Title: Uncertainties in regional climate models outputs Author: RNDr. Eva Holtanová Supervisor: doc. RNDr. Jaroslava Kalvová, CSc. Department: Dept. of Meteorology and Environment Protection Faculty of Mathematics and Physics Charles University in Prague Present doctoral thesis focuses on the analysis of uncertainties in regional climate model outputs in the area of the Czech Republic. Generally, the uncertainties in model outputs come from inaccuracies of initial and boundary conditions, further from the necessity to parameterize the small scale processes, and the structure of the model, e.g. the choice of numerical schemes or spatial resolution. In case of the simulations of future climate, another source of uncertainty arises. It is the unknown development of forcings that influence the climate system. The analysis in this work focuses on two multi-model ensembles, that come from two international European projects PRUDENCE and ENSEMBLES. The simulated 30-year mean seasonal air temperature and precipitation amounts are used, for the reference period 1961- 1990, and several future time periods. Two techniques were employed to assess the uncertainties. The first one was aimed at dividing the variance of a multi-model ensemble into contributions of regional model, driving global model and emission...

Les épisodes de sécheresse et de canicule qui frappent épisodiquement les régions tempérées ont des conséquences préjudiciables sur les plans sanitaire, économique, social et écologique. Afin de pouvoir enclencher des stratégies de préparation et de prévention avec quelques semaines ou mois d'anticipation, les attentes sociétales en matière de prévision sont élevées, et ce d'autant plus que les projections climatiques font craindre la multiplication de ces épisodes au cours du 21ème siècle. Néanmoins, la saison d'été est la plus difficile à prévoir aux moyennes latitudes. Les sources connues de prévisibilité sont plus ténues qu'en hiver et les systèmes de prévision climatique actuels peinent à représenter correctement les mécanismes de téléconnexion associés. Un nombre croissant d'études a mis en évidence un lien statistique dans certaines régions entre l'humidité du sol au printemps et les températures et précipitations de l'été qui suit. Ce lien a été partiellement confirmé dans des modèles numériques de climat mais de nombreuses interrogations subsistent. L'objectif de cette thèse est donc de mieux comprendre le rôle joué par l'humidité du sol sur les caractéristiques et la prévisibilité du climat de l'été dans les régions tempérées. Grâce notamment au modèle couplé de circulation générale CNRM-CM, nous avons mis en œuvre des ensembles de simulations numériques qui nous ont permis d'évaluer le degré de persistance des anomalies d'humidité du sol printanière. En effet, une longue persistance est une condition nécessaire pour que ces anomalies influencent le climat à l'échelle de la saison, via le processus d'évapotranspiration de la surface. En imposant dans notre modèle des conditions initiales et aux limitées idéalisées d'humidité du sol, nous avons mis en évidence des régions du globe pour lesquelles l'état moyen et la variabilité des températures et des précipitations en été sont particulièrement sensibles à ces conditions. C'est notamment le cas sur une grande partie de l'Europe et de l'Amérique du nord, y compris à des latitudes élevées. Pour toutes ces régions, l'humidité du sol est une source prometteuse de prévisibilité potentielle du climat à l'horizon saisonnier, bien que de fortes incertitudes demeurent localement sur le degré de persistance de ses anomalies. Une expérience de prévisibilité effective coordonnée avec plusieurs systèmes de prévision montre qu'une initialisation réaliste de l'humidité du sol améliore la prévision de températures estivales principalement dans le sud-est de l'Europe. Dans d'autres régions, comme l'Europe du Nord, le désaccord des modèles provient de l'incertitude sur la persistance des anomalies d'humidité du sol. En revanche, sur les Grandes Plaines américaines, aucun modèle n'améliore ses prévisions qui restent donc très médiocres. La littérature ainsi que nos évaluations de sensibilité du climat à l'humidité du sol ont pourtant identifié cette région comme un "hotspot" du couplage entre l'humidité du sol et l'atmosphère. Nous supposons que l'échec de ces prévisions est une conséquence des forts biais chauds et secs présents dans tous les modèles sur cette région en été, qui conduisent à un dessèchement excessif des sols. Pour le vérifier, nous avons développé une méthode qui corrige ces biais au cours de l'intégration des prévisions avec CNRM-CM6. Les prévisions qui en résultent sont nettement améliorées sur les Grandes Plaines. La compréhension de l'origine des biais continentaux en été et leur réduction dans les prochaines générations de modèles de climat sont des étapes essentielles pour tirer le meilleur parti de l'humidité du sol comme source de prévisibilité saisonnière dans les régions tempérées. / Severe heat waves and droughts that episodically hit temperate regions have detrimental consequences on health, economy and society. The design and deployment of efficient preparedness strategies foster high expectations for the prediction of such events a few weeks or months ahead. Their likely increased frequency throughout the 21st century, as envisaged by climate projections, further emphasizes these expectations. Nevertheless, the summer season is the most difficult to predict over mid-latitudes. Well-known sources of predictability are weaker than in winter and current climate prediction systems struggle to adequately represent associated teleconnection mechanisms. An increasing number of studies have shown a statistical link over some regions between spring soil moisture and subsequent summer temperature and precipitation. This link has been partly confirmed in climate numerical models, but many questions remain. The purpose of this PhD thesis is to better understand the role played by soil moisture onthe characteristics and predictability of the summer climate in temperate regions. By means of the CNRM-CM coupled general circulation model, we have designed a range of numerical simulations which help us evaluate the persistence level of spring soil moisture anomalies. Indeed, a long persistence is a necessary condition for these