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CLIMATECHANGE–GEOPHYSICALFOUNDATIONSANDECOLOGICALEFFECTSEditedbyJuanBlancoandHoushangKheradmand Climate Change – Geophysical Foundations and Ecological Effects Edited by Juan Blanco and Houshang Kheradmand Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech 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 InTech, 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 Iva Lipovic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright Sergey Vasilyev, 2010. Used under license from Shutterstock.com First published August, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Climate Change – Geophysical Foundations and Ecological Effects, Edited by Juan Blanco and Houshang Kheradmand p. cm. ISBN 978-953-307-419-1 free online editions of InTech Books and Journals can be found atwww.intechopen.com Contents Preface IX Part 1 Climate Variability 1 Chapter 1 Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 3 Martin Dameris and Diego Loyola Chapter 2 Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 25 Maria Lanfredi, Tiziana Simoniello, Vincenzo Cuomo and Maria Macchiato Chapter 3 The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 43 Arne Max Erich Winguth Chapter 4 Temporal Variability of Rain-Induced Floods in Southern Quebec 65 Assani Ali Arkamose, Landry Raphaëlle, Quessy Jean-François and Clément Francis Chapter 5 Detecting of a Global and Caribbean Climate Change 81 Nazario D. Ramirez-Beltran, Joan Manuel Castro and Oswaldo Julca Chapter 6 Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 113 Valiya M. Hamza and Fábio P. Vieira Chapter 7 Climate Change Impacts on Atmospheric Circulation and Daily Precipitation in the Argentine Pampas Region 137 Olga C. Penalba and María Laura Bettolli VI Contents Chapter 8 Holocene Vegetation Responses to East Asian Monsoonal Changes in South Korea 157 Sangheon Yi Chapter 9 Climate Signals from 10Be Records of Marine Sediments Surrounded with Nearby a Continent 179 Kyeong Ja Kim and Seung-Il Nam Chapter 10 Drought Analysis Based on SPI and SAD Curve for the Korean Peninsula Considering Climate Change 195 Minsoo Kyoung, Jaewon Kwak, Duckgil Kim, Hungsoo Kim and Vijay P. Singh Part 2 Changes in Fauna and Flora 215 Chapter 11 Review of Long Term Macro-Fauna Movement by Multi-Decadal Warming Trends in the Northeastern Pacific 217 Christian Salvadeo, Daniel Lluch-Belda, Salvador Lluch-Cota and Milena Mercuri Chapter 12 Global Heating Threatens the `I`iwi (Vestiaria coccinea), Currently a Common Bird of Upper Elevation Forests in Hawaii 231 Anthony Povilitis Chapter 13 Possible Effects of Future Climate Changes on the Maximum Number of Generations of Anopheles in Monsoon Asia 247 Shunji Ohta and Takumi Kaga Chapter 14 Climate Change and Shifts in the Distribution of Moth Species in Finland, with a Focus on the Province of Kainuu 273 Juhani H. Itämies, Reima Leinonen and V. Benno Meyer-Rochow Chapter 15 Effects and Consequences of Global Climate Change in the Carpathian Basin 297 János Rakonczai Chapter 16 Climate Change Impact on Quiver Trees in Arid Namibia and South Africa 323 Danni Guo, Renkuan Guo, Yanhong Cui, Guy F. Midgley, Res Altwegg and Christien Thiart Chapter 17 Changes in the Composition of a Theoretical Freshwater Ecosystem Under Disturbances 343 Ágota Drégelyi-Kiss and Levente Hufnagel Contents VII Chapter 18 The Use and Misuse of Climatic Gradients for Evaluating Climate Impact on Dryland Ecosystems - an Example for the Solution of Conceptual Problems 361 Marcelo Sternberg, Claus Holzapfel, Katja Tielbörger, Pariente Sarah, Jaime Kigel, Hanoch Lavee, Aliza Fleischer, Florian Jeltsch and Martin Köchy Part 3 Changes in Alpine and Boreal Landscapes 375 Chapter 19 Climate-Driven Change of the Stand Age Structure in the Polar Ural Mountains 377 Valeriy Mazepa, Stepan Shiyatov and Nadezhda Devi Chapter 20 Mountains Under Climate and Global Change Conditions – Research Results in the Alps 403 Oliver Bender, Axel Borsdorf, Andrea Fischer and Johann Stötter Chapter 21 Are Debris Floods and Debris Avalanches Responding Univocally to Recent Climatic Change – A Case Study in the French Alps 423 V. Jomelli, I. Pavlova, M. Utasse, M. Chenet, D. Grancher, D. Brunstein and F. Leone Chapter 22 Glaciers Shrinking in Nepal Himalaya 445 Samjwal R. Bajracharya, Sudan B. Maharjan and Finu Shrestha Chapter 23 Subglacial and Proglacial Ecosystem Responses to Climate Change 459 Jacob C. Yde, Teresa G. Bárcena and Kai W. Finster Chapter 24 Why Do We Expect Glacier Melting to Increase Under Global Warming? 479 Roger J. Braithwaite Chapter 25 Estimation of the Sea Level Rise by 2100 Resulting from Changes in the Surface Mass Balance of the Greenland Ice Sheet 503 Xavier Fettweis, Alexandre Belleflamme, Michel Erpicum, Bruno Franco and Samuel Nicolay Preface Climate is a fundamental part of the world as we know it. The landscape andeverythingon itaredeterminedbyclimateactingover longperiodsof time (Pittock2005).Therefore,anychangeonclimatewillhaveeffectssoonerorlaterontheworldaround us. These changes have happened before in the past, and theywill likelyhappenagain in the future.Climatevariabilitycanbebothnaturaloranthropogenic(SimardandAustin2010). Ineithercase, thechange in thecurrentclimatewillhaveimpactsonthebiogeophysicalsystemoftheEarth.Asallhumanactivitiesarebuiltonthissystem,oursocietywillbeimpactedaswell.Asaconsequence,climatechangeisincreasingly becoming one of themost important issues, generating discussions ineconomy,science,politics,etc.There isnodiscrepancyamongscientists thatclimatechange is real and it has the potential to change our environment (Oreskes andConway2010),butuncertaintyexistsaboutthemagnitudeandspeedatwhichitwillunfold(Mossetal.2010).Themostdiscussedeffectofglobalwarmingistheincreaseoftemperatures,althoughthisincreasewillnotbehomogeneousthroughtheseasons,with the winters expected to warm up significantly more than the summers. 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Lett., Vol. 2, 215-218 2 Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms Maria Lanfredi, Tiziana Simoniello, Vincenzo Cuomo and Maria Macchiato Institute of Methodologies for the Environmental Analysis-IMAA-CNR, Department of Physics- University “Federico II”, Italy 1. Introduction Observational time series of climatic variables exhibit substantial changeability on spatial and temporal scales over many orders of magnitude. In statistical terms, this implies a continuous variance distribution involving all resolvable time scales (frequencies), starting from those comparable with the age of the Earth. A correct causal interpretation of such a variability is very difficult even in the context of a cognitive approach (e.g., von Storch, 2001) to the problem. Cognitive models are minimum complexity models aiming at the scientific understanding of the most relevant processes occurring at any given temporal and spatial scale. Although generally they cannot be useful for management decisions straightforwardly, their role is fundamental especially for understanding the internal climatic variability that cannot be passively related to external forcing factors. The concept of stochastic process is essential in this framework, since it synthesizes collective behaviours which contribute as a whole to the overall dynamics. As stochastic processes are the macroscopic result of many degrees of freedom, the characterization of their correlation properties across different scales through the analysis of observational data is a problem of statistical inference and their modelling is usually a mechanical-statistical problem. Maybe, the most famous early effort aiming to summarize the climate variance distribution among different frequencies, which is commonly referred as climate spectrum, is the ideal sketch proposed by Mitchell (1976) (see Fig. 1). All the features of this spectrum that deviate from the flat behaviour typical of white noise (pure random process) deserve dynamical interpretation in order to understand climate. Within the traditional picture of the climate dynamics, the variance distribution among different temporal scales is seen as the superposition of oscillations generated by astronomical cycles (spectral spikes), quasi-periodic or aperiodic fluctuations with a preferred scale (broad spectral peaks), and internal stochastic processes whose temporal correlation decays according to characteristic time scales. These last are responsible for all the continuous broad-band deviations of the spectrum from flatness. Within this picture, the variance accumulations that do not appear in the form of peaks and spikes, such as that we Climate Change – Geophysical Foundations and Ecological Effects 26can observe in-between the red vertical lines of Fig. 1 by scanning the figure from the short to the long periods, are due to the superposition of stochastic processes with different scales. This “redness”(optical analogy: dominance of low frequencies) would reflect the thermal inertia of slow climatic subsystems, such as ocean and cryosphere, and would be the result of a progressive addition of variance “shelves” (Mitchell, 1976) generated by ever slower scale-dependent processes. Hasselmann (1976) proposed an interesting interpretation of this redness by assuming that the heat-storage capacity of “slow” Earth’s sub-systems act to integrate random “fast” disturbances in a dynamical context that is therefore characterized by separation between short and long scales. As an example, ocean would act as a long term integrator of the meteorological atmospheric forcing (white noise on climatic scales) thus providing “memory” to the atmosphere-ocean system in the form of non zero correlation among different scales, that is redness. The resulting simplest paradigm for this integration is Brownian motion (random walk) that is a non-stationary scale free process whose variance increases linearly with scale (Mandelbrot & van Ness, 1968). Such an ideal motion is able to produce random trends of any length but within climate dynamics the presence of dissipative phenomena is expected to dump such integration. Then, dissipation introduces a characteristic time scale that marks the temporal horizon for the decay of the fluctuations toward the mean value and also oceanic processes approach white noise asymptotically (e.g., Von Storch et al, 2001). Fig. 1. Idealized sketch of the planetary climate variance spectrum (after Mitchell 1976). To this day, the necessity of understanding the actual origin of the climate variability in the entire spectral range is still recognised as a “primary goal” of the climate research, especially Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 27 if we aim to address the impact of human activities on climate. Currently, the availability of historical records of atmospheric temperature, which is the key variable of any terrestrial process, and the possibility of enlarging the observational time window back to about 400,000 years ago (proxy paleoclimatic data) give us the unique opportunity to get realistic insights into the correlation structures that characterise climate regimes from the meteorological to the glacial-interglacial domain. Contextually, the development of new mathematical-statistical tools, devised for enhancing specific correlation features (e.g. fractal persistence), make it possible to better discriminate such correlations from structures ascribable to more traditional superpositions of fluctuations and cycles. For many years, the scientific community has worked to rightly interpret the collection of observational data in order to improve the current understanding of the climate dynamics, evaluate the performances of models, and detect signatures of climate change blurred within regime variability. In particular, many works have focused on red spectral patterns in order to explore the possibility that the scale free dynamics typical of fractals, either non stationary (fractal Brownian motion) or stationary (fractional Brownian noise) (Mandelbrot & van Ness, 1968), could provide a description of climate better than the traditional one. A wide literature, based on both classical and new mathematical-statistical tools, is now available which reports analysis results and possible dynamical scenarios able to explain the sample time scale laws (e.g.,Konscielny-Bunde et al., 1996, 1998; Govindan et al., 2001;Eichner et al., 2003; Kurnaz, 2004; Varotsos et al., 2006; Vecchio & Carbone, 2010 ). These works suggest long range persistence (power law correlation) rather than scale dependence (exponential correlation) as a good statistical paradigm for explaining the climate spectrum redness on scales up to about 102 years. Also some analyses of pre-historical records (Pelletier, 1998; Huybers & Curry, 2006) support scale-invariance, since a random walk spectrum appears in the time scale range from 102 to 104 years. In both historical and pre-historical climate, scale separation seems to fail giving place to a continuum of self-organized scales. In this case, weather would be the only dynamical framework where it works well. In spite of the wide consensus around these studies, there are contradictory results about the universality of the scaling and the dependence of the exponent on the distance to sea (e.g., Vyushin et al., 2004a, 2004b; Blender and Fraedrich, 2004). More in general the interpretation of such a scaling is rather controversial because of the many drawbacks of the methodologies adopted (e.g., Hu, 2001; Kantelhardt, 2001; Metzler, 2003; Mauran et al., 2004; Gao et al., 2006; Rust, 2006; Lanfredi et al, 2009, Simoniello et al, 2009). This chapter discusses the state of the art of the studies of historical time series of atmospheric temperature, particularly focused on the interpretation of redness, and provides new analysis results for enhancing the debate on paleoclimatic observations. The core of the chapter is the discussion of the correlation structures estimated from observational data and their reliability. This is a typical problem of statistical inference that is crucial for identifying the right class of dynamical models to be used in the climate modelling. It is shown that the most popular recent interpretations, supporting power-law correlation, are not the only possible. The traditional simpler explanations are also acceptable and may work better than the complex ones. The discussion is inserted into the framework of the stochastic approach to the climate approximation, although our arguments are useful for climate modelling also within a non-stochastic approach to the problem. The chapter is organised according to the following principal points: Climate Change – Geophysical Foundations and Ecological Effects 28Section 2 summarises the main physical and statistical concepts and tools used in the chapter. A short overview of the basic models and operational implications concerning scale separation and scale invariance is provided; analysis tools and their potential weak points are discussed. Section 3 discusses the analyses of historical and pre-historical data. Detailed statistical estimates and literature results are provided in order to support the discussion. Then, the debate on the dynamical nature of redness is extended to millennial time scales. Finally, section 4 concerns the conclusive part of the chapter. 2. Basic concepts and statistical tools In this Section we summarise the main physical and statistical concepts and tools used in the chapter. These substantially concern the main general forms of correlation, scale dependence (short-range correlation) and scale invariance (long-range correlation), which are useful for the selection of the right class of stochastic models for climate. Of course, the discussion is not exhaustive but merely aims to provide the basic background that is necessary for the understanding of the chapter’s content. 2.1 Autoregressive processes, scale dependence, and their role in the traditional stochastic climate Stationary stochastic processes are often fruitfully modelled by means of autoregressive processes, which are filters whose input is a Gaussian independent process (white noise) t (e.g., Jenkins & Watts, 1968). The output of an autoregressive process AR(p) of order p is: 1pt i t i tiX a X    (1) where (ai) are the autoregressive coefficients and t is a Gaussian random process with zero mean and variance 2. In particular, the paradigmatic model of the meteorological fluctuations is the first order autoregressive process AR(1): t t i tX aX    ; 1a  (2) where the index t indicates the daily step. The autocovariance function is: 221aaXXnntt  (3) Thislast decays with the characteristic length =-1/ln(a). For continuous processes, the correlation function is: /( ) te    (4) For very long time scales AR(1) is completely stationary with variance: 2 2211tXa   t>> (5) Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 29 AR(1) is the most simple example of scale-dependent process: for t>. Within the traditional approach to climate approximation, this white noise describes the variability of meteorological variables in a scale range satisfying the condition: m ct     (6) where m is the meteorological characteristic scale ( a few days) and c is the closest characteristic scale of climate (e.g. that of the oceans). More in general, it describes elementary stochastic processes whose superposition can generate redness through the progressive addition of variance. In fact, according to Eqs. 3 and 5, the fluctuations of an AR(1) produce low variance (high covariance) on scales shortest than its characteristic one; such a variance increases with scale up to the value in Eq. 5 on asymptotic scales. Roughly speaking, if we consider the superposition of different first order autoregressive processes ARi (1) (i=1,…,n) and separated time scales 10.5 characterizing persistent processes is particularly interesting since the theoretical correlation implies non zero probability that disturbances survive on times as long as infinity (long range memory). Such ideal processes may be useful within empirical studies aiming to describe observational stationary time series which show interdependence between very distant samples without approaching white noise. In these cases, the most classical models that are characterized by exponential decorrelation ( )te    (e.g., autoregressive processes) could fail to account for such a long-range dependence. 2.3 Drawbacks of time series analysis for the detection of scale invariance; detrended fluctuation analysis Generally, the investigation of time series aims to identify a class of theoretical processes able to synthesize some given correlation features of observational data: the class of the processes is assumed to be unknown. As a consequence, in order to propose a given model as a realistic descriptor of the investigated dynamics, we have to demonstrate both the compatibility of the tested theoretical correlation structure with that estimated from data (necessary condition) and to exclude any other alternative forms of correlation (sufficient condition). Actually, the procedures that are used to identify the existence of power-law correlation do not allow us to satisfy both these conditions. It is well known that the variance spectrum is very sensitive to any form of non stationary behaviour. It is suitable for investigating stationary or cyclo-stationary signals or, more in general, signals with weak local features. As far as climatic time series, this condition cannot be guaranteed. Any external forcing such as volcanic eruptions and externally induced temporary warming/cooling trends can produce misleading results. Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 31 In order to avoid these drawbacks, some authors developed alternative tools, such as Detrended Fluctuation Analysis (DFA) (Peng et al, 1995), aiming to minimize externally-induced non-stationary effects describable in the form of low-order polynomials. We shortly recall how this methodology works. The time series to be analysed is integrated and divided into N boxes of length n. In each box, a least square polynomial yn(k), representing the trend in that particular box, is fitted to the integrated data y(k). Then, the root-mean-square fluctuation:  21( ) ( ) ( ) /NnkF n y k y k N  (12) is calculated. This computation is repeated on many time-scales (box sizes) in order to characterize F(n) as a function of n. Power-law (fractal) scaling implies a linear relationship in a log-log plot. Under such conditions fluctuations can be characterized by a scaling exponent  (=H for fGn). In this chapter the 2nd-order Detrending (DFA2) is adopted in order to minimize the effects of discontinuities and linear trends. This methodology, that is generally considered the most powerful for identifying fGn, may produce many false positive results. This point is well stressed in Mauran et al., (2004). This is a method developed to discover fractals blurred in noise. In practice, it intrinsically postulates that a fractal is present and try to estimate the scaling coefficient minimizing external disturbances. It satisfies the necessary condition above (if a fractal is present it is generally able to find it) but is not able to satisfy the sufficient condition, since if there is not any fractal the estimation of a linear best fit in a log-log plot of sample statistics is not sufficient for supporting the actual existence of a power law. In particular, log-log collinearity should be carefullyverified. 3. Results from time series analysis of atmospheric temperature In this Section we discuss some examples of analyses of temperature time series aiming to detect long range persistence. We refer to bibliography for in-depth information. 3.1 Historical data The rationale behind most of the investigations on historical data is the more or less explicit use of white noise as null hypothesis. Within the classical stochastic approach to climate approximation the fastest processes we deal with are the meteorological processes, whose time scale is considered well-separated from all the slower climatic time scales. Such a meteorological variability has been traditionally explained by low-order autoregressive processes such as the paradigmatic first-order autoregressive process (AR1): t t i tX aX    ; (13) where Xi is the meteorological variable, a is the first-order autocorrelation coefficient, and i represents white noise. According to this model, the parameter a accounts for rapid inter-day correlation decay so that the asymptotic behaviour, starting from scales of a few weeks, is uncorrelated and unpredictable: Xii Climate Change – Geophysical Foundations and Ecological Effects 32More recently, in the wake of the great success of empirical fractal tools devised for enhancing power-law correlation in noised and biased observational data (e.g., Peng et al., 1995; Konscielny-Bunde et al., 1998; Freeman et al., 2000; Matsoukas et al., 2000; Haggerty et al., 2002; Bunde et al., 2002; Kandelhardt et al., 2003, 2006; Blender and Fraedrich, 2003), many researches have focused on historical atmospheric temperature time series for exploring the possibility that long range persistence characterizes climate after the meteorological correlation is decayed (e.g., Konscielny-Bunde et al., 1996, 1998; Govindan et al., 2001; Eichner et al., 2003; Kurnaz, 2004; Varotsos et al., 2006). Their analyses, based on the estimation of the Hurst coefficient prevalently by means of DFA, seem to put into evidence slightly long range persistent features and their conclusion is that the asymptotic noise I is not white but is a power law correlated noise (see Kiraly & Janosi, 2002 for a fractal version of Eq. 13). According to these works Fractional Gaussian noise has been suggested as a realistic model for explaining the statistical dependence of atmospheric temperature anomalies (deviations from the mean annual trend) on climatic time scales. Fig. 2. Plot of the detrended fluctuation function for daily atmospheric temperature time series (Klein Tang, 2002) recorded in Prague (filled squares), Wien (stars), St. Petersburg (empty squares), Potsdam (triangles) (after Lanfredi et al, 2009). Fig. 2 shows the results of DFA applied to four atmospheric temperature time series widely analysed literature (Lanfredi et al, 2009 and references therein). The apparent linear behaviour of the fluctuation function on decadal scales is rather evident and the value of the Hurst coefficient greater than 0.5 indicates a long range persistent behaviour. Nevertheless, just the well known redness of the climatic spectrum suggests that white noise is not the right null hypothesis against long range persistence. The actual problem is to establish whether the power law is the best representation for the atmospheric temperature correlation or instead alternative time scale laws are acceptable. In practice there is a problem of functional form goodness for the linear fit. Fig.3 (Lanfredi et al., 2009) shows the residuals from the power law best fit of Fig. 2 which should be a stationary noise in the time range where the time series is fractal. On the contrary, the residuals are arranged in a non-linear way in all the cases. Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 33 Fig. 3. Plots of the ratio F(n)/n in logarithmic scale for the four time series of Fig. 2: (a) Prague; (b)Wien; (c) St. Petersburg; (d) Potsdam (after Lanfredi et al., 2009). Fig. 4. Estimates of (n) for (a) Prague; (b)Wien; (c) St. Petersburg; (d) Potsdam (after Lanfredi et al., 2009). In addition, within the scaling regime, the scale invariant law F(kn)=kF(n) should hold for any k. Thus, the function (n)=logk[F(kn)/F(n)] should provide an estimation of the local scaling coefficient. Again, (n) should be a stationary noise where a scaling regimes occurs. Fig. 4 shows the estimates of (n) for the four time series of Fig. 2. On short time scales the high value of (n) accounts for a strong correlation that progressively decays approaching a noised and irregular behaviour that does not allow us to detect scaling regimes unquestionably. Most likely, the apparent scaling is due to the emergence of slower fluctuations that add “shelves” (Mitchell,1976) to the time series variance. Climate Change – Geophysical Foundations and Ecological Effects 34In order to assess how short range dependent processes appear when examined by means of fractal tools, we can investigate time series simulated on the basis of observational data and modelled according to scale separation (Lanfredi et al, 2009). Fig. 5 and 6 show the analysis results of a simple two-scales (weather-climate) process, modelled on the basis of the autocorrelation function of the Prague’s data. The analogies with the real data (Figs 2,3 and 4) are very impressive. The two-scale model is able to account for the whole results obtained from the fractal investigation. The mechanism that produces scaling is clear. Correctly, the total fluctuation function F(n) ends as a white noise (Fig. 5b) only in the latest part of the plot. Nevertheless, since the variance produced by the slow climatic variable emerges only on the long time scales, if we try to fit the function globally from the short to the long time scales (Fig. 5a), a spurious scaling occurs for the presence of the variance shelf. 1,E-021,E-011,E+001,E+011,E+021,E+00 1,E+01 1,E+02 1,E+03 1,E+04 1,E+05n[days]F(n)=0.5=1.41,E+001,E+011,E+021,E+031,E+00 1,E+01 1,E+02 1,E+03 1,E+04n(days)F(n)=0.65=0.631,E+001,E+011,E+021,E+031,E+00 1,E+01 1,E+02 1,E+03 1,E+04n(days)F(n)=0.65=0.63n (days) n (days)1,E-021,E-011,E+001,E+011,E+021,E+00 1,E+01 1,E+02 1,E+03 1,E+04 1,E+05n[days]F(n)=0.5=1.41,E+001,E+011,E+021,E+031,E+00 1,E+01 1,E+02 1,E+03 1,E+04n(days)F(n)=0.65=0.631,E+001,E+011,E+021,E+031,E+00 1,E+01 1,E+02 1,E+03 1,E+04n(days)F(n)=0.65=0.631,E-021,E-011,E+001,E+011,E+021,E+00 1,E+01 1,E+02 1,E+03 1,E+04 1,E+05n[days]F(n)=0.5=1.41,E+001,E+011,E+021,E+031,E+00 1,E+01 1,E+02 1,E+03 1,E+04n(days)F(n)=0.65=0.631,E+001,E+011,E+021,E+031,E+00 1,E+01 1,E+02 1,E+03 1,E+04n(days)F(n)=0.65=0.63n (days) n (days)a) b) Fig. 5. a) Results of DFA for real (filled squares) and simulated (empty squares) anomalies. The continuous line shows the empirical power law reported in literature; b) effect of an hidden long scale within an asymptotic noise, the high scaling coefficient of the hidden process =1.4 on short scales is an indication of strong correlation and is compatible with values estimated for the ocean (after Lanfredi et al, 2009). Fig. 6. Residuals from the linear best fit, and estimation of the local scaling exponent of a two-scales (weather-climate) autoregressive process (after Lanfredi et al, 2009). Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 35 3.2 Paleoclimatic data The temperature time series obtained from the Vostok ice core dataset (Petit et al, 1999) provides a unique source of information about climate changes over glaciological scales. Although unevenly sampled in time and affected by reconstructionerrors, such as non-temperature effects, observational uncertainty, age-model uncertainty, etc., it includes structures generated by those time scale laws we are searching for. Above all, they can inform us about possible common correlation structures unifying climate dynamics on historical and paloclimatic eras. Fig. 7 shows this paleorecord that describes temperature variability for the past 420,000 years. The time series appears to be rather noised even if some near systematic behaviours are detectable. Among them, the longest oscillations (Milankovitch cycles) account for the alternation between glacial and interglacial eras. Although the astronomical variability that drives them are known to be a combination of cyclical changes of the Earth-Sun geometry (eccentricity, obliquity, precession), there is not yet a shared interpretation of the underlying dynamics (e.g., Meyers et al 2008). These data include information on the effects of the so-called “Pacemaker of the Ice Ages “ (Hayes, et al., 1976) on the terrestrial internal climatic variability. Just this variability under the action of the astronomical forcing could provide useful insights on the mechanisms that govern the mutual interactions between the different climatic subsystems. Also in this case, we do not discuss this specific dynamical problem but illustrate the difficulty related to the inference of time scale laws from this dataset. Temporal distance from the present(years)Temporal distance from the present(years) Fig. 7. Reconstructed temperature data from the Vostok Ice Core dataset (Petit et al., 1999). Temperature is the difference from the mean recent time value (red line). Climate Change – Geophysical Foundations and Ecological Effects 36Maybe, the most famous work proposing scale invariance as the main tool for explaining climate variability over millennia is that by Huybers & Curry, (2006). It gathers both historical and paleoclimatic data and discusses their power spectrum within an unified theory based on a fractal continuum of time scales. The estimated variance spectrum is reported in Fig. 8. The low frequency scaling coefficient for the paleorecord corresponds to a value of the Hurst’s coefficient H=0.32, which is signature of anti-persistent fractional Brownian motion. Quite similar results were also found by Pelletier, (1998) who estimated a coefficient compatible with random walk. =2H+1=1.640.04=2H+1=1.640.04 Fig. 8. Sample estimation of the planetary climate spectrum (after Huybers & Curry,2006). The parameter  is the estimated scaling coefficient. The visual inspection of this spectrum in the low frequency range , so as it is, raises some questions. Differently from the high frequency cycles (annual frequency and sub-harmonics), which appear as spikes well separated from the continuous spectrum of the stochastic component, the millennial cycles are difficult to be separated from noise: it is necessary to know them a priori for interpreting the spectrum correctly. As already specified above, the variance spectrum is not the best tool for investigating complex signals where trends and oscillations could introduce spurious scaling (Gao et al, 2006). Huybers & Curry (2006) estimated the paleorecord scaling in the frequency range between 1/100 and 1/15,000 years to minimize the influence from the Milankovitch bands. Nevertheless, cyclic trends occur in the analysed band too (e. g., Kerr, 1996). Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 37 In order to delve into this problem we can investigate time scale laws in the time domain by estimating the second order structure function (Kolmogorov, 1941): 2( ) [ ( ) ( )]X t X t       (14) which is the best statistical tool for studying fractional Brownian motion, since it can be applied to non-stationary data and 2( ) H    when the time series X(t) is an fBm. Second order structure function coincides with the variogram used in Geostatistics (Cressie,1993), which is a well known tool for investigating time scale dependence also when data are unevenly sampled. Fig. 9 illustrates the structure function of the Vostok time series normalised to 22X. 10 kyears20 kyears60 kyears 100 kyears120 kyears200 kyearsn (years)(n)2 - 4 k years10 kyears20 kyears60 kyears 100 kyears120 kyears200 kyearsn (years)(n)10 kyears20 kyears60 kyears 100 kyears120 kyears200 kyearsn (years)(n)2 - 4 k years Fig. 9. Second order structure function of the Vostok Ice Core dataset. Arrows indicates approximately the time scales where cycles exhibit minimum or maximum values. The level 1)(  corresponds to the variance of the total time series. Differently from the sample spectrum, the structure function reveals long time oscillations explicitly. They are clear in spite of the strong noised character of the estimations due to the uneven and limited sampling etc.. In the time domain, maximum values are associated to odd multiple of semiperiods whereas minimum values correspond to multiple periods. In a composition of cycles and noises, the minimum values reached in the periodic part of (n) (red dashed line in Fig. 8) mark the percentage contribution due to pure noise. The scale where this plateau is intercepted for the first time (a few thousand of years) marks the crossover between the scales where the truly stochastic noise is observable and that where the contribution of the cycles starts to appear. Then the scaling would occur in a scale range where the non stationary character of the oscillations contaminates the variance of the noise. By looking at Fig. 10 in the temporal range where scaling should appear (red line from 102 to 1.5 x 104 years), a direct estimation provides the value H=0.53, which is in a rather good Climate Change – Geophysical Foundations and Ecological Effects 38agreement with the estimations of Pelletier (1998). Nevertheless, we can observe that the scales shorter than 103 years are evidently not collinear with the subsequent ones. The same is true above 104 years, where (n) appears flatter. If we estimate H by progressively shortening the Huybers & Curry range from the short scale side, its value increases. The same is true if we shorten it by starting from the long time scales. The maximum value H=0.6 is obtained about in the middle of the initial range but this does cover not even one decade, which is the minimum requirement for keeping confidence in scale invariance. In addition, we can note that the apparent linearity ends with a maximum value that corresponds to one half period of the 20 k years oscillation. The central about linear behaviour seems to be an inflection transient between the short time scales (concavity up) and those belonging to cycles where the function (n) exhibits a different curvature (concavity down). 10 k yearsn (years)H=0.53updownH=0.6 ( n ) ( n )10 k yearsn (years)H=0.53updownH=0.610 k yearsn (years)H=0.53updown10 k yearsn (years)H=0.53updownH=0.6 ( n ) ( n ) Fig. 10. As Fig. 9 but in double logarithmic scale. The peak at about 10 k years is the maximum anticorrelation associated to a cycle of about 20 k years. The red line is the best fit computed on the time scales indicated by Huybers & Curry (2006): 102 years- 1.5 x 103 years. Arrows indicate the concavity semi-planes. 4. Conclusion In all the studies on the dynamics of the natural world, observational time series play a fundamental role since they are the main source of information for inferring the underlying causal mechanisms. Especially in a stochastic context, when the number of degree of freedom is high, observational time series can provide those time scale laws that rule temporal correlation thus helping us to identifythe right reference class of theoretical models. Nevertheless, the interpretation of time laws estimated from real data can be rather difficult because the analysis results can be rather ambiguous in many cases. Dynamical Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms 39 inferences from climate observations fall just in this class. The analysis results illustrated in this chapter put into evidence that no conclusive interpretation of the sample variance spectrum is available yet. It is clear that the analyses performed have to be carefully supervised, preferring those mathematical and statistical tools that are less sensitive to local (in time) disturbances, trends, and cycles that can trick analysts. Spurious scaling can easily appear thus suggesting an erroneous modelling of the deep dynamical characteristics of the climate. 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(2004a) Volcanic forcing improves atmosphere-ocean coupled general circulation model scaling performance, Geophys. Res. Lett., Vol. 31,(January 2004), pp. L10206, ISSN 0094- 8276. Climate Change – Geophysical Foundations and Ecological Effects 42Vyushin, D., Zhidkov, I., Havlin, S., Bunde, &, and Brenner, S. (2004b) Reply to the Comment on “Volcanic forcing improves atmosphere-ocean coupled general circulation model scaling performance”, Geophys. Res. Lett., Vol. 31, (January 2004),pp. L22210, ISSN 0094-8276. 