The recent elevated North Atlantic hurricane activity has generated considerable interests in the interaction between tropical cyclones (TCs) and climate change. The possible connection between TCs and the changing climate has been indicated by observational studies based on historical TC records; they indicate emerging trends in TC frequency and intensity in some TC basins, but the detection of trends has been hotly debated due to TC track data issues. Dynamical climate modeling has also been applied to the problem, but brings its own set of limitations owing to limited model resolution and uncertainties. The final goal of this study is to project the future changes of North Atlantic TC behavior with global warming for the next 50 years using the Nested Regional Climate Model (NRCM). Throughout the course of reaching this goal, various uncertainties and limitations in simulating TCs by the NRCM are identified and explored. First we examine the TC tracking algorithm to detect and track simulated TCs from model output. The criteria and thresholds used in the tracking algorithm control the simulated TC climatology, making it difficult to objectively assess the model's ability in simulating TC climatology. Existing tracking algorithms used by previous studies are surveyed and it is found that the criteria and thresholds are very diverse. Sensitivity of varying criteria and thresholds in TC tracking algorithm to simulated TC climatology is very high, especially with the intensity and duration thresholds. It is found that the commonly used criteria may not be strict enough to filter out intense extratropical systems and hybrid systems. We propose that a better distinction between TCs and other low-pressure systems can be achieved by adding the Cyclone Phase technique. Two sets of NRCM simulations are presented in this dissertation: One in the hindcasting mode, and the other with forcing from the Community Climate System Model (CCSM) to project into the future with global warming. Both of these simulations are assessed using the tracking algorithm with cyclone phase technique. The NRCM is run in a hindcasting mode for the global tropics in order to assess its ability to simulate the current observed TC climatology. It is found that the NRCM is capable of capturing the general spatial and temporal distributions of TCs, but tends to overproduce TCs particularly in the Northwest Pacific. The overpredction of TCs is associated with the overall convective tendency in the model added with an outstanding theory of wave energy accumulation leading to TC genesis. On the other hand, TC frequency in the tropical North Atlantic is under predicted due to the lack of moist African Easterly Waves. The importance of high-resolution is shown with the additional simulation with two-way nesting. The NRCM is then forced by the CCSM to project the future changes in North Atlantic TCs. An El Nino-like SST bias in the CCSM induced a high vertical wind shear in tropical North Atlantic, preventing TCs from forming in this region. A simple bias correction method is applied to remove this bias. The model projected an increase both in TC frequency and intensity owing to enhanced TC genesis in the main development region, where the model projects an increased favorability of large-scale environment for TC genesis. However, the model is not capable of explicitly simulating intense (Category 3-5) storms due to the limited model resolution. To extrapolate the prediction to intense storms, we propose a hybrid approach that combines the model results and a statistical modeling using extreme value theory. Specifically, the current observed TC intensity is statistically modeled with the General Pareto distribution, and the simulated intensity changes from the NRCM are applied to the statistical model to project the changes in intense storms. The results suggest that the occurrence of Category 5 storms may be increased by approximately 50% by 2055.

The Georgia General Assembly, like many states, has enacted pre-defined, comprehensive, drought-mitigation apparatus, but they need rainfall outlooks. Global circulation models (GCMs) provide rainfall outlooks, but they are too spatially course for jurisdictional impact assessment. To wed these efforts, spatially averaged, time-smoothed, daily precipitation observations from the National Weather Service cooperative network are fitted to eight points of 700 mbar atmospheric data from the NCEP/NCAR Reanalysis Project for climate downscaling and drought prediction in the Apalachicola-Chattahoochee-Flint (ACF) river basin. The domain is regionalized with a factor analysis to create specialized models. All models complied well with mathematical assumptions, though the residuals were somewhat skewed and flattened. All models had an R-squared > 0.2. The models revealed map points to the south to be especially influential. A leave-one-out cross-validation showed the models to be unbiased with a percent error of < 20%. Atmospheric parameters are estimated for 2008–2011 with GCMs and empirical extrapolations. The transfer function was invoked on both these data sets for drought predictions. All models and data indicate drought especially for 2010 and especially in the south.

