in order to minimize the numerical artifacts observed when processing seismic signals with conventional tools, such as the Fourier transform. It provides a two-dimensional unfolding of a one-dimensional signal by decomposing it into scale (playing the role of the inverse of the frequency) and time coefficients. These coefficients are constructed through a function ψ, called the wavelet, by means of dilatations and translations. For more details about the wavelet transform, the reader is referred to Daubechies (1992); Keller (2004); Mallat (1999); Meyer (1989); Torresani (1995). Let s be a (square integrable) signal; the continuous wavelet transform is the function W defined as x − t dx W [s](t, a) = s( x )ψ¯ ( ) , a a where ψ¯ denotes the complex conjugate of ψ. The parameter a > 0 is the scale (i.e. the dilatation factor) and t the time translation variable. In order to be able to recover s from W [s], the wavelet ψ must be integrable, square integrable and satisfy the admissibility condition

|ψˆ (ω )|2 dω < ∞, |ω |

where ψˆ denotes the Fourier transform of ψ. In particular, this implies that the mean of ψ is zero, ψ( x ) dx = 0.

This explains the denomination of wavelet, since a zero-mean function has to oscillate. The wavelet transform can be interpreted as a mathematical microscope, for which position and magnification correspond to t and 1/a respectively, the performance of the optic being determined by the choice of the lens ψ (see Freysz et al. (1990)). The continuous wavelet transform has been successfully applied to numerous practical and theoretical problems (see e.g. Arneodo et al. (2002); Keller (2004); Mallat (1999); Nicolay (2006); Ruskai et al. (1992)). 3.2 Wavelets for frequency-based studies

One of the possible applications of the continuous wavelet transform is the investigation of the frequency domain of a function. For more details about wavelet-based tools for frequency analysis, the reader is referred to Mallat (1999); Nicolay (2006); Nicolay et al. (2009); Torresani (1995). Wavelets for frequency-based studies have to belong to the second complex Hardy space. Such a wavelet is given by the Morlet wavelet ψ M whose Fourier transform is given by

( ω − Ω )2 ω Ω ) − exp(− ) exp(− ), 2 2 2 where Ω is called the central frequency; one generally chooses Ω = π 2/ log 2. For such a wavelet, one directly gets ψˆ M (ω ) = exp(−

W [cos(ω0 x )](t, a) =

1 exp(iω0 t)ψˆ¯ M ( aω0 ). 2

Climate Change and Variability

Since the maximum of ψˆ M (·ω0 ) is reached for a = Ω/ω0 , if a0 denotes this maximum, one has ω0 = Ω/a0 . The continuous wavelet transform can thus be used in a way similar to the windowed Fourier transform, the role of the frequency being played by the inverse of the scale (times Ω). There are two main differences between the wavelet transform and the windowed Fourier transform. First, the scale a defines an adaptative window: the numerical support of the function psi (./a) is smaller for higher frequencies. Moreover, if the first m moments of the wavelet vanish, the associated wavelet transform is orthogonal to lower-degree polynomials, i.e. W [s + P] = W [s], where P is a polynomial of degree lower than m. In particular, trends do not affect the wavelet transform. In this study, we use a slightly modified version of the usual Morlet wavelet with exactly one vanishing moment, πω ω − Ω )2 ψˆ (ω ) = sin( ) exp(− ). 2Ω 2 3.3 The scale spectrum

Most of the Fourier spectrum-based tools are rather inefficient when dealing with non-stationary signals (see e.g. Titchmarsh (1948)). The continuous wavelet spectrum provides a method that is relatively stable for signals whose properties do not evolve too quickly: the so-called scale spectrum. Let us recall that we are using a Morlet-like wavelet. The scale spectrum of a signal s is Λ( a) = E|W [s](t, a)|, where E denotes the mean over time t. Let us remark that this spectrum is not defined in terms of density. Nevertheless, such a definition is not devoid of physical meaning (see e.g. Huang et al. (1998)). It can be shown that the scale spectrum is well adapted to detect cycles in a signal, even if it is perturbed with a coloured noise or if it involves “pseudo-frequencies” (see Nicolay et al. (2009)). As an example, let us consider the function f = f 1 + f 2 + , where f 1 ( x ) = 8 cos(2πx/12), f 2 ( x ) = (0.6 +

log( x + 1) 2π log( x + 1) ) cos( x (1 + )) 16 30 100

and () is an autoregressive model of the first order (see e.g. Janacek (2001)), n = αn−1 + σηn , where (η ) is a centered Gaussian white noise with unit variance and α = 0.862, σ = 2.82. The parameters α and σ have been chosen in order to simulate the background noise observed in the surface air temperature of the Bierset weather station (see Section 4). The function f (see Fig. 1) has three components: an annual cycle f 1 , a background noise () and a third component f 2 defined through a cosine function whose phase and amplitude evolve; f 2 is represented in Fig. 2. As we will see, such a component is detected in many climatic time series. As shown in Fig. 3, the scale spectrum of f displays two maxima, associated with the cycles of 12 months and 29.56 months respectively. The components f 1 and f 2 are thus detected, despite the presence of the noise (). Furthermore, the amplitudes associated with f 1 and f 2 are also recovered.

Multi-months cycles observed in climatic data

14 12 10 8 6 4 2 0 -2 -4

120

240

360

480

600

[months] Fig. 1. The function f simulating an air surface temperature time series. The abscissa represent the months. 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8

120

240

360 480 600 [months] Fig. 2. The component f 2 (solid lines) of the function f , compared with the function 0.6 cos(2πx/30) (dashed lines). The abscissa represent the months.

Unlike the Fourier transform, which takes into account sine or cosine waves that persisted through the whole time span of the signal, the scale spectrum gives some likelihood for a wave to have appeared locally. This method can thus be used to study non-stationary signals.

Climate Change and Variability

8 7 6 5 4 3 2 1 0

[months] Fig. 3. The scale spectrum Λ of f . The abscissa (logarithmic scale) represent the months.

Let us remark that the scale spectra computed in this work do not take into account values that are subject to border effects.

4. Results 4.1 Scale spectra of global temperature records

The scale spectra of the global temperature data (CRUTEM3gl) display two extrema corresponding to the existence of two cycles c1 = 30 ± 3 months and c2 = 43 ± 3 months. The second cycle is also observed in the scale spectra of time series where the SST is taken into account (HadCRUT3, HadCRUT3v, HadSST2 and GLB-T). The existence of c1 in these data is not so clear. The scale spectra of these series are shown in Fig. 4 and Fig. 5. CRUTEM3gl

HadSST2

0.11 30.6 m41.8 m

0.1

0.07

anomalies [K]

0.09 0.08

11 m

30.6 m

0.06 0.05

0.07 0.06

0.04

0.05

0.03

0.04

41.8 m

0.08

30 [months]

0.02

10.1 m

30 [months]

Fig. 4. The scale spectra of global temperature records. Crutem3 (left panel) and HadSST2 (right panel).

Multi-months cycles observed in climatic data

HadCRUT3

GLB-T 41.8 m

0.08

anomalies [K]

0.07

30.6 m

0.06

0.05

10.8 m

0.05

10.1 m

0.04

0.03 0.02

29.6 m

0.07

0.06

0.04

41.8 m

0.09

0.03 12

30 [months]

0.02

30 [months]

Fig. 5. The scale spectra of global temperature records: HadCRUT3 (left panel) and GLB-T (right panel). When considering hemispheric data, c1 and c2 are still observed. The scale spectra of the global temperature time series in the Northern Hemisphere display a maximum corresponding to c1 . This cycle is more clearly observed in the data where the SST is not taken into account (i.e. with CRUTEM3nh), while c2 is more distinctly seen in the other time series (NH-T, HadCrut3, HadSST2), as seen in Fig. 6 and Fig. 7. The spectra related to the Southern Hemisphere still display a maximum corresponding to c2 . For the CRU time series (HadCRUT3sh and HadSST2sh), the observed cycle that is the closest to c1 is about 25 months, while the scale spectrum of the GISS data (SH-T) display a cycle c1 as marked as the cycle c2 . The scale spectra of these series are shown in Fig. 8 and Fig. 9. CRUTEM3nh 0.16 0.14

11.6 m

0.06

40.4 m

0.05

0.08

0.045 0.04

0.06

0.035

0.04

28.6 m

0.055

0.1

0.02

41.9 m

0.065 29.6 m

0.12 anomalies [K]

HadSST2 NH 0.07

12 m

0.03 12

30 [months]

0.025

30 [months]

Fig. 6. The scale spectra of Northern Hemisphere temperature records: Crutem3 (left panel) and HadSST2 (right panel).

4.2 Scale spectra of local temperature records

In Nicolay et al. (2009), the scale spectra of a hundred near-surface air temperature time series have been computed using GISS Surface Temperature Analysis data (only the most complete data were chosen). The cycles detected in some weather stations are given by Fig. 10 and Table 1 (the location, the amplitude of the cycles found and the associated class of climate

Climate Change and Variability

HadCRUT3 NH 0.085

NH-T

28.6 m 41.8 m

0.08 0.075 anomalies [K]

0.07 0.065

0.12 0.11

41.8 m

0.1 29.6 m

0.09

10.5 m

0.08

0.06

0.07

0.055

0.06

0.05

0.045 0.04

0.04

0.035

0.03

11.6 m

30 [months]

0.02

30 [months]

Fig. 7. The scale spectra of Northern Hemisphere temperature records: HadCRUT3 (left panel) and NH-T (right panel). CRUTEM3sh

HadSST2 SH

0.12

0.1 43.3 m

0.11

43.3 m

0.09

anomalies [K]

0.08 0.1

31.7 m

0.07

0.09 0.08

0.05

11.2 m

0.04

0.07 0.06

24.9 m

0.06

10.8 m

0.03 12

30 [months]

0.02

30 [months]

Fig. 8. The scale spectra of Southern Hemisphere temperature records: CRUTEM3 (left panel) and HadSST2 (right panel). are also presented). These stations were selected in order to cover most of the typical climate areas (see for Rudloff (1981) more details). As expected, the scale spectrum leads to a correct estimation of the annual temperature amplitude (the difference between the mean temperature of the warmest and coldest months). The weather stations located in Europe and Siberia are clearly affected by the cycle c1 , while weather stations in areas such as California, Brazil, Caribbean Sea and Hawaii are influenced by c2 . The North American Weather stations time series analysis shows the presence of both c1 and c2 . Roughly speaking, the temperature amplitudes induced by the cycles c1 and c2 represent about one tenth of the annual amplitude.

Multi-months cycles observed in climatic data

HadCRUT3 SH

SH-T

0.1

0.09 43.3 m

0.09

0.08

11.2 m

anomalies [K]

0.08

27.6 m 43.3 m 0.07

0.07 24.9 m

0.06

0.05 0.04

0.05 10.8 m

0.04

0.03 0.02

30 [months]

0.03

30 [months]

Fig. 9. The scale spectra of Southern Hemisphere temperature records: HadCRUT3 (left panel) and SH-T (right panel). Weather stations

Lat.

Long.

Cycle (m)

Cycle amp.

An. amp.

Classif.

Uccle (Belgium) Zaragoza (Spain) The Pas (Canada)

50.8◦ N

41.6◦ N 54.0◦ N

4.3◦ E

DO BS EC

64.8◦ N

147.9◦ W

40 K

Verhojansk (Siberia) Jakutsk (Siberia) San Francisco (California) Lander (Wyoming) Manaus (Brazil) Belo Horizonte (Brazil) Tahiti (French Polynesia) Lihue (Hawaii) Colombo (Sri Lanka) Minicoy (India)

67.5◦ N 62.0◦ N 37.6◦ N 42.8◦ N 3.1◦ S 19.9◦ S 17.6◦ S 22.0◦ N 6.9◦ N 8.3◦ N

133.4◦ E 129.7◦ E 122.4◦ W 108.7◦ W 60.0◦ W 43.9◦ W 149.6◦ W 159.3◦ W 79.9◦ E 73.2◦ E

0.4 K 0.3 K 0.6 K 0.8 K 0.8 K 0.8 K 0.8 K 0.8 K 0.3 K 0.6 K 0.3 K 0.5 K 0.2 K 0.3 K 0.2 K 0.2 K

15 K 18 K 38 K

Fairbanks (Alaska)

30.4 ± 2.7 28.4 ± 2.4 28.5 ± 2.6 44.8 ± 2.4 28.5 ± 2.5 40.4 ± 2.5 31.7 ± 2.5 28.6 ± 2.4 41.8 ± 2.7 41.8 ± 2.6 43.3 ± 2.4 41.8 ± 2.4 41.8 ± 2.5 41.8 ± 2.5 44.5 ± 2.6 41.8 ± 2.6

64 K 60 K 8K 28 K 3K 4K 3K 4K 2K 2K

EC EC Cs DC Ar Aw Ar Ar Ar Aw

0.9◦ W 101.1◦ W

Table 1. Cycles found in some world weather stations (the errors are estimated as in Nicolay et al. (2009)). The stations were selected to represent the main climatic areas. For the class of climates, see Rudloff (1981). To show, that c1 and c2 affect the whole planet, the scale spectrum of each grid point of the NCEP/NCAR reanalysis has been computed. As displayed in Fig. 11 and Fig. 12, 92% of the Earth area is associated to at least one of these cycles. Fig. 11 shows that c1 is mainly seen in Alaska, Eastern Canada, Europe, Northern Asia and Turkey, while Fig. 12 reveals that c2 is principally seen in Equatorial Pacific, Northern America and Peru. Roughly speaking, the cycle c1 is observed in regions associated with the Arctic Oscillation, while c2 is detected in regions associated to the Southern Oscillation. 4.3 Scale spectra of atmospheric indices

Advection causes the transfer of air masses to neighboring regions, carrying their properties such as air temperature. The climatic indices characterize these air mass movements.

Climate Change and Variability

Uccle

The Pas

0.6

0.8

0.55 0.45 [K]

0.4

0.7

30.4 m

0.65

0.35 0.3

0.6

0.25

0.55

0.2

30 43 [months]

0.45

Verhojansk

[K]

0.8

0.36 0.34

0.6

0.28

0.5

0.26

41.9 m

0.3

0.24

0.4

0.22 30 [months]

0.2

Manaus 0.3

30 [months]

Lihue 0.4

43.3 m

0.28

0.35

0.26

41.8 m

0.3

0.24 [K]

0.38 31.7 m

0.32

0.25

0.22 0.2

0.2

0.18

0.15

0.16

0.1

0.14 0.12

0.4

0.7

0.3

30 [months]

San Francisco

1 0.9

28.6 m

0.5

0.15 0.1

44.8 m

0.75

0.5

30 [months]

0.05

30 [months]

Fig. 10. Scale spectra of near-surface air temperature time series: Uccle (Belgium), The Pas (Canada), Verhojansk (Siberia), San Francisco (California), Manaus (Brazil), Lihue (Hawaii). The cycles detected in the main climatic indices are reported in Table 2. Almost all these indices display a cycle corresponding to c1 , the notable exceptions being the NP, PNA and global-SST ENSO indices. The cycle c2 is observed in the AO (CPC), NP, PDO, PNA and SOI* indices, as well as the indices related to the Southern Oscillation (such as the ENSO indices). The scale spectra of these indices are shown in Fig. 13, 14, 15 and 16.

Multi-months cycles observed in climatic data

Fig. 11. NCEP/NCAR reanalysis data. The grid points where a cycle corresponding to c1 has been detected are coloured.

Fig. 12. NCEP/NCAR reanalysis data. The grid points where a cycle corresponding to c2 has been detected are coloured. Index

cycle c1

cycle c2

AO (CPC) 34 ± 2.6 43 ± 2.5 QBO 29 ± 2 Global-SST ENSO 45 ± 2.1 MEI ENSO 30 ± 2.1 45 ± 2.1 NAO (CPC) 34 ± 2.1 NAO (CRU) 34 ± 2.1 NOI 32 ± 2.3 NP 43 ± 2.4 PDO 26 ± 2.4 40 ± 2.3 PNA 45 ± 2.4 SOI 30 ± 2.2 SOI* 30 ± 2.5 44 ± 2.6 Table 2. Cycles found in the main climatic indices (the errors are estimated as in Nicolay et al. (2009)).

Climate Change and Variability

AO (CPC)

NAM

0.5 0.45 0.4

50 45

12 m

0.35

0.25

0.2

0.15

0.1

0.05

30 [months]

34 m

34 m 43.3 m

0.3

12 m

NAO (CPC) 0.4

NAO (CRU) 0.7

11 m

0.35

9.75 m

0.6

0.3

0.5

34 m

0.25

0.4

0.2

0.3

0.15

0.2

0.1

30 [months]

0.1

[months]

24.9 m

34 m

30 [months]

Fig. 13. Scale spectra of the climatic indices related to the Northern Atlantic Oscillation.

4.4 A statistical validation for the observed cycles

Although many evidences attest the validity of the method described above, a question naturally remains: Is there a high probability that the maxima observed in the scale spectra occurred by pure chance? In Nicolay et al. (2010), to check if the cycles observed in the time series can be trusted, the scale spectra of the NCEP/NCAR reanalysis data have been compared with the spectra of signals made of an autoregressive model of the first order (AR(1) model, see e.g. Janacek (2001)), in which maxima could occur fortuitously. Such processes are observed in many climatic and geophysical data (see e.g. Allen & Robertson (1996); Percival & Walden (1993)) and are well suited for the study of climatic time series (see e.g. Mann & Lees (1996); Mann et al. (2007)). An artificial signal (yn ) can be associated to the temperature time series ( xn ) of a grid point of the NCEP/NCAR reanalysis data as follows: • One first computes the climatological anomaly time series (δn ) of ( xn ), i.e. for each month, the mean temperature is calculated from the whole signal and the so-obtained monthly-sampled signal (mn ) is subtracted to ( xn ), δn = xn − mn . • The anomaly time series (δn ) is fitted with an AR(1) model (n ), n = αn−1 + σηn ,

Multi-months cycles observed in climatic data

Global-SST ENSO

MEI ENSO

0.8

0.7

44.8 m

0.6

30.6 m

0.5

0.4

0.3

0.2

6 4

44.8 m

30 [months]

0.1

SOI

30 [months]

SOI*

1.4 29.6 m

0.9

53.3 m

44.8 m

1.3 1.2

0.8

1.1

0.7

29.6 m

0.9 9.1 m

0.6

0.8 0.5 0.4

0.7 12

0.6

[months]

NOI

PDO

1.3

0.5 55 m

1.2

0.45

11.6 m

25.7 m 40.4 m

0.4 1.1

10.5 m

0.35

31.7 m

0.3

19.5 m

0.25

0.9

0.2 0.8 0.7

0.15 12

30 [months]

0.1

30 [months]

Fig. 14. Scale spectra of the climatic indices related to the Southern Oscillation. where ηn is a Gaussian white noise with zero mean and unit variance (see e.g. Janacek (2001)). • The artificial signal (yn ) associated to ( xn ) is defined by replacing (δn ) with (n ), yn = m n + n . Let us remark that (yn ) is indeed a stochastic process; several simulations of the same signal ( xn ) will thus yield different realizations.

Climate Change and Variability

PNA (CPC)

PNA (JISAO)

0.5 0.45

45 40

11.2 m

0.4

18.2 m

0.35 0.3

19.5 m 29.6 m

0.2

0.15

10 12

30 [months]

44.4 m

44.8 m

0.25

0.1

10.1 m

30 [months]

Fig. 15. Scale spectra of the PNA indices. NP

EOF3 (JISAO)

1.2

1.1

0.9

43.3 m

0.8

0.7

0.6

0.5

19.5 m

29.6 m 53.3 m

0.4

0.3 0.2

8.8 m

30 [months]

Fig. 16. Scale spectra of other climatic indices. To check if the cycles c1 and c2 appearing in the time series did not occur by pure chance, the subsequent methodology can be applied to each temperature time series ( xn ) of the NCEP/NCAR reanalysis data: • N = 10, 000 realizations (yn ) of ( xn ) are computed. • The distribution of the highest local maximum y M of the scale spectrum of the data in the range of 26 to 47 months is estimated from these artificial signals, i.e. one computes the distribution of ˜ ( a ), y M = sup Λ 26≤ a≤47

˜ is the scale spectrum of a realization (yn ). where Λ • The probability P to obtain a maximum of higher amplitude than the one corresponding to c1 or c2 observed in the scale spectrum of ( xn ) is finally computed, using the distribution previously obtained. It is shown in Nicolay et al. (2010) that such a methodology yields reliable data. The probability values concerning c1 and c2 are displayed in Fig. 17 and Fig. 18 respectively. The coloured area correspond to regions where the cycle is significant. These figures show that most of the

Multi-months cycles observed in climatic data

cycles associated with c1 and c2 can be considered as significant. The cycle observed in the climatic indices are also significant, since one always get P < 0.1 (see Mabille & Nicolay (2009); Nicolay et al. (2010)). Finally, let us remark that c1 and c2 can also be detected through the Fourier transform, if the time series are preprocessed in order to free the corresponding spectrum from the dominating cycle corresponding to one year (for more details, see Nicolay et al. (2010)).

Fig. 17. The probability values associated with c1 (NCEP/NCAR reanalysis data). The cycles observed in a zone corresponding to the colour white are not significant.

Fig. 18. The probability values associated with c2 (NCEP/NCAR reanalysis data). The cycles observed in a zone corresponding to the colour white are not significant.

5. Discussion and conclusions The wavelet-based tool introduced in Sect. 3.1 provides a methodology for detecting cycles in non-stationary signals. Its application to climatic time series has led to the detection of two statistically significant periods of 30 and 43 months respectively. When looking at the global temperature time series, since most of the lands are situated on the Northern Hemisphere, the cycle c1 seems to be influenced by the continents, while the cycle c2 appears to be more influenced by the oceans. However, considering that only a small number

Climate Change and Variability

of stations is taken into account in the construction of these records, the above comment has to be taken with circ*mspection. Weather station records and NCEP/NCAR reanalysis show that the cycle c1 is mainly seen in the regions under the influence of the Arctic Oscillation, while the cycle c2 is observed all over the globe, but more frequently in the regions under the influence of the Southern Oscillation. As a matter of fact, the same cycles are observed in the corresponding indices. In particular c1 is observed in the spectrum of the AO index and c2 is detected in the ENSO indices. As observed in Mabille & Nicolay (2009); Nicolay et al. (2009), the temperature amplitude induced by these cycles always lies between 0.2 and 0.8 K and represents about ten percents of the annual amplitude. Since the sun is one of the origins of the air mass flows and since the cycles c1 and c2 are observed in both the temperature time series and the indices describing the air mass flows, a possible explanation for the existence of these cycles is the solar activity variability. If such hypothesis is true one should find a corresponding cycle in the solar indices such as the solar flux and the sun spot number. Indeed, a cycle corresponding to a period of about 37 months is observed in these data (see Nicolay et al. (2009)). The climate regions could then induce a change of period going from 30 months for continental climates to 43 months for oceanic climates. This cycle corresponding to 37 months detected in the sun is a “flip-flop” type behavior: following Mursula & Hiltula (2004), the solar rotation periodicity undergoes a phase reversal cycle. In Takalo & Mursula (2002), this period is estimated to be about 38 months in the last 40 years, in good agreement with findings based on long series sunspot observations obtained in Berdyugina & Usoskin (2003).

6. References Allen, M.R. & Robertson, A.W. (1996). Distinguishing modulated oscillations from coloured noise in multivariate datasets, Clim. Dyn., 12, 775–784. Arneodo, A.; Audit, B.; Decoster, N.; Muzy, J.-F. & Vaillant, C. (2002). Climate Disruptions, Market Crashes and Heart Attacks, In: The Science of Disaster, A. Bunde and H.J. Sc heelnhuber, (Eds.), pp. 27–102, Springer, Berlin. Baldwin, M.P. et al. (2001). The quasi-biennial oscillation. Rev. Geophys., Vol. 39, 179–229. Barnston, A. & Livezey, R. (1987). Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., Vol. 115, 1083–1126. Berdyugina, S.V. & Usoskin, I.G. (2003). Active longitutes in sunspot activity: Century scale persistence. Astron. Astrophys., Vol. 405, 1121. Brohan, P.; Kennedy, J.J.; Harris, I.; Tett, S.F.B. & Jones P.D. (2006). Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850. J. Geophys. Res., Vol. 111, D12,106. Daubechies, I. (1992). Ten lectures on wavelets, SIAM, Philadelphie. Fedorov, A.V. & Philander, S.G. (2000). Is El Niño changing? Science, Vol. 288, 1997–2002. Freysz, E.; Pouligny, B.; Argoul, F. & Arneodo, A. (1990). Optical wavelet transform of fractal aggregates. Phys. Rev. Lett., Vol. 64, pp. 745–748. Goupillaud, P.; Grossman, A. & Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration , Vol. 23, 85–102. Hansen J.; Ruedy R.; Glascoe, J. & Sato M. (1999). GISS analysis of surface temperature change. J. Geophys. Res., Vol. 104, 30997–31022.

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Huang, N.E. et al. (1998). The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London A, Vol. 454, pp. 903–995. Hurrell, J.W. (1995). Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science, 269, 676–679. Janacek, G. (2001). Practical time series, Arnold, London. Jones, P.D.; Jonsson, T. & Wheeler, D. (1997). Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and South-West Iceland. International Journal of Climatology, Volume 17, 1433–1550. Jones, P.D. et al. (2001). Adjusting for sampling density in grid box land and ocean surface temperature time series. J. Geophys. Res., Vol. 106, 3371–3380. Kalnay, E. et al. (1996). NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., Vol. 77, 437–471. Keller, W. (2004). Wavelets in geodesy and geodynamics, de Gruyter, Berlin. Klein Tank, A.M.G. et al. (2002). Daily dataset of 20th-century surface air temperature and precipitation series for the European climate assessment. International Journal of Climatology, Vol. 22, 1441–1453. Klingbjer, P. & Moberg A. (2003). A composite monthly temperature record from Torneladen in Northern Sweden, 1802–2002. International Journal of Climatology, Vol. 23, 1465–1494. Kronland-Martinet, R.; Morlet, J. & Grossmann, A. (1987). Analysis of sound patterns through wavelet transforms. Int. J. Pattern Recogn. Artific. Intellig., Vol. 1, 273–302. Mabille, G. & Nicolay, S. (2009). Multi-year cycles observed in air temperature data and proxy series. Eur. Phys. J. Special Topics, Vol. 174, 135–145. Mallat, S. (1999). A wavelet tour of signal processing, Academic Press, New-York. Mantua, N.J.; Hare S.R.; Zhang, Y.; Wallace, J.M. &, Francis, R.C. (1997). A pacific interdecadal climate oscillation with impacts on salmon production. Bulletin of the American Meteorological Society, Vol. 78, 1069–1079. Mann, M.E. & Lees, J. (1996). Robust estimation of background noise and signal detection. Climatic Change, 33, 409–445. Mann, M.E.; Rutherford, S.; Wahl, E. & Ammann, C. (2007). Robustness of proxy-based climate field reconstruction methods. J. Geophys. Res., 112, D12109. Matyasovszky, I. Improving the methodology for spectral analysis of climatic time series. Theor. Appl. Climatol., to appear, DOI: 10.1 007/s00704-009-0212-z. Meyer, Y. (1989). Ondelettes et opérateurs, Hermann, Paris. Mursula, K. & Hiltula, T. (2004). Systematically asymmetric heliospheric magnetic field: Evidence for a quadrupole mode and non-axis symmetry with polarity flip-flops. Sol. Phys., Vol. 224, 133–143. Nelder, J. &, Mead R. (1965). A simplex method for function minimization. Comput. J., Vol. 7, 308–313. Newton, H. & Milsom, A. (1955). Note on the observed differences in spottedness of the Sun’s Northern and Southern Hemispheres. Monthly Not. R. Astron. Soc., Vol. 115, 398:404. Nicolay, S. (2006). Analyse de séquences ADN par la transformée en ondelettes, Ph.D. Thesis, University of Liège. Nicolay, S.; Mabille, G.; Fettweis, X. & Erpicum, M. (2009). 30 and 43 months period cycles found in air temperature time series using the Morlet wavelet method. Clim. Dyn., Vol. 33, pp. 1117–1129.

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Nicolay, S.; Mabille, G.; Fettweis, X. & Erpicum, M. (2010). A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method. Nonlin. Processes Geophys., 17, 269-272. Paluš, M. & Novotná, D. (2006). Quasi-biennal oscillations extracted from monthly NAO index and temperature records are phase-synchronized. Nonlin. Processes Geophys., Vol. 13, 287–296. Percival, D.B. & Walden, A.T. (1993). Spectral analysis for physical applications, Cambridge University Press, Cambridge. Rayner, N.A. et al. (2006). Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: The HadSST2 dataset. Journal of Climate, Vol. 19, 446–469. Rudloff, W. (1981). World Climates. Wissenschaftliche Verlagsgesellschaft mbH, Stuttgart. Ruskai, M.B.; Beylkin, G.; Coifman, R.; Daubechies, I.; Mallat, S.; Meyer, Y. & Raphael, L. (eds.) (1992). Wavelets and their Applications, Jones and Bartlett, Boston. Schwing, F.; Murphree, T. & Green, P. (2002). The northern oscillation index (NOI): A new climate index for the northeast pacific. Progress in Oceanography, Vol. 53, 115–139. Smith, T.M. & Reynolds, R.W. (1997). Extended reconstruction of global sea surface temperature based on COADS data. J. Climate, Vol. 16, 1495–1510. Smith, D.; Cusack, S.; Colman, A.; Folland, C.; Harris, G. & Murphy, J. (2007). Improved surface temperature prediction for the coming decade from global climate model. Science, 317, 796–799. Takalo, J. & Mursula, K. (2002). Annual and solar rotation periodicities in IMF components: Evidence for phase/frequency modulation. Geophys. Res. Lett., Vol. 29, 31–1–31–4. Titchmarsh, E.C. (1948). Introduction to the theory of Fourier integrals, Oxford University Press. Torresani, B. (1995). Analyse continue par ondelettes, CNRS Éditions, Paris. Trenberth, K. & Hurrell, J.W. (1994). Decadal atmosphere-ocean variations in the pacific. Climate Dynamics, Vol. 9, 303–319. Wolter, K. & Timlin, M.S. (1993). Monitoring ENSO in COADS with a seasonally adjusted principal component index. Proc of the 17th Climate Diagnostics Workshop, Norman, OK, NOAA/N MC/CAC, NSSL, Oklahoma Clim. Survey, CIMMS and the School of Meteor, 52–57. Wolter, K. & Timlin, M.S. (1998). Measuring the strength of ENSO events – how does 1997–98 rank. Weather, Vol. 53, 315–324. Zhang, Y., Wallace, J.M. & Battisti, D.S. (1997). ENSO-like interdecadal variability. J. Climate, Vol. 10, 1004–1020. Zhou, S.; Miller, A.J.; Wang, J. & Angell, J.K. (2001). Trends of NAO and AO and their associations with stratospheric processes. Geophys. Res. Lett., Vol. 28, 4107–4110.

Summer-Time Rainfall Variability in the Tropical Atlantic

x3 Summer-Time Rainfall Variability in the Tropical Atlantic Guojun Gu

Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD & Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD U.S.A. 1. Introduction Convection and rainfall in the tropical Atlantic basin exhibit intense variations on various time scales (e.g., Nobre & Shukla, 1996; Giannini et al., 2001; Chiang et al., 2002). The Atlantic Intertropical Convergence Zone (ITCZ) stays always north of the equator with the trade winds converging into it, representing a prominent climate feature on the seasonal time scale. The ITCZ, though generally over the open ocean, extends to the northeast coast of South America during boreal spring and to the West African continent during boreal summer. Longer-than-seasonal time scale variability is also evident and has been extensively explored in the past through both observational analyses and modeling studies (e.g., Lamb, 1978a, b; Carton & Huang, 1994; Nobre & Shukla, 1996; Sutton et al., 2000). The Atlantic Niño and an interhemispheric sea surface temperature (SST) gradient mode are discovered to be the two major local forcing mechanisms (e.g., Zebiak, 1993; Nobre & Shukla, 1996), in addition to the two other remote large-scale forcings: the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) (e.g., Curtis & Hastenrath, 1995; Chiang et al., 2002; Wang, 2002). The most intense year-to-year variability in the tropical Atlantic is usually observed during boreal spring [March-April-May (MAM)], specifically in the western basin and over the northeastern portion of South America, when the marine ITCZ approaches the equator (e.g., Hastenrath & Greischar, 1993; Nobre & Shukla, 1996). Therefore, most of past studies have been primarily focused on this season (e.g., Chiang et al., 2002; Gu & Adler, 2006). Intense interannual variability has also been seen during boreal summer [June-July-August (JJA)] in the tropical Atlantic (e.g. Sutton et al., 2000; Gu & Adler, 2006, 2009). The Atlantic Niño becomes mature during this season, and the impact of the interhemispheric SST gradient mode and ENSO can still be felt in the equatorial region (e.g., Sutton et al., 2000; Chiang et al., 2002). Particularly, evident interannual variations exist in various distinct severe weather phenomena such as African easterly waves (AEW) and associated convection, and Atlantic hurricane activity (e.g., Thorncroft & Rowell, 1998; Landsea et al., 1999). These severe weather systems frequently appear during boreal summer and fall, and

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usually propagate westward near the latitudes of the ITCZ (e.g., Chen & Ogura, 1982; Gu & Zhang, 2001). Hence, based on the two recent studies (Gu & Adler, 2006, 2009), summer-time rainfall variations within the tropical Atlantic basin are further explored here with a focus on the effects of two local SST modes and ENSO. The global monthly precipitation information from the Global Precipitation Climatology Project (GPCP) is applied (Adler et al., 2003). On a global 2.5o2.5o grid, this satellite-based product is combined from various data sources: the infrared (IR) rainfall estimates from geostationary and polar-orbiting satellites, the microwave estimates from Special Sensor Microwave/Imager (SSM/I), and surface rain gauges from the Global Precipitation Climatological Centre (GPCC) in Germany. The global SST anomalies and three climatic modes are computed using a satellite-based SST product from the National Centers for Environmental Prediction (NCEP) (Reynolds et al., 2002). This product, archived on 1o1o grids, is built through recons-spatial interpolation, and lasts from January 1982 to present. The results shown here are focused on the time period of January 1982-December 2006. Additionally, monthly surface wind anomalies are derived using the products from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) reanalysis project (Kalnay et al., 1996). All monthly anomalies, and rainfall and SST indices constructed here are de-trended as we primarily focus on the variations on the interannual time scale.