3 The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles Arne Max Erich Winguth University of Texas at Arlington USA 1. Introduction It is predicted that by the year 2300, the atmospheric CO2 concentration will exceed ~2000 ppmv (Caldeira & Wickett, 2003; Mikolajewicz et al., 2007), corresponding to a release of 4000 x 1015 g carbon (PgC) by fossil fuel emissions and land use changes since the beginning of the industrial revolution. The anthropogenic carbon will eventually sequester on time scales of 100,000 yrs as organic carbon into the ocean and land biosphere and as CaCO3 into the geosphere (Archer et al., 1998). This carbon transfer in the atmosphere-ocean system is comparable to that at the Paleocene-Eocene boundary (55 Ma), when a massive release of carbon into the climate system led to a prominent global warming event referred to as the Paleocene-Eocene Thermal Maximum (PETM). The PETM is characterized by a major (>3.0‰) negative carbon isotope excursion, documented in marine and terrestrial fossils (e.g. Koch et al., 1992; Kelly et al., 1998; Handley et al., 2008), and a worldwide seafloor carbonate dissolution horizon (e.g. Bralower et al., 1997; Lu et al., 1998; Schmitz et al., 1996; E. Thomas et al., 2000) as well as shoaling of the lysocline and carbonate compensation depth (Zachos et al., 2005). These changes are consistent with the release of more than 2000 PgC of isotopically depleted carbon into the ocean-atmosphere system within less than 10,000 years (Panchuk et al., 2008; Zachos et al., 2007, 2008), pointing to a greenhouse gas-driven warming (see Fig. 1). Recent estimates from Cui et al. (2011) indicate a slow emission rate of 0.3-1.7 PgC yr-1 as compared to the present-day emission of carbon dioxide of ~9.9 PgC yr-1 from fossil fuel emissions (Boden et al., 2010) and land-use changes (Houghton, 2008). Surface temperatures increased by 5°C in the tropics (Tripati & Elderfield, 2005; Zachos et al., 2005) and mid-latitudes (Wing et al., 2005), and by 6-8°C in the ice-free Arctic and sub-Antarctic (Hollis et al., 2009; Kennett & Stott, 1991; Moran et al., 2006; Sluijs et al., 2006, 2007, 2008a, 2011; E. Thomas et al., 2000; Weijers et al., 2007), and deep-sea temperatures increased by 4-6°C (Tripati and Elderfield, 2005; Zachos et al., 2008), relative to Paleocene temperatures (see Fig. 1). At the same time, large-scale changes in the climate system occurred, for example in the patterns of atmospheric circulation, vapor transport, precipitation (Robert & Kennett, 1994; Pagani et al., 2006a; Brinkhuis et al., 2006; Sluijs et al., 2008a, 2011; Wing et al., 2005), intermediate and deep-sea circulation (Nunes & Norris 2006; D.J. Thomas, 2004; D.J. Thomas et al., 2008) and a rise in global sea level (Sluijs et al., 2008b; Handley et al., 2011). The sea level rise is caused by various factors, including thermal Climate Change – Geophysical Foundations and Ecological Effects 44expansion, decrease in ocean basin volume, decrease in mountain glaciers, as well as local tectonic changes. Topography and bathymetry during the PETM differed significantly from today with respect to the distribution of landmasses, sizes of ocean basins and width and depth of seaways. Fig. 1. Evolution of atmospheric pCO2 concentration and deep-sea temperature reconstruction over the past 65 million years (from Zachos et al., 2008). a) Atmospheric pCO2 for the period 0 to 65 million years ago. The dashed horizontal line shows the minimum pCO2 for the early Eocene (1,125 ppmv), as given by calculations of equilibrium with Na–CO3 mineral phases (vertical bars, where the length of the bars indicates the range of pCO2 over which the mineral phases are stable) that are found in Neogene and early Eocene lacustrine deposits. The vertical distance between the upper and lower colored lines shows the range of uncertainty for the alkenone and boron proxies. b) Deep-sea benthic foraminiferal oxygen-isotope curve based on records from Deep Sea Drilling Project and Ocean Drilling Program sites. [Reproduced by permission of AAAS; copyright 2008 AAAS.] The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 45 2. Climate change and variability at the beginning of the PETM The causes leading to the warming event at the Paleocene-Eocene boundary are still controversial (Fig. 2). Fig. 2. Major feedbacks for the initial warming at the Paleocene-Eocene boundary. Note that the feedbacks and their magnitudes are still controversial (see e.g. Bowen and Zachos, 2010). One possible sequence of events inferred from paleoproxies begins with a volcanically induced greenhouse gas (water vapor, CO2, CH4, and other constituents) increase that would have produced a global increase in surface temperature (Bralower et al., 1997; Kennett & Stott, 1999; Sluijs et al., 2007, 2011; E. Thomas et al., 2000). Various climate-modeling studies have investigated the warming event at the Paleocene-Eocene boundary in response of the elevated greenhouse gas concentrations. These studies utilized atmospheric general circulation models (Sloan & Barron, 1992; Sloan & Rea, 1995; Huber & Sloan, 1999; Shellito et al., 2003; Shellito & Sloan, 2006), ocean general circulation models (Bice et al., 2000; Bice & Marotzke, 2002), or more recently coupled comprehensive climate models (Heinemann et al., 2009; Huber & Sloan, 2001; Huber & Caballero, 2003; Huber & Caballero, 2011; Lunt et al., 2010; Shellito et al., 2009; Winguth et al., 2010) to simulate the mean climate Climate Change – Geophysical Foundations and Ecological Effects 46and its variability during the Eocene, but they have not been able to reproduce the high temperatures of the PETM in the high latitudes, and were controversial regarding the causeof this warming (Pagani et al., 2006b; Zeebe et al., 2009). Some of the more recent studies have investigated the climate feedbacks with a sequence of different greenhouse gas concentrations (e.g. Heinemann et al., 2009; Lunt et al., 2010; Winguth et al., 2010). In the following, we summarize key findings of the paper of Winguth et al. (2010), using a complex earth system model, the comprehensive Community Climate System Model version 3 (CCSM-3; Collins et al., 2006), in order to investigate PETM climate feedbacks in response to rises in the greenhouse gas concentrations. Huber & Caballero (2011) used the same model, but with a different dust concentration in the atmosphere. The simulated increase by 2.5°C from 4xCO2 to 8xCO2 in CCSM-3 could be explained by CO2 emissions due to enhanced volcanic activity at the beginning of the PETM (Fig. 3). Fig. 3. Zonally averaged (50-yr mean) surface air temperature (in °C) for present-day (solid), 4xCO2 PETM (long-dashed), 8xCO2 PETM (short-dashed), and 16xCO2 PETM (dashed-dotted)(from Winguth et al., 2010). Surface temperatures in the tropics rise by only ~2°C from 4xCO2 to 8xCO2, in agreement with temperature reconstructions (Pearson et al., 2007) and with future climate predictions (IPCC, 2007) of a more extreme warming at high latitudes vs. low latitudes in a warmer world. Temperature increase over land exceeds that over the ocean (Fig. 4) due to reduced latent heat fluxes and lower heat capacity. Over the continents, the 30C isotherm in the 8xCO2 simulation reaches up to 30 latitude, about 5 more poleward than for the present-day simulation. Maximum simulated temperatures, comparable to extreme temperatures in the present-day Sahara, are simulated over subtropical Africa and South America (~50°C for 8xCO2), resulting in warm sea-surface temperatures in the adjacent oceans through advection. Simulated minimum surface temperatures (for 8xCO2) are between 3°C and 7C over the Arctic and about -10C over northeast Asia. The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 47 Fig. 4. Surface air temperature (SAT) in °C for a) the difference between the 8x and the 4xCO2 PETM experiment corresponding to opening of the Atlantic by massive volcanism and b) differences between the 16x and the 8xCO2 PETM experiment by the release of carbon from the marine and terrestrial carbon stocks (100-year mean). Fig. 5. Differences between reconstructed surface temperatures (in °C) and 50-year annual mean temperature from the CCSM-3 climate simulation with an atmospheric CO2 concentration of 4x (blue), 8x (purple), and 16x (red) the preindustrial level (see Winguth et al., 2010). For reference, paleolatitude is listed for each location. While data-inferred paleotemperatures are relatively well represented in the tropical regions, a significant bias between model results and data remains for the Arctic Ocean (Sluijs et al., 2006) and for the area around New Zealand (Fig. 5, Waipara River; Hollis et al., 2009). The bias in the northern polar region (IODP core 302 A; Sluijs et al., 2006) is of complex nature and could for example be associated with the concentration of cloud Climate Change – Geophysical Foundations and Ecological Effects 48condensation nuclei used in CCSM-3 (Huber & Caballero, 2011; Kump & Pollard, 2008), the uncertainties in paleolocations (N-S position, or distance form shore), or with skewing of data towards summer temperatures (Sluijs et al., 2006). The causes for model-data discrepancies at high southern latitudes remain controversial. A positive climate-carbon cycle feedback loop leading to further PETM warming due to destabilization of methane hydrates is shown in Fig. 2. There is sufficient evidence from various sites around the globe, including the New Jersey shelf (Sluijs et al., 2007), the North Sea (Bujak & Brinkhuis, 1998; Sluijs et al., 2007), the Southern Ocean (Kennett and Stott, 1991), and New Zealand (Hollis et al., 2009) that 13C-depleted carbon in the form of isotopically light CO2 and/or CH4 was released from the sea floor (Dickens et al., 1995, 1997; Higgins & Schrag, 2006; Pagani et al., 2006a) or from wetlands (Pancost et al., 2007) into the atmosphere-ocean-biosphere system (Sluijs et al., 2007; Bowen and Zachos, 2010). As shown in Figs. 3 and 4, such a change in the radiative forcing from 8xCO2 to 16xCO2 leads to a simulated additional warming of ~2°C globally, with 4°C at the poles, 5C over South America and South Africa, and 2°C at the equator. For the Southern Ocean, cool water masses moderate the climate over the polar southern hemisphere, so that south of 60, the temperature increase in response to the increase of CO2-radiative forcing is smaller than in the northern hemisphere (Fig. 4b). In mid-latitudes, the bias between the 16xCO2 simulation and reconstructed PETM surface temperatures is reduced compared to simulations with a lower atmospheric CO2 level with high dust concentration; the values are comparable to the 8xCO2 scenario with lower dust concentration in Huber & Caballero (2011). For the tropics, evidence from fossil remains of a giant boid snake in northeastern Colombia (Head et al., 2009) and modeling studies (Winguth et al., 2010; Huber & Caballero, 2011) support warm average temperatures of 30-34C. b)a)Precipitation (mm/day)()16xCO28xCO24xCO2Present-Day16xCO28xCO24xCO2Present-Day Fig. 6. Zonally averaged (50-yr mean) precipitation (in mm/day) (a) and evaporation minus precipitation (in mm/day) (b) for present-day (solid), 4xCO2 PETM (long-dashed), 8xCO2 PETM (short-dashed), and 16xCO2 PETM (dashed-dotted) (from Winguth et al., 2010). The increase in global warming during the PETM leads to extremes in the hydrological cycle (droughts in the subtropics and higher precipitation and flooding in the tropics and high latitudes). The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 49 Rapid warming at the beginning of the Eocene has been inferred from the widespread distribution of dinoflagellate cysts (or dynocysts). The abundance of one dynocyst species, Apectodinium, dramatically increased at different locations worldwide (Bujak & Brinkhuis, 1998; Crouch et al., 2001; Heilmann-Clausen & Egger, 2000; Sluijs et al., 2007), implying a change in environmental conditions such as warmer sea surface temperatures and increased food availability in form of phytoplankton (Burkholder et al., 1992) due to increased nutrient delivery by weathering (Ravizza et al., 2001; Zachos & Dickens, 2000) and erosion (Fig. 2). The climate-carbon cycle feedback associated with an increase in greenhouse gases (Fig. 2) might also have been enhanced by an increase in the atmospheric water vapor fluxes (Figs. 6 and 7); for instance, latent heat flux by evaporation and precipitation rises with the warming of the surface (Fig. 6). Higher precipitation and lower sea surface salinity values are derived for the Arctic from isotopic measurements as well as from dinocyst assemblages (Pagani et al., 2006a; Sluijs et al., 2008a). The enhanced precipitation at high latitudes is consistent to patterns simulated for future climate scenarios (e.g. Cubasch et al., 2001; Meehl et al., 2006; Mikolajewicz et al., 2007). For the southern high latitudes, a simulated increase in precipitation is confirmed by clay-mineral indicators from the Antarctic continent, pointing towards humid conditions at the PETM (Robert & Kennett, 1994). Compared to present-day, differences in the geography and mountain height cause remarkable changes. For instance, a higher than present-day ratio of tropical land-to-ocean area at the PETM reduces the tropical ocean surface and hence the oceanic source of atmospheric moisture (Barron et al., 1989). This change in tropical surface area not only reduces significantlytropical precipitation, but also poleward moisture transport from the tropics. However, increase in precipitation by higher than present-day greenhouse gases counteracted this effect during the PETM. Fig. 7. Evaporation minus precipitation (in mm/day) for a) the difference between the 8x and the 4xCO2 PETM experiment, and b) the difference between the 16x and the 8xCO2 PETM simulation. The increase in global warming during the PETM leads to extremes in the hydrological cycle (increases in aridity in the subtropics are shaded red, and in humidity in the tropics are shaded blue). An initial increase in CO2 in the atmosphere by volcanic outgassing would have increased the strength of the hydrological cycle. Model simulations suggest that the subtropics at ~30° became drier and that precipitation at 60° increased significantly (Fig. 6), which is consistent to future climate projections (IPCC, 2007). Over North America during summer, the Climate Change – Geophysical Foundations and Ecological Effects 50simulated total amount of rainfall decreases from lower to mid-latitudes in response to a northward-directed monsoonal moisture transport over the Mississippi watershed from the Gulf (Sewall & Sloan, 2006; Winguth et al., 2010). Sedimentary records from the mid-latitudes of the North American continent have produced conflicting evidence for hydrological changes in this region. For example, a ~25% increase in relative humidity for the northern continental mid-latitudes (Bighorn Basin, Wyoming, paleolatitude ~49 °N) has been inferred from an amplification in the carbon isotope excursion in soil organic matter (Bowen et al., 2004), but vegetation analysis inferred a decrease of ~40% in precipitation at the beginning of the PETM (Wing et al., 2005). Drier PETM conditions occurred probably in Utah (USA, paleolatitude ~45 °N; Bowen & Bowen, 2008); these findings are, however, controversial, since other studies (Retallack, 2005) suggest enhanced rainfall for this region (Bowen & Bowen, 2009; Retallack, 2009). Droughts by reduced soil moisture and biomass burning through wildfires in the subtropics during the PETM could have provided a significant carbon release to the atmosphere (Fig. 7). In western Europe, sedimentary records from the Spanish Pyrenees (Schmitz & Pujalte, 2007) indicate seasonally increased precipitation during the PETM, leading to enhanced runoff into the Tethys Ocean and thus enhanced productivity by a rise in nutrient availability in the near-shore areas (Schmitz et al., 1996; Speijer & Wagner, 2002; Gavrilov et al., 2003). Increased precipitation over England (~+1 mm/day change from 4xCO2 to 16xCO2) would also have generated a feedback on the carbon cycle, for example enhanced carbon emission from wetlands (Pancost et al., 2007). 3. Feedbacks associated with the PETM ocean circulation In this section, two feedback loops involving the carbon cycle and climate are discussed. The first is associated with the rise of greenhouse gas concentrations (water vapor, CO2, CH4, and other gases) in the atmosphere due to tectonic changes such as volcanism (Bralower et al., 1997; Kennett & Stott, 1991; Lyle et al., 2008; Sluijs et al., 2007; Storey et al., 2007; Svensen et al., 2004) and the second with the response of the climate system to regional or global sea level change by tectonic uplift and climatic changes (Fig. 2). Evidence of marine transgression during the PETM (Handley et al., 2011; Maclennan & Jones, 2006; Schmitz & Pujalte, 2003; Sluijs et al., 2008b) related to changes in spreading rate, volcanism, and regional perturbations as well as climatic changes (melting of glaciers, thermal expansion, and changes in ocean circulation) suggests that sea levels rose by approximately 20-30 m. The increase in surface temperatures and freshening of the sea surface by enhanced poleward moisture transport in response to a rise of greenhouse gases changes the regional buoyancy and momentum fluxes, leading to changes in vertical density gradients and stratification of the deep sea. Warmer and more saline subtropical water masses are modeled associated with the initial PETM warming (~0.2 psu higher in salinity for the 8xCO2 than for the 4xCO2 experiment), originating near the Gulf of Mexico and mixed via the eastern North Atlantic into intermediate layers. For intermediate water masses in the North Atlantic Ocean, simulated temperature rises by ~4°C (from ~11°C to ~15°C) (Fig. 8a). In the Pacific, the increase of the atmospheric CO2 to 8xCO2 results in an increase in the vertical density gradient, since surface waters become significantly lighter with the warming. Deep-sea temperatures increase by ~2.5°C due to the global warming (Fig. 8b). The simulated Pacific circulation in the 4xCO2 scenario is nearly symmetric about the equator, with deep-sea ventilation occurring in the polar regions of the northern and The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 51 southern hemisphere (Fig. 9a), in agreement with analyses of Nd isotope data that indicated a bimodal ventilation (D.J. Thomas et al., 2008). The northward-directed Atlantic deep-sea circulation of ~4 Sv (1 Sv = 106 m3 s-1) in the 4xCO2 scenario with a source of deep-water formation in the South Atlantic is comparable in strength with the modern but reversed. With an increase of the CO2-radiative forcing to 8xCO2, the ventilation of the deep sea is reduced and the age of water masses in intermediate depth is increased (Fig. 9b), particularly in the southern high latitudes. The Atlantic deep-sea circulation in the 8xCO2 scenario remains reversed, in agreement with Zeebe & Zachos (2007), who used inferred [CO32-] gradients in the deep sea, but in contrast with the reconstruction of an abrupt shift in the deep-sea circulation during the PETM to a North Atlantic deep-water source, based on benthic carbon isotope records (Nunes & Norris, 2006). a) b) PETM 8xCO2 - 4xCO2 Atlantic OceanPETM 8xCO2 - 4xCO2 Pacific Oceanc) d) PETM 16xCO2 - 8xCO2 Pacific Ocean PETM 16xCO2 - 8xCO2 Atlantic Ocean Fig. 8. Vertical sections of the potential temperature (50-year mean) differences of the 8xCO2 and 4xCO2 PETM experiments for the Pacific Ocean (a) and the Atlantic Ocean (b) for the beginning of the warming, and differences of the 16xCO2 and the 8xCO2 PETM experiments for the Pacific Ocean (c) and the Atlantic Ocean (d) (from Winguth et al., 2010). The warming of intermediate and deep-water masses could have had a positive feedback on the ocean circulation (Fig. 2), as proposed in Bice & Marotzke (2002). The warming of the ocean by changes in the buoyancy forcing (heat and freshwater fluxes) and circulation lowers the depth of methane hydrate stability, which depends on pressure, temperature, salinity, and gas composition, from ~900 m to ~1500 m (Fig. 10; Dickens et al., 1995). This change might have triggered a massive methane hydrate release into the atmosphere-ocean Climate Change – Geophysical Foundations and Ecological Effects 52system, which in turn accelerated the global warming (Archer & Buffett, 2005). The potential consequences of such an amplification are displayed in Figs. 8c and d, for the assumption that the carbon release corresponded to ~4400 PgC (16xCO2 experiment) relative to the 8xCO2 experiment. A temperature increase of >3.5˚C is simulated for high-latitude intermediate water masses in the Pacific due to an increase in vertical density gradients. The increase in the ideal age of water masses is shown in Fig. 9c. Fig. 9. Change of residence time of water masses in ~1800 m depth (idealized age in yrs) for a) 4xCO2 PETM simulation, b) 8xCO2 PETM simulation, and c) 16xCO2 PETM simulation (100-yr mean). Increase of idealized age corresponds to an increase in stratification. Locations of deep-sea ventilation are in the Northern and Southern Pacific. Thedeep-sea ventilation decreases particularly in the southern ocean with higher atmospheric CO2 radiative forcing. The feedback loop associated with the sea level changes at the PETM would have affected the oceans by an enhanced freshing from Arctic Ocean. An increased flow via the Turgay Strait, the passage between the Arctic and Tethys Ocean, has been inferred from the abundance of dinoflagellate cysts (e.g. Iakokleva et al., 2001). Higher sea levels might also have allowed a throughflow via the Fram and Bering Straits, as supported by Nd-Sr isotopes in fish fossils (Gleason et al., 2009; Roberts et al., 2009), paleogeographic reconstructions (Scotese, 2011) and climate simulations (Cope & Winguth, 2011; Heinemann et al., 2009). A freshwater input from the Arctic Ocean into the North Pacific Ocean (Marincovich & Gladenkov, 1999) would have produced an increase in the vertical density gradients and led to a weakening of the North Pacific intermediate water masses by 2.5 Sv at 30°N, and a comparable increase in the Pacific deep-sea circulation. The opening of the Bering Strait would have shifted formation of intermediate water masses in the North Pacific more equatorward towards the arid subtropics, by that increasing temperature and salinity of intermediate water masses (Fig. 11). Such a temperature change in intermediate waters at The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 53 the beginning of the PETM warming could have contributed to the release of methane hydrates (e.g. Kennett & Stott, 1991; Sluijs et al., 2007). Most of the methane released from the hydrates would have ultimately reached the atmosphere or oxidized as CO2 and thus increased the greenhouse gas radiative forcing during the PETM (Fig. 2). Fig. 10. Methane-hydrate temperature-depth (pressure) diagram from Dickens et al. (1995), adapted after Dickens & Quinby-Hunt (1994). The triple point (Q1) is the point where all three phases (methane and sea ice, methane and sea water, methane hydrates) meet. Above the triple point to the left are conditions where methane and sea ice and to the right where methane and seawater exist. The area below the curve denotes the conditions under which methane hydrates are stable (at present-day with bottom water temperatures of -1.5 °C and depths below 250 m). For the pre-PETM, the critical depth below which methane hydrates were stable was around 900 m. A 4°C water temperature increase at the PETM would have lowered the critical depth by ~600 m to ~1500 m. [Reproduced by permission of American Geophysical Union; copyright 1995 American Geophysical Union.] 4. Feedbacks associated with the atmospheric chemistry during the PETM Many potentially important feedback processes are associated with atmospheric chemistry (Beerling et al., 2007). Possible changes associated with clouds at the beginning of the PETM are for example cloud albedo, cloud optical depth, or heat transport by tropical cyclones. Clouds interfere with the transfer of radiation because they reflect a certain amount of radiation back to space and they act as a blanket for thermal radiation. The reflectivity of clouds is influenced by cloud condensation nuclei (CCN). While today’s major source for CCN over land is due to pollution, CCN concentrations over remote ocean areas are linked to marine productivity via dimethyl sulfide (DMS) emission from the ocean. DMS emitted from certain phytoplankton groups is mixed into the troposphere and is oxidized to sulfate particles, Climate Change – Geophysical Foundations and Ecological Effects 54which then act as CCN for marine clouds. The CCN concentration affects cloud droplet size and distribution, which influences cloud reflectivity and hence the climate. Climate change on the large scale, in turn, affects the ocean circulation, nutrient cycles and consequently the phytoplankton concentration in the oceans and thereby closes via DMS emission the feedback loop, as first hypothesized by Charlson et al. (1987). If global productivity had declined during the PETM by ocean stagnation and reduced equatorial upwelling, the concentration of CCN would also have been reduced. PETM 8xCO2 Bering Strait minus Turgay Strait Fig. 11. Vertical section of the potential temperature (50-year mean) difference of the 8xCO2 PETM simulation with a passage between the Arctic Ocean and the Pacific (Bering Strait) minus the simulation with a passage between the Arctic Ocean and the Indian Ocean. The changes in throughflow might have been caused by sea level rise due to tectonic and climatic changes (Fig. 2). A change of freshwater input from the Arctic might ultimately have caused warming of water masses and thus triggered a positive feedback loop between the climate and the carbon cycle. This would have affected the cloud optical depth (Kump and Pollard, 2008), leading to high-latitude warming and a further increase in ocean stratification and stagnation of the deep-sea circulation, probably similar to the one modeled in the 16xCO2 experiment by Winguth et al. (2010). Polar stratospheric clouds (Sloan & Pollard, 1998; Kirk-Davidoff et al., 2002) or intensified tropical cyclone activity (Korty et al., 2008) could have further exaggerated warming at the PETM. Another feedback between the carbon cycle and the climate that may have played an important role during the PETM are volatile organic compounds (VOCs; Beerling et al., 2007). VOCs are emitted by plants, for example isoprene with present-day emission rates comparable to that of methane (Guenther et al., 2006; Prather & Erhalt, 2001). Isoprene is a major player in the oxidative chemistry of the troposphere and influences the formation of tropospheric ozone (Fehsenfeld et al., 1992), decreases the hydroxyl radical concentration, increases the residence time of CH4, and is involved in forming organic aerosols influencing the climate by acting as CCN (Beerling et al., 2007). High CH4 emissions during the PETM could have increased the atmospheric methane concentration and enhanced radiative forcing (with a ~21 times higher global warming potential than CO2 over a time span of 100 years; IPCC, 1990). Methane in the atmosphere is typically either reduced by oxidation to CO2 or interacts with other chemical components. The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 55 Emission scenarios for the PETM considering atmospheric chemistry involving NOx and ozone reactions indicate that the life-time of methane in the atmosphere increases with increasing emission of methane, thus leading to an increased radiative forcing influencing the climate and methane hydrate destabilization within a positive feedback loop (Schmidt & Shindell, 2003). 5. Feedbacks associated with weathering during the PETM While the feedbacks listed in the previous sections illustrate the complexity of the PETM warming, the rapid recovery phase after the CIE remains controversial as well. Rapidly regrowing organic carbon stocks on land and in the ocean on climatic time scales 104 yrs), carbon sequestration by weathering of continental rocks becomes an important process. Atmospheric CO2 and H2O reacts with rocksalthough this isprobablythemostuncertainoftheeffectsofglobalwarming.Theseuncertaintieshighlighttheneedformoreresearchonhowglobaleventshaveeffectsatregionalandlocalscales,buttheyalsoindicatedtheneedforthesocietyatlargetoassumearisk‐freeapproachto avoid theworse effectsof climate change inour socio‐economical and ecologicalsystems(IPCC2007).Humanshavebeendealingwith risk‐relatedactivities fora long time.Forexample,whenbuyingacarorhomeinsurance,thediscussionisnotaboutwhethertheadverseeffectswillhappenornot,butonhow to reduce its effectsand recoverand if theyhappen.Inmanycountries,havingcarinsuranceiscompulsorytodriveacar,evenifonlyasmallpercentageofdriverssuffercaraccidentscomparedtothetotalnumberofcars.Inaddition,themostriskymanoeuvres(i.e.excessivespeed,notstoppingonredlight,etc.)arebanned toreduce therisksofaccidents.Similarly,developingpoliciesand practices that reduce andminimize the risks and effects of climate change areX Preface needed, even if theworse situationswill never happen. If not,wewill be in theequivalent of driving without insurance and without respecting the signals. Allpoliciesandpracticesforeconomic,industrialandnaturalresourcemanagementneedtobefoundedonsoundscientificfoundations.Thisvolumeoffersaninterdisciplinaryviewofthebiophysicalissuesrelatedtoclimatechange,andprovidesglimpseofthestate‐of‐the‐art research carried out around the world to inform scientists,policymakersandotherstakeholders.Anyscientificdisciplinelearnsfromexperience,andthescienceofclimatechangeisnotdifferent.Climatechange isdefinedasaphenomenonbywhich the long‐termaverages ofweather events (i.e. temperature, precipitation,wind speed, etc.) thatdefinetheclimateofaregionarenotconstantbutchangeovertime.Climateisalsothe result of very complex interactions between physical, chemical and biologicalvariables. As a result, at geological scales of time, climate is constantly goingthroughperiodsofrelativelystableconditionsfollowedbyperiodsofchange.Therehave been a series of past periods of climatic change, registered in historical orpaleoecologicalrecordsthatcanbestudiedfromdifferentgeophysicalvariables.Inthefirstsectionofthisbook,aseriesofstate‐of‐the‐artresearchprojectsexplorethebiophysicalcauses forclimatechangeandthe techniquescurrentlybeingusedanddevelopedfor itsdetection inseveralregionsoftheworld.Inthissection,Damerisand Loyola describe the interactions between physical, dynamical, and chemicalprocesses in Earth atmosphere. Manfredi et al. provide a new statisticalmethodology to study changes in historical climatic data.Winguth discusses thefeedbacksbetweenclimatechangeandbiogeochemicalcyclesduringthePaleocene‐Eocene Thermal Maximum. Historical and current changes in climatic andgeophysicalvariablescanbe foundaround theglobe. InNorthAmerica,Assanietal. study the temporal variability of rain‐induced floods in southern Quebec(Canada),whereasRamírez‐Beltránetal.showcaseastudy todetect thechange inclimatic conditions at global scale and in theCaribbean basin. In SouthAmerica,Hamzaetal.studiedthechangesinsoiltemperaturetodetectvariabilityinclimateforthe lastdecades,whilePenalbaandBettollianalyzethechanges inatmosphericcirculation and daily precipitation caused by climate change in the ArgentineanPampas. InAsia,Yistudies thepollenrecords inKorea toestablish thechanges inclimate during the Holocene, whereas Kim and Nam analyzing the records ofberylliumdeposits inthemarinesediment,andKyoungetal.discusstheeffectsofclimatechangeindroughtsintheKoreanpeninsula.Theknowledgeofpastchanges in theenvironmentwillbeofgreatvalue to try tounderstandwhatwill happen under future climatic conditionsdifferent from thecurrent ones. However, the effects of climate change on ecosystems around theworld are not something of the future. They are happening now all around theglobe.Ecologicalchanges in thephenologyanddistributionofplantsandanimalsare occurring in all aquatic and terrestrial ecosystems. Predator‐prey and plant‐Preface XI insect interactions have been disruptedwhen interacting species have respondeddifferentlytowarming(Parmesan2006).Thesecondsectionofthebookexplorestheeffectsthathavealreadybeenreportedonthefloraandfauna.Thesechangesaffectall types of ecosystems and creatures. In thePacificOcean, Salvadeo et al. reportchangesinthemovementofmarinemammalsalongtheNorthAmericancoast,andPovilitis examines the worrying situation of birds in Hawaii, threatened by thechanging climate. In Asia, the changes in Monsoon patterns could change thenumberofgenerationsofmosquitoes,asOhtaandKagareport.InEurope,Itämieset al. describe how the populations ofmoths have shifted in the last decades inFinland,whileRakonczai explores the ecological changes already observed in theCarpathians. InAfrica,Guo et al. study the consequence of changes in climaticpattersonquiver treesgrowthanddistribution.To finish thissection,Sternbergetal.discuss theuseofclimaticgradientsassimilesofclimatechangeandDrégelyi‐Kiss and Hufnagel provide a theoretical study on climate‐induced changes infreshwaterecosystems.Beingtheecosystemsmostpotentiallyaffectedbyclimatechange,thearcticandalpineregions are already experiencing some of themost noticeable and fastest changes.Range‐restricted species, particularly polar and mountaintop species, show severerangecontractionsandhavebeen the firstgroups inwhichentirespecieshavegoneextinct due to recent climate change (Parmesan 2006). The last section of the bookprovidessomeofthelatestresearchintheseecosystems.Mazepaetal.describesomeofthechangesdetectedinthestandstructureofforestsinarcticRussia.Benderetal.describe the researchbeingdone in theAlps related to climate change.Also in theAlps, Jomelli et al. study if avalanches are already being affected by the change inclimate,whereasBajracharyaetal. reporton the shrinkingofHimalayanglaciers inNepal.Ydeetal. review thedifferentenvironmentaleffects that climate change cancause in glacial ecosystems. Among these changes, the increasingmelt of glacierscould have important effects. Braithwaite explains the reasons for this trend, andFettweisetal.estimate the increase insea levelriseby themeltingofGreenland icesheet.All things considered, these 25 chapters provide a good overview of the differentchanges thathavealreadybeendetected inall theregionsof theworld.Theyareanintroduction to theresearchbeingdonearound theglobe inconnection to this topic.However,climatechangeisnotjustatheoreticalissueonlyimportantforscientistsorenvironmentalists.Italsohasdirectimplicationsinoursocio‐economicalsystems.Theothertwobooksofthisseries“Climatechange–Socioeconomiceffects”and“ClimateChange – Research and Technology for Adaptation andMitigation” explore thesetopicsindetail,andweencouragethereadertoand is converted into aqueous bicarbonate that is transferred to the oceans via riverine discharge and eventually deposited on the seafloor as biogenic carbonates (Walker et al., 1981; Berner, 2004). The hothouse climate during the PETM with an increase in precipitation and plant growth likely accelerated weathering. The associated large input of dissolved bicarbonates into the ocean would have neutralized the oceans’ acidity and led to post-CIE deepening of the lysocline (Zachos et al., 2005), and preservation of calcareous marine sediments (Fig. 12; Kelly et al., 2010). This negative weathering feedback would ultimately have led to a draw-down of atmospheric CO2, climatic cooling and reduced weathering. 6. Conclusive remarks The PETM, represented by the largest perturbation in climate and carbon cyle during the last 60 million years (Fig. 1; Pearson & Palmer, 2000; Royer et al., 2007) can be considered as an analog for future climate change. The analysis of ice bubbles trapped in the Antarctic suggests a variability of the atmospheric CO2 concentration over the last 800,000 yrs ranging from 172 ppmv to 300 ppmv for the preindustrial period. As a result of human activities, CO2 in the atmosphere rose over the last couple of hundred years with a pace not seen in recent geological history. In the year 2011, the atmospheric CO2 concentration exceeded 390 ppmv (Tans & Keeling, 2011), and a doubling of the pre-industrial atmospheric CO2 level is expected by the end of this century. The climate sensitivity for this doubling in CO2 is estimated to be 1.9–6.2 K due to the positive forcings, i.e. the rise in greenhouse gases, and including the negative forcing arising from the cooling effects of aerosols (IPCC, 2007; Andreae, 2007). A release of ~2000 PgC into the atmosphere in the next couple of hundred years could eventually trigger the release of an additional 2000-4000 PgC from marine sediments (Archer & Buffett, 2005), a flux comparable to that observed at the PETM (Zachos et al., 2008) and more than 10 times higher than observed during the last million years. The additional carbon release would act as a positive feedback, accelerating the warming. Climate Change – Geophysical Foundations and Ecological Effects 56 Fig. 12. Schematic changes in the carbonate–silicate geochemical cycle associated with the PETM (from Kelly et al., 2010). a) A rapid release of massive amounts of carbon into the ocean–atmosphere-biosphere system raises atmospheric pCO2 levels, increases the carbon flux into the oceanic reservoir, thus raises the calcium carbonate compensation depth (CCD), and reduces the biogenic calcification. Preservation of carbonates is restricted to shallow areas on the seafloor. b) In the recovery phase of the atmosphere–ocean-biosphere system, silicate weathering is accelerated, reducing the atmospheric partial pressure of CO2 and increasing the flux of dissolved bicarbonate ions and silicic acid to the ocean, thus neutralizing ocean acidification, and leading to a deepening of the CCD and preservation of carbonates in deeper areas on the seafloor. [Reproduced by permission of Elsevier; copyright 2010 Elsevier.] The climatic and biogeochemical response to remarkable carbon emissions would likely be severe, for example a more frequent occurrence of climate extremes (heat waves, droughts and floods), particularly over the continents and at high latitudes, as well as ocean warming and stagnation. Another likely effect is ocean acidification and a rise of the calcite dissolution depth (Zachos et al., 2005), affecting marine organisms with calcareous shells (E. Thomas, 1998, 2003, 2007). The increased vertical gradients in the ocean together with warmer temperatures might produce near-anoxic conditions in the oxygen minimum zone The Paleocene-Eocene Thermal Maximum: Feedbacks Between Climate Change and Biogeochemical Cycles 57 (comparable with dead zones in the Black Sea or the Gulf of Mexico). Geochemical evidence for the PETM supports a downward expansion of the oxygen-minimum zone below 1500 m (Chun et al., 2010; Nicolo et al., 2011) in agreement with foraminiferal evidence. 7. Acknowledgments All model simulations were done on NCAR computers, supported by NSF. The work is supported by NSF Grant EAR-0628336. 8. References Andreae, M.O., 2007: Atmospheric aerosols versus greenhouse gases in the twenty-first century. Phil. Trans. R. Soc. London, A, 365, 1915–1923. doi:10.1098/rsta.2007.2051. Archer, D., & Buffett, B., 2005: Time-dependent response of the global ocean clathrate reservoir to climatic and anthropogenic forcing. 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Zeebe, R.E., Zachos, J.C., & Dickens, G.R., 2009: Carbon dioxide forcing alone insufficient to explain Palaeocene-Eocene Thermal Maximum warming. Nature Geoscience, 2, 576-580. 