anomalies to influence the climate at the seasonal scale, through the process of evapotranspiration. By imposing in our model idealized initial and boundary soil moisture conditions, we have highlighted areas of the globe for which the average state and the variability of temperatures and precipitation in summer is particularly sensitive to these conditions. This is the case in particular for Europe and North America, including over high latitudes. Soil moisture is therefore a promising source of potential seasonal climate predictability for these regions, although the persistence of soil moisture anomalies remains locally very uncertain. An effective predictability coordinated experiment, bringing together several prediction systems, shows that a realistic soil moisture initialization improves the forecast skill of summer temperatures mainly over southeast Europe. In other regions, such as Northern Europe, the disagreement between models comes from uncertainty about the persistence of soil moisture anomalies. On the other hand, over the American Great Plains, even the forecasts with improved soil moisture initialization remain unsuccessful. Yet, the literature as well as our assessment of climate sensitivity to soil moisture have identified this region as a "hotspot" of soil moisture - atmosphere coupling. We assume that the failure of these predictions relates to the strong hot and dry bias present in all models over this region in summer, which leads to excessive soil drying. To verify this assumption, we developed a method that corrects these biases during the forecast integration based on the CNRM-CM6 model. The resulting forecasts are significantly improved over the Great Plains. Understanding the origin of continental biases in the summer and reducing them in future generations of climate models are essential steps to making the most of soil moisture as a source of seasonal predictability in temperate regions

Title: Links between atmospheric circulation and surface air temperature distributions in climate models Abstract: This thesis comprises a collection of five papers dealing with validation of regional climate model (RCM) simulations over Central Europe. The first paper illustrates and discusses problems with observed data that are used for model validation and how the choice of reference dataset affects the outcomes in validating the RCMs' performances. The second paper evaluates daily temperatures, and it indicates that some temperature biases may be related to deficiencies in the simulations of large- scale atmospheric circulation. RCMs' ability to simulate atmospheric circulation and the observed links between circulation and surface air temperatures are examined in detail in the third paper. This article also compares performances of individual RCMs with respect to the driving data by analysing the results for the driving data themselves. The fourth paper focuses on biases in the diurnal temperature range within RCMs and their possible causes by examining links of the errors to the at- mospheric circulation and cloud amount. The last paper investigates the observed relationships between atmospheric circulation and daily precipitation amounts over three regions in the Czech Republic, as well as how these...

Droughts can have far-reaching and devastating effects on all sectors of society and ecology and future changes to drought and flood patterns are uncertain. This uncertainty has led to a lax response from local officials in dealing with mitigation and adaptation, particularly in Sweden. As such, this study focused on providing more localized estimates of future drought trends in Sweden so that policy makers can make informed decisions. To assess impacts to different sectors, the results from ten different climate model simulations between 1961-2100 under different emission scenarios, along with hydrological model simulations, were evaluated throughout Sweden for 50 different catchments using a variety of meteorological and hydrological drought indices. We projected a consistent and significant increase in drought severity, duration, and intensity over the course of the 21st century in many parts of Sweden under both moderate and high emission scenarios (RCP 4.5 and RCP 8.5). However, models were in less agreement on the sign of change of drought frequency. These results are highly consistent with more regional pan-European studies on drought, but also show significant departures due to local catchment-specific variability in some forms of drought. Local impacts to agriculture, energy production, water supply, public health, and fresh-water ecosystems are briefly discussed. These results are likely underestimates of future drought due to biases in the models. Improved formulations of drought indices along with a more robust statistical handling of the model output could reduce these uncertainties. / Torka kan ha förödande effekter på samhälle och ekosystem. På grund av ökande nederbörder och temperaturer är förändringar i torka och översvämningar osäkra. Dessa osäkerheter har lett till slappa åtgärder från politiker och lokala tjänstemän vars syfte är att anpassa samhället inför torka och översvämningar. I och med detta fokuserade denna studie på att ge mer lokaliserade uppskattningar av framtida torktrender i Sverige så att beslutsfattare kan fatta välgrundade beslut. För detta utvärderades resultat från olika klimatmodeller och simuleringar av avrinnesområden mellan 1961-2100 under olika utsläppsscenarier. Dessa utfördes i hela Sverige för 50 olika avrinningsområden för att bedöma effekterna av framtida torka på olika samhällsfunktioner, så som jordbruk, energiproduktion, vattenförsörjning, folkhälsa, och ekosystem. De flesta modellerna visade en överensstämmande ökning av torkans magnitud, varaktighet och intensitet under 2007-2100 i många delar av Sverige under både måttliga och höga utsläppsscenarier. Modellerna var dock mindre överens om förändring av torkfrekvensen. Dessa resultat överensstämmer mycket väl med mer regionala studier av Europa men visar också tydliga skillnader på grund av lokal variation, vilket har olika effekter på olika samhällsfunktioner. Det visade sig även att klimatmodellerna och våra metoder hade en tendens att överskatta bland annat nederbörd, vilket innebär att dessa resultat sannolikt är underskattningar av framtida torka.