2. Seasonal-mean rainfall and SST variations during JJA and MAM Detailed seasonal variations in the tropical Atlantic have been examined in past studies (e.g., Mitchell & Wallace, 1992; Biasutti et al., 2004; Gu & Adler, 2004). Here we just give a brief summary of seasonal-mean features of rainfall and SST, and their corresponding interannual variances during JJA and MAM (Fig. 1). Seasonal-mean oceanic rainfall generally stays over warm SST (27oC), featuring the climatological state of the ITCZ. The marine rainfall band is also closely connected to the rainfall zones over the two neighboring continents. During MAM (Fig. 1c), the most intense rainfall zone is located over the coastal region of the northeastern South America; simultaneously the oceanic rainfall band or the marine ITCZ approaches the equator, following the warmest SST in the equatorial region due to the relaxation of trade winds particularly in the eastern basin. During JJA (Fig. 1a), an equatorial cold tongue-ITCZ complex forms with the maritime ITCZ becoming strongest and moving to the north, with two rainfall zones concomitantly being seen over the northern South America and West Africa, respectively. During MAM (Fig. 1d), the most intense rainfall variability occurs in the western equatorial region, especially along the coastline. In contrast, the maximum SST variances are generally observed in the eastern basin, and are generally concentrated in three areas: tropical north Atlantic (~5o-25oN), the equatorial region, and tropical south Atlantic (~10o-25oS). Furthermore, the most intense SST variability tends to be away from the equator, with thus a much weaker SST variability along the equator. During JJA (Fig. 1b), rainfall variances are mostly found to be over the open ocean along the mean latitudes of the ITCZ. Major SST variability is located in the equatorial region, corresponding to the frequent appearance of the Atlantic Niño (e.g., Zebiak, 1993; Carton & Huang, 1994). Interestingly, rainfall variances within the interior of two continents, particularly over West Africa, are much weaker compared with over ocean and in the coastal zone.

Summer-Time Rainfall Variability in the Tropical Atlantic

Fig. 1. Seasonal mean rainfall (mm day-1; contours) and SST (oC; color shades) during (a) June-August (JJA) and (c) March-May (MAM); Seasonal mean variances of rainfall (mm2 day2; contours) and SST [(oC)2; color shades] during (b) JJA and (d) MAM.

3. Spatial structures of SST anomalies associated with rainfall variations in the tropical Atlantic To quantitatively investigate rainfall variability, two indices are defined to represent the strength (PITCZ) and latitude (LatITCZ) variations of the marine ITCZ, respectively, with another one denoting the rainfall variability within the entire tropical Atlantic basin (Pdm). The tropical (25oS-25oN) meridional rainfall peak (Pmax(j), here j is an index of latitude) of monthly rainfall averaged along the longitudinal band of 15o-35oW, is determined for each month, including its latitude (Latmax(j)). Then, this maximum rainfall value (Pmax(j)) is weighted by the ones at the two neighboring latitudes (P(j-1) and P(j+1)) to yield a relatively smoother value for the ITCZ strength (PITCZ). The ITCZ latitude (LatITCZ) is then estimated based on Latmax(j) and its two neighboring latitudes (Lat(j-1) and Lat(j+1)) weighted by their corresponding rainfall values. The resulting ITCZ latitude (LatITCZ) thus depends on all these three rainfall values. The GPCP monthly rainfall product has a coarse (2.5o2.5o) spatial resolution. This interpolation method can provide a relatively smooth (and reasonable), latitudinal change of the ITCZ north-south migration. The resultant ITCZ latitudes are in general confirmed by derived from both the similar NOAA/NCEP-CMAP

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satellite-based monthly precipitation product and a merged, short-record (1998-2006) 1o1o TRMM (3B43) monthly rainfall product (not shown). The basin-mean rainfall is computed over a domain of 15oS-22.5oN, 15o-35oW. Finally, PITCZ, LatITCZ, and Pdm are determined by subtracting their corresponding mean seasonal cycles. Time series for these three indices are depicted in Fig. 2. Rainfall changes during these two seasons are comparable calibrated by either PITCZ or Pdm. However, the ITCZ does not change much its mean latitudes during JJA, in contrast to evident fluctuations during MAM. Thus the major rainfall changes during JJA are related to the variability of the ITCZ strength and/or the basin-mean rainfall. This probably implies a lack of forcing mechanism on the ITCZ location during JJA. Past studies suggested that the Atlantic interhemispheric SST mode, though a dominant factor of the ITCZ position during MAM, becomes secondary during JJA (e.g., Sutton et al., 2000; Gu & Adler, 2006).

Fig. 2. Time series of (a) the domain-mean rainfall (Pdm), (b) the ITCZ strength (PITCZ), and (c) the ITCZ latitudes (LatITCZ) during JJA (solid) and MAM (dash-dot). Simultaneous correlations between SST anomalies with PITCZ and LatITCZ are estimated during both seasons (Fig. 3). During JJA, the major high-correlation area of SST anomalies with PITCZ is located west of about 120oW in the tropical central-eastern Pacific, and the correlations between SST anomalies and LatITCZ are generally weak in the tropical Pacific. Within the tropical Atlantic, significant, positive correlations with PITCZ roughly cover the

Summer-Time Rainfall Variability in the Tropical Atlantic

entire basin from 20oS to 20oN. It is of interest to note that the same sign correlation is found both north and south of the equator, suggesting a coherent, local forcing of rainfall variability during JJA. Furthermore, evident negative correlations between SST anomalies and LatITCZ are seen within the deep tropics especially along and south of the equator. These confirm the weakening effect of the interhemispheric SST gradient mode during JJA (e.g., Sutton et al., 2000). During MAM, the ITCZ strength is strongly correlated to SST anomalies in both the equatorial Pacific and Atlantic (e.g., Nobre & Shukla, 1996; Sutton et al., 2000; Chiang et al., 2002). However, the significant negative correlations tend to appear along the equator in the central-eastern equatorial Pacific (east of 180oW) and along the western coast of South America, quite different than during JJA. Roughly similar correlation patterns can also be observed for LatITCZ in the tropical Pacific. Within the tropical Atlantic basin, PITCZ tends to be correlated with SST anomalies along and south of the equator, but the high correlation area shrinks into a much smaller one compared with that during JJA. The lack of high (negative) correlation north of the equator further confirms that the interhemispheric SST mode strongly impacts the ITCZ locations (Fig. 3d), but has a minor effect on the ITCZ strength (e.g., Nobre & Shukla, 1996).

Fig. 3. Correlations of SST anomalies with (a, c) the ITCZ strength (PITCZ) and (b, d) the ITCZ latitude (LatITCZ) during (a, b) MAM and (c, d) JJA. The 5% significance level is 0.4 based on 23 degrees of freedom (dofs). During these two seasons there are also two major large areas of high correlation for both PITCZ and LatITCZ in the tropical western Pacific, though with different spatial features: One is along the South Pacific Convergence Zone (SPCZ), another is north of 10oN. These two features are probably associated with the ENSO effect and other factors, and not directly related to the changes in the tropical Atlantic, which are supported by weak regressed SST anomalies (not shown).

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4. The effects of three major SST modes To further explore the relationships between rainfall anomalies in the tropical Atlantic and SST variability, particularly during JJA, three major SST indices are constructed. Here, Nino3.4, the mean SST anomalies within a domain of 5oS-5oN, 120o-170oW, is as usual used to denote the interannual variability in the tropical Pacific. As in Gu & Adler (2006), the SST anomalies within 3oS-3oN, 0-20oW are defined as Atl3 to represent the Atlantic Equatorial Oscillation (e.g., Zebiak, 1993; Carton & Huang, 1994). SST variability in the tropical north Atlantic is denoted by SST anomalies averaged within a domain of 5o-25oN, 15o-55oW (TNA). In addition, another index (TNA1) is constructed for comparison by SST anomalies averaged over a slightly smaller domain, 5o-20oN, 15o-55oW. We are not going to focus on the interhemispheric SST mode here because during boreal summer this mode becomes weak and does not impact much on the ITCZ (e.g., Sutton et al., 2000; Gu & Adler, 2006), and the evident variability of the ITCZ is its strength rather than its preferred latitudes (Fig. 2). Same procedures are applied to surface zonal winds in the western basin (5oS-5oN, 25o45oW) to construct a surface zonal wind index (UWAtl). As discovered in past studies (e.g., Nobre & Shukla, 1996; Czaja, 2004), evident seasonal preferences exist in these indices (Fig. 4). ENSO usually peaks during boreal winter. The most intense variability in the tropical Atlantic appears during boreal spring and early summer. The maxima of both TNA and TNA1 are in April, about three months later than the strongest ENSO signals (e.g., Curtis & Hastenrath, 1995; Nobre & Shukla, 1996). Surface zonal wind anomaly in the western equatorial region (UWAtl) attains its maximum in May, followed by the most intense equatorial SST oscillation (Atl3) in June. Münnich & Neelin (2005) suggested that there seems a chain reaction during this time period in the equatorial Atlantic region. It is thus further arguable that the tropical western Atlantic (west of 20oW) is a critical region passing and/or inducing climatic anomalies in the equatorial Atlantic basin.

Fig. 4. Variances of various indices as a function of month. The variance of Nino 3.4 is scaled by 2.

Summer-Time Rainfall Variability in the Tropical Atlantic

Fig. 5. Correlation coefficients between various indices as function of month. The 5% significance level is 0.41 based on 21 dofs. 4.1 Relationships between various indices Simultaneous correlations between SST indices are computed for each month (Fig. 5). The Pacific Niño shows strong impact on the tropical Atlantic indices. Significant correlations are found between Nino3.4 and TNA during February-April with a peak in March. The negative correlation between Nino3.4 and Atl3 becomes statistically significant during April-June, showing the impact of the ENSO on the Atlantic equatorial mode (e.g., Delecluse et al., 1994; Latif & Grötzner, 2000). UWAtl is consistently, negatively correlated with Nino3.4 during April-July except in June when the correlation coefficient is slightly lower than the 5% significance level. Interestingly, there are two peak months (April and July) for the correlation between UWAtl and Nino3.4 as discovered in Münnich & Neelin (2005). High correlations between Atl3 and UWAtl occur during March-July. These correlation relations tend to support that zonal wind anomalies at the surface in the western basin is a critical part of the connection between the equatorial Pacific and the equatorial Atlantic. Münnich & Neelin (2005) even showed a slightly stronger correlation relationship. Atl3 is also significantly correlated to UWAtl in other several months, i.e., January, September, and November, probably corresponding to the occasional appearance of the equatorial oscillation event during boreal fall and winter (e.g., Wang, 2002; Gu & Adler, 2006). Within the tropical Atlantic basin, the correlations between Atl3 and SST anomalies north of the equator (TNA and TNA1) become positive and strong during late boreal summer, particularly between Atl3 and TNA1 (above the 5% significance level during AugustOctober). As shown in Fig. 4, SST variations north of the equator become weaker during boreal summer. Simultaneously the ITCZ and associated trade wind system move further to the north. It thus seems possible to feel impact in the TNA/TNA1 region from the equatorial region during this time period for surface wind anomalies-driven ocean transport (e.g., Gill, 1982).

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Lag-correlations between various SST indices are estimated to further our understanding of the likely, casual relationships among them (Figs. 6, 7, and 8). The base months for SST indices are chosen according to their respective peak months of variances (Fig. 4). The strongest correlation between Atl3 in June and Nino3.4 is found when Nino3.4 leads Atl3 by one month (Fig. 6), further confirming the remote forcing of ENSO on the Atlantic equatorial mode (e.g., Latif & Grötzner, 2000). The 1-3 month leading, significant correlation of UWAtl to Atl3 in June with a peak at one-month leading indicates that the equatorial oscillation is mostly excited by surface zonal wind anomalies in the western basin likely through oceanic dynamics (e.g., Zebiak, 1993; Carton & Huang, 1994; Delecluse et al., 1994; Latif & Grötzner, 2000).

Fig. 6. Lag-correlations between Atl3 in June with Nino3.4 and UWAtl, respectively. Positive (negative) months indicate Atl3 leads (lags) Nino3.4 and UWAtl. The 5% significance level is 0.42 based on 20 dofs. The lag-correlation between UWAtl in May and Nino3.4 is depicted in Fig. 7. The highest correlation appears as Nino3.4 leads UWAtl by one-month, suggesting a strong impact from the equatorial Pacific (e.g., Latif & Grötzner, 2000), and this impact probably being through anomalous Walker circulation and not passing through the mid-latitudes. North of the equator, TNA and TNA1 both peak in April (Fig. 4). Simultaneous correlations between these two and Nino3.4 at the peak month are much weaker than when Nino3.4 leads them by at least one-month (Fig. 8). It is further noticed that the consistent high lagcorrelations are seen with Nino3.4 leading by 1-7 months. Significant correlations of TNA and TNA1 in April with Nino3.4 can actually be found as Nino3.4 leads them up to 10 months (not shown). These highly consistent lag-relations suggest that the impact from the equatorial Pacific on the tropical north Atlantic may go through two ways: the PacificNorth-American (PNA) teleconnection and the anomalous Walker circulation (e.g., Nobre & Shukla, 1996; Saravanan & Chang, 2000; Chiang et al., 2002), with the trade wind anomalies being the critical means. Most previous studies generally emphasized the first means being

Summer-Time Rainfall Variability in the Tropical Atlantic

available during bo oreal winter and spring (e.g., Curttis & Hastenrath, 1995; Nobre & Shukla, S 19996).

Fig g. 7. Lag-correlation between UWAtl in May with h Nino3.4. Posittive (negative) months m W ind dicate UWAtl leadss (lags) Nino3.4. The T 5% significan nce level is 0.42 b based on 20 dofs.

Fig g. 8. Lag-correlatiions between TN NA and TNA1 in April with Nino o3.4. Positive (neg gative) mo onths indicate TN NA and TNA1 leaad (lag) Nino3.4. The 5% significance level is 0.422 based on n 20 dofs. 4.2 2 Spatial structu ures of three SST T modes related variations Taables 1 and 2 illusstrate the simultaaneous correlatio ons between the tthree SST indicess (Atl3, TN NA, and Nino3.44) and two rainffall indices (PITCCZ and LatITCZ). T The ENSO even nts can efffectively impact rainfall variabilitty in the tropica al Atlantic (e.g., N Nobre & Shukla,, 1996;

Climate Change and Variability

Enfield & Mayer, 1997; Saravanan & Chang, 2000; Chiang et al., 2002; Giannini et al., 2004). A higher correlation (-0.62) can even be obtained between Nino3.4 and Pdm, implying a basin-wide impact in the equatorial region. The correlation between Nino3.4 and LatITCZ is relatively weak during JJA, in contrasting to a much stronger impact during MAM. Significant correlations appear between Atl3, and PITCZ and LatITCZ during JJA and MAM (Tables 1 & 2). Even though the Atlantic equatorial warm/cold events are relatively weak and the ITCZ tends to be located about eight degrees north of the equator during boreal summer, the results suggest that the Atlantic Niño mode could still be a major factor controlling the ITCZ strength. For the effect of TNA, large seasonal differences exist in its correlations with the rainfall indices (Tables 1 & 2). During JJA, TNA is significantly correlated with PITCZ. During MAM, however this correlation is much weaker. The correlation coefficient even changes sign between these two seasons. On the other hand, TNA is significantly correlated to LatITCZ during MAM, but not during JJA. Nino3.4 Atl3 TNA  -0.51 0.68 0.51 PITCZ -0.65 LatITCZ 0.39 0.04 Table 1. Correlation coefficients () between PITCZ (mm day-1) and LatITCZ (degree), and various SST indices during JJA. =0.40 is the 5% significance level based on (n-2=) 23 dofs. Nino3.4 Atl3 TNA  -0.50 0.56 PITCZ -0.18 0.57 -0.67 0.41 LatITCZ Table 2 Correlation coefficients () between PITCZ (mm day-1) and LatITCZ (degree), and three SST indices during MAM. =0.40 is the 5% significance level based on (n-2=) 23 dofs. The modulations of the three major SST modes on the tropical Atlantic during JJA and MAM are further quantified by computing the regressions based on their seasonal-mean magnitudes normalized by their corresponding standard deviations. Fig. 9 depicts the SST, surface wind, and precipitation associated with Atl3. During JJA, the spatial patterns generally agree with shown in previous studies that primarily focused on the peak months of the Atlantic equatorial mode (e.g., Ruiz-Barradas et al., 2000; Wang, 2002). Basin-wide warming is seen with the maximum SSTs along the equator and tends to be in the eastern basin (Fig. 9b). Surface wind anomalies in general converge into the maximum, positive SST anomaly zone. Accompanying strong cross-equatorial flows being in the eastern equatorial region, anomalous westerlies are seen in the western basin extending from the equator to about 15oN. These wind anomalies are related to the equatorial warming (Figs. 5, 6), and also might be the major reason for the warming-up in the TNA/TNA1 region. Off the coast of West Africa, there even exist weak southerly anomalies between 10o-15oN. Positive rainfall anomalies are dominant in the entire basin, corresponding to the warm SSTs. It is interesting to note that these rainfall anomalies tend to be over the same area as the seasonal mean rainfall variances (Fig. 1). Particularly, over the open ocean the maximum rainfall anomaly band is roughly sandwiched by the marine ITCZ and the equatorial zone with maximum SST variability (Figs. 1c, 9b, and 9d), confirming the strong modulations of the equatorial mode during this season (Fig. 2). During MAM,

Summer-Time Rainfall Variability in the Tropical Atlantic

positive SST anomalies already appear along the equator (Fig. 9a). However, in addition to the SST anomalies along the equator, the most intense SST variability occurs right off the west coast of Central Africa, reflecting the frequent appearance of the Benguela Niño peaking in March-April (e.g., Florenchie et al., 2004). North of the equator, negative SST anomalies, though very weak, can still be seen off the West African coast. This suggests that the Atlantic Niño may effectively contribute to the interhemispheric SST mode peaking in this season, particularly to its south lobe (Figs. 1b and 9a). Negative-positive rainfall anomalies across the equator forming a dipolar structure are evident, specifically west of 20oW (Fig. 9c). In the Gulf of Guinea, positive rainfall anomalies, though much weaker than in the western basin, can still be observed extending from the open ocean to the west coast of Central Africa, roughly following strong positive SST anomalies.

Fig. 9. Regression onto Nino3.4 of SST and surface wind (a, b), and precipitation (c, d) anomalies during JJA (a, c) and MAM (b, d). The SST, surface wind, and rainfall anomalies associated with TNA are shown in Fig. 10. Positive SST anomalies appear north of the equator during MAM, but become weaker during JJA. Surface wind vectors converge into the warm SST region, resulting in the decrease in the mean trade winds north of the equator. Cross-equatorial flow is strong during MAM, implying TNA's contribution to the interhemispheric SST mode. On the other hand, no evident SST anomalies appear along and south of the equator supporting that the two lobes of the interhemispheric mode are probably not connected (e.g., Enfield et al., 1999). A negative-positive rainfall dipolar feature occurs during MAM with much weaker anomalies east of 20oW, consistent with previous studies (e.g., Nobre & Shukla, 1996; RuizBarradas et al., 2000; Chiang et al., 2002). During JJA, however only appears a single band of positive rainfall anomalies between 5o-20oN, covering the northern portion of the mean rainfall within the ITCZ and its variances (Figs. 1c, 1d, and 10d). Interestingly this band tilts

Climate Change and Variability

from northwest to southeast, tending to be roughly along the tracks of tropical storms. This may reflect the impact of TNA on the Atlantic hurricane activity (e.g., Xie et al., 2005).

Fig. 10. Regression onto Atl3 of SST and surface wind (a, b), and precipitation (c, d) anomalies during JJA (a, c) and MAM (b, d). Fig. 11 illustrates the SST, surface wind, and rainfall regressed onto the seasonal mean Nino3.4. During MAM, positive-negative SST anomalies occur in the tropical region, shaping a dipolar structure accompanied by strong cross-equatorial surface winds. Compared with Figs. 9a and 10a, it is likely that ENSO may contribute to both lobes of the interhemispheric SST mode during this season (e.g., Chiang et al., 2002). Rainfall anomalies tend to be in the western basin and manifest as dipolar as well. Compared with Figs. 9a and 9c, it is noticeable that along and south of the equator, ENSO shows a very similar impact feature as the Atlantic equatorial mode except with the opposite sign. This enhances our discussion about their relations (Figs. 5 and 6). During JJA, SST anomalies almost disappear north of the equator. South of the equator, negative SST anomalies can still be seen but become weaker, accompanied by much weaker equatorial wind anomalies. Rainfall anomalies move to the north, as does the ITCZ. The dipolar feature can hardly be discernible. Again, the rainfall anomalies show a very similar pattern as those related to Atl3 (Figs. 9d and 11d), though their signs are opposite. This seems to suggest that during JJA the impact of ENSO on the tropical Atlantic may mostly go through its influence on the Atlantic equatorial mode (Atl3).

Summer-Time Rainfall Variability in the Tropical Atlantic

Fig. 11. Regression onto TNA of SST and surface wind (a, b), and precipitation (c, d) anomalies during JJA (a, c) and MAM (b, d). Therefore, generally consistent with past results (e.g., Nobre & Shukla, 1996; Enfield & Mayer, 1997; Saravanan & Chang, 2000; Chiang et al., 2002; Giannini et al. 2004), these three SST modes all seem to influence rainfall variations in the tropical Atlantic, though through differing means. However, strong inter-correlations have been shown above among these SST indices and in past studies (e.g., Münnich & Neelin, 2005; Gu & Adler, 2006). Nino3.4 is significantly correlated with Atl3 during both JJA (-0.46) and MAM (-0.53), and with TNA (0.52) during MAM. Previous studies have demonstrated that the Pacific ENSO can modulate SST in the tropical Atlantic through both mid-latitudes and anomalous Walker circulation (e.g., Horel & Wallace, 1981; Chiang et al., 2002; Chiang & Sobel, 2002). While no significant correlation between Nino3.4 and Atl3 was found in some previous studies (e.g., Enfield & Mayer, 1997), high correlations shown here are generally in agreement with others (e.g., Delecluse et al., 1994; Latif & Grötzner, 2000). Thus, the correlations shown above, particularly the effect of Nino3.4, may be complicated by the inter-correlations among the SST indices. For instance, the high correlation between Nino3.4 and PITCZ may primarily result from their respective high correlations with Atl3 (Tables 1 & 2), and hence may not actually indicate any effective, direct modulation of convection (PITCZ) by the ENSO. It is thus necessary to discriminate their effects from each other. Thus, linear correlations and second-order partial correlations are estimated and further compared (Figs. 12 and 13). The second-order correlation here represents the linear correlation between rainfall and one SST index with the effects of other two SST indices removed (or hold constant) (Gu &Adler, 2009). With or without the effects of Nino3.4 and TNA, the spatial structures of correlation with Atl3 do not vary much. With the impact of Nino3.4 and TNA removed, the Atl3 effect

Climate Change and Variability

only becomes slightly weaker during both JJA and MAM. Given a weak relationship between Atl3 and TNA (0.17 during JJA and -0.16 during MAM; Enfield et al., 1999), this correlation change is in general due to the Pacific ENSO.

Fig. 12. Correlation maps of seasonal-mean rainfall anomalies in the tropical Atlantic with (a, d) Nino3.4, (b, e) Atl3, and (c, f) TNA during JJA (left) and MAM (right). The 5% significance level is 0.4 based on 23 dofs. During JJA, Nino3.4 and Atl3 have very limited impact on the TNA associated rainfall anomalies, likely due to TNA’s weak correlation with both Nino3.4 (-0.06) and Atl3 (0.17). The second-order partial correlation between TNA and PITCZ slightly increases to 0.57. This high correlation coefficient seems to be reasonable because the marine ITCZ is then directly over the tropical North Atlantic (Fig. 1). During MAM, the effect of TNA on rainfall over the tropical open ocean is generally weak. With the effects of Nino3.4 and Atl3 removed, the large area of negative correlation in the western basin near South America shrinks into a much smaller region. Hence, the direct influence of ENSO through the anomalous Walker circulations could play a role, but in general is confined in the western basin and over the northeastern South American continent where the most intense deep convection and variations are located

Summer-Time Rainfall Variability in the Tropical Atlantic

during MAM (Fig. 1). During JJA, this kind of modulation of deep convection disappears because the ITCZ moves to the north and stays away from the equator. The ENSO impact on rainfall anomalies in the tropical Atlantic may hence primarily go through its effect on the two local SST modes. In particular, its effect on Atl3 seems to be the only means during JJA by means of modulating surface winds in the western basin (Figs. 6 and 7; e.g., Latif & Grötzner, 2000; Münnich & Neelin, 2005). These wind anomalies are essential components for the development of the Atlantic Niño mode (e.g., Zebiak, 1993; Latif & Grötzner, 2000).

Fig. 13. Partial-correlation maps of seasonal-mean rainfall anomalies in the tropical Atlantic with (a, d) Nino3.4, (b, e) Atl3, and (c, f) TNA during JJA (left) and MAM (right). The second-order partial correlations are estimated by limiting the effects of any two other indices. The 5% significance level is 0.41 based on 21 dofs.

5. Summary and conclusions Seasonal-mean rainfall in the tropical Atlantic during JJA shows intense interannual variabilities, which are comparable with during MAM based on both the ITCZ strength and the basin-mean rainfall. The latitudes of the marine ITCZ however do not vary much from-

Climate Change and Variability

year-to-year during JJA, in contrasting to evident variations occurring during MAM. Hence the summer-time rainfall variability is mostly manifested as the variations in the ITCZ strength and the basin-mean rainfall. Rainfall variations associated with the two local SST modes and ENSO are further examined. The Atlantic Niño mode can effectively induce rainfall anomalies during JJA through accompanying anomalous surface winds and SST. These rainfall anomalies are generally located over the major area of rainfall variance. TNA can contribute to the rainfall changes as well during this season, but its impact is mostly limited to the northern portion of the ITCZ. The ENSO teleconnection mechanism may still play a role during boreal summer, although it becomes much weaker than during boreal spring. It is noticed that the ENSO-associated spatial patterns tend to be similar to those related to the Atlantic Niño though with an opposite sign. This suggests that the impact of ENSO during JJA may go through its influence on the Atlantic Niño mode. During MAM, TNA shows an evident impact on rainfall changes specifically in the region near and over the northeastern South America. The correlation/regression patterns are generally consistent with those using the index representing the interhemispheric SST mode (e.g., Ruiz-Barradas et al., 2000), though the TNA-associated SST anomalies are weak and mostly north of the equator. This suggests a strong contribution of TNA to this interhemispheric mode and also its independence from the SST oscillations south of the equator (e.g., Enfield et al., 1999). Atl3 and Nino3.4 can contribute to the interhemispheric SST mode too, in addition to their direct modulations of rainfall change in the basin. Particularly in the western basin (west of 20oW), corresponding to evident oscillations of the ITCZ locations, a dipolar feature of rainfall anomalies occurs in the regression maps for both indices. Simultaneously appear strong surface wind anomalies with evident cross-equatorial components. To further explore the relationships among the two local SST modes and ENSO, contemporaneous and lag correlations are estimated among various indices. ENSO shows strong impact on the Atlantic equatorial region and the tropical north Atlantic. Significant, simultaneous correlations between Nino3.4 and TNA are seen during February-April. Significant lag-correlation of TNA at its peak month (April) with Nino3.4 one or several months before further confirms that the impact from the tropical Pacific is a major contributor during boreal spring (e.g., Chiang et al., 2000). Nino3.4 is highly correlated with Atl3 during April-June. The correlations between Nino3.4 and zonal wind index in the west basin (UWatl) also become high during April-July. Moreover the maximum correlation between UWatl in May (peak month) and Nino3.4 is seen as Nino3.4 precedes it by one month, indicating the remote modulations of wind anomalies. The Pacific ENSO can effectively modulate convection and surface winds during boreal spring through both ways: the PNA and the anomalous Walker cell (e.g., Nobre & Shukla, 1996; Chiang & Sobel, 2002). Trade wind anomalies are a pathway for the SST oscillations north of the equator (e. g., Curtis & Hastenrath, 1995; Enfield & Mayer, 1997). Along and south of the equator, convective and wind anomalies in the western basin are the critical means for the ENSO impact. During JJA, the pathway from the mid-latitudes becomes impossible due to seasonal changes in the large-scale mean flows, and the ITCZ moves away from the equator. Hence, the ENSO impact on the tropical region is greatly limited. The lag-correlations between Atl3 at the peak month (June) and Nino3.4 and UWatl, respectively, tend to suggest that the equatorial oscillation is excited by the preceding surface wind anomalies in the west basin

Summer-Time Rainfall Variability in the Tropical Atlantic

that are closely related to the ENSO. The lag and simultaneous correlations of Atl3 with UWatl further confirm that it is a coupled mode to a certain extent. It is interesting to further note that high positive correlations can be found between Atl3 and TNA/TNA1 during JulyOctober, implying that during JJA the Atlantic equatorial mode may have a much more comprehensive impact, in addition to its influence on the ITCZ, than expected. A second-order partial correlation analysis is further applied to discriminate the effects of these three SST modes because of the existence of inter-correlations among them. With the effects of Atl3 and TNA removed, ENSO only has a very limited direct impact on the open ocean in the tropical Atlantic, and its impact is generally confined in the western basin and over the northeastern South America. Therefore, during JJA, the two local SST modes turn out to be more critical/essential for rainfall variations in the tropical Atlantic. The effect of the Pacific ENSO on the tropical Atlantic is in general through influencing the Atlantic Niño mode, and surface zonal wind anomalies in the western basin are the viable means to realize this effect.

6. References Adler, R.; & Coauthors (2003). The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeor, 4, 1147-1167. Biasutti, M.; Battisti, D. & Sarachik, E. (2004). Mechanisms controlling the annual cycle of precipitation in the tropical Atlantic sector in an atmospheric GCM. J. Climate, 17, 4708-4723. Carton, J. & Huang, B. (1994). Warm events in the tropical Atlantic. J. Phys. Oceanogr., 24, 888-903. Chen, Y. & Ogura, Y. (1982). Modulation of convective activity by large-scale flow patterns observed in GATE. J. Atmos. Sci., 39, 1260-1279. Chiang, J.; Kushnir, Y. & Zebiak, S. (2000). Interdecadal changes in eastern Pacific ITCZ variability and its influence on the Atlantic ITCZ. Geophys. Res. Lett., 27, 3687-3690. Chiang, J.; Kushnir, Y. & Giannini, A. (2002). Reconstructing Atlantic Intertropical Convergence Zone variability: Influence of the local cross-equatorial sea surface temperature gradient and remote forcing from the eastern equatorial Pacific. J. Geophys. Res., 107(D1), 4004, doi:10.1029/2000JD000307. Chiang, J. & Sobel, A. (2002). Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J. Climate, 15, 2616-2631. Curtis, S. & Hastenrath, S. (1995). Forcing of anomalous sea surface temperature evolution in the tropical Atlantic during Pacific warm events. J. Geophys. Res., 100, 1583515847. Czaja, A. (2004). Why is North Tropical Atlantic SST variability stronger in boreal spring? J. Climate, 17, 3017-3025. Delecluse, P.; Servain, J., Levy, C., Arpe, K. & Bengtsson, L. (1994). On the connection between the 1984 Atlantic warm event and the 1982-1983 ENSO. Tellus, 46A, 448464. Enfield, D. & Mayer, D. (1997). Tropical Atlantic sea surface temperature variability and its relation to El Niño-Southern Oscillation. J. Geophys. Res., 102, 929-945

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Enfield, D.; Mestas-Nunez, A., Mayer, D. & and Cid-Serrano, L. (1999). How ubiquitous is the dipole relationship in tropical Atlantic sea surface temperature? J. Geophys. Res., 104, 7841-7848. Florenchie, P.; Reason, C., Lutjeharms, J., Rouault, M., Roy, C. & Masson, S. (2004). Evolution of interannual warm and cold events in the southeast Atlantic Ocean. J. Climate, 17, 2318-2334. Giannini, A.; Chiang, J., Cane, M., Kushnir, Y. & Seager, R. (2001). The ENSO teleconnection to the tropical Atlantic Ocean: Contributions of the remote and local SSTs to rainfall variability in the tropical Americas. J. Climate, 14, 4530-4544. Giannini, A.; Saravanan, R. & Chang, P. (2004). The preconditioning role of tropical Atlantic variability in the development of the ENSO teleconnection: Implication for the predictability of Nordeste rainfall. Climate Dyn., 22, 839-855. Gill, A. (1982). Atmosphere-Ocean Dynamics. Academic Press, 662pp. Gu, G., & Adler, R. (2004). Seasonal evolution and variability associated with the West African monsoon system. J. Climate, 17, 3364-3377. Gu, G. & Adler, R. (2006). Interannual rainfall variability in the tropical Atlantic region. J. Geophys. Res., 111, D02106, doi:10.1029/2005JD005944. Gu, G. & Adler, R. (2009). Interannual Variability of Boreal Summer Rainfall in the Equatorial Atlantic. Int. J. Climatol., 29, 175-184, doi: 10.1002/joc.1724. Gu, G. & Zhang, C. (2001). A spectrum analysis of synoptic-scale disturbances in the ITCZ. J. Climate, 14, 2725-2739. Hastenrath, S. & Greischar, L. (1993). Circulation mechanisms related to northeast Brazil rainfall anomalies. J. Geophys. Res., 98, 5093-5102. Kalnay, E.; & Coauthors (1996). The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-471. Lamb, P. (1978a). Large scale tropical Atlantic surface circulation patterns during recent subSaharan weather anomalies. Tellus, 30, 240-251. Lamb, P. (1978b). Case studies of tropical Atlantic surface circulation patterns during recent sub-Saharan weather anomalies: 1967 and 1978. Mon. Wea. Rev., 106, 482-491. Landsea, C.; Pielke Jr., R., Mesta-Nunez, A. & Knaff, J. (1999). Atlantic basin hurricanes: Indices of climate changes. Climate Change, 42, 89-129. Latif, M. & Grötzner, A. (2000). The equatorial Atlantic oscillation and its response to ENSO. Climate Dyn., 16, 213-218. Mitchell, T. & Wallace, J. (1992). The annual cycle in equatorial convection and sea surface temperature. J. Climate, 5, 1140-1156. Münnich, M. & Neelin, J. (2005). Seasonal influence of ENSO on the Atlantic ITCZ and equatorial South America. Geophys. Res. Lett., 32, L21709, doi:10.1029/2005GL023900. Nobre, P. & Shukla, J. (1996). Variations of sea surface temperature, wind stress, and rainfall over the tropical Atlantic and South America. J. Climate, 9, 2464-2479. Reynolds, R.; Rayner, N., Smith, T., Stokes, D. & and Wang, W. (2002). An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625. Ruiz-Barradas, A.; Carton, J. & Nigam, S. (2000). Structure of interannual-to-decadal climate variability in the tropical Atlantic sector. J. Climate, 13, 3285-3297. Saravanan, R. & Chang, P. (2000). Interaction between tropical Atlantic variability and El Niño-Southern oscillation. J. Climate, 13, 2177-2194.