4 Temporal Variability of Rain-Induced Floods in Southern Quebec Assani Ali Arkamose, Landry Raphaëlle, Quessy Jean-François and Clément Francis Université du Québec à Trois-Rivières, Québec Canada 1. Introduction Although the impact of climate warming on streamflow in general and on floods in particular is a much-debated topic (e.g. Koutsoyiannis et al., 2008; Kundzewicz et al., 2008; Räisanen, 2007; Sun et al., 2007), there is general agreement about the geographical variability of these changes. In Quebec, a region characterized by a temperate, continental- and maritime-type climate, a consensus is forming in light of results of climate and hydrological modeling regarding the effects of climate warming on flood magnitude. These effects will depend on the season and the underlying cause of floods. Thus, whereas climate and hydrological models predict a decrease in the magnitude of spring floods resulting from snowmelt (freshets) due to a decrease in the amount of snow falling in winter, they also predict an increase in the magnitude of rain-induced floods as a result of increasing rainfall intensity during summer and fall. Thus, Roy et al. (2001) predicted a significant increase in the intensity of heavy precipitation (20 and 100-year recurrence intervals) which will result in a much greater increase in the magnitude of summer floods. In the Châteauguay River, for instance, peak flow for a flood induced by a 20-year rainfall event will double or triple, depending on initial soil moisture conditions, by the end of the century. However, according to Zhang et al. (2000), no significant increase in rainfall intensity has been observed in Quebec or Canada over the past century, which would explain the absence of any significant change in the interannual variability of the magnitude of rain-induced floods observed in many regions of Canada (Cunderlik & Ouarda, 2009). While Assani et al. (2011) have shown that the amount of rainfall from August to November has significantly increased in southeastern Quebec, south of parallel 46oN on the South Shore of the St. Lawrence River, no study has looked at the impact of this increase in rainfall on the interannual variability of rain-induced floods. Analysis of the interannual variability of snowmelt-induced spring floods (spring freshets) has revealed no generalized significant decrease in their magnitude (Assani et al., 2010), despite a recorded increase in temperature since the 1970’s in Quebec. On the contrary, a significant increase in the magnitude of spring floods on the North Shore of the St. Lawrence is recorded from 1934 to 2000, which is thought to result from the continental nature of climate in this region rather than from increasing temperature. Assani et al. (2010) have also shown that the interannual variability of snowmelt-induced spring floods is significantly correlated with the AMO climate index on the North Shore, and with the SOI and AO climate indices on the South Shore, north and south of parallel 47oN, respectively. Climate Change – Geophysical Foundations and Ecological Effects 66In Quebec, rain-induced floods can sometimes be more devastating than snowmelt-related floods. For instance, in July 1996, the Saguenay-Lac-Saint-Jean area of central Quebec, north of the St. Lawrence River, was the scene of a series of floods which caused more than 1 billion dollars in damage and 10 fatalities. More recently, in the Rivière-au-Renard area of eastern Quebec, south of the St. Lawrence, floods caused several million dollars in damage and two deaths in August 2007. These events led to speculation about the likely increase in the magnitude of rain-induced flood due to climate warming, as predicted byclimate and hydrological models. To see whether or not these speculations are supported by observational data, the following two main goals were set for this study: 1. Analyze the interannual variability of the magnitude of rain-induced floods in different regions of Quebec, to see whether this magnitude increased significantly over time, particularly in the southeastern part of the province, south of parallel 46oN on the South Shore of the St. Lawrence River, where the amount of rainfall has increased over time. 2. Determine which climate factors (climate indices) affect the interannual variability of the magnitude of rain-induced floods, in order to see if snowmelt-induced (spring) floods and rain-induced floods are affected by the same climate indices. 2. Methods 2.1 Streamflow data sources and selection of climatic indices The streamflow data come from Environment Canada’s Hydat CD-ROM (2004). Eighteen rivers for which the daily streamflows are measured continuously over a relatively long period wherever possible were selected (table 1 and fig.1). To be able to analyze a greater number of stations, we delimited the study period between 1934 and 2004. Streamflow in these rivers is not affected by the presence of dams. To constitute the seasonal maximum flow series for each year, we selected the highest daily average flow measured in the period from July to October, as this is the period with the highest frequency of rain-induced floods in Quebec. Several authors have already analyzed the relationship between the climatic indices and streamflows in Québec, Canada and North America. For instance, in Québec, Anctil and Coulibaly (2004) observed a positive correlation between the annual average flows and the PNA (Pacific-North America) and AO indices in the St. Lawrence watershed during the 1938-2000 period. Apart from these two indices, Déry and Wood (2004, 2005) also observed a correlation between streamflows and the PDO (Pacific Decadal Oscillation), the NINO3.4 and SOI (Southern Oscillation index) indices in the other two major Québec watersheds (Hudson Bay and Ungava Bay) during the 1964-2000 period. On the scale of Eastern Canada, a correlation was observed between NAO (North Atlantic Oscillation) and the river flows (see Kingston et al., 2006). On the scale of the North American continent, AMO and PDO are correlated to the annual average flows in many regions of the United States (Enfield et al., 2001; McCabe et al., 2004). Curtis (2008) observed a correlation between AMO and heavy summer rains in the United States and Mexico. At the daily scale, Assani et al. (2010) noted a significant correlation between maximum spring flows and AMO on the North Shore, and AO and ENSO on the South Shore. In this study, we correlated the annual maximum flows to all the climatic indices already correlated to streamflows: AMO, AO, NAO, NINO3.4, PDO and SOI. The data for the AMO, SOI, NINO3.4 and PDO indices are taken from the following websites: http://www.cdc.noaa.gov/ClimateIndices/List. (2007-10-08), and NAO is taken from http://www.cgd.ucar.edu/cas/jhurrell/indices.data.html (2006-10-08), and Temporal Variability of Rain-Induced Floods in Southern Quebec 67 AO is taken from http://jisao.washington.edu/data/ao/ (2006-10-08). For each climatic index and for each year, we first derived the seasonal mean of the monthly indices over four months (April-July, May-August, June-September, July-October, and August-November), then over three months (April-June, May-July, June-August, July-September, August-October, and September-November). Fig. 1. Location of stations grouped in four modes derived from principal component analysis. 2.2 Statistical data analysis 2.2.1 Interannual variability modes of seasonal daily maximum flows The first step was to apply principal components analysis (PCA) to the seasonally maximum flow (August to November) data measured at different stations (e.g. Hannachi et al., 2007). We chose this method for comparison with the results of previous work (e.g.Anctil & Coulibaly, 2004; Assani et al., 2010). Moreover, it is widely used in hydroclimatology to analyze the influence of climate factors on the temporal variability of precipitations and streamflows (e.g., McCabe et al., 2004; Vicente-Serrano, 2005). To determine the number of mode patterns (temporal variability modes of annual maximum flow), principal component analysis (mode S) was applied to the correlation matrix (and not to the covariance matrix), based on the correlations calculated between the seasonal flows measured at the different (individual) stations in order to eliminate the influence of extreme values (Bigot et al. 1997), and the effect provoked by a site’s local variability (Siew-Yan-Yu et al. 1998). Thus, we analyzed a matrix consisting of 71 lines (number of years of streamflow measurement, from 1934 to 2004) and 18 columns (number of rivers analyzed). We applied the Varimax rotation method to maximize the saturation values of the stations on the principal components and obtain more stable and physically more robust mode patterns. Climate Change – Geophysical Foundations and Ecological Effects 68No River Area (km²) Latitude (N)Longitude (W) MAF (m³/s) 1 Richelieu 22000 45°18’ 73°15’ 327 2 Chateaugay 2500 45°17’ 73°48’ 26.0 3 Eaton 642 45°28’ 71°39’ 8.6 4 Etchemin 1130 46°38’ 71°02’ 28.5 5 Nicolet de sud-ouest 544 45°47’ 71°58’ 8.4 6 Beaurivage 709 46°39’ 71°17’ 10.5 7 Du Sud 826 46°49’ 70°45’ 14.7 8 Ouelle 802 47°25’ 69°56’ 15.6 9 Du Loup 1050 47°49’ 69°31’ 15.4 10 Trois-Pistoles 932 48°05’ 69°11’ 13.4 11 Rimouski 1610 48°24’ 68°33’ 26.5 12 Matane 826 48°46’ 67°32’ 33.4 13 Blanche 208 48°46’ 67°39’ 4.8 14 De La Petite Nation 1330 45°47’ 75°05’ 19.1 15 Du Nord 1170 45°47’ 74°00’ 21.5 16 L’Assomption 1340 46°00’ 73°25’ 23.5 17 Matawin 1390 46°41’ 73°54’ 22.4 18 Vermillon 2670 47°39’ 72°57’ 37.2 MAF = Mean annual flow Table 1. Rivers analyzed The rationale for this maximization is the fact that the criterion used for grouping rivers into modes or homogeneous hydrological regions is based on the values of loadings of rivers on the significant principal components using the "maximum loading" rule. According to this rule, a station is associated with a significant principal component if its loading value on this component is larger than on other components (Vicente-Serrano, 2005). Thus, all stations for which the loadings values are largest on a given principal component define a variability mode or homogeneous hydrological region. However, this loading value must be statistically significant, and since it is not possible rigorously to test a loading value using a statistical test, the correlation between streamflow in each river and the scores of each significant principal component was calculated. This correlation, whose value corresponds to the loading value of the river on a significant component, was thus tested using Student’s t test (Assani et al., 2010). A river is therefore correlated to a principal component if its loading value, whose significance has been indirectly tested using the corresponding correlation coefficient, is statistically significant at the 5% level. The Kaiser (1960) criterion based on the eigenvalues of principal components was used to determine the number of significant components, since any principal component with eigenvalue equal to or larger than 1 is considered significant. The grouping of the stations in temporal variability modes was based on “the maximum loading rule”. According to this rule, a station is associated with a principal component when the value of its loading is higher on this component than on the others (Vicente-Serrano, 2005). Temporal Variability of Rain-Induced Floods in Southern Quebec 69 2.2.2 Analysis of the interannual variability of the magnitude of rain-induced seasonal daily maximum flows To test the stationarityof the temporal modes, we first applied the regression method (a parametric test) to the scores of the significant principal components (Kundzewicz et al., 2005). However, because it is not possible, using this method, to determine the exact date of a shift in the mean of a hydrologic series, nor whether this shift is abrupt or progressive, the Lombard (1987) method was used to derive these two parameters (date and type of shift). Suppose we have a series of observations, noted 1 ,..., ,nX X where Xi is the observation taken at time .T i these observations are supposed to be independent. One question of interest is to see whether the mean of this series has changed. If i refers to the theoretical mean of ,iX then a possible pattern for the mean is given by Lombard’s smooth-change model, where 1121 2 12 1( ) ( )ii TT T   , 11 221 ;;.if i Tif iT Tif i nT    (1) In other words, the mean changes gradually from 1 to 2 between times 1T and 2T . As a special case, one has the usual abrupt-change model when 2 1 1.T T  In order to test formally whether the mean in a series is stable, or rather follows model (1), one can use the statistical procedure introduced by Lombard (1987). To this end, define iR as the rank of iX among X1,…Xn. Introduce the Wilcoxon score function   2 1u u   and define the rank score of Xi by  1, 1,..., ,1iiR i nZn          (2) where 111niin n       and 22111niin n        (3) Lombard’s test statistic is 1 21 2 11251 11,n nn T TT T TS Ln     (4) where 21 21,1 1jTiT Tj iTL Z     (5) At the 5% level of significance, one concludes that the mean of the series changes significantly according to a pattern of type (1) whenever nS > 0.0403. Note that the test is suitable for the detection of all kinds of patterns in equation (1), including abrupt changes. A Climate Change – Geophysical Foundations and Ecological Effects 70complete investigation of the power and robustness of nS and of five other test statistics proposed by Lombard is given in Quessy et al. (2011). 2.2.3 Comparison of the magnitude of rain-induced floods with the magnitude of two- and five-year floods To determine the extent of a potential change in the magnitude of rain-induced floods, this magnitude was compared with that of annual floods with a 2-year recurrence interval in each watershed. Two-year flood flows were estimated from annual series of measured daily maximum flows for each watershed using the regional approach developed by Anctil et al. (1998). This method is based on the regionalized law of general extreme values (GEV). This estimate was produced follows: - First, we calculated the quantiles (QR) corresponding to the two-year recurrence by means of the formulas developed by Anctil et al. (1998) in the natural homogeneous hydrologic regions. These have been defined in Québec by means of the Hosking and Wallis method. The following equations were used:  RBQ  (6) ( 1)1 lnTBT         (7) where T is the return period; ,  and  respectively are the shape, location and scale parameters of the standardized parameters of the regional GEV distribution. These parameters are estimated by means of the L-moments method, for which the values were calculated by Anctil et al.(1998) in the natural homogeneous hydrologic regions defined in Québec. - Finally, we estimated the two-year recurrence quantile (Q2) downstream from the dams by means of the following equation: 2 R mQ Q Q (8) Qm is the mean of daily maximum flows for a given river, derived from an annual series compiled from daily flow data measured from October (yr-1) to September of each hydrological year. Using equation 9, it is possible to compare the intensity of the magnitude of rain-induced floods with that of snowmelt-induced floods. 2.2.4 Analysis of the relationship between the annual maximum flow and the climatic indices The relationship between the seasonal maximum daily flows and the climatic indices was calculated by means of canonical correlation analysis (CANCOR). Compared to other methods of multivariate analysis, CANCOR takes into account both intra-group relationships and the cross correlations between variables of two groups. Indeed, it creates factors (linear transformations of variables, commonly called canonical variables) in the first group (dependent variables) simultaneously to factors in the second group (independent variables). It requires those factors to be orthogonal to each other within the same group, so Temporal Variability of Rain-Induced Floods in Southern Quebec 71 they are interpreted as independent dimensions of the phenomenon expressed by a group of variables. Thus, the canonical analysis helps to maximize the correlation between the first factor of the first group and the first factor of the second group than between the second factors of the two groups, each one considered orthogonal to the two factors of the first pair, than between the third factors and so on. Each pair of factors expresses a type of relationship between variables in both groups. The intensity of the relationship of a determined type is measured by a canonical correlation coefficient, which is the correlation coefficient between the factors of the same pair. This method allows simultaneous correlation of several dependent variables (streamflows) and several independent variables (climatic indices). It is widely used in climatology (e.g., Chen & Chen, 2003; Dukenloh & Jacobett, 2003; Repelli & Nobre, 2004). The two correlation methods were calculated between the annual climatic indices and the principal components scores (McCabe et al., 2004). 3. Results 3.1 Modes and long-term trend of the variability of rain-induced maximum seasonal flows in Quebec Using principal component analysis, it was possible to group the 18 rivers into four variability modes each defined by a statistically significant principal component (Fig. 1 and Table 3). The first principal component (East-Central Mode) is correlated with rivers located between 45°30’N and 48°N on the South Shore. The second principal component (Southwest Mode) is correlated with all rivers located on the North Shore. And the last two principal components (East and Southeast Modes) are correlated with rivers on the South Shore located respectively north of 48°N and south of 45°30’N. The total variance explained by the four components exceeds 70%. The interannual variability of the principal component (PC) scores is shown in Figure 2, and linear regression and Lombard method results are summarized in Tables 3 and 4. Recall that the interannual variability of PC scores reflects the interannual variability of streamflow in rivers with which the principal components are significantly correlated. A statistically significant increase in streamflow is only observed in the Southeast Mode, south of 45°30’N on the South Shore (Figs. 2-3 and Tables 3 and 4). However, analysis of regression results reveals a significant decrease in the interannual variability of the scores of the first principal component, which is correlated with rivers located between 45°30’N and 48°N (East-Central Mode), and a significant increase in the interannual variability of the scores of the second principal component (Southwest Mode). However, the Lombard method could not confirm these changes in principal components I and II (PC I and II). Hence, the above results do not point to any generalized, province-wide increase in the interannual variability of the magnitude of rain-induceddaily maximum flows. To determine the extent of the increase observed in the Southeast Mode, the number of times the two-year flood flow calculated from an annual daily maximum flow series was reached or exceeded was determined for the four modes (Table 5). The two-year flood flow was reached or exceeded in 50% of the watersheds, and analysis of its geographical distribution reveals that the two-year flood magnitude is attained more frequently on the South Shore south of 48°N (reached or exceeded in 7 out of 8 watersheds) than elsewhere. Climate Change – Geophysical Foundations and Ecological Effects 72N° Rivers PCI PCII PCIII PCIV Southeast Mode1 Richelieu 0.040 0.145 0.055 0.848 2 Chateaugay 0.074 0.202 0.116 0.848 3 Eaton 0.420 0.202 0.070 0.533 East-Central Mode4 Etchemin 0.876 0.170 0.108 -0.089 5 Nicolet de sud-ouest 0.766 0.162 -0.156 0.386 6 Beaurivage 0.721 0.175 0.210 0.067 7 Du Sud 0.652 0.081 0.235 0.127 8 Ouelle 0.689 0.098 0.389 -0.001 9 Du Loup 0.710 0.112 0.509 0.015 East Mode10 Trois-Pistoles 0.377 0.068 0.818 -0.073 11 Rimouski 0.310 0.240 0.814 0.025 12 Matane 0.132 0.218 0.727 0.157 13 Blanche 0.031 0.092 0.753 0.108 Southwest Mode14 De La Petite Nation 0.023 0.829 0.140 0.317 15 Du Nord 0.320 0.816 0.053 0.133 16 L’Assomption 0.262 0.804 0.172 0.152 17 Matawin 0.082 0.890 0.166 0.180 18 Vermillon 0.092 0.772 0.145 -0.041 Explained variance (%) 22.2 20.7 17.2 11.8 The higher values of Rivers loadings on PCs show in the bold. Table 2. Principal components Loadings of Rivers PC (Mode) a b R² Fc PC I (East-Central Mode) -0.013 25.28 0.070 5.194 PC II (Southwest Mode) 0.011 22.19 0.0541 3.946 PC III (East Mode) 0.007 13.29 0.0194 1.365 PCIV (Southeast Mode) 0.019 36.63 0.148 11.986 a = slope of the curve; b = y-intercept; R² = coefficient of determination; Fc = value of the Fisher-Snedecor test statistic. Fc values which are statistically significant at the 95% level are shown in bold. Table 3. Regression parameters for curves fitted to the factorial scores of the principal components Temporal Variability of Rain-Induced Floods in Southern Quebec 73 Fig. 2. Interannual variability of the Principal components scores (1934-2004). PCI = East-Central Mode (blue curve); PCII = Southwest Mode (red curve); PCIII = East Mode (red curve); PCIV = Southeast Mode (black curve); Principal Components (Mode) Sn Year of change PC I (East-Central) 0.0257 - PC II (Southwest) 0.0281 - PC III (East) 0.0132 - PC IV (Southeast) 0.1152 1958 The value of Sn > 0.043, shown in bold, is statistically significant at the 95% level. Table 4. Analysis of the interannual variability of PC scores (1934-2004). Lombard test results. 3.2 Relationship between climate indices and streamflow (PC scores) Results of the canonical analysis of correlations are shown in Table 6. Each of the principal components, which represent the variability of streamflow in the four modes, is correlated with a canonical variable. This reflects the fact that the principal components are independent from one another, the first one being correlated with V3, the second, with V2, the third, with V4 and the last, with V1. As for climate indices, only quarterly indices derived from the means of the September to November indices show a significant correlation with principal components. The AMO index is correlated with the canonical variable W1. Since the V1 and W1 canonical variables are correlated, AMO is correlated with the last principal component, which represents streamflow variability (PC scores) in the Southeast Mode. This correlation is negative. AO is positively correlated with the second principal component (PC II), which encompasses rivers on the North Shore (Southwest Mode). The SOI climate index is correlated with the third principal component (PC III) linked to rivers located north of 48oN. Finally, the first principal component is not significantly correlated with any climate index. As for explained variance, it is larger for Climate Change – Geophysical Foundations and Ecological Effects 74canonical variables correlated with principal components than for those correlated with climate indices. No River Qmax Q2-year (m³/s) Fr Southeast Mode 1 Richelieu 896 865 0 2 Châteauguay 418 403 3 3 Eaton 183 177 2 Center-east Mode 4 Etchemin 253 244 2 5 Nicolet 142 137 2 6 Beaurivage 179 173 0 7 Du Sud 246 237 1 8 Ouelle 119 112 3 9 Du Loup 170 160 0 East Mode 10 Trois-Pistoles 212 200 0 11 Rimouski 268 252 0 12 Matane 377 355 1 13 Blanche 41 38 4 Southwest Mode 14 De La Petite Nation 82 79 0 15 Du Nord 191 184 0 16 L’Assomption 155 150 2 17 Matawin 142 137 0 18 Vermillon 224 216 0 Qmax = mean of annual daily maximum flows (October (yr-1) to September) calculated for the 1934-2004 interval; Q2 = two-year flood flow estimated from the annual series using the regional method; Fr= number of times Q2 was reached or exceeded from August to October during the 1934-2004 interval. Table 5. Number of times (Fr) the two-year annual flood flow (estimated using the regional method) was reached or exceeded in the various watersheds, from 1934 to 2004. Temporal Variability of Rain-Induced Floods in Southern Quebec 75 Fig. 3. Interannual variability of the fall daily maximum flow of a few river. a = Nicolet Sw (blue curve) and Etchemin (red curve) rivers in Center-East Mode; Rimouski (blue curve) and Trois-Pistoles (red curve) Rivers in East Mode; Châteaugay (blue curve) and Richelieu (red curve) Rivers in Southeast Mode; De la Petite Nation (blue curve) and Matawin (red curve) Rivers in Southwest Mode Climate Change – Geophysical Foundations and Ecological Effects 76Variables V1 V2 V3 V4 W1 W2 W3 W4 PC I -0.127 0.440 0.862 0.286 PC II 0.528 0.840 -0.314 -0.054 PC III -0.371 0.142 -0.410 0.821 PC IV 0.873 -0.408 0.160 0.291 AMOfall -0.864 0.388 0.435 -0.236 AOfall 0.316 0.832 -0.363 0.369 NAOfall -0.249 0.197 0.032 0.187 PDOfall 0.477 0.035 0.429 -0.599 SOIfall -0.028 -0.195 0.328 0.884 EV (%) 29.9 27.0 25.9 20.4 22.7 18.4 12.3 26.0 EV = explained variance. The higher values of coefficient of correlation show in bold. Table 6. Correlation between the principal components and canonical variables (V), and correlation between climatic indices and canonical variables (W). 4. Discussion and conclusion In light of the interannual variability of snowmelt-induced floods (Assani et al., 2010), analysis of the interannual variability of rain-induced floods in Quebec during the period from 1934 to 2004 led to four significant results: (i) The 18 rivers analyzed were grouped into four modes: one on the North Shore and three on the South Shore, the latter being located south of 45°30’N, between 45°30’N and 48°N, and north of 48°N, respectively. For spring snowmelt-induced floods, the same rivers defined three modes: one on the North Shore and the other two on the South Shore, on either side of parallel 47oN. The effect of local factors on the origin of floods could account for the presence of an extra mode for rain-induced floods. Thus, rain-induced floods may be caused by three factors: summer storms resulting from convective motion (convective rainfall), polar front-induced rainfall (frontal rainfall), and rainfall caused by other tropical cyclones in the Atlantic basin. Local factors have a stronger effect on convective rainfall than on the other two types of rainfall. However, because the necessary data were not available, the effect of each of the above three factors on flood genesis in Quebec could not be quantified. In springtime, floods are almost exclusively caused by snowmelt. As such, the effect of local factors on snowmelt-induced flood is limited. (ii) Analysis of the interannual variability of streamflow only revealed a significant increasein the southeast, south of 45°30’N on the South Shore (Southeast Mode). Two factors may account for this increase: - An increase in agricultural surface area, as this is a region of Quebec in which agricultural lands make up more than 20% of all watersheds. This high proportion of farmland could lead to significant runoff which, in turn, would result in an increase in flood magnitude over time. However, since Muma et al. (2011) showed that in increase in agricultural surface area in a watershed has no impact on the magnitude of rain-induced flood flows in Quebec, this factor cannot account for the increase in magnitude of flows observed over time in the region. Temporal Variability of Rain-Induced Floods in Southern Quebec 77 - An increase in precipitation. Analysis of the interannual variability of seasonal precipitation from August to November revealed a significant increase in the amount of rainfall south of parallel 46°N from 1950 to 2000 on the South Shore, as shown in Figure 3 for a number of stations in the Richelieu and Châteauguay watersheds. This increase is thought to be the main cause of the increase in rain-induced flows observed in that part of the province, and could also account for the higher frequency of attainment and/or exceedance of the two-year annual flood magnitude in the Southeast Mode than in the other three modes. Fig. 5. Interannual variability of August to November precipitation over the period from 1950 to 2000 at two stations in the Châteauguay and Richelieu rivers watersheds, south of 45°30’N. Les Cèdres station: blue curve; Magog station : red curve (iii) Analysis of the relationship between rain-induced flood flows and climate indices revealed a correlation between the interannual variability of flood flows and climate indices in three modes. In the Southeast Mode, located south of 45°30’N and characterized by a significant increase in streamflow and precipitation, flood flows show a negative correlation with the AMO index; in the Southwest Mode, flows are positively correlated with the AO index; and in the East-Central Mode, flows are negatively correlated with AO. For snowmelt-induced spring flows, AMO is negatively correlated with streamflow in North Shore rivers (Southwest Mode), this mode being characterized by a significant increase in spring flood flows over the 1930-2000 interval. AO is negatively correlated with streamflow in South Shore rivers located south of 47°N (Southeast and East-Central Modes) and SOI is positively correlated with streamflow in rivers located north of 47oN (Assani et al., 2010). This comparison leads to the conclusion that rain- and snowmelt-induced floods are not correlated with the same climate indices in the Southeast and Southwest Modes. However, it also shows that AMO is correlated with rain-induced (Southeast Mode) and snowmelt-induced (Southeast Mode) floods characterized by a significant increase in flow over time in Quebec. The effect of these three indices (AMO, AO, and SOI), which show a significant correlation with streamflow in the three modes, on the interannual variability of streamflow in Quebec Climate Change – Geophysical Foundations and Ecological Effects 78has been described by Assani et al. (2010). AMO is correlated negatively to precipitation and streamflow in many regions of North America (e.g. Curtis, 2008; Enfield et al., 2001; McCabe et al., 2004). In fact, positive values of the index (positive phase) coincide with a decrease in precipitation and streamflow in many regions of North America in general, and in Quebec in particular, whereas negative values (negative phase) of the index are associated with an increase in precipitation and streamflow. During a positive AMO phase, more frequent changes in the circulation and shear of the westerly and a weakening of cyclonic activities and transfer of water vapour from the Atlantic Ocean to the continent are observed. These factors trigger a decrease in precipitation and streamflow in Quebec. As for AO, it is positively correlated with rain-induced flood flows in Quebec. According to the scheme proposed by Kingston et al. (2006), when AO is in positive phase (high values), an increase in SSTs (surface ocean temperatures) is observed (more northerly Gulf Stream position), along with a reduced influence of the East Coast trough. As a result, the frequency of southerly airflow increases, and storm tracks coincide with the coast more often. Thus, streamflow increase in Québec. Finally, the influence of ENSO would lead to an increase in atmospheric humidity and cyclonic activities in the region during El Niño episodes. These two factors are responsible for an increase in summer and winter precipitation in Québec. This study shows that, in the region characterized by a significant increase in rainfall, the magnitude of flood flows has significant increased. Moreover, the frequency of flows larger than the two-year annual flood flow has also increased in the watersheds. These findings confirm climate model predictions about the impact of climate warming on the intensity of rain-induced floods in Quebec. However, this increase is not a generalized, province-wide phenomenon. 5. References Anctil, F., Martel, F., Hoang, V.D. (1998). Analyse régionale des crues journalières de la province du Québec. Canadian Journal of Civil Engineering, Vol. 25; pp.125–146, ISNN 0315-1468 Anctil, F. & Coulibaly, P. (2004). Wavelet analysis of the interannual variability in Southern Québec Streamflow. Journal of climate, Vol. 17, pp. 163-73, ISNN 0894-8755 Assani, A.A., Charron, S., Matteau, M. & Mesfioui, M. (2010b) Temporal variability modes of floods for catchements in the St.lawrence Watershed (Quebec, Canada). Journal of Hydrology, Vol. 385, pp.292-299, ISNN 0022-1694 Assani, A.A., Landry, R. & Laurencelle, M. (2011) Comparison of interannual variability modes and trends of spring streamflow, fall precipitation and streamflow, and winter streamflow in southern Québec (Canada). Rivers Research and Application (accepted), ISNN 1534-1459 Bigot, S., Camberlin, P., Moron, V. & Richard, Y. 1997. Structures spatiales de la variabilité des précipitations en Afrique : une transition climatique à la fin des années 1960 ? Comptes Rendus de l’Académie des Sciences, Paris, série II a, Vol. 324, pp.181-188. Chen, D. & Chen, Y. (2003). Association between winter temperature in China and upper air circulation over East Asia revealed by canonical correlation analysis. Global and Planetary change, Vol.37, pp.315-325, ISNN: 0921-8181 Cunderlik, J.M. & Ouarda, T.B.J.M. (2009). Trends in the timing and magnitude of floods in Canada. Journal of Hydrology,Vol. 375, pp.471-480, ISNN: 0022-1694 Temporal Variability of Rain-Induced Floods in Southern Quebec 79 Curtis, S. (2008). The Atlantic multidecadal oscillation and extreme daily precipitation over the US and Mexico during the Hurricane season. Climatic Dynamics, Vol.30, pp.343-351. Déry, S.J. & Wood, E.F. (2004). Teleconnection between the Arctic Oscillation and Hudson Bay river discharge. Geophysical Research Letters, Vol. 31: LI18205, doi: 1029/2004GL020729, ISNN 0094-8276 Muma, M., Assani, A.A., Landry, R., Quéssy, J-F. & Mesfioui, M. (2011). Effects of the change from forest to agriculture land use on the spatial variability summer extreme daily flow characteristics in Southern Quebec (Canada). Journal of Hydrology, ISNN: 0022-1694 (Accepted) Déry, S.J. & Wood, E.F. (2005). Decreasing river discharge in northern Canada. Geophysical Research Letters, Vol. 32: L10401, doi: 10.1029/2005GL022845, ISNN 0094-8276 Dunkeloh, A. & Jacobett, J. (2003). Circulation dynamics of Mediterranean precipitation variability 1948-98. International Journal of Climatology, Vol. 23, pp.1843-1866, ISNN 0899-8418. Enfield, D.B., Mestas-Nuñez, A.M. & Trimble, P.J. (2001). The Atlantic multidecadal oscillation and its relationto rainfall and river flows in the continental US. Geophysical Research Letters, Vol. 28, pp.2077-2080, ISNN 0094-8276 Environment Canada. (2004). Données sur les débits de rivières. Province du Québec, CD-Rom, Ottawa (Canada). Hannachi, A., Jolliffe, I.T. & Stephenson, D.B. 2007. Empirical orthogonal function and related techniques in atmospheric science: a review. International Journal of Climatology, Vol.27, pp.1119-1152, ISNN 0899-8418 Kaiser, H.F. (1960) The application of electronic computers to factor analysis. Educational and Psychological Measurement, Vol. 20, pp. 141-151, ISNN 0013-1644 Kingston, D.G., Lawler, D.M. & McGregor GR (2006) Linkages between atmospheric circulation, climate and streamflow in the northern North Atlantic: research prospects. Progress in Physical Geography, Vol. 30, pp.143-174, ISNN 0309-1333 Koutsoyiannis, D., Montanari, A., Lins, H.F. & Cohn, T.A. (2008). Discussion of “The implications of projetcted climate change for freshwater resources and their management”. Hydrological Sciences-Journal, Vol. 54, pp.394-405, ISNN 0262-6667 Kundzewicz, Z.W., Graczyk, D., Maureer, T., Pinskwar, I., Radziejewski, M., Svensson, C. &Szwed, M. 2005. Trend detection in river flow series: 1. Annual maximum flow. Hydrological Sciences Journal, Vol.50, pp.797-810, ISNN 0262-6667 Kundzewicz, Z.W., Mata L.J., Arnell, N.W., Döll, P., Jimenez, B., Miller, K., Oki, T., Sen, Z. & Shiklomanov, I. (2008). The implications of projected climate change for freshwater resources and their management. Hydrological Sciences Journal, Vol.53, pp.3-10, ISNN 0262-6667 McCabe, G.J., Palecki, M.A. & Betancourt JL. (2004) Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proceedings of National Academy of Sciences, USA, Vol. 101, pp. 4136-4141. Lombard, F. (1987). Rank tests for changepoint problems. Biometrika, Vol. 74, pp.615-624, ISNN 0006-3444 Quessy, J.-F., Favre, A.-C., Saïd M. & Champagne, M. (2011) Statistical inference in Lombard’s smooth-change model. Environmetrics, doi: 10.1002/env.1108 (in press), ISNN 1180-4009 Climate Change – Geophysical Foundations and Ecological Effects 80Räisänen, J. (2007). How reliable are climate models? Tellus, Series A: Dynamic Meteorology and Oceanography, Vol. 59, pp.2-29, ISNN 0280-6509 Ripelli, C.A. & Nobre, P. (2004). Statistical prediction of sea-surface temperature over the Tropical Atlantic. International Journal of Climatology, Vol. 24, pp.45-55, ISNN 0899-8418 Roy, L., Leconte, R., Brissette, F.P. & Marche, C. (2001). The impact of climate change on seasonal floods of a southern Quebec River Basin. Hydrological Processes, Vol.15, pp.3167-3179, ISNN 0885-6087 Siew-Yan-Yu, T.O., Rousselle, J., Jacques, D. & Nguyen, V.-T.