The observed summertime drying over Northern Central India (NCI) during the latter half of the twentieth century is not reproduced by the Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensemble average. At the same time, the spread between precipitation trends from individual model realizations is large, indicating that internal variability potentially plays an important role in explaining the observed trend. Here we show that the drying is indeed related to the observed 1950–1999 positive trend of the Pacific Decadal Oscillation (PDO) index and that the relationship is even stronger for a simpler index (S1). Adjusting the CMIP5-simulated precipitation trends to account for the difference between the observed and simulated S1 trend increases the original multimodel average NCI drying trend from −0.09 ± 0.31 mm d−1 (50 years)−1 to −0.54 ± 0.40 mm d−1 (50 years)−1. Thus, our estimate of the 1950–1999 NCI drying associated with Pacific decadal variability is of similar magnitude as our previous CMIP5-based estimate of the drying due to anthropogenic aerosol. The drying (moistening) associated with increasing (decreasing) S1 can partially be attributed to a southeastward (northwestward) shift of the boundary between ascent and descent affecting NCI. This shift of the ascent region strongly affects NCI but not Southeast Asia and south China. The average spread between individual model realizations is only slightly reduced when adjusting for S1 as smaller-scale variability also plays an important role.

The observed summertime drying over Northern Central India (NCI) during the latter half of the twentieth century is not reproduced by the Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensemble average. At the same time, the spread between precipitation trends from individual model realizations is large, indicating that internal variability potentially plays an important role in explaining the observed trend. Here we show that the drying is indeed related to the observed 1950–1999 positive trend of the Pacific Decadal Oscillation (PDO) index and that the relationship is even stronger for a simpler index (S1). Adjusting the CMIP5-simulated precipitation trends to account for the difference between the observed and simulated S1 trend increases the original multimodel average NCI drying trend from −0.09 ± 0.31 mm d−1 (50 years)−1 to −0.54 ± 0.40 mm d−1 (50 years)−1. Thus, our estimate of the 1950–1999 NCI drying associated with Pacific decadal variability is of similar magnitude as our previous CMIP5-based estimate of the drying due to anthropogenic aerosol. The drying (moistening) associated with increasing (decreasing) S1 can partially be attributed to a southeastward (northwestward) shift of the boundary between ascent and descent affecting NCI. This shift of the ascent region strongly affects NCI but not Southeast Asia and south China. The average spread between individual model realizations is only slightly reduced when adjusting for S1 as smaller-scale variability also plays an important role.

Long-term changes of El Niño/Southern Oscillation (ENSO) are studied with the ensemble run of Simple Ocean Data Assimilation (SODA 2.2.6) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). An eight member ocean reanalyses (SODA 2.2.6) from 1871 to 2008 is produced by using forcing from eight ensemble members of an atmospheric reanalysis. The ensemble reanalysis shows that El Niño has prominent decadal variability. Weak El Niños occur throughout the entire record whereas the occurrence of strong El Niños varies, with strong El Niño at the beginning and end of the record. The strength of La Niña is weaker than for El Niño, and has less variability. Although for any given El Niño year all ensemble members show the occurrence of El Niño, in some ensemble members the El Niño is strong while in others it is weak. When the timing of the onset of Westerly Wind Bursts (WWBs) occurs earlier in the year and the strength of WWBs is stronger, strong El Niño occurs. To study the importance of the background state in the tropical Pacific Ocean on ENSO, long-term trends of tropical Pacific SST, wind stress, subsurface temperature and the sub-tropical cells (STCs) are analyzed. The reanalysis shows that there is a slight cooling trend of SST in the central tropical Pacific due to an enhanced tropical Pacific circulation. Subsurface temperature also has a cooling trend. The STCs, which consist of equatorial upwelling, Ekman transport, extra-tropical subduction and pycnocline transport from the sub-tropical to the tropical region, strengthen from 1900 to 2008. When the STCs are accelerated, equatorial upwelling increases bringing cold water from the subsurface that cools the surface. ENSO variability is also analyzed in the CMIP5 historical experiments. Results show that most of the models have a realistic representation of the strength of ENSO; however, the location of warming generally extends too far to the west. Overall, properties of ENSO do not show a significant change in most of the CMIP5 models. One distinguishing difference between the CMIP5 models and SODA 2.2.6 is that ENSO in SODA 2.2.6 has prominent asymmetry between El Niño and La Niña, whereas ENSO in the CMIP5 models tends to have fairly symmetric El Niño and La Niña. In contrast with the reanalysis most of the CMIP5 models have warming trends at the surface and the transport of the STCs has a decreasing trend.