A statistical downscaling technique is employed to link atmospheric circulation produced by climate models at the large-scale to precipitation recorded at individual weather stations on Vancouver Island. Relationships between the different spatial scales are established with synoptic typing, coupled with non-homogeneous Markov models to simulate precipitation intensity and occurrence in historical and future periods. Types are generated through a clustering algorithm which processes daily precipitation observations recorded by Environment Canada weather stations spanning 1971 to 2000. Large-scale atmospheric circulation data is taken from an ensemble of climate model projections made under the IPCC AR4 SRES A2 scenario through the end of the 21st century. Atmospheric predictors used to influence the Markov model are derived from two versions of the data: Averages of model grid cells selected by correlation maps of circulation and precipitation data; a new approach involving Common Empirical Orthogonal Functions (EOFs) calculated from model output over the Northeast Pacific Ocean. Circulation-based predictors capture the role of sea level pressure (SLP), and winds in influencing coastal precipitation over Vancouver Island. The magnitude and spatial distribution of the projected differences are dependent on the predictors used. Projections for 2081 to 2100 made using common EOFs result in most stations reporting no statistically significant change compared to the baseline period (1971 to 2000) in both seasons. Projections using averaged grid cells find winter season (Nov-Feb) precipitation anomalies produce values that are modestly positive, with typical gains of 6.5% in average precipitation, typical increases of 7.5% rising up to 15% in extreme precipitation, and little spatial dependence. In contrast, average and extreme summer precipitation intensity (Jun-Sep) declines negligibly at most island weather stations with the exception of those on the southern and western sections, which experience reductions of up to 20% relative to the latter thirty years of the twentieth century. Precipitation occurrence decreases slightly in both seasons at all stations with declines in the total days with measurable precipitation ranging from 2% to 8% with reductions also seen in the length of extended periods of precipitation in both seasons.

Dans un contexte de changement climatique, l'intégration des énergies renouvelables aux systèmes électriques est un enjeu majeur des décennies à venir. Les énergies liées au climat (photovoltaïque, éolien et hydro-électricité) peuvent contribuer à une réduction des émissions de gaz à effet de serre. Cependant, elles sont fortement intermittentes et la production électrique associée peine à répondre à la demande.Cette étude vise à évaluer la faisabilité météorologique du développement d'un système de production électrique basé sur les sources d'énergie liées au climat (CRE - Climate-Related Energy). Nous considérons uniquement leurs variations spatiotemporelles et supposons un équilibre entre production et demande moyennes. Nous avons développé CRE-Mix, une chaîne de modèles permettant de convertir les variables météorologiques en chroniques énergétiques. Cet outil permet l'estimation des fluctuations spatiotemporelles de production et de demande énergétiques résultant de la variabilité hydro-climatique. Pour une sélection de régions en Europe, nous évaluons la facilité d'intégration des CRE en fonctions de leur cohérence temporelle avec la demande. Pour chaque source d'énergie et de multiples mix énergétiques nous estimons successivement (i) le taux de pénétration moyen (PE), qui quantifie la proportion de demande satisfaite sur une longue période et (ii) les caractéristiques des périodes de faible pénétration pour lesquelles le taux journalier de demande satisfaite reste bas pendant plusieurs jours consécutifs. Les résultats montrent que les systèmes basés sur une seule source ont du mal à répondre à la demande et souffrent de longues périodes de faible PE, en raison de leur variabilité temporelle. Cependant, une combinaison d'énergies, l'utilisation de systèmes de stockage ou l'échange d'énergie entre régions, permettent d'augmenter fortement la fiabilité des CRE (PE proche de 100% et rares/courtes périodes de faible pénétration). Cette étude, basée sur 30 ans, a été étendue à l'ensemble de XXème siècle afin d'évaluer les fluctuations basse fréquence des CRE résultant de la variabilité