-V. (1998). Régionalisation du régime des précipitations dans la région des Bois-francs et de l’Estrie par l’analyse en composantes principales. Canadian Journal of Civil Engineers, Vol.25, pp.105-1058 ISNN 0315-1468 Vicente-Serrano, S.M. (2005) El Niño and La Niña influence on droughts at different timescales in the Iberian Peninsula. Water Resources Research, Vol. 41: W12415, doi: 10.1029/2004WR003908, ISNN 0043-1397 Zhang, X., Vincent, L.A., Hogg, W.D. & Niitsoo, A. (2000). Temperature and precipitation trends in Canada during the 20th century. Atmosphere-Ocean, Vol. 38, pp.395-429, ISNN 0705-5900 5 Detecting of a Global and Caribbean Climate Change Nazario D. Ramirez-Beltran, Joan Manuel Castro and Oswaldo Julca University of Puerto Rico at Mayaguez, Puerto Rico 1. Introduction Weather is defined as what is happening to the atmosphere at any given time while climate is what would be expected to occur at any given time of the year based on many years of meteorological observations. Change in climate constitutes shifts in meteorological conditions lasting a few years or longer. The climate change can occur in a single meteorological variable or in a group of variables affecting a region or the entire Earth (Burroughs, 2001). It is expected that a climate change can be expressed by the behaviour of time series of meteorological variables and in this study, a meteorological variable that expresses a climate change is called a climate indicator. Over the years, the climate of the Earth has changed due to natural or anthropogenic factors, and the research community is concentrating on the identification of the evidences of these changes. However, there are some uncertainties about the occurrence of a significant climate change and especially the time when the changes have become evident. The main purpose of this chapter is to introduce a statistical test to determine when a significant climate change has occurred assuming that a climate indicator is available. A climate indicator is a meteorological variable that reveals the climate of a region or a given part of the Earth. The suggested statistical test will be applied to detect climate changes at the global and Caribbean scale using several climatic indicators. During the last 140 years the Earth has been experimented several climate changes, which have been documented by several researchers (Huntingford et al., 2006; Hansen 2005; Easterling et al., 2000; Battisti el al., 1997; She and Krueger 2004; and Barnett et al., 1999). For example, Easterling et al., (1997) reported that the global mean surface air temperature has risen about 0.5° C during the 20th century. A large part of the world ocean has shown coherent changes of heat content during the last 50 years (Leuliette et al., 2004). Frich et al., (2002) claim that during the second half of the 20th century the world has become both warmer and wetter for global land areas and currently wet periods produce significantly larger rainfall than a few decades ago. These observed extreme events are in line with the expected changes due to the new greenhouse conditions. Global warming is affecting human lives, and in particular is severely impacting the agriculture forestry and in general the economy (Salinger 2005). For instance, Easterling et al., (2000) pointed out that in the United States since 1987 more than 360 weather events have produced losses in excess of $5 million each event with several catastrophic consequences. The temperature in globe has increased during the last 140 years, because that the number of heat waves has increased (Schar et al., 2004; Changnon et al., 2000). Global warming is a real process that is leading to Climate Change – Geophysical Foundations and Ecological Effects 82catastrophic consequences. It has been documented that the global warming is mainly due to anthropogenic factors (Huntingford et al., 2006; Hansen 2005; Easterling et al., 2000). The International Project of Climate Change (IPCC) has established that most of the observed increment in global temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations. The anthropogenic activities that affect the global warming are the emission of greenhouse gases and changes in land use, such as urbanization and agriculture. Recent simulation results have shown that the global warming during the last 20th century cannot only be explained by external forces, but also by natural variability that play an important role (Huntingford 2006, IPCC 2001, IPCC 2007). Battist et al. (1997) pointed out that the global warming may be attributed to natural variability, which can be observed in the energy transported by the atmosphere and ocean circulation. Kruger (2004) and Barnett et al., (1999) have established that the natural variability is due to volcanic eruption (Robock A., 2000), and the solar flux variability. Atmospheric dynamics simulations at global and regional scales have been conducted over different scenarios to predict the most likely future climate impacts (IPCC 2001; Angeles 2005; Huntingfordet al., 2006; Hansen 2005). Stott and Ketteborough (2002) claim that predicting Earth surface temperature is almost impossible since the anthropogenic and the natural variability include a large amount of uncertainties that may be difficult to predict. However, Angeles et al., (2007) uses global outputs and a regional atmospheric model to project that during the next five decades there will be large concentrations of rainfall episodes in smaller areas across the Caribbean basin. Climate change detection and attribution techniques usually apply global or regional circulation models and/or statistical techniques to detect climate changes (Easterling et al., 1997, 2000; Barnett et al., 1999; Schar et al., 2004; Smith et al., 2002; Santer et al., 2005; Meehl et al., 2004; Menne et al., 2005; Tomé and Miranda 2004; Feldstein 2002; IPCC 2001; Smith et al., 2002). A climate change may be expressed as a change in the mean or in the autocorrelation function of the underlying climate indicator. A statistical algorithm for climate change detection is introduced here with the intention of providing a tool to determine when a significant climate change has occurred. The algorithm is based on determining when the mean of the underlying climate indicator exhibits a significant deviation from a selected reference. The algorithm will divide the climate indicator in two parts the reference data and the testing data. The reference data will be used to identify the deterministic and stochastic components of the reference data, and the testing part is used to measure the deviation from the reference. Thus if a significant deviation from the reference data is found a climate change is detected at the identified point in time. The algorithm can detect climate changes that occur in the trend, in the seasonal or in the stochastic component. A simulation technique was used to design a climate indicator with three components and a postulated change was used to validate the performance of the proposed test. Real climate indicators were also used to detect climate change. Regression techniques were used to model the trend and seasonal components, and a time series model was used to represent the stochastic component of real time series data. Climate changes at global and Caribbean scale were studied. The second section of this chapter will present the basis of the proposed algorithm for detecting changes in a climate indicator. The third section presents a simulation exercise to illustrate the performance of the detection test. The fourth section will describe the data and sources of information, as well as some applications of the test at the Global and Caribbean climate scale, and the last section will present some conclusions. Detecting of a Global and Caribbean Climate Change 83 2. Methodology The deterministic components of a climate indicator, such as trend, and periodicity will be identified and removed from the data to estimate the stochastic component, which will be modelled by using a time series model. The identified time series model is also removed from the data to obtain a white noise process. Finally, a sequential statistical test will be implemented to detect whether or not a significant deviation from the white noise process has occurred. The algorithm is based on the fact that if a climate indicator does not contain any climate change then the entire time series will behave as a white noise process. On the other hand, if a climate indicator involves a climate change the stochastic behaviour of the testing part will show that the underlying climate change caused a significant deviation from the white noise process. The algorithm includes six major steps: (1) collect the largest time series of a climate indicator; (2) divide the data sets in two parts: the first par will be called the reference data and the second part as the testing data; (3) identify periodicity and trend components based on the reference data, and remove periodicity and trend components from the entire time series and call the resulting time series the estimated of the stochastic component; (4) identify an autoregressive moving average (ARMA) model to the first part of the estimated stochastic component; (5) compute the ARMA fingerprint; and (6) use a sequential hypothesis testing procedure to determine whether or not a significant change has occurred on the mean or in the autocorrelation function of the process. This study will focus on detecting changes on the mean of the process, where the mean of the process may be a constant or a time dependent function. 2.1 Step one: select a climate indicator It is assumed that climate properties of a given part of the world are expressed by a sequence of a meteorological variable, which we referred as a climate indicator. Thus, a climate indicator can be a time series of air temperature, sea level, rainfall, etc. It is required that the selected time series has no missing values and observations have been obtained at equal time intervals. The climate change can occur at different time scales and to be able to detect a climate change it is required to select a climate indicator that contains observations before and after the climate change. It is desirable that the time series will be large enough to identify the deterministic and stochastic components of the underlying meteorological process and leaving a significant part of data for testing. The length of the data will be established in step two. 2.2 Step two: dividing the time series The time series will be divided into two parts. The first part will be called the reference data and the second part will be the testing data. The reference data will be used as a reference level to measure the deviation (if any) of the testing data with respect to the reference data. If the underlying time series is a periodic series, the length of the reference data must contain at least three times the length of the period. On the other hand, if the time series is not a periodic series, it is recommended that reference data would contain at least 50 observations. The reference data will be located on the left (older values of the series) and the testing part on the right hand side (more recent values) of the series. Typically, the reference data is located at the beginning of the time series; however, it could be placed in almost any part of the series as long as enough observations are available. The testing part will be at least 50 observations and will be used to measure whether or not there exists a Climate Change – Geophysical Foundations and Ecological Effects 84significant change with respect to the reference data. It should be noted that the change detection test will depend on the meteorological properties of the selected reference and testing data, which can be expressed as follows: Reference data: x , t = 1, 2, . . . , m (1) Testing data: x , t = m + 1,m + 2,… , n,m ≥ 50, andn ≥ 2m (2) where x represents the underlying climate indicator at time t; m is sample size of the reference data, and n is the total number of observations considered for climate change detection, and n ≥ 2m. 2.3 Step three: identifying the deterministic components A climate indicator may be stationary or a nonstationary process and consequently it may have deterministic and stochastic components. The deterministic component may be a trend and/or a periodic component. Thus, the reference data of the climate indicator may be expressed as follows: x = T + P + s ,t = 1,… ,m (3) where T and P are the deterministic trend and periodic components, respectively at time t, and s is the stochastic components at time t. The trend component can be modeled by a polynomial in time and the periodic component by a sinusoidal function of time. The autocorrelation function and the periodogram of x can be used to identify the trendconsultthemaswell.The Editorswant to finish this preface acknowledging the collaboration and hardworkofall theauthors.We arealso thankful to thePublishingTeamof InTech forXII Preface their continuous support and assistance during the creation of this book. SpecialthanksareduetoMsAnaPantarforinvitingustoleadthisexcitingproject,andtoMsIvaLipovicforcoordinatingthedifferenteditorialtasks.Dr.JuanBlancoDep.ForestSciences,FacultyofForestryUniversityofBritishColumbia,CanadaDr.HoushangKheradmandLCT/LCAandSustainableDevelopmentExpertScientificandSteeringCommitteememberFédérationFrançaisepourlessciencesdelaChimieFranceReferencesIPCC,2007:SummaryforPolicymakers.In:ClimateChange2007:ThePhysicalScienceBasis.ContributionofWorkingGroupItotheFourthAssessmentReportofthe Intergovernmental Panel on Climate Change [Solomon, S.,D.Qin,M.Manning,Z.Chen,M.Marquis,K.B.Averyt,M.TignorandH.L.Miller(eds.)].CambridgeUniversity Press,Cambridge,UnitedKingdom andNewYork,NY,USA.Moss,R.H.,Edmonds, J.A.,Hibbard,K.A.,Manning,M.R.,Rose, S.K., vanVuuren,D.P.,Carter,T.R.,Emori,S.,Kainuma,M.,Kram,T.,Meechl,G.A.,Mitchell,J.F.B.,Nakicenovic,N.,Riahi,K., Smith, S.J., Stouffer,R.J.,Thomson,A.M.,Weyant, J.P., Wilbanks, T.J. (2010). The next generation of scenarios forclimatechangeresearchandassessment.Nature,Vol463,p747‐756.Oreskes,N.,Conway,E.M. (2010).MerchantsofDoubt:HowaHandfulofScientistsObscured the Truth on Issues from Tobacco Smoke to Global Warming.BloomsburyPress,NewYork.ISBN9781596916104.Parmesan,C. (2006).Ecologicalandevolutionaryresponses torecentclimatechange.AnnualReviewsofEcologyandEvolutionarysystematic,Vol37,p637‐669.Pittock,A.B. (2005).Climate change.Turningup theheat.Earthscan,London. ISBN0643069343.Simard,S.W.,Austin,M.E.(2010).Climatechangeandvariability.InTech,Rijeka.ISBN978‐953‐307‐144‐2. Part 1 Climate Variability 1 Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere Martin Dameris1 and Diego Loyola2 1Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre 2Deutsches Zentrum für Luft- und Raumfahrt, Institut für Methodik der Fernerkundung Oberpfaffenhofen, Germany 1. Introduction The climate system of the Earth atmosphere is affected by a complex interplay of dynamical, physical and chemical processes acting in the troposphere (atmospheric layer reaching from the Earth surface up to about 12 km height) and the Middle Atmosphere, i.e. the stratosphere (from about 12 to 50 km) and the mesosphere (from 50 to 100 km). Moreover, mutual influences between these atmospheric layers must be taken into account to get a complete picture of the Earth climate system. An outstanding example which can be used to describe some of the complex connections of atmospheric processes is the evolution of the ozone layer in the stratosphere and its interrelation with climate change. The stratospheric ozone layer (located around 15 to 35 km) protects life on Earth because it filters out a large part of the ultraviolet (UV) radiation (wavelength range between 100 nm and 380 nm) which is emitted by the sun. The almost complete absorption of the energy-intensive solar UV-B radiation (280-320 nm) is especially important. UV-B radiation particularly affects plants, animals and people. Increased UV-B radiation can, for example, adversely impact photosynthesis, cause skin cancer and weaken the immune system. In addition, absorption of solar UV radiation by the stratospheric ozone layer causes the temperature of the stratosphere to increase with height, creating a stable layer that limits strong vertical air movement. This plays a key role for the Earth’s climate system. Approximately 90% of the total ozone amount is found in the stratosphere. Only 10% is in the troposphere; ozone concentrations in the troposphere are much lower than in the stratosphere. Data derived from observations (measurements from satellites and ground-based instruments) and respective results from numerical simulations with atmospheric models are used to describe and explain recent alterations of the dynamics and chemistry of the atmosphere. Since the beginning of the 1980ies in each year the ozone hole develops over Antarctica during spring season (i.e. September to November), showing a decrease in the total amount of ozone of up to 70% (see Figure 4). Especially in the lower stratosphere (about 15-25 km altitude), ozone is almost completely destroyed during this season. Relatively shortly after the discovery of the ozone hole, the extreme thinning of the ozone layer in the south-polar Climate Change – Geophysical Foundations and Ecological Effects 4 stratosphere was explained as a combination of special meteorological conditions and changed chemical composition induced by industrially manufactured (anthropogenic) chlorofluorocarbons (CFCs) and halons. 1.1 Ozone chemistry In the atmosphere, ozone (O3) is produced exclusively by photochemical processes. Ozone formation in the stratosphere is initiated by the photolysis of molecular oxygen (O2). This produces two oxygen atoms (O) which recombine with molecular oxygen to form ozone. Since ozone is created by photochemical means, it is mainly produced in the tropical and subtropical stratosphere, where sunshine is most intensive throughout the year. At the same time, the ozone molecules formed in this way are destroyed again by the photolysis of ozone and by reaction with an oxygen atom. These reactions form the basis of stratospheric ozone chemistry, the so-called Chapman mechanism (Chapman, 1930). But if stratospheric ozone amounts are determined via this simple reaction system and the known rate constants and photolysis rates, the results obtained are about twice as high as the measured values. Since the early 1950ies, it has been known that fast so-called catalytic cycles reduce the determined ozone amounts to the observed values. By the early 1970ies, the catalysts had been identified as the radical pairs OH/HO2 and NO/NO2, which are formed from water vapour (H2O) and nitrous oxide (N2O) respectively (Bates and Nicolet, 1950; Crutzen, 1971; Johnston, 1971). In the mid-1970ies, the radical pairs Cl/ClO (from CFCs) and Br/BrO (from halons) were identified as further significant contributors (Molina and Rowland, 1974; Wofsy et al., 1975). The important point is that a catalyst can take part in the reaction cycle several thousand times and therefore is very effective in destroying ozone molecules. The increased occurrence of CFCs and halons due to anthropogenic emissions has significantly accelerated stratospheric ozone depletion cycle over recent decades, triggering a negative stratospheric ozone trend which is most obvious in the Southern polar stratosphere during spring time where the ozone hole is found. In the troposphere, CFCs and halons are mostly inert. Over time (several years), they are transported into the stratosphere. Only there they are photolysed and converted into active chlorine or bromine compounds. In particular, ozone is depleted via the catalytic Cl/ClO-cycle in polar spring. However, the kinetics of these processes are very slow, because the amount of UV radiation is limited due to the prevailing twilight conditions. In the polar stratosphere, it is mainly chemical reactions on the surface of stratospheric ice particles that are responsible for activating chlorine (and also bromine) and then driving ozone depletion immediately after the end of polar night (Solomon et al., 1986). In the very cold lower polar stratosphere, polarand the periodic components; i. e., if the autocorrelation function dies out very fast, the underlying process is stationary and the process do not exhibit trend; otherwise, a trend should be identified. Regression techniques can be used to estimate the parameters of the trend and the sinusoidal function. The Fourier-based method or a more accurate method based on wavelet techniques (Nicolay, et al., 2010) can be used to estimate the size of the required periods. The trend and periodic components are removed from the original time series to estimate the stochastic component, s . s = x − T − P ,t = 1,… , n (4) where T and P are the estimates of the trend and periodic components, respectively. In time series analysis is customary to remove the trend and periodic components by using the appropriate difference operators. For example: ∇ x = (1 − B) x (5) where ∇ is the difference-of-order d operator and B is the backshift operator, which is defined as follows: B x = x . The operator to remove periodicity can be written as follows: ∇ x = (1 − B ) x (6) where D is the order of seasonal difference and p is the period of the time series. Unfortunately, these operators cannot be applied in this case since the application of these operators will remove not only the trend and periodic components but also will remove the Detecting of a Global and Caribbean Climate Change 85 climate change signal; and consequently, these operators are not suitable for the purposes of detecting a climate change. 2.4 Step four: identifying the stochastic component The first m values of the stochastic component, s , are used to identify the autocorrelation structure of the time series. Most of the stochastic components of climate indicators are a sequence that can be represented by autoregressive moving average (ARMA) models (Box and Jenkins 1976). The reference data will be tested first to determine whether or not the reference data is an autocorrelated or a white noise process. If the underlying process is a white noise there is no need of removing the autocorrelation structure. On the other hand, if the autocorrelated structure of the reference data is significant, this dependent structure can be identified by using an ARMA model, which can be expressed as follows: s = ⋯⋯ a ,t = 1, 2, … ,m (8) where s , B and m were defined previously; θ′s and ϕ’s are the parameters of the moving average and the autoregressive components of the model, respectively; a is a sequence of independent random variables with mean equal to zero and a constant variance. It should be noted that the transformed reference data should be a stationary process since the trend and periodicity components have been removed from the original time series. Stationary in the sense that the mean and the autocorrelation function will not change over time. The identification of an ARMA model consists of determining the values of r and q. The identification is accomplished by using the autocorrelation and partial autocorrelation functions of the stochastic component and a numerical parameters estimation algorithm, as described in several time series textbooks (Box and Jenkins 1976; Brockwell and Davis 2002; Wei, 1990; Pandit and Wu 1983). Nonlinear regression techniques are used to estimate the parameters of the ARMA model and several statistical programs are available to perform this estimation task, for instance: Statgraphics, Minitab, Matlab, etc. It should be noted that the main purpose of identifying an ARMA model is to remove the autocorrelation structure, as shown in the next step. 2.5 Step five: computing the ARMA fingerprint The ARMA fingerprint is the sequence created by the difference at each point in time between the estimated of stochastic component, s , and the estimated stochastic component from the ARMA model, s . The ARMA fingerprint can be computed as follows: f = s − s ,t = 1,2, … , n (8) s = ⋯⋯ a ,t = 1, 2, … ,m (9) where s is an estimated of the stochastic component and is computed by using eq. (4); whereas, s is an estimated of s and is computed by evaluating eq. (9), and f is the ARMA fingerprint; a are the residuals for the stochastic component; θ’s and ϕ’s are parameter estimated that must be computed with the m values of the stochastic component, s . It should be noted that the model fitting is computed with t=1,…,m; however, the finger print is computed for the entire time series. Climate Change – Geophysical Foundations and Ecological Effects 86Thus, if no change has occurred in the underlying process then the fingerprint will behave as a white noise sequence; where a white noise process is a sequence formed by independent random variables with zero mean and constant variance. However, if the process exhibits a significant deviation from the white noise, the ARMA model will show a unique characteristic which will be exhibited either in the mean or in the autocorrelation function of the given sequence and this special sequence will be called here the ARMA fingerprint. Thus, if a significant change occurs in the mean of the process, the ARMA fingerprint will also exhibit a significant deviation from the mean. On the other hand, if a change occurs in the second moment of the process, the fingerprint may also exhibits a significant deviation in the autocorrelation function. 2.6 Step six: sequential hypothesis testing If external forces affected the climate indicator, its ARMA fingerprint will present an autocorrelation function with a significant deviation from the autocorrelation of the white noise process. The suggested procedure will detect changes on the mean, and changes in the autocorrelation function. The exponentially weighted moving average (EWMA) test is adopted to detect the change on the mean of the process at every point in time. EWMA test was proposed by Roberts (1959) and adopted here because it is an efficient test to detect a small shift in the mean and also because it is a robust test in the senses that it is not affected by moderate deviations from the Gaussian process as well as because it is not affected by weak autocorrelated time series. Thus, if a climate change induced a strong autocorrelation function it will be detected by EWMA test. The exponentially weighted average, z , of the fingerprint is defined as: z = λf + (1 − λ)z ,t = 1,… , n (10) A significant increment in the mean occurs at time t if z > U and a significant decrement occurs in the mean at time t if zP = 8cos + 4 + ( ) cos t 1 + ( (15) s = 0.2s + 0.6s − 0.5a + a (16) ψ = 3σ,t > 0,otherwise (17a) ψ = 0.1tt >0,otherwise (17b) The variable x is the climate indicator at time t; the variable T is a quadratic trend; P is a periodic component and is composed by two functions, the first function includes a large amplitude with period equal to twelve, and the second function has a moderated amplitude with a function of time and a fixe period equal to 30; the stochastic component is an ARMA(2,1) process, a is a Gaussian noise sequence with zero mean and variance 9. A similar function was also used by Nicolay (2010) to represents a climate indicator. The variable ψ represents the induced climate change, which may have a step (17a) or a ramp function (17b) that starts when t = m + 1. The step function represents a sudden increment of a meteorological variable whereas the ramp function is a slow and persistent increment of climate change. It is assumed that the step change occurred at time m+1 and will persist up to the end of the series with a magnitude of 3 times the standard deviation of the white noise,σ. When the ramp function is used the persistent climate change will be very small at the beginning of the change and the simulated climate change will become evident after the time approaches to n. It is expected that the climate change will be detected right after the change has occurred for the case of the step function; however, when the ramp function is simulated the change is detected after some delay (about 35 units of time). Figure 1 shows the different components of the simulated time series. The top panel on the right of Figure 1 shows the simulated trend and the top panel on the left shows the periodic function. The middle panel on the left of Figure 1 shows the stochastic component whereas the middle panel on the right shows the step function. The bottom panel of Figure 1 shows the simulated climate indicator, which is the sum of the previously plotted variables. As described in the previous section, the procedure to implement the detection test includes six steps. The first step consists on selecting a climate indicator to be studied and in this case, the simulated climate indicator x , is the selected time series. The second step consists on dividing the time series in two sets, the first set includes one hundred values, which were assigned to the reference data; i.e., m=100, and the remaining observations correspond the testing data. The third step consists on identifying the deterministic components, which includes the trend and the periodic component. Figure 2 shows the sample autocorrelation function of Climate Change – Geophysical Foundations and Ecological Effects 88the simulated reference data, x . This autocorrelation function indicates that the underlying time series is not stationary and includes a strong periodic component. Thus, it is necessary to remove first the trend component to properly identify the periodic functions, and finally, remove the periodic component to develop a stationary time series. Fig. 1. Panels a, b, c and d show the trend component, the periodic component, the stochastic component, and the step change, respectively. Panel at the bottom shows the climate indicator, which is the result of the sum of the described components. A quadratic model was used to estimate the trend of the climate indicator. Thus, the following regression model was identified: T = 7.5 + 0.041t + 0.0003t ,t = 1,… ,100 (18) Detecting of a Global and Caribbean Climate Change 89 Statistical analysis shows that the included parameters are significant at 5% level. The simulated reference data and the estimated trend are given on Figure 3. In this figure, the continuous and smooth line shows the estimated trend and the broken and continues line is the simulated reference data. Fig. 2. Autocorrelation function of the simulated reference data. Fig. 3. Simulated reference data and estimated trend. 0 10 20 30 40 50 60-1-0.8-0.6-0.4-0.200.20.40.60.81Lagscorrelation0 10 20 30 40 50 60 70 80 90 100-10-50510152025timevalue Climate Change – Geophysical Foundations and Ecological Effects 90The trend was removed from the entire time series to create the detrended sequence, which is given below: y = x − T , t = 1,… ,200 (19) The Fast Fourier Transform was used to develop the periodorgram and two significant ordinates were found indicating that the periodic component includes two harmonic functions, as exhibits in Figure 4. The estimated periods of these functions are 11.9 and 28.4. Fig. 4. The periodogram of the deterended time series shows that there are two significant harmonics with the largest ordinates at the frequencies of 0.084 and 0.035 that correspond to the period of 11.9 and 28.4, respectively. Periodogram and autocorrelation function show that the detrended reference data has a periodic component that can be modeled by using the following sinusoidal function: P = 0.0098 − 0.5705sin . + 8.1084 cos . − 0.9133sin . + 4.3805 cos . (20) Figure 5 shows the reference data with the estimated periodic component. The sinusoidal continuous line shows the estimated periodic component while the broken and continuous line shows the stochastic data. The trend and the periodic components are removed from the simulated reference data to compute the stochastic component as follows: s = x − T − P (21) 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50246810121416x 104FrequencyPeriodogram ordinate Detecting of a Global and Caribbean Climate Change 91 Fig. 5. This figure shows the reference data after removing the trend component and the estimated periodic component. The smooth and continuous line shows the estimated periodic component. The fourth step consists on identifying a time series model for the stochastic component. The sample autocorrelation function and a parameter estimation algorithm are used simultaneously to identify the corresponding ARMA model. It can be shown that the theoretical autocorrelation function for and ARMA(2,1) can be written as follows (Ramirez-Beltran and Sastri 1997): ρ = (22) where γγγ = 1 −ϕ −ϕ−ϕ 1 − ϕ 0−ϕ −ϕ 1 1 + θ(ϕ + θ)θ0 σ (23) γ = ϕ γ + ϕ γ ,k ≥ 3 (24) where ρ and γ are the autocorrelation and autocovariance functions at lag k of the stochastic component, respectively; ϕ′s are the autoregressive parameters, and θ is the moving average parameter, and σ is the variance of the white noise. The simulation was based on the following values: ϕ = 0.2, ϕ = 0.6, θ = −0.5 and σ = 3. The left panel of Figure 6 shows the theoretical autocorrelation function of and ARMA(2,1) model, and the right panel of Figure 6 shows the sample autocorrelation function of s . 0 10 20 30 40 50 60 70 80 90 100-20-15-10-505101520timevalue Climate Change – Geophysical Foundations and Ecological Effects 92The Matlab computer software (MathWorks, 2000) was used to estimate the parameters of the ARMA(2,1), and the estimated model can be written as follows: s = 0.19s + 0.56s − 0.68a ,t = 1,… ,100 (26) Data also provide information to estimate the standard deviation of the noise, which is σ = 2.74. Fig. 6. Theoretical (left) and sample (right) autocorrelation functions for the stochastic component. The fifth step consists of deriving the ARMA fingerprint. The estimated parameters in the previous step are used to compute the stochastic time series and removed the autocorrelation structure from the simulated stochastic component. Thus, the ARMA fingerprint was computed as follows: f = s − s ,t = 1,… ,200 (27) Figure 7 shows the ARMA fingerprint, and the firstone hundred values resembles to the pattern of a white noise process. However, the last part of the fingerprint shows a significant deviation from the white noise. The sixth step consists on applying the sequential hypothesis test for detecting the induced climate change. Essentially, the EWMA test includes a 95% confidence interval for the mean of a white noise process. Thus, the values that are outside of the interval indicate that a significant deviation has occurred on the mean; and consequently, that particular observation shows the time when the climate change becomes evident. The left panel of Figure 8 shows that the induced climate change was detected at time t=102, and the red stats that fall beyond the 95 confidence interval indicate that the climate change is evident during this period of time. In this particular exercise, the size of the step change was three time the standard deviation of the noise (3σ = 9).A climate change was also simulated by using the ramp function. Thus, the equation (17b) was used to induce a slowly increasing climate change. The right panel of Figure 8 shows the results for the ramp function, which indicates that after 35 time units the change becomes evident. The right panel of Figure 8 also shows 0 5 10 15 20 25-1-0.8-0.6-0.4-0.200.20.40.60.81Lagscorrelation0 5 10 15 20 25-1-0.8-0.6-0.4-0.200.20.40.60.81Lagscorrelation Detecting of a Global and Caribbean Climate Change 93 the sequential hypothesis test, which indicates the climate change is large enough to be detected, and the detection occurs at time t=135. Fig. 7. The ARMA fingerprint of the simulated process. Fig. 8. The left panel shows the performance of the detection test when a step change has been induced and this change has been detected at t=102. On the other hand, the right panel shows results when the climate change was induced by a ramp function. In this case the change was detected at t=135; i.e., after 35 units of delay. 4. Climate change detection Four data sets were used to implement the climate change detection test. The first two sets are associated with the factors that induced a climate change, and other two are climate indicators related to meteorological variables that exhibited the vestiges of a climate change embedded along the time series. 0 20 40 60 80 100 120 140 160 180 200-10-505101520timevalue0 20 40 60 80 100 120 140 160 180 200-4-202468101214 Observation Exp weigthed averages 0 20 40 60 80 100 120 140 160 180 200-4-202468101214 Observation Exp weigthed averages Climate Change – Geophysical Foundations and Ecological Effects 944.1 Attribution variables The factors that induced a climate change are known as attribution variables and in this study, we selected two attribution variables (IPCC, 2001). The sunspots are considered as a natural attribution variable; i.e., the induced changes are the result of sun energy variations that directly impact the Earth climate conditions, whereas the carbon dioxide (CO2) emissions are considered as an anthropogenic attribution variable. 4.1.1 Sunspots Sun exhibits signs of varying activity in the form sunspots. These are dark areas, which are seen at lower latitudes and crossing the phase of the sun as it rotates, and are cooler than the surrounding chromospheres. A sunspot consist of two regions a dark central umbra at temperature of around 4,000°K and surrounding lighter penumbra at around 5,000°K. Thus, the darkness is purely a matter of contrast that appears dark compared to the general brightness of the sun. A spot may be from 1x103 to 2x105 km in diameter with a life cycle from hours to months (Burroughs, 2001). If the amount of energy emitted by the sun varies over the time and the Earth is receiving a radiation from the sun; consequently, some changes on the Earth surface temperature may be attributed to variation of solar radiation. In 1843 Heinrich Schwabe (Burroughs, 2001) discovered that the number of sun spots exhibits a periodically behaviour; however, it was until 1980 when satellite data confirmed such discovery. She et al. (2004) pointed out that there is a relationship between temperatures observed in mesopause and the effect of solar cycle. Satellite data has been used as a medium to support that an indirect measurement of solar radiation can be obtained by studying the behaviour of sunspots. For instance, Julca (2007) shows that sunspots and observed solar irradiances exhibit 0.77 of correlation coefficient and this result confirms that the solar activity may be studied by analysing the behaviour of sunspots. Figure 9 exhibits the comparison of observed sunspot pattern and solar irradiance during the period 1979-2005. The analysed time series of sunspots was obtained from the Royal Observatory of Belgium (http://sidc.oma.be/sunspot-data/), and the studied period was from January 1750 until February 2011. Figure 10 shows the patterns of the sunspots over the studied period. Fig. 9. Sunspots and solar radiation. The left vertical axis shows the scale of solar radiation and is given in W/m2; whereas, the right scale shows the number of sunspots. W/m2 Sunspots Detecting of a Global and Caribbean Climate Change 95 Fig. 10. The monthly sunspots form January 1749 to February 2011. The algorithm for detecting climate change was applied to identify whether or not the sunspots attribution variable exhibits a significant change on the mean. The half of the available observations was used as the reference data (from January 1749 to December 1880) and the remaining part of the series was used as the testing data (from January 1881 to February 2011). The selected reference data do not exhibit a significant trend; and consequently, no trend was removed. However, sunspots show an unstable variance; i.e., a data transformation should be explored to stabilize the variance. Logarithmic transformation was discarded since it produces extreme lower values and a significant bias is introduced to the data. The squared root transformation was applied to data and better results were found. Six harmonic functions were needed to model the periodic component. Table 1 shows the parameter estimation of the sinusoidal functions. After removing the periodic component, an ARMA(2,1) model was identified into the stochastic component. Thus, the fingerprint of sunspots was computed as follows: f = s − s (28) where s = x − P (28) P = −0.201 + ∑ b sin + c cos (29) s = .. . a (30) where f is the ARMA fingerprint and x is the number of sunspots at the month t (t =1,… , n), n = 3146, and m = 1573; the standard deviation of the estimated white noise 1750 1800 1850 1900 1950 2000050100150200250SunspotsyearNumber of sunspots Climate Change – Geophysical Foundations and Ecological Effects 96process is 1.099. After removing the seasonal component the ARMA fingerprint technique was implemented and the monthly stochastic component shows a significant increment during about three decades from 1955 to 1989 and especially during the first decade (1955-1965) it was detected a significant increment of sunspots as shown in Figure 11. i b c p (months) 1 -0.6842 1.8636 135 2 1.3737 -1.1725 899 3 -0.1579 1.2634 121 4 0.19 1.2516 166 5 -0.4951 -0.9182 101 6 0.4223 -0.3006 111 Table 1. Parameter estimation of the sinusoidal function Fig. 11. Sunspots show an evident increasing amount of sunspots from 1955 to 1989, with a significant increment during the decade (1955 to 1965). The reference data were from January 1749 to December 1880, and the testing data from January 1881 to February 2011. The direct link between sunspot number and solar output fits with the hypothesis thatcold period known as the Little Ice Age and the colder weather of the late seventeen century was the result of an almost complete absence of sunspots known as the Maunder minimum (Burroughs, 2001). The climate impact of changes in solar radiance in the ultraviolet (UV) region has been suggested. Because wavelengths between 200 to 300 nm are absorbed high in the stratosphere by oxygen and ozone, they initiate photochemical reactions which influence the weather at lower levels. It has been shown that the amount of solar radiation entering the lower atmosphere varies with solar activity, as a result of alterations in 1750 1800 1850 1900 1950 2000-1-0.500.511.5Sunspot change detection year Exp weigthed averages Detecting of a Global and Caribbean Climate Change 97 stratospheric ozone concentrations caused by the changing UV flux. This reduces the amount of solar energy reaching the lower atmosphere in middle and high latitudes in winter when solar activity is high. These changes could have a significant impact on global circulation; the increase solar UV radiance in the lower tropical stratosphere will expand the Hadley circulation leading to a pole ward shift of the sub-tropical westerly jet (Burroughs, 2001). Another consequence of changing UV fluxes reaching the lower atmosphere is to affect the formation of free-radicals in the lower atmosphere (hydroxyl radical). This alters the production of condensation nuclei and hence the formation of clouds. In effect, more UV radiation reaching the troposphere will increase the concentration of condensation nuclei and hence make it cloudier environment. Thus, varying of solar activity it will modify the climate over the Earth. 