Dans la plupart des études, on s'intéresse au changement climatique futur en analysant l'évolution du climat entre une référence actuelle fixée et une période future. Le réchauffement est de plus en plus fort au fil du 21ème siècle. Dans un contexte où les conditions climatiques sont toujours en train d'évoluer, les écosystèmes doivent continuellement s'adapter à des modifications diverses du climat. Dans le cadre de cette thèse, je propose d'analyser les projections climatiques sous un angle alternatif. Afin d’être caractéristique des représentations des populations urbaines et rurales, je définis et analyse des indicateurs liés à la vitesse des changements de température, de précipitations et de végétation. Un ensemble de simulations CMIP5 de 18 modèles de climat est sélectionné. La vitesse est représentée par des différences entre deux périodes successives de 20 ans. Cette notion de vitesse pourrait offrir de nouveaux outils pour interagir avec les communautés scientifiques travaillant sur les impacts et l'adaptation.Sans politiques d’atténuation du changement (scénario RCP8.5), le réchauffement global sera au moins deux fois plus rapide à la fin du siècle qu’actuellement, et même trois fois dans certaines régions. Près de la moitié des surfaces continentales, principalement les zones tropicales, seront touchées par des décalages significatifs de la distribution de la température entre deux périodes de 20 ans d’ici à 2060, i.e. au moins 4 fois plus qu’actuellement. Dans ces régions, des années extrêmement chaudes ayant un temps de retour de 50 ans deviendront habituelles en l’espace de 20 ans seulement. La fraction de la population mondiale étant exposée à ces changements pourrait atteindre environ 60% (i.e. 6 milliards de personnes et 7 fois plus qu’actuellement). Il suffit de relativement légères mesures d’atténuation (RCP6.0) pour que la vitesse du réchauffement ne dépasse pas les valeurs actuelles et que 3 fois moins de personnes soient exposées à des décalages significatifs de température.Les vitesses d’humidification et d’assèchement en termes de précipitations augmenteront de 30 à 40%. Leur répartition géographique deviendra plus stable spatialement et les tendances tendront à persister sur les mêmes régions, et ce malgré l’accélération du réchauffement global. Cette stabilisation résulte de la contribution grandissante des processus thermodynamiques par rapport à ceux contrôlés par la circulation générale. La combinaison de l’accélération des tendances et de leur persistance peut avoir un impact sur l’adaptation des sociétés et des écosystèmes, particulièrement sur le bassin méditerranéen, en Amérique centrale, en Inde et dans les régions arctiques. Une telle évolution est déjà visible actuellement, mais pourrait disparaître avec de fortes mesures d’atténuation (RCP2.6).Les changements de la végétation peuvent être des repères visuels du changement climatique. Dans les moyennes et hautes latitudes Nord, le cycle saisonnier des arbres et des herbacées suit la vitesse du réchauffement. Sans politiques d’atténuation, le début de la saison foliaire avance et sa durée augmente plus rapidement au fil du siècle. La couverture de la végétation se densifie quelque soit le scénario proportionnellement à l’augmentation de la température. Le cycle saisonnier des cultures des moyennes latitudes dépend directement de la température et celui des cultures tropicales de l’évolution des caractéristiques de la saison des pluies. Sous les autres latitudes, aucune évolution robuste du cycle saisonnier n’est projetée. La vitesse des changements de répartition de la végétation a déjà doublé entre 1880 et 1950 correspondant à un changement marqué de l'utilisation des sols. Elle est stable tout au long du siècle si la végétation interagit dynamiquement avec le climat dans les modèles, traduisant un ralentissement du changement de l'utilisation des sols et l'accélération des changements de végétation sous l'effet du changement climatique. / In most climate studies, climate change is approached by focusing on the evolution between a fixed current baseline and a future period, emphasizing stronger warming as we move further over the 21st century. Under climate conditions that are continuously evolving, human and natural systems might have to constantly adapt to a changing climate. This thesis proposes an alternative approach to climate projections. Here, I consider and analyze indicators of the pace of changes relative to temperature, precipitation and vegetation in order to be relevant for both urban and rural populations. An ensemble of CMIP5 simulations from 18 climate models is selected. The pace is represented by differences between two subsequent 20-year periods. Considering the pace of change would be beneficial for climate impacts and adaptation analyses.The models predict that the warming rate strongly increases without any mitigation policies (RCP8.5 scenario). It is twice as high by the end of the century compared to the current period, and even three times higher in some regions. Significant shifts in temperature distributions between two subsequent 20-year periods are