interne du climat. De longues chroniques régionales de production et de demande ont été générées grâce au développement d'une méthode de descente d'échelle statistique basée sur les analogues atmosphériques (SCAMP). Cet outil génère des scénarios météorologiques multivariés physiquement cohérents. Les résultats montrent que les variations basse fréquence des CRE sont influencées par les grandes oscillations océano-climatiques. De plus, on montre que les variations multi-décennales de l'hydro-électricité sont particulièrement importantes avec notamment une différence en PE supérieure à 15% d'une décade à l'autre et des périodes de faible pénétration aux caractéristiques très irrégulières.Enfin, nous évaluons la pertinence de systèmes électriques basés sur les CRE en climat futur. SCAMP permet de produire des scénarios régionaux de variables météorologiques à partir des modèles climatiques issus des simulations CMPI5. Pour les précipitations, les tendances simulées par SCAMP sont en désaccord avec de nombreuses études. L'application de SCAMP en "modèle parfait" semble indiquer que le lien entre les situations atmosphériques de grande échelle et les précipitations totales, mais également convectives et stratiformes, change en climat futur. / In the context of climate change, the integration of renewables in electric power systems is one of the main challenges of the coming decades. Climate-Related-Energy sources (CRE - solar, wind and hydro power) can contribute to reduce the greenhouse gas emissions. However, they exhibit large spatio-temporal fluctuations and the associated intermittent electricity generation often leads to an incomplete supply-demand balance. This study aims to evaluate the meteorological feasibility of developing an electric power system that would only rely on CRE sources. We focus on the multi-scale spatio-temporal fluctuations of these renewables by assuming a balance between mean electricity production and mean energy load. We develop and use CRE-mix, a suite of models able to convert meteorological conditions into CRE time series. It gives an assessment the spatio-temporal fluctuations of power production and energy demand, resulting from the multi-scale hydro-climatic variability. For a set of European regions, we assess the ease of integration of CRE sources, regarding their temporal consistency with energy demand. For each CRE source and multiple CRE mixes, we consider in turn (i) the mean penetration rate (PE), which quantifies the proportion of satisfied demand over a long period and (ii) the characteristics of low penetration periods, defined as sequences of days for which the penetration rate is lower than a given threshold. This study proves that single CRE sources have difficulty to meet the energy demand and suffer from long low penetration periods, due to their multi-scale temporal variations. However, using some integrating factors (multi-sources, storage systems, inter-regions electric power transmission), efficiently improves the reliability of CRE-based power systems with PE rates close to 100% and rare low penetration periods.These analyses, based on a 30-yr period, are extended to the entire 20th century in order to assess the low frequency fluctuations of CRE sources resulting from the internal variability of climate. Long regional series of production and demand, were generated thanks to the development of a statistical downscaling method based on atmospheric analogues (SCAMP). It simulates physically-consistent multivariate series of meteorological parameters. The results demonstrate that these fluctuations are related to some large scale oceano-climatic oscillations. Moreover, the multi-decennial variations of hydro power are particularly large: changes in PE rates exceeding 15% from one decade to the other and uneven energy droughts characteristics.Finally, we evaluate the relevance of the CRE sources under future climate conditions. SCAMP is used to produce downscaled projections of meteorological drivers of CRE sources for the 21st century from a selection of CMIP5 climate models. The resulting scenarios for precipitation are not consistent with other studies focusing of the future modifications of this variable in Europe. The application of SCAMP in a perfect-model approach seems to indicate that the large-scale-meteorology/local-precipitation relationship is changing in the course of the 21st century, for all total, convective and stratiform precipitation.