Summer-Time Rainfall Variability in the Tropical Atlantic

Sutton, R.; Jewson, S. & Rowell, D. (2000). The elements of climate variability in the tropical Atlantic region. J. Climate, 13, 3261-3284. Thorncroft, C. & Rowell, D. (1998). Interannual variability of African wave activity in a general circulation model. Int. J. Climatol., 18, 1306-1323. Wang, C. (2002). Atlantic climate variability and its associated atmospheric circulation cells. J. Climate, 15, 1516-1536. Xie, L.; Yan, T. & Pietrafesa, L. (2005). The effect of Atlantic sea surface temperature dipole mode on hurricanes: Implications for the 2004 Atlantic hurricane season. Geophys. Res. Lett., 32, L03701, doi:10.1029/2004GL021702. Zebiak, S. (1993). Air-sea interaction in the equatorial Atlantic region. J. Climate, 6, 1567-1586.

Climate Change and Variability

Tropical cyclones, oceanic circulation and climate

4 x

Tropical cyclones, oceanic circulation and climate Lingling Liu

Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China 1. Introduction Tropical cyclone, also popular known as hurricane or typhoon, is a non-frontal synoptic scale warm-core system characterized by a large low-pressure center. It forms over most of the world’s tropical waters between about 5° and 22° latitude in an environment with sufficient sea surface temperature (>26.5°C), moisture instability and weak vertical shear, including North Indian, western North Pacific, eastern North Pacific, North Atlantic, South Indian and western South Pacific (Fig.1). Environmental conditions in the eastern South Pacific and South Atlantic are not favorable for the tropical cyclone’s genesis. Thus, so far there has only been one documented tropical cyclone in the South Atlantic basin and it was quite weak. Mostly for the purpose of providing useful warnings, tropical cyclones are categorized according to their maximum wind speed. Tropical cyclones, with maximum winds of 17ms-1 or less, are known as tropical depressions; when their wind speeds are in the range of 18 to 32 ms-1, inclusive, they are called tropical storms, whereas tropical cyclones with maximum winds of 33 ms-1 or greater are called hurricanes in the western North Atlantic and eastern North Pacific regions, typhoons in the western North Pacific, and severe tropical cyclones elsewhere.

Fig.1. The tracks and intensity of nearly 150 years of tropical cyclones. (http://earthobservatory.nasa.gov/IOTD/view.php?id=7079)

Climate Change and Variability

A tropical cyclone is driven principally by heat transfer from the ocean. Thus, the genesis and development of the tropical cyclones and its variability of number and intensity are influenced by the oceans importantly. Meanwhile, a tropical cyclone can affect the thermal structure and currents of the upper ocean. Beginning with the observations published by Leipper (1967), a number of studies have been made in order to understand the various aspects of the ocean response to tropical cyclones (e.g. Price, 1981; Black, 1983; Greatbatch, 1983; Ginis, 1995; Jacob et al., 2000) and the tropical cyclone-ocean interaction (Chang & Anthes, 1979; Sutyrin & Khain, 1979; Ginis et al., 1989). It became obvious that the tropical cyclones have a profound effect on the uppermost 200-300m of the ocean, deepening the mixed layer by many tens of meters, cooling the surface temperature by as much as 5°C, and causing near-inertial surface currents of 1-2ms-1, detectable at depths up to at least 500m (Withee & Johnson, 1976). However, most of the previous studies focused on the local response of the ocean to the passing tropical cyclone. Relatively little is known about the influence of tropical cyclones on the mean climatology. Emanuel (2001) estimated the oceanic heat transport induced by tropical cyclone activity, comparable the observed peak meridional heat transport by the Meridional Overturning Circulation (MOC) as estimated by Macdonald & Wunsch (1996), suggesting that tropical cyclones may play an important role in driving the thermohaline circulation and thereby in regulating climate. As we known, the world’s oceans is an extremely important part of the Earth’s climate control system because the world’s oceans carry roughly half of the net equator-to-pole heat flux necessary to balance the meridional distribution of net radiative flux at the top of atmosphere (Macdonald & Wunsch, 1996) and thus play a critical role in setting the global temperature distribution. Furthermore, tropical cyclones threaten lives and property because of their high winds, associated storm surge, excessive rain and flooding, and ability to spawn tornadoes. Of all the natural phenomena that affect our planet, tropical cyclone, which account for the majority of natural catastrophic losses in the developed world, is among the most deadly and destructive. It is therefore of critical importance to understand the mutual influence of the tropical cyclones, oceanic circulation and climate. Our discussion here focuses on the role of tropical cyclones in regulating the general oceanic circulation and climate, section 2, and the effects of the ocean on tropical cyclones, section 3.

2. The role of tropical cyclones in regulating oceanic circulation and climate 2.1 It's role in ENSO El Niño/Southern Oscillation (ENSO) is a climate pattern that occurs across the tropical Pacific Ocean on average every four years, but over a period which varies from two to seven years. ENSO is composed of an oceanic component, called El Niño (or La Niña, depending on its phase), which is defined as a warming or cooling of at least 0.5°C (0.9°F) averaged over the east-central tropical Pacific Ocean, and an atmospheric component, Southern Oscillation, which is characterized by changes in surface pressure in the tropical Western Pacific. Measurements from satellite, ships, and buoys reveal El Niño to be a complex phenomenon that affects ocean temperatures across virtually the entire tropical Pacific, also affecting weather in other parts of the world. People are gradually interested in El Niño just because it is usually accompanied with abnormality of global circulation (Horel & Wallace, 1981).

Tropical cyclones, oceanic circulation and climate

It has been recognized that tropical cyclones, strong nonlinear events in the low and mid-latitudes in the weather system, can influence ENSO greatly. Most tropical cyclones form on the side of the subtropical ridge closer to the equator, then move poleward past the ridge axis before recurving into the main belt of the westerlies. It is well known that surface westerlies on the equator are an essential part of the development of El Niño events. Several studies have pointed out that a single tropical cyclone can also generate significant equatorial westerlies (Harrison & Giese, 1991; kindle & Phoebus, 1995). Gao et al. (1988) proposed a triggering mechanism of the near-equatorial cyclones on El Niño. They pointed out that the near-equatorial tropical cyclones developing equatorward of 10°N can intensify equatorial westerlies and produce Kelvin waves, which propagate to the South American Coasts in about 2-3 months, inducing SST to rise there. According to their result, the near-equatorial cyclones play an essential role in El Niño in its beginning, continuous, and developing period. Sobel & Camargo (2005) argued that western North Pacific tropical cyclones play an active role in ENSO dynamics, by helping a warm event which is already taking place to persist or strengthen. They proposed that tropical cyclones in the western North Pacific can produce equatorial surface westerly anomalies near the dateline, and an associated SST increase in the central and eastern Pacific. These signals are of the right sign to contribute to the enhancement of a developing El Niño event. 2.2 Tropical cyclone-induced mechanical energy input and its variability According to the new theory of oceanic general circulation, external sources of mechanical energy are required to maintain the quasi-steady oceanic circulation. Wind stress and tidal dissipation are the primary sources of mechanical energy. However, tropical cyclone, a vitally important component of the atmospheric circulation system at low- and mid-latitudes, may be an important mechanical energy source, which have been ignored because in the commonly used low spatial resolution wind stress data, these strong nonlinear events are smoothed out. Nillson (1995) estimated the energy input to the inertial waves induced by tropical cyclones theoretically as 0.026 TW, while Shay & Jacob (2006) estimated as 0.74TW using the averaged downward vertical energy flux of 2ergs cm-2 s-1 based on the observational data profile during the passage of the hurricane Gilbert. Based on a hurricane-ocean coupled model (Schade & Emanuel, 1999), the mechanical energy input to the world’s oceans induced by tropical cyclones was estimated (Liu et al., 2008). As shown in Fig.1, tropical cyclones vary greatly in their location and strength and its activity is different each year; thus, for the study of their contribution to the general oceanic circulation and climate, the most objective approach is to estimate the annual mean contribution from these storms. Then the energy input to the ocean induced by over 1500 tropical cyclones from 1984 to 2003 was calculated: (a) One of the major forms of energy transfer from wind to the ocean is through surface waves. The annual energy input to the surface waves induced by tropical cyclones averaged from 1984 to 2003 is 1.62TW. (b) The wind energy input to the surface currents, including both the geostrophic and ageostrophic components, by tropical cyclones is 0.1TW. (c) Tropical cyclones are excellent generators of near-inertial motions, which are the most likely contributor to the subsurface turbulence, internal waves, and the subsurface diapycnal mixing, because of their large wind stress that change on the inertial time scale.

Climate Change and Variability

The generation of inertial motions by tropical cyclones has been discussed in previous studies (e.g. Price, 1981, 1983). The energy flux due to wind forcing associated with tropical cyclones averaged from 1984 to 2003 is 0.03TW. (d) Tropical cyclone-induced cooling in the upper ocean is a striking phenomenon, which has been documented in many studies. Within the vicinity of a tropical cyclone, strong winds blowing across the sea surface drive strong ocean currents in the mixed layer. The vertical shear of the horizontal current at the base of mixed layer induces strong turbulence, driving mixing of warm/old water across the mixed layer base (Emanuel, 2005). As a result, sea surface temperature is cooled down. Most importantly, the warming of water below the mixed layer raises the center of mass, and the gravitational potential energy (GPE) of the water column is increased. According to the calculation, the annual mean GPE increase induced by tropical cyclones averaged from 1984 to 2003 is 0.05TW. The relationship between the increase of GPE and the energy input to the near-inertial currents and the surface currents for each individual tropical cyclone over the past 20 years are demonstrated in Fig. 2a and 2b, respectively. It is clearly seen that the near-inertial energy input alone cannot account for the increase of the GPE when the hurricanes are strong. The ratio of GPE increase to the wind energy input to the near-inertial currents and the total surface currents versus normalized PDI (power dissipation index: PDI  

Tlife

3 vmax dt¸ ,

which indicates the strength of the tropical cyclones) are shown in Fig. 2c and 2d, respectively. For weak tropical cyclones the increase of GPE is limited and it may be dominated by the contribution from the near-inertial energy from the wind.

Fig.2. Relationship between the increase of GPE and the energy sources from the hurricanes: (a) GPE vs near-inertial components; (b) GPE vs wind energy input to the surface currents; (c) the ratio of GPE increase to near-inertial energy from the wind vs PDI; and (d) the ratio of GPE increase to energy input from the wind input to the surface currents vs PDI. In the upper panels the solid lines indicate best-fit power laws (Liu et al., 2008).

Tropical cyclones, oceanic circulation and climate

For hurricanes, however, the near-inertial energy from the wind can only supply a small portion of the energy needed for GPE increase, and the remaining portion of energy should be supplied by subinertial components of the wind energy input to the surface currents. Therefore, when the hurricane is strong, wind energy input to the subinertial motion is not totally dissipated in the mixed layer; instead, it contributes to the increase of GPE. Moreover, the conversion rate of kinetic energy input from the wind to GPE also increases as the strength of the hurricane increases. The distribution of the energy input to the near-inertial motions from tropical cyclones averaged from 1984 to 2003 is shown in Fig.3. It is readily seen that most of this energy is distributed in the latitudinal band from 10° to 30°N in the western Pacific and in the North Atlantic, with approximately half of the total energy being input into the western North Pacific (The distribution of the other forms of energy generated from tropical cyclones has similar patterns).

Fig. 3. (right) Energy input to near-inertial motions induced by tropical cyclones averaged from 1984 to 2003 (units: mW m-2); (left) the meridional distribution of the integrated energy. (Liu et al., 2008) According to the previous studies, the energy input induced by smoothed wind stress to the surface geostrophic currents is estimated as 0.88TW (Wunsch, 1998), the energy input to surface waves is 60TW (Wang & Huang, 2004a) and the energy input to Ekman layer is about 3TW, including 0.5-0.7TW over the near-inertial frequency (Alford, 2003; Watanabe & Hibiya, 2002) and 2.4TW over the subinertial range (Wang & Huang, 2004b). It seems that the energy input by tropical cyclones is much smaller than that from smoothed wind field. However, it may also have a non-ignorable role in the oceanic circulation and climate. Figure 4 shows the distribution of the energy input to surface waves (induced by NCEP-NCAR wind field and tropical cyclones) averaged from 1984 to 2003. The left panel is the meridional distribution of the zonally integrated results, where the blue line is the energy input from the smoothed wind field and the red line is the total energy input. From the meridional distribution, it is readily seen that the energy generated by tropical cyclones greatly enhances the energy input at the midlatitude. In the latitudinal band from 10° to 30°N, tropical cyclones account for 22% increase of the energy, and in the western North Pacific, they account for 57% increase of energy, compared with results calculated from smoothed wind data. Although the total amount of energy input by tropical cyclones is much smaller than that by smoothed wind field, it may be more important for many applications including ecology, fishery, and environmental studies since they occur during a short time period at the midlatitude band where stratification is strong.

Climate Change and Variability

Fig. 4. (right) Distribution of energy input generated from smoothed wind field and tropical cyclones to surface waves averaged from 1984 to 2003 (units: mW m-2) and (left) the meridional distribution of the zonal integrated energy source. The blue line is the energy input generated by smoothed wind stress, and the red line is the total energy input, including contributions due to tropical cyclones. (Liu et al., 2008) Emanuel (2001) argued that subtropical cyclones are one of the strongest time-varying components in the atmospheric circulation. Accordingly, great changes in energy input to the ocean induced by tropical cyclones are expected. Figure5 shows the decadal variability of the normalized annual mean energy input to the ocean induced by tropical cyclones, the energy input to the ocean based on the NCEP-NCAR wind stress dataset (Huang et al., 2006), the normalized PDI and the normalized number of global tropical cyclones. The energy input from tropical cyclones show strong interannual and decadal variability with an increasing rate of 16% over the past 20 years, which is similar as the variability of the PDI, and the correlation coefficient is 0.92. That is, the energy input induced by tropical cyclones depends upon the strong hurricanes. Moreover, the energy input is also associated with the number of tropical cyclones in each year, and the correlation coefficient is 0.33. In addition, it can be readily seen that the energy input from tropical cyclones varies much more greatly than that from smoothed wind field, which may have an important role in the climate variability.

Fig.5. The normalized annual-mean energy input to surface waves from hurricane (black solid line), from the NCEP-NCAR wind stress dataset (blue dash-dot line), the normalized PDI (red dashed line) and the number of tropical cyclones (magenta dotted line).

Tropical cyclones, oceanic circulation and climate

2.3 The mixed layer deepening induced by tropical cyclones The strong activity of tropical cyclones can deepen the mixed layer at low- and mid-latitudes. Huang et al. (2007) have shown that the mixed layer deepening at low and middle latitudes can enhance the meridional pressure difference and thus the overturning circulation and poleward heat flux, and at the same time, take less mechanical energy to support to subsurface diapycnal mixing. Then a natural question is, how much do the tropical cyclones contribute to the mixed layer deepening at low and mid-latitudes at the global scale? Owing to the strong wind associated with tropical cyclones, mixing in the ocean is greatly enhanced, deepening the mixed layer. The mixed layer deepening for an individual tropical cyclone is defined as the difference between the initial mixed layer depth and the maximal mixed layer depth obtained from the model at a given station during the whole process of the passing through of a tropical cyclone. However, there is a possibility that several tropical cyclones passed through the same grid point within one year, each time the mixed layer deepening is denoted as dh j . If there are N tropical cyclones that passed through this grid in one year, the total mixed layer deepening at this grid is dH   j 1 dh j , and the N

distribution in the world’s oceans is shown in Fig.6. The maximum mixed layer deepening induced by tropical cyclones is on the order of 100m. It is readily seen that the mixed layer deepening induced by tropical cyclones accumulates at low- and mid-latitude, which may be much important for the meridional overturning circulation, and thus the climate.

Fig.6. Annual mean (accumulated) mixed layer deepening (m) induced by tropical cyclones averaged from 1984 to 2003. (Liu et al., 2008) In general, with the passing of a tropical cyclone, the mixed layer depth can be increased remarkably. Furthermore, after the passing through of the tropical cyclone, the mixed layer gradually relaxes back to the initial state. Woods (1985) have demonstrated that the mixed layer deepening/shoaling process can play the most important critical roles in watermass formation. The deepening of the mixed layer enables a mass exchange from the pycnocline, leading to obduction, which indicates the irreversible mass flux from the permanent pycnocline to the mixed layer; on the other hand, mixed layer retreating leaves water mass behind, leading a mass flux from the mixed layer, and thus an enhancement of subduction, which indicates the irreversible mass flux from the mixed layer to the permanet pycnocline.

Climate Change and Variability

Thus, the mixed layer deepening/shoaling process induced by tropical cyclones may be an important mechanism for the watermass formation/erosion. The total volume flux of mixed layer deepening induced by tropical cyclones is estimated as 39 Sv (1 Sv=106 m3 s-1), with 22.4 Sv in the North Pacific. Qiu and Huang (1995) discussed subduction and obduction in the oceans; they estimated that the basin-integrated subduction rate is 35.2 Sv and obduction rate 7.8 Sv in the North Pacific (10° off the equator). That is, the total rate of mixed layer deepening induced by tropical cyclones is approximately 50% of the subtropical water mass formation rate through subduction and it is much larger than obduction. However, the volume flux of mixed layer deepening induced by tropical cyclones cannot be simply regarded as the induced subduction/obduction rate enhancement. In the study of water mass movement, the upper ocean can be divided vertically into four layers: the Ekman layer, the mixed layer, the seasonal pycnocline, and the permanent pycnocline. Water parcels entrained into the mixed layer during the passage of tropical cyclones may be originated from the seasonal pycnocline, rather than from the permanent pycnocline; similarly, water parcels released during the mixed layer retreating period may be re-entrained into the mixed layer downstream. On the other hand, the subduction/obduction rate for the stations near the passage of tropical cyclone can also be affected if the subducted/obducted water parcels pass through the typhoon region during the mixed layer deepening period, which can re-entrain the water parcels in the pycnocline to the mixed layer. However, so far how to estimate the subduction/obduction enhancement induced by tropical cyclones remains unclear. Consequently, the mixed layer deepening and shoaling process induced by tropical cyclones must have a major impact on water mass balance in the regional and global oceans, which needs our further study and water mass balance without taking into consideration of the contribution of tropical cyclones may not be acceptable. 2.4. The vertical diffusivity induced by tropical cyclones Ocean mixing affects global climate because it is linked to the ocean’s ability to store and transport heat (Wunsch & Ferrari, 2004). The winds associated with tropical cyclones are known to lead to localized mixing of the upper ocean (Price, 1981; Jacob et al., 2000; D’Asaro, 2003). Furthermore, they are important mixing agents at the global scale. Sriver & Huber (2007) estimated the vertical diffusivity induced by tropical cyclones based on the temperature data. However, the assumption that all mixing in a given year is achieved during the single largest cooling event may underestimate the vertical mixing rate induced by tropical cyclones. In our study, the vertical diffusivity averaged over the lifetime of a tropical cyclone can be defined by the following scaling: k  wdh , where dh is the mixed layer deepening due to cyclone stirring, and w  dh / Tlife is the equivalent upwelling velocity averaged over the life cycle of tropical cyclone. However, for the study of oceanic general circulation, it is more appropriate to define the contribution due to each tropical cyclone in terms of the annual mean vertical diffusivity k j  w j  dh j  dh 2j / Tyear (1) The annual meaning (accumulated) vertical diffusivity is defined as

Tropical cyclones, oceanic circulation and climate

j 1

Tyear

k  kj 

 dh j 1

2 j

(2)

Fig. 7. Annual-mean vertical diffusivity induced by tropical cyclones from 1984 to 2003 (units: 10-4 m2 s-1): (right) the horizontal distribution and (left) the zonally averaged vertical diffusivity. (Liu et al., 2008) The horizontal distribution of the tropical cyclone-induced vertical diffusivity is shown in Fig. 7. It indicates that vertical diffusivity induced by tropical cyclones is on the order of (1-6)×10—4m2s-1, which is approximately 10 to 100 times larger than the low environmental diffusivity (0.05-0.1) ×10—4m2s-1 observed in the subtropical ocean below the mixed layer. Thus, the winds associated with tropical cyclones generate strong, near-inertial internal waves, making them efficient upper ocean mixers. Although the hurricane-induced mixing takes place in a vertical location in the water column quite different from that of deep mixing induced by tides, they all contribute to the maintenance of the oceanic general circulation and climate. Climate is especially sensitive to mixing variations in the tropical ocean (Bugnion et al., 2006) because this region of strong stratification is the most efficient place for mixing to drive strong heat transport (Scott & Marotzke, 2002; Nof & Gorder, 1999, 2000; McWilliams et al., 1996). The cyclone-induced mixing is a fundamental physical mechanism that may act to stabilize tropical temperature, mix the upper ocean, and cause amplification of climate change (Sriver & Huber, 2007).

3. How the tropical cyclone’s behavior is affected by the ocean? During the past decade or so, many researchers have suggested that because of global warming, the sea surface temperature will likely increase, which will then lead to an increase in both the number and intensity of tropical cyclones (Chan & Liu, 2004). Emanuel (2005) found that the hurricane power dissipation, which indicates the potential destructiveness of hurricanes, is highly correlated with tropical sea surface temperature, reflecting well-documented climate signals. Moreover, the total power of dissipation has increased markedly since the mid-1970s due to both longer storm lifetimes and greater storm intensities. Thus, it was proposed that the future warming may lead to an upward

Climate Change and Variability

trend in tropical cyclone destructive potential. Sriver and Huber (2006) further proposed that a 0.25°C increase in mean annual tropical sea surface temperature corresponds roughly to a 60% increase in global Power Dissipation of tropical cyclones. Webster et al. (2005) showed that in an environment of increasing sea surface temperature, a large increase was seen in the number and proportion of hurricanes reaching categories 4 and 5 while the number of cyclones and cyclone days has decreased in all basins except the North Atlantic during the past decade. However, in the more recent work published by Emanuel et al. (2008), based on a new technique for deriving hurricane climatologies from global data, he stated that global warming should reduce the global frequency of hurricanes, though their intensity may increase in some locations. Emanuel (2001) argued that tropical cyclones are one of the strongest time-varying components in the atmospheric circulation. The interannual variations of tropical cyclone activity received much more attention. Among these, most studies focus on the effect of the El Niño/Southern Oscillation (ENSO). ENSO affects tropical cyclones strongly in several basins, though its influence is different in each. For the western North Pacific, during warm ENSO events, there is the eastward and equatorial shift in genesis location of tropical cyclones and longer lift span (Wang & Chan, 2002). However, due to the differences in data and technique, the results about the influence of ENSO on the frequency of tropical cyclones may be somewhat controversial. Chan (1985, 2000), Wu& Lau (1992), and others reached a common conclusion that the number of tropical storm formation over the western North Pacific is less than normal during El Niño years. However, in more recent work (Chan & Liu, 2004; Camargo & Sobel, 2005), it is shown that the mean annual tropical cyclones in the western North Pacific is higher (lower) during El Niño (La Niña) year. For the eastern North Pacific, the formation points shifts west, more intense hurricanes are observed, and tropical cyclones track father westerward and maintain a longer lifetime in association with warm ENSO events (Schroeder & Yu, 1995; Irwin & Davis, 1999; Kimberlain, 1999) . Moreover, in the South Pacific, tropical cyclones originated farther east, resulting in more storms in the eastern South Pacific and fewer in the western South Pacific during El Niño years (Revell & Goulber, 1986). In the North Atlantic, when El Niño is present, there are fewer and/or weaker storms and its genesis father north (Gray et al., 1993; Knaff, 1997).

4. Conclusions Climate variability and any resulting change in the characteristics of tropical cyclones have become topics of great interest and research. As we discussed above, the climate signals, including ENSO, global warming, can greatly influence the tropical cyclone activity, including its number and intensity. On the other hand, a tropical cyclone can affect the local thermal structure and currents of the upper ocean, which has been discussed much in the previous studies. However, relatively little is known about the influence of tropical cyclone activity on the oceanic circulation and climate. Therefore, in this study we mainly focus on the role of tropical cyclones in regulating the general oceanic circulation and climate. The tropical cyclones can influence the oceanic circulation in many aspects: 1) Tropical cyclones play an essential role in modulating, even triggering ENSO; 2) Tropical cyclones are an important mechanical energy sources required to maintain the quasi-steady oceanic

Tropical cyclones, oceanic circulation and climate

circulation. 3) Mixed layer deepening induced by tropical cyclones at low and midlatitudes can enhance the meridional pressure difference and thus the overturning circulation and poleward heat flux. Moreover, the mixed layer deepening/shoaling processes induced by tropical cyclones can also affect the water mass formation/erosion, which need the further study. 4) Tropical cyclones can lead to strong localized mixing which affects global climate. Nevertheless, these studies are only the first step toward unraveling the complicated roles of tropical cyclones in the oceanic circulation and climate system. Many important questions remain unclear at the present time. For example, tropical cyclones are active only for a small fraction of the annual cycle. How do the energy input and the increase of vertical diffusivity induced by tropical cyclones over a small fraction of time in a year affect the general oceanic circulation? How do the mixed layer deepening/shoaling processes induced by tropical cyclones affect the formation/erosion, and then the subduction/obduction? To answer these questions further study is clearly needed.

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Ginis, I. (1995). Ocean response to the tropical cyclone. Global Perspective on Tropical Cyclones. WMO/TD-NO.693, 198-260. Gray, W. M., C. W. Landsea, P. W. Mielke, Jr., & K. J. Berry (1993). Predicting Atlantic basin seasonal tropical cyclone activity by 1 August. Weather and Forecasting, 8, 73-86. Greatbatch, R. J. (1983). On the response of the ocean to a moving storm: The nonlinear dynamics. J. Phys. Oceanogr., 13, 357-367. Harrison, D. E., & B. S. Giese (1991). Episodes of surface westerly winds as observed from islands in the western tropical Pacific. J. Geophys. Res., 96, 3221-3237. Horel. J. D. & J. M. Wallace (1981). Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev., 109, 813-829. Huang, R. X., W. Wang & L. L. Liu (2006). Decadal variability of wind-energy input to the world ocean. Deep-Sea Res. II., 53, 31-41. Huang, R. X., C. J. Huang & W. Wang (2007). Dynamical roles of mixed layer in regulating the meridional mass/heat fluxes. J. Geophys. Res., 112, C05036, doi: 10.1029/2006JC004046. Irwin, R. P. & R. E. Davis (1999). The relationship between the Southern Oscillation Index and tropical cyclone tracks in the eastern North Pacific. Geophys. Res. Lett., 20, 2251-2254. Jacob, S. D., L. K. Shay, A. J. Mariano & P. G. Black (2000). The 3D oceanic mixed layer response to Hurricane Gilbert. J. Phys. Oceanogr., 30, 1407-1429. Kimberlain, T. B. (1999). The effects of ENSO on North Pacific and North Atlantic tropical cyclone activity. In preprints of the 23 rd conference on Hurricanes and Tropical Meteorology, 250-253. Boston: American Meteorological Society. Kindle, J. C., & P. A. Phoebus (1995). The ocean response to operational westerly wind bursts during the 1991-1992 El Nino. J. Geophys. Res., 100, 4893-4920. Knaff, J. A. (1997). Implications of summertime sea level pressure anomalies in the tropical Atlantic region. J. Clim., 10, 789-804. Leipper, D. F. (1967). Observed ocean conditions in Hurricane Hilda. J. Atmos. Sci., 24, Liu, L. L., W. Wang & R. X. Huang (2008). The mechanical energy input to the ocean induced by tropical cyclones. J. Phys. Oceanogr., 38, 1253-1266. Macdonald, A. M. & C. Wunsch (1996). The global ocean circulation and heat flux. Nature, 382, 436-439. McWilliams, J. C., G. Danabasoglu & P. R. Gent (1996). Tracer budgets in the warm water sphere. Tellus A, 48, 179-192. Nilsson, J. (1995). Energy flux from traveling hurricanes to the internal wave field. J. Phys. Oceanogr., 25, 558-573. Nof, D. & Van Gorder, S. (1999). A different perspective on the export of water from the south Atlantic. J. Phys. Oceanogr., 29, 2285-2302. Nof, D. & Van Gorder, S. (2000). Upwelling into the thermocline of the Pacific Ocean. Deep-Sea Res. I, 47, 2317-2340. Price, J. F. (1981). Upper ocean response to a hurricane. J. Phys. Oceanogr., 11, 153-175. Price, J. F. (1983). Internal wave wake of a moving storm. Part I: Scales, energy budget and observations. J. Phys. Oceanogr., 13, 949-965. Qiu, B., & R. X. Huang (1995). Ventilaiton of the North Atlantic and North Pacific: Subduction Versus Obduction. J. Phys. Oceanogr., 25, 2374-2390.

Tropical cyclones, oceanic circulation and climate

Revell, C. G. & S. W. Goulter (1986). South Pacific tropical cyclones and the Southern Oscillation. Mon. Wea. Rev., 114, 1138-1145. Schade, L. R. & K. A. Emanuel (1999). The ocean’s effect on the intensity of tropical cyclones: Results from a simple coupled Atmosphere-Ocean Model. J. Atmos. Sci., 56, 642-651. Schroeder, T. A. & Z. P. Yu (1995). Interannual variability of central Pacific tropical cyclones. In Preprints of the 21st conference on Hurricanes and Tropical Meteorology, 437-439. Boston: American Meteorological Society. Scott, J. R. & J. Marotzke (2002). The location of diapycnal mixing and the meridional overturning circulation. J. Phys. Oceanogr., 32, 3578-3595. Shay, L. K. & S. D. Jacob (2006). Relationship between oceanic energy fluxes and surface winds during tropical cyclone passage. Atmosphere-Ocean Interactions II: Advances in Fluid Mechanics, W. Perrie, Ed., WIT Press, Southampton, United Kingdom, 115-142. Sobel, A. H. & S. J. Camargo (2005). Influence of western North Pacific tropical cyclones on their large-scale environment. J. Atmos. Sci., 62, 3396-3407. Sriver, R. L. & M. Huber (2006). Low frequency variability in globally integrated tropical cyclone power dissipation. Geophys. Res. Lett., 33, L11705, doi: 10.1029/ 2006GL026167. Sriver, R. L. & M. Huber (2007). Observational evidence for an ocean heat pump induced by tropical cyclones. Nature, 447, 577-580. doi: 10.1038/nature05785. Sutyrin, G. G. & A. P. Khain (1979). Interaction of the ocean and atmosphere in the area of moving tropical cyclones. Dokl. Akad. Nauk SSSR, 249, 467-470. Wang, B. & J. C. L. Chan (2002). How strong ENSO events affect tropical storm activity over the western North Pacific. J. Clim., 15, 1643-1658. Wang, W. & R. X. Huang (2004a). Wind energy input to the surface waves. J. Phys. Oceanogr., 34, 1276-1280. Wang, W. & R. X. Huang (2004b). Wind energy input to the Ekman layer. J. Phys. Oceanogr., 34, 1267-1275. Watanabe, M. & T. Hibiya (2002). Global estimate of the wind-induced energy flux to the inertial motion in the surface mixed layer. Geophys. Res. Lett., 29, 1239, doi: 10.1029/2001GL04422. Webster, P. J., G. J. Holland, J. A. Curry & H. R. Chang (2005). Changes in tropical cyclone number, duration and intensity in a warming environment. Science, 309, 1844-1846. Withee, G. W., & A. Johnson (1976). Data report: buoy observations during Hurricane Eloise (September 19 to October 11, 1975), US Dep. Commer., NOAA, NSTL Station, MS Woods, J. D. (1985). The physics of pycnocline ventilation. Coupled ocean-Atmosphere Models. J.C.J.Nihoul, Ed., Elservier Sci. Pub., 543-590. Wu, G. & N. C. Lau (1992). A GCM simulation of the relationship between tropical-storm formation and ENSO. Mon. Wea. Rev., 120, 958-977. Wunsch, C. (1998). The work done by the wind on the oceanic general circulation. J. Phys. Oceanogr., 28, 2332-2340. Wunsch, C. & Ferrari, R. (2004). Vertical energy flux, and the general circulation of the oceans. Annu. Rev. Fluid Mech, 36, 281-314.

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Possible impacts of global warming on typhoon activity in the vicinity of Taiwan

x5 Possible impacts of global warming on typhoon activity in the vicinity of Taiwan Chia Chou

Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan Department of Atmospheric Sciences, National Taiwan University, Taipei Taiwan

Jien-Yi Tu

Department of Atmospheric Sciences, Chinese Culture University, Taipei Taiwan

Pao-Shin Chu Department of Meteorology, SOEST, University of Hawaiiat Manoa, Honolulu Hawaii 1. Introduction

Typhoons are one of the most extreme natural events over the western North Pacific–East Asian (WNP–EA sector). Typhoons often affect the spatial distribution of regional precipitation in summer since they are a major source of rainfall over this region. For example, in 2004, 10 typhoons occurred in Japan and brought more than usual precipitation, causing widespread damage, whereas drought occurred in the Philippines and southern China (Kim et al., 2005; Levinson et al., 2005; Wu et al., 2005). Typhoon-related climate studies often focus on the variation of typhoon intensity, frequency, and track in multitemporal scales ranging from intraseasonal to interdecadal (Chan, 1985, 2000; Chia and Ropelewski, 2002; Chu, 2004; Ho et al., 2006; Matsuura et al., 2003; Wang and Chan, 2002). In recent years, the influence of global warming on the intensity of tropical cyclones has received much attention. Emanuel (2005), Hoyos et al. (2006), and Webster et al. (2005) found increasing trends in the western Pacific and Atlantic based on some available besttrack datasets. Chan and Liu (2004) and Klotzbach (2006) found small or no trends using alternate analysis techniques. Other studies (e.g., Landsea et al., 2006) have shown opposite trends to those found by Emanuel in the west Pacific by examining other best-track datasets. Besides observations, model simulations also show that intense tropical cyclones will be more frequent in the future warmer climate, while the total number of tropical cyclones tends to decrease (Bender et al., 2010; Emanuel et al., 2008; Zhao et al., 2009). In addition to the increase in tropical cyclone intensity, one study also found a detectable shift of the typhoon track over the WNP–EA in the past four decades (Wu et al., 2005). Such a change also affects regional precipitation (Ren et al., 2006). For a future climate projection, the typhoon track over the WNP–EA region may potentially be affected by global warming (Wu and Wang, 2004). In our study, we also focus on the variation of typhoon tracks,

Climate Change and Variability

particularly on the abrupt change of typhoon tracks from a historical perspective. Observational data for Taiwan are useful for studying variations of typhoon tracks over the WNP–EA region because of the island’s unique location. Taiwan is located at the turning point of the track for most typhoons in the WNP–EA region (Camargo et al., 2007). Figure 1 shows two major typhoon paths over the WNP–EA region: one is moving westward to the South China Sea directly and the other is turning north to either Japan or Korea. Taiwan is located just between these two major tracks, so the number of typhoon landfalls on Taiwan is sensitive to the shift of the typhoon track. Thus, we use the number of typhoons that passed through the vicinity of Taiwan (21º–26ºN, 119º–125ºE) as an index to examine the variation of the typhoon track in the WNP–EA region. The results here are mainly from Tu et al. (2009), along with a discussion of global warming impacts on typhoon track. The data and statistical methods used in this study are briefly discussed in section 2. We first identified the abrupt shift of the typhoon track in section 3 and then discussed its association with large-scale environmental changes in section 4. A possible association of this change in typhoon track with global warming was discussed in section 5, followed by a discussion and conclusions.