projected to involve almost half of all land surfaces and most tropical areas by 2060 onwards (i.e. at least four times as many regions than currently). In these regions, an extremely warm year with a return period of about 50 years would become quite common only 20 years later. The fraction of the world population exposed to such shifts might reach about 60% (6 billion people, i.e. seven times more than currently). Low mitigation measures (RCP6.0) allow the warming rate to be kept at current values, and reduce the fraction of the world population exposed to significant shifts of temperature distributions by one third.Under RCP8.5, rainfall moistening and drying rates both increase by 30-40% above current levels. As we move further over the century, their patterns become geographically stationary and the trends become persistent. The stabilization of the geographical rate patterns that occurs despite the acceleration of global warming can be physically explained: it results from the increasing contribution of thermodynamic processes compared to dynamic processes in the control of precipitation change. The combination of intensification and increasing persistence of precipitation rate patterns may affect the way human societies and ecosystems adapt to climate change, especially in the Mediterranean basin, Central America, South Asia and the Arctic. Such an evolution in precipitation has already become noticeable over the last few decades, but it could be reversed if strong mitigation policies were quickly implemented (RCP2.6).Changes in vegetation could be visual landmarks of climate change. In mid- and high-latitudes of the Northern Hemisphere, the phenology of grass and trees follows the warming rate. Without any mitigation policies, the start of spring occurs earlier, and its duration is extended faster as we move over the century. The vegetation cover becomes denser, regardless of the selected pathway, in proportion to the temperature rise. The seasonal cycle of mid-latitude crops also depends on the temperature, and the seasonal cycle of tropical crops directly follows the features of the wet season. In all other latitudes, no robust evolution of the seasonal cycle is projected. The pace of change of vegetation cover since 1880 already doubled before 1950, mainly due to a strong change in land use. This pace is then projected to be stable over the entire 21st century if the vegetation dynamically interacts with the climate system in the models. This corresponds to a reduction of land-use change and to the acceleration of changes of vegetation cover under climate change.

By the end of 2016 surveillance and reconnaissance satellites will have been monitoring Arctic-wide sea ice conditions for decades. Situated at the boundary between atmosphere and ocean, Arctic sea ice retreat has been one of the most conspicuous indication of climate change, especially in the two most recent decades. The 2001 annual minimum extent of Arctic sea ice marks the last year above the 1981 -- 2012 long-term average extent. Ever since then only lower than average Arctic sea ice has been observed at the end of each summer's melt season. For more than a century climate scientists have postulated that the darkening of the Arctic due to retreating sea ice and therefore more exposed open ocean would be the consequence of global warming. In the first decade of the 2000s the human influence on that warming in the Arctic was indeed detected in observations and attributed to increasing atmospheric greenhouse-gas concentrations. In this study we direct our attention to a potential offsetting effect from other anthropogenic (OANT) forcing agents, mainly aerosols, that has potentially out masked a fraction of greenhouse-gas induced warming by a combined cooling effect. We acknowledge that multiple sources of uncertainty exist in our method, in particular in the observed records of Arctic sea ice and corresponding simulations from climate models. No formal detection and attribution (DA) analysis has yet been carried out to try to detect the combined cooling effect from aerosols in observations of Arctic sea ice extent. We use three publicly available observational data sets of Arctic sea ice and climate simulations from eight models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). In our detection and attribution study observations are regressed on model-derived climate response pattern, or fingerprints, under all known historical (ALL), greenhouse-gas only (GHG) and known natural-only (NAT) forcing factors using an optimal fingerprinting method. We estimate regression coefficients (scaling factors) for each forcing group that scale the fingerprints to best match the observed record. From the scaled ALL, GHG and NAT fingerprints we calculate the relative contribution of the observed sea ice decline attributable to OANT forcing agent. Based on our DA results we show that the simulated climate response patterns to changes in GHG, OANT and NAT forcing are detected in the observed records of September Arctic sea ice extent for the 1953 to 2012 period. / Graduate