4.1.2 Carbon dioxide The carbon dioxide time series was obtained from Mauna Loa station Hawaii. Data are reported as a dry air mole fraction defined as the number of molecules of carbon dioxide divided by the number of all molecules in air, including carbon dioxide (CO2) itself, after water vapor has been removed. Thus, a mole fraction of CO2 is expressed as parts per million (ppm) and is the number of molecules of CO2 in every one million molecules of dried air. The CO2 monthly data is the monthly mean CO2 mole fraction determined from daily averages. This data set includes about 53 years of data from March 1958 to February 2011. The underlying data includes a few missing values which were estimated by the data source (http://www.esrl.noaa.gov/gmd/ccgg/trends/). Information from this station has the longest continuous record of CO2 concentrations in the world. This climate attribution variable has been considered as one of the most favourable locations for measuring undisturbed air because of the Hawaii environmental conditions. It should be noted that the volcanic events were excluded from the records (Keeling and Whorf 2005). Figure 12 shows the behaviour of the observed CO2. Fig. 12. Monthly carbon dioxide at Mauna Loa Hawaii, from March 1958 to February 2011. 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010310320330340350360370380390Carbon Dioxide Monthly Datayear ppm Climate Change – Geophysical Foundations and Ecological Effects 98The carbon dioxide shows a strong trend and seasonality components. The reference data includes 312 monthly values and correspond to the period of 26 years from March 1958 to February 1984 and the testing data includes 27 years of data from March 1984 to February 2011. To perform a climate change detection analysis there may be two possibilities, depending of the identified trend. It can be fitted either a linear or a quadratic trend to the reference data, and the parameters of both the linear and the quadratic trend are significant and also the proportions of explained variance are about the same; therefore, it is justifiable the application of a linear or a quadratic trend. However, the results are quite different and the interested reader should be aware about the interpretation of results. The analyses of linear and quadratic trend are given in Table 2. The first column indicates the component to be analysed and the second and the third columns refer to results from the linear and the quadratic analysis, respectively. Component Linear Trend Quadratic Trend Fingerprint f = s − s f = s − s Stochastic s = x − T –P s = x − T –P Trend T = 312.95 + 0.0918t T = 315.34 + 0.0461t + 0.000145t Periodic component P = b sin 2πtp + c cos 2πtp P = b sin 2πtp + c cos 2πtp i b c p(months) i b c p (months) 1 2.5017 0.971 12 1 2.5011 0.969 12 2 -0.6331 -0.3541 6 2 -0.6334 -0.3545 6 Stochastic s = 1 − 0.5882B1 − 1.235B − 0.2431B a s = 1 − 0.6327B1 − 1.217B + 0.2723B a Standard deviation of a 0.3146 0.3036 Table 2. Analysis for a linear and a quadratic trend. The left panel of Figure 13 shows the linear trend that was fitted to the reference data and this function was evaluated for t during the testing period(t = 313, 314, … ,636). This figure indicates that the expected mean (straight line) is smaller than the actual mean during the testing period. Therefore, the detection test should indicate that there is a significant increment on the mean during the testing data. This figure indicates that during the reference data the mean of CO2 has an increasing rate of 0.0918 ppm per month; however, during the testing period the increasing rate growth larger than the linear trend, since the sequential test detects an increment with respect to the mean (linear trend). On the other hand, the right panel of Figure 13 shows a quadratic trend that was fitted to the reference data and this function was evaluated for t during the testing period. This figure indicates that the expected mean (parabola) is larger than the actual mean during the testing period. Therefore, the detection test should indicate that there is a reduction with respect to the mean (quadratic trend). Apparently, the emissions of CO2 during the last decade have been reduced with respect to the quadratic emission rate. However, the reduction on emission is still larger than the linear trend. Detecting of a Global and Caribbean Climate Change 99 Fig. 13. The left panel shows the observed CO2 and a linear trend, which was fitted to the reference data and evaluated during the testing data. The right panel shows the observed CO2 and the quadratic trend, which was fitted to the reference data and evaluated during the testing data. The reference data is from March 1958 to February 1984 and the testing data is from March 1984 to February 2011. The left panel of Figure 14 shows that in 1988, the emission rate of CO2 is larger than the mean rate 0.0918 ppm per month and this event becomes evident during the period of 1995-2011. The right panel of Figure 14 shows that the reduction with respect to the mean (parabola) is evident during the period 1990-2011. Thus, the CO2 during the testing period exhibited an increasing emission rate that is larger than the linear but smaller than the quadratic rate and this phenomenon is clearly exhibited during the period 1990-2011. Fig. 14. The left panel shows that in 1988, the emissions of CO2 are larger than 0.0918 ppm per month and this event becomes evident during 1995-2008. The right panel shows the relative reduction of concentration of CO2 when using a quadratic trend. The relative reduction occurred during 1992-2008. 4.2 Climate indicators A climate indicator is a time series of a meteorological variable that contains the behaviour of a climate of the Earth and may exhibit a climate change. The selected climate indicators were the surface temperature, and cloud cover. The information contained in the climate 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010310320330340350360370380390Observed CO2 and Liner Trendyear CO2 (ppm)1960 1965 1970 1975 19801985 1990 1995 2000 2005 2010310320330340350360370380390Observed CO2 and Quadratic Trendyear CO2 (ppm)1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010-0.4-0.200.20.40.60.8Carbon Dioxide (when using linear trend) year Exp weigthed averages 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010-2-1.5-1-0.50Carbon Dioxide (when using quadratic trend) year Exp weigthed averages Climate Change – Geophysical Foundations and Ecological Effects 100 indicators come from ground meteorological stations, satellite observations, and numerical weather prediction models. 4.2.1 Global surface temperatures The global surface temperature is a climate indicator that has been used to show evidence that the Earth is warming and has become much stronger during recently years as reported in the Third Assessment Report (IPCC 2001). The IPCC (2007) also reported that from 1995-2006 were the warmest years since 1850. The global surface temperatures were provided by the Goddard Institute for Space Studies (GISS), and include the period from January 1880 to December 2010. The surface data set was developed based on the Global Historical Climatology Network (GHCN). This analysis included observations from 6,300 ground stations located in different parts of the world (Hansen et al. 2010). Data from stations were confirmed with satellite data, and global climate model. Global temperature analyses are routinely either omitting urban stations or adjusting their long-term trends to try to eliminate or minimize the urban effect. The GISS analysis used 1951–1980 as the base period to develop the global temperature anomalies and the detailed description of data analysis is presented by Hansen et al. (2010). The monthly anomalies of the global land-ocean surface (AGLO) temperatures are shown in Figure 15, this product was selected because involves both the land and ocean measurements. These data were acquired at the web site http://data.giss. nasa.gov/gistemp/. Fig. 15. Monthly anomalies of global land-ocean surface temperatures (1880-2010). The AGLO temperatures were divided in two parts. The reference data includes the first one hundred years from January 1880 to December 1979 (m = 1200) and thirty-one years for the testing data from January 1980 to December 2010. The reference data show a significant linear trend with and increment of 0.00029°C per month. After removing the linear trend, the autocorrelation function and the periodogram were computed and it was found that this data do not include a significant periodic component, this is because that climatology was 1880 1900 1920 1940 1960 1980 2000-1-0.500.51Global land-ocean surface temperaturesyearTemperature anolmaly (in C) Detecting of a Global and Caribbean Climate Change 101 removed by GISS working group. The Autocorrelation function shows that the stochastic component can be represented by and ARMA(1,1). Thus, the ARMA fingerprint can be computed as follows: f = s − s (31) where s = x − T = x + 0.28 − 0.00029t (32) s = .. a (33) The variable x represents the AGLO temperatures; the standard deviation of a for t =1,… ,m was 0.169°C. Figure 16 shows that the EWMA sequential test indicating a significant increment of AGLO temperature, which becomes evident in 1998. During the reference part (1880-1890), there are a few observations that exhibit some cooling behaviour or may be the presence of outliers. Fig. 16. The AGLO temperatures show a significant increment that becomes evident en 1998. 4.2.2 Caribbean surface air temperatures Air temperatures for the major Caribbean islands were obtained from GHCN version 3. (ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/). The studied Caribbean islands are Cuba (CU), Jamaica (JA), Puerto Rico (PR), and La Espanola, which includes Dominican Republic (DR) and Haiti (HA). Table 3 shows the summary of stations that were used in this work. The monthly air temperature used in this analysis includes the following period: from January 1948 to February 2011. The quality of the data set was improved by removing inhomogeneities from the data record associated with non-climatic influences such as changes in instrumentation, and station environment (Peterson and Easterling, 1994). The monthly surface air temperatures from the National Center for Environmental Prediction 1880 1900 1920 1940 1960 1980 2000 2020-0.25-0.2-0.15-0.1-0.0500.050.10.150.20.25Global land-ocean surface temperatures year Exp weigthed averages Climate Change – Geophysical Foundations and Ecological Effects 102 (NCEP) reanalysis data were also used as a proxy variable to estimate some of the missing values that were encounter in some stations. The nearest grid point to each island was used to derive a regression equation between the temperature at a given station and the temperature of the nearest NCEP-grid-point. Regression equations exhibit an average of 0.9 of correlation coefficient between station temperature and NCEP data. Country Cuba Dominican Republic Haiti Jamaica Puerto Rico Number of stations 14 28 1 5 15 Table 3. Number of stations over the Caribbean area. Thirty years of data (from January 1948 to December 1977) were used to estimate climatology and anomalies; and after performing this calculations some of the data do not exhibit seasonal component. Figure 17 shows the monthly temperature anomalies for the major Caribbean islands from January 1948 to February 2011. Since Haiti provides only one station, the analysis for this country was omitted. (a) (b) (c) (d) Fig. 17. Monthly temperature anomalies for the major Caribbean island. Data set includes observations from January 1948 to February 2011. 1940 1950 1960 1970 1980 1990 2000 2010 2020-2-1.5-1-0.500.511.522.5Puerto RicoyearTemperature anolmaly (in C)1940 1950 1960 1970 1980 1990 2000 2010 2020-2-1.5-1-0.500.511.52Dominican RepublicyearTemperature anolmaly (in C)1940 1950 1960 1970 1980 1990 2000 2010 2020-3-2.5-2-1.5-1-0.500.511.52CubayearTemperature anolmaly (in C)1940 1950 1960 1970 1980 1990 2000 2010 2020-2-1.5-1-0.500.511.52JamaicayearTemperature anolmaly (in C) Detecting of a Global and Caribbean Climate Change 103 The reference data of monthly anomalies for Puerto Rico exhibit a linear trend with an increasing rate of temperature of about 0.00095ºC per month. The trend was removed and the periodicity component was very weak since climatology was subtracted from data; and consequently, this component was therefore deleted. The Autocorrelation function shows that the stochastic component can be represented by and autoregressive process (AR) of order one, AR(1). The AR process is a particular case of the ARMA model in which the moving average component is missing. The procedure to calculate the fingerprint is outlined in Table 4. The sequential statistical test was implemented to detect if there is any deviation from the mean. Figure 18a shows that Puerto Rico air temperatures indicate an additional increment in temperature that become evident in year 2010. This figure also shows some false alarms that occurred on 1988 and 2000. This pattern shows a weak climate-change signal over the island of Puerto Rico. (a) (b) (c) (d) Fig. 18. a) This figure shows that in 2010 a significant climate change occurred in Puerto Rico. This figure also shows two false alarms occurred on 1988 and 2000; b) For Dominican Republic there was a significant trend during the referencedata and no climate change during testing data, only a couple of false alarms occurred on 1981, 2005, 2009, and 2010; c) Cuba shows no trend during the reference data; however, there was a significant climate change that occurred during 2007-2010; d) Jamaica shows the largest trend during the reference data and a reduction on surface air temperature during the testing data, which indicates that the there were an over estimation of the linear trend during the reference data. 1950 1960 1970 1980 1990 2000 2010-0.3-0.2-0.100.10.20.30.4Puerto Rico year Exp weigthed averages 1950 1960 1970 1980 1990 2000 2010-0.4-0.3-0.2-0.100.10.20.3Dominican Republic year Exp weigthed averages 1950 1960 1970 1980 1990 2000 2010-0.4-0.200.20.40.6Cuba year Exp weigthed averages 1950 1960 1970 1980 1990 2000 2010-0.5-0.4-0.3-0.2-0.100.10.20.3Jamaica year Exp weigthed averages Climate Change – Geophysical Foundations and Ecological Effects 104 The first thirty years of Dominican Republic were used to estimate climatology and also were used as the reference data (from January 1948 to December 1977). The reference data of temperature anomalies for Dominican Republic exhibit a linear trend with an increasing rate of about 0.00073ºC per month. The harmonic analysis and the autocorrelation function show that there is a significant periodic component with period equal to 12 months. The stochastic component was computing after removing the trend and periodic component, as shown in Table 4. The autocorrelation function of the stochastic component shows that this process can be represented by an autoregressive (AR) process of orders one. Figure 18b shows that there was no significant climate change for Dominican Republic. This figure also exhibits a couple of false alarms that occurred on the following years: 1981, 2005, 2009, and 2010. Component Puerto Rico Dominican Republic Fingerprint f = s − s f = s − s Stochastic s = x − T s = x − T –P Trend T = −0.40 + 0.00095t T = −0.1372 + 0.00073t Periodic component none P = −0.0057 sin 2πt12 − 0.0054cos 2πt12 Stochastic s = 11 − 0.703B a s = 11 − 0.626B a standard deviation of a 0.30 C 0.36 C Component Cuba Jamaica Fingerprint f = s − s f = s − sStochastic s = x – P s = x − T Trend None T = −0.4998 + 0.00270t Periodic component P= −0.0103 sin 2πt12− 0.0065 cos 2πt12 None Stochastic s = 11 − 0.4248B a s = 1 − 0.3753B1 − 0.9071B a standard deviation of a 0.47 C 0.32 C Table 4. Calculations of the ARMA fingerprint for the major Caribbean islands. As in the previous islands, the first thirty years were used to estimate climatology of the surface air temperature of Cuba. The reference data include monthly observations from January 1948 to December 1978. The anomalies of reference-data of air temperature from Cuba exhibit a no significant trend. The harmonic analysis and the autocorrelation function show that there is a significant periodic component with period equal 12 months. The autocorrelation function of the stochastic component shows that this process can be represented by an AR(1) process. Estimates of a sinusoidal function and the stochastic component are given in Table 4. Figure 18c shows that there was a significant climate Detecting of a Global and Caribbean Climate Change 105 change occurred on Cuba and becomes evident during 2007 to 2010. This figure also shows that there are some false alarms that occurred on 1981, and 1991. In Table 4 the variable x represents the anomaly temperatures for the corresponding country,B is back shift operator; m = 360andn = 758. The reference data of monthly anomalies for Jamaica exhibit a linear trend with an increasing rate of temperature of about 0.00270 ºC per month. The trend was removed and periodicity component was not significant component. The autocorrelation function shows that the stochastic component can be represented by and ARMA(1,1). The fingerprint procedure is outlined in Table 4 and estimates are also given in this table. The sequential statistical test was implemented to detect an addition deviation from the mean in the testing data. Figure 18d shows that Jamaica air temperatures indicate there is a possible reduction with respect to the linear trend; i.e., there was an over estimation of the increasing rate given for a reference data. It should be noted that Jamaica during the first two decades (1948 to 1968) shows a significant increasing rate of temperature and caused an over estimation of trend during the reference data. Jamaica increasing rate is about three times higher than Puerto Rico and four times than Dominican Republic. In summary, Jamaica exhibits the largest increasing rate of surface air temperature and this phenomenon occurred during 1948 to 1968. Cuba shows no increasing rate during the reference data; however, exhibits a significant increment during 2007 to 2010. Puerto Rico shows a linear trend during the reference data in addition to a significant increment of temperature in 2010. Dominican Republic shows a significant trend during the reference data and no more changes during the testing data. In general, the climate change exhibited in the Caribbean islands is marginal compared to a global scale. 4.2.3 Global cloud cover The cloud cover monthly time series was obtained from the International Satellite Cloud Climatology Project (ISCCP). The cloud products were generated from sensors located on seven satellites, and the D2 product provides the properties of the clouds observed at every three hours and presented in monthly time series during the period of July 1983 to June 2005. Some of the included variables in this data set are cloud cover, top-cloud temperature, top-cloud pressure, optical thickness, and water path. The clouds are classified based on optical thickness and on top pressure. More information can be found in the following site: http://iridl.ldeo.columbia.edu/SOURCES/.NASA/.ISCCP/.D2/.all/. Quispe (2006) developed a user friendly computer program to read and manage the cloud data files. The global cloud cover file includes 6,596 grids and the cloud cover was selected at global and at Caribbean scales. Figure 19 shows the global cloud cover. Data from July 1983 to June 1994 were used as reference and from July 1994 to June 2005 as the testing data; the following values were selected for n = 264 months, and m = 132 months. A significant linear trend was identified and the harmonic analysis shows that there are three significant harmonics at 12, 132 and 66 months. Thus, the linear trend and the sinusoidal functions were removed from the original data and the stochastic process follows an AR(1) model. The estimates of the trend, the sinusoidal function, and stochastic model are given in Table 5. The left panel of Figure 20 shows the performance of the sequential test, which shows and an additional reduction of cloud cover that occurs during 1997 to 2001; however, increment was also detected from 2004 to 2005 and a possible false alarm occurs on 1994. With the purpose of better understanding the behaviour of the clouds, a second analysis was conducted without trend; i.e., in this case we want to measure the deviation Climate Change – Geophysical Foundations and Ecological Effects 106 with respect to the constant average of the reference data. The right panel of Figure 20 indicates that a significant reduction occurs with respect to the reference mean during 1995 to 2004. In summary, it can be concluded that a significant reduction of global cloud cover occurs and especially during the period of 1995 to 2004. Fig. 19. Global cloud cover. Fig. 20. The left panel shows that a significant reduction was detected during 1997 to 2001. The right panel shows an analysis withouttrend, and confirmed that a significant reduction of global cloud cover occurred during 1995 to 2004. 4.2.4 Caribbean cloud cover The selected Caribbean area includes the following geographical location: latitude from 17 N to 24 N and longitude from 87 W to 64 W, as shown in Figure 21. The data was organized as follows: the reference data include from July 1983 to June 1994 and the testing data are from July 1994 to June 2005. The reference data exhibit a significant reduction rate (-0.0259 per month) of cloud cover and the harmonic analysis shows that there is a significant periodic function with period equal to twelve. The remaining stochastic component can be modelled by a moving average of order one; i.e., MA(1). This model is also a particular case of the ARMA model, in which the model does not contain the autoregressive part. The 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004646566676869Global cloud coveryear% 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004-1-0.500.511.5Global cloud cover year Exp weigthed averages 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004-2.5-2-1.5-1-0.500.5Global cloud cover (without trend) year Exp weigthed averages Detecting of a Global and Caribbean Climate Change 107 Component Global cloud coverFingerprint f = s − sStochastic s = x − T –PTrend T = 68.49 − 0.0137tPeriodic component P = b sin 2πtp + c cos 2πtp i b c p (months) 1 -0.3819 -0.5707 12 2 0.7968 -0.625 132 3 0.0189 -0.4304 66 Stochastic s = 11 − 0.2327B a Standard deviation of a 0.605Table 5. Calculations for the ARMA fingerprint for the global cloud cover. procedure to compute the fingerprint and the estimation of parameters are given in table 6. The sequential test indicates that no additional change was found in the Caribbean cloud clover. Table 6 shows that Caribbean cloud cover during the reference data exhibit a reduction rate of -0.0259 % per month. Figure 22 Shows that Caribbean clouds cover no additional climate change is detected. Component Caribbean cloud coverFingerprint f = s − sStochastic s = x – PTrend T = 52.76 − 0.0259tPeriodic component P = 5.4444 sin 2πt12 + 2.869 cos 2πt12 Stochastic s = (1 + 0.2263B)aStandard deviation of a 5.99Table 6. Estimation of the fingerprint for the Caribbean cloud cover. Fig. 21. The selected Caribbean region to be studied. Climate Change – Geophysical Foundations and Ecological Effects 108 Fig. 22. The Caribbean clouds cover from July 1983 to June 2005. This data were extracted from ISCCP D2 using the Quispe (2006) program. Fig. 23. Caribbean clouds cover shows a no additional reduction of cloud cover. 5. Conclusion The major factors that have forced climate change are due to natural and anthropogenic causes such as solar radiation, volcano eruptions, increasing concentration of greenhouse gases, etc. A statistical test is proposed to detect when a significant climate change has 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 20043035404550556065Caribbean cloud coveryear% 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004-6-4-20246Caribbean cloud cover year Exp weigthed averages Detecting of a Global and Caribbean Climate Change 109 occurred. Usually, a climate indicator can be decomposed into three major features: trend, seasonal, and stochastic component. A climate change can be exhibited in any of the components of a given climate indicator. The introduced statistical test detects a climate change that can be observed in any of the three component of the process. The test consists on dividing the underlying time series in two parts. The first part of the observations is used for identifying trend, seasonality and stochastic components and these components are removed from the entire time series. The test consists of determining whether or not the remaining part of the series exhibits a significant deviation from the white noise. Thus, if a significant deviation from the white noise process occurs a climate change is detected; otherwise, no change has recorded. The proposed test was implemented to detected climate changes at global and Caribbean levels. The studied variables were: sunspots, concentration of carbon dioxide, surface air temperature, and cloud cover. At the Caribbean levels, it was found that cloud cover exhibits a significant reduction rate whereas the air temperature shows a significant increasing rate. Rainfall processes across the main Caribbean islands shows no significant trend during the studied period, which suggests that heavy rainfall events are being concentrated in small areas to maintain the rainfall process with no trend. The global cloud cover also shows a significant decreasing trend whereas the land-ocean surface temperature shows an increasing trend. Smaller clouds cover areas and high temperatures across the world also suggests that heavy rainfall processes will be concentrated in small continental areas causing flooding, landslide, and human and economic catastrophic impacts. A sequential statistical test has been introduced to detect when as significant climate change has occurred. The major contribution of this work is to introduce a statistical tool to determine without ambiguity when a climate change has occurred. One of the advantages of the proposed procedure is its simplicity; however, it requires of a large sequence of a reliable climate indicator, where a climate indicator is a meteorological variable that reveals the intrinsic climatic characteristic of a given region of the Earth. It is very important to understand the interaction of the physical processes and how the climate indicators are related. One of the major limitations of the proposed detection test is that some climate indicators after removing the trend and the periodic components still retain characteristics of a nonstationary process and difference operators to induce stationary behaviour are not applicable, since the difference operator not only removes the nonstationary behaviour but also remove the climate change. 6. Acknowledgments This research has been supported by National Aeronautics and Space Administration (NASA) EPSCoR program with grant NCC5-595 and also by the University of Puerto Rico. 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Lett., 31, L02207, doi:10.1029/ 2003GL019100 Wei, W.W.S (1990), Time Series Analysis: Univariate and Multivariate Methods, 1st Edition, Addison-Wesley Publishing Company http://www.statgraphics.com/downloads.htm http://www.minitab.com/en-US/default.aspx http://www.mathworks.com/ 6 Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles Valiya M. Hamza and Fábio P. Vieira National Observatory – ON/MCT Brazil 1. Introduction A detailed understanding of the nature of past climate changes is important in assessment of the effects of global warming trends identified in meteorological records (Hansen and Lebedeff, 1987). Nevertheless, there are large uncertainties in the reconstruction of the climate history of times prior to the period of instrumental records, there being considerable difficulties in experimental determination of past climate changes. In this context geothermal methods, based on results of temperature logs in boreholes stands out as one of the few methods that allow direct measurement of thermal signals in the subsurface induced by climate changes of the past. Geothermal methods have been employed during the last few decades in extracting information on climate changes of the recent past for several regions of the northern hemisphere (e.g. Cermak, 1971; Lachenbruch et al, 1982; Beltrami et al, 1992; Bodri and Cermak, 1995; Duchkov and Sokolova, 1998; Harris and Chapman, 2001; Majorowicz and Safanda, 2001; Golovanova et al, 2001; Beltrami and Burlon, 2004). Nevertheless, very few attempts have been made in using geothermal data for examining climate variations in low latitudes of the southern hemisphere. Among the studies in this category are the works carried out in Australia (Cull, 1979, 1980; Torok and Nicholls, 1996; Taniguchi et al, 1999a, 1999b; Appleyard, 2005), Brazil (Hamza, 1991; Hamza, 1998; Hamza et al, 1991; Cavalcanti and Hamza, 2001; Cerrone and Hamza, 2003) and South Africa (Tyson et al, 1998; Jones et al, 1999). Much of the work carried out in Brazil remain as publications of limited access, such as internal reports (Hamza et al, 1978; Eston et al, 1982; Hamza et al, 1987), academic theses (Vitorello, 1978; Araújo, 1978; Santos, 1986; Ribeiro, 1988; Del Rey, 1989; Cavalcanti, 2003) and meeting proceedings (Ribeiro, 1991; Souza et al, 1991; Hamza and Cavalcanti, 2001; Cerrone and Hamza, 2003; and Conceição and Hamza, 2006). In the present work we provide a synthesis of these earlier works, with emphasis on progress obtained during the period 2006 – 2011. As prelude to the discussion of results presented in this work we provide first a brief description of the sources and characteristics of the geothermal data employed and the criteria used for data selection. Details of the methods used for extracting information on past climateare set out in the next section. The results obtained in model simulations of temperature-depth profiles are classified into groups, representative of the major geographic Climate Change – Geophysical Foundations and Ecological Effects 114 zones. Finally, the climate history of South America, deduced from geothermal data, is compared with results of geothermal climate reconstructions from other continental areas. 2. Characteristics of the data base According to the recent compilations carried out by the National Observatory (Observatório Nacional – ON/MCT) in Brazil geothermal measurements have been carried out in over 5000 localities in South America (Hamza et al, 2010; Vieira and Hamza, 2010). Most of the earlier data were acquired as parts of basic research projects for heat flow determinations and also as parts of applied research projects for oil exploration and geothermal energy assessments. The focus of data acquisition in the earlier works has been on determining temperature gradients in the deeper parts of the boreholes. The characteristics of these data sets are variable, depending on the methods used for primary data acquisition. Of these, only the ones acquired using the so-called conventional (CVL) method provide direct information on the vertical distribution of temperatures at shallow depths and hence are potentially suitable for climate related investigations. The conventional method has been employed for geothermal studies in 134 localities, which is slightly more than 10% of the overall data set. It includes mainly temperature logs in bore holes and wells and thermal property measurements on samples representative of local geologic formations. In some cases estimation of radiogenic heat production was also carried out. The details of the experimental techniques employed for temperature and thermal conductivity measurements have been discussed in academic theses (e.g. Vitorello, 1978; Araújo, 1978; Del Rey, 1989) and in publications dealing with heat flow measurements (Hamza et al, 1987; Hamza and Muñoz, 1996; Gomes and Hamza, 2005). A direct evaluation of the quality of data acquired in the earlier works is a difficult task since the experimental techniques used for temperature and thermal conductivity measurements have undergone substantial changes over the last few decades. The sources of conventional data sets employed in the present work may be considered as falling into five main groups: - New data acquired in ten different localities in the Amazon region, during the period of 2006 to 2008 (Hamza, 2006; Hamza, 2007); - Results of recent geothermal measurements in the cordilleran region of Colombia (Hamza et al, 2009; Alfaro et al, 2009). - Data acquired during the period of 2000 - 2005, as part of geothermal projects for mapping heat flow variations in the coastal area of southeast Brazil (Gomes, 2003; Gomes and Hamza, 2005; Hamza et al, 2005). Some of these have been employed in studies of climate change the state of Rio de Janeiro (Cerrone and Hamza, 2003; Hamza et al, 2003); - Data acquired during the decade of 1980, mainly in the state of São Paulo, as parts of hydrocarbon and geothermal energy exploration programs. Some of these data are reported as parts of academic theses of the 1980s (Santos, 1986; Ribeiro, 1988; Del Rey, 1989) while some are parts of related publications (Santos et al, 1986; Del Rey and Hamza, 1989; Hamza et al, 1987); - Data acquired during the decade of 1970 in the southern and eastern parts of Brazil. Most of the results have been published in the Brazilian Geothermal Data Collection – Volume 1 (Hamza et al, 1978). Some are part of academic theses of the late 1970s (Vitorello, 1978 and Araújo, 1978) and related publications (Vitorello et al, 1978; Hamza, Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 115 1982). Data acquired for four boreholes in the cordilleran region of Peru is reported in the IHFC data collection by Huang and Pollack (1998). The geographic distribution of the overall data sets is illustrated in the map of Figure (1). Fig. 1. Localities of geothermal measurements in South America (Vieira and Hamza, 2010). Letters in the legend refer to Koppen climate classification: A – Tropical; B – Dry; C – Temperate; D – Continental; E - Polar (Koppen, 1936; Pidwirny, 2006). 2.1 Selection criteria for climate studies The characteristics of the conventional (CVL) data set were examined carefully to screen out records with indications of possible perturbations arising from non-climatic effects. Also, it was necessary to eliminate those which do not provide fairly reliable determinations of both the steady and the transient components of the subsurface thermal field. In an attempt to guarantee the reliability of the data set the following quality assurance conditions were imposed: a. The depth of borehole is sufficiently large that the lower section of the thermal profile allows a reliable determination of the geothermal gradient, free of the effects of recent climate changes. Order of magnitude calculations indicate that surface temperature Climate Change – Geophysical Foundations and Ecological Effects 116 changes of the last centuries would penetrate to depths of nearly 150meters, in a medium with a thermal diffusivity of 10-6 m2/s. Thus boreholes of at least 200 m deep are necessary for a reliable determination of the local geothermal gradient. The choice of this depth limit is rather arbitrary since the possibility that low amplitude climate signals of earlier periods are present at larger depths of up to several hundreds of meters cannot entirely be ruled out. However it is a reasonable compromise for examining subsurface thermal effects of ground surface temperature (GST) variations of the last few centuries; b. The temperature-depth profile is free from the presence of any significant non-linear features in the bottom parts of the borehole, usually indicative of advection heat transfer by fluid movements, either in the surrounding formation or in the borehole itself; c. The time elapsed between cessation of drilling and measurements in boreholes is at least an order of magnitude large compared to the duration of drilling, minimizing thereby the influence of eventual thermal perturbations generated during the drilling activity; d. The lithologic sequences encountered in the borehole have relatively uniform thermal properties and are of sufficiently large thickness that the gradient changes related to variations in thermal properties does not lead to systematic errors in the procedure employed for extracting the climate related signal; and e. The elevation changes at the site and in the vicinity of the borehole are relatively small so that the topographic perturbation of the subsurface temperature field at shallow depths is not significant. The sites of these selected boreholes are distributed in the eastern parts of Brazil (in the states of Santa Catarina, Paraná, São Paulo, Minas Gerais, Rio de Janeiro and Bahia), in the Amazon region, central cordillera in Colombia and eastern cordillera in Peru. The majority of the selected temperature logs are from boreholes with depths greater than 200 meters. Some log data for depths less than 200 m were also considered, as these are found to provide complementary information on subsurface temperature fields at shallow depths which may be compared with those encountered in areas where deeper boreholes are situated. Most of the data sets acquired during the decades of 1970 and 1980 have temperature measurements at depth intervals of ten to twenty meters. In more recent logs measurements have been made at intervals of two meters. Such recent logs may be considered as capable of providing more robust estimates of the background temperature gradients. Data from boreholes with depthsshallower than 150 meters were not considered in the present work in view of the potential uncertainties in the determination of the local undisturbed gradient and consequent difficulties in extraction of the climate signal. On the other hand, information on climate changes of the recent past available in such logs may be used in obtaining qualitative estimates of GST changes. Data acquired at shallow depths of less than 20 meters were excluded from analyses for climate changes, avoiding thereby eventual perturbing effects of diurnal and seasonal variations in the reconstruction of surface temperature history. In an earlier study Hamza et al (2007) reported results of geothermal measurements for a number of sites in the Brazilian territory. In the present we have included results of additional studies carried out during the period of 2006 to 2011. The data sets have been classified into groups, designated as subtropical highlands, subtropical humid zones (of the interior and of the coastal areas), tropical Amazon region, semi-arid zones and cordilleran regions in western Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 117 parts of the continent. The area extents of these geographic sectors are in large part similar to the prevailing climate zones indicated in Figure (1). Typical examples of temperature profiles encountered in boreholes in Brazil, Peru and Colombia are illustrated in Figure (2). In this figure some of the temperature-depth profiles have been shifted laterally to convenient positions along the temperature axis, to avoid overlap and to allow for easy visualization. Consequently, the temperature axis in Figure (2) displays only relative values. Fig. 2. Vertical distributions of temperatures in selected localities in Brazil, Colombia and Peru. The climate changes of the recent past are often considered responsible for the non-linear features in temperature logs. The vertical distributions of temperatures in boreholes, some examples of which are illustrated in Figure (2) reveal several remarkable features. For example, the temperature depth profiles at shallow depths are consistently concave towards the depth axis, which is indicative of surface warming events of relatively recent times. The widespread occurrence of such temperature-depth profiles in almost all of the major geographic zones, irrespective of the local geological complexities and changes in soil type, is considered a clear indication that the observed features are generated by surface warming events of large spatial dimensions. Temperature profiles that are convex towards the depth axis, and hence characteristic of cooling events, were not encountered. 