2. Data, statistical methods, and climate model 2.1 Data The number of typhoons in the vicinity of Taiwan from 1970 to 2006 is provided by the Central Weather Bureau (CWB) in Taiwan (Chu et al., 2007). Independently, the typhoontrack information for the period of 1970-2009 is obtained from the Regional Specialized Meteorological Center (RSMC) Tokyo—Typhoon Center. Here we defined the maximum surface wind over 34 kt as a typhoon case. To understand the influence of the large-scale environment on typhoon activity, the following two global datasets were analyzed: (1) a monthly optimum interpolation (OI) sea surface temperature (SST) with 1º spatial resolution from January 1982 to December 2009 (Reynolds et al., 2002), and (2) other large-scale variables, such as the geopotential height and wind field, are derived from (1979–2009) the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Reanalysis 2 (NCEP-2) data (Kanamitsu et al., 2002), with a horizontal resolution of 2.5º latitude × 2.5º longitude. 2.2 Statistical Methods To detect abrupt shifts in tropical cyclone records, we use a Bayesian changepoint analysis (Chu and Zhao, 2004; Zhao and Chu, 2006). Because typhoon occurrence in the study domain is regarded as a rare event, a Poisson process is applied to provide a reasonable representation of typhoon frequency. Poisson process is governed by a single parameter: the Poisson intensity. A detail description can be found in Tu et al. (2009). Besides tropical cyclones, it is also of interest to investigate whether there is any change point in the SST records or typhoon passage frequency series. Since these variables do not follow a Poisson process, we use a different method to detect abrupt shifts in the temperature or passage frequency series: a log-linear regression model in which a step function is expressed as an independent variable is adopted. If the estimated slope is at least twice as large as its standard error, one would reject the null hypothesis (i.e., slope being

Possible impacts of global warming on typhoon activity in the vicinity of Taiwan

zero) at the 5% significance level. This model is similar to that used in Elsner et al. (2000) and Chu (2002). To evaluate the difference in the mean circulation between two samples, we use a classic nonparametric test, known as the Wilcoxon-Mann-Whitney test (Chu, 2002). To perform this test, the two data batches need to be pooled together and ranked. The null hypothesis assumes that the two batches come from the same distribution. The details can also be found in Tu et al. (2009). To calculate the trends of SST and typhoon activity in 1982-2009 and 1970-2009 respectively, these variables are computed for each typhoon season by a rank regression method, i.e., minimizing a product of usual variable times its rank in a centered ranking system (Hollander and Wofle, 1999; Neelin et al., 2006). A Spearman-rho test is used to examine the statistical significance of the trend. 2.3 Model A coupled ocean–atmosphere–land model of intermediate complexity (Neelin and Zeng, 2000; Zeng et al., 2000) is used in this study. Based on the analytical solutions derived from the Betts–Miller moist convective adjustment scheme (Betts and Miller, 1993), typical vertical structures of temperature, moisture, and winds for deep convection are used as leading basis functions for a Galerkin expansion (Neelin and Yu, 1994; Yu and Neelin, 1994). The resulting primitive-equation model makes use of constraints on the flow by quasiequilibrium thermodynamic closures and is referred to as the quasi-equilibrium tropical circulation model with a single vertical structure of temperature and moisture for deep convection (QTCM1). Because the basic functions are based on vertical structures associated with convective regions, these regions are expected to be well represented and similar to a general circulation model (GCM) with the Betts–Miller moist convective adjustment scheme. Instead of coupling a complicated ocean general circulation model, a slab mixed layer ocean model with a fixed mixed layer depth of 50 m is used. By specifying Q flux, which crudely simulates divergence of ocean transport (Hansen et al., 1988, 1997), SST can be determined by the energy balance between surface radiative flux, latent heat flux, sensible heat flux, and Q flux. The Q flux can be obtained from observations or ocean model results (Doney et al., 1998; Keith, 1995; Miller et al., 1983; Russell et al., 1985). In general, the Q flux varies from ocean to ocean as well as from season to season. QTCM version 2.3 is used here, with the solar radiation scheme slightly modified.

3. Abrupt shift of typhoon track A small area of 21º–26ºN, 119º–125ºE is defined as the vicinity of Taiwan (Chu et al., 2007). If a typhoon passes through this area, we count it as one that influences Taiwan. Figure 2 shows that a majority (over 90%) of typhoons pass through this region in June–October (JJASO), which is a typical typhoon season over the WNP–EA region (Chu et al., 2007). Thus, we count the number of typhoons only for these months of each year, instead of for the entire 12 months. Figure 3a is the time series of seasonal (JJASO) typhoon numbers in the vicinity of Taiwan from 1970 to 2006. The interannual variation is relatively small before 1982, but becomes much stronger after 1982. An increase of the number of typhoons in recent years is also evident. Figure 3b displays the posterior probability mass function of the changepoint plotted as a function of time (year). High probability on year i implies a more

Climate Change and Variability

likely change occurring, with year i being the first year of a new epoch, or the so-called changepoint. In Fig. 3b, we note a great likelihood of a changepoint on the typhoon rate in 2000. The average typhoon rate is 3.3 yr-1 during the first epoch (1970–99), but increased to 5.7 yr-1 during the second epoch (2000–06), thus almost doubling from the first to the second. Because there are two parameters to estimate for each epoch, given a changepoint, the minimum sample size is two. To achieve robust results, however, Chu and Zhao (2004) and Zhao and Chu (2006) suggested the use of 5 yr at each end of the dataset to estimate the prior parameters. Because the data observed after the changepoint year in 2000 are quite consistent, showing higher typhoon counts relative to the first epoch, we feel that our approach and the sample size (n=7) are appropriate to establish a consistent estimation for the “after changepoint” period. Separately, a nonparametric test also shows that the difference of the mean of typhoon numbers (Fig. 3a) between the two epochs is significant at the 5% level. To further substantiate the abrupt shift in typhoon activity, Fig. 3c displays the posterior density function of the rate parameter before and after the changepoint year. The posterior distribution represents a combination of the prior distribution and the likelihood function. Note the very little overlapping areas in the tail areas between two posterior distributions in Fig. 3c, supporting the notion of a rate increase from the first epoch to the second. To understand the association of the variation of typhoon number over the vicinity of Taiwan with the spatial distribution of typhoon activity over a larger area, such as the entire WNP–EA region, the frequency of the typhoon occurrence is counted for each 6-h interval for each grid box of 2.5º× 2.5º (Stowasser et al., 2007; Wu and Wang, 2004). Figure 3d shows the difference in the mean typhoon rate between the two epochs, that is, the period of 2000– 06 minus the period of 1970–99. The spatial distribution exhibits a pronounced increase of typhoon frequency north of 20ºN and a decrease south of 20 ºN over the western part of the WNP–EA region (100º–140ºE), implying a northward shift of the typhoon frequency since 2000. The area with a significance level at the 5% in Fig. 3d (northeast of the Taiwan vicinity) is slightly different from the vicinity of Taiwan defined in Fig. 3a. This is because of the way typhoon counts constructed in Fig. 3a are somewhat different from the typhoon frequency in Fig. 3d, which contains mixed information of typhoon numbers and typhoon translation speed. Examining the total number of typhoon formations over the entire WNP, no noticeable changes are found. However, on a regional scale, we did find a reduction of the formation number over the South China Sea and the Philippine Sea, but little change over the vicinity of Taiwan (not shown). Overall, this suggests that the typhoon track over the western part of the WNP–EA region has shifted northward from the South China Sea and the Philippine Sea toward the vicinity of Taiwan and the East China Sea since 2000. Thus, the increased typhoon number over the vicinity of Taiwan after 2000 (Fig. 3a) is not an isolated local feature, but is consistent with the northward shift of the typhoon track over the WNP–EA region. We further examined the variation of the typhoon frequency in three subregions of the WNP–EA region with large changes of typhoon frequency (Fig. 4): the South China Sea (15º– 20ºN, 110º–120ºE), the Philippine Sea (15º–20ºN, 120º–130ºE), and the Taiwan–East China Sea region (25º–30ºN, 120º–130ºE). Over the South China Sea (Fig. 4a), a clear interannual variation and a downward linear trend of typhoon frequency, which is obtained from a simple best-fit (least squares) method, are found during the past 37 yr (1970–2006). The linear trend is consistent with the findings of Wu et al. (2005). This downward trend is

Possible impacts of global warming on typhoon activity in the vicinity of Taiwan

related to a reduction in the number of typhoon formations over the South China Sea. Over the Philippine Sea (Fig. 4b), the variation is more likely characterized by a decadal variation with a phase shift around 1975, 1985, and the late 1990s. Over the Taiwan–East China Sea region (Fig. 4c), it shows a similar variation of typhoon frequency to that in Fig. 3a, with a statistically significant shift occurring in 2000 at the 5% level, as confirmed by the classical changepoint analysis described in section 2b. In other words, the abrupt shift of the typhoon frequency in the vicinity of Taiwan is a part of the change over the Taiwan–East China Sea region (Figs. 3a and 4c), not associated with the changes in the South China Sea and the Philippine Sea.

4. The association with the large-scale pattern To understand possible causes for such a northward shift of the typhoon track, we examine variations of the large-scale environment, such as the westward extension of the Pacific subtropical high ridge between two epochs. Because the SST data start in 1982, the period of 1982–99 is used to represent the first epoch, instead of 1979–99, for consistency in analyzing the spatial distribution of large-scale variables. We note that the difference of the analyzed periods between the typhoon data (e.g., Figs. 3 and 4) and the large-scale variables (Figs. 5 and 6) may cause an inconsistency. Moreover, the small sample size of the second epoch may also create some uncertainties in the composite analysis. However, the composite analysis may yield insight to the possible mechanisms that induce the abrupt shift of the typhoon number in the Taiwan vicinity. The ridge of the Pacific subtropical high in the WNP–EA region is the main steering flow controlling the typhoon track (Ho et al., 2004; Wu et al., 2005). Figure 5 shows the 500-hPa geopotential height in the first (dotted line) and second epochs (solid line). The 5880-gpm contour is commonly used to represent the variation of the subtropical high ridge over the WNP–EA region (e.g., Chang et al., 2000). However, the 5875-gpm contour is used in this study because it is closer to the vicinity of Taiwan than the 5880-gpm contour. The results discussed below are not sensitive to the contour that we chose. Averaged over the entire typhoon season (JJASO), the subtropical ridge tends to retreat eastward from the first to the second epoch (Fig. 5a). Because of this possible weakening of the subtropical high, the typhoon track during the second epoch tends to move a little more northward than those in the first epoch. We further examined the subseasonal variation of the subtropical high over this region since the subtropical high over this region experiences a strong subseasonal variation (e.g., LinHo and Wang, 2002). According to LinHo and Wang (2002), the entire typhoon period (JJASO) is dominated by three major natural periods in the WNP–EA region: June (the first period of summer), July–September (JAS; the second period of summer), and October (early fall). In June, the subtropical high during the second epoch tends to retreat eastward relative to the first epoch (Fig. 5b). In this period, most typhoons move westward into the South China Sea because of the strong westward extent of the subtropical high. The area where the height difference between two epochs is statistically significant is observed over the Taiwan Philippine region. When the subtropical high retreats eastward, such as shown in Fig. 5b, the typhoon track tends to move northward. In JAS, on the other hand, the subtropical high moves northward slightly during the second epoch (Fig. 5c). This period is the peak phase of the typhoon season over the vicinity of Taiwan, which has the most typhoons passing

Climate Change and Variability

through (Fig. 2). In this period, the subtropical high tends to move northward, so most typhoons move straight to Taiwan or turn northward to Japan and Korea. Thus, the possible northward retreat of the subtropical high (Fig. 5c) also favors the typhoon track shifting northward. In October, unlike the other two periods discussed before, the subtropical high actually becomes stronger and extends more westward (Fig. 5d), so it is not favorable for typhoons moving northward. However, the typhoon frequency in this period is lower than those in the other two periods (Chu et al., 2007). Overall, the eastward retreat of the subtropical high in JJASO, which is associated with the northward shift of typhoon track, could be a result of change of the subtropical high in summer, that is, June–September (JJAS). We note that the differences of the 500-hPa geopotential height over the western North Pacific in the peak season (JAS) are not statistically significant, possibly resulting from the small sample size of the second epoch, so the weakening of the subtropical high should be examined in the future when the second epoch becomes long enough. We also examined the changes of other large-scale variables between two epochs. Figure 6a shows the change of low-level winds at 850 hPa. A cyclonic circulation anomaly is found between 30ºN and the equator over the WNP–EA region during the second epoch, which implies a strengthening of the Asian summer monsoon trough, a favorable condition for typhoon activity. In Fig. 6b, positive and statistically significant vorticity anomalies are also found over the abovementioned region, including Taiwan, which supports the notion of an enhanced Asian summer monsoon trough. Thus, the strengthening of the Asian summer monsoon trough is accompanied by the weakening or eastward retreat of the subtropical high shown in Fig. 5a. We note that positive low-level vorticity anomalies may also favor tropical cyclone genesis. However, it is not a dominant factor here because the number of typhoon formations is decreased over the South China Sea and the Philippine Sea. Vertical wind shear is another factor that may affect typhoon activity (Chia and Ropelewski, 2002; Frank and Ritchie, 2001; Gray, 1979; Kurihara and Tuleya, 1981). Figure 6c does show an increase of vertical wind shear in the region of 0º–15ºN, 105º–150ºE, which is unfavorable for typhoon formation and development, but this increase of vertical wind shear is not statistically significant. On the other hand, to the north of 15ºN in the area where the lowlevel vorticity anomalies are positive and the cyclonic circulation anomaly dominates, the vertical wind shear has not changed appreciably, so this condition is at least not unfavorable for typhoon activity. Another important environmental condition for tropical cyclone activity is SST. Warmer SST tends to create a favorable thermodynamic condition for tropical cyclones through the air– sea heat flux exchange (Emanuel, 1999). In the North Atlantic, it is well established that SST is one of the factors impacting the number and severity of tropical cyclones (Landsea et al., 1998; Shapiro, 1982; Shapiro and Goldenberg, 1998). Thus, SST is examined here. In Fig. 6d, warm SST anomalies are found over the equatorial region from 120ºE to 120ºW. However, only the warm SST anomalies west of the date line are statistically significant. Another region with warm SST anomalies is in the North Pacific region from 30º to 50ºN, which is associated with a negative phase of the Pacific decadal oscillation (Mantua et al., 1997). Both regions are away from the region with strong typhoon activity. However, SST anomalies can alter large-scale atmospheric circulation, and thus affect the typhoon track. To understand which regions of the warm SST anomalies are responsible for creating the favorable conditions of typhoon activity over the WNP–EA region, a climate model with an intermediate complexity (Neelin and Zeng, 2000; Zeng et al., 2000) is used. In these

Possible impacts of global warming on typhoon activity in the vicinity of Taiwan

simulations, the warm SST anomalies are prescribed separately in the equatorial region (5ºS–5ºN, 130º–175ºE) and the midlatitude region (25º–45ºN, 140ºE–120ºW), while a mixed layer ocean is used outside those two regions. The experiment design is similar to that used in Lau and Nath (2000). The results shown in Fig. 7 are averages of 10 ensemble runs. Given the warm SST anomalies in the equatorial region, low-level cyclonic circulation anomalies are noted over the WNP–EA region (Fig. 7a). This is a typical Rossby wave response to the equatorial warm SST anomalies (e.g., Gill, 1980; Wang et al, 2003). This pattern over the WNP is qualitatively similar to that in Fig. 6a. In contrast, the warm SST anomalies over the midlatitude region have little bearing on the tropical circulation over the WNP (Fig. 7b). We note that the simulated low-level wind anomalies over the midlatitude Pacific (Fig. 7) are different than the observation shown in Fig. 6a. Thus, both the equatorial and midlatitude warm SST anomalies are not responsible for the wind anomalies over midlatitude Pacific, which may be contributed to by some complicated effects. Accordingly, the warm SST anomalies over the equatorial western and central Pacific are postulated as a possible cause for inducing the favorable conditions for the northward shift of the typhoon track. We then examine the temporal variation of the SST over the equatorial region. Figure 8 shows the time series of monthly SST anomalies, with the annual cycle removed, along the equatorial western and central Pacific (5ºS–5ºN, 130º–175ºE) from 1982 to July 2007. The SST anomalies exhibit an interannual variation, which is roughly related to the three strongest El Niño events of the twentieth century: 1982/83, 1991/92, and 1997/98, a sharp reduction of the SST anomalies from the growing year (the year before the El Niño peak phase, such as 1997) to the decaying year (the year after the El Niño peak phase, such as 1998). We used the regression model discussed in Chu (2002) to analyze the variation of the monthly SST anomalies. A statistically significant shift occurs in August 2000 (Fig. 8). Specifically, before August 2000, the average of the SST anomalies is about -0.1 ºC, but it increases to 0.3ºC after August 2000. Although this difference seems to be small, it is considerable when viewed in the context of multiyear seasonal means at the equator. In the interannual time scale, the typhoon number over the vicinity of Taiwan is also strongly associated with the SST anomalies over the equatorial western and central Pacific (C.-T. Lee, 2008, personal communication).We also examined the six typhoons that passed through the vicinity of Taiwan in 2000 and found that five out of six typhoons occurred in or after August 2000.After further examining the SST anomalies in 2007, we found that warm SST anomalies still exist over the equatorial western and central Pacific (Fig. 8), which is consistent with the above-normal number of typhoons (five) that we found so far in 2007. Thus, combining results from observations, especially in Figs. 3 and 8, and model simulations, the warm SST anomalies over the equatorial western to central Pacific appear to be main factors that induced the abrupt change of the northward shift of the typhoon track over the WNP–EA region.

5. Impacts of global warming Global warming has been observed for more than one hundred years (e.g., Trenberth et al., 2007). Under global warming, El Niño–like warm SST anomalies are often found over the equatorial Pacific region (Meehl and Washington, 1996; Meehl et al., 2007; Teng et al., 2006). In this study, we also found that the mean state of the equatorial Pacific SST transitioned from a cold to a warm phase in 2000. The question that arises is as follows: Do the equatorial

Climate Change and Variability

SST anomalies that are associated with the abrupt northward shift of the typhoon track over the WNP–EA region result from global warming? Figure 9 shows the variation of globally averaged SST in 1982-2009. A clear upward trend of SST is found, which is consistent to the global warming found in the previous studies (e.g., Trenberth et al., 2007). The spatial distribution of the SST trend is shown in Figure 10. This clearly resembles the SST anomalies shown in Fig. 6d. In other words, the SST anomalies that induce the abrupt shift of the typhoon track are similar to the warming trend of SST over the past three decades. Thus, it implies that global warming could be at least partially responsible for the abrupt shift of the typhoon track over the western North Pacific. We further examined the trend of typhoon activity in 1970-2009 and found decreasing trends over the South China Sea and slightly increasing trends north of Taiwan (Fig. 11). We note that the decreasing trends are statistically significant, but the increasing trends are not. The decreasing trend is similar to the pattern found in Fig. 3d, while the increasing trend is much weaker than that shown in Fig. 3d even though the tendency is same. It is consistent with the results shown in Fig. 4: a clear decreasing trend of typhoon activity over the South China Sea (Fig. 4a) and an abrupt increase of typhoon activity north of Taiwan (Fig. 4c). Overall, the warming trend of SST does show an influence on the abrupt shift of the typhoon track over the western North Pacific.

6. Discussion and conclusion Long-term climate variability of typhoon activity over the western North Pacific–East Asian (WNP–EA) sector has been analyzed here. Because Taiwan is located at a unique location along the typhoon path, the number of typhoons that pass through the vicinity of Taiwan is used to study the movement of the typhoon track over this region. Our analysis suggests an abrupt change in typhoon numbers in 2000, which is consistent with the northward shift of the typhoon track over the WNP–EA region and the abrupt increase of typhoon frequency over the Taiwan–East China Sea region. This northward shift of the typhoon track is mainly associated with warm SST anomalies over the equatorial western and central Pacific, which exhibited an abrupt change in August 2000, concurrent with the change of the typhoon track. Both observations and model simulations show that the equatorial warm SST anomalies tend to induce an eastward and northward retreat of the subtropical high, an enhanced lowlevel vorticity, and a monsoon trough, which all favor the northward shift of the typhoon track. A further examination shows that the equatorial warm SST anomalies are partially associated with a global warming trend of SST. Is this global warming trend of SST in 19822009 induced by anthropogenic forcings, such as the greenhouse effect? This is an interesting question that should be answered in the future.

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Hansen, J.; Fung, I.; Lacis, A.; Rind, D.; Lenedeff, S.; Ruedy R. & Russell, G. (1988). Global climate changes as forecast by Goddard Institute for Space Studies threedimensional model. J. Geophys. Res., 93D, 9341–9364 Hansen, J.; Sato, M. & Ruedy, R. (1997). Radiative forcing and climate response. J. Geophys. Res., 102, 6831–6864 Ho, C.-H.; Baik, J.-J.; Kim, J.-H.; Gong, D.-Y. & Sui, C. H. (2004). Interdecadal changes in summertime typhoon tracks. J. Climate, 17, 1767–1776 Ho, C.-H.; Kim, J.-H.; Jeong, J.-H.; Kim, H.-S. & Chen, D. (2006). Variation of tropical cyclone activity in the south Indian Ocean: El Niño–Southern Oscillation and MaddenJulian Oscillation effects. J. Geophys. Res., 111, D22101, doi:10.1029/2006JD007289 Hollander, M. & Wolfe, D. A. (1999). Nonparametric Statistical Methods, 2nd ed., John Wiley, New York Hoyos, C. D.; Agudelo, P. A.; Webster, P. J. & Curry, J. A. (2006). Deconvolution of the factors contributing to the increasing global hurricane intensity. Science, 312, 94–97 Kanamitsu, M.; Ebisuzaki, W.; Woollen, J.; Yang, S.-K.; Hnilo, J. J.; Fiorino, M. & Potter, G. L. (2002). NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643 Keith, D. W. (1995). Meridional energy transport: Uncertainty in zonal means. Tellus, 47A, 30–44 Kim, J.-H.; Ho, C.-H., & Sui, C.-H. (2005). Circulation features associated with the recordbreaking typhoon landfall on Japan in 2004. Geophys. Res. Lett., 32, L1472, doi:10.1029/2005GL022494 Klotzbach, P. J. (2006). Trends in global tropical cyclone activity over the past twenty years (1986–2005). Geophys. Res. Lett., 33, L10805, doi:10.1029/2006GL025881 Kurihara, Y. & Tuleya, R. E. (1981). A numerical-simulation study on the genesis of a tropical storm. Mon. Wea. Rev., 109, 1629–1653 Landsea, C. W.; Bell, G. D.; Gray, W. M. & Goldenberg, S. B. (1998). The extremely active 1995 Atlantic hurricane season: Environment condition and verification of seasonal forecasts. Mon. Wea. Rev., 126, 1174–1193 Landsea, C. W.; Harper, B. A.; Hoarau, K. & Knaff J. A. (2006). Can we detect trends in extreme tropical cyclones? Science, 313, 452–454 Lau, N.-C. & Nath, M. J. (2000). Impact of ENSO on the variability of the Asian-Australian monsoons as simulated in GCM experiments. J. Climate, 13, 4287–4309 Levinson, D. H. & Coauthors (2005). State of the climate in 2004. Bull. Amer. Meteor. Soc., 86, S1–S86 LinHo & Wang B. (2002). The time–space structure of the Asian–Pacific summer monsoon: A fast annual cycle view. J. Climate, 15, 2001–2019 Mantua, N. J.; Hare, S. R.; Zhang, Y.; Wallace, J. M. & Francis R. C. (1997). A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1079 Matsuura, T.; Yumoto, M. & Iizuka, S. (2003). A mechanism of interdecadal variability of tropical cyclone activity over the western North Pacific. Climate Dyn., 21, 105–117 Meehl, G. A. & Washington, W. M. (1996). El Niño-like climate change in a model with increased atmospheric CO2 concentrations. Nature, 382, 56–60 Meehl, G. A. & Coauthors (2007). Global climate projection. Climate Change 2007: The Physical Science Basis, S. Solomon et al. Eds., Cambridge University Press, 747–845

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Miller, J. R.; Russell, G. L. & Tsang, L.-C. (1983). Annual oceanic heat transports computed from an atmospheric model. Dyn. Atmos. Oceans, 7, 95–109 Neelin, J. D. & Yu, J.-Y. (1994). Modes of tropical variability under convective adjustment and the Madden–Julian oscillation. Part I: Analytical theory. J. Atmos. Sci., 51, 1876– 1894 Neelin, J. D. & Zeng, N. (2000). A quasi-equilibrium tropical circulation model— Formulation. J. Atmos. Sci., 57, 1741–1766 Neelin, J. D.; Münnich, M.; Su, H.; Meyerson, J. E. & Holoway, C. E. (2006). Tropical drying trends in global warming models and observations. Proc. Natl. Acad. Sci. U. S. A., 103, 6110-6115 Ren, F.; Wu, G.; Dong, W.; Wang, X.; Wang, Y.; Ai, W. & Li, W. (2006). Changes in tropical cyclone precipitation over China. Geophys. Res. Lett., 33, L20702, doi:10.1029/2006GL027951 Reynolds, R. W.; Rayner, N. A.; Smith, T. M.; Stokes, D. C. & W. Wang, W. (2002). An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609–1625 Russell, G. L. ; Miller, J. R. & Tsang, L.-C. (1985). Seasonal oceanic heat transports computed from an atmospheric model. Dyn. Atmos. Oceans, 9, 253–271 Shapiro, L. J. (1982). Hurricane climate fluctuations. Part II: Relation to large-scale circulation. Mon. Wea. Rev., 110, 1014–1040 Shapiro, L. J. & Goldenberg, S. B. (1998). Atlantic sea surface temperature and tropical cyclone formation. J. Climate, 11, 578–590 Stowasser, M.; Wang, Y. & Hamilton, K. (2007). Tropical cyclone changes in the western North Pacific in a global warming scenario. J. Climate, 20, 2373–2396 Teng, H.; Buja, L. E. & Meehl, G. A. (2006). Twenty-first-century climate change commitment from a multi-model ensemble. Geophys. Res. Lett., 33, L07706, doi:10.1029/2005GL024766 Trenberth, K. E. & Coauthor (2007). Observations: Surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis, S. Soloman et al., Eds., Cambridge University Press, 235-336 Tu, J.-Y.; Chou, C. & P.-S. Chu (2009). The abrupt shift of typhoon activity in the vicinity of Taiwan and its association with western North Pacific-East Asian climate change. J. Climate, 22, 3617-3628 Wang, B. & Chan, J. C. L. (2002). How ENSO regulates tropical storm activity over the western North Pacific. J. Climate, 15, 1643–1658 Wang, B.; Wu, R. & Li, T. (2003). Atmosphere–warm ocean interaction and its impacts on Asian–Australian monsoon variation. J. Climate, 16, 1195–1211 Webster, P. J.; Holland, G. J.; Curry, J. A. & Chang, H. R. (2005). Changes in tropical cyclone number, duration and intensity in a warm environment. Science, 309, 1844–1846 Wu, L. & Wang, B. (2004). Assessing impacts of global warming on tropical cyclone tracks. J. Climate, 17, 1686–1698 Wu, L.; Wang, B. & Geng, S. (2005). Growing typhoon influence on east Asia. Geophys. Res. Lett., 32, L18703, doi:10.1029/2005GL022937 Yu, J.-Y. & Neelin, J. D. (1994). Modes of tropical variability under convective adjustment and the Madden–Julian oscillation. Part II: Numerical results. J. Atmos. Sci., 51, 1895–1914

Climate Change and Variability

Zeng, N.; Neelin, J. D. & Chou, C. (2000). The first quasiequilibrium tropical circulation model-implementation and simulation. J. Atmos. Sci., 57, 1767–1796 Zhao, M.; Held, I. M.; Lin, S.-J. & Vecchi G. A. (2009). Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50km resolution GCM, J. Climate, 22, 6653-6678 Zhao, Z. & Chu, P.-S. (2006). Bayesian multiple change point analysis of hurricane activity in the eastern North Pacific: A Markov chain Monte Carlo approach. J. Climate, 19, 564–578

Fig. 1. June-October (JJASO) typhoon frequency climatology averaged over the period of 1970-2009. The contour interval is 0.5 per season (JJASO) per grid box (2.5°×2.5°). The bold arrows represent the majority of typhoon paths in the Western North Pacific–East Asian region. The box represents the area in the vicinity of Taiwan.

Fig. 2. Monthly percentage of typhoons impacting Taiwan averaged over the period of 1970-2009.

Possible impacts of global warming on typhoon activity in the vicinity of Taiwan

Fig. 3. (a) Time series of seasonal (JJASO) typhoon numbers passing the vicinity of Taiwan from 1970 to 2006 as compiled by the Central Weather Bureau. The vicinity was defined as 21°N-26°N and 119°E-125°E. (b) The conditional posterior probability mass function of change-points is plotted as a function of time. (c) Posterior density function of seasonal typhoon rate before (dashed line) and after (solid) the shift, with the change-point year being set in 2000. (d) June-October typhoon frequency differences for the period of 20002006 minus the period of 1970-1999 from the Regional Specialized Meteorological Center, Tokyo. The contour interval is 0.5; shading denotes that the difference between the mean of the two epochs is statistically significant at the 5% level. Adapted from Tu et al., (2009).

Climate Change and Variability

Fig. 4. Time series of seasonal (JJASO) typhoon frequency departure from 1970 to 2006 for three sub regions of the western North Pacific: (a) the South China Sea, (b) the Philippine Sea and (c) the Taiwan and East China Sea region. The thicker dashed line in the upper panel is a best-fit least square linear trend and the thinner dashed lines denote one standard deviation for each area. The unit in the y-axis is the typhoon number per season (JJASO) per grid box (2.5°×2.5°). Adapted from Tu et al. (2009).

Possible impacts of global warming on typhoon activity in the vicinity of Taiwan

Fig. 5. The 5875 gpm contour of 500hPa geopotential height for the period of 1982-1999 (thick dotted line) and 2000-2006 (thick solid line) in (a) June-October (JJASO), (b) June, (c) July-September (JAS) and (d) October. The contours are the 500hPa geopotential height differences of the second minus first epoch, shaded by the 10% significance level. Adapted from Tu et al. (2009).

Climate Change and Variability

Fig. 6. (a) 850 hPa wind difference between 2000-2006 and 1982-1999 for JJASO; (b) same as (a) but for 850 hPa relative vorticity; (c) same as (a) but for vertical wind shear (200hPa850hPa); and (d) same as (a) but for sea surface temperature (SST). The contour interval for 850 hPa relative vorticity is 1.5E+6 (s-1), for vertical wind shear is 0.8 (m s-1), and for SST anomalies is 0.2°C. Dotted areas indicate regions where the difference in the mean between two epochs is significant at the 5% level. In (b), (c), and (d), negative values are dashed. Adapted from Tu et al. (2009).

Possible impacts of global warming on typhoon activity in the vicinity of Taiwan

Fig. 7. SST anomalies (contour) and 850 hPa wind anomalies from the model simulations with the prescribed SST anomalies over (a) the equatorial region (130°E-175°E, 5°S-5°N) and (b) mid-latitudes (140°E-120°W, 25°N-45°N). Adapted from Tu et al. (2009).

Fig. 8. Variation of monthly SST anomalies averaged over the area of 130°E-175°E and 5°S-5°N from January 1982 to July 2007. The short dashed lines are the means averaged over 1982-1999 (-0.1°C) and 2001-2006 (0.3°C) respectively. Adapted from Tu et al. (2009).

Climate Change and Variability

Fig. 9. Globally averaged SST in JJASO for the period of 1982-2009.

Fig. 10. Trend of SST in JJASO for the period of 1982-2009. The unit is ºC per decade. The dotted area denotes that the trend is statistically significant at the 5% level.

Fig. 11. Trend of typhoon frequency in JJASO for the period of 1970-2009. The unit is per season (JJASO) per grid box (2.5°×2.5°). The dotted area denotes that the trend is statistically significant at the 5% level.

Influence of climate variability on reactive nitrogen deposition in temperate and Arctic climate

x6 Influence of climate variability on reactive nitrogen deposition in temperate and Arctic climate Lars R. Hole

Norwegian Meteorological Institute (met.no) Norway 1. Introduction

Depending on wetness of the climate, a large fraction of reactive nitrogen deposited from the atmosphere is deposited as wet deposition, ranging from 10 to 90%. The remaining fraction is deposited as dry deposition (gas and particles) (Delwiche, 1970; Galloway et al., 2004; Wesely & Hicks, 2000). Deposition of long-range transported reactive nitrogen (Nr) has been an issue of concern Europe and North America for a long time. In 1983 the Convention on Long-Range Transboundary Air Pollution entered into force, while the Protocol concerning the Control of Nitrogen Oxides or their Transboundary Fluxes was signed in 1988. While measures to reduce sulphur (S) emissions have been quite successful, nitrogen (N) emissions have proven more difficult to reduce (www.emep.int). Effects of N deposition on terrestrial ecosystems include surface water acidification (Stoddard, 1994) and reductions in biodiversity (Bobbink et al., 1998) while forest growth effects are more difficult to substantiate (Tietema et al., 1998; Emmett et al., 1998). Retention of N in many boreal and temperate ecosystems is usually high, which leads to soil N enrichment which in turn may lead to ‘N saturation’ of soils and increased leaching of N to surface waters, leading to water acidification (Stoddard, 1994). Recent studies indicate that climate change may affect the biogeochemical Nr cycle profoundly. Evidence is accumulating that interactions between N deposition and terrestrial processes are influenced by climate warming (De Wit et al., 2008). There are few studies on the linkage between Nr deposition and climate variability in Northern Europe. By coupling of a regional climate model and the Mesoscale Chemical Transport (CTM) Model MATCH, Langner et al. (2005) showed that changes in the precipitation pattern in Europe have a substantial potential impact on deposition of oxidised nitrogen, with a global warming of 2.6 K reached in 2050-2070. Air mass trajectories have been shown to be affected by climate warming and this may potentially lead to changes in N deposition. Fowler et al (2005) were not able to establish a clear connection between Nr wet deposition in the UK and the North Atlantic Oscillation Index (NAOI), suggesting that a much more detailed approach with analysis of individual precipitation events and trajectory studies would have to be used in order to establish relationships between Nr deposition trends and climate variation. In Norway, Hole and Tørseth (2002) reported the total sulphur and nitrate deposition in

Climate Change and Variability

five-year periods from 1978-1982 to 1997-2001 by interpolating national and EMEP (European Monitoring and Evaluation Programme) station measurements to the EMEP 50x50 km grid. They found that the total (wet+dry) Nr deposition in the last period had been reduced with 16% compared to the first period although the total precipitation had increased with 10% (Fig 1). However the decline in deposition since the early 1980s is not steady since EMEP area NOx emissions reached a peak around 1990 and the period 19881992 was the wettest in Norway of the periods studied. Grid cell total deposition for NOx in the last period varied from 0.04 to 1.2 g N m-2 yr-1 while corresponding numbers for NHy was 0.06 to 0.9 g N m-2 yr-1. According to Hanssen-Bauer (2005) mean annual precipitation in Norway has increased in 9 of 13 climate regions into which Norway is divided (Fig. 1), with a 15-20% increase in northwestern regions (between Bergen and Trondheim) in the last century.