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The Arctic region shows a particularly high susceptibility to climate change, which historically manifests in an amplification of the near-surface warming in the Arctic relative to the global mean. This Arctic amplification (AA) has impacts on the climate system also beyond the northern polar regions, which highlights the importance to adequately represent it in numerical models. While state-of-the-art climate models widely agree on the presence of AA, they simulate a large spread in the magnitude of Arctic-amplified warming. This thesis addresses the need to evaluate the performance of global climate models in projecting AA and its most important drivers. For the latter, the focus is on the three amplifying climate feedbacks (ACFs) that largely drive the meridional warming structure leading to AA. The ACFs include the sea-ice-albedo feedback (SIAF), the Planck feedback, and the lapse-rate feedback (LRF). These feedbacks arise from the relevant changes in Arctic sea ice, near-surface temperatures, and the deviation from the near-surface temperature change through the atmosphere, respectively. In the thesis, two observational constraints are presented to narrow the range of climate models of the sixth Coupled Model Intercomparison Project (CMIP6) regarding their projection of AA and the ACFs in both past and future climate. While for the past, the models representation of near-surface processes can often be directly evaluated against observations, it is particularly the LRF that is difficult to constrain as it incorporates the entire atmospheric warming structure. As a consequence, the historical constraint focuses on the LRF, while the future constraint gives a prediction range for the evolution of AA and all three ACFs through the 21st century. The main results are highlighted in the view of the changing atmospheric energy budget (AEB) of the Arctic under anthropogenic climate forcing. The AEB provides a framework to address Arctic climate change at large scales, and further helps to decide on the relevant aspects that provide appropriate metrics for constraining both AA and the ACFs. In other words, the perspective of a changing Arctic AEB highlights important alterations of the energetics under climate change, that further link to changes in climate aspects that partly explain the inter-model spread in simulated AA and the ACFs. The main results of the cumulative thesis are formulated on the basis of three published research papers, papers I, II, and III. Paper I addresses the Arctic AEB which is typically characterised by an equilibrium between net radiative cooling and advective heating, and mostly an absence of convection. This radiative-advective equilibrium (RAE) approximates well the energy budget and thermal structure of the Arctic atmosphere. The main outcome of paper I is that with continuous warming as simulated by CMIP6 models in an idealised setup, a deviation from the RAE increasingly develops, resulting from sea ice retreat and increased ocean-to-atmosphere heat fluxes. These changes are further concomitant with a depletion of the typical surface-based temperature inversion and a decrease in advective heating, which is byword for the convergence of atmospheric energy transport in the Arctic. Since the RAE currently explains much of the basic thermal structure of the Arctic atmosphere, those changes have the potential to further mediate the LRF. Paper II builds on paper I and evaluates the performance of climate models in representing the key aspects of the Arctic LRF in CMIP6 historical simulations that have the best estimates of the transient climate forcings during the observational period. In particular it is found that CMIP6 models that realistically simulate both the lower thermal structure of the atmosphere and the poleward atmospheric energy transport are more trustworthy in informing about the LRF and how much it contributed to Arctic warming during the past few decades. The evaluation is based on observations of surface-based temperature inversions during the year-long Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, and atmospheric energy transport convergence computations from reanalyses. Paper III expands the constraint approach of paper II and carries out an emergent constraint (EC) on future AA and the ACFs that further elaborates on the physical relationships between the constraining metrics and future climate projections. Previous work has highlighted that parts of the inter-model spread in simulated AA is explained through the spread in contemporaneous sea ice loss across climate models. The thesis confirms this link by showing that CMIP6 models with a stronger climatological sea ice loss project a stronger AA in the future under the assumption of a high emission scenario. By further linking the degree of future ice loss to the current-climate sea ice amount in CMIP6 models, paper III facilitates an EC on the future evolution of AA and the ACFs. In particular, models with a lower contemporary sea ice amount project a larger magnitude of AA by setting the stage for stronger climatological ice loss and near-surface warming, linking to the relevant ACFs. From the corresponding prediction it is evident that AA is expected to continue at a warming rate that is more than twice or three times larger than global-mean