0100200300400500Depth (m)Relative TemperatureCosmopolis - BrazilJacarei - BrazilSeropédica - BrazilCaraiba - BrazilPoço Fora - BrazilPapanduva - BrazilP. caldas - BrazilLobitos - PeruBogotá - Colombia Climate Change – Geophysical Foundations and Ecological Effects 118 3. Methods employed in data analysis Three different methods have been employed in data analysis: forward models, inversion methods and signal backstripping methods. Brief descriptions of these methods are provided in the following sections. 3.1 Forward models The basics of forward modeling approach has been discussed extensively in the literature (Birch, 1948; Cermak, 1971; Vasseur et al, 1983; Lachenbruch et al, 1986, 1988). For the case where surface temperature variation can be represented by a power law relation, analytic solutions are readily available. Thus, for a linear (or ramp type) change in surface temperature the relation between the amplitude of the climate signal (ΔT) and the time elapsed (t) at any depth (z) is given by the relation (Carslaw and Jaeger, 1959):  2( ) 4 / 4T z T i erfc z t    (1) where i2 erfc is the second integral of the complementary error function and κ the thermal diffusivity of the medium. The best fitting ramp function is obtained by inverting the above relation using iterative procedures such as linearized Newton’s method. The iterative procedure for this model, referred to as Ramp Inversion (Chisholm and Chapman, 1992; Golovanova et al, 2001; Roy et al, 2002), allows simultaneous determination of the magnitude of surface temperature change and the period of its occurrence. The main limitation of the forward model approach is that it resolves mainly the first-order features in the GST history. This is a consequence of the implicit assumption that the bottom parts of the log data, employed in determination of background temperature gradients, are free of transient perturbations. The basic steps in forward model approach include identification and separation of the steady and transient components present in temperature profiles at shallow depths. The steady state component is determined by the flow of heat from the interior of the Earth while the transient component is induced by downward propagation of a climate related thermal signal induced at the surface. Usually a linear fit to the deeper portion of the log data, where the climate perturbation is practically absent, allows determination of the local temperature gradient. However some care is necessary in selecting the depth interval for determination of the gradient. If the temperature gradient is calculated using a small subset of data from the lowermost part of the borehole its standard deviation (σG) is likely to be relatively large, a consequence of the large root mean square (rms) deviation associated with the small number of data points. Progressive inclusion of data from the overlying parts in least square analysis leads to a steady initial decrease in σG, as the estimation of gradient becomes more robust. However, as more data are included from shallower depths (where non-linear features are present) this tendency is reversed and σG increases. In the present work, the depth corresponding to the minimum value of σG is considered as indicative of the top of the unperturbed zone. The background gradient determined for the depth interval below this zone is used for calculating the steady component of the temperature field. Subtracting it from the observed temperatures allows determination of the transient component. A similar procedure has also been employed by Roy et al (2002) in the separation of steady and transient components of temperature profiles in the Indian subcontinent. Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 119 3.2 Inverse models In the inverse problem approach (Tarantola and Valette, 1982) a priori information is explicitly incorporated in constraining the solution. The functional space inversion (FSI) method discussed by Shen and Beck (1991, 1992) and Shen et al (1992) makes use of the non-linear least squares theory in solving the one dimensional heat conduction equation in a layered half space. The algorithm employed finds the model that minimizes the misfit function:         1 10 0 0 01( )2t td mS m d d C d d m m C m m                (2) where d and d0 are respectively the calculated and observed temperatures, m and m0 the calculated and a priori model parameters and Cd and Cm the covariance matrices of d0 and m0. The term Cd indicates the uncertainty in the observed temperature-depth data while Cm indicates uncertainty in the a priori model. The selection of appropriate values of a priori standard deviations for the temperature (σd0) and thermal conductivity (σk0) data are important in determining the solutions. The main advantage of FSI formulation is that it does not predetermine the steady state temperature profile. Instead, both the steady state and transient profiles are estimated simultaneously. In addition,stratospheric clouds (PSCs) form during polar night (Figure 1). PSCs develop at temperatures below about 195 K (= -78 °C) where nitric acid trihydride crystals form (NAT, HNO3 ·3H2O). Under the given conditions in the lower stratosphere ice particles develop at temperatures below approx. 188 K (= -85 °C). Due to different land-sea distributions on the Northern and Southern Hemisphere, the lower stratosphere over the south pole cools significantly more in winter (June – August) than the north polar stratosphere (December – February) (see Section 1.2). The climatological mean of polar winter temperatures of the lower Arctic stratosphere is around 10 K higher than that of the lower Antarctic stratosphere. While the Antarctic stratosphere reaches temperatures below PSC-forming temperatures for several weeks every year, there is a pronounced year-on-year variability in the north polar stratosphere: relatively warm winters, where hardly any PSCs develop are Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 5 observed, as well as very cold winters, with conditions similar to that of Antarctica. This means that expansive PSC fields develop in the Antarctic stratosphere every year, but are seldom seen over the Arctic (see Section 2.1). A detailed description of chemical processes affecting ozone is given by Dameris (2010). Fig. 1. Polar stratospheric clouds over Finland. The picture was taken on January 26, 2000 from the DLR research aircraft Falcon. 1.2 Importance of stratospheric dynamics Since the 1990ies it became obvious that the ozone layer was not just getting thinner over Antarctica, but over many other regions, too, although to a lesser extent (see Figure 5). Many observations from satellite instruments and ground based techniques (incl. radiosondes) have shown a clear reduction of the amount of stratospheric ozone, e.g. in middle geographical latitudes (about 30°-60°) of both hemispheres. From that time on observational evidences and the actual state of understanding have been reviewed in WMO/UNEP Scientific Assessments of Ozone Depletion (WMO, 1992; 1995; 1999; 2003; 2007; 2011). It turned out that the thickness of the stratospheric ozone layer is not solely controlled by chemical processes in the stratosphere. Physical and dynamic processes play an equally important role. The polar stratosphere during winter is dominated by strong west wind jets, the polar vortices. Due to the different sea-land distribution in the Northern and Southern Hemisphere these wind vortices develop differently in the two hemispheres. Large-scale waves with several hundreds kilometres wavelength are generated in the troposphere, for example during the overflow of air masses over mountain ridges. These waves propagate upward into the stratosphere and affect the dynamics there including the strength of the polar wind jets. The polar vortex in the Southern Hemisphere is less disturbed and therefore the mean zonal wind speed is stronger than in the Northern Hemisphere. In the Southern Hemisphere this leads to a stronger isolation of stratospheric polar air masses in winter and a more pronounced cooling of the polar stratosphere during polar night (see Section 1.1). Climate Change – Geophysical Foundations and Ecological Effects 6 Additionally, atmospheric trace gas concentrations are affected by air mass transports, which are determined by wind fields (wind force and direction). The extent to which such a transport of trace gases takes place depends on the lifetime of the chemical species in question. Only if the chemical lifetime of a molecule is longer than respective dynamical timescales, the transport contributes significantly to the distribution of the chemical substance. For example, in the lower stratosphere the chemical lifetime of ozone is long enough that transport processes play a key role in geographical ozone distribution there. At these heights, ozone can be transported to latitudes where, photochemically, it is only produced to an insignificant extent. In this way, ozone generated at tropical (up to about 15°), sub-tropical (about 15°-30°) and middle latitudes is transported particularly effectively in the direction of the winter pole (i.e. towards the north polar region from December to February and towards the south polar region from June to August), due to large-scale meridional (i.e. north-south) circulation. There, it is mixed in with the local air. This leads to an asymmetric global ozone distribution with peaks at higher latitudes during the corresponding spring months and not over the equator (see Figures 7 and 8). At higher stratospheric latitudes, it is thus particularly difficult to separate chemical influences on ozone distribution (ozone depletion rates) from the changes caused by dynamic processes. With respect to climate change due to enhanced greenhouse gas concentrations (i.e. in particular carbon dioxide, CO2, methane, CH4, and nitrous oxide, N2O) caused by human activities, it is expected that temperatures in the troposphere will further increase (IPCC, 2007) and that they will further decrease in the stratosphere due to radiation effects (Chapter 4 in WMO, 2011). Since the reaction rates of many chemical reactions are directly depending on atmospheric temperature, climate change will directly influence chemical processes and therefore the amount and distribution of chemical substances in Earth atmosphere. Moreover, changes in atmospheric temperature and temperature gradients are modifying dynamic processes that drive the circulation system of the atmosphere. This would result in changing both, the intensity of air mass transports and the transportation routes, with possible long-term consequences for the atmospheric distribution of radiatively active gases, including ozone. Changes in distribution of the climate-influencing trace gases in turn affect the Earth’s climate. 2. Measuring ozone with satellite instruments and numerical modelling Since many years ozone distribution in the stratosphere is observed by ground-based and satellite instruments (see Section 2.1). In particular measurements from space help to get a global view of the state of the stratospheric ozone layer and its temporal evolution including short-term fluctuations and long-term changes (i.e. trends). An outstanding task is to combine multi-year observations derived from different sensors flown on different satellites in a way that at the end one gets consistent and homogeneous data products which enable solid scientific investigations of processes causing the basic state of the atmosphere and its variability. In addition to a detailed analysis of existing measurements, numerical models of the atmosphere are used to reproduce as best as possible recent atmospheric conditions and the modulation in space and with time. Sensitivity studies help to identify those processes most relevant to describe climatological mean atmospheric conditions as well as spatial and temporal changes. For example, changes in climate, the temporal evolution of the ozone layer and the connections between them are simulated by atmospheric models which consider all known and relevant dynamical, physical as well as chemical processes (see Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 7 Section 2.2). In such numerical studies, it is important to consider natural processes and their variations, as well as human activities relevant to atmospheric processes. A comprehensive evaluation of data derived from numerical model simulations with respective observations helps to identify the strength and weaknesses of the applied model systems which to a great part reflect the current state of the knowledge about processes acting in Earth atmosphere (see Section 3). A good understanding of all crucialit allows consideration of the vertical distribution of thermo-physical properties and their uncertainties as model parameters, allowing thereby determination of a more detailed GST history where it is possible to identify second order features. Also, FSI formulation includes as model parameters all variables that govern the conductive thermal regime (background heat flow density, the GST history as a function of time and thermal properties as functions of depth). As prelude to the presentation of the results obtained by the functional space inversion method we provide brief descriptions of the steps taken in data processing and analysis. These include specifying the depth intervals and time periods used for inversion, setting a priori standard deviations of temperature and thermal conductivity data sets and measures taken to minimize the undesirable consequences of the null hypothesis in the inversion scheme. The estimates of the depth at which the thermal regime is supposedly untouched by the GST variations and the time limit beyond which GST variations cannot be recovered from the given temperature log data, were chosen in accordance with the depth extent of the available temperature log data. In particular, the depth estimate is set to be greater than the deepest data point because the calculated data are projected (interpolated) from the finite element solution. As for the time limit there is no harm in setting a value compatible with or greater than the depth extent of the borehole (Shen and Beck, 1992). On the other hand, use of a shorter-than-necessary time span would end up in “telescoping” the GST history. Unless there are independent evidences indicating a rapid return to unperturbed conditions it seems prudent to assume that this return take place gradually. Use of shorter time spans leads to slight reductions in the magnitude and duration of the cooling events of the earlier periods. As pointed out by Shen and Beck (1991; 1992) the results of GST history, determined by functional space inversion, is sensitive to a priori standard deviations of thermal conductivity. The preferred values for the standard deviations are based on considerations of the trade-off between consistency of the solution and data resolution. In the present work we have used 50mK for standard deviation of the temperature data (σd0) Climate Change – Geophysical Foundations and Ecological Effects 120 and 1W/m/K for standard deviation of the thermal conductivity (σk0). These values are of the same order of magnitude as those adopted by Safanda and Rajver (2001) and Golovanova et al (2001). FSI inversion makes use of an a priori null hypothesis in obtaining robust estimates of the prior steady state. However, this built-in feature can potentially lead to undesirable results when inversions are attempted for determining GST history of periods not correlated with the subsurface temperature data. Thus, in the absence of suitable temperature data for shallow depths the inversion scheme generates artificial values for the late part of GST history. It is clear that acquisition of reliable temperature data for shallow depths is important in determining GST history of the last few decades. On the other hand, the results also show that occurrence of artificial cooling trends for the decades prior to 1970 are possible only in cases where temperature measurements are restricted to depths greater than 100m. 3.3 Signal back stripping approach Both the conventional and the Bayesian inversion methods have inherent difficulties in identification of individual thermal signals originating from climate variations that are episodic. In such cases methods based on signal back stripping approach are more convenient. Following the standard practice we also assume that the residual temperature profile represents is a superposition of individual perturbations. The characteristics of such perturbations vary, mainly as a result of differences in the magnitudes and time periods of individual GST episodes. The essence of the procedure adopted in the present work can be understood by considering magnitudes of temperature perturbations at two conveniently selected depths z1 and z2 in the residual profile:  1 1 / 4 'dT Aerfc z t  (3a)  2 2 / 4 'dT Aerfc z t  (3b) Dividing (3a) by (3b) and designating the ratio dT1/dT2 by δ we have:   112 2/ 4 '/ 4 'erfc z tdTdT erfc z t  (4) Note that equation (4) does not depend on the magnitude of the temperature perturbation but only on the selected value of the time period. Iterative methods may now be employed for determining the appropriate value of δ for the depth interval. The magnitude of this perturbation can be obtained from the relation:  11 / 4 'dTAerfc z t (5) In applying this procedure it is necessary to start with results of the lowermost section of the residual temperature profile. If the borehole is sufficiently deep it is fairly reasonable to assume that the residual temperatures of the deeper parts retain the effects of only the Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 121 earliest perturbation. The magnitude and period of this earliest perturbation can be determined through the use of equations (3) and (5). It also opens up the possibility of removing the effects of this particular perturbation from the original residual temperature profile. The result is a back stripped profile free of the effects of the earliest perturbation. The procedure is repeated successively for identifying and removing the perturbations arising from later climate episodes. 4. Estimates of surface temperature changes 4.1 Forward model results A summary of the results obtained in fitting forward models to the observational data discussed in this work, is presented in Table (1). It includes magnitudes of the GST change (ΔT) and their duration (t) as well as the values of the root mean square (rms) misfit between the model and the observational data. For reasons of brevity, we present here only the results for the ramp function model. Climate changes inferred on the basis of this model are grouped together for the five major geographic zones: subtropical highlands (elevations >400 meters) subtropical lowlands (elevationsthe period of approximately 1750 to 1850. A closer examination of the results illustrated in Figure (3) reveals some marked differences in the GST values within individual geographic zones. The primary reason for the occurrence of such intra-zonal variations is unknown at the moment, but it is likely that they are related to microclimatic histories of individual sites. Also, the depth distribution of the transient components indicates that the magnitude of warming event is relatively smaller (in the range of 1.4 to 2.20C) for the semi-arid zone in north eastern parts of Brazil. The close agreement between the results for the localities in Table (1) is considered as indication that local changes in vegetation cover and soil types have only a minor influence on the surface thermal budget in semi-arid zones. The GST change in this region seems to have had its beginning during the time period of 1850 to 1900, significantly earlier than the corresponding periods for other geographic zones. Climate Change – Geophysical Foundations and Ecological Effects 122 Climate Zone Locality Coordinates ΔT (oC) Climate variation rms (mK) Duration Year of Onset Subtropical Highlands Á. Lindóia 220 29’/460 38’ 3.2 105 1877 4.4 Amparo 22° 43’/46° 46’ 2.8 115 1852 5.5 Araras 22° 21’/47° 22’ 3.4 40 1942 6.4 Cosmópolis 230 43’/470 12’ 3.8 75 1907 5.5 Itapira 220 28’/460 43’ 3.8 40 1942 10.2 Rafard 23° 00’/47° 31’ 3.8 60 1922 6.8 Jacarei 230 18’/450 57’ 2.4 105 1880 3.5 São Paulo 23° 34’/46° 44’ 2.1 105 1905 7.2 Serra Negra 22° 36’/46° 32’ 2.0 105 1877 3.9 P. Caldas 210 55’/460 25’ 2.0 50 1926 13.7 Teresópolis 22° 26’/ 42° 57’ 2.0 55 1945 5.5 L. Muller 28º 40´/49º 30´ 2.6 80 1895 10.0 Subtropical Lowlands Seropédica 22º 46´/43º 39´ 3.8 145 1860 23.1 Miracema 22º 01'/41º 06' 3.6 30 1969 7.3 Campos 21º 46'/41º 17' 3.0 20 1980 6.7 Itapemirim 19° 06’/410 04’ 3.6 65 1910 14.1 S. Sebastião 23° 48’/45° 25’ 2.7 110 1880 7.8 Subtropical Humid Itu 230 15’/470 19’ 1.2 60 1922 3.2 Jundiaí 230 10’/460 52’ 1.2 50 1932 7.2 Papanduva 260 23’/500 08’ 1.2 90 1885 15.0 Cach. Sul 30º 00´/52º 55´ 1.8 70 1905 12.0 Maricá 22° 54’/42° 45’ 0.4 15 1985 2.9 Rio Bonito 21° 25’/42° 12’ 1.8 40 1960 7.7 Semi-Arid Arraial 12° 32’/42° 50’ 1.4 85 1890 8.4 Caraiba 09° 28’/39° 50’ 1.4 110 1865 9.3 Jacobina 11° 11’/40° 31’ 1.9 150 1828 8.7 Poço Fora 090 41’/390 51’ 1.8 105 1870 4.9 Tropical Rain Forest Manaus 02º 57´/60º 01´ 1.3 40 1960 15.0 Belém 01º 27´/48º 27´ 2.2 55 1953 9.4 Salinópolis 00º 38´/47º 20´ 2.1 65 1943 8.6 Dom Eliseu 04º 17´/47º 34´ 1.9 60 1948 9.2 Cordilleran Regions Pen 742 81º 10´/04º 17´ 4.0 200 1779 7.4 Lobitos 81º 16´/04º 27´ 2.5 200 1779 8.3 Lomitos 70º 39´/17º 16´ 2.0 200 1779 7.2 Bogotá 74º 05´/04º 38´ 2.8 150 1860 9.5 Table 1. Results of Ramp Inversions of GST changes, for selected localities in South America. ΔT is the magnitude of ramp change, and rms the root mean square misfit. Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 123 Fig. 3. Magnitudes of climate changes and its vertical distributions, deduced from forward model fits to borehole temperature profiles at selected localities in South America. A careful examination of figure (3) reveals that some of the transient temperature profiles do have small negative values for depth intervals corresponding to the lower parts of the transient sections. The negative residuals are usually considered as arising from cooling events prior to recent warming episodes. However, the magnitudes of such events appear as subdued features, because of the implicit assumption in forward model approach that the bottom parts of the borehole are free of transient perturbations. Results of numerical simulations indicate that occurrence of negative residuals is quite sensitive to subtle changes in the value adopted for the background temperature gradient. This is the main limitation of the forward models, which resolves mainly for the first-order features in the GST history. We conclude by noting that residual temperature profiles similar to those obtained in the present work were also reported by Golovanova et al (2001) for the Urals region and Roy et 050100150200250300-0.5 0.5 1.5 2.5 3.5Depth (m)Magnitude of Climate Change (oC)Cosmópolis - BrazilJacarei - BrazilA. Lindóia - BrazilSeropédica - BrazilJacobina - BrazilP. Fora - BrazilBogotá - ColombiaLobitos - Peru Climate Change – Geophysical Foundations and Ecological Effects 124 al (2002) for the Indian Peninsula. A complementary analysis of this problem is provided in section (4.2) below, where we discuss the residual temperature profiles derived using the inversion method. 4.2 Results of functional space inversion The method of Functional space inversion (FSI) was employed in analysis of temperature-depth profiles for 14 localities distributed over three main geographic zones of Brazil. The criteria used in the selection of profiles included availability of both thermal property data of subsurface layers and supplementary information on the history of changes in the vegetation cover. In discussing the results it is important to point out that the FSI method provides more detailed information on the GST history than that provided by the forward model approach. However, in comparing the GST histories of several localities it is convenient to work with deviations from the site specific mean rather than the absolute value. In the present work, GST deviations are calculated by subtracting the model results from the a posteriori estimate of the site specific mean. The results of FSI method, illustrated in figure (4), reveal several characteristic features in the GST history of the study area. Foremost among these are the indications that surface temperatures have increased by as much as 1 to 4oC, over the last century. This observation is in reasonable agreement with the results of the ramp function model discussed in the previous section. However, in all localities the warming events seem to be preceded by cooling episodes occurring over the time period of approximately 1700 to 1900. The amplitudes of the cooling events are much less, falling generally in the range of 0.5 to 1oC. For time periods prior to the 17th century the resolving power of the inversion method is poor, a consequence of the limitations in sensitivity and precision of sensors used for temperature measurements in boreholes. Hence FSI model calculations for periods prior to 1700 may not necessarily be representative of true climate history. Even though the data set is poor the results of Figure (4) seem to indicate that the warming trends are less pronounced in semi-arid regions relative to the tropical humid regions. The results also reveal a small time shift in the occurrence of climate warming in highlands regions compared with that for semiarid regions. For example, the warming event in the highlands region had its beginning during the time period of 1850 to 1900 while that for semi-arid regions seems to have had its beginning during the period of 1670 to 1860. Also the durations of both warming and cooling episodes appear to be relatively smaller in highland regions when compared with those for the semi-arid regions. Such differences have important implications for understanding evolutionary trends in climate history of eastern Brazil. The vertical distributions of transient components derived from FSI inversions are similar to those found for the forward model results, illustrated in Figure (3). The magnitudes of the transient components decrease rapidly with depth. However, the residual temperatures become significantly negative at depth intervals corresponding to the lower parts of the transient sections. Such negative residuals are indicative of the occurrence of cooling events prior to recentwarming episodes. We conclude that the vertical distributions of residual temperatures in FSI method are better representations of subsurface transient thermal regimes than those derived from the forward model approach. Another conspicuous feature of the results by the FSI method in Figure (4) is the presence of brief cooling episodes for the recent decades, since 1970. There is a possibility that short period cooling episodes are spurious, a consequence of the null hypothesis employed in the Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 125 computational process of the FSI method (Shen and Beck, 1992). The potential undesirable effects of the null hypothesis may be minimized by limiting the GST history to periods compatible with reliable subsurface temperature data. In the present work temperature data are available for depths less than 50 meters in all of the data sets employed in inversion schemes. Consequently the cooling trends observed in our results, for the decades prior to 1970, cannot be attributed to the spurious effects of the null hypothesis. Examples of cooling trends similar to those found in the present work can also be seen in the results reported for several localities in Europe and North America (Bodri and Cermak, 1995; Rajver et al, 1998; Stulc et al, 1998; Golovanova et al, 2001). Fig. 4. History of ground surface temperature variations associated with climate changes of the recent past, deduced on the basis of functional space inversion models fits to borehole temperature profiles, for selected localities in South America (Adapted with modifications from Hamza et al, 2007). The uncertainties in the estimates of GST in the inversion method can somewhat be improved by carrying out simultaneous inversion of temperature profiles of several sites in the same geographical province. In the present work we have carried out simultaneous inversions only for repeat measurements at Seropédica, in the state of Rio de Janeiro. In view of notable differences in the local soil conditions and vegetation cover, no attempt has been made for carrying out simultaneous inversions of temperature profiles from different locations discussed in the present work. Also, most of these sites are separated by large distances and fall within areas with distinctly different microclimate conditions and geographic characteristics. -2-112341500 1700 1900GST Deviations (oC)Time (Year A.D.)Águas LindóiaAmparoJacareiJacobinaPapanduvaCaraibaCosmopolis Seropédica Climate Change – Geophysical Foundations and Ecological Effects 126 A summary of the results of inverse modelling is presented in Table (2). The summary includes maximum and minimum values of ground surface temperatures and their respective times of occurrence. Also given in this table are the difference between the maximum and minimum values of GST, the time elapsed between the maximum and minimum and aposteriori estimates of undisturbed GST. The values of the differences in magnitudes are in the range of 1.3 to 30C, which is slightly lower than the range indicated by the results of the forward model approach. On the other hand, the values of the differences in the time periods are in the range of 62 to 147 years, which is comparable to the ranges indicated by the results of the forward model approach. Locality Maximum Minimum Difference T0 (°C) Log Year °C Age °C Age Mag. Yr Amparo 21,1 1949 18.1 1802 3.0 147 18.9 1982 Itu 20.7 1982 19.4 1920 1.3 62 19.9 1982 Araras 22.8 1982 19.8 1910 3.0 72 20.9 1982 Campos 25.7 1999 22.7 1936 3.0 64 23.9 2000 C. Itapemirim 23.7 1971 20.0 1883 3.7 88 21.2 1976 Arraial 28.8 1967 27.2 1863 1.6 104 27.7 1976 Caraíbas 30.1 1946 28.6 1812 1.6 134 29.0 1976 Poço de Fora 30.4 1976 28.2 1822 2.1 154 28.7 1976 Table 2. Results of functional space inversions for selected data sets. Maximum and minimum values of GST variations and their respective years of occurrences are given in columns 2 - 5. Also given are model results for GST deviations and the respective periods. T0 is the aposteriori steady state temperature. 4.3 Results of signal back-stripping approach Techniques of signal back-stripping were used in analysis of temperature log data from 15 localities in south east Brazil. In the present work we limit the discussion to the results obtained for the locality of Seropédica (Rio de Janeiro). The vertical distributions of temperatures obtained during the various stages of the back stripping process are illustrated in the set of upper and lower panels of Figure (5). The segment on the left side of the upper panel in this figure refers to results of the first stage of the back stripping process. Here the blue curve indicates the initial reduced temperatures and the red curve the first signal extracted by the back stripping method. The extracted signal points to an episode of climate change that took place 115 years back in time and had a magnitude of 2.90C. Similarly, the segments on the right side of the upper panel and that on the left side of the lower panel of this figure illustrate, respectively, the results of the second and third stages of the back stripping process. According to the results obtained the signal extracted in the second stage points to an episode of climate change that took place 93 years back in time and had a magnitude of 0.20C. On the other hand, the signal extracted in the third stage points to an episode of climate change that took place 21 years back in time and had a magnitude of 0.80C. The segment on the right side of the lower panel illustrates the residual temperatures, after extracting the above mentioned three climate signals. The back stripping process is terminated at this third stage since the magnitudes of the residual temperatures are less than 0.050C, below the sensitivity limit of experimental system used in data acquisition. The overall history of climate change derived from the back stripping method is illustrated in Figure (6). Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 127 Fig. 5. Results of signal back stripping method employed in disentangling the climate history of Seropédica (Rio de Janeiro). Climate Change – Geophysical Foundations and Ecological Effects 128 Fig. 6. Climate history of Seropedica (Rio de Janeiro) derived from results of the signal back stripping method. 5. Discussion 5.1 Climate changes in South America The results obtained in the present work have contributed to substantial improvements in data base for climate change in South America. The area covered in the data base includes several geographic zones in Brazil, Colombia and Peru. Classical inverse models were employed in the analysis of temperature logs from over 30 localities and, in addition, Bayesian inverse modelling was carried out for data from 20 selected sites. The model results have allowed determination of the magnitude as well as the duration of ground surface temperature changes in the major geographic zones of South America. The map of recent climate changes derived from geothermal data is presented in Figure (7). According to the results obtained the magnitude of GST changes are in the range of 2 to 3.5oC but have had their beginning during the early decades of the 20th century. Nearly similar trends are seen in temperature-depth profiles of bore holes in tropical as well as subtropical zones of the interior and coastal areas. The data from semi arid zones also indicate occurrence of surface warming events but the magnitudes are in the range of 1.4 to 2.2oC while the duration of the warming event is larger, extending back into the last decades of the 19th century. The magnitudes of GST variations are relatively largein localities which have undergone recent changes in vegetation cover. Also there are indications that GST changes are practically insignificant in areas of tropical rain forest. 5.2 Global climate changes The improved data base on GST changes in the South American continent has contributed to a better understanding of climate changes in the southern hemisphere. In this context, it is convenient to examine also its implications for global variations. It is clear that improved assessments of global changes require an integrated analysis of both the geothermal data sets as well as the meteorological data sets (such as HadCRUT3) reported for oceanic and continental regions (Brohan et al, 2006; Jones and Moberg, 2003; Rayner et al, 2003). Major obstacles in such a venture springs from the wide disparities in data density, and it is Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 129 Fig. 7. Map of recent changes in ground surface temperatures derived from geothermal data sets, and supplemented with ground data for selected localities. convenient to adopt procedures that minimize problems arising from the non-homogeneous distribution. In the present case, the surface area of the globe is divided into a regular grid system composed of 5 x 5 degree grid cells and average values of surface temperature variation in the grid elements calculated. Experimental data are available for almost all of the grid elements covering the continental regions and a significant part of the grid system for the oceanic regions. Following the common practice employed in deriving maps of global representations interpolated values were used for grid elements without data. Techniques of spherical harmonic representation (Hamza et al, 2008) were employed in analysis of global variations. The harmonic representation of surface temperature variations (q) may be represented as: -80 -70 -60 -50 -40 -30South Latitude-60-50-40-30-20-10010West Longitude00.61.21.82.43 Climate Change – Geophysical Foundations and Ecological Effects 130          0 0, cos cos sin cosN nnm nm nm nmn mq A m P B m P             (7) where  is the longitude  = 90 – ψ, is the colatitude, P´nm(cos ) is the associated Legendre function that is fully normalized and Anm and Bnm the coefficients of the harmonic expansion. The expression for evaluation of P´nm is: ´ mnm nm nP P K (8) where Pnm is the associated Legendre function given by: 2( 2 )0( 1) (2 2 )!