2. Trend analysis of nitrogen deposition and relation to climate variability 2.1 Measurement network studied In the following, we explore relations between climate variability and wet N deposition at 7 locations in south Norway, including a range in annual precipitation and atmospheric Nr deposition. We have tested whether various climate indices are significantly correlated with i) bulk concentrations of Nr in precipitation ii) monthly precipitation iii) Nr deposition during summer and winter. Our main focus is deposition. We have separated summer and winter data to test whether there are seasonal differences in the correlations. More details on the measurement network can be found in Hole et al. (2008). 2.2 Climate indices Different climate indices have been tested for correlation with Nr deposition, precipitation and Nr concentration in precipitation. In addition to the North Atlantic Oscillation Index (NAOI) we have tested for the Arctic Oscillation Index (AOI), the European Blocking Index (EUI), the Scandinavian blocking Index (ScandI) and the East Atlantic Index (EAtlI). The Arctic oscillation (AO) is the dominant pattern of non-seasonal sea-level pressure (SLP) variations north of 20N, and it is characterized by SLP anomalies of one sign in the Arctic and anomalies of opposite sign centered about 37-45N. The North Atlantic oscillation (NAOI) is a climatic phenomenon in the North Atlantic Ocean of fluctuations in the difference of sea-level pressure between Iceland and the Azores. It controls the strength and direction of westerly winds and storm tracks across the North Atlantic and is a close relative of the AO (www.cpc.noaa.gov). The European blocking index is based on observations of pentad (5-day average) wind over the region 15W to 25E and 35n to 55N. If the pentad zonal wind equals the climatological value for that time period, the index is zero. If the pentad zonal wind is less than average the index is positive (a blocking high pressure persist over central Europe), while the opposite is true if the index is negative. Similarly, positive ScandI and EatlI are associated with blocking anticyclones over Scandinavia and the East Atlantic, respectively. Jet stream intensity and orientation at the storm trackexit, and in the vicinity of Norway in particular, vary with the phase of these climate patterns. (Orsolini and Doblas-Reyes, 2003). The winter of 1990 (which was warm and wet with prevailing westerlies in S Norway) was a strong positive event in NAOI whilst the dry and cold winter of 1996 was a prolonged

Influence of climate variability on reactive nitrogen deposition in temperate and Arctic climate

negative event. It also appears that the NAOI and AOI behave similarly and they are also correlated, particularly in winter (Rsummer = 0.55, Rwinter = 0.81).

Fig. 1. Total deposition of nitrogen (oxidized + reduced) 1988-92 (maximum total Nr deposition in the monitoring period) and 1997-2001 (minimum total Nr deposition in the monitoring period) in mainland Norway. The unit is mg N/m2 year. From Hole and Tørseth (2002). Precipitation zones from Hanssen-Bauer (2005) are also indicated. 2.3 Statistical method Precipitation data from seven monitoring stations are presented here as monthly values in winter (December-February) and summer (June-August). In this way we can see seasonal differences since strong anticyclones in the Atlantic with westerlies are particularly common in winter during negative NAOI events. Precipitation concentrations were weighted according to precipitation amount. Existence of a monotonic increasing or decreasing trend in the time series 1980-2005 and 1990-2005 was tested with the nonparametric Mann-Kendall test at the 10% significance level as a two-tailed test (Gilbert, 1987). Some of the stations opened in the 1970s, but we choose to test for the same periods at all stations to be able to compare trends. An estimate for the slope of a linear trend was calculated with the nonparametric Sen’s method (Sen, 1968). The Sen’s method is not greatly affected by data outliers, and it can be used when data are missing (Salmi et al., 2002). It is likely that significant trends in deposition are partly a result of changes in emissions. However, it is not obvious which emission areas contribute to deposition in Norway, even though a sector analysis has been carried out for parts of the period studied (Tørseth et al,

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Climate Change and Variability

2001). The relative contribution could also vary from year to year depending on transport climate. Here, we have tested whether removing significant trends in the data have any influence on the correlations we observe.

Fig. 2. Monthly average NO3 wet deposition summer and winter (mg/m2). Solid lines are 1990-2005 trends, dashed lines are 1980-2005 trends. 2.4. Observed trends Significant Sen slopes (10% level) in nitrate and ammonia deposition for 1980-2005 and 19902005 are shown in Figures 2-3. Trends in nitrate concentrations since 1980 corresponds to a reduction of up to 50% at Kårvatn in summer (Aas et al, 2006) and less at the other stations. For the longest period, there are negative trends (summer, winter or both) in nitrate wet deposition at five out of seven sites. For the shortest period there are negative trends in nitrate wet deposition at four of seven sites, including the most coastal site (Haukeland), where there is also a very strong increase in summer precipitation (32 mm/decade). For the longest period there are few sites with significant trends in nitrate wet deposition and this could be caused by increasing precipitation in the period, although the data analysed here show significant increase in precipitation at only three sites. For 1990- 2005 decreasing nitrate concentration in precipitation is accompanied by decreasing nitrate wet deposition only at the driest site (Langtjern). The positive trend in ammonia wet deposition at Tustervatn could be caused by changes in local farming activity. We should keep in mind

Influence of climate variability on reactive nitrogen deposition in temperate and Arctic climate

101

that the 25 year studied here is a very short time to detect climatic trends, since there is much variability on decadal scale (Hanssen-Bauer, 2005).

Fig. 3. Monthly average NH4 wet deposition summer and winter (mg/m2). Solid lines are 1990-2005 trends, dashed lines are 1980-2005 trends. 2.5 Climate indices and connection to concentrations, precipitation and deposition First, we test correlations between Nr concentrations and climate indices. For most stations there was no correlation. The strongest correlation found was R=-0.45 for nitrate concentration and NAOI at Haukeland in winter. Nitrate wet deposition at the western sites (Haukeland and Skreådalen) are well correlated with NAOI and strongest in winter (R=0.60 at Skreådalen) (Table 1). A cluster analysis where the western sites are combined gives R=0.56 for the western sites in winter, and a much lower correlation (R=0.22) for the southern sites (Birkenes and Treungen). For precipitation the corresponding correlations coefficients are 0.75 and 0.38 respectively. Interestingly AOI has a similar regional correlation pattern, but it has a higher correlation at the northern site Tustervatn (R = 0.47 in winter). This regional pattern reflexes the correlation with precipitation in which again corresponds well with Hanssen-Bauer (2005). High correlations with NAOI and AOI in winter is not surprising since strong cyclonic systems in the Atlantic leads to high precipitation at the west coast. Local air temperature is also strongly correlated with winter nitrate wet

102

Climate Change and Variability NAOI

AOI

Birkenes

0.15

Treungen

European blocking

East Atlantic blocking

-0.01

-0.06

0.31

0.09

0.01

0.24

Langtjern

0.10

-0.03

-0.05

0.11

Kårvatn

0.20

0.21

-0.20

0.08

Haukeland

0.46

0.30

-0.18

0.13

Skreådalen

0.38

0.21

-0.19

0.37

Tustervatn

0.11

0.14

0.19

-0.01

Birkenes

0.24

0.16

-0.45

0.25

0.24

Treungen

0.25

0.13

-0.47

0.25

0.23

Langtjern

0.21

0.06

-0.46

0.23

0.32

Kårvatn

0.04

0.16

0.14

-0.27

-0.15

Haukeland

0.53

0.60

0.13

-0.20

0.20

Skreådalen

0.60

0.57

-0.20

-0.22

0.39

Tustervatn

0.28

0.47

0.24

-0.12

0.22

Winter

Scandinavian blocking

Summer

Station name

Table 1. Correlation coefficients, R, for nitrate deposition vs climate indices 1980-2005. deposition at the coastal sites (R=0.84), suggesting that mild, humid winter weather with strong transport from west and south-west (positive NAOI) brings high deposition, mostly as rain, and transport from the UK. For the other sites R40 years of historical weather data (ERA40) and dynamically downscaled climate scenarios for Europe to the year 2100 have been used to assess the linkage between climate variability and N deposition by means of the MATCH (Multi-scale Atmospheric Transport and Chemistry) model (Hole & Enghardt, 2008). Total nitrate (NO3)and total ammonium (NH4) concentrations in precipitation decreased significantly at the Swedish EMEP stations from the mid 1980s to 2000 (Lövblad et al., 2004). During the same period the pH of precipitation increased from ~4.2 to 4.6. Data from the national throughfall network (Nettelblad et al., 2005) measurements of air- and precipitation chemistry at around 100 sites across Sweden confirm the downward trend in concentrations of NO3 and NH4 in rain. The trend was particularly pronounced in southern Sweden. Due to increasing precipitation amounts during the same period, however, the total deposition of reactive nitrogen (NO3 and NH4) has not decreased; instead it has remained roughly unchanged. Increasing precipitation in a region will obviously result in increasing wet deposition if atmospheric N concentrations are unchanged. Altered precipitation patterns and temperatures are also likely to affect mobilisation of N pools in the soil and runoff to rivers, lakes and fjords (de Wit et al., 2008). Since many aquatic ecosystems in Scandinavia are N limited, increasing N fertilization will disturb the natural biological activity. In the following we focus on future N deposition in northern Europe (Fennoscandia and the Baltic countries) as a result of future climate change. There are substantial regional differences in factors such as topography, annual mean temperature and precipitation in this area, and hence a regional discussion is required. Our purposes are to examine (1) regional and seasonal differences in climate change effects on nitrogen deposition, (2) whether changes in wet deposition are proportional to changes in precipitation, and (3) the distribution between dry and wet deposition. The MATCH model and the experimental set-up applied is described in Hole & Enghardt (2008) and references therein. 4.2 Deposition in future climate – comparison with current climate Figures 7 and 8 show the calculated relative change in annual mean deposition of NOy and NHx over northern Europe. The figures display the difference of the 30-year mean of annually accumulated deposition during a future 30-year period minus the 30-year period labelled “current climate” normalised by the “current climate”. The Norwegian coast will experience a large increase in total N deposition due to increased precipitation projected by the present climate change scenario (ECHAM4/OPYC3–RCA3, SRES A2). The changes are most likely connected to the projected changes in precipitation in northern Europe. On an annual basis the whole of Fennoscandia is expected to receive more precipitation in 2071-2100 compared to “current climate”. The deposition of NOy and NHx display similar increasing trends along the coast of Norway. In northern Fennoscandia and in parts of southeast Sweden NHx decreases, while NOy is projected to increase. East and south of the Baltic Sea, the increase in NHx deposition is much smaller than the increase in NOy deposition. This is mostly because scavenging of NHx is more effective in

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Climate Change and Variability

source areas than scavenging of NOy.

Fig. 7. Relative change in annually accumulated deposition of oxidised nitrogen (NOy) from the period 1961-1990 to 2021-2050 (top row) and from 1961-1990 to 2071-2100 (bottom row). Left panel is total deposition, middle panel is wet deposition, right panel is dry deposition.

Fig. 8. Same as Fig. 7, but for reduced nitrogen (NHx).

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The total deposition of NOy over Norway is expected to increase from 96 Gg N year-1 during current climate to 107 Gg N year-1 by the year 2100 due only to changes in climate (Hole & Enghardt, 2008). The corresponding values for Sweden are more modest, 137 Gg N year-1 to 139 Gg N year-1. Finland, the Baltic countries, Poland and Denmark will also experience increases in total NOy deposition. A large part of the increase in total NOy deposition south and east of the Baltic is due to increased dry deposition. Reduced precipitation and increased atmospheric lifetimes of NOy results in higher surface concentrations here, which drive up the dry deposition. In Norway and Sweden the change in annual dry deposition from current to future climate is only minor and virtually all change in total NOy deposition emanates from changes in wet deposition. The total deposition of NHx decreases marginally in many countries around the Baltic Sea. Decreasing wet deposition of NHx causes the decrease in total deposition in Sweden, Poland and Denmark. Norway will experience a moderate increase in total NHx deposition in both during 2021-2050 and 2071-2100 compared to “current climate” (52 Gg N year-1 and 53 Gg N year-1 compared to 50 Gg N year-1). Trends in deposition pattern for the two compounds are not identical because primary emissions occur in different parts of Europe and because their deposition pathways differ. NHx generally has a shorter atmospheric lifetime than NOy; the increased scavenging over the coast of Norway will leave very little NHx to be deposited in northern Finland and the Kola Peninsula, where NHx emissions are minor. The relative increase in deposition is slightly smaller than the predicted increase in precipitation. In Fig. 9 this dilution effect for NOy is apparent along the Norwegian coast (where precipitation will increase most), but further north and east it is stronger because much of the NOy is scavenged out before it reaches these areas.

Fig. 9. Relative change in concentration of oxidised nitrogen in precipitation from the period 1961-1990 to 2021-2050 (left) and from 1961-1990 to 2071-2100 (right).

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4.3 What can we say from these model results? The accuracy of our results is determined by the accuracy of the utilised models and the input to the models. MATCH has been used in a number of previous studies and has proven capable to realistically simulate most species of interest. The model has, however, always had limitations in its capability to simulate NHx species. This we have attributed to relatively larger uncertainties in the emission inventory of NH3 and to the fact that subgrid emission/deposition processes not fully resolved in the system. The model (RCA3) used to create the meteorological data in the present study has been evaluated in Kjellström et al. (2005). Using observed meteorology (ERA40 from ECMWF; “perfect boundary condition”) on the boundaries they compare the model output with observations from a number of different sources. The increase in resolution from ERA40 produces precipitation fields more in line with observations although many topographical and coastal effects are still not resolved. This could explain the underestimation of precipitation at the sites located in western Norway. The precipitation in northern Europe is also generally overestimated in RCA3 when ECHAM4/OPYC3 is used on its boundaries. The degree of certainty we can attribute to RCA3’s predictions of future climate is not only dependent on the climate model’s ability to describe “current climate” and how the regional climate will respond to the increased greenhouse gas forcing. The RCA3 results are to a large degree forced by the boundary data from the global climate model. The EU project PRUDENCE and BALTEX presented a wide range of possible down-scaled scenarios for northwestern Europe showing, for example, that winter precipitation can increase by 20 to 60% in Scandinavia (see (Christensen et al., 2007) and references therein). These uncertainties are thus of the same order of magnitude as the projected changes in N deposition. Estimates of precursor (NOX, VOCs, CO etc.) emission strengths comprise a large uncertainty when assessing future N deposition. In order to only study the impact that possible climate changes may have on the deposition of N species we have kept emissions at their 2000-levels. This is a simplification and future N loading in north-western Europe will also be affected by changes in Europe as well as America and Asia. This study has focussed on the change in N deposition due to climate change and not evaluated the relative importance of altered precursor emissions or changed inter-hemispheric transport. The change in deposition over an area may not always be the result of changes in the driving meteorology over that area. It can of course also be due to changes in atmospheric transport pathways or deposition en route to the area under consideration.

5. Discussion and conclusions In section 2 we studied observations of N deposition and its relation to climate variability. We showed that 36 % of the variation in winter nitrate wet deposition is described by the North Atlantic Oscillation Index in coastal stations, while deposition at the inland station Langtjern seems to be more controlled by the European blocking index. The Arctic Oscillation Index gives good correlation at the northernmost station in addition to the coastal (western) stations. Local air temperature is highly correlated (R=0.84) with winter nitrate deposition at the western stations, suggesting that warm, humid winter weather results in high wet deposition. For concentrations the best correlation was found for the coastal station Haukeland in winter (R=-0.45). In addition, there was a tendency in the data

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that high precipitation resulted in lower Nr concentrations. Removing trends in the data did not have significant influence on the correlations observed. However, a careful sector analysis for each month and for each station could improve the understanding of the separate effects of emission variability and climate variability on the deposition. For the Business as Usual (BAU) emission scenarios, northern hemisphere sulphur emissions will only decline from 52.3 mt to 51.3 mt from 2000 to 2020 (section 3). For the Most Feasible Reduction (MFR) scenario 2020 emissions will be only 20.2 mt. However, the two different scenarios show much smaller differences in concentration and deposition of sulphur in the Arctic. This is because the largest potential for improvement in SO2 emissions is in China and SE Asia. These regions have little influence on Arctic pollution according to Stohl (2006) and others. For oxidized and reduced nitrogen compounds there is more reduction in the emissions in Russia and Europe in the MFR scenario, and hence the potential for improvement in the Arctic is larger. SO42- concentrations are decreasing significantly at many Arctic stations. For NO 3- and NH4+ the pattern is unclear (some positive and some negative trends). There are few signs of significant trends in precipitation for the period studied here (last 3 decades). However, expected future occurrence of rain events in both summer and winter can result in increasing wet deposition in the Arctic (ACIA, 2004, www.amap.no/acia). There is relatively good monitoring data coverage in Fennoscandia and on Kola peninsula in Russia, but there are otherwise few stations for background air and precipitation concentration measurements in the Arctic. In our observations there are few differences between summer and winter observations, although NO3- wet deposition is higher in winter in some stations in NW Russia and Fennoscandia (Pinega, Oulanka, Bredkal and Karasjok). The explanation for this is not clear, but in Hole et al (2006b) seasonal exposure differences for SO2 at Oulanka are revealed which can indicate that transport path differences are part of the explanation for the seasonal pattern. Because of new technologies and climate change, future emissions and deposition are particularly uncertain due to the expected increase in human activities in the polar and subpolar regions. Increased extraction of natural resources and increased sea traffic can be expected. Climate change is also likely to influence transport and deposition patterns (ACIA, 2004, www.amap.no/acia). There is a need for a deeper insight in plans and consequences with respect to the Arctic. Modelling results presented here seem to rule out SE Asia as an important contributor to pollution close to the surface in the Arctic atmosphere. This is in accordance with earlier studies (e.g. Iversen and Jordanger, 1985, Stohl, 2006) giving thermodynamic arguments why SE Asian emissions will have minor influence in the Arctic. As for the relation between future Nr deposition and climate scenarios in temperate climate (section 4), our results suggest that prediction of future Nr deposition for different climate scenarios most of all need good predictions of precipitation amount and precipitation distribution in space and time. Climate indices can be a tool to understand this connection. Regional differences in the expected changes are large. This is due to expected large increase in precipitation along the Norwegian coast, while other areas can expect much smaller changes. Country-averaged changes are moderate. Wet deposition will increase relatively less than precipitation because of dilution. In Norway the contribution from dry deposition will be relatively reduced because most of the N will be effectively removed by wet deposition. In the Baltic countries both wet and dry deposition will increase. Dry deposition

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will increase here probably because of increased occurrence of wet surfaces. According to our model results, northwestern Europe will generally experience small changes in N deposition as a consequence of climate change. The exception is the west coast of Norway, which will experience an increase in N deposition of 10-20% in the period 20212050 and 20-40% in 2071-2100 (compared to current climate). Although Norway as a whole will only experience a moderate increase in N deposition of about 10%, there are large regional differences. RCA3/MATCH forced by ECHAM4/OPYC3 (SRES A2) prescribes that a large part of the Norwegian coast is expected to receive at least 50% increase of the precipitation during the period 2071-2100 compared to period 1961-1990, which is in line with other regional climate scenarios. This region has already experienced increasing precipitation in recent decades. The total effect on soil and watercourse chemistry of the dramatic change in these regions remains to be thoroughly understood. Our studies shows that expected reduction in future N deposition (as a consequence of emission reductions in Europe) could be partly offset due to increasing precipitation in some regions in the coming century. Future long term N emissions in Europe are difficult to predict, however, since they depend on highly uncertain factors such as the future use of fossil fuels and farming technology. The same uncertainty obviously also applies to the greenhouse gas emission scenarios.

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x7 Climate change: impacts on fisheries and aquaculture 1Central

Bimal P Mohanty1, Sasmita Mohanty2, Jyanendra K Sahoo3 and Anil P Sharma1

Inland Fisheries Research Institute, Barrackpore, Kolkata 700120; 2School of Biotechnology, KIIT University, Bhubaneswar 751024, 3Orissa University of Agriculture & Technology, College of Fisheries, Berhampur760007; India. Climate change has been recognized as the foremost environmental problem of the twentyfirst century and has been a subject of considerable debate and controversy. It is predicted to lead to adverse, irreversible impacts on the earth and the ecosystem as a whole. Although it is difficult to connect specific weather events to climate change, increases in global temperature has been predicted to cause broader changes, including glacial retreat, arctic shrinkage and worldwide sea level rise. Climate change has been implicated in mass mortalities of several aquatic species including plants, fish, corals and mammals. The present chapter has been divided in to two parts; the first part discusses the causes and general concerns of global climate change and the second part deals, specifically, on the impacts of climate change on fisheries and aquaculture, possible mitigation options and development of suitable monitoring tools.

1. Global Climate change: Causes and concerns Climate change is the variation in the earth’s global climate or in regional climates over time and it involves changes in the variability or average state of the atmosphere over durations ranging from decades to millions of years. The United Nations Framework Convention on Climate Change (UNFCCC) uses the term ‘climate change’ for human-caused change and ‘climate variability’ for other changes. In last 100 years, ending in 2005, the average global air temperature near the earth’s surface has been estimated to increase at the rate of 0.74 +/0.18 °C (1.33 +/- 0.32 °F) (IPCC 2007). In recent usage, especially in the context of environmental policy, the term ‘climate change’ often refers to changes in the modern climate.

2. Causes of climate change There are both natural processes and anthropogenic activities affecting the earth’s temperature and the resultant climate change. The steep increases in the global

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anthropogenic greenhouse gas (GHG) emissions over the decades are major contributors to the global warming. 2.1. Natural processes affecting the earth’s temperature Sun is the primary source of energy on earth. Though the sun’s output is nearly constant, small changes over an extended period of time can lead to climate change. The earth’s climate changes are in response to many natural processes like orbital forcing (variations in its orbit around the Sun), volcanic eruptions, and atmospheric greenhouse gas concentrations. Changes in atmospheric concentrations of greenhouse gases and aerosols, land-cover and solar radiation alter the energy balance of the climate system and causes warming or cooling of the earth’s atmosphere. Volcanic eruptions emit many gases and one of the most important of these is sulfur dioxide (SO2) which forms sulfate aerosol (SO4) in the atmosphere. 2.2 Greenhouse gases Greenhouse gases (GHGs) are those gaseous constituents of the atmosphere, both natural and anthropogenic, that are responsible for the greenhouse effect, leading to an increase in the amount of infrared or thermal radiation near the surface. While water vapor (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and ozone (O3) are the primary greenhouse gases in the Earth’s atmosphere, there are a number of entirely human-made greenhouse gases in the atmosphere, such as the halocarbons and other chlorine- and bromine-containing substances. Halocarbons such as CFCs (chlorofluorocarbons) are completely artificial (man-made), and are produced from the chemical industry in which they are used as coolants and in foam blowing. Increases in CO2 are the single largest factor contributing more than 60% of humanenhanced increases and more than 90% of rapid increase in past decade. Most CO2 emissions are from the burning of fossil fuels such as coal, oil, and gas. Rising CO2 is also related to deforestation, which eliminates an important carbon sink of the terrestrial biosphere (www.ncdc.noaa.gov/oa/climate/globalwarming.html; Shea et al., 2007). Currently, the atmosphere contains about 370 ppm of CO2, which is the highest concentration in 420000 years and perhaps as long as 2 million years. Estimates of CO2 concentrations at the end of the 21st century range from 490 to 1260 ppm, or a 75% to 350% increase above preindustrial concentrations (WMO World Data Centre for Greenhouse Gases. Greenhouse gas bulletin, 2006; Shea KM and the Committee on Environmental Health, 2007).

3. Impacts of climate change Although it is difficult to connect specific weather events to global warming, an increase in global temperatures may in turn cause broader changes, including glacial retreat, arctic shrinkage, and worldwide sea level rise. Changes in the amount and pattern of precipitation may result in flooding and drought. Other effects may include changes in agricultural yields, addition of new trade routes, reduced summer stream flows, species extinctions, and increases in the range of disease vectors (Understanding and responding to Climate Change. 2008: http://www.national-academies.org).

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Most models on Global climate change indicate that snow pack is likely to decline on many mountain ranges in the west, which would bring adverse impact on fish populations, hydropower, water recreation and water availability for agricultural, industrial and residential use. Partial loss of ice sheets on polar land could imply meters of sea level rise, major changes in coastlines and inundation of low-lying areas, with greatest effects in river deltas and low-lying islands. Such changes are projected to occur over millennial time scales, but more rapid sea level rise on century time scales cannot be excluded. Current models of climate change predict a rise in sea surface temperatures of between 2 °C and 5 °C by the year 2100 (IPCC Third Assessment Report, 2001: Done et al., 2003). Climate change will affect ecosystems and human systems like agricultural, transportation and health infrastructure. The regions that will be most severely affected are often the regions that are the least able to adept. Bangladesh is projected to lose 17.5 % of its land if sea level rises about 1 meter (39 inches), displacing millions of people. Several islands in the South Pacific and Indian oceans may disappear. Many other coastal regions will be at increased risk of flooding, especially during storm surges, threatening animals, plants and human infrastructure such as roads, bridges and water supplies. There are many ways in which climate change might affect human health, including heat stress, heat (sun) stroke, increased air pollution, and food scarcities due to drought and other agricultural stresses. Because many disease pathogens and carriers are strongly influenced by temperature, humidity and other climate variables, climate change may also influence the spread of infectious diseases or the intensity of disease outbreaks. During the last 100 years, anthropogenic activities related to burning fossil fuel, deforestation and agriculture has led to a 35% increase in the CO2 levels in the temperature and this has resulted in increased trapping of heat and the resultant increase in the earth’s atmosphere. Most of the observed increase in globally-averaged temperatures has been attributed to the greenhouse gas concentrations. The globally averaged surface temperature rise has been projected to be 1.1-6.4 °C by end of the 21st century (2090-2099) which is mainly due to thermal expansion of the ocean (www.searo.who.int/en/Section260/Section2468_ 14335.htm, 2008). The global average sea level rose at an average rate of 1.8 mm per year from 1961 to 2003 and the total rise during the 20th century was estimated to be 0.17 m (The Fourth Assessment Report of IPCC, 2007). Due to such surface warming it is predicted that heat waves and heavy precipitations will continue to become more frequent with more intense and devastating tropical cyclones (typhoons and hurricanes). Due to the resultant disruption in ecosystem’s services to support human health and livelihood, there will be strong negative impact on the health system. IPCC has projected an increase in malnutrition and consequent disorders, with implications for child growth and development. Increased burden of diarrheal diseases and infectious disease vectors are expected due to the erratic rainfall patterns. Climate change is likely to lead to some irreversible impacts. Approximately 20- 30 % of species assessed so far are likely to be at increased risk of extinction if increases in global average warming exceed 1.5-2.5 °C (relative to 1980-1999). As global average temperature increase exceeds about 3.5 °C, model projections suggest significant extinctions (40-70 % of species assessed) around the globe. Some projected regional impacts of Climate change have been systematically listed in the IPCC Fourth Assessment Report, 2007.

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4. Impacts of Climate Change on Fisheries and Aquaculture Fish has been an important part of the human diet in almost all countries of the world. It is highly nutritious; it can provide vital nutrients absent in typical starchy staples which dominate poor people’s diets (FAO, 2005a; FAO, 2007a). Fish provides about 20 % of animal protein intake (Thorpe et al., 2006) and is one of the cheapest sources of animal proteins as far as availability and affordability is concerned. While it serves as a health food for the affluent world owing to the fish oils rich in polyunsaturated fatty acids (PUFAs), for the people in the other extreme of the nutrition scale, fish is a health food owing to its proteins, oils, vitamins and minerals and the benefits associated with the consumption of small indigenous fishes (Mohanty et al., 2010a). Although aquaculture has been contributing an increasingly significant proportion of fish over recent decades, approximately two-thirds of fish are still caught in capture fisheries. The number of people directly employed in fisheries and aquaculture is estimated at 43.5 million, of which over 90 % are small –scale fishers (FAO, 2005a). In addition to those directly employed in fishing, over 200 million people are thought to be dependent on smallscale fishing in developing countries, in terms of other economic activities generated by the supply of fish (trade, processing, transport, retail, etc.) and supporting activities (boat building, net making, engine manufacture and repair, supply of services to fisherman and fuel to fishing boats etc.) in addition to millions for whom fisheries provide a supplemental income (FAO, 2005a). Fisheries are often available in remote and rural areas where other economic activities are limited and can thus be important sources for economic growth and livelihoods in rural areas with few other economic activities (FAO, 2005a) 4.1 Potential impacts of climate change on fisheries Climate change is projected to impact broadly across ecosystems, societies and economics, increasing pressure on all livelihoods and food supplies. The major chunk of earth is encompassed by water that harbors vast majority of marine and freshwater fishery resources and thus likely to be affected to a greater extent by vagaries of climate change. Capture fisheries has unique features of natural resource harvesting linked with global ecosystem processes and thus is more prone to such problems. Aquaculture complements and increasingly adds to the supply chain and has important links with capture fisheries and is likely to be affected when the capture fisheries is affected. The ecological systems which support fisheries are already known to be sensitive to climate variability. For example, in 2007, the International Panel on Climate Change (IPCC) highlighted various risks to aquatic systems from climate change, including loss of coastal wetlands, coral bleaching and changes in the distribution and timing of fresh water flows, and acknowledged the uncertain effect of acidification of oceanic water which is predicted to have profound impacts on marine ecosystems (Orr et al., 2005). Similarly, fishing communities and related industries are concentrated in coastal or low lying zones which are increasingly at risk from sea level rise, extreme weather events and wide range of human pressures (Nicholls et al., 2007a). While poverty in fishing communities or other forms of marginalization reduces their ability to adapt and respond to change, increasingly globalized fish markets are creating new vulnerabilities to market disruptions which may result from climate change.

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Fisheries and fisher folk may have the impact in a wide range of ways due to climate change. The distribution or productivity of marine and fresh water fish stocks might be affected owing to the processes such as ocean acidification, habitat damage, changes in oceanography, disruption to precipitation and freshwater availability (Daw et al., 2009). Climate change, in particular, rising temperatures, can have both direct and indirect effects on global fish production. With increased global temperature, the spatial distribution of fish stocks might change due to the migration of fishes from one region to another in search of suitable conditions. Climate change will have major consequences for population dynamics of marine biota via changes in transport processes that influence dispersals and recruitment (Barange and Perry, 2009). These impacts will differ in magnitude and direction for populations within individual marine species whose geographical ranges span large gradients in latitude and temperature, as experimented by Mantzouni and Mackenzie (2010) in cod recruitment throughout the north Atlantic. The effects of increasing temperature on marine and freshwater ecosystems are already evident, with rapid pole ward shifts in distributions of fish and plankton in regions such as North East Atlantic, where temperature change has been rapid (Brander, 2007). Climate change has been implicated in mass mortalities of many aquatic species, including plants, fish, corals, and mammals (Harvell et al., 1999; Battin et al., 2007). Climate change will have impact on global biodiversity; alien species would expand into regions in which they previously could not survive and reproduce (Walther et al., 2009). Climate driven changes in species composition and abundance will alter species diversity and it is also likely to affect the ecosystems and the availability, accessibility, and quality of resources upon which human populations rely, both directly and indirectly through food web processes. Extreme weather events could result in escape of farmed stock and contribute to reduction in genetic diversity of wild stock affecting biodiversity. Climate variability and change is projected to have significant effects on the physical, chemical, and biological components of northern Canadian marine, terrestrial, and freshwater systems. According to a study conducted by Prowse et al. (2009), the northward migration of species and the disruption and competition from invading species are already occurring and will continue to affect marine, terrestrial, and freshwater communities. This will have implications for the protection and management of wildlife, fish, and fisheries resources; protected areas; and forests. Shifting environmental conditions will likely introduce new animal-transmitted diseases and redistribute some existing diseases, affecting key economic resources and some human populations. Stress on populations of iconic wildlife species, such as the polar bear, ringed seals, and whales, will continue as a result of changes in critical sea-ice habitat interactions. Where these stresses affect economically and culturally important species, they will have significant effects on people and regional economies. Further integrated, field-based monitoring and research programs, and the development of predictive models are required to allow for more detailed and comprehensive projections of change to be made, and to inform the development and implementation of appropriate adaptation, wildlife, and habitat conservation and protection strategies. Fisheries will also be exposed to a diverse range of direct and indirect climate impacts, including displacement and migration of human populations; impacts on coastal communities and infrastructure due to sea level rise; and changes in the frequency, distribution or intensity of tropical storms. Inland fisheries ecology is profoundly affected

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by changes in precipitation and run-off which may occur due to climate change. Lake fisheries in Southern Africa for example, will likely be heavily impacted by reduced lake levels and catches. The variety of different impact mechanisms, complex interactions between social, ecological and economic systems and the possibility of sudden and surprising changes make future effects of climate change on fisheries difficult to predict. In fact, understanding the ecological impacts of climate change is a crucial challenge of the twenty-first century. There is a clear lack of general rules regarding the impacts of global warming on biota. A study conducted by Daufresne et al. (2009) provided evidence that reduced body size is the third universal ecological response to global warming in aquatic systems besides the shift of species ranges toward higher altitudes and latitudes and the seasonal shifts in life cycle events. Apart from fisheries, global primary production (planktonic primary production) which is related to global fisheries catches at the scale of Large Marine Ecosystems appears to be declining, in some part due to climate variability and change, with consequences for the near future fisheries catches (Chassot et al., 2010). Other climatic change impacts on fisheries include surface winds, high CO2 levels and variability in precipitations. While surface wind would alter both the delivery of nutrients in to the photic zone and strength and distribution of ocean currents, higher CO2 levels can change the ocean acidity and variability in precipitation would affect sea levels. Global average sea level is rising at an average rate of 1.8 mm per year since 1961 and there is evidence of increased variability in sea level in recent decades. It is recently reported that ocean temperature and associated sea level increases between 1961 and 2003 were 50% larger than estimated in the 2007 IPCC Report. All coastal ecosystems are vulnerable to sea level rise and more direct anthropogenic impacts. Sea level rise may reduce intertidal habitat areas in ecologically important regions thus affecting fish and fisheries. 4.2 Impact of climate change on the parasites and infectious diseases of aquatic animals The potential trends of climate change on aquatic organisms and in turn in fisheries and aquaculture are less well documented and have primarily concentrated on coral bleaching and associated changes. An increase in the incidence of disease outbreaks in corals and marine mammals together with the incidence of new diseases has been reported. It was suggested that both the climate and human activities may have accelerated the global transport of species, bringing together of pathogens and previously unexposed populations (Harvell et al., 1999; De Silva and Sato, 2009). Climate changes could affect productivity of aquaculture systems and increase the vulnerability of cultured fish to diseases. All aquatic ecosystems, including freshwater lakes and rivers, coastal estuarine habitats and marine waters, are influenced by climate change (Parry et al., 2007; Scavia et al., 2002; Schindler, 2001). Relatively small temperature changes alter fish metabolism and physiology, with consequences for growth, fecundity, feeding behavior, distribution, migration and abundance (Marcogliese, 2008). The general effects of increased temperature on parasites include, rapid growth and maturation, earlier onset of spring maturation, increased parasite mortality, increased number of generations per year, increased rates of parasitism and disease, earlier and prolonged transmission, the possibility of continuous, year-round transmission (Marcogliese, 2001).