For analyzing the effect of climate change on the streamflow at a regional scale, six General Circulation Models (GCMs) were selected from among eighteen GCMs from the Coupled Model Intercomparison Project (CMIP5) for the Pajaro River Watershed in central California. The 1/8° latitude-longitude resolution bias-corrected and downscaled CMIP5 projections were utilized for an ensemble of GCMs under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The twenty-first century is segregated into three time-periods (2016-2039, 2040-2069 and 2070-2099) for comparing the streamflow against changing precipitation and temperature according to the CMIP5 projections. The daily maximum and daily minimum temperature are projected to consistently rise through to the latter part of the century. Csiro-mk3-6 and canesm2 models project an increase of 3.1°C in annual average daily maximum temperature and 3.4°C in annual average daily minimum temperature respectively in 2070-2099 period under RCP8.5 scenarios. Future precipitation is projected to increase in January and February, which means the wet months in the Pajaro River Watershed are likely to get more rainfall. The dry months would continue to receive diminished precipitation throughout the century. The streamflow was increasing on future January, and sporadically, in February months but diminished during the dry months. The range of annual average streamflow for the future years stretched from 0.1 to 29.1 m3/s for the GCM ensemble, mostly close to the lower limit. The results suggest considering multiple climate change scenarios and evaluating alternative setups would provide a robust basis for hydrological assessment.

Previous studies found large biases between individual observational and model estimates of historical ocean anthropogenic carbon uptake. We show that the largest bias between the Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble mean and between two observational estimates of ocean anthropogenic carbon is due to a difference in start date. After adjusting the CMIP5 and observational estimates to the 1791-1995 period, all three carbon uptake estimates agree to within 3Pg of C, about 4% of the total. The CMIP5 ensemble mean spatial bias compared to the observations is generally smaller than the observational error, apart from a negative bias in the Southern Ocean and a positive bias in the Southern Indian and Pacific Oceans compensating each other in the global mean. This dipole pattern is likely due to an equatorward and weak bias in the position of Southern Hemisphere westerlies and lack of mode and intermediate water ventilation.

La neige est une composante essentielle du système climatique arctique. Au nord de l'Eurasie et de l'Amérique du Nord, la couverture neigeuse est présente de 7 à 10 mois par an et son extension saisonnière maximale représente plus de 40% de la surface terrestre de l'hémisphère nord. La neige affecte une variété de processus climatiques et de rétroactions aux hautes latitudes. Sa forte réflectivité et sa faible conductivité thermique ont un effet de refroidissement et modulent la rétroaction neige-albédo. Sa contribution au bilan radiatif de la Terre est comparable à celle de la banquise. De plus, en empêchant d'importantes pertes d'énergie du sol sous-jacent, la neige limite la progression de la glace et le développement du pergélisol saisonnier. Réserve d'eau naturelle, la neige joue un rôle essentiel dans le cycle hydrologique aux hautes latitudes, notamment en ce qui concerne l'évaporation et le ruissellement. La neige est l'une des composantes du système climatique présentant la plus forte variabilité. Le réchauffement de l'Arctique étant deux fois plus rapide que celui du reste du globe, la variabilité présente et future des caractéristiques de la neige est cruciale pour une meilleure compréhension des processus et des changements climatiques.Cependant, notre capacité à observer l'Arctique terrestre étant limitée, les modèles climatiques jouent un rôle clé dans notre aptitude à comprendre les processus liés à la neige. À cet égard, la représentation des rétroactions associées à la neige dans les modèles climatiques, en particulier pendant les saisons intermédiaires (lorsque la couverture neigeuse de l'Arctique présente la plus forte variabilité), est primordiale.Notre étude porte principalement sur la représentation de la neige terrestre arctique dans les modèles de circulation générale issus du projet CMIP5 (Coupled Model Intercomparison Project) au cours du printemps (mars-avril) et de l’automne (octobre-novembre) de 1979 à 2005. Les caractéristiques de la neige des modèles de circulation générale ont été validées par rapport aux mesures de neige in situ, ainsi qu’à des produits satellitaires et à des réanalyses.Nous avons constaté que les caractéristiques de la neige dans les modèles ont un biais plus marqué au printemps qu'en automne. Le cycle annuel de la couverture neigeuse est bien reproduit par les modèles. Cependant, les cycles annuels d'équivalent en eau de la neige et de sa profondeur sont largement surestimés par les modèles, notamment en Amérique du Nord. Il y a un meilleur accord entre