(cos ) cos! ( )! ( 2 )!2n mInttmn m tnm ntn tsenPt n t n m t         (9) and 0 0(2 1)( )!,( )!0 2mnif m HH n n mKn mif m H          (10) In equation (9)  2Int n m is the largest integer that is lower than  / 2n m . Full normalization of associated Legendre functions (Pnm) requires that the following equations be satisfied:  220 0(cos ) ( ) 4nmP sen m sen d d         (11a)  220 0(cos )cos( ) 4nmP m sen d d         (11b) The coefficients Anm and Bnm are evaluated by fitting the harmonic expansion to the set of experimental data, which are the values of surface temperature changes (q) and their respective geographic coordinates ( and ). The results obtained on the basis of harmonic representation of the mixed data set are illustrated in the global map of surface temperature changes, in Figure (8). According to features discernible in this figure the thermal signals of climate change in continental areas of North America, Europe, Asia, West Africa and Eastern South America have magnitudes in excess of one degree centigrade, while those in most of the oceanic regions is less than one degree centigrade. On the other hand most of the oceanic regions are characterized by surface temperature variations of less than 0.80C. 6. Conclusions Much of the information on surface temperature changes of the recent past in South America are derived from historical records of local meteorological stations. Some of the Climate Changes of the Recent Past in the South American Continent: Inferences Based on Analysis of Borehole Temperature Profiles 131 Fig. 8. Spherical harmonic representation of surface temperature variations associated with climate changes of the recent past. Note the systematic differences in magnitudes of climate change between continental and oceanic areas. main problems in the use of such records include non-uniformity in the accuracy and precision of sensors employed in data acquisition, gaps in the time series of observations and influence of urban warming effects (most of the meteorological observatories are located in or close to towns or cities). The results reported in the present work, based on geothermal methods, are in general free of such problems. An important result emerging from studies based on geothermal methods is that the climate was relatively cooler during the 17th and 18th centuries. The climate histories, deduced from geothermal data, are found to be consistent with the results of available meteorological records in Brazil, Colombia and Peru. Comparative studies indicate that the magnitudes and duration of recent climate changes in South America are similar to those found in other continental areas such as North America, Asia and Europe. Analysis of surface temperature changes on global scale reveal that climate warming effects in the northern hemisphere are more pronounced because of the relatively large areas of continental blocks in these regions. On the other hand, the climate warming effects are relatively subdued in the southern hemisphere, where relatively larger proportions of oceans dominate. It is possible that systematic difference in magnitudes of global warming between continental and oceanic regions is a consequence of large scale mixing of near surface waters with those of deep ocean circulation systems. This mixing process is considered to be responsible for pronounced mitigation of the effects of global warming in the southern hemisphere. 7. Acknowledgments This work was carried out as part of a research project with funding from Conselho Nacional de Desenvolvimento Científico – CNPq (Project No. 301865/2008-6; Produtividade Climate Change – Geophysical Foundations and Ecological Effects 132 de Pesquisa - PQ). We thank the topic editor and reviewers for critical comments and suggestions. Dr. Andres Papa of the Geophysics Department (ON-MCT, Rio de Janeiro) provided institutional support. The source code for functional space inversion program was provided by Dr. Paul Shen, University of Western Ontario, Canada. 8. References Alfaro, C., Alvarado, I., Quintero, W., Hamza, V.M., Vargas, C., Briceño, L.A., (2009). Preliminary map of geothermal gradients of Colombia., XII Congreso Colombiano de Geologia, 7-11 September, Paípa - Boyacá, Columbia. Appleyard, S.J. (2005). Late Holocene temperature record from southwestern Australia: evidence of global warming from deep boreholes, Australian J. of Earth Sciences, 52, 161-166. 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Penalba1 and María Laura Bettolli1,2 1Departamento de Ciencias de la Atmósfera y los Océanos, FCEN, UBA 2Consejo Nacional de Investigaciones Científicas y Técnicas Argentina 1. Introduction The relevant findings of the Intergovernmental Panel on Climate Change (IPCC, 2007) highlight the increment of the global mean temperature and the need to understand how this increment will affect the climate variability and change of the regional environment. The changes in the climate are a consequence of both internal variability of the climate system and external factors, the latter being both natural and anthropogenic. The Fourth Assessment Report of IPCC describes the scientific progresses that have been achieved by researchers in the understanding the observed changes of the climatic system, the processes involved, and the establishment of future climate change projections (IPCC, 2007). The number of studies that discuss this problematic have increased considerable during the last years. However, there are many issues that need further investigation, in particular for developing countries. Extreme climate anomalies have a negative impact on the population and economy of the affected regions. The climate has a fundamental role for regions where the economy is based on agriculture. The process of growing crops can be seriously affected by extreme temperatures, and the precipitation can be a limiting factor which conditions the success or failure of the production. The region of interest in this study is the Pampas region, which comprises the most productive agricultural lands of Argentina. The most important grains of the country, like soybean, corn, wheat and sunflower are grown in this region. Together with their by-products, these crops promote the social and productive system of the region, and are one of the principal sources of fiscal incomes. In the campaign of 2008/09, more than 24 million hectares, compared to of the country’s total of 28 million cultivated hectares of these grains, corresponded to the Pampas region (http://www.sagpya.mecon.gov.ar/). This region, located in the center east of Argentina, southeastern South America, have an extension of more than 600.000 km2. Since the grains are cultivated extensively without artificial irrigation, the precipitation is one of the climatic variables of main influencefor the production, and is also a condition for the management of the crops. Therefore, the spatial and temporal distributions of the precipitation in the region, and its surplus or deficit, are of extreme importance for the successful harvests. Climate Change – Geophysical Foundations and Ecological Effects 138 From the 1960s, the Pampas region was favored by an increase of precipitation on both the annual and seasonal scales (Fernández et al., 2006; Liebmann et al., 2004). This increase showed a non-stationary variability (Penalba &Vargas, 2004). Depending on the region and the time of the year, the observed cycles were: inter-decadal variability, trends, jumps or discontinuities (Barros et al., 2008; Boulanger et al., 2005; Penalba & Vargas, 2008). This hydrological condition displaced the agricultural border with around 200 km to the west, which favored substantially the agricultural activity, especially in the semiarid subregions. However, these inter-decadal and inter-annual variations were observed in the extreme precipitation, on an annual scale and during the months of maximum precipitation (Penalba & Vargas, 2008). On a daily scale, the frequency of rainy days and of days of extreme precipitation, showed the same temporal variability (Penalba & Robledo, 2010). Furthermore, the temporal variability was greater, increasing the risk of droughts and their consequent negative impacts (Penalba et al., 2010). During 2008 and during almost the entire 2009, a severe rainfall deficit occurred in the region (Bidegain et.al., 2010), impacting strongly the gross domestic product. In Entre Ríos, which is a province within the Pampas region, the producers lost more than 50 millions of dollars in the corn harvest (Riani, 2009). Rainfall events depend on, among other factors, the large-scale atmospheric fields. Therefore, the study of these circulation structures, their frequency, distribution and temporal variability are important elements for diagnosis and forecast, particularly in the context of future climate change. Global Circulation Models (GCMs) are fundamental tools for climate change studies. Various studies shows that the GCMs have good capacity of representing characteristics of the South American circulation climatology, on temporal scales from monthly to decadal (Di Luca et al., 2006; Solman & Le Treut, 2006; Solman & Pessacg, 2006). On the other hand, the intermodel variability in the representation of monthly and seasonal characteristics of the precipitation and temperature in different regions of South America is high (Gulizia et al., 2009; Marengo et al., 2010; Rusticucci et al., 2010; Silvestri & Vera, 2008; Vera et al., 2006). The skill of the GCMs in representing the circulation that conducts or conditions the precipitation events has an important role in the evaluation of future climate projections. However, the capacity of the GCMs in representing the circulation on a synoptic scale has been little explored up to now. The usefulness of the GCMs in local studies is restricted by their poor spatial resolution. Techniques of scale reduction have been developed as bridges between the large scale information generated by the GCMs and the local scale information, with the purpose of performing short- to midrange forecasts and to study the potential impacts of future climate change. In this way, the use of daily results from the GCMs for studies of local climate is subject to their aptitude in representing the atmospheric systems on a regional scale. In this context, the present chapter is structured to fulfill the following objectives: to characterize the rainfall conditions and their probability of occurrence in the Pampas region; to identify daily circulation patterns in southern South America and to associate them with different rainfall conditions in the Pampas region; to evaluate the representation of the daily circulation patterns as simulated by a set of 12 GCMs; and to analyze the projected changes of the same patterns at different time horizons of the 21th century. In the second section of this chapter, the area, data and methods of this study are described. In the third section, the results are analyzed and discussed and in the last section the conclusions are presented. Climate Change Impacts on Atmospheric Circulation and Daily Precipitation in the Argentine Pampas Region 139 2. Data and methods 2.1 Data The following data-sets were used in this study: a. Daily mean sea level pressure (SLP) fields from NCEP reanalysis 2, provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA: http://www.cdc.noaa.gov/, were used to represent observed circulation for the period 1979-1999. The domain extends from 15ºS to 60ºS and from 42.5ºW to 90ºW on a 2.5º latitude-longitude grid, including the Pacific and the Atlantic Oceans and the Andes Mountains, geographical features that have a significant influence on the circulation over South America (Figure 1). Fig. 1. (a) The Argentine Pampas Region (shaded rectangle) and the domain chosen for the atmospheric circulation fields (dashed line). (b) Locations of the five meteorological stations used in this chapter. The focus of this analysis is to examine the model veracity with respect to sea level pressure. Previous studies (e.g. Bettolli et al., 2010) observed that the upper level patterns are less variable than the surface patterns. Furthermore, the upper level patterns can be associated with different surface synoptic types. The surface fields with their embedded synoptic systems provide a first-order control on spatial and temporal variations in precipitation. b. Observed daily rainfall series from stations in the Pampas region were provided by the Argentine National Meteorological Service. The five stations that had both less than ten per cent of missing data and a continuity of their records were chosen for analysis (Figure 1) Although this dataset goes further back in time, we only analyze the period that coincide with the NCEP data period. c. A set of 12 GCM SLP daily fields was used to describe present and future low level circulation (Table 1). The 20C3M experiment was used for the period 1979-1999 and the -64 -62 -60 -58Longitude-36-34-32-30Latitude-90 -80 -70 -60 -50 -40 -30Longitude-60-50-40-30-20-100Latitude(a)South AmericaPampas Region(b) Climate Change – Geophysical Foundations and Ecological Effects 140 SRES A1B 720 ppm stabilization scenario was used for the periods 2046-2065 and 2081-2099. These simulations are available from the Program for Climate Model Diagnosis and Intercomparison (PCDMI) and from the ENSEMBLES CERA archives. The SLP fields of the models were interpolated to the NCEP reanalysis grid with an inverse distance weighting method in order to facilitate comparisons. Table 1. List of GCMs used for the study. 2.2 Methods In order to identify the dominant spatial structures and their degree of contribution to the total variance, Principal Component Analysis (PCA) was performed on observed NCEP SLP field (Jolliffe, 2002; Richman, 1986). The method was applied in the T-mode, withthe correlation matrix as input and the SLP daily fields as variables and the gridpoints as observations. Only the principal components (PCs) that correspond to large eigenvalues are expected to contain an interpretable signal and are retained for further analysis. Craddock & Flood (1969) suggest plotting the log eigenvalue diagram (LEV diagram) and cutting the number of PCs just behind a section where the graph approximates to a line with a relatively small slope. Following their criterion, the first 6 PCs for summer and the first 8 PCs for winter, accounting for 94.3% and 94.6% of the total variance respectively, were retained for further analyses. The first unrotated PC of the raw data can be identified with the time mean pattern (Compagnucci & Vargas,1986; Huth, 2000). The first PC was calculated for each GCM in order to analyze the ability of the GCMs to reproduce the basic characteristics of the daily circulation at low levels. The cluster analysis was coupled with PCA to determine the dominant circulation types (CT) of NCEP (as in Romero et al., 1999a; Rodrigues Chaves & Cavalcanti, 2001). The analysis was carried out in the subspace given by the leading unrotated PCs. As shown by Gong & Richman (1995), this combination of methods provides the most separable cluster system. The clustering algorithm used in this study is the ‘k-means’ method, which is a partitioning method that classifies all days into a predefined, optimal number of clusters (MacQueen, 1967). The method minimizes the variability within each cluster and maximizes the variability between clusters. The choice of optimal number of clusters was established by the pseudo-F statistic (Calinski & Harabasz, 1974). This statistics assess the among- and within-cluster sum of squares Identification Model Original Grid Resolution A BCCR-BCM2.0 2.7905° x2.8125 ° B CNRM-CM3 2.79° x 2.8125° C CSIRO-Mk3.0 1.865° x 1.8750° D ECHAM5/MPI-OM 1.865° x 1.8750° E EGMAM 3.71059° x 3.75° F GFDL-CM2.0 2° x 2.5° G GFDL-CM2.1 2° x 2.5° H GISS-EH 3° x 5° I GISS-ER 3° x 5° J INGV-SXG 1.1215° x 1.125° K IPSL-CM4 2.5352° x 3.75° L UKMO-HadCM3* 2.5° x 3.75° * Available period for present climate: 1979-1989 Climate Change Impacts on Atmospheric Circulation and Daily Precipitation in the Argentine Pampas Region 141 relationship. The number of maximum local peaks in its plot indicates an appropriate number of clusters (Romero et al., 1999b). A progressive number of k clusters, from 2 to 30 were tested, and the pseudo-F statistic suggested a five-cluster solution for summer and a seven-cluster solution for winter. The SLP fields from the GCMs, were classified using the cluster centroids from the NCEP original typing. Each GCM SLP daily field was assigned to the NCEP circulation type that correlated best with the daily field. In order to outline and summarize the model-data comparisons, Taylor diagrams were constructed (Gleckler et al., 2008). These diagrams convey information with clarity and they quantify the degree of statistical similarity between two fields (in this study, between the observed NCEP circulation and the simulated by the GCMs) considering the correlation coefficient, the standard deviation and the root mean squared error (RMSE). The shape of the configurations can be compared, to a certain degree, through the correlation coefficient. The spatial patterns are compared directly from the values of atmospheric pressure through the standard deviation and the RMSE. The GCMs are considered to characterize and estimate the spatial patterns better, when the cloud of points is more concentrated and closer to the reference point. The probability of occurrence of a given rainfall event conditioned to a specific circulation type (CT) was compared to the climatological probability of its occurrence (Bettolli et al., 2010). The Z statistic was used to quantify the difference between the probabilities (Infante Gil & Zárate de Lara, 1984 ). 3. Results 3.1 Precipitation The Pampas region has a humid temperate climate and a flat relief. The mean annual rainfall is around 900 mm. The annual rainfall is characterized by a spatial variation in the NE-SW direction, with a significant decrease from east to west from the meridian of 65° (Penalba & Vargas, 2008). Of the annual amount, only 20% reaches the sea as water and the remaining 80% evaporates, runs off or changes the soil water amount (Berbery & Mechoso, 2001). The mean annual cycle of the region is characterized by a wet season with around 110 mm per month from October to April (warm months). During the transition months, the monthly rainfall decreases, reaching its lowest values during austral winter (around 30 mm/month) (Bettolli et. al., 2010). In some recent application studies, the number of rain days appeared to be the key to fluctuations in total rainfall amounts; in some, variation depended on rainfall intensity; and in others, on both variables. It has also been found that the lack of water can affect crop production. Robledo & Penalba (2008) analyzed the climatology of the different components that affect the monthly rainfall of Argentina. They calculated the amount and the frequency of the mean daily intensity and the daily extreme rainfall, using different thresholds according to the regions. In the Pampas, the spatial patterns and seasonal variation of daily rainfall above the 75th percentile show similar behavior. From October to April, the value of this threshold is around 16 mm/day, meanwhile during winter this value decreases to 5 mm/day. The frequency of rain days during the wet months is around 25%, decreasing to 16% during the austral winter months. Climate Change – Geophysical Foundations and Ecological Effects 142 Considering the dry condition, the average length of a dry sequence is 5 days or 8 days in summer and winter, respectively. The maximum dry period length presents more spatial variability with values around 20 days in summer and 40 days in winter (Llano & Penalba, 2011). In this chapter, we are interested in the characteristics of the rainfall that condition the production of crops. Winter and summer coincide with key stages of the growing season of main crops of the region (Pascale & Damario, 2003). Dry or wet conditions are defined at a regional scale, analyzing the joint information of the five stations. We analyze days with no rainfall in the five stations of the region (dry days), days with rainfalls in at least one station in the region greater than 0.1mm (rain days, R0.1) and greater than the 75th percentile (heavy rain days). As mentioned above, the 75th percentile for winter is 5mm (R5) and 16mm (R16) for summer. The annual cycle of the percentage of days corresponding to different rainfall conditions is shown in Figure 2. The annual cycle pattern of the three humid conditions is conserved in the region, with low variability on a monthly time scale for both winter and summer seasons. The highest variability between the months of these seasons is found in the R0.1 condition. During summer, in general, the probability of rainfall at least at one station is around 50% and of intense rainfall 20%. For winter, these probabilities are 30% and 10% respectively. Although the region is classified as humid, due to its high annual precipitation amount, the annual percentage of dry days is more than 50 per cent. Fig. 2. Annual cycle of the probability of days per months corresponding to different rainfall conditions: 0.1 mm (R0.1), 5mm (R5), 16mm (R16) in at least one station in the region. The daily rainfall of 10mm plays an important role for the hydrological balance of the summer months, since this amount approximates the daily evaporation (R10) (Vargas, 1979). Due to the hydrological characteristics of the Pampas region, the analysis focus on winter (JJA) and summer (DJF), coinciding with key stages of the growing season of different main Climate Change Impacts on Atmospheric Circulation and Daily Precipitation in the Argentine Pampas Region 143 crops in the region (wheat, corn and soybean). The thresholds of study are set 1 to R0.1 and no rain for both seasons and to R5 for winter and to R10 and R16 for summer. 3.2 Climatic characteristics of the atmospheric circulation One important aspect when comparing the properties of the GCMs and NCEP dataset is the reproduction of the PCs spatial structures. In particular, the first PC spatial pattern approximates the time mean pattern; whereas the remaining PCs spatial patterns can be interpreted as deviations from the time mean (Huth, 2000). This feature is used to analyze the climatology of the GCMs. The climatic characteristicsof the mean SLP patterns, represented by the first PC of NCEP datasets, show a notorious seasonality (Figure 3). This seasonality is of great importance in determining the low-level circulation and its associated moisture advection to the region. During summer, the high pressure cells over the eastern South Pacific and western South Atlantic are positioned in their southernmost location, limitating the Westerlies to the south of 50ºS. A clear thermo-orographic low is located in the center of the continent over the eastern Andes mountain range. During winter, both the semi-permanent high systems and the Westerlies are in their northernmost position and the thermo-orographic low is absent. These climatic characteristics seem to be better captured by the GCMs in winter than in summer (Figure 3). The scatter plot diagrams of Figure 3 show that the cloud of points is more concentrated and closer to the point of high correlation (between the spatial patterns of the PC1 of NCEP and of the models), and lesser RMSE. The spatial patterns of the best and worst performing GCMs, that is, the GCMs located at the extreme points of the scatter plot diagrams, are also shown in Figure 3. During winter, the GCMs tend to displace the Westerlies equatorward, attenuating the contribution of the subtropical highs, whereas in summer, both semi-permanent high systems are more extended to the south. For some GCMs, the difficulty in representing the Andes orographic effect on the circulation is noteworthy. As examples, the model GISS-ER and EGMAM are shown in Figure 3. GISS-ER extends the thermo-orographic low of summer towards a larger region over the mountain range, while the model EGMAM represents both semi permanent anticyclones over the continent, penetrating the range. The variance explained by each PC can be interpreted as a measure of the strength of each spatial pattern. Therefore, an analysis of the variance values explained by the first PC is used as a simple indicator of the ability of the GCMs in representing the mean fields of the low level circulation over the region. The percentages of variance explained by the first PC are shown in Figure 4. For the NCEP dataset, values reach 70.6% and 54.6% for summer and winter respectively, indicating that the cold season is more perturbed than the warm season. During summer, the percentage of variance explained by the first PC of 10 out of the 12 GCMs is below than observed. Thus, most models tend to represent a lesser incidence of the mean pattern and, therefore, a higher presence of perturbations. During winter, 6 models keep the circulation closer to its mean than is observed (i.e., overestimation in the percentages of variance explained by the first PC in BCCR-CM2.0, CNRM-CM3, CSIROMk3.0, GFDL-CM2.0, UKMO-HadCM3 and IPSL-CM4). However, the inter-model dispersion is lower when compared with summer. It is worth mentioning that most models are capable of reproducing the seasonality of the percentage of variance explained by the first PC, which is lower in winter. The exception is IPSL-CM4 with a lower percentage in summer. Climate Change – Geophysical Foundations and Ecological Effects 144 Fig. 3. Spatial pattern of the PC1 of the NCEP SLP data for summer and winter. Scatterplot of the correlation coefficients versus the RMSEs between PC1 of NCEP and of the GCMs. Spatial pattern for some selected models are also shown. 0.800.850.900.951.000.0 0.2 0.4 0.6 0.8RMSDCorrelation Coefficient-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-90 -80 -70 -60 -50-60-50-40-30-20-90 -80 -70 -60 -50-60-50-40-30-20ECHAM5/MPI-OMSummerGISS-ERPC1 NCEPRMSE0.800.850.900.951.000.0 0.2 0.4 0.6 0.8RMSDCorrelation Coefficient-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-90 -80 -70 -60 -50-60-50-40-30-20-90 -80 -70 -60 -50-60-50-40-30-20CSIRO-Mk3.0WinterEGMAMPC1 NCEPRMSEClimate Change Impacts on Atmospheric Circulation and Daily Precipitation in the Argentine Pampas Region 145 Fig. 4. Percentage of variance explained by the PC1 for summer and winter. 3.3 Observed circulation types The circulation types for summer (CTiS, i=1,…,5) are shown in Figure 5. Fig. 5. Observed CTs and percentage of days corresponding to each CT for summer. The dashed (solid) lines represent sea level pressure values lower (higher) than 1013 hPa. The contour interval is 2 hPa. Taylor diagrams of the observed CTs of NCEP and the CTs of the GCMs (red letters) and of the model ensemble (green point). 4050607080NCEP BCCR-BCM2.0CNRM-CM3CSIRO-Mk3.0ECHAM5/MPI-OMEGMAM GFDL-CM2.0GFDL-CM2.1GISS-EH GISS-ER UKMO-HadCM3IPSL-CM4 INGV-SXG% Variance ExplainedPC 1 Summer PC 1 Winter-60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGHIJKL 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefAB CDEFGHI JKL-60-50-40-30-20Latitude-60-50-40-30-20LatitudeSummerCT2s26.8%CT1s7.1% 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGHIJKL 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGHIJKL 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGHI JKLCT3s15.6%CT4s19%CT5s31.5%-60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGHIJKL 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefAB CDEFGHI JKL-60-50-40-30-20Latitude-60-50-40-30-20LatitudeSummerCT2s26.8%CT2s26.8%CT1s7.1%CT1s7.1% 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGHIJKL 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGHIJKL 500 1000040008000120010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGHI JKLCT3s15.6%CT3s15.6%CT4s19%CT4s19%CT5s31.5%CT5s31.5% Climate Change – Geophysical Foundations and Ecological Effects 146 CT1S is characterized by an intensification of the southern Pacific anticyclone associated with a trough axis in the northwest-southeast direction. This structure could be connected with a post-frontal anticyclone that moves forward on the continent inducing an anomalous flow from the east-southeastover the Pampas region. CT2S is characterized by a perturbation over the continent and a weakening of the Atlantic anticyclone that could be related to a cold front affecting the region. In CT3S, a belt of high pressures is extended to the South, reaching around 45ºS. This CT is accompanied by a centre of low pressure values to the north. CT4S shows an intensification and expansion of the southern Atlantic anticyclone, which interrupts the passage of the eastern perturbations and diverts them to the south. CT5S is the pattern that is most similar to the mean SLP field of summer (compare with the spatial pattern of the NCEP PC1 in Figure 3). This is the most frequent summer pattern, with 31.5% of the studied cases. Fig. 6. Idem Figure 5 for winter. The winter patterns (CTiW, i=1,…,7) show more variable spatial structures than the summer patterns (Figure 6). This is due to the higher baroclinicity of the winter season and therefore, the greater contribution of synoptic perturbations. In CT1W, a cyclonic perturbation -60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-60-50-40-30-20Latitude-60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-60-50-40-30-20Latitude-60-50-40-30-20LatitudeCT1w8.5%CT2w14.3%CT3w16.5%CT4w19.1%CT7w27.1%CT6w7.6%CT5w6.9%Winter 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefAB CDEFGHI J KL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGH IJKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGHI JKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGH IJKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGHIJK L 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGH IJKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGH IJKL-60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-60-50-40-30-20Latitude-60-50-40-30-20Latitude-90 -80 -70 -60 -50Longitude-60-50-40-30-20Latitude-60-50-40-30-20Latitude-60-50-40-30-20LatitudeCT1w8.5%CT1w8.5%CT2w14.3%CT2w14.3%CT3w16.5%CT3w16.5%CT4w19.1%CT4w19.1%CT7w27.1%CT7w27.1%CT6w7.6%CT6w7.6%CT5w6.9%CT5w6.9%Winter 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefAB CDEFGHI J KL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGH IJKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGHI JKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGH IJKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGHIJK L 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDE FGH IJKL 500 100004000800012000150010.990.950.90.80.70.60.50.40.30.20.10Standard DeviationCorrelationRMSDRefABCDEFGH IJKLClimate Change Impacts on Atmospheric Circulation and Daily Precipitation in the Argentine Pampas Region 147 dominates the circulation over the southern South Pacific Ocean, while the opposite occurs in CT2W for which a high pressure system extends towards south, entering over the continent. CT3W shows an intensification of the Atlantic anticyclone, inducing an anomaly of the northern-northeastern flow at the southern tip of the continent. CT3W shows an extension towards the north of the Westerlies, which restricts the action of both anticyclones to act to the north of 40ºS. CT5W corresponds to an intense high pressure centered over Patagonia in the south of Argentina that extends over almost the whole southern region of the continent and over adjacent oceans. CT6W can be associated with a cold front that advances towards northeast with its postfrontal anticyclone generating southern advection when getting in over the continent. Finally, CT7W can be linked with the mean SLP field of winter, similarly to summer, with a frequency of 27.1% (compare with the spatial pattern of the NCEP PC1 in Figure 3). 3.4 Observed circulation types and daily rainfall This section quantifies the relationship between the CTs for each season and rainfall amount and persistence over the Pampas region. The purpose is to evaluate how much rainfall information for the core crop-producing region is contained in the circulation structures at a regional scale. Then, the probability of occurrence of a rainy day conditioned to a specific CT is compared with the probability of occurrence of a rainy day for the rest of the data by means of the Z-statistic. Values and significance from the Z-statistics are shown in Table 2. Table 2. Z-statistics of the comparison between the conditional probability of occurrence of a day with a certain rainfall condition in each CT for summer and winter and the climatological probability of that day. If the Z-statistic value is positive (negative) and is significant, the specific circulation pattern has (does not have) a significant contribution to the rainfall event. In red, significant values at 95% and 90% (*). For summer, CT4S has the highest contribution to the dry days, showing positive and significant values of the Z-statistic for this condition (first column in Table 2). The configuration of SLP of CT4S corresponds to an intensification of the southern Atlantic Summer Dry Days R0.1 R10 R16 CT1s -2.65 2.65 -0.06 -1.13 CT2s -2.22 2.22 2.25 2.29 CT3s 0.67 -0.67 -0.99 -1.51 CT4s 3.68 -3.68 -3.05 -2.16 CT5s -0.05 0.05 1.00 1.15 Winter Dry Days R0.1 R5 CT1w -1.20 1.20 0.75 CT2w 1.21 -1.21 -2.26 CT3w 0.33 -0.33 -0.71 CT4w 1.78* -1.78* -1.36 CT5w -4.72 4.72 3.06 CT6w -1.56 1.56 0.44 CT7w 1.99 -1.99 0.34 Climate Change – Geophysical Foundations and Ecological Effects 148 anticyclone, which interrupts the passage of the eastern perturbations and diverts them to the south. This anticyclone induces stability at low levels and can be significantly associated to the dryprocesses is necessary, for example, for reliably estimating the future development of the ozone layer (Section 4). In this context, alterations in atmospheric processes due to climate change must be considered. 