warming. Furthermore, the three ACFs continue to contribute to Arctic warming, with the SIAF leading the warming contribution response. Lastly, the consideration of statistically strong and physically plausible relationships across climate models makes the EC a valuable technique to constrain climate model simulations in conjunction with observations. This thesis highlights the potential of combining the advantages of both presented constraints: Using multiple process-relevant aspects instead of one singular metric (paper II), but considering the mechanistic couplings between these metrics and the climate projection of interest (paper III) will improve our model-evaluation techniques and further help guiding the design of future climate simulations.:Summary of the dissertation List of papers Author’s contribution Supervision statement 1 Introduction 2 Research focus 3 The Arctic atmospheric energy budget 3.1 The atmospheric column model 3.2 The annual atmospheric energy budget 4 Arctic amplification and climate feedbacks 4.1 Amplifying climate feedbacks 4.2 A comment on process coupling 5 Methods and data 5.1 Energy budget equations 5.2 Quantifying Arctic amplification and climate feedbacks 5.3 Climate model data 5.3.1 CMIP6 idealised simulations 5.3.2 CMIP6 historical simulations 5.3.3 CMIP6 ssp585 simulations 5.4 Observational constraints 5.4.1 Constraint on historical Arctic lapse-rate feedback 5.4.2 Constraint on future Arctic amplification and relevant climate feedbacks 6 Results 6.1 Paper I - Deviations from the Arctic radiative-advective equilibrium under anthropogenic climate change 6.2 Paper II - Constraining the Arctic lapse-rate feedback during past decades by contemporary observations 6.3 Paper III - Constraining future Arctic amplification and the relevant climate feedbacks based on the recent sea ice climatology 7 Summary and outlook References Lists Acknowledgements Appendix A: Paper I Appendix B: Paper II Appendix C: Paper III

[ES] La gestión de los recursos hídricos es de vital importancia para la comprensión de las sequias a largo plazo. En la actualidad, se presentan problemas debido a la disponibilidad y manejo del recurso hídrico. Además, el cambio climático afecta de manera negativa las variables climáticas y la disponibilidad del recurso hídrico. El tomar decisiones en base a información confiable y precisa conlleva un arduo trabajo y es necesario contar con diferentes herramientas que permitan llegar a la gestión de los recursos hídricos. La modelización de las variables climáticas es parte fundamental para determinar la disponibilidad del recurso hídrico. Las más importantes son la precipitación y temperatura o precipitación y evapotranspiración. Los modelos estocásticos se encuentran en un proceso de evolución que permiten reducir la escala de análisis. En esta investigación se ha abordado la modelación de variables climáticas con detalle diario. Se ha planteado una metodología para la generación de series sintéticas de precipitación y temperatura mediante modelización estocástica continua multivariada a escala diaria. Esta metodología también incorpora la corrección del sesgo para precipitación y temperatura de los escenarios de cambio climático con detalle diario. Los resultados de la presente tesis indican que los modelos estocásticos multivariados pueden representar las condiciones espaciales y temporales de las diferentes variables climáticas (precipitación y temperatura). Además, se plantea una metodología para la determinación de la evapotranspiración en función de los datos climáticos disponibles. Por otro lado, los modelos estocásticos multivariados permiten la corrección del sesgo con resultados diarios, mensuales y anuales más realistas que otros métodos de corrección de sesgo. Estos modelos climáticos son una herramienta para pronosticar eventos o escenarios futuros que permiten tomar mejores decisiones de manera anticipada. Estos modelos se programaron en el entorno de MatLab con el objetivo de aplicarlos a diferentes zonas de estudio de manera eficiente y automatizada. Los análisis realizados en la presente tesis se realizaron para la cuenca del Júcar con un buen desempeño para las condiciones de la cuenca. / [CA] La gestió dels recursos hídrics és de vital importància per a la comprensió de les sequeres a llarg termini. En l'actualitat, es presenten problemes a causa de la disponibilitat i maneig del recurs hídric. A més, el canvi climàtic afecta de manera negativa les variables climàtiques i la disponibilitat del recurs hídric. El prendre decisions sobre la base informació de confiança i precisa comporta un ardu treball i és necessari comptar amb diferents eines que permeten arribar a la gestió dels recursos hídrics. La modelització de les variables climàtiques és part fonamental per a determinar la disponibilitat del recurs hídric. Les més importants són la precipitació i temperatura o precipitació i evapotranspiració. Els models estocàstics es troben en un procés d'evolució que permet la incorporació de més detalls reduint l'escala d'anàlisi. En aquesta investigació s'ha abordat el modelatge de variables climàtiques amb detall diari. S'ha plantejat una metodologia per a la generació de sèries sintètiques de precipitació i temperatura mitjançant modelització estocàstica contínua multivariada a escala diària. Aquesta metodologia també incorpora la correcció del biaix