Les glaciers des régions sub-polaires entre 45 et 60°S ont reculé dramatiquement au cours du dernier siècle. L'archipel des Kerguelen (49°S, 69°E) constitue un site unique dans ces régions où peu d'observations sont disponibles pour comprendre le recul glaciaire. Situés à faible altitude et proches de l'océan, ses glaciers ont montré une sensibilité particulière aux variations atmosphériques et océaniques. Ainsi, depuis les années 60, la calotte Cook (~400 km2) a reculé de manière spectaculaire, perdant 20% de sa surface en 40 ans. L'objectif de mon travail de thèse était d'évaluer l'état actuel et futur de la calotte, et de comprendre les causes de ce recul tout en les replaçant dans un contexte global. Pour ce faire, un réseau météorologique et glaciologique a été mis en place en 2010 sur l'archipel et des campagnes de mesures ont depuis été réalisées annuellement. L'analyse de ces mesures nous permet de confirmer le bilan de masse négatif de la calotte. Parallèlement, l'étude de l'albédo de l'ensemble de la calotte Cook à partir d'images satellites MODIS (MODerate resolution Imaging Spectroradiometer) permet d'évaluer l'évolution de la ligne de neige de la calotte depuis 2000, mettant en évidence une réduction importante de sa zone d'accumulation au cours des dix dernières années. La modélisation du bilan de masse de la calotte Cook à l'aide d'un modèle degré-jour couplé à une routine dynamique révèle par ailleurs que son retrait est principalement dû à une forte diminution des précipitations sur l'archipel depuis les années 60. Afin de replacer le recul des glaciers aux îles Kerguelen dans un contexte global, les tendances climatiques sur l'ensemble des zones subpolaires sont étudiées, faisant apparaître que la zone sub-Antarctique est actuellement celle où les retraits sont les plus forts à l'échelle du globe. Pour comprendre ces variations, nous analysons un ensemble complet de jeux d'observations de terrain, de satellites et de résultats de modélisation : réanalyses, modèles de l'exercice CMIP5 (Coupled Model Intercomparison Project phase 5), observations de température atmosphérique et océanique, précipitations, etc. Ceux-ci révèlent un réchauffement et un assèchement quasi généralisé de l'ensemble de la zone 40-60° S, lié à un déplacement vers le sud des zones dépressionnaires en réponse aux phases de plus en plus fréquemment positives du mode annulaire austral (Southern Annular Mode, SAM). Le recul récent des glaciers des îles Kerguelen, mais également d'autres zones glaciaires des régions subpolaires de l'hémisphère sud, est donc principalement lié à un déficit d'accumulation causé par le SAM, et amplifié par le réchauffement atmosphérique. L'évolution future du bilan de masse de la calotte Cook aux îles Kerguelen est évaluée grâce au Modèle Atmosphérique Régional (MAR), forcé à ses frontières par les modèles de l'exercice CMIP5. Des simulations du bilan de masse récent sont d'abord effectuées sur base des réanalyses ERA-Interim et NCEP1, et comparées aux observations in situ. Parallèlement, des simulations d'un an sont réalisées avec le désagrégateur de précipitations SMHiL (Surface Mass balance High resolution downscaLing) en sortie du MAR, à différentes échelles, afin d'évaluer l'impact du changement d'échelle sur la représentation des précipitations. Une évaluation des modèles CMIP5 par rapport à ERA-Interim sur la période récente est ensuite réalisée sur base de certaines variables climatiques-clé. Le modèle le plus proche d'ERA-Interim sur la période récente, et les deux modèles les plus extrêmes sont ensuite utilisés pour forcer le MAR sur le prochain siècle, et les sorties de bilan de masse de surface sont analysées de manière critique. L'analyse du retrait de la calotte des îles Kerguelen à l'aide de différents outils a permis de mieux comprendre le lien entre glaciers et climat, mettant en évidence le rôle majeur du SAM, mais a également soulevé de nouvelles questions. / Glaciers of the southern hemisphere sub-polar regions between 45 and 60°S have declined dramatically over the last century. The islands of Kerguelen archipelago (49°S, 69°E) represent a unique location in regions where few data are available to understand glacier retreat. Situated at low altitudes and close to the ocean, their glaciers have shown particular sensitivity to atmospheric and oceanic variations. Thus, since the 1960s, the Cook Ice Cap (~400km2) has retreated spectacularly, losing 20% of its area in 40 years. The aim of my thesis was to assess the present and future state of the ice cap, and to understand the causes of this decline while putting them in a global context. To do so, a meteorological and glaciological network