les modèles et les observations dans la position de la marge de neige au printemps plutôt qu'en automne. Les amplitudes de variabilité interannuelle pour toutes les variables de la neige sont nettement sous-estimées par la plupart des modèles CMIP5. Pour les deux saisons, les tendances des variables de la neige dans les modèles sont principalement négatives, mais plus faibles et moins significatives que celles observées. Les distributions spatiales des tendances de la couverture neigeuse sont relativement bien reproduites par les modèles, toutefois, la distribution spatiale des tendances en équivalent-eau et en profondeur de la neige présente de fortes hétérogénéités régionales.Enfin, nous concluons que les modèles CMIP5 fournissent des informations précieuses sur les caractéristiques de la neige en Arctique terrestre, mais qu’ils présentent encore des limites. Il y a un manque d’accord entre l’ensemble des modèles sur la distribution spatiale de la neige par rapport aux observations et aux réanalyses. Ces écarts sont particulièrement marqués dans les régions où la variabilité de la neige est la plus forte. Notre objectif dans cette étude était d'identifier les circonstances dans lesquelles ces modèles reproduisent ou non les caractéristiques observées de la neige en Arctique. Nous attirons l’attention de la communauté scientifique sur la nécessité de prendre compte nos résultats pour les futures études climatiques. / Snow is a critical component of the Arctic climate system. Over Northern Eurasia and North America the duration of snow cover is 7 to 10 months per year and a maximum snow extension is over 40% of the Northern Hemisphere land each year. Snow affects a variety of high latitude climate processes and feedbacks. High reflectivity of snow and low thermal conductivity have a cooling effect and modulates the snow-albedo feedback. A contribution from terrestrial snow to the Earth’s radiation budget at the top of the atmosphere is close to that from the sea ice. Snow also prevents large energy losses from the underlying soil and notably the ice growth and the development of seasonal permafrost. Being a natural water storage, snow plays a critical role in high latitude hydrological cycle, including evaporation and run-off. Snow is also one of the most variable components of climate system. With the Arctic warming twice as fast as the globe, the present and future variability of snow characteristics are crucially important for better understanding of the processes and changes undergoing with climate. However, our capacity to observe the terrestrial Arctic is limited compared to the mid-latitudes and climate models play very important role in our ability to understand the snow-related processes especially in the context of a warming cryosphere. In this respect representation of snow-associated feedbacks in climate models, especially during the shoulder seasons (when Arctic snow cover exhibits the strongest variability) is of a special interest.The focus of this study is on the representation of the Arctic terrestrial snow in global circulation models from Coupled Model Intercomparison Project (CMIP5) ensemble during the melting (March-April) and the onset (October-November) season for the period from 1979 to 2005. Snow characteristics from the general circulation models have been validated against in situ snow measurements, different satellite-based products and reanalyses.We found that snow characteristics in models have stronger bias in spring than in autumn. The annual cycle of snow cover is well captured by models in comparison with observations, however, the annual cycles of snow water equivalent and snow depth are largely overestimated by models, especially in North America. There is better agreement between models and observations in the snow margin position in spring rather than in autumn. Magnitudes of interannual variability for all snow characteristics are significantly underestimated in most CMIP5 models compared to observations. For both seasons, trends of snow characteristics in models are primarily negative but weaker and less significant than those from observations. The patterns of snow cover trends are relatively well reproduced in models, however, the spatial distribution of trends for snow water equivalent and snow depth display strong regional heterogeneities.Finally, we have concluded CMIP5 general circulation models provides valuable information about the snow characteristics in the terrestrial Arctic, however, they have still limitations. There is a lack of agreement among the ensemble of models in the spatial distribution of snow compared to the observations and reanalysis. And these discrepancies are accentuated in regions where variability of snow is higher in areas with complex terrain such as Canada and Alaska and during the melting and the onset season. Our goal in this study was to identify where and when these models are or are not reproducing the real snow characteristics in the Arctic, thus we hope that our results should be considered when using these snow-related variables from CMIP5 historical output in future climate studies.