per a precipitació i temperatura dels escenaris de canvi climàtic amb detall diari. Els resultats de la present tesi indiquen que els models estocàstics multivariats poden representar les condicions espacials i temporals de les diferents variables climàtiques (precipitació i temperatura). A més es planteja una metodologia per a la determinació de l'evapotranspiració en funció de les dades climàtiques disponibles. D'altra banda, els models estocàstics multivariats permeten la correcció del biaix amb resultats diaris, mensuals i anuals més realistes que altres mètodes de correcció de biaix. Aquests models climàtics són una eina per a pronosticar esdeveniments o escenaris futurs que permeten prendre millors decisions de manera anticipada. Aquests models es van programar a l'entorn de Matlab amb l'objectiu d'aplicar-los a diferents zones d'estudi de manera eficient i automatitzada. Les anàlisis realitzades en la present tesi es van realitzar per a la conca del Xúquer amb un bon acompliment per a les condicions de la conca. / [EN] Management of the water resources is important for understanding long-term droughts. Currently, there are problems due to the availability and management of water resources. Furthermore, climate change negatively affecting climate variables and the availability of water resources. Making decisions based on reliable and accurate information involves hard work and it is necessary to have different tools to achieve the management of water resources. The modeling of the climatic variables is a fundamental part to determine the availability of the water resource. The most important are precipitation and temperature or precipitation and evapotranspiration. Stochastic models are in a process of evolution that allows the incorporation of more details by reducing the scale of analysis. In this research, the modeling of climatic variables has been approached in daily detail. A methodology has been proposed for the generation of synthetic series of precipitation and temperature by means of multivariate continuous stochastic modeling on a daily scale. This methodology also incorporates the bias correction for precipitation and temperature of the climate change scenarios with daily detail. The results of this thesis indicate that multivariate stochastic models can represent the spatial and temporal conditions of the different climatic variables (precipitation and temperature). In addition, a methodology is proposed for the determination of evapotranspiration based on the available climatic data. On the other hand, multivariate stochastic models allow bias correction with more realistic daily, monthly and annual results than other bias correction methods. These climate models are a tool to forecast future events or scenarios that allow better decisions to be made in advance. These models were programmed in the MatLab software with the aim of applying them to different study areas in an efficient and automatically. The work in this thesis was carried out for the Júcar basin with a good performance for the conditions of the basin / Hernández Bedolla, J. (2022). Análisis estocástico de datos climáticos como predictor para la gestión anticipada de sequías en recursos hídricos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182095 / TESIS

En Europe, l'augmentation des températures moyennes de surface de l'air projetée au cours du 21ème siècle s'accompagne d'une augmentation des extrêmes chauds et d'une diminution des extrêmes froids. Dans les dernières décennies, des indices témoignent déjà de ces changements, comme l'établissement récurrent de nouveaux records de chaleur ou l'augmentation des canicules. Nous étudions l'évolution des extrêmes journaliers de température au cours du 20ème et du 21ème siècle en France et en Europe, et ce en termes d'occurrence et d'intensité. Un intérêt particulier est aussi porté aux mécanismes responsables de ces futurs extrêmes climatiques, ainsi qu'aux futures températures maximales. Nous nous intéressons tout d'abord à l'évolution des records journaliers de température à partir d'observations et de modèles de climat. Entre 1950 et 1980, l'évolution théorique des records dans le cadre d'un climat stationnaire représente correctement l'évolution observée des records chauds et froids. Depuis les années 1980, un écart à ce climat stationnaire est observé, avec respectivement une augmentation et une diminution de l'occurrence des records chauds et froids. Les modèles climatiques suggèrent une accentuation de ces changements au cours du siècle. L'occurrence moyenne des records chauds à la fin du siècle présente une forte augmentation par rapport aux premières décennies de la période observée. L'augmentation la plus importante des records chauds est projetée en été, en particulier dans la région méditerranéenne. Quant aux records froids, les modèles indiquent une diminution très importante de leur occurrence, avec une occurrence quasi-nulle dans les dernières décennies. Les variations observées d'occurrence de records sont, au début du 21ème siècle, toujours dans l'éventail des fluctuations de la variabilité interne du climat. Au cours du siècle, l'émergence de l'influence anthropique de ces fluctuations est détectable dans l'évolution des records chauds et froids en été, et ce respectivement autour des décennies 2030 et 2020. À l'horizon de la fin du siècle, les