2.1 Observations from satellite Satellite remote sensing of ozone started in 1970 with the Backscatter Ultraviolet Spectrometer (BUV) onboard the NASA satellite Nimbus-4. The first Total Ozone Mapping Spectrometer (TOMS) was launched in 1978 onboard the Nimbus-7 satellite and was followed by a series of Solar Backscatter UV Instrument (SBUV). TOMS measured the total column of atmospheric ozone content whereas the SBUV measured height resolved stratospheric ozone profiles. The last TOMS instrument operated until 2007, the Ozone Mapper Profiler Suite (OMPS) to be launched in 2011 will continue this data record. The European contribution to satellite base measurements of atmospheric composition started with the Global Ozone Monitoring Experiment (GOME) sensor onboard the ESA satellite ERS-2 launched in 1995. GOME measured not only ozone (total column, profiles and tropospheric column) but also a number of atmospheric composition gases like nitrogen dioxide, sulphur dioxide, bromine monoxide, water vapour, formaldehyde, chlorine dioxide, glyoxalin as well as clouds and aerosols (see Burrows et al., 1999). The GOME data record is continued with the SCIAMACHY sensor onboard the ESA satellite ENVISAT launched in 2002, with the Dutch sensor OMI onboard the NASA satellite AURA launched in 2004, and with the GOME-2 sensor onboard the EUMETSAT satellite MetOp-A launched in 2006. This 16 years data record will be continued with the GOME-2 sensors on the EUMETSAT satellites MetOp-B (to be launched in 2012) and MetOp-C (to be launched in 2017). The ESA’s Sentinel 5 precursor mission (to be launched in 2015), Sentinel 4 and Sentinel 5 with further extend this data record with similar sensor systems in the next decades. Remote Sensing in the UV/VIS spectral range between 280 nm and 450 nm is based on measurements of backscattered radiation from the Earth-atmosphere system. The Differential Optical Absorption Spectroscopy (DOAS) fitting technique is used to derive trace gas slant column amounts along the viewing path of the GOME-type instruments. The spectral structure of ozone in the Huggings bands (Figure 2) measured by a satellite sensor is compared to laboratory measurements to quantify the ozone content on the atmosphere. The slant columns determined with DOAS are finally converted to geometry-independent vertical column amounts through division by appropriate air mass factors (Van Roozendael et al., 2006) which result from radiative transfer calculations (see Figure 3). Air mass factors describe the enhanced absorption of a given trace gas due to slant paths of incident light in the atmosphere. The ozone retrieval must also take into account the influence of clouds and other atmospheric effects (Loyola et al., 2011). Satellite total ozone measurements are systematically compared with ground-based measurements and the differences are typically lower than 1%. Nevertheless satellite ozone data from different instruments may show spatial and temporal differences due to sensor Climate Change – Geophysical Foundations and Ecological Effects 8 Fig. 2. Schematically representation of the DOAS principle used for the retrieval of ozone content from the Huggings bands between 325 nm and 335 nm. The differential structure of satellite measurements (top left) and laboratory measurements (top right) are fitted together (low panel) to determine the current ozone amount. Fig. 3. The satellite measured ozone slant column (brown path) is converted to viewing geometry independent vertical column of ozone (black path). Clouds AtmosphereSeaEffective Slant Column LandGOME SunVertical Column Density325 327 329 331 333 335Wavelength [nm]-0.5 -0.4 -0.3 -0.2 -0.1 0.0 Measurement SpectrumBroad-scale FeaturesDifferential AbsorptionMeasured Spectra in the Huggings Bands Absorption Cross-section [1019 cm2 mol-1] Broad-scale Features O3 Huggins BandsReference Spectrum 0.51.01.52.0325 327 329 331 333 335 Wavelength [nm]325 327 329 331 333 335Wavelength [nm]-0.15-0.050.050.15Differential Absorption [-]Residual O3 FittReferenceLeast-squares shift & squeeze FitFitChemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 9 specific characteristics and drifts. Therefore some corrections are needed before merging data from different satellites to create long-term homogenous climate data records that can be used for ozone trend studies. In this chapter we use the merged satellite TOMS/OMI data record (Stolarski et al., 2006) starting in 1979 and the merged GOME/SCIAMACHY/GOME-2 data record (Loyola et al., 2009) starting in 1995. An ozone hole is said to exist when the total ozone column sinks to values below 220 DU, which is around 30% under the norm. Dobson Units are column densities  a measure of the total amount of ozone in a column over a specific place. At standard temperature and pressure (1000 hPa, 0 °C), a 0.01-mm thick ozone layer corresponds to 1 DU. A 300-DU thick ozone layer at the Earth’s surface would thus correspond to a pure ozone column of 3 mm. Figure 4 shows the evolution of ozone hole as measured by the TOMS sensor onboard the Nimbus 7 satellite between 1979 to 1992, TOMS data from the Meteor satellite between 1993 to 1994, GOME data from the ERS-2 satellite between 1995 and 2002, SCIAMACHY data from the ENVISAT satellite between 2003 and 2006, and GOME-2 data from the MetOp-A satellite between 2007 and 2010. The average ozone from October 1st to 3rd is plotted for all the years with the exception of 1993 and 2002 where data from September 23rd to 25th are used. In 1993 no TOMS data were available at the beginning of October and in 2002 the data from September are plotted to show the atypical split of the ozone hole due to the unusual meteorological conditions in the stratosphere occurring only in 2002. Corresponding results for Northern Hemisphere spring time conditions are presented in Figure 5. There, average total ozone column from March 25th to 27th is plotted for all years between 1979 and 2011 except 1995 where no satellite data is available. Obviously the ozone depletion is not as strong as in the Southern Hemisphere and the trend towards lower ozone amount is much less visible. The interannual variability is high which can be explained by the variability of stratospheric dynamics (see Sections 1.1 and 1.2). Nevertheless, most clearly seen in years like 1997 and 2011, the dynamic situation of the Arctic stratosphere can be very similar to the Antarctic, i.e. showing a well-pronounced and undisturbed polar vortex in winter with temperatures low enough to form PSCs in large extent. Other years which also show a significant reduction of total ozone in northern spring are 1990, 1993, 1996, and 2007. On the other hand, in years like 1998 and 2010 when stratospheric temperatures are enhanced due to disturbed stratospheric dynamic conditions, total ozone values are much higher. It is also obvious that total ozone values at low latitudes (i.e. tropical and sub-tropical regions) are naturally low. 2.2 Simulations with chemistry-climate models Chemistry-climate models (CCMs) are numerical tools which are used to study connections between atmospheric chemistry and climate (Figure 6). They are composed of two basic modules: An Atmospheric General Circulation Model (AGCM) and a Chemistry Model. An AGCM is a three-dimensional model describing large-scale (i.e. spatial resolution of a few hundred km) physical, radiative, and dynamical processes in the atmosphere over years and decades. It is used to study changes in natural variability of the atmosphere and for investigations ofdays of the region. CT4S is the most persistent pattern, with the 19% of the events in sequences lasting from four to seven-day (Table 3). For rainy days, positive and significant values of the Z-statistic are observed for the CT2S and CT1S patterns (R0.1, R10 and R16 columns in Table 2). Rainy days (R0.1) are significantly benefited by patterns that could be related to a post-frontal intense anticyclone that induces east-southeast anomalous flow and consequently increases moisture advection over the region (CT1s). This pattern is the less frequent NCEP pattern of the season (7.1%, Figure 5) and is also one of the less persistent patterns, with 90% of the events in sequences of one to three days (Table 3). Heavy rainy days are significantly related with a cyclonic disturbance at the centre of the continent associated with a cold front passage (CT2S in Figure 5). Table 3. Probability of the persistence (in days, D) of the different CTs. Table 4. Schematic summary of results. Summer Winter D CT1s CT2s CT3s CT4s CT5s CT1w CT2w CT3w CT4w CT5w CT6w CT7w 1 54.3 41.1 60.5 34.8 46.7 36.6 50.3 36.8 52.5 59.5 51.3 48.3 2 24.3 23.7 22.2 31.2 22.2 33.8 25.5 27.8 20.1 21.5 30.0 24.6 3 11.4 17.8 9.0 12.1 12.8 11.3 14.5 16.5 12.3 12.7 6.3 12.7 4 4.3 8.7 4.8 8.5 7.4 7.0 5.5 9.0 7.3 2.5 10.0 3.8 5 1.4 3.2 0.6 4.3 4.7 5.6 2.1 5.3 2.8 3.8 2.5 5.5 6 2.7 0.0 4.3 1.9 2.8 1.4 1.5 2.8 0.8 7 4.3 0.9 2.4 2.1 0.8 1.4 0.0 0.0 1.1 1.7 8 1.4 1.4 1.6 1.4 0.7 2.3 1.1 0.4 9 0.6 0.7 1.2 0.8 10 0.4 11 0.5 0.7 0.4 0.4 12 13 0.4 14 15 0.4 Summer Winter CT1s Rainy Days Least Frequent CT1w CT2s Heavy Rainy Days CT2w CT3s Least persistent CT3w Most persistent CT4s Dry Days Most persistent CT4w Dry Days CT5s Most frequent CT5w Rainy and heavy rainy Days Least persistent Least frequent CT6w Rainy Days CT7w Dry Days Most frequent Climate Change Impacts on Atmospheric Circulation and Daily Precipitation in the Argentine Pampas Region 149 In Table 4, a schematic summary of the results described above is found, which will serve as a basis for the comparison with the GCMs in the next section. Winter dry days are significantly favored by a high pressure system that extends from the Atlantic Ocean to the centre of the continent (CT4W) and also by CT7W, the pattern that resembles the mean pattern for winter (positive and significant values of the Z-statistic for this condition in the first column of Table 2). Rainy days and heavy rainy days are significantly benefited by structures with a high pressure system at the south of the continent, enhancing an anomalous flow from the east-southeast to the central region of Argentina and a corresponding moisture advection at low levels (CT5W). This CT is the less persistent pattern with 93.7% of the events in sequences of one- to three-day lasting (Table3) and it is also the less frequent one (6.9%, Figure 6). CT3W is the most persistent pattern of winter (Table 3), coinciding with what was found for the summer CT4S. 3.5 Comparison between NCEP and GCMs 3.5.1 Present climate A diversity of aspects should be taken into account when comparing the ability of the GCMs in representing the synoptic patterns of NCEP, given that the surface climate depends on the representation of these characteristics. The comparison of the mean spatial patterns is summarized in the Taylor diagrams of Figures 5 and 6. Although the correlation is expected to be high, since the projection of the GCM fields was defined over the centriods of the observed NCEP fields, a certain dispersion is found. In summer, CT2S and CT5S shows the smallest dispersion, bounded between the values 0.95 and 0.99 of the Taylor diagrams. This means that the GCMs are able to reproduce the structure and position of these atmospheric systems. In particular, the accurate representation of CT2S is essential for the generation of the heavy rainfall events in the region. In winter, the greatest correlations are close to 0.99 and are found for the CT2W, CT3 W, CT4W and CT7 W. Unlike what occurs for summer, the CTs that are best represented by the GCMs are the ones associated with dry days (CT4 W y CT7w). CT3w is the most persistent structure with an intensification of the Atlantic anticyclone (Table 3) that could be linked to the blocking events occurring in the Atlantic ocean (around 40°W) that are more frequent during winter (Alessandro, 2003). From the Taylor diagram, it is clear that the spatial structure of CT3w is well represented, which is key for the location of the blocking and its consequent effect on the surface variables (Figure 6). The comparison of the standard deviation and the root mean squared error indicate an inter model dispersion according to the CT and the time of the year. For summer, the standard deviations are distributed around the observed NCEP value for all cases except for CT2s, for which most models underestimate the standard deviation (points to the left of the reference point in the Taylor diagram of Figure 5). This indicates that the GCMs tend to underestimate the amplitude of the variation of the SLP of CT2s, and consequently underestimate the depth of the systems that are associated directly with heavy rainfall. During winter, the dispersion of the standard deviations is uniform for CT2W, CT3 W, CT4 W y CT7W, which also are the CTs with best estimations of the spatial structures (Figure 6). Most GCMs overestimate the standard deviation of the types CT1W, CT5 W and CT6 W, Climate Change – Geophysical Foundations and Ecological Effects 150 increasing the depth of the systems, and in particular of those that are associated with rainfall or heavy rainfall of winter (CT5 W and CT6 W, Table 4). In all cases, the root mean squared errors are lower than 400 hPa. Considering that the standard deviations of NCEP vary between 536 and 1088 hPa, the model errors are lower than the proper variability of the observed mean fields. Also, the majority of the errors do not reach higher values than 50% of the standard deviation. Another aspect to take into account is the relative frequency of each CT estimated by the GCMs. Figure 7.a shows that the ensemble is able to reproduce these frequencies, although the model dispersion is considerable, especially in summer for CT3s and CT5s. In summer, the frequencies of the models overestimate the frequencies of CT3s while the frequencies of CT5s are mostly underestimated. The latter coincides with what was found in the analysis of the dominant summer pattern, PC1. CT5s is similar to the summer mean field and an underestimation of its frequencies implicates a higher contribution of the other CTs, representing the perturbations. The pattern that represents an intensification of the southern Atlantic anticyclone and its stability (CT4s) is the pattern that is best represented by the GCMs in terms of frequency. This pattern is significantly associated to the dry days of the region. For winter, the dispersion among the models is lower than for summer. CT5W and CT6W are best represented with frequencies close to observed values. This implies that the models are capable of a quite good representation of the frequencies of the structures that are significantly associated with rain days and heavy rain days. The ensemble reproduces the observed frequencies very well in all cases. 3.5.2 Future climate The future frequency changes of each CT show a considerable dispersion among the GCMs (Figures 7.b and 7.c), especially for the warm season. Nevertheless, the signs of the tendencies are equal for all CTs and for the two time horizons (2046-2065 and 2081-2099). It is important to point out that the future changes of the CT frequencies are smaller than the 20th century observed frequency dispersion (Figure 7). In this sense, it is difficultclimate effects of radiatively active trace gases (greenhouse gases) and aerosols (i.e. natural and anthropogenic particles in the atmosphere), along with their interactions and feedbacks. Usually, AGCM calculations employ prescribed concentrations of radiatively active gases, e.g. CO2, CH4, N2O, CFCs, and O3. Changes of water vapour (H2O) concentrations due to the hydrological cycle are directly simulated by an AGCM. The Climate Change – Geophysical Foundations and Ecological Effects 101979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total Ozone [Dobson Units] 150 200 250 300 350 400 450 Fig. 4. Evolution of the ozone hole derived from satellite measurements in early October from 1979 until 2010. The purple area over the South Polar Region indicates the area of the ozone hole (see text). Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 11 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total Ozone [Dobson Units] 200 250 300 350 400 450 500 Fig. 5. As Figure 4, but for the Northern Hemisphere and using a different colour scale. Evolution of the ozone derived from satellite measurements in late March from 1979 until 2011 (no data available for 1995, see text). Climate Change – Geophysical Foundations and Ecological Effects 12temporal development of sea surface temperatures (SSTs) and sea ice coverage are prescribed in these models. The chosen boundary conditions for concentrations of radiatively active gases and SSTs (incl. sea ice) represent a specific period of time, e.g. some years or decades. In a CCM, i.e. an AGCM interactively coupled to a model describing in detail atmospheric chemistry, the simulated concentrations of the radiatively active gases are used in the calculations of heating rates (e.g. due to the absorption of short-wave solar radiation) and cooling rates (e.g. due to the emission of long-wave infrared radiation). Changes in the abundance of these gases due to chemistry and advection influence heating and cooling rates and, consequently, variables describing atmospheric dynamics such as temperature and wind. This gives rise to a dynamical-chemical coupling in which the chemistry influences the dynamics (via radiative heating and cooling) and vice versa (via temperature and advection). As an example, ozone represents the dominant radiative-chemical feedback in the stratosphere. Simulations with CCMs also consider gradual changes in concentrations of radiatively active gases and other boundary conditions (e.g., emissions). The temporal development of source gas emissions are prescribed for a specific episode (years to decades). Fig. 6. Schematic of a Chemistry-Climate Model (CCM). The core of a CCM (oval symbols) consists of an atmospheric general circulation model (AGCM; i.e. dynamics and radiation part) that includes calculation of the heating and cooling rates and a detailed chemistry module. They are interactively coupled. Arrows indicate the direction of effect. Rectangular boxes denote external forcing. Dynamics Temperature and Wind Sea Surface Temperatures Chemistry Radiation Photolysis andHeating Rates Emission of Natural and Anthropogenic Gases Concentrations of Radiatively Active Gases Volcanic and non-Volcanic Aerosols Solar Cycle Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 13 As an example, in the following a brief description of the CCM E39CA is presented providing some useful details to better understand how such a model system works and respective simulations are performed. E39CA consists of the dynamic part E39 and the chemistry module CHEM. “E39” is an AGCM, based on the circulation model ECHAM4 (Roeckner et al., 1996). It has a vertical resolution of 39 levels from the Earth surface up to the top layer centred at 10 hPa (Land et al., 2002). The horizontal model grid on which the tracer transport, model physics and chemistry are calculated, has a mesh size of approximately 3.75°· 3.75° (latitude, longitude). The chosen time step for model integration is 24 minutes. The chemistry module “C” (Steil et al., 1998) is based on a family concept which combines related chemical constituents with short lifetimes (shorter than that of the dynamics or the model time-step used) into one family with a life-time larger than the time-step. “C” includes stratospheric homogeneous and heterogeneous ozone chemistry and the most relevant chemical processes for describing the tropospheric background chemistry with 107 photochemical reactions, 37 chemical species and four heterogeneous reactions on PSCs and on sulphate aerosols. Heating and cooling rates and photolysis rates are calculated on-line from the modelled distributions of the radiatively active gases O3, CH4, N2O, H2O and CFCs, and the actual cloud distribution. In the following some boundary conditions of an E39CA model simulation are described covering the years from 1960 to 2050 (simulation “R2”). The model simulation considers various natural and anthropogenic forcings like the 11-year activity cycle of the sun with varying intensity of solar radiation (particularly in the ultraviolet which affects ozone chemistry), the quasi-biennial oscillation (QBO) of tropical zonal winds in the lower stratosphere (i.e. the direction of the zonal wind is alternating between west and east with a mean period of 28 months; see Baldwin et al., 2001), chemical and direct radiative effects of gases and aerosols (i.e. particles) emitted during major volcanic eruptions, and the increase in greenhouse gas concentrations. R2 is a simulation performed to assess the past and future evolution assuming a consistent set of boundary conditions which are partly based on observations (for the past) and on particular assumptions for future developments. For example, the future concentrations of long-lived greenhouse gases (CO2, CH4, and N2O) are based on a ‘business as usual’ scenario (i.e. the A1B future scenario described in IPCC, 2001); future concentrations of ozone depleting substances follow the A1 scenario prescribed in WMO (2003), e.g. assuming a phase out of the CFCs in the troposphere and stratosphere over the coming decades leading to a continuous decrease of stratospheric chlorine content in future. Moreover, the wind phases of the QBO which were observed in the past are consistently continued. The solar activity signal observed between 1977 and 2007 is continually repeated until 2050. Furthermore, no major volcanic eruptions have been adopted in years up to 2050. Sea surface temperatures (SSTs) and sea ice coverage are prescribed using a continuous dataset derived from the coupled atmosphere-ocean model HadGEM1, provided by the UK Met Office Hadley Centre (Johns et al., 2006). The results from HadGEM1 are taken from a transient simulation with prescribed anthropogenic forcing as observed in the past, and following the SRES-A1B scenario in the future. More details about the CCM E39CA and the respective assumptions made in the numerical simulation are provided in Stenke et al. (2009) and Garny et al. (2009).3. Confronting model results with observations – a basement for predictions In this section data derived from multi-year space-borne measurements are compared with respective data derived from simulation R2 with the CCM E39CA. It should provide some Climate Change – Geophysical Foundations and Ecological Effects 14insight into current capabilities of numerical modelling of atmospheric processes and how model results are evaluated on the basement of observations. The evaluation of results derived from numerical modelling with observations gives indications about the quality of the applied model which partly reflects our current understanding of atmospheric processes and the cause and effect relationships leading to changes in atmospheric behaviour. The following examples of E39CA are only exemplary; more detailed comparisons including evaluations with other CCMs are provided in the SPARC CCMVal report (2010). The evolution of the total ozone column in the atmosphere and respective standard deviation (both in Dobson Units, DU) as a function of latitude and time derived from GOME/SCIAMACHY/GOME-2 satellite measurements and the E39CA R2 simulation are presented in Figure 7. Note that the colour bars of the total ozone (upper two figures) are different for satellite and model data to better compare the latitude-time patterns (see discussion below regarding Figure 8). It is obvious that the overall variations of total ozone with latitude and time are well reproduced by E39CA. Fig. 7. Latitudinal evolution of total ozone (top) and standard deviation (bottom) from June 1995 to May 2008. GOME/SCIAMACHY/GOME-2 satellite data are presented on the left side (a, c) and E39CA model on the right side (b, d). Satellite measurements from April 2004 are not available; the corresponding model data are therefore also neglected (Figure 8 in Loyola et al., 2009). Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 15 The standard deviation of a given quantity (here total ozone in the lower two figures) is a measure for variability of the respective system, describing the range of variability in a specific region and period of time. Again, the agreement between model results and observations is satisfactory, i.e. the spatial and temporal structures are well reproduced. The latter result is important because it indicates that E39CA is able to reproduce adequately the internal variability of stratospheric dynamics and chemistry which is different in the Northern and Southern Hemisphere and the tropics (see Sections 1.1 and 1.2). Fig. 8. Seasonal mean values of total ozone (June 1995 to May 2008) from GOME/SCIAMACHY/GOME-2 satellite instruments (top), the E39CA simulation (middle), and the difference between satellite measurements and model results (bottom) (Figure 6 in Loyola et al., 2009). Climate Change – Geophysical Foundations and Ecological Effects 16Figure 8 provides a more detailed evaluation of the absolute accuracy of total column ozone values as derived from E39CA simulations. Here, seasonal mean values of total ozone derived from satellite instrument measurements and E39CA are once again presented for the time period from June 1995 to May 2008. Please note that the colour bars here are also different for satellite and model data since E39CA total ozone values have a positive bias: A general shift to higher total ozone values is found ranging from about 5 DU in high northern latitudes during winter (DJF) to about 100 DU in high southern latitudes during winter (JJA). This finding indicates that there are still some weaknesses in the applied model system leading to an overall overestimation of total column ozone. Nevertheless, it is obvious that the meridional structure is well represented by E39CA in all seasons. The seasonal changes are well reproduced by the model. Particularly in the Northern Hemisphere, the latitudinal structure compares in a reasonable way. For example, the position of the polar vortex during winter and spring, which is indicated by lower ozone values over Eurasia, is correctly simulated by E39CA. While the Northern Hemisphere is dominated by a clear zonal wave number 1 pattern (i.e. one maximum and one minimum along a latitudinal circle), the distribution of ozone in the Southern Hemisphere has a much more zonally symmetric structure during all seasons which is captured by the model. In addition to Figure 7, Figure 9 shows seasonal means of the standard deviation of total ozone, again for satellite data and model results. The overall seasonal change and the hemispheric patterns of the standard deviation in the model follow quite well the respective values from observations, but there are some differences in details. For example, in the distribution of the standard deviation in northern winter (DJF) high latitudes show some obvious differences: While in E39CA, the variability is low in the centre of the polar vortex (approximately between northern Europe and the North Pole) and higher in the surroundings, the satellite data show high variability in the vortex centre and a lower standard deviation over North America and eastern Asia. This finding can be explained by the fact that the polar vortex is too stable in E39CA, i.e. the number of minor and major warmings is lower than observed (e.g. Stenke et al., 2009). In the summer hemisphere (DJF in the Southern Hemisphere) the standard deviation is much higher in the model, but the region of maximum variability agrees again well with those derived from observed values. Another clear difference is found in the Southern Hemisphere spring months (SON) indicating a weaker variability in the South Polar Region (see also lower part of Figure 11). This model behaviour is explained by a too cold polar lower stratosphere in E39CA (‘cold pole problem’) reducing the dynamical variability in this region strongly (Stenke et al., 2009). The comparisons shown so far were based on climatological and seasonal mean values. They are mainly used to rate the basic state of a numerical model system. Also important is the evaluation of the temporal evolution of an atmospheric quantity. The adequate reproduction of short-term variability and long-term changes, i.e. trends, is another prerequisite for robust assessment of future developments. Some examples are presented in the following section. 4. Prognostic studies As a result of international agreements on protecting the stratospheric ozone layer (Montreal Protocol in 1987 and its amendments), the rapid increase in concentrations of the main CFCs in the troposphere has been stopped. Since the mid-1990ies, a decline in Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 17 Fig. 9. Seasonal mean values of total ozone standard deviations (June 1995 to May 2008) from GOME/SCIAMACHY/GOME-2 satellite instruments (top) and the E39CA simulation (bottom) (Figure 7 in Loyola et al., 2009). tropospheric CFC content has been observed (WMO, 2011). Consequently, a slight decrease in stratospheric chlorine concentrations has also been detected for several years now. However, due to the long lifetimes of CFCs in the atmosphere, it will take until the middle of this century for the stratosphere’s chlorine content to go back to values resembling those observed in the 1960ies. Therefore, it is expected that the strong chemical ozone depletion observed over the past three decades will decrease in the near future. In this context, a solid assessment of the timing of the ozone layer recovery and particular the closure of the ozone hole is not a trivial task since the future evolution of the ozone layer is affected by several processes. In particular ongoing climate change will have an influence on atmospheric dynamics (including the transport of ozone) andozone chemistry (via temperature changes). CCMs are suitable tools to perform prognostic studies regarding the evolution of stratospheric ozone content, because they are considering the complex interactions of dynamical, physical and chemical processes. Based on prognostic studies with CCMs it is expected that the ozone layer will build up again in the next decades and that the ozone hole over Antarctica will be closed (see Chapter 3 in WMO, 2011; Chapter 9 in SPARC CCMVal, 2010). Figure 10 shows an example of the temporal evolution of total ozone deviations regarding a mean ozone value (1995-2009) for the near global mean (i.e. global mean values neglecting polar regions). Looking into the past it is obvious that E39CA is able to reproduce seasonal and interannual fluctuations in a Climate Change – Geophysical Foundations and Ecological Effects 18sufficient manner, although the amplitudes of ozone anomalies are slightly underestimated. Model data and data derived from satellite observations clearly show the signal of the 11-year solar cycle. The absolute minimum ozone values observed in years 1993-95, which are caused by the eruption of the volcano Pinatubo, are not adequately reproduced by E39CA. The simulated increase in stratospheric ozone amount after year 2010 is a direct consequence of the prescribed decrease of stratospheric chlorine content. The speed at which the ozone layer will rebuild in future depends on a range of other factors, however. Rising atmospheric concentrations of radiatively active gases (such as CO2, CH4 and N2O) do not just cause the conditions in the troposphere to change (i.e. the greenhouse effect warms the troposphere), but also in the stratosphere which cools down with increasing CO2 concentrations. The regeneration of the ozone layer thus takes place under atmospheric conditions different to those prevailing during the ozone depletion processes of recent decades. Due to climate change, it is highly unlikely that the ozone layer will return to exactly the way it was before the time of increased concentrations of ozone-depleting substances. Fig. 10. Average deviations of the total ozone column (in %) for the region between 60°N and 60°S. The mean annual cycle for the period 1995-2009 was subtracted in each case. The orange and red curves represent data obtained from satellite instruments (TOMS, GOME, SCIAMACHY and GOME-2). The blue curve shows results from a numerical simulation (R2) using a chemistry-climate model (E39CA). Due to further increasing greenhouse gas concentrations, global atmospheric temperatures will continue to change over the coming decades, i.e. it is expected that the troposphere will continue to warm up and that the stratosphere will cool down further due to radiation effects. In addition, it must also be taken into account that, due to the expected build-up of the ozone layer, stratospheric ozone heating rates (absorption of solar ultraviolet radiation by ozone) will increase again, to some extent counteracting the increased cooling due to Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 19 rising greenhouse gas concentrations. However, as the ozone concentration depends largely on the background temperature, there will be some feedback. Since climate change also involves a change in the stratosphere’s dynamics, “dynamic” heating of the stratosphere can also occur, depending on the time of year and place, which leads to local stratospheric heating, rather than cooling. That’s why it is important to take the interactions between chemical, physical and dynamic processes into account, both for the interpretation of observed changes in the ozone layer and for prognostic studies. It must be always considered that ozone and climate connections are influencing each other in both directions. Therefore, estimates of future stratospheric ozone concentration developments and climate change are not trivial and bring uncertainties with them. Future prognostics with CCMs also clearly indicate that ozone regeneration will be faster in some areas than in others, where it’s quite possible that the recovery of the ozone layer will be delayed (Chapter 3 in WMO, 2011). The results of E39CA also show that the regeneration of the ozone layer will vary from region to region and does not represent a simple reversal of the depletion observed over recent years. Examples are presented in Figure 11, showing the evolution of the stratospheric ozone layer in the Northern and Southern polar regions. In contrast to Figure 10, only the data for respective spring months are shown when ozone depletion maximises. First of all, E39CA reproduces nicely the different evolution of the ozone layer in the Northern and Southern Hemisphere in the past showing a more pronounced thinning of the ozone layer in the Southern Hemisphere due to the formation of the ozone hole. Interannual fluctuations are well captured by E39CA in the Northern Hemisphere while they are underestimated in the Southern Hemisphere. Here, for example, the model does not create dynamical situations leading to weak polar vortices in late winter and early spring and therefore higher ozone values as particularly observed in 1988 and 2002 (see also Figure 4). Obviously, the recreation speed of the ozone layer is different in the Northern and in the Southern Hemisphere: In the Northern Hemisphere ozone values found in the 1960ies and 1970ies are reached again around 2030 and further increase afterwards. In the Southern Hemisphere the 2050-values are still below the values found in the 1960ies (see also Figure 12). Looking again to Figure 10, here the ozone values after 2030 are also higher than before 1980. What is the reason for this different behaviour in different stratospheric regions? Due to the continued increase in greenhouse gas concentrations in the atmosphere, the stratosphere will further cool down, resulting in faster ozone-layer regeneration especially in the middle and upper stratosphere. Here, lower temperatures slow down ozone destroying (temperature depending) chemical reactions. In the lower polar stratosphere, in particular in the Southern Hemisphere, the rebuilding of the ozone layer may slow down during spring. There, lower temperatures lead to an increased formation of polar stratospheric clouds (PSCs), which are the necessary prerequisite to start ozone depletion. On the other hand, climate change will affect ozone transport to higher latitudes. E39CA predict enhanced transport of stratospheric air masses from tropical to extra-tropical regions. Caused by the isolation of the Southern Hemisphere polar stratosphere (originated by the high zonal wind speed there; see Section 1.2) this change does not lead to a faster closure of the ozone hole (see also Figure 12); contrastingly, in the Northern Hemisphere the transport of ozone rich air towards polar latitudes is intensified, leading to a quicker back formation of the ozone layer. Overall, model simulations indicate that the ozone layer is expected to recreate faster in the Arctic than in the Antarctic stratosphere. Climate Change – Geophysical Foundations and Ecological Effects 20 Fig. 11. As Figure 10, but now for the polar regions (top: Northern Hemisphere for months February, March, and April; bottom: Southern Hemisphere for months September, October, and November). Deviations are given with regard to the mean value of the period 1995-2009 (in %) for the region between 60° and 90°. Notice the different scales on the y-axis. Figure 12 provides another view of the temporal evolution of the ozone hole as it is simulated by E39CA from the 1960ies to the 2040ies. Here, decadal mean total ozone values for October are presented. Respective mean values derived from satellite instruments are shown for comparison (notice again that the satellite and theCCM plots use different colour bars representing Dobson Units.) These images of simulated total ozone columns also indicate that the closure of the ozone hole will be delayed regarding the prescribed temporal decrease of the stratospheric chlorine content, i.e. will be not completed before 2050 (see also lower part of Figure 11). Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 21 Satellite 1970-1979 Satellite 1980-1989 Satellite 1990-1999 Satellite 2000-2009 E39CA 1960-1969 E39CA 1970-1979 E39CA 1980-1989 E39CA 1990-1999 E39CA 2000-2009 E39CA 2010-2019 E39CA 2020-2029 E39CA 2030-2039 E39CA 2040-2049 Fig. 12. Decadal evolution of the ozone hole in October (monthly mean value) derived from satellite measurements (TOMS, GOME, SCIAMACHY, GOME-2) from 1970 to 2009 and the simulation with the CCM E39CA from 1960 until 2049. 5. Discussion of uncertainties and conclusion Obviously, reliable estimates of the future evolution of atmospheric behaviour are difficult because of strong interactions between physical, dynamical and chemical processes which, moreover, are expected to be modified in a future climate with enhanced greenhouse gas concentrations. Further uncertainties must be taken into account regarding the future development of both the stratospheric ozone layer and the climate. The future development of stratospheric water vapour concentrations is currently highly uncertain. Prognostic studies with numerical models indicate that tropospheric water vapour concentrations would increase with increasing temperatures in the troposphere, which would also enhance the amount of water vapour being transported from the troposphere into the lower tropical stratosphere (e.g. WMO, 2007). Higher stratospheric water vapour concentrations could lead to an increased PSC-forming potential in the polar regions during the winter months and thus enhance the ozone depletion potential. As water vapour is also an important greenhouse gas, changes of Climate Change – Geophysical Foundations and Ecological Effects 22atmospheric concentrations would affect atmospheric radiation balance. Stratospheric water vapour concentrations would also increase with rising methane (CH4) concentration (e.g. caused by rice cultivation, intensive livestock farming, thawing of permafrost soils) due to oxidation of CH4, which would itself increase ozone production in the lower stratosphere. On the other hand, rising CH4 concentrations would bind reactive chlorine in the atmosphere. However, CH4 is also an important greenhouse gas. Therefore, higher atmospheric CH4 concentrations would influence both the climate and atmospheric chemistry. A further rise in atmospheric nitrous oxide (N2O) concentrations would increase the amount of atmospheric nitrogen oxides, thus decreasing the ozone content of the middle and upper stratosphere. N2O (although known as laughing gas) stems from both natural (for example oceans and tropical forests) and anthropogenic sources (for example emissions from cultivated soil, industrial processes, the combustion of fossil fuels, biomass, and biofuels). N2O emissions near the Earth’s surface are the most important source of nitrogen oxides in the stratosphere. N2O is also a key greenhouse gas. This is another example which nicely illustrates the close relationship between problems associated with climate change and those relating to changes in the stratospheric ozone layer. Regulating the atmospheric N2O content in the atmosphere is not just important for protecting the Earth’s climate, but also for the future evolution of the stratospheric ozone layer. A reduction in N2O emissions would both lower the anthropogenic greenhouse effect and positively influence the recovery of the ozone layer. To summarise: Clearly, natural effects such as the variability of solar radiation and particle emissions that are due to strong volcanic eruptions influence the stratospheric ozone concentration. Internal fluctuations of stratospheric circulation affect the thermal structure of the stratosphere and air mass transport. Chemical production and depletion of ozone are determined by photochemical processes, homogeneous gas-phase reactions, and heterogeneous chemistry on the surface of particles (aerosols and PSCs). Understanding atmospheric processes and the interconnections between the various processes is made even more difficult by the fact that atmospheric conditions change over the long term owing to increased greenhouse gas concentrations. Climate change influences the overall stratospheric ozone production (i.e. the sum of ozone depletion and production) both directly and indirectly, and thus determines the rate of ozone regeneration, which will vary with altitude and latitude. Furthermore, changes in stratospheric circulation can potentially modify the development of the ozone layer in the 21st century. For example, a stronger meridional circulation in an atmosphere with increased greenhouse gas concentrations could cause the stratospheric wind systems to change during the winter months, thus for example resulting in decreased zonal wind speeds. This could lead to higher mean stratospheric temperatures in the polar regions and thus less PSCs. The climate-chemistry connections presented in this chapter clearly demonstrate that in assessing atmospheric changes it is not enough to look at the processes independent of each other. Changes in climate and in the chemical composition of the atmosphere are closely interrelated, sometimes in very complex ways. Therefore, surprising developments cannot be ruled out in the future, either. 6. Acknowledgment Thanks to NASA for the provision of the TOMS satellite data, ESA/DLR for the provision of the GOME and SCIAMACHY data, O3M-SAF/EUMETSAT/DLR for the provision of the GOME-2 data. We would like to thank colleagues from BIRA-IASB (Belgium), DLR (Germany), RT Solutions Inc. (USA) and AUTH (Greece) for their work on ozone retrieval Chemistry-Climate Connections – Interaction of Physical, Dynamical, and Chemical Processes in Earth Atmosphere 23 algorithms from the European satellites and corresponding geophysical validation. Special thanks to the DLR colleagues, M. Coldewey-Egbers for merging the GOME/SCIAMACHY/GOME-2 measurements and providing the ozone deviation data, and A. Stenke and H. Garny for the development of the E39CA model and providing the simulation data. 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