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Many diseases display greater virulence at higher temperatures that might be the result of reduced resistance of the host due to stress or increased expression of virulence factors/ increased transmission of the vectors. Some examples have been summarized in table 1. Host

Disease /Parasite

Response to high temperature

Reference

Largemouth bass (Micropterus salmoides) Mosquitofish (Gambusia affinis) Trout (Onchorhynchus spp.) Juvenile coho salmon (O. kisutch) A variety of reef fish

Red sore disease /bacterium Aeromonas hydrophila

Susceptibility to the disease increases

Esch and Hazen (1980)

Asian fish tapeworm (Bothriocephalus acheilognathi) Whirling disease / Myxozoan Myxobolus cerebralis Blackspot disease/ trematode larvae (metacercariae) Ciguatera fish poisoning (CFP) caused by bioaccumulation of algal toxins Infected with Ichthyophonus sp.

-do-

Granath and Esch (1983)

-do-

Hiner and Moffitt (2001)

Virulence is directly correlated with daily maximum temperature Increased incidence of CFP due to increased temperature

Cairns et al., 2005

More rapid onset of disease, higher parasite load, more severe host tissue reaction and reduced mean-day-todeath at higher temperature Exacerbate PKD outbreaks and increase the geographic range of PKD as a result of the combined responses of T. bryosalmonae and its bryozoan hosts to higher temperatures.

Kocan et al., 2009

Rainbow trout, Oncorhynchus mykiss

Freshwater bryozoans infected with myxozoan, Tetracapsuloides bryosalmonae

Tester et al., 2010

Spores released from Tops et al., sacs produced by the 2009 parasite during infection of freshwater bryozoans are infective to salmonid fish, causing the devastating Proliferative Kidney Disease (PKD) Table 1. Impact of climate change on parasitic and other diseases of aquatic animals. As the emergence of disease is linked directly to changes in the ecology of hosts or pathogens, or both (Harvell et al., 1999), climate change will have a profound impact on the spread of parasites and disease in aquatic ecosystems (Harvell et al., 1999; Marcogliese, 2001; Harvell et al., 2002). Climate change will affect parasite species directly resulting from the extension of the geographical range of pathogens (Harvell et al., 2002). In addition,

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increased temperature may cause thermal stress in aquatic animals, leading to reduced growth, sub- optimal behaviors and reduced immunocompetence (Harvell et al., 1999; Harvell et al., 2002; Roessig et al., 2004) resulting in changes in the distribution and abundance of their hosts (Marcogliese, 2001). In the oceans, diseases are shown to increase in corals, sea urchins, molluscs, sea turtles and marine mammals, although not all can be linked unequivocally to climate alone (Lafferty et al., 2004). However, it was recently suggested that diseases may not increase with climate change, although distributions of parasites and pathogens will undoubtedly shift (Lafferty, 2009). Other factors may dominate over climate in controlling the distribution and abundance of pathogens, including: habitat alteration, invasive species, agricultural practices and human activities. Effects on parasites

Effects on hosts

Effects on transmission

Faster embryonic development and hatching Faster rates of development and maturation Decreased longevity of larvae and adults Increased mortality of all stages

Altered feeding

Earlier reproduction in spring

Altered behavior

More generations per year

Altered range

Prolonged transmission in the fall

Altered ecology Potential transmission year round Reduced host resistance Table 2. General effects of increased temperature on parasite life cycles, their hosts and transmission processes (Marcogliese, 2008) Outbreaks of numerous water- borne diseases in both humans and aquatic organisms are linked to climatic events, although it is often difficult to disentangle climatic from other anthropogenic effects. In some cases, these outbreaks occur in foundation or keystone species, with consequences throughout whole ecosystems. There is much evidence to suggest that parasite and disease transmission, and possibly virulence, will increase with global warming. However, the effects of climate change will be superimposed on a multitude of other anthropogenic environmental changes. Climate change itself may exacerbate these anthropogenic effects. Moreover, parasitism and disease may act synergistically with these anthropogenic stressors to further increase the detrimental effects of global warming on animal and human populations, with debilitating social economic ramifications (Marcogliese, 2008). The repercussions of climate change are not limited solely to temperature effects on hosts and their parasites, but also have other possible effects such as: alteration in water levels and flow regimes, eutrophication, stratification, changes in acidification, reduced ice cover, changes in ocean currents, increased ultra- violet (UV) light penetration, run off, weather extremes (Cochrane et al., 2009).

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5. Anticipated impacts in next few decades In addition to incremental changes of existing trends, complex social and ecological systems such as coastal zones and fisheries, may exhibit sudden qualitative shifts in behaviour when forcing variables past certain thresholds (Daw et al., 2009). For example, IPCC originally estimated that the Greenland ice sheet would take more than 1000 years to melt, but recent observations suggest that the process is already happening faster owing to mechanisms for ice collapse that were not incorporated into the projections (Lenton et al., 2008). The infamous collapse of the Northwest Atlantic northern cod fishery provides a non-climaterelated example where chronic over fishing led to a sudden, unexpected and irreversible loss in production from this fishery. Thus, existing observations of linear trends cannot be used to reliably predict impacts within the next 50 years (Daw et al., 2009). A study by Veron et al. (2009) also emphasizes impact of increasing atmospheric CO2 levels due to global warming on mass coral bleaching world-wide. According to this group, temperature-induced mass coral bleaching causing mortality on a wide geographic scale started when atmospheric CO2 levels exceeded approximately 320 ppm. At today's level of approximately 387 ppm, allowing a lag-time of 10 years for sea temperatures to respond, most reefs world-wide are committed to an irreversible decline. Mass bleaching will in future become annual, departing from the 4 to 7 years return-time of El Niño events. Bleaching will be exacerbated by the effects of degraded water-quality and increased severe weather events. In addition, the progressive onset of ocean acidification will cause reduction of coral growth and retardation of the growth of high magnesium calcite-secreting coralline algae. If CO2 levels are allowed to reach 450 ppm (due to occur by 2030-2040 at the current rates), reefs will be in rapid and terminal decline world-wide from multiple synergies arising from mass bleaching, ocean acidification, and other environmental impacts. Damage to shallow reef communities will become extensive with consequent reduction of biodiversity followed by extinctions. Reefs will cease to be large-scale nursery grounds for fish and will cease to have most of their current value to humanity. There will be knock-on effects to ecosystems associated with reefs, and to other pelagic and benthic ecosystems. This is likely to have been the path of great mass extinctions of the past, adding to the case that anthropogenic CO2 emissions could trigger the Earth's sixth mass extinction (Veron et al., 2009).

6. Climate change impacts on inland fisheries - the Indian scenario In recent years the climate is showing perceptible changes in the Indian subcontinent, where the average temperature is on the rise over the last few decades. In India, observed changes include an increase in air temperature, regional monsoon variation, frequent droughts and regional increase in severe storm incidences in coastal states and Himalayan glacier recession (Vass et al., 2009). In some states like West Bengal, the average minimum and maximum temperatures has increased in the range of 0.1 - 0.9 °C throughout the state. The average rainfall has decreased and monsoon is also delayed; consequently, the climate change impact is being felt on the temperature of the inland water bodies and on the breeding behavior of fishes. It is well known that temperature is an important factor which strongly influence the reproductive cycle in fishes. Temperature, along with rainfall and photoperiod, stimulate the endocrine glands of fishes which help in the maturation of the gonads. In India, the inland aquaculture is centered on the Indian major carps, Catla catla,

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Labeo rohita and Cirrhinus mrigala and their spawning occurs during the monsoon (June-July) and extend till September. In recent years the phenomenon of IMC maturing and spawning as early as March is observed, making it possible to breed them twice a year. Thus, there is an extended breeding activity as compared to a couple of decades ago (Dey et al., 2007), which appears to be a positive impact of the climate change regime.

Fig. 1. Course of the River Ganga showing different stretches (http://www.gits4u.com/ water/ganga1.gif) The mighty river Ganga forms the largest river system in India and not only millions of people depend on its water but it provides livelihood to a large group of fishermen also. The entire length of the river, with a span of 2,525 km from source to mouth is divided into three main stretches consisting of upper (Tehri to Kanauji), middle (Kanpur to Patna) and lower (Sultanpur to Katwa) (Figure 1). From analysis of 30 years’ time series data on river Ganga and water bodies in the plains, Vass et al. (2009) reported an increase in annual mean minimum water temperature in the upper cold-water stretch of the river (Haridwar) by 1.5 °C (from 13 °C during 1970-86 to 14.5 °C during 1987-2003) and by 0.2- 1.6 °C in the aquaculture farms in the lower stretches in the Gangetic plains. This change in temperature clime has resulted in a perceptible biogeographically distribution of the Gangetic fish fauna. A number of fish species which were never reported in the upper stretch of the river and were predominantly available in the lower and middle stretches in the 1950s (Menon, 1954) have now been recorded from the upper cold-water region. Among them, Mastocembelus armatus has been reported to be available at Tehri-Rishikesh and Glossogobius gurius is available in the Haridwar stretch (Sinha et al., 1998) and Xenentodon cancila has also been reported in the cold-water stretch (Vass et al., 2009). The predator-prey ratio in the middle stretch of the river has been reported to be declined from 1:4.2 to 1:1.4 in the last three decades. Fish production has been shown to have a distinct change in the last two decades

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where the contribution from IMCs has decreased from 41.4% to 8.3% and that from catfishes and miscellaneous species increased (Vass et al., 2009).

7. Adaptation and mitigation options Adaptation to climate change is defined in the climate change literature as an adjustment in ecological, social or economic systems, in response to observed or expected changes in climatic stimuli and their effects and impacts in order to alleviate adverse impacts of change, or take advantage of new opportunities. Adaptation is an active set of strategies and actions taken by peoples in response to, or in anticipation to the change in order to enhance or maintain their well being. Hence adaptation is a continuous stream of activities, actions, decisions and attitudes that informs decisions about all aspects of life and that reflects existing social norms and processes (Daw et al., 2009). Many capture fisheries and their supporting ecosystems have been poorly managed, and the economic losses due to overfishing, pollution and habitat loss are estimated to exceed $50 billion per year (World Bank & FAO, 2008). The capacity to adapt to climate change is determined partly by material resources and also by networks, technologies and appropriate governance structures. Improved governance, innovative technologies and more responsible practices can generate increased and sustainable benefits from fisheries. There is a wide range of potential adaptation options for fisheries. To build resilience to the effects of climate change and derive sustainable benefits, fisheries and aquaculture managers need to adopt and adhere to best practices such as those described in the FAO ‘Code of Conduct for Responsible Fisheries’, reducing overfishing and rebuilding fish stocks. These practices need to be integrated more effectively with the management of river basins, watersheds and coastal zones. Fisheries and aquaculture need to be blended into National Climate Change Adaptation Strategies. In absence of careful planning, aquatic ecosystems, fisheries and aquaculture can potentially suffer as a result of adaptation measures applied by other sectors such as increased use of dams and hydro power in catchments with high rainfall, or the construction of artificial coastal defenses or marine wind farms (ftp://ftp.fao.org/FI/brochure/climate_change/policy_brief.pdf). Mitigation solutions reducing the carbon footprint of Fisheries and Aquaculture will require innovative approaches. One example is the recent inclusion of Mangrove conservation as eligible for reducing emissions from deforestation and forest degradation in developing countries, which demonstrates the potential for catchment forest protection. Other approaches to explore include finding innovative but environmentally safe ways to sequester carbon in aquatic ecosystems, and developing low-carbon aquaculture production systems (ftp://ftp.fao.org/FI/brochure/climate_change/policy_brief.pdf). There is mounting interest in exploiting the importance of herbivorous fishes as a tool to help ecosystems recover from climate change impacts. Aquaculture of herbivorous species can provide nutritious food with a small carbon footprint. This approach might be particularly suitable for recovery of coral reefs, which are acutely threatened by climate change. Surveys of ten sites inside and outside a Bahamian marine reserve over a 2.5-year period demonstrated that increases in coral cover, including adjustments for the initial sizedistribution of corals, were significantly higher at reserve sites than those in non-reserve sites: macroalgal cover was significantly negatively correlated with the change in total coral cover over time. Reducing herbivore exploitation as part of an ecosystem-based

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management strategy for coral reefs appears to be justified (Mumby and Harborne, 2010). Furthermore, farming of shellfish, such as oysters and mussels, is not only good business, but also helps clean coastal water, while culturing aquatic plants help to remove waste from polluted water. In contrast to the potential declines in agricultural yields in many areas of the world, climate change opens new opportunities for aquaculture as increasing numbers of species are cultured (ftp://ftp.fao.org/FI/brochure/climate_change/policy_brief.pdf). Marine fish is one of the most important sources of animal protein for human use, especially in developing countries with coastlines. Marine fishery is also an important industry in many countries. The depletion of fishery resources is happening mainly due to anthropogenic factors such as overfishing, habitat destruction, pollution, invasive species introduction, and climate change. The most effective ways to reverse this downward trend and restore fishery resources are to promote fishery conservation, establish marineprotected areas, adopt ecosystem-based management, and implement a "precautionary principle." Additionally, enhancing public awareness of marine conservation, which includes eco-labeling, fishery ban or enclosure, slow fishing, and MPA (marine protected areas) enforcement is important and effective (Shao, 2009). The assessment report of the 4th International Panel on Climate Change confirms that global warming is strongly affecting biological systems and that 20-30% of species risk extinction from projected future increases in temperature. One of the widespread management strategies taken to conserve individual species and their constituent populations against climate-mediated declines has been the release of captive bred animals to wild in order to augment wild populations for many species. Using a regression model based on a 37-year study of wild and sea ranched Atlantic salmon (Salmo salar) spawning together in the wild, McGinnity et al. (2009) showed that the escape of captive bred animals into the wild can substantially depress recruitment and more specifically disrupt the capacity of natural populations to adapt to higher winter water temperatures associated with climate variability, thus increasing the risk of extinction for the studied population within 20 generations. According to them, positive outcomes to climate change are possible if captive bred animals are prevented from breeding in the wild. Rather than imposing an additional genetic load on wild populations by releasing maladapted captive bred animals, they propose that conservation efforts should focus on optimizing conditions for adaptation to occur by reducing exploitation and protecting critical habitats.

8. Monitoring stress in aquatic animals and HSP70 as a possible monitoring tool Temperature above the normal optimum are sensed as heat stress by all organisms, Heat stress (HS) disturbs cellular homeostasis and can lead to severe retardation in growth and development and even death. Heat shock (stress) proteins (HSP) are a class of functionally related proteins whose expression is increased when cells are exposed to elevated temperatures or other stress. The dramatic up regulation of the HSPs is a key part of heat shock (stress) response (HSR). The accumulation of HSPs under the control of heat shock (stress) transcription factors (HSFs) play a central role in the heat stress response (HSR) and acquired thermo tolerance. HSPs are highly conserved and ubiquitous and occur in all organisms from bacteria to yeast to humans. Cells from virtually all organisms respond to different stress by rapidly synthesizing the HSPs and therefore, HSPs are widely used as

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biomarkers for stress response (Jolly and Marimoto, 2000). HSPs have multiple housekeeping functions, such as activation of specific regulatory proteins and folding and translocation of newly synthesized proteins. HSPs are usually produced in large amounts (induction) in response to distinct stressors such as ischemia, hypoxia, chemical/toxic insult, heavy metals, oxidative stress, inflammation and altered temperature or heat shock (Marimoto, 1998). Out of different HSPs, the HSP70 is unique in many ways; it acts as molecular chaperone in both unstressed and stressed cells. HSC70, the constitutive HSP70 is crucial for the chaperoning functions of unstressed cells, where as the inducible HSP70 is important for allowing cells to cope with acute stress, especially those affecting the protein machinery. HSP70 in marine mussels are widely used as a potential biomarker for stress response and aquatic environmental monitoring of the marine ecosystem (Li et al., 2000). The success of any organism depends not only on niche adaptation but also the ability to survive environmental perturbation from homeostasis, a situation generally described as stress (Clark et al., 2008a). Although species-specific mechanisms to combat stress have been described, the production of heat shock proteins (HSPs), such as HSP70, is universally described across all taxa. We have studied expression profile of the HSP70 proteins, in different tissues of the large riverine catfish Sperata seenghala (Mohanty et al., 2008), freshwater catfish Rita rita (Mohanty et al., 2010b), Indian catfish Clarias batrachus, Indian major carps Labeo rohita, Catla catla, Cirrhinus mrigala, exotic carp Cyprinus carpio var. communis and the murrel Channa striatus, the climbing perch Anabas testudineus (CIFRI, 2009; Mohanty et al., 2009). Out of these, the IMCs are the major aquaculture species and therefore are of much economic significance. Similarly, Anabas and Channa fetch good market value and their demand is increasing owing to their perceived therapeutic value (Mohanty et al., 2010a). The large riverine catfish S. seenghala comprises the major fisheries in majority of rivers and reservoirs and the freshwater catfish Rita rita has a good market demand and these two comprise a major share of the capture fisheries in India. Monoclonal anti-HSP70 antibody (H5147, Sigma), developed in mouse against purified bovine brain HSP70, in immunoblotting localizes both the constitutive (HSP73) and inducible (HSP72) forms of HSP70. The antibody recognizes brain HSP70 of bovine, human, rat, rabbit, chicken, and guinea pig. We observed immunoreactivity of this antibody with HSP70 proteins in different organs and tissues of a variety of fish species (Table 3). The strong immunoreactivity indicates that the HSP70 proteins of bovine and this riverine catfish Rita rita share strong hom*ology although fish belong to a clade phylogenetically distant from the bovines. Persistent, high level of expression of HSP70 was observed in muscle tissues of Rita rita and for this reason, we have used and recommend use of white muscle tissue of Rita rita as a suitable positive control in analysis of HSP70 expression in tissues of other organisms (Mohanty et al., 2010b). Early studies on heat shock response in Antarctic marine ectoderms had led to the conclusion that both microorganisms and fish lack the classical heat shock response, i.e. there is no increase in HSP70 expression when warmed (Carratti et al., 1998; Hofmann et al., 2000). However, later it was reported that other Antarctic animals, show an inducible heat shock response, at a level probably set during their temperate evolutionary past (Clark et al., 2008 a, b); the bivalve (clam) Laternula elliptica and gastropod (limpet) Nacella concinna show an inducible heat shock response at 8 °C and 15 °C, respectively and these are temperatures in excess of that which is currently experienced by these animals, which can be attributed to

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the global warming (Waller et al., 2006). Permanent expression of the inducible HSP70 genes, species-specific high expression of HSC70 (N. concinna) and permanent expression of GRP78 (N concinna and L. elliptica) indicates that, as for fish, chaperone proteins form an essential part of the adaptation of the biochemical machinery of these animals to low but stable temperatures. High constitutive levels of HSP gene family member expression may be a compensatory mechanism for coping with elevated protein damage at low temperature analogous to the permanent expression of HSP70 in the Antarctic notothenoids (Clark et al., 2008 a). Such studies clearly indicate that both genetics and environment play important role in spatio-temporal gene expression. Remarks Mohanty et al. 2009 CIFRI 2009; Mohanty et al. 2009 Cyprinous carpio var communis ++ ++ ++ -doAnabas testudineus ++ ++ -doChanna punctatus ++ -doSperrata seenghala ++ ++ ++ + Mohanty et al. 2008 Rita rita ++ ++ ++ + Mohanty et al. 2010b Table 3. HSP70 expression profile in different tissues of some freshwater fishes, both aquacultured and wild stock. Fish species Labeo rohita Cirrhinus mrigala

Liver ++

Muscle ++ -

Kidney ++ -

Gill ++ ++

There is need to standardize tools suitable for monitoring stress resulting from global warming and climate change impacts, in the aquatic animals from both aqua culture and capture fisheries systems. As HSP70 expression has been reported in many fish species (Table 3) it might serve as a suitable tool for monitoring impact of thermal stress/global warming; however, as HSP70 proteins are expressed under other conditions also, it is necessary to identify the heat shock (stress) transcription factors (HSFs) that can be specifically attributed to global warming (thermal stress) and climate change. It is also necessary to distinguish the constitutive and induced forms of the transcripts/proteins by qPCR/proteomic analysis so that specific HSP70 forms suitable for monitoring performance of the farmed fishes can be monitored for better management of aquacultured animals. IPCC have predicted an average global warming between +2 and +6 °C, depending on the scenarios, within the next 90 years (IPCC 2007). The consequences of this increase in temperature are now well documented on both the abundance and geographic distribution of numerous taxa i.e. at population or community levels; in contrast, studies at the cellular level are still scarce. The study of the physiological or metabolic effects of such small increases in temperature is difficult because they are below the amplitude of the daily or seasonal thermal variations occurring in most environments. The underground water organisms are highly thermally buffered and thus are well suited for characterization of cellular responses of global warming. Colson-Proch et al. (2010) studied the genes encoding HSP70 family chaperones in amphipod crustaceans belonging to the ubiquitous subterranean genus Niphargus and HSP 70 sequence in 8 populations of 2 complexes of species of this genus (Niphargus rhenorhodanensis and Niphargus virei complexes). Expression profiles of HSP70 were determined for one of these populations by reverse transcription and quantitative polymerase chain reaction, confirming the inducible nature of this gene. An

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increase of 2 °C seem to be without any effect on N. rhenorhodanensis physiology whereas a heat shock of + 6 °C represented an important thermal stress for these individuals. Thus this study showed that although Niphargus individuals do not undergo any daily or seasonal thermal variations in underground water, they display an inducible HSP70 heat shock response (Colson-Proch et al., 2010).

9. Epilogue There are opposing viewpoints on the predicted impacts of ‘global warming’ also. Scientists warn against overselling climate change. Some experts feel that the data produced by models used to project weather changes, risk being over-interpreted by governments, organizations and individuals keen to make plans for a changing climate, with dangerous results. The point made is that the Global Climate Models (GCMs) help us understand pieces of the climate system, but that does not mean we can predict the details. Thus, indications of changes in the earth’s future climate must be treated with the utmost seriousness and with the precautionary principle uppermost in our minds. Extensive climate change may alter and threaten the living conditions of much of mankind. They may induce large-scale migration and lead to greater competition for the earth’s resources. Such changes will place particularly heavy burdens on the world’s most vulnerable countries. There may be increased danger of violent conflicts and wars, within and between states. A wide array of adaptation options is available, but more extensive adaptation than is currently occurring is required to reduce vulnerability to climate change. Although the understanding of climate change has advanced significantly during the past few decades, many questions remain unanswered. The task of mitigating and adapting to the impacts of climate change will require worldwide collaborative input from a wide range of experts from various fields. The common man’s contribution will play a major role in reducing the impacts of climate change and protecting the earth from climate change-related hazards. The impacts of climate change to freshwater aquaculture in tropical and subtropical region is difficult to predict as marine and freshwater populations are affected by synergistic effects of multiple climate and noncelibate stressors. If such noncelibate factors are identified and understood then it may be possible for local predictions of climate change impacts to be made with high confidence (De Silva and Soto, 2009). Coastal communities, fishers and fish farmers are profoundly affected by climate change. Climate change is modifying the distribution and productivity of marine and freshwater species and is already affecting biological processes and altering food webs, thus making the consequences for sustainability of aquatic ecosystems for fisheries and aquaculture, and for the people dependent on them, uncertain. Fisheries, aquaculture and fish habitats are at risk. Deltas and estuaries are in the fore front and thus, most vulnerable to climate change. Mitigation measures are urgently needed to neutralize and alleviate these growing threats, to adapt to their impacts and also to build our knowledge base on Complex Ocean and aquatic processes. The prime need is to reduce the global emissions of GHGs, which is the primary anthropogenic factor responsible for climate change (ProAct Network, 2008). Healthy aquatic ecosystems contribute greatly to food security and livelihoods. They are critical for production of wild fish and for some of the seed and much of the feed (trash fish) for aquaculture. Coastal ecosystems provide food, habitats and nursery grounds for fish. Estuaries, coral reefs, mangroves and sea grass beds are particularly important. Mangroves

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create barriers to destructive waves from storms and hold sediments in place with their extensive root systems thereby reducing coastal erosion. Healthy coral reefs, sea grass beds and wetlands provide similar benefits. Thus, these natural systems not only support fisheries, but help protect communities from the terrible impacts of natural hazards and disasters also (ProAct Network, 2008). In freshwater systems, ecosystem health and productivity is linked to water quality and flow and the health of wetlands. Ecosystem-based approaches to fisheries and coastal zone management are highly beneficial as such approaches recognize the need for people to use the ecosystem for their food security and livelihoods while enabling these valuable natural assets to adapt to the effects of climate change, and to reduce the threats from other environmental stresses (Hoegh-Guldberg et al., 2007). Fish and shellfish provide essential nutrition for 3 billion people and about 50% of animal protein and micronutrients to 400 million people in the poorest countries of the world. Fish is one of the cheapest sources of animal proteins and play important role in preventing protein-calorie malnutrition. The health benefits of eating fish are being increasingly understood by the consumers. Over 500 million people in the developing countries depend on fisheries and aquaculture for their livelihoods. Aquaculture is the world’s fastest growing food production system, growing at 7% annually. Fish products are among the most widely traded foods internationally (ftp://ftp.fao.org/FI/brochure/climate_change/ policy_brief.pdf). Implementing adaptation and mitigation pathways for communities dependent on fisheries, aquaculture and aquatic ecosystems will need increased attention from policy-makers and planners. Sustainable and resilient aquatic ecosystems will benefit the fishers as well as the coastal communities and will provide good and services at national and global levels. Fisheries and aquaculture need specific adaptation and mitigation measures like: improving the management of fisheries and aquaculture as well as the integrity and resilience of aquatic ecosystems; responding to the opportunities for and threats to food and livelihood security due to climate change impacts; and helping the fisheries and aquaculture sector reduce GHG emissions. To conclude, the present generation is already facing the harmful effects of the climate change; however, the future generations will suffer most of the harmful effects of global climate change. So, the present generation need to decide, whether to aggressively reduce the chances of future harm at the cost of sacrificing some luxuries or to let our descendants largely fend for themselves (Broome, 2008). Thus, how we handle the issue of Climate Change is more of an ethical question and the global community must act sensibly and responsibly.

10. References Barange, M., & Perry, R.I. (2009) Physical and ecological impacts of climate change relevant to marine and inland capture fisheries and aquaculture In: Climate change implications for fisheries and aquaculture overview of current scientific Knowledge, Cochrane, K., Young, C. De, Soto, D., & Bahri, T. (Eds). FAO Fisheries and Aquaculture Technical paper: No. 530, pp. 7-106, FAO, Rome. Battin, J., Wiley, M. W., Ruckelshaus, M. H., Palmer, R. N,. Korb, E., Bartz, K. K., & Imaki, H. (2007) Projected impacts of climate change on salmon habitat restoration, Proc. Natl. Acad. Sci, USA, 104, 6720-6725.

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Brander, K. M. (2007) Global fish production and climate change, Proc. Natl. Acad. Sci., USA, 104, 19709-19714. Broome, J. (2008) The ethics of climate change, Sci. Am., 298, 96-100. Cairns, M. A., Ebersole, J. L., Baker, J. P., Wigngton, P. J. Jr., Lavigne, H. R., & Davis, S. M. (2005) Influence of summer stream temperatures on black spot infestation of juvenile coho salmon in the Oregon Coast Range, Trans. Am. Fish. Soc., 134, 1471-1479. Carrattù, L., Gracey, A. Y, B.uono, S., & Maresca, B. (1998) Do Antarctic fish respond to heat shock? In: Fishes of Antarctica. A Biological Overview. di Prisco, G., Pisano, E., Clarke, A. (Eds) Springer, Italy. Chassot, E., Bonhommeau, S., Dulvy NK, Mélin F, Watson R, Gascuel D, Le Pape O. (2010) Global marine primary production constrains fisheries catches. Ecol Lett., Feb 5. [Epub ahead of print] CIFRI (2009) Annual Report. Central Inland Fisheries Research Institute, Barrackpore, Kolkata, India. ISSN 0970 6267. Clark, M. S., Fraser, K. P. P., & Peck, L. S. (2008a) Antarctic marine molluscs do have an HSP70 heat shock response, Cell Stress Chaperon., 13, 39-49. Clark, M. S., Geissler, P., Waller, C., Fraser, K. P. P., Barnes, D. K. A., & Peck, L. S. (2008b) Low heat shock thresholds in wild Antarctic inter-tidal limpets (Nacella concinna). Cell Stress Chaperon., 13, 51-58. Cochrane, K., Young, C. De, Soto, D., & Bahri, T. (2009) Climate change implications for fisheries and aquaculture: overview of current scientific knowledge. FAO Fisheries and Aquaculture Technical paper: No. 530,FAO, Rome. Colson-Proch, C., Morales, A., Hervant, F., Konecny, L., Moulin, C., & Douady, C. J. (2010) First cellular approach of the effects of global warming on groundwater organisms: a study of the HSP70 gene expression. Cell Stress Chaperon., 15, 3, 259-270. Daufresne, M., Lengfellner, K., & Sommer, U. (2009) Global warming benefits the small in aquatic ecosystems. Proc Natl Acad Sci USA., 106, 31, 12788-12793. Daw, T., Adger, W. N., Brown, K., & Badjeck, M.-C. (2009) Climate change and capture fisheries: potential impacts, adaptation and mitigation. In: Climate change implications for fisheries and aquaculture overview of current scientific Knowledge, Cochrane, K., Young, C. De, Soto, D., & Bahri, T. (Eds). FAO Fisheries and Aquaculture Technical paper: No. 530, pp.107-150, FAO, Rome. De Silva, S. S. and Soto, D. 2009, Climate change and aquaculture: potential impacts, adaptation and mitigation In: Climate change implications for fisheries and aquaculture overview of current scientific Knowledge, Cochrane, K., Young, C. De, Soto, D., & Bahri, T. (Eds). FAO Fisheries and Aquaculture Technical paper: No. 530, pp. 151212, FAO, Rome. Dey, S., Srivastava, P. K., Maji, S., Das, M. K., Mukhopadhyay, M. K., & Saha, P. K. (2007) Impact of climate change on the breeding of Indian major carps in West Bengal. J. Inland Fish. Soc. India, 39, 1, 26-34. Done, T., Whetton, P., Jones, R. et al. (2003) Global climate change and coral bleaching on the Great Barrier Reef. Final report to the State of Queensland Greenhouse taskforce through the Department of Natural Resources and Mines, Queensland,. Esch, G. W., & Hazen, T. C. (1980) Stress and body condition in a population of largemouth bass: implications for red-sore disease, Trans. Am. Fish. Soc., 109, 532-536.

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FAO (2005) Increasing the contribution of small-scale fisheries to poverty alleviation and food security. FAO Technical Guidelines for Responsible Fisheries. No. 10, 79 p., FAO, Rome. FAO (2007) The state of world fisheries and aquaculture – 2006, 162 p., FAO, Rome. Granath, W. O. Jr., & Esch, G. W. (1983) Survivorship and parasite- induced host mortality among mosquio fish in a predator- free, North Carolina cooling reservoir, Am. Midland Naturalist, 110, 314-323. Harvell, C. D., Kim, K., Burkholder, J. M., Colwell, R. R., Epstein, P. R., Grimes, D. J., Hofmann, E. E., Lipp, E. K., & Osterhaus, A. D. Overstreet RM et al. (1999) Emerging marine diseases- climate links and anthropogenic factors, Science, 285, 1505-1510. Harvell, C. D., Mitchell, C. E., Ward, J. R., Altizer, S., Dobson, A. P., Ostfeld, R. S & Samuel, M.D. (2002) Climate warming and disease risks for terrestrial and marine biota, Science, 296, 5576, 2158-2162. Hiner, M., & Moffitt, C. M. (2001) Variation in infections of Myxobolus cerebralis in fieldexposed cutthroat and rainbow trout in Idaho, J. aquat. Anim. Hlth, 13, 124-132. Hoegh-Goldberg, O. et al. (2007) Coral reefs under rapid climate change and ocean acidification. Science, 318, 1737-1742. Hofmann, G. E., Buckley, B. A., Airaksine, S., Keen, J. E., & Somero, G. N. (2000) Heat-shock protein expression is absent in the Antarctic fish Trematomus bernacchii family Nototheniidae. J Exp Biol., 203, 2331-2339. IPCC (2007) Fourth Assessment Report - Climate Change 2007: Synthesis Report, 2007. IPCC (2001) Climate Change 2001. IPCC Third Assessment Report, 2001. Jolly, C., & Marimoto, R. I. (2000) Role of the heat shock response and molecular chaperones in oncogenesis and cell death, J. Natl Cancer Inst. 92, 1564-1572. Kocan, R., Hershberger, P., Sanders, G., & Winton, J. (2009) Effects of temperature on disease progression and swimming stamina in Ichthyophonus-infected rainbow trout, Oncorhynchus mykiss (Walbaum), J Fish Dis., 32, 10, 835-43. Lafferty, K. D. (2009) The ecology of climate change and infectious diseases. Ecology, 90, 888-900. Lafferty, K. D., Porter, J. W & Ford, S. E. (2004) Are diseases increasing in the ocean? Ann. Rev. Ecol. Evol. Syst., 35, 31-54. Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008) Tipping elements in the earth’s climate system, Proc Natl Acad Sci, USA, 105, 6, 1786-1793. Li, C. Y., Lee, J. S., Ko, Y. G., Kim, J. I. & Seo, J. S. (2000) Heat shock protein 70 inhibits apoptosis downstream of cytochrome c release and upstream of caspase-3 activation. J. Biol. Chem., 275, 25665-25671. Ling, S. D., Johnson, C. R., Frusher, S. D., & Ridgway, K. R. (2009) Overfishing reduces resilience of kelp beds to climate-driven catastrophic phase shift, Proc Natl Acad Sci USA., 106, 52, 22341-22345. Mantzouni, I., & Mackenzie, B. R. (2010) Productivity responses of a widespread marine piscivore, Gadus morhua, to oceanic thermal extremes and trends. Proc Biol Sci. Feb 10. [Epub ahead of print]. Marcogliese, D. J. (2008) The impact of climate change on the parasites and infectious diseases of aquatic animals, Rev. sci. tech. Off. int. Epiz., 27, 2, 467-484. Marimoto, R.J. (1998) Regulation of the heat shock transcriptional response: cross talk between a family of heat shock factors, molecular chaperones and negative regulators. Genes Dev., 12, 3788-3796.