changements moyens d'occurrence de records ne peuvent pas être uniquement expliqués par des fluctuations naturelles. Nous nous sommes ensuite intéressés aux futures températures estivales extrêmes, ainsi qu'aux canicules intenses qui peuvent être à l'origine de ces extrêmes. Pour cela, l'utilisation de modèles climatiques globaux est associée à la modélisation climatique régionale et à des stations d'observations en France. Tout d'abord, l'augmentation maximale des valeurs maximales des records journaliers de température en été en France est estimée à partir d'une simulation régionale à haute résolution spatiale. À l'horizon 2100, les projections indiquent une augmentation maximale de ces valeurs extrêmes en été comprise entre de 6.6°C et 9.9°C selon les régions de la France. La comparaison de ces projections avec un ensemble de modèles climatiques indique que ces augmentations maximales pourraient être plus importantes. La médiane de la distribution des modèles indique en effet une augmentation maximale de ces valeurs maximales des records journaliers de température de 11.8°C en été et en France. Puis, des expériences de modélisation de canicules intenses du climat européen de la fin du 21ème siècle ont été réalisées à partir d'événements particuliers d'un modèle de climat. Ces expériences ont mis en évidence le rôle des interactions entre le sol et l'atmosphère dans l'amplification des températures extrêmes lors de futurs évènements caniculaire intenses. L'occurrence de telles canicules est d'abord dépendante de la circulation atmosphérique, mais l'intensité des températures peut ensuite être fortement amplifiée en fonction du contenu en humidité des sols avant la canicule, et donc des conditions climatiques des semaines et des mois précédents. / Over the 21st century, the mean increase in surface air temperatures is projected to be associated with an increase in warm temperature extremes and a decrease in the cold ones. Over the last decades, evidence already suggests these changes, as for example recurrent warm record-breaking temperatures or the increase in heatwave occurrence. We investigate the evolution of daily temperature extremes over the 20th and the 21st centuries in France and in Europe, their possible changes in frequency and intensity. We also focus on the mechanisms responsible for these projected climate extremes, as well as the maximum values of temperature extremes at the end of the century. First, we investigate the evolution of daily record-breaking temperatures in Europe based on the observations and an ensemble of climate models. From the 1950s to the 1980s, the theoretical evolution of the records in a stationary climate correctly reproduce the observed one, for both cold and warm records. From 1980, a shift from that theoretical evolution is observed, with an increase in the occurrence of warm records and a decrease in the occurrence of the cold ones. Climate models suggest an amplification of these changes over the century. At the end of the 21st century, the mean number of warm records shows a strong increase compared to the first decades of the observed period. The strongest increase in warm record-breaking temperatures is found in summer, and particularly over the Mediterranean edge. On the contrary, the occurrence of cold record-breaking temperatures is projected to strongly decrease, with almost no new records in the last decades of the century, for all seasons and over the entire European domain. Observed variations of daily record-breaking temperatures are still, at the beginning of the 21st century, consistent with internal climate variability only. Over the century, the anthropogenic influence emerge from these fluctuations in the summer record evolutions, around the 2030 and the 2020 for the warm and cold records respectively. By 2100, the mean changes in record occurrences cannot be explained by the internal climate variability solely, for all seasons and over the entire European domain. Then, we investigate future extreme temperatures at the end of the 21st century, as well as severe heatwaves leading to these extremes. Climate models analyses are associated with regional climate modeling and a French station-based dataset of observations. The summer 21st century evolution of the maximum values of daily warm record-breaking temperatures is first examined in the observations and the high resolution simulation of the regional model. By 2100, an increase of these values is projected, with maximum changes between +6.6°C and +9.9°C in summer among the French regions. These projections assessed from a regional model may underestimate the changes. The multi-model mean estimate of the maximum increase of these values is indeed around +11.8°C in summer over France. Finally, regional modeling experiments of severe heatwaves in the climate of the end of the 21st century in Europe are performed. These severe heatwaves are selected cases from a global climate model trajectory. The experiments results show the role of the soil-atmosphere interactions in the amplification of the extreme temperatures during such future severe warm events. The occurrence of the heatwave is first caused by the atmospheric circulation, but the temperature anomaly can then be amplified according to the soil moisture content before the event, and thus the climatic conditions of the preceding weeks and months.