was set up in 2010 on Kerguelen archipelago and field campaigns have been carried out annually since then. Analysis of these measurements confirms the negative mass balance of Cook Ice Cap. In parallel, the study of the albedo over the whole ice cap from MODIS satellite images (MODerate resolution Imaging Spectroradiometer) gives us access to the evolution of the snow line since 2000, highlighting an important reduction of Cook Ice Cap accumulation area over the last decade. Mass balance modelling of the Cook Ice Cap using a degree-day model coupled to a simple ice motion routine further reveals that its retreat is mainly due to a strong decrease in precipitation over the Kerguelen Islands since the 1960s. In order to put the decline of the cryosphere on Kerguelen in a global context, climatic trends over the whole sub-polar regions are studied, revealing that the sub-Antarctic area is currently the one where glacier retreat is the strongest. To understand these variations, we analyse a complete set of field and satellite observations and modelling results : reanalyses, models from the CMIP5 (Coupled Model Intercomparison Project phase 5) experiment, atmospheric and oceanic temperature and precipitation observations, etc. The latter show warming and quasigeneralised drying of the whole 40-60°S area, linked to the southward shift of storm tracks in response to the more frequent positive phases of the Southern Annual Mode (SAM). Recent glacier retreat on Kerguelen archipelago, and for other glaciers and ice caps located at similar latitudes, is thus mainly due to a deficit of accumulation caused by the SAM, and amplified by atmospheric warming. The future evolution of Cook Ice Cap mass balance is evaluated using the MAR (Modèle Atmosphérique Régional) model, forced at its boundaries by CMIP5 models. Recent mass balance simulations are first carried out using ERA-Interim and NCEP1 reanalyses, and compared to in situ observations. In parallel, one-year simulations are produced with the precipitation desagregation scheme SMHiL (Surface Mass balance High resolution downscaLing) on MAR outputs, at various scales, in order to evaluate the impact of downscaling on precipitation. An evaluation of CMIP5 models over the recent period against ERA-Interim is then carried out, considering certain key climatic variables. The model closest to ERA-Interim as well as the two most extreme models are then used to force the MAR model over the next century, and surface mass balance outputs are critically analysed. The analysis of the decline of the Kerguelen ice cap using different tools and techniques brought new insights on the link between glaciers and climate, highlighting the major role of the SAM, but also raised new questions.

Les scénarios hydrologiques requis pour les études d'impacts hydrologiques nécessitent de disposer de scénarios météorologiques non biaisés et qui soient de surcroît adaptés aux échelles spatiales et temporelles des hydro-systèmes considérés. Les scénarios météorologiques obtenus en sortie brute des modèles de climat et/ou des modèles de prévision numérique du temps sont de ce fait non appropriées. Les sorties de ces modèles sont par suite souvent adaptées à l'aide de Méthodes de Descente d'Echelle Statistique (MDES). Depuis les années 2000, les MDES ont beaucoup été utilisées pour la génération de scénarios météorologiques en un site. En revanche, la génération de scénarios spatiaux couvrant de larges territoires est une tâche plus difficile, en particulier lorsque l'on souhaite respecter la cohérence spatiale des précipitations à prédire. Parmi les MDES usuelles, les approches basées sur la recherche de situations analogues passées permettent de satisfaire cette contrainte. Dans cette thèse, nous évaluons la capacité d'un Modèle Analog (MA) – où l'analogie porte sur les géopotentiels 1 000 et 500 hPa – pour la génération de scénarios de précipitation spatialement cohérents pour le territoire Français métropolitain. Dans un premier temps, la transposition spatiale du modèle MA est évaluée : le modèle s'avère utilisable pour la génération de scénarios spatiaux cohérents sur des territoires couvrant plusieurs dizaines de milliers de kilomètres carrés dès lors qu'aucune barrière climatique n'est rencontrée. Dans un second temps, nous évaluons la sensibilité des performances de prédiction à l'agrégation spatiale de la variable à prédire. L'augmentation de performance avec l'agrégation s'explique alors par la diminution de la variabilité du prédictand, pour autant que les variables de grande échelle considérées soient de bons prédicteurs pour la région considérée. Dans une dernière étude, nous explorons la possibilité