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McGinnity, P., Jennings, E., DeEyto, E., Allott, N., Samuelsson, P., Rogan, G., Whelan, K., & Cross, T. (2009) Impact of naturally spawning captive-bred Atlantic salmon on wild populations: depressed recruitment and increased risk of climate-mediated extinction, Proc Biol Sci. 276, 1673, 3601-3610. Menon, A. G. K. (1954) Fish geography of the Himalayas. Zoological Survey of India, Calcutta. 11, 4, 467-493. Mohanty, B. P., Mondal, K., Bhattacharjee, S., & Vass, K. K. (2008) HSP 70 expression profile in tissues of the large riverine catfish Aorichthys seenghala (Sykes). P-GNB-58, p.153. 8th Indian Fisheries Forum 22-26 Nov 2008, Kolkata, India; jointly organized by CIFRI, Inland Fisheries Society of India and Indian Fisheries Forum. ISBN-8185482-14-4. Mohanty, B. P., Bhattacharjee, S, Mondal, K., & Das, M. K. (2009) HSP 70 expression in different tissues of some important tropical freshwater fishes. 96th Indian Science Congress, 3-7 January 2009, organized by NEHU, Shillong, India. Mohanty, S., & Mohanty, B. P. (2009) Global climate change: a cause of concern, Natl Acad Sci Lett, 32, 5 & 6, 149-156. Mohanty, B. P., Behera, B. K., & Sharma, A. P. (2010a) Nutritional significance of small indigenous fishes in human health. Bulletin No. 162, Central Inland Fisheries Research Institute, Barrackpore, Kolkata, India. ISSN 0970-616X. Mohanty, B. P., Bhattacharjee, S., Mondal, K., & Das, M. K. (2010b) HSP70 expression profiles in white muscles of riverine catfish Rita rita show promise as biomarker for pollution monitoring in tropical rivers. Natl Acad Sci Lett., 33, 5 & 6, 177-182. Mumby, P. J., & Harborne, A. R. (2010) Marine reserves enhance the recovery of corals on Caribbean reefs, PLoS One 5, 1, e8657. National Climate Data Centre, National Oceanic and Atmospheric Administration. Global warming: frequently asked questions. Available at: www.ncdc.noaa.gov/oa/ climate/globalwarming.html. Accessed December 9, 2008. Nicholls, R. J., Wong, P. P., Burkett, V. R., Codignotto, J. O., Hay, J. E., McLean, R. F., Ragoonaden, S., & Woodroffe, C. D. (2007) Coastal systems and low-lying areas. In: Climate Change 2007: impacts, adaptation and vulnerability, Parry, M. L., Canziani, O. F., Palutikof, J. P., Linden, V. D. & Hanson, C. E., (Eds.), pp. 315-356. Contribution of working group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK. Orr, J. C., Fabry, V. J., Aumont, O., Bopp, L., Doney, S. C., Feely, R. A., Gnanadesikan, A., Gruber, N., Ishida, A., Joos, F., Key, R. M., Lindsay, K., Maier-Reimer, E., Matear, R., Monfray, P., Mouchet, A., Najjar, R. G., Plattner, G-K, Rodgers, K. B., Sabine, C. L., Sarmiento, J. L., Schlitzer, R., slater, R. D., Totterdell, I. J., Weirig, M-F., Yamanaka, Y., & Yool, A. (2005) Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature, 437, 681-686. ProAct Network (2008) The role of environmental management and eco-engineering in disaster risk reduction and climate change adaptation. Prowse, T. D., Furgal, C., Wrona, F. J., & Reist, J. D. (2009) Implications of climate change for northern Canada: freshwater, marine, and terrestrial ecosystems, Ambio, 38, 5, 282-289. Regional Framework for action to protect human health from effects of climate change in the South East Asia and Pacific Region. 2007. Available at http://www.searo.who.int/ en/Section260/Section2468_14335.htm. Accessed December 9, 2008.

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Scott, M. A., Locke, M., & Buck, L. T (2003) Tissue- specific expression of inducible and constitutive Hsp70 isoforms in the western painted turtle, J Exptl. Biol., 206, 303-311. Shao, K. T. (2009) Marine biodiversity and fishery sustainability. Asia Pac J Clin Nutr., 18, 4, 527-531. Shea, K. M., & the Committee on Environmental Health. (2007) Global Climate Change and children’s health. Pediatrics, 120, e1359-e1367. Sinha, M., De, D. K., & Jha, B. C. (1998) The Ganga- Environment and Fishery. Central Inland Fisheries Research Institute, Barrackpore, Kolkata, India. Tester, P. A., Feldman, R. L., Nau, A. W., Kibler, S. R., & Wayne Litaker, R. (2010) Ciguatera fish poisoning and sea surface temperatures in the Caribbean Sea and the West Indies. Toxicon. Mar 3. [Epub ahead of print] Thorpe, A., Reid, C., Anrooy, R. V., Brugere, C., & Becker, D. (2006) Poverty reduction strategy papers and the fisheries sector: an opportunity forgone?, J Intl. Dev., 18, 4, 487-517. Tops, S., Hartikainen, H. L., & Okamura, B. (2009) The effects of infection by Tetracapsuloides bryosalmonae (Myxozoa) and temperature on Fredericella sultana (Bryozoa). Int J Parasitol., 39, 9, 1003-1010. Understanding and responding to Climate Change. 2008 Edn. pp. 1-24. The National Academies, USA (http://www.national-academies.org) Vass, K. K., Das, M. K., Srivastava, P. K. & Dey, S. (2009) Assessing the impact of climate change on inland fisheries in River Ganga and its plains in India. Aqu Ecosys Health & Management., 12, 2, 138-151. Veron, J. E., Hoegh-Guldberg, O., Lenton, T. M., Lough, J. M., Obura, D. O., Pearce-Kelly, P., Sheppard, C. R., Spalding, M., Stafford-Smith, M. G., & Rogers, A. D. (2009) The coral reef crisis: the critical importance of90%, we can call the grid point the analogue for the scenario 5.2. Analogue regions First we looked for the analogue regions for the base period. We found that we got back our regions after looking for the analogues in the future. We found that the analogue regions are south of Debrecen. This climate shifting was the same in case of different scenarios because in the first decades they do not differ very much but we can see more differences in the middle of the century. Finally, we defined the analogue regions for the scenarios and time periods. We found that the climatic shifting would be 250-450 km in the next decades and 450- 650 km by the middle of the century. Unfortunately we could not find any similar regions for the end of the century, but some analogues can be found in North Africa.

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TIME

Base period For validating the method, analogues for the observed climate were calculated. As a result we got back the region of Debrecen.

19611990

A1F1

20102019

20202029

20302039

20402069

Fig. 8. Analogue regions in the next decades and in case of different climate scenarios for Debrecen

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We developed a new method to find inverse analogue regions; these are the regions the climate of which will be similar to that of our study area in the future. We found a same shifting to the north. These analogue regions are in Poland (Fig. 9.) and were defined for the scenario A2. Similarity (%)

2011-2040

2041-2070

Analogue regions: PL11, PL43, PL41 Analogue regions: PL61, PL12, PL31 Fig. 9. Analogue regions in the next decades and in case of different climate scenarios for Debrecen It can be seen that analogue regions are south-east of Debrecen about 250-450 km away but later this distance is larger. The analogue regions are Vojvodina in Serbia as well as the RO04 (Sud-Vest) and the RO03 (Sud) NUTS regions in Romania. For further analyses only these regions were taken into consideration (Fig. 10.). We calculated the diversity of croplands and the land use and we found opposite changes. While the diversity of croplands is lower than in Hungary, the diversity of land use is higher. It is mostly because of the main crops. The ratio of wheat and maize is higher in the South, just because the climatic conditions are better for them. Meanwhile the yield is lower; it is more economical to use these crops because the better conditions necessitate less agronomic techniques. Diversity of the croplands. In the analogue regions the diversity is lower, so the structure of the croplands in Hungary will probably change, too.

Diversity of the land use types is higher in the analogue regions, but it could be caused by the topography as well.

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Ratio of the maize fields in the arable land is higher in the analogue regions than in Hungary. Climate change could have a positive effect on the Hungarian maize production, too. The ratio of the wheat fields in the analogue regions is similar to the present situation in Hungary. Climate change may not have an effect on it.

Fig. 10. Land use and cropland ratio of the analogue regions 5.3. Discussion Debrecen, the basic object of our calculations, is an important centre of agricultural production in Hungary, so we would like to interpret the results in this aspect. Climate, especially temperature and precipitation, basically determines agricultural production. Results show that in Hungary we have to count on an increase in temperature and decrease in precipitation. The possible future climate predicted by the scenarios will be similar to the present climate of South-Southeast Europe. Of course climate depends also on other effects, especially on elevation, topography and storm-track conditions, which could not be considered in this kind of analysis. However, the method of spatial analogies seems to be a good tool to understand and interpret the results of the GCM scenarios and the effects of climate change, so we want to go ahead in this research. This method and additional data on the analogue regions can provide information on the impacts of climate change on ecosystems or on agricultural production, such as the changes in land use, cropping system or yields and on the possibilities for disappearing or introducing new crops or weeds and pests into an area. Increase in mean annual temperatures in our region, if limited to two or three degrees, can generally be expected to extend the growing season. In case of crops (or animals), where phonological phases depend on accumulated heat units, the phenophases can become shorter. Whether crops respond to higher temperatures with an increase or decrease in yield depends on whether their yield is currently strongly limited by insufficient warmth or the temperature is near or little above the optimum. In Central Europe, where temperatures are near the optimum under current climatic conditions, increases in temperature would probably lead to decreased yields in case of several crops. Increased temperature could be favourable for example for pepper and grapes; however, it is unfavourable for green peas and potato. Decrease in precipitation could be a great limiting factor in agriculture. If we accept the results of the GCMs, according to the A1FI scenario for the period 2011-2040, the analogue regions of Debrecen will be the Vojvodina region in Serbia and South Romania. It means a shifting of about 250-450 km south, which corresponds to other international results. The detailed analyses of the analogue regions can help us to adapt to the changing climate. From the analogue regions we should collect all kind of available ecological, agricultural,

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economic, social and public sanitation data. We can study what kind of problems there are, and what the solutions are. We can learn from there how to solve the possible problems and develop strategies. This would be a good base for further research and an important base for decision makers. With the method of spatial analogy we can build a new way of knowledge transfer from where we can learn adaptation techniques and to where we can transfer our knowledge.

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Sipkay, Cs., Kiss, K. T., Vadadi-Fülöp, Cs. & Hufnagel, L. (2009). Trends in research on the possible effects of climate change concerning aquatic ecosystems with special emphasis on the modelling approach. Applied Ecology and Environmental Research 7(2): 171-198. Sipkay, Cs., Horváth, L., Nosek, J., Oertel, N., Vadadi-Fülöp, Cs., Farkas, E., Drégelyi-Kiss, Á. & Hufnagel, L. (2008a). Analysis of climate change scenarios based on modelling of the seasonal dynamics of a Danubian copepod species. Applied Ecology and Environmental Research 6(4): 101-108. Sipkay, Cs., Hufnagel, L. Révés,z A. & Petrányi, G. (2008b). Seasonal dynamics of an aquatic macroinvertebrate assembly (Hydrobiological case study of Lake Balaton, №. 2) Applied Ecology and Environmental Research 5(2): 63-78. Solymosi N., Kern, A. Horváth L., Maróti-Agócs Á., Erdélyi K. (2008). TETYN: An easy to use tool for extracting climatic parameters from Tyndall datasets, Environmental Modelling and Software 948-949 p. Sommer, U. (1995). An experimental test of the intermediate disturbance hypothesis using cultures of marine phytoplankton. Limnology and Oceanography, 40, 1271 – 1277, 0024-3590 Spellerberg, I.F. (1991). Monitoring ecological change, Cambridge University Press, Cambridge Straile, D. (2005). Food webs in lakes – seasonal dynamics and the impacts of climate variability. In: Belgrano, A, Scharler, U.M, Dunne, J & Ulanowicz, R.E. Szenteleki K. (2007). A Kötnyezet-Kockázat- Társadalom (KLIMAKKT) Klímakutatás adatbázis kezelő rendszerei, Klíma21 füzetek vol51. 89-115 p. (in Hungarian) Vadadi-Fülöp Cs., D. Türei, Cs. Sipkay, Cs. Verasztó, Á. Drégelyi-Kiss, L. Hufnagel (2009). Comparative Assessment of Climate Change Scenarios Based on Aquatic Food Web Modeling. Environmental Modeling and Assessment 14(5) : 563-576 Viner, B. & Kemp, L. (1983). The effect of vertical mixing on the phytoplankton of Lake Rotongaio (July 1979 -January 1981). New Zealand Journal of Marine and Freshwater Research, 17, 407 – 422, 0028-8330 Wood, R.J., Boesch, D.F., Kennedy, V.S. (2002): Future consequences of climate change for the Chesapeake Bay ecosystem and its fisheries. In: McGinn, N.A. (ed): Fisheries in a changing climate. American Fisheries Society, Bethesda, MD, pp. 171–184.

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X9 Pelagic ecosystem response to climate variability in the Pacific Ocean off Baja California Gilberto Gaxiola-Castro1, Bertha E. Lavaniegos1, Antonio Martínez2, Rubén Castro2, T. Leticia Espinosa-Carreón3

1Centro

de Investigación Científica y Educación Superior de Ensenada. Departamento de Oceanografía Biológica. Carretera Ensenada-Tijuana No. 328. Fracc. Zona Playitas. Ensenada, Baja California, México. C.P. 22860. 2Universidad Autónoma de Baja California. Facultad de Ciencias Marinas. Kilómetro 107 carretera Tijuana-Ensenada. Ensenada, Baja California, México. C.P. 22810. 3Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional (CIIDIR) Unidad Sinaloa-IPN. Blvd. Juan de Dios Bátiz Paredes #250. Col. San Joachin. C.P. 81101. Guasave, Sinaloa, México. 1. Introduction The oceans enclose 72 percent of the Earth’s surface, controlling the global deliveries of heat and freshwater which drive our climate and weather. In turn, the enormous and varied oceanic ecosystems are affected by the ocean climate, generating changes mainly in the upper part of the water column which can be detected as response to this variability. In particular, the Pacific Ocean environment is affected by changes in the world climate, responding to seasonal, interannual, and interdecadal variability, as well as El Niño-La Niña cycles. The California Current System (CCS) located in the northeastern Pacific Ocean, is one of the largest marine ecosystems of the world, with physical and biological mechanism forced by regional (coastal upwelling, eddies, wind driven currents), and large scale processes (e.g. El Niño-La Niña cycles). One example of these large scale processes affecting the CCS region was the anomalous intrusion of Subartic fresher water into during the 2002-2006 periods, which disturb the California Current (CC) ecosystems from Canada to Mexico (Bograd & Lynn, 2003; Durazo et al., 2005; Durazo, 2009; Freeland et al., 2003; Gaxiola-Castro et al., 2008; Lavaniegos, 2009). The ocean climate response of the CC region off Baja California is particularly significant because this is a transitional ocean basin, highly influenced by the equatorward flow of Subartic waters mainly during spring and summer (Durazo & Baumgartner, 2002), and by subtropical signatures triggered by poleward flows mostly during late summer and autumn (Bograd et al., 2000; Durazo, 2009). Moreover, CCS off Baja California is a useful region to understand large and mesoscale ocean climate effects on physical and biological variability of the marine environment, and for instance of climate change.

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In this contribution we examine the associations between large-scale temporal climate physical forcing and the plankton variability off Baja California. The pelagic ecosystem off Baja California has been influenced by large scale processes like the 1997-1998, and 20082009 El Niño events, which were characterized by low chlorophyll, high sea surface height, high surface salinity, and high sea surface temperature, with opposite conditions during the 1998-1999 and 2007-2008 La Niña events (Espinosa-Carreon et al., 2004; McClatchie et al., 2009). Seasonal and interannual patterns are also observed off Baja California, with a maximum of phytoplankton Chlorophyll-a occurring in spring, as a result of the phytoplankton growth in response of the seasonal maximum of upwelling-favorable winds (Espinosa-Carreon et al. 2004; Perez-Bruinus et al. 2007). Zooplankton biomass is larger in summer and autumn, characterized by abundance of copepods, euphausids, and other minor groups, higher abundance of salps during the 1997-1998 El Niño event, and by jelly fish and ctenophores abundances in April 2009 (Lavaniegos et al., 2010). The main objectives of this chapter are to describe the general conditions in the CCS off Baja California coast from 1997-2010, reviewing the governing ocean climate and physical forcing at different time and scales, that might have influenced the pelagic ecosystem of the study region.

2. Climate conditions off Baja California Northeast Pacific Ocean atmospheric circulation is dominated by the North Pacific High Pressure center (NPH). In February, this pressure system is weak and located ~25N-130W off Baja California coast. During summer the NPH migrates near of ~38N-145W, where it reaches the maximum values. Between spring and summer the strong pressure gradient generate high equatorward upwelling favorable winds between northern California and the Baja California coast (Huyer, 1983; Strub et al., 1987; Strub & James, 2002; Perez-Brunius et al., 2007; Castro & Martinez, 2010). In the vicinity of Baja California coast the wind is highly persistent with a south-southeast direction. Wind speeds are stronger and less variables in spring and summer than during autumn and winter. Annual harmonic of the wind has small amplitude, with low (200 m depth). Here only data of copepods and euphausiids are presented separately, while the rest of the taxa were combined in the category of other zooplankton. They were counted from a fraction (1/8, 1/16, or 1/32) of the original sample. Zooplankton biomass and abundance were log-transformed to normalize data. Biomass and abundance anomalies for the period 1997-2008 were estimated removing the long-term seasonal mean. Time series of abundance anomalies for copepods and euphausiids were subject to linear regression analyses. Data of daily upwelling indices (UI) from www.pfeg.noaa.gov/products/products.html, were also used to illustrate the variability in coastal upwelling activity. The data were obtained for two sites: off Punta Baja (30ºN, 119ºW) and off Punta Eugenia (27ºN, 116ºW; see figure 1). UI monthly means (and standard deviations) were estimated. Further, UI anomalies were calculated removing long-term monthly means from the period 1997-2008. In addition, we used oceanic climate indices (MEI, Multivariate ENSO Index; http://www.esrl.noaa.gov/psd/people/klaus.wolter/MEI/; PDO, Pacific Decadal Oscillation; http://jisao.washington.edu/pdo/PDO.latest; NPGO, North Pacific Gyre Oscillation; http://ocean.eas.gatech.edu/npgo/), which are contrasted with integrated water column Chlorophyll-a anomalies, computed from the IMECOCAL hydrographic data.

4. Physical Forcing Analysis The response to the forcing at interannual variability scales off the Baja California coast is evident from the time series on figure 2. In this figure, the wind stress magnitude, wind stress curl, SST (Sea Surface Temperature) and SSH (Sea Surface Height) anomalies were averaged on the overall IMECOCAL area, and compared with the MEI. Anomalies were computed averaging spatially all data over the area on figure 4, subtracting the temporal mean and dividing by the standard deviation. The correlation coefficient (r) between wind stress (magnitude and curl) and MEI was very low (r~0.1). Negative anomalies of the wind stress occurred during 1997, 2002, and 2004-2005, coinciding roughly with positive values of MEI, which could be associated to El Niño conditions and relatively weak winds on the northeastern Pacific (Peterson & Schwing, 2003). The strength of the wind anomalies showed poor agreement with MEI; for example in 1997, negative anomalies of the wind stress reached values as low as -2.0, while during the 2004

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event were just ~-0.7. Wind positive anomalies were present from 1998 to 2000, matching much of the time with La Niña. At the second half of 2005, high positive wind anomalies were observed between the last part of El Niño 2004-2005 and the transition to negative values of MEI (weak La Niña conditions). Summarizing, the MEI does not represent adequately the wind events off Baja California coast.

Fig. 2. (a) Anomalies of the wind stress magnitude and wind stress curl; (b) SST (Sea Surface Temperature) and SSH (Sea Surface Height). Multivariate El Niño Southern Oscillation Index (MEI) is included in both figures as reference. Interannual SST anomalies were better correlated with the MEI than the wind (Fig. 2). The correlation (at 0 lag) between SST and MEI was relatively high (r= 0.66). The most remarkable SST events were during 1997-1998, and 2006 corresponding to El Niño conditions, where both MEI and SST showed positive anomalies (Espinosa-Carreon et al., 2004). Negative SST anomalies occurred during 2002-2003 with positive MEI values, which possibly was related with a strong incursion of subartic waters to the region (Durazo et al., 2005). SST negatives anomalies were evident during La Niña episodes, between winter 1998 and 2000-2001, and remained until 2003. A shift between anomalies of SST-MEI is evident; with SST anomalies preceding MEI (figures 2, 4a). This lag has been reported by Lynn et al. (1998). SSH anomalies were well correlated with MEI (r= 0.52), except during the La Niña conditions (1999-2000) (Fig. 3). During La Niña 1999-2000 the MEI and SST showed negative values, while SSH was positive, which is not consistent with a thermal expansion.

Pelagic ecosystem response to climate variability in the Pacific Ocean off Baja California

(a)

(b)

(c)

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(d)

Fig. 3. Hovmuller diagram: (a) SST; (b) SSH; (c) wind stress curl magnitude; (d) wind setress curl anomalies. Each variable has been low passed filtered and averaged on the along shore direction. The horizontal axis represent the distance in km from the coast (at x=0). The vertical axis represents the time beginning in January 1996. Time response of the Pacific Ocean along the Baja California Peninsula, as observed in figure 3, shows a lagged relation for each variable. In particular, the positive anomalies of SST (Fig. 3a) clearly preceded positive anomalies of SSH (Fig. 3b) during El Niño years. Casey & Adamec (2002) found that the relationship between SST-SSH is consistent with thermal expansion related to ENSO, which was proposed before by Leuliette & Wahr (1999). The lag between SST and SSH is more evident if we relate those variables with some diagnostic index like the MEI. The correlation was made between the MEI and the weekly-unfiltered time series of each variable on every grid point to show the short term lag. SST is somewhat correlated with MEI. The maximum correlation (Fig. 4a) occurs around Punta Eugenia (28N). In general the correlation is low (but significant at the 95% confidence level). The lag between MEI and SST (black contour line in Fig. 4a) shows that SST precedes MEI by 1 to 1.5 months and shows a northward propagation. By the other side, the correlation between SSH and MEI (Fig. 4b) is much higher for the most part of the area. In contrast, the lag shows that the MEI leads SSH by 1.5 months along the coast, and the lag decreases on the offshore direction to 0.5 months. It is important to mention that if the SST and SSH data are lowpassed filtered, the correlation, as expected, will increase significantly, but the short-term lag will be lost. In addition, figure 3 also shows a rich offshore structure of the wind stress curl (Fig. 3c) and magnitude (Fig. 3d). The wind stress curl is positive most of the time near the coast, and changes sign away from the drop off zone (Castro & Martinez, 2010). The effect of the wind stress curl seems to be related to SST in such way that when the wind stress curl is weak (negative anomaly in figure 3c) SST increases. Almost two years previous to the 1997-1998 El Niño, the wind stress curl was very weak next to the coast (Fig. 3c), as was the wind intensity (Fig. 3d). In contrast, during the 2002 El Niño, the SST anomaly was negative (colder). There was not a response of SSH, with a positive wind stress curl anomaly next to the coast, and the intensification of the wind magnitude. The 2006 El Niño event produced

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very similar (but moderate) response on SST, SSH, and wind magnitude anomalies to the 1997-1998 event. It is important to notice that the wind magnitude anomalies in figure 3d are more intense off the coast, while the wind stress curl has large values near the coast.

(a)

(b)

Fig. 4. Correlation between the MEI with SST (a), and MEI with SSH (b) on every grid point. Data cover the period from 1996-2007, without filtered. The black contour-line represents the lag in days. To investigate a possible dynamical relationship connecting SST and the other analyzed variables, we calculate the zero-lagged time correlation of the low passed filtered data described previosly. The correlations between SST and SSH, wind stress curl (x), wind stress major component (x) and wind stress minor component (y) were calculated for every grid point and then averaged on the along-coast direction. Just the significant correlations were considered for the averaging. The black line in figure 5 shows the correlation between SST and SSH. Largest correlation was on the coast vicinity (offshore distance  150 km). Beyond this point the correlation decaes gradually. The positive sign shows two possible mechanism involved: the upwelling scenario, where sea level and SST decreases; and the thermal expansion scenario, where as the water temperature increases sea level also increases. The latest scenario does not seems to be probable in this analysis since figure 5 shows the zero lag correlation, namely the short term response. The major component of the wind stress (x) is almost aligned with the shore line. It is well correlated with SST, except near the coast (red line in figure 5). The minor component of the wind stress (y), does not show a clear relation

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with SST, and the wind stress curl shows the strongest relation with SST at about 120 km off the coast.

Fig. 5. Time correlation between SST and SSH (black line). Wind stress principal component x (red line) and minor component y (blue), and wind stress curl (magenta). The correlation was done for every point and then averaged on the along shore direction. Near the coast (~120 km off shore), SST is well related with SSH, being the wind stress curl the second dominant forcing. Beyond this point, the dominant forcing is the component of the wind stress parallel to the coast (x). The negative sign of the SST-x correlation indicates upwelling (since the major component is esentially positive), but from figures 3, and 5, the SST is mainly related with the wind 150 km away from the coast. It is clear that the averaged correlation shown in figure 5, fades the local relation between SST, SSH, and the wind. The relevance of the relationships derived from figure 5, is that the dependence of SST on the wind or SSH, can not be sumarized on a single mechanism. The wind stress curl dominates on the wind intensity near the coast. This result implies that Ekman pumping is also important on the near coast area. Ekman pumping could be a possible alternative mechanism to explain the presence of SST anomalies. Cold (warm) SST during La Niña (El Niño) events seems to be in phase with positive (negative) anomalies of the wind stress curl (Fig. 3a,c). Also the role of SSH is important on the coast neighborhood, as it is well correlated with SST. Offshore, the wind component parallel to the coast dominates.

5. Remote sensed and in situ Phytoplankton Chlorophyll The fraction of available remote sensed composite imagery of chlorophyll (CHL) SeaWiFS 1997-2010 data for the region off Baja California (percent of current data in the weekly

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images) show high values (>80%) near coastal zone, diminishing offshore, with lowest values ( wheat>. But there are not significant differences between other selected crops to reduce water erosion and vulnerability. Potato are now identified as the best land use to reduce wind erosion while wheat and maize are the worth one. Alfalfa, Barley and sugar beet have the same results versus wind erosion. On the other hand, as reclamation for crop production or for improved pasture is very difficult but will be practical if the other characteristics of the soil are favorable and erosion is controlled by soil conservation techniques, for example by construction of terraces. Therefore, it is interested to assume cultivation practices (e.g., contouring and terraces) impact to control the movement of water over the soil surface and those effects on land vulnerability classes for the climate change era. The differences between two practices are shown in (Figure 10a & 10b) which will be achieved in the far future (Shahbazi, 2008). According to these results, terrace application without attention to economical condition and financial costs could be better than contouring to reduce risk of vulnerabilities. Also, the area covered with none level risk in the first examined item is 38% more than the second chosen one where 5% of a total are scattered near the Garangah and Mardekatan natural regions previously distinguished as low level risk will be altered to high level risk by selecting contouring practice instead of terrace procedure.

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Fig. 10a. Terrace practice and climate change impact on land vulnerability caused by actual water erosion (Shahbazi, 2008)

Fig. 10b. Contouring practice and climate change impact on land vulnerability caused by actual water erosion (Shahbazi, 2008) 5.2.3. Contaminants Risks In general terms, the agrocontamination risk is considered to be directly related to the capacity of soils to store and immobilize toxic chemicals. The surface runoff transports high amounts of substances, such as phosphates in over-fertilized soils. Many biophysical and management factors control substance release from the soil to the water. The leaching of agricultural chemicals results from a complex interaction of physical, chemical and biological processes and attempts have been made to model these by equations based on classical mechanistic physics, and on a statistical or stochastic framework (De la Rosa & Crompvoets, 1998). However, models are not yet reliable enough to predict accurately the behavior of agrochemicals in the field. Soils are heterogeneous, climate and management factors vary, both in the short and long-terms. The development of land evaluation models is thus justified in terms of providing a tool with which to assess large amounts of soil information, such as that obtained from soil surveys, in order to yield the most practicable strategy for environmental protection (De la Rosa et al., 1993). The excesses of mineral nutrients and organic pesticides seem to be the most significant potential contaminants. However, impurities in fertilizers, manure and wastes can also be an important source of pollution especially with heavy metals. Therefore, the studied vulnerability types in west

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Asia are: phosphorus, nitrogen, heavy metals and pesticides same as Mediterranean region. For Pantanal model establishment main following witticisms have been considered: Phosphate substances are basically transported by runoff and constitute a possible source of eutrophication of waters. However, the phosphate fixation on clay minerals, along with its interaction with other soil components, was also estimated although the mobility of phosphate is usually very low in relation to other mineral nutrients. The amount of phosphate adsorbed by soil depends greatly on pH values, and also on particle size distribution and organic matter. Nitrate is the major nitrogen derived pollutant and the main source of groundwater contamination because of its high mobility. Along with land qualities associated with the rainfall partitioning, cation adsorption and denitrification are expected to predict this contamination risk. Retention of the heavy metals: copper, zinc and cadmium, by soils is analyzed considering the pH, as indicative of soil carbonate content, the main land characteristic controlling the different reactions. Organic matter content strongly affects adsorption–desorption and biodegradation of many pesticides, although other soil properties such as particle size distribution and CEC are also considered decision factors (De la Rosa & Crompvoets, 1998). 5.2.3.1. Case Study in East Azerbaijan General contamination assessing in Ahar area revealed that only soil profiles under using of apple garden between the 44 studied profiles because of having artificial drainage has classified as moderate level risk (V2). Therefore, a total of 1560 ha (17.3%) are susceptible to contamination effect. In the current situation and without any climate and management changes risks of vulnerability raised by nitrogen and phosphorous (28% and 23% of studied area, respectively) are many times more than pesticides and heavy metals. It can be described as false management practices for using nitrogen fertilizers which are now presented in the whole are (88% area except not investigated lands where had been identified as marginal area by Cervatana model). Besides of that 57% area are distinguished as susceptible correspond to pesticides, correct management practices caused to be reduced the actual vulnerability compared with attainable one. Attainable and actual vulnerability classes for two hypothetical scenarios are summarized in (Table 5). Vulnerability classes V1 V2 V3 V4 V5 V1 V2 V3 V4 V5

Attainable

Actual

Current and future scenarios (% of total area) Phosphorous

Nitrogen

Heavy metals

Pesticides

32 25 4 27 ---10 29 ---26 23

55 32 1 ------------55 32 1

57 ---31 ------15 47 ---26 ----

1 2 49 36 ---3 11→12 26→41 48→32 ----

Table 5. Summary of Pantanal model application as a point by point view in Ahar area * V1= none; V2= low; V3= moderate; V4= high; V5= extreme; → (impact of climate change)

According to the results, climate change will not effect on contamination vulnerabilities as well as water or wind erosion in part of Asia. The most important management practices accompany

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with climate change was examined as follows: Intensive wheat, barley, alfalfa, maize, potato, and sugar beet. Following orders present the best practice to decrease land vulnerability raised by: I) phosphorous; II) nitrogen; III) pesticides and IV) heavy metals, respectively. I. Maize> Sugar beet> Barley> Wheat> Alfalfa> Potato II. Alfalfa> Maize- Sugar beet- Wheat- Alfalfa- Potato III. Potato> Maize> Barley> Sugar beet> Alfalfa> Wheat IV. Maize> Barley- Sugar beet- Potato> Wheat 5.2.3.2. Case Study in West Azerbaijan for the Climate Change Era Agro-ecological field vulnerability evaluation was compiled in Souma area where is closed to Urmia. Raizal model application resulted that for rainfall erosion, 72% of Souma lands are at none level of risk (ClassV1), and a further 28% at a very low and medium level. The medium risk area is more scattered in the north of study area which has established on plateau unit and characterised by a medium soil texture. In the simulated hypothetical scenario by long-term these results will be constant. Also, the study area is susceptible for wind vulnerability erosion and will increase in the future by climate change. The highest risk areas (V10) are located at the north-west and south-east of study area and refer to shallow Entisols. Soils No 2 and 6 areas will be altering from very high to extreme vulnerable land by climate change. Besides 10% extreme vulnerable land, 70% of the total area will be susceptible to vulnerability risks. A point-to-point application of Pantanal model results were summarized in (Table 6). Soil No 1 2 3 4 5 6 7 8 9 V1 V2

Phosphate current future V4 V4 V2 V2 V1 V1 V2 V2 V1 V1 V2 V2 V2 V2 V1 V1 V2 V2 18.63** (45%) 18.9 (45%)

18.63 (45%) 18.9 (45%)

4.03 (10%)

Nitrogen Heavy metals current future current future V3 V3 V3 V3 V2 V1 V1 V1 V2 V1 V1 V1 V2 V1 V1 V1 V2 V1 V1 V1 V2 V1 V1 V1 V2 V1 V1 V1 V2 V1 V1 V1 V2 V1 V1 V1 Vulnerability classes* 37.53 37.53 37.53 0 (90%) (90%) (90%) 37.53 0 0 0 (90%) 4.03 4.03 4.03 4.03 (10%) (10%) (10%) (10%) 0

Pesticides current future V4 V4 V3 V3 V3 V2 V4 V3 V4 V3 V4 V3 V3 V3 V3 V3 V4 V3 0 0 22.05 (53%) 19.51 (47%)

0 1.25 (3%) 36.28 (87%) 4.03 (10%)

Table 6. Summary of contamination vulnerability risk evaluation assessment in Souma (Shahbazi et al., 2009c)

* V1 = None; V2 = Low; V3 = Moderate; V4 = High, ** Area extention = km2 According to obtained results, 10% of Souma area is at a high risk (Class V4) by phosphate while more than 45% is at a low level risk, and also 45% of the area presents no risk (ClassV1) of contamination. Reaction from local staff to the quality of the evaluation results for the current situation in Souma area was positive, although additional work on sensitivity and validation testing are needed in order to improve the prediction capacity of the risk evaluation approach (Shahbazi et al., 2009c).

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6. Conclusion Remarks Agro-ecological land evaluation appears to be a useful way to predict the potential index and/or general capability to distinguish the best agricultural land resulting from interactive changes in land use and climate. Due to bioclimatic deficiency is the most-sensitive factor affected by climate change; irrigation is indicated as very important in this semi-arid agriculture. However, the cultivation of rainfed wheat can be recommended instead of irrigated wheat in order to reduce the tillage operation costs. Also, the use of modern irrigation methods is recommended for the studied area in the future. Determining the impacts of climate change on land use systems involves also biophysical effects on agricultural management practices. Climate change might constrain or mandate particular land management strategies (e.g., irrigation); however, these options will be different for each particular site. In summary, the application of the land evaluation decision support system MicroLEIS DSS for planning the use and management of sustainable agriculture is suggested in west Asia region, for present and future climate conditions.