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variables (e.g., temperature, humidity, mean sea level pressure etc.) to hydrologic variables (e.g., precipitation and streamflow), at regional scale. Statistical downscaling methods are generally used in the hydrological impact assessment studies for downscaling climate projections provided by the General Circulation Models (GCMs). GCMs are climate models designed to simulate time series of climate variables globally, accounting for the greenhouse gases in the atmosphere. The statistical techniques used to bridge the spatial and temporal resolution gaps between what GCMs are currently able to provide and what impact assessment studies require are called as statistical downscaling methods. Generally, these methods involve deriving empirical relationships that transform large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system. The research reported in this thesis contributes towards (i) development of methodologies for climate change impact assessment of various components of a water resources system, such as water quality, water availability, irrigation and reservoir operation, and (ii) quantification of GCM and scenario uncertainties in hydrologic impacts of climate change. Further, an integrated reservoir operation model is developed to derive optimal operating policies under the projected scenarios of water availability, irrigation water demands, and water quality due to climate change accounting for various sources of uncertainties. Hydropower generation is also one of the objectives in the reservoir operation. The possible climate change impact on river water quality is initially analyzed with respect to hypothetical scenarios of temperature and streamflow, which are affected by changes in precipitation and air temperature respectively. These possible hypothetical scenarios are constructed for the streamflow and river water temperature based on recent changes in the observed data. The water quality response is simulated, both for the present conditions and for conditions resulting from the hypothetical scenarios, using the water quality simulation model, QUAL2K. A Fuzzy Waste Load Allocation Model (FWLAM) is used as a river water quality management model to derive optimal treatment levels for the dischargers in response to the hypothetical scenarios of streamflow and water temperature. The scenarios considered for possible changes in air temperature (+1 oC and +2 oC) and streamflow (-0%, -10%, -20%) resulted in a substantial decrease in the Dissolved Oxygen (DO) levels, increase in Biochemical Oxygen Demand (BOD) and river water temperature for the case study of the Tunga-Bhadra River, India. The river water quality indicators are analyzed for the hypothetical scenarios when the BOD of the effluent discharges is at safe permissible level set by Pollution Control Boards (PCBs). A significant impairment in the water quality is observed for the case study, under the hypothetical scenarios considered. A multi-variable statistical downscaling model based on Canonical Correlation Analysis (CCA) is then developed to downscale future projections of hydro¬meteorological variables to be used in the impact assessment study of river water quality. The CCA downscaling model is used to relate the surface-based observations and atmospheric variables to obtain the simultaneous projection of hydrometeorological variables. Statistical relationships in terms of canonical regression equations are obtained for each of the hydro-meteorological predictands using the reanalysis data and surface observations. The reanalysis data provided by National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used for the purpose. The regression equations are applied to the simulated GCM output to model future projections of hydro-meteorological predictands. An advantage of the CCA methodology in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, by retaining the explained variance between the two sets. The CCA method is used to model the monthly hydro-meteorological variables in the Tunga-Bhadra river basin for water quality impact assessment study. A modeling framework of risk assessment is developed to integrate the hydro¬meteorological projections downscaled from CCA model with a river water quality management model to quantify the future expected risk of low water quality under climate change. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold DO level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is adopted to evaluate the future fractional removal policies for each of the dischargers by including the predicted future risk levels. The hydro-meteorological projections of streamflow, air temperature, relative humidity and wind speed are modeled using MIROC 3.2 GCM simulations with A1B scenario. The river water temperature is modeled by using an analytical temperature model that includes the downscaled hydro-meteorological variables. The river water temperature is projected to increase under climate change, for the scenario considered. The IFWLAM uses the downscaled projections of streamflow, simulated river water temperature and the predicted lower and upper future risk levels to determine the fraction removal policies for each of the dischargers. The results indicate that the optimal fractional removal levels required for the future scenarios will be higher compared to the present levels, even if the effluent loadings remain unchanged. Climate change is likely to impact the agricultural sector directly with changes in rainfall and evapotranspiration. The regional climate change impacts on irrigation water demands are studied by quantifying the crop water demands for the possible changes of rainfall and evapotranspiration. The future projections of various meteorological variables affecting the irrigation demand are downscaled using CCA downscaling model with MIROC 3.2 GCM output for the A1B scenario. The future evapotranspiration is obtained using the Penman-Monteith evapotranspiration model accounting for the projected changes in temperature, relative humidity, solar radiation and wind speed. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops quantified at nine downscaling locations covering the entire command area of the Bhadra river basin, used as a case study, are projected to increase for the future scenarios of 2020-2044, 2045-2069 and 2070-2095 under the climate change scenario considered. The GCM and scenario uncertainty is modeled combinedly by deriving a multimodel weighted mean by assigning weights to each GCM and scenario. An entropy objective weighting scheme is proposed which exploits the information contained in various GCMs and scenarios in simulating the current and future climatology. Three GCMs, viz., CGCM2 (Meteorological Research Institute, Japan), MIROC3.2 medium resolution (Center for Climate System Research, Japan), and GISS model E20/Russell (NASA Goddard Institute for Space Studies, USA) with three scenarios A1B, A2 and B1 are used for obtaining the hydro-meteorological projections for the Bhadra river basin. Entropy weights are assigned to each GCM and scenario based on the performance of the GCM and scenario in reproducing the present climatology and deviation of each from the projected ensemble average. The proposed entropy weighting method is applied to projections of the hydro-meteorological variables obtained based on CCA downscaling method from outputs of the three GCMs and the three scenarios. The multimodel weighted mean projections are obtained for the future time slice of 2020-2060. Such weighted mean hydro-meteorological projections may be further used into the impact assessment model to address the climate model uncertainty in the water resources systems. An integrated reservoir operation model is developed considering the objectives of irrigation, hydropower and downstream water quality under uncertainty due to climate change, uncertainty introduced by fuzziness in the goals of stakeholders and uncertainty due to the random nature of streamflow. The climate model uncertainty originating from the mismatch between projections from various GCMs under different scenarios is considered as first level of uncertainty, which is modeled by using the weighted mean hydro-meteorological projections. The second level of uncertainty considered is due to the imprecision and conflicting goals of the reservoir users, which is modeled by using fuzzy set theory. A Water Quantity Control Model (WQCM) is developed with fuzzy goals of the reservoir users to obtain water allocations among the different users of the reservoir corresponding to the projected demands. The water allocation model is updated to account for the projected demands in terms of revised fuzzy membership functions under climate change to develop optimal policies of the reservoir for future scenarios. The third level of uncertainty arises from the inherent variability of the reservoir inflow leading to uncertainty due to randomness, which is modeled by considering the reservoir inflow as a stochastic variable. The optimal monthly operating polices are derived using Stochastic Dynamic Programming (SDP), separately for the current and for the future periods of 2020-2040 and 2040-2060 The performance measures for Bhadra reservoir in terms of reliability and deficit ratios for each reservoir user (irrigation, hydropower and downstream water quality) are estimated with optimal SDP policy derived for current and future periods. The reliability with respect to irrigation, downstream water quality and hydropower show a decrease for 2020-2040 and 2040-2060, while deficit ratio increases for these periods. The results reveal that climate change is likely to affect the reservoir performance significantly and changes in the reservoir operation for the future scenarios is unable to restore the past performance levels. Hence, development of adaptive responses to mitigate the effects of climate change is vital to improve the overall reservoir performance.