Appropriate and defensible statistical frameworks are required in order to make credible inferences about future climate based on projections derived from multiple climate models. It is shown that a two-way analysis of variance framework can be used to estimate the response of the actual climate, if all the climate models in an ensemble simulate the same response. The maximum likelihood estimate of the expected response provides a set of weights for combining projections from multiple climate models. Statistical F tests are used to show that the differences between the climate response of the North Atlantic storm track simulated by a large ensemble of climate models cannot be distinguished from internal variability. When climate models simulate different responses, the differences between the re- sponses represent an additional source of uncertainty. Projections simulated by climate models that share common components cannot be considered independent. Ensemble thinning is advocated in order to obtain a subset of climate models whose outputs are judged to be exchangeable and can be modelled as a random sample. It is shown that the agreement between models on the climate response in the North Atlantic storm track is overestimated due to model dependence. Correlations between the climate responses and historical climates simulated by cli- mate models can be used to constrain projections of future climate. It is shown that the estimate of any such emergent relationship will be biased, if internal variability is large compared to the model uncertainty about the historical climate. A Bayesian hierarchical framework is proposed that is able to separate model uncertainty from internal variability, and to estimate emergent constraints without bias. Conditional cross-validation is used to show that an apparent emergent relationship in the North Atlantic storm track is not robust. The uncertain relationship between an ensemble of climate models and the actual climate can be represented by a random discrepancy. It is shown that identical inferences are obtained whether the climate models are treated as predictors for the actual climate or vice versa, provided that the discrepancy is assumed to be sym- metric. Emergent relationships are reinterpreted as constraints on the discrepancy between the expected response of the ensemble and the actual climate response, onditional on observations of the recent climate. A simple method is proposed for estimating observation uncertainty from reanalysis data. It is estimated that natural variability accounts for 30-45% of the spread in projections of the climate response in the North Atlantic storm track.

A growing human population and demands for food, freshwater and energy are causing extensive changes in the water and biogeochemical cycles of river catchments around the world. Addressing and investigating such changes is particularly important for transboundary river catchments, where they impose additional risk to a region’s stability. This thesis investigates and develops data-driven methodologies for detecting hydro-climatic and nutrient load changes and their drivers with limited available data and on different catchment scales. As a specific case study, we analyze the Sava River Catchment (SRC) and compare its results with other world regions. A past–present to future evaluation of hydro-climatic data is done on the basis of a water balance approach including analysis of historic developments of land use and hydropower development data and projections of the Coupled Model Intercomparison Project, Phase 5 (CMIP5) output. Using observed water discharge and nutrient concentration data, we propose a novel conceptual model for estimating and spatially resolving total nitrogen (TN) and total phosphorus (TP) input and delivery-retention properties for a river catchment and its nested subcatchments, as well as detection of nutrient hotspots. The thesis identifies hydroclimatic change signals of hydropower-related drivers and finds consistency with other world regions. The proposed nutrient screening methodology provides a good distinction between human-related nutrient inputs and landscape-related transport influences on nutrient loading at subcatchment to catchment scale. A cross-regional comparison of the SRC data with the Baltic region shows similarity between nutrient-relevant indicators and driving socio-economic and hydro-climatic conditions. The study highlights a number of complexities with regard to CMIP5 model representation of water fluxes. The large intermodel range of CMIP5 future projections of fluxes calls for caution when using individual model results for assessing ongoing and future water and nutrient changes. / <p>QC 20170516</p> / VR 2009-3221 / FORMAS 2014-43