7. Abbreviations and Acronyms AKi: Arkley index; ARi: Aridity index; CDBm: Monthly Climate database; CRDY: Dry land, Cropland, Pasture; CRWO: Cropland-Woodland mosaic; CWANA: Central and West Asia and North Africa; ENSO: El Niño-Southern Oscillation; Eng & Tec: Engineering and Technology Prediction; Ero & Con: Erosion and contamination modelling; ETo: Potential evapotranspiration; GIS: Geographic Information System; GRAS: Grassland; GS: Growth season; HUi: Humidity index; ICCD: Impacts of Climate Changes on Drylands; Imp & Res: Impact and Response simulation; ImpelERO: Integrated Model to Predict European Land use for erosion; Inf & Kno: Information and Knowledge databases; IPCC: Intergovernmental Panel on Climate Change; LES: Land Evaluation Systems; LESA: Land evaluation and site assessment; LD: Land degradation; LUP: Land use planning; MDBm: Management database; MicroLEIS: Mediterranean land evaluation information system; MFi: Modified Fournier index; ONEP: Office of Natural Resources & Environmental Policy and Planning; p: Monthly precipitation; P: Annual precipitation; PCi: precipitation concentration index; Pro & Eco: Production and Ecosystem modelling; SAVA: Savanna; SDBm plus: The multilingual soil database software; SHRB: Shrub land

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19 x

Simulated potato crop yield as an indicator of climate variability and changes in Estonia Triin Saue1,2 and Jüri Kadaja1

1 Estonian

2Department

Research Institute of Agriculture of Geography, University of Tartu Estonia

1. Introduction During the recent decades, global climate change has been at the centre of quite many scientific studies. Although the consensus is that climate is changing on a global scale, change on a regional or local scale is often more subtle and variable. Global climate change is mostly evaluated using the changes of annual average ambient temperature indicators, however, regional climate scenarios are not always consistent with global indicators. Consequently, the search for, and identification of, clear and unambiguous indicators of the impact of global climate change at a regional or local level is of vital importance. Interactions between the biosphere and the atmosphere are obvious and have long been studied by several disciplines (e.g. Budyko, 1971, 1984; Fritts, 1976; Bolin, 1977; Tooming, 1977, 1984; sem*nov and Porter, 1995; Scheifinger et al., 2002; Menzel, 2003; Aasa et al., 2004; McPherson, 2007). It has long been recognized that climate decides what can be cultivated, whereas soils indicate mainly to what extent climatic opportunities can be realized. The crops that continue to be grown in a particular location will primarily be determined by the changes in climate, and the seasonal distribution of rainfall and temperature that they experience. The main effect of temperature derives from the control of the growing period duration (Woodward, 1988), but also other processes linked with the accumulation of dry matter (leaf area expansion, photosynthesis, respiration, evapotranspiration etc.) are affected by temperature. Rainfall and soil water availability may affect the duration of growth through leaf area duration and the photosynthetic efficiency. These general climatic constraints on agricultural production are modified by local climatic constraints. In Northern countries the length of growing season, late spring and early autumn frost and solar radiation availability are typical climatic constraints, limiting the productivity of crops. For example, in Germany the growing season is one to three months longer than in Scandinavian countries (Mela, 1996). Not surprisingly, also the reverse relation is true – biological and agricultural data can be used in climate assessments. Several biology-related indicators have been used by several scientists to assess past and present climate, its changes and variability, such as Palmer Drought Severity Index (e.g. Makra et al., 2002; Szep et al., 2005; Burke et al., 2006; Mpelasoka et al., 2007), growth season beginning and length (e.g. Menzel and Fabian, 1999;

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Chmielewski & Köhn, 2000; Schwartz & Reiter, 2000; Sparks & Tryjanowski, 2007), dates of phenological phases (e.g. Ahas et al., 2004; Badeck et al, 2004; Chuine et al., 2004; Donnelly et al., 2004), etc. One of the complex variables, integrally describing summer weather conditions, is the biological production of plants and yield of agricultural crops. In this chapter, the potentiality of using the biological production and yield of agricultural crops as an indicator of summer climate variability and possible change is discussed. This approach is based on the postulate that the primary requirement for the success of a plant in a particular area is that its phenology would fit the environment. The signals of climate change usually occur more clearly in species growing at the borders of their distribution areas (Pensa et al., 2006) or whose growth is strongly influenced by climate, such as many arable crops (Hay & Porter, 2006). Trends in individual climate variables or their combination into agro-climatic indicators show that there is an advance in phenology in large areas of North America and Europe, which has been attributed to recent regional warming. In temperate regions, there are clear signals of reduced risk of frost, longer growing season duration, increased biomass, insect expansion, and increased forest-fire occurrence that are in agreement with regional warming. Still, no detectable change in crop yield directly attributable to climate change has been reported for Europe (IPCC, 2007). Experimental studies of climate change through plant productivity are complicated indeed, as it is hard to distinguish the impact of climate variability or change from the effects of soil, landscape, and management. The worldwide trends in increasing productivity (yield per hectare) of most crops over the last 40 years, primarily due to technological improvements in breeding, pest and disease control, fertilisation and mechanisation, also make identifying climate-change signals difficult (Hafner, 2003). Thus, although the yield of agricultural crops is a quite commonly measured value, there is usually no long hom*ogeneous time series of field crop yields. Therefore, the use of a simulated time series of crop yields, computed with dynamic plant production process models, is a more convenient and efficient way to draw climate estimations. These models are compiled from our knowledge of the different physiological processes in plants, and integrate different daily or more frequent weather data, calculating the development of plant production step-by-step. Traditionally, crop models are useful tools for translating climate forecasts and climate change scenarios into changes in yield, net returns, and other outcomes of different management practices. Additionally, those results can be turned backward and model-calculated yields can be used as an indicator to describe climate resources. In this chapter the concept of meteorologically possible yield (MPY) - the maximum yields under given meteorological conditions - is applied to derive qualitatively new information about climate variability. We will describe series of weather-reliant potato yields based on real existing meteorological series. Trends and variability changes within the series are assessed and compared to variability in the series of meteorological data. Probable range of temperature and precipitation in years 2050 and 2100 is applied to construct possible distribution of MPY in those years; future changes in mean values and variability are examined.

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2. Material and methods 2.1 The model and the category of meteorologically possible yield Plant productivity and thus the yields of field crops depend on many different closely interrelated factors. To introduce all of them into the model simultaneously is complicated. In our approach, the concept of the separation of factors, the principle of reference yields (Tooming, 1984; Kadaja & Tooming, 2004) was applied based on the principle of maximum plant productivity: such adaptation processes take place in a plant and plant community which are directed towards providing the maximum productivity of net photosynthesis possible under the existing environmental conditions (Tooming, 1967, 1970, 1977, 1984, 1988). Proceeding from this principle, maximum plant production is observed under different limiting factors, which can be divided into agroecological groups: biological, meteorological, soil, and agrotechnical groups. These groups of factors are included separately in the model, step by step, starting from the optimal conditions for the plant community (Tooming, 1993, 1998; Kadaja, 1994). Because the conditions specified as optimal involve no limitations, no input information regarding their optimal and limiting ranges is necessary. The corresponding categories of reference yields, as limits between the aforesaid groups, are in descending order: potential yield (PY), MPY, practically possible yield, and commercial yield (Fig. 1). This concept is applied in the dynamic model POMOD to model the potato production process and yield (Sepp & Tooming, 1991; Kadaja & Tooming, 2004). In the present state, POMOD allows the computation of the PY and the MPY. The PY is the maximum yield of a given species or variety possible under the existing conditions of solar radiation, with all the other environmental and agricultural factors considered to be optimal. Therefore, PY is determined by the biological properties of the variety and the solar radiation available for utilization, and it expresses the radiation resources in units of biomass produced. The MPY is the maximum yield conceivable under the existing irradiance and meteorological conditions, with optimal soil fertility and agrotechnology, the levels of soil nutrients and the agrotechnology used do not limit production, and the effects of plant diseases, pests, and weeds are excluded. Only those soil properties related to the determination of the soil water content are applied.

Fig. 1. The concept of yield limiting factors and corresponding reference yields (Zhukovsky et al., 1989).

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As a result, MPY expresses agrometeorological resources, while its mean value and variability distribution over a long period characterize the agroclimatic resources in yield units. Using the category of MPY and the model of crop production, we can transform the complex of meteorological conditions into their yield equivalent and easily assess the agrometeorological resources of different years and the agroclimatic resources at different locations. The underlying parameters of POMOD are the total biomass and the masses of plant organs (leaves, stems, roots, and tubers) per unit ground area (Kadaja & Tooming, 2004). The total growth of the plant biomass is calculated as the difference between the gross photosynthetic and respiration rates, integrated over time and leaf area index. The gross and net photosynthetic rates are expressed by equations derived from the principle of maximum plant productivity (Tooming, 1967). The meaning of parameters of gross and net photosynthesis irradiance curves are illustrated in Fig. 2. The initial slope a is the slope of tangent to the gross photosynthesis irradiance curve drawn from the origin of co-ordinates. Ra is the PAR flux density at the tangential point of net photosynthesis irradiance curve and its tangent drawn from the origin of co-ordinates. The intensity of photosynthetically active radiation (PAR) in the canopy is calculated from the total radiation and the leaf area above a particular level. The distribution of the total increase in biomass between different plant organs is determined using growth functions (Ross, 1966), which are given in the model as functions of accumulated positive temperatures. MPY is calculated taking into account the impact of meteorological factors on photosynthesis and respiration, and the influence of temperature on development rate.

Fig. 2. Gross and net photosynthesis irradiance curves and their characteristics (Tooming, 1984). The biological parameters of the potato varieties were determined on the basis of field experiments, not limited by nutrient deficiency, properly cultivated, weed and pest free, and regularly protected from late blight (Sepp & Tooming, 1991; Kadaja, 2004). The computed yields have proved similar to the real yields under these conditions, if the reduction in leaf area from late blight, not totally avoidable by protection, is included in the model. Differences in the real and computed yields did not exceed 5% in independent data collected under extremely good and bad growing conditions (Sepp & Tooming, 1991). Further verification of the model has been made on the basis of 20-year yield series at four stations

Simulated potato crop yield as an indicator of climate variability and changes in Estonia

369

of the Estonian Variety Control Network, with relatively stable cultivation and soils maintained during the period. Significant correlations between actual yields and calculated MPY were verified at three stations, whereas at the fourth, the correlation was not significant because of an increased level of plant diseases, grown without crop rotation.

Prec Kuressaare

Prec Tartu

Temp Kuressaare

Temp Tartu

100 90 80 70 60 50 40 30 20 10 0

13 8 3 -2

Temperature, C

Precipitation sum, mm

2.2 Locations To simulate time series of meteorologically possible yield, we compiled series of meteorological and agrometeorological data from the archives of the Estonian Meteorological and Hydrological Institute. We used the data from two stations: Tartu (58°15´N, 26°27´E) and Kuressaare (58°15´N, 22°29´E). These stations are located in regions with different local climates. Local climatic differences in Estonia result from, above all, the proximity of the Baltic Sea, which warms the coastal zone in winter and cools it especially in spring. According to the climatic classification of Estonia based on its air temperature regime, as proposed by Jaagus & Truu (2004), Tartu is located in the Mainland Estonia climatic region, characterized by a more continental climate and practically no climatic effect of the Baltic Sea, and Kuressaare is located in the Island Estonia region, with a much more maritime climate. Spring is much warmer in Tartu and summer starts earlier. In addition to different temperature regimes, there are considerable differences in precipitation between the two stations (Fig. 3). Furthermore, climate change effects appear to be different in the continental and coastal areas (Jaagus, 2006). For instance, because of the direct influence of the sea, the evident increase in annual mean temperature (1.0-1.7 °C at the different stations in Estonia during the second half of the 20th century) is less intense in spring in Kuressaare compared to that in Tartu. A significant increase in winter precipitation has also taken place in Estonia, but is much lower on the westernmost coast. In the same period, precipitation has increased remarkably in the coastal region in spring.

-7

Month

9 10 11 12

Fig. 3. Monthly mean temperatures (lines) and precipitation sums (bars) in Kuressaare and Tartu in 1965-2006.

370

Climate Change and Variability

2.3 Input data: calculations with current climate The input information for the model can be divided into four groups: daily meteorological data, annual information, parameters of location, and biological parameters of the potato variety (Kadaja & Tooming, 2004). The first group includes daily data on global radiation, air temperature, and precipitation for the growing period. For Tartu, meteorological data were available from 1901, for Kuressaare from 1923. Calculations were carried out up to 2006. As Kuressaare meteorological station was closed in 2001, the data for last years were calculated there on the basis of an adjacent station (Virtsu, Sõrve, Vilsandi, or Ristna, depending on which had the highest correlation for a particular factor or period). Direct measurements of global radiation have only been made since 1954 in Tartu. We computed the missing daily sums of global radiation from sunshine duration, using regression equations established separately for every month in Tartu. Annual information included the year, the date and the value of the initial water storage in the soil (or the date when the soil moisture fell below the field capacity), the date of the permanent increase in temperature to above 8 °C in the spring, the dates of the last and first night frosts (≤ -2 °C), and the date of the permanent drop in temperature to below 7 °C in autumn. The initial soil moisture value is used as a basis for further calculations of soil moisture progression throughout the vegetation period. The dates of the temperature transitions are used as ‘planting’ and ‘harvesting’ dates for potatoes. We obtained the dates of night frosts and temperature transitions from the meteorological data sets of the stations. The data for the soil water status in spring was collected from the reports of the agrometeorological network using observations at Tartu-Erika (adjacent to Tartu) and at Karja on the island of Saaremaa (for Kuressaare). For the earlier period (up to the end of the 1940s) and for some later years when the agrometeorological network was not working, the data were derived from the meteorological data at the stations. The locations are characterized by their geographical latitudes and the hydrological parameters of the soil, such as the wilting point, field capacity, and maximum water capacity. We used the parameters of the field soils (Kitse, 1978) prevalent at the locality. For Tartu, the parameters of a region with Albeluvisol (World Reference Base for Soil Resources) were used; for Kuressaare, the Skeletic Regosol prevails. All the soils are sandy silt loam, with quite similar hydrological parameters. As parameters of variety, the model requires the parameters for photosynthesis, respiration, and the growth functions. We used the parameters of the early variety ‘Maret’ and the late variety ‘Anti’, both bred for Estonian conditions. The variety-specific photosynthesis variables, the initial slope of the photosynthesis irradiance curve a (kg CO2 s--1W--1), the irradiation density of adaptation Ra (W m--2), and the photosynthesis and respiration rates at the saturated PAR density given per unit mass of leaves, 1 and 2 respectively (kg CO2 kg--1 s--1), were estimated initially from the literature and adjusted for the specified varieties by a calibration method from experimental field data (Saue, 2006). Parameters σ2 and α were considered constant throughout the vegetation period, while σ1 and Ra were studied as variables. To associate parameters amongst each other, measured data of specific leaf weight of leaves,  were used. Specifically, different values were given to the maximum value of σ1 and to the parameters describing its change within the temperature sums. The scope of change of σ1 were first estimated by literature data (Tooming, 1977). Ra was calculated through σ1, α and  . To find the most optimal σ1 value, relative errors between measured

Simulated potato crop yield as an indicator of climate variability and changes in Estonia

371

and modelled data at different σ1 values were calculated. Data of leaf area index and the biomass of all organs at all measurement dates were used. Growth functions (Fig. 4) were determined on the basis of field experiments made from 2001 to 2006 (Kadaja, 2004, 2006). 1,0

1,0

Anti

0,8

Maret

0,8

0,6

0,4

0,2

0,0

Roots Leaves Tubers Stems

0,0

200

400

600

800

1000

1200

1400

1600

1800

200

400

600

800

1000

1200

Fig. 4. Experimentally determined growth functions of late potato variety ‘Anti’ and early variety ‘Maret’. Vertical lines denote the beginning of calculations. 2.4 Input data: calculations with future climate Climate change could considerably affect the growth and yield of most crops (Adams et al., 1990; Easterling et al., 1992a, b). For model simulations of future potato production, future weather data were required. To achieve temperature and precipitation data for the years 2050 and 2100, climate change scenarios were generated for Estonia using a simple coupled gas-cycle/climate model MAGICC (Model for the Assessment of Greenhouse-gas Induced Climate Change) that drives a spatial climate-change scenario generator (SCENGEN). MAGICC has been one of the primary models used by IPCC since 1990 to produce projections of future global-mean temperature and sea level rise; we used the 5.3 version of the software, which is consistent with the IPCC Fourth Assessment Report (http://www.cgd.ucar.edu/cas/wigley/magicc/UserMan5.3.v2.pdf). Because projections of climate change depend heavily upon future human activity, climate models are run against scenarios. There are over 40 different scenarios, each making different assumptions for future greenhouse gas pollution, land-use and other driving forces. Assumptions about future technological development as well as the future economic development are thus made for each scenario. Four alternative illustrative emission scenarios were used in our study to generate climate change scenarios for Estonia: A1B, a scenario of an integrated world with rapid economic growth, slowing population increase and a quick spread of new and efficient technologies with a balanced emphasis on all energy sources; A2, a scenario of a more divided world with continuously increasing population and an emphasis on family values and local traditions; B1, scenario of a world of “dematerialization” and introduction of clean technologies with rapid economic growth and increasing population; B2, a scenario of a world with an emphasis on local solutions to economic and environmental sustainability, with moderate economic growth and slowed population increase (Nakićenović & Swart, 2000). The highest climate warming is projected by A2; the lowest by B1. The year 1990 is used as the reference year in MAGICC/SCENGEN, all the climatic changes are calculated with respect to this year. Data of changes in mean monthly air temperature and precipitation, averaged over 18 GCM experiments available on SCENGEN were applied. The idea of averaging more than one

372

Climate Change and Variability

GCM experiment and constructing a composite pattern for future climate change was first introduced by Santer et al. (1990); later Hulme et al. (2000) reported the clear supremacy of the technique over just only one model. The data are displayed in MAGICC/SCENGEN in a grid resolution of 2.5º latitude/longitude, thus the Estonian territory is covered by three grid boxes, with medium coordinates 58.8ºN/21.3ºE, 58.8ºN/23.8ºE and 58.8ºN/26.3ºE. Kuressaare and Tartu fall into two outermost boxes. However, the direct use of the SCENGEN output is not possible, because these predictions are available as changes in monthly means, but the crop model depends on daily time-series of weather as one of its main inputs. To calculate the future values of MPY, we used observed daily weather data in those stations during the baseline period 1965-2006. This shorter period is applied instead of previously used longer periods, since in climate change calculations it is necessary to use data outside the heretofore growing period. Global radiation was assumed not to change. Future daily temperatures and precipitation were calculated by adding the predicted monthly corrections to the observed series of daily data. This way, not just the one average predicted future value for temperature and precipitation, but 41 possible series of those meteorological elements were obtained for the two target years, suggesting the possible future weather distribution. Such setup also leads to the variability in the future climates being almost identical to the variability of the historical climate. Although the variability of climate in the future may alter (Rind et al., 1989; Mearns, 2000), inducing possible decrease in mean crop yields (sem*nov & Porter, 1995; sem*nov et al., 1996), some researchers (Barrow et al., 2000; Wolf, 2002) have reported that for potato, changes in climatic variability in northern Europe generally resulted in no changes in mean yields and its coefficient of variation. Thus converted future weather data series are employed to calculate the date and the value of the initial water storage in the soil (or the date when the soil moisture falls below the field capacity), the date of the permanent increase in temperature to above 8 °C in the spring, the dates of the last and first night frosts (≤ -2 °C), and the date of the permanent drop in temperature to below 7 °C in autumn for each individual year of the new series. For determination of the soil water status in spring a relationship between radiation balance Rfc from permanent transition of temperature over 0º C to soil moisture fall below the field capacity, and meteorological data was derived using 30-year data of 13 stations of the Estonian Agrometeorological Network. To calculate Rfc , incoming global radiation and evaporative energy of precipitation (precipitation multiplied by latent evaporative heat) were accounted. The strongest correlations of Rfc were achieved with temperature sums from March to April T3-4 and precipitation sums from February to April U2-4: Rfc= 468.2 – 1.587 T3-4 – 0.517 U2-4

r = 0.66

(1)

To apply relationship (1) into the future dataset, a submodel calculates Rfc as well as permanent date of temperature rise over 0º C for each year of the new weather data series for 2050 and 2100. Next, from that date, the running radiation balance is summarized dayby-day. The date when the running radiation balance exceeds Rfc is counted as the date of achieving the soil field capacity and it is considered as the ‘first possible’ planting date. Additionally, ‘optimal planting date’ is applied – the date achieved by postponing the day of planting in model calculations day-by-day until the maximum yield is obtained. To prevent staying to a side maximum this postponing is conducted until the MPY drops below 70% of its maximum value, or until the date of summer equinox.

Simulated potato crop yield as an indicator of climate variability and changes in Estonia

373

The dates of last and first night frosts in the future series are found on the basis of the earlier determined relationships between mean daily air temperature and ground level minimum temperature, dependent on the radiation sum of previous day.

3. Results 3.1 Time series of meteorological resources: current climate Series of meteorologically possible yield were compiled for early and late maturing potato varieties in two different Estonian localities. In Table 1 we present long-term mean yields calculated with existing meteorological data series, using real and computed (both first possible and optimal) planting dates; the yields thus describe real, possible and optimal climatic resources for plant growth during given period. With real planting dates, there was practically no difference in average values of the MPY between long and short (from 1965) series. As expected, the late variety produced higher yields at all locations. Overall, the MPY series showed only weak and insignificant trends (Fig. 5), although reliable trends are apparent for some shorter periods. The longest period with a significant (P < 0.05) decreasing trend was observed in Kuressaare from 1977 to 2006. Generally, ‘Anti' demonstrated higher variance in yields. For both varieties, the variability reached higher in Kuressaare. Variability increases in all cases when using computed planting dates instead of real dates. Closer investigation of the MPY variability showed a significant increase in variance in Tartu since the early 1980s. In the MPY calculations contrived with real meteorological data, the standard deviation of MPY was significantly lower for ‘Maret’ in 1901-1980 compared to 1981-2006 (P = 0.006, according to F test); for ‘Anti’, the change was smaller yet significant (P = 0.046). When using shorter time series and optimal planting times, the same difference in yield variance was detected both for ‘Maret’ (P = 0.002) and ‘Anti’ (P = 0.015). The meteorological elements series revealed no similar changes in climate variability. Reliable dispersion differences were detected only in the precipitation series, but their significance was lower than that of the yields. ANTI Tartu MPY Real dates Long series to 2006 1965-2006 1901-1980 1981-2006 1923-1938 1939-2006 Computed dates 1965-2006, first planting date 1965-2006, optimum planting date

Var. coeff.

55.5 54.5 56.1 53.9

0.20 0.21 0.18 0.25

58.8 58.9

Kuressaare MPY Var. coeff. 50.3 50.3

0.27 0.28

51.0 50.1

0.16 0.29

0.24

49.8

0.23

50.2

MARET Tartu MPY Var. coeff.

Kuressaare MPY Var. coeff.

45.0 45.1 45.5 43.5

0.16 0.19 0.14 0.22

37.8 37.7

0.21 0.22

0.33

42.4

0.18

38.2

0.27

0.32

44.0

0.19

39.3

0.26

Table 1. Mean values of MPY and corresponding coefficient of variation for different periods.

374

Climate Change and Variability

Therefore, the separate meteorological elements did not reflect the influence of their combined effect on the variability of biological production. Significant differences in yield variability, not identified in the meteorological series, were also observed for ‘Anti’ at Kuressaare, where the standard deviation was approximately two times lower before 1939 than in later periods (P < 0.017).

1965-2006, optimal planting Long series to 2006, real planting

40 Tartu Maret

y = -0,16x + 368,4 r = -0.24; p=0.1

y = 0,007x + 31,3 r = 0,03; p=0.8

y = -0,02x + 102,5 r = -0,07; p=0.5

Tartu Anti

y = 0,009x + 41,9 r = 0.01; p=0.9

15 65

y = -0,004x + 44,7 r = -0,01; p=0.9

y = -0,09x + 209,3 r = -0,1; p=0.5

Kuressaare Maret

y = -0,02x + 83 r = -0,03; p=0.8 y = 0,03x - 9,4 r = 0,02; p=0.9 Kuressaare Anti

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year

Fig. 5. Series of MPY of the early potato variety ‘Maret’ and the late potato variety ‘Anti’ in Tallinn and Kuressaare.

Simulated potato crop yield as an indicator of climate variability and changes in Estonia

375

3.2 Relationships between MPY and other indicators In Estonia, like elsewhere in temperate zone, crop yield variation is highly influenced by weather conditions (Carter, 1996; Karing et al.,1999). When using real, measured potato yield data, potato yield variance was found to be mostly dependent on weather conditions, while the impact of fertilization and soil management proved less significant and in interaction with weather (Saue et al., 2010). Of meteorological conditions, potato proved the most susceptible to spring temperatures, yielding higher in years with a warm spring; negative linear relation between yields and precipitation during the same period concurred. The positive influence of precipitation was expressed after flowering. In this paragraph, we will compare simulated yields and direct meteorological series of precipitation, temperature and solar radiation, using accumulated values for those meteorological elements over different periods, in order to explain the extent to which individual factors allow us to describe the whole complex. Correlation analyses (linear and second-order polynomial) were performed. In Tartu , linear correlations between MPY and the accumulated meteorological factors were weak, although they were significant in some cases since the series were long (Table 2). The correlations with temperature were slightly higher, but only for the early variety. In Kuressaare, significant (P < 0.01) linear correlations were identified between MPY and all the accumulated meteorological factors in the selected periods: positive for precipitation and negative for solar radiation and temperature. In general, the period with the highest correlations began earlier for precipitation (from May for ‘Maret’ and from June for ‘Anti’), and later for temperature and radiation (from June and July, respectively). The results for Kuressaare are quite different from those for Tartu because its location on the island of Saaremaa in the western part of Estonia is characterised by a mild marine climate and dry summers. Low precipitation at the beginning of summer causes dry conditions, so water deficit is the main limiting factor there. The relationships between MPY and solar radiation and temperatures are largely indirect, and these factors correlate negatively with precipitation. As a rule, if a curve with a maximum describable by a second-order polynomial is applied, better correlation will be apparent between MPY and the accumulated meteorological elements. This means that for all factors, the limitation derives from both deficit and excess. Again, the highest correlations occurred in Kuressaare: for ‘Anti’ with precipitation (JuneAugust: r = -0.77, May-August: r = -0.76), and for ‘Maret’ with temperature from June to September (r = -0.71). The only exception, where the correlations are almost equal on the linear and polynomial curves, is the early variety in Kuressaare. There, the conditions are dry, especially in the first half of summer, so the limiting factor for the early variety in most years is a deficit of precipitation. For the late variety, the decrease in yield is occasionally caused by an excess of water. However, the latter is much more common in inland regions, represented by Tartu, where intense rainy periods produce soil moisture near its maximum content in June and July, causing the loss of soil aeration and a very significant reduction in yield. The limiting from two sides and high variances between MPY and the cumulative meteorological elements allow us to conclude that, under our conditions, MPY gives qualitatively new information about climate variability in summer, especially regarding climatic favourableness, by integrating the effects of different weather factors. In conditions with one very dominant limiting factor, there is no need for such an indicator, e.g., near the

376

Climate Change and Variability

Tartu

P T

Kuressaare

R P T

Relation -ship

Meteoelement

Station

Polar Circle, where MPY correlates very well with temperature (Sepp et al., 1989) or in arid regions, where the dominant factor is water deficit. For the stations analyzed in our work, Kuressaare is the most likely to be affected by a single dominant limiting factor, but the variance is still quite high there. Early variety 'Maret' May-Aug

June-Aug

'Late variety Anti'

May-Sept

May-Aug

June-Aug

May-Sept

LIN

0,03

0,02

0.03

0,01

-0,03

0.02

POL

--0,36

--0,41

--0.31

--0,47

--0,52

--0.43

LIN

--0,07

--0,02

--0.13

--0,06

--0,12

--0.03

POL

--0,53

--0,40

--0.49

--0,64

--0,56

--0.40

LIN

--0,26

--0,37

--0.24

--0,04

--0,20

--0.03

POL

--0,35

--0,50

--0.29

--0,41

--0,55

--0.35

POL

--0,25

--0,32

--0.26

--0,34

--0,35

0.34

LIN

0,19

0,27

0.05

0,26

0,34

0.10 --0.42

POL

--0,31

--0,33

--0.34

--0,42

--0,46

LIN

--0,17

--0,41

--0.24

0,14

--0,09

0.08

POL

--0,41

--0,52

--0.34

--0,46

--0,44

--0.41

LIN

--0,50

--0,55

--0.51

--0,46

--0,56

--0.45

POL

--0,50

--0,55

--0.51

--0,47

--0,57

--0.47

LIN

0,65

0,61

0.64

0,65

0,72

0.61

POL

--0,68

--0,66

-0.65

--0,76

--0,77

--0.69

LIN

--0,56

--0,68

--0.61

--0,30

--0,44

--0.35

POL

--0,58

--0,69

--0.62

--0,48

--0,57

--0.51

Table 2. Correlation coefficients r for the linear (LIN) and polynomial (POL) relationships between meteorologically possible yield (MPY) and accumulated solar radiation (R), precipitation (P), and temperature (T) at two stations. Bold indicates significance levels of P < 0.01. 3.3 Climate Change Most climate change scenarios project that greenhouse gas concentrations will increase through 2100 with a continued increase in average global temperatures (IPCC, 2007). Results of the four emission scenarios, each containing 18 General Circulation Models (GCM) experiments used in SCENGEN provide a wide variety of possible climate change scenarios (Table 3). In this paragraph we will look at the results by four illustrative emission scenarios, achieved by using the multi-model average for two locations in Estonia. All scenarios project the increase in annual mean temperature, the highest warming is supposed to take place during the cold part of the year (Fig. 6). During the plant-growth period (April to September), the increase of air temperature will be lower. Average annual precipitation is also predicted to increase (Fig. 7), however, changes in the annual range of monthly precipitation vary highly between models and scenarios and are less certain than changes in temperature. On average, the highest change in precipitation is predicted for January and

Simulated potato crop yield as an indicator of climate variability and changes in Estonia

377

November; August and September are predicted a small increase or even a slight decrease. All the projected climatic tendencies have already been noted during the last century (Jaagus, 2006), indicating evident climate warming in Estonia. In previous analogous works (Keevallik, 1998; Karing et al., 1999; Kont et al., 2003), temperature rise has been predicted higher; however we believe that moderate warming is more realistic.

Tartu

Kuressaare

Tartu

Kuressaare

Precipitation change, %

Scenario

2100

2050

Year

Temperature change, º C

A1B A2 B1 B2 A1B A2 B1 B2

2.40 2.60 1.73 2.25 4.65 5.78 3.11 4.13

2.37 2.54 1.71 2.24 4.64 5.72 3.14 4.13

8.5 10.0 6.2 8.1 16.2 20.7 10.7 14.7

8.1 8.8 5.8 8.0 16.3 19.5 11.2 14.4

Table 3. Changes in annual air temperature and precipitation calculated as a mean of experiments by 18 different GCM for four different emission scenarios.

Temperature change, C

Tartu 2100 A2

Tartu 2100 B1

-10

11 Year 12

Temperature change, C

-10

-10 2

11 Year 12

Kuressaare 2100 B1

Kuressaare 2100 A2

11 Year 12

Year 12

Fig. 6. Changes in monthly mean temperature (º C) predicted by 18 global climate models for the A2 and B1 emissions scenarios for year 2100 compared to the baseline period (1961– 1990) at two Estonian sites. Lines connect the values of monthly mean change, boxes mark mean change ± standard deviation and whiskers mark the range of all models.

378

Climate Change and Variability

Precipitation change, %

Tartu 2100 A2

Tartu 2100 B1

200

100

-100

-200

11 Year 10 12

-200

Precipitation change, %

Kuresaare 2100 A2

11 Year 12

Kuressaare 2100 B1

200

100

-100

-200

-200 11 Year 1 3 5 7 9 11 Year 2 4 6 8 10 12 2 4 6 8 10 12 Fig. 7. Changes in monthly sum of precipitation (%) predicted by 18 global climate models for the A2 and B1 emissions scenarios for year 2100 compared to the baseline period (1961– 1990) at two Estonian sites. Lines connect the values of monthly mean change, boxes mark mean change ± standard deviation and whiskers mark the range of all models.

3.4 MPY in the future From now on, all changes in MPY are referred as compared to baseline period (1965-2006) and we will discuss the yields achieved with optimal planting time. The productivity and yield changes related to the rise of CO2 in the atmosphere rise are not considered. For the late variety ‘Anti’, the long-term mean MPY values, calculated by using historical climate data of 1965-2006 with computed optimal planting time, describing the optimal climatic resources for plant growth, are 58.9 t ha-1 in Tartu and 50.2 in Kuressaare (see Table 1). For the early variety ‘Maret’ the values are 44.0 and 39.3, respectively. For early variety, all four considered scenarios predict losses in all given localities (Fig. 8). Stronger scenarios cause higher losses, up to 37% in Tartu and 32% in Kuressaare by 2100. In Kuressaare, the change in mean MPY is statistically significant for the year 2050 only by the strongest, A2 scenario (p=0.03); for the year 2100 all scenarios predict significant loss (p

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The temperature characteristics of a region are influenced by natural factors such as latitude, elevation and the presence of ocean currents. The precipitation characteristics of a region are influenced by factors such as proximity to mountain ranges and prevailing winds.

What are the six major controls of climate? ›

There are six major controls of the climate of any place. They are: latitude, altitude, pressure and wind system, distance from the sea, ocean currents and relief features.

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What is the real answer to climate change? ›

Because we are already committed to some level of climate change, responding to climate change involves a two-pronged approach: Reducing emissions of and stabilizing the levels of heat-trapping greenhouse gases in the atmosphere (“mitigation”); Adapting to the climate change already in the pipeline (“adaptation”).

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What is the best explanation for climate change? ›

These changes are caused by extra heat in the climate system due to the addition of greenhouse gases to the atmosphere. These additional greenhouse gases are primarily input by human activities such as the burning of fossil fuels (coal, oil, and natural gas), deforestation, agriculture, and land-use changes.

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What is climate change in very short answer? ›

Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun's activity or large volcanic eruptions.

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How do we respond to climate change? ›

Mitigation involves reducing carbon dioxide gas emissions and stopping the problem of climate change from growing. This means burning less fossil fuel (coal, oil and natural gas) and producing more renewable energy from technologies such as wind, solar and hydro power.

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