L’océan Pacifique est le siège de variabilités climatiques interannuel et multi-décennale, El Niño Southern Oscillation (ENSO) et la Pacific Decadal Oscillation (PDO), dont les répercussions (via des téléconnections) peuvent être mondiales. Des impacts importants sur les populations, les activités socio-économiques et sur l’environnement ont été attribuées à ENSO. Il est alors primordial d’améliorer notre compréhension de la dynamique Pacifique et notamment du phénomène ENSO ainsique son évolution sous l’effet du changement climatique.Les mesures géochimiques (Sr/Ca et 818O) réalisées sur les coraux constituent des enregistrements paléoclimatiques de choix pour l’étude de l’évolution d’ENSO et sont essentielles pour mettre en perspective la dynamique actuelle du climat par rapport à sa dynamique passée. Après avoir évaluer la robustesse du paléothermomètre géochimique corallien (Sr/Ca), cette thèse a permis la reconstruction de température de surface océanique (SST) à partir de coraux de l’atoll de Clipperton (Pacifique tropical Est) et de l’archipel des Marquises (Pacifique tropical centre) couvrantplusieurs parties du dernier millénaire. Nos résultats suggèrent que la structure spatiale d’ENSO étaitplutôt stable au cours des deux derniers siècles, montrant majoritairement une structure de type ENSOcanonique (Est Pacifique) par opposition à l’ENSO Modoki (centre Pacifique). Bien qu’encore débattue, cette structure spatiale pourrait avoir évoluée très récemment, en liaison avec le changement climatique global (et cela pourrait continuer dans le futur). A l’échelle décennale, nos deux zones d’étude (centre et Est Pacifique) sont influencées par la PDO.Les résultats de cette thèse tendent également à suggérer que l’activité d’ENSO actuelle (sous l’effet du forçage anthropique) n’est pas atypique à l’échelle du dernier millénaire. En effet, son intensité et sa fréquence étaient plus fortes au début du petit âge glaciaire (LIA, 16ème siècle). La comparaison deces résultats avec un ensemble de simulations climatiques (PMIP3) montre que la variabilité ENSO estbien reproduite par ces modèles climatiques mais qu’ils échouent à reproduire correctement l’état moyen des températures du Pacifique. / The Pacific Ocean is the place of interannual and multi-decadal climate variabilities, namely the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). There can have globals impacts via teleconnections. Major impacts on populations, economic and environmental activitieshave been attributed to ENSO. It is therefore essential to improve our understanding of the Pacificdynamic, particularly ENSO activity and its evolution under recent climate change.Geochemical measurements (Sr/Ca and 818O) performed on corals are relevant paleoclimatic records for studying the evolution of ENSO and are essential to put into perspective the current climatedynamic in comparison to past climate.After an evaluation of the robustness of the coral geochemical paleothermometer (Sr/Ca), we present the reconstruction of sea surface temperature (SST) from Eastern tropical Pacific coral (Clippertonatoll) and central tropical Pacific coral (Marquesas archipelago) covering several parts of the last millennium. Our results suggest that ENSO spatial pattern was relatively stable over the past two centuries, mainly indicating an eastern Pacific ENSO pattern (canonical) in comparison to the centralPacific ENSO (Modoki). Although still debated, this spatial pattern could have recently changed dueto global climate change (and this could continue in the future). At the decadal timescale, both studiedareas (central and eastern Pacific) are influenced by the PDO.The results of this Phd thesis also suggest that the present day ENSO activity (under the influence ofanthropogenic forcing) is not atypical throughout the last millennium. The intensity and frequency of ENSO were stronger in the early Little Ice Age (LIA, 16th century). These results are compared withan ensemble of climate simulations (PMIP3) and indicate that ENSO variability is correctly reproduced by numerical climate models but that these models fail to correctly reproduce the mean temperature state of the Pacific.