d'améliorer la performance locale du modèle analogue par l'ajout de prédicteurs locaux. Le modèle combiné qui en résulte permet d'accroître sensiblement les performances de prédiction par l'adaptation du lien d'échelle sur la base d'un jeu de prédicteurs additionnels. Il apparaît de plus que la pertinence de ces prédicteurs dépend de la situation de grande échelle rencontrée ainsi que de la région considérée. / Hydrological scenarios required for the impact studies need to have unbiased meteorological scenarios adapted to the space and time scales of the considered hydro-systems. Hence, meteorological scenarios obtained from global climate models and/or numerical weather prediction models are not really appropriated. Outputs of these models have to be post-processed, which is often carried out thanks to Statistical Downscaling Methods (SDMs). Since the 2000's, SDMs are widely used for the generation of scenarios at a single site. The generation of relevant precipitation fields over large regions or hydro-systems is conversely not straightforward, in particular when the spatial consistency has to be satisfied. One strategy to fulfill this constraint is to use a SDM based on the search of past analog situations. In this PhD, we evaluate the ability of an Analog Model (AM) – where the analogy is applied to the geopotential heights 1000 and 500 hPa – for the generation of spatially coherent precipitation scenarios over the French metropolitan territory. In a first part, the spatial transferability of an AM is evaluated: the model appears to be usable for the generation of spatial coherent scenarios over territories covering several tens of thousands squared kilometers if no climatological barrier is met in between. In a second part, we evaluate the sensitivity of the prediction performance to the spatial aggregation of the predictand. The performance increases with the aggregation level as long as the large scale variables are good predictors of precipitation for the region under consideration. This performance increase has to be related to the decrease of the predictand variability. We finally explore the possibility of improving the local performance of the AM using additional local scale predictors. For each prediction day, the prediction is obtained from a parametric regression model, for which predictors and parameters are estimated from the analog dates. The resulting combined model noticeably allows increasing the prediction performance by adapting the downscaling link for each prediction day. The selected predictors for a given prediction depend on the large scale situation and on the considered region.

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In a rapidly changing world, sustainability is becoming more and more of a priority for organizations. This paper evaluates the possibility of using the Doughnut Economics Model (DEM) as a tool to implement sustainability within an organization on the firm-level, highlighting the potential opportunities and limitations that it poses. Through case studies conducted with two organizations (Apotea AB &amp; Proton Group), both common and firm-specific gaps within sustainability strategies are identified, and the applicability of the DEM is appraised as a tool to help fill these gaps. A qualitative research method was employed, and interviews were held with sustainability managers from Apotea AB and Proton Group. A qualitative thematic analysis process led to the generation of initial codes, themes, and patterns that emerged throughout the interviews held. The results from this study highlighted the illustrative and visual nature of the DEM, and how it could help firms view sustainability from different perspectives. The visualisation of the model helps stimulate conversations about sustainability within the firm, and raising awareness on the topic of sustainability, promoting it within organizational culture. This study additionally concluded that the implementation of the DEM in only a firm-specific, directly impacted area, could help the firm with pinpointing niche areas where the enterprise can make its largest contribution towards a safe and just space for humanity. On the other hand, this study found and supported existing claims through past research on the model’s limitations in terms of its downscaling, as the planetary boundaries are designed for a global scale. Moreover, the model lacks in defining policies, indicators, or measurements regarding areas of improvement. The opportunities that lie in the DEM are plentiful, however, the downscaling process on a firm-scale is extremely challenging, and little-to-no existing research or literature exists on the topic.

Information on habitat preferences is critical for the successful conservation of endangered species. For many species, especially those living in remote areas, we currently lack this information. Time and financial resources to analyze habitat use are limited. We aimed to develop a method to describe habitat preferences based on a combination of bird surveys with remotely sensed fine-scale land cover maps. We created a blended multiband remote sensing product from SPOT 6 and Landsat 8 data with a high spatial resolution. We surveyed populations of three bird species (Yellow-breasted Bunting Emberiza aureola, Ochre-rumped Bunting Emberiza yessoensis, and Black-faced Bunting Emberiza spodocephala) at a study site in the Russian Far East using hierarchical distance sampling, a survey method that allows to correct for varying detection probability. Combining the bird survey data and land cover variables from the remote sensing product allowed us to model population density as a function of environmental variables. We found that even small-scale land cover characteristics were predictable using remote sensing data with sufficient accuracy. The overall classification accuracy with pansharpened SPOT 6 data alone amounted to 71.3%. Higher accuracies were reached via the additional integration of SWIR bands (overall accuracy = 73.21%), especially for complex small-scale land cover types such as shrubby areas. This helped to reach a high accuracy in the habitat models. Abundances of the three studied bird species were closely linked to the proportion of wetland, willow shrubs, and habitat heterogeneity. Habitat requirements and population sizes of species of interest are valuable information for stakeholders and decision-makers to maximize the potential success of habitat management measures.

Urgent scientifically-informed action is needed to stabilise the Earth System amidst anthropogenic climate change. Particularly, the notable transgression of the ‘biosphere integrity’ Planetary Boundary needs to be addressed. Modern Earth System Models struggle to accurately represent regional to local-scale climate features and biodiversity aspects. Recent developments allow to tackle these issues using Artificial Intelligence. This dissertation focuses on two main aspects: (i) deriving high spatio-temporal resolution climate data from coarser models; and (ii) integrating high-temporal-resolution climate data into Species Distribution Models. Three specific objectives were defined: Obj1 Improving Perfect Prognosis – Statistical Downscaling methods through modern Deep Learning algorithms. Obj2 Downscaling a high-resolution multivariate climate ensemble. Obj3 Employ the resulting dataset to improve Species Distribution Models’ projections. The objectives are connected to the three articles that support this cumulative dissertation. Its scope is limited to the Free State of Saxony, Germany, where local high-resolution climate data and high-quality observations of endangered vascular plant species were employed. From a broader perspective, these efforts should contribute to the overarching goal of bridging the gap between the scales of species distribution and climate models while establishing open-source, reproducible, and scalable containerised frameworks. Recent Deep Learning algorithms were leveraged to accomplish (i). The proposed frameworks enhance previous performance of Perfect Prognosis – Statistical Downscaling approaches, while ensuring repeatability. The key near-surface variables considered are precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature. The assumptions that support the Perfect Prognosis approach were thoroughly examined, confirming the robustness of the methods. The downscaled ensemble exhibits a novel output resolution of daily 1 km, which can serve as input for multiple climate impact studies, especially for local-scale decision-making and in topographically complex regions. Considerable methodological implementations were proposed and thoroughly analysed to achieve (ii). Despite notable limitations, Species Distribution Models are frequently used in climate change conservation planning. Thus, recent developments in climate data resolution could improve their usefulness and reliability, which have been previously constraint to coarse temporal aggregates in the projection domain. The presented framework provides fine-grained species suitability projections and satisfactory spatio-temporal transferability, albeit worrying trends. These improved projections are a step forward towards tailored conservation efforts. Limitations of Machine Learning methods and Species Distribution Models are addressed. Substantial avenues for future improvements are thoroughly discussed. As results suggest further reduction of suitable habitats, yet another call for swift action towards low-carbon societies is made. This requires maximising climate change mitigation and adaptation measures, along with a swift transition from short-term profit-driven policies to long-term sustainable development, but primarily, a collective shift in consciousness from anthropocentric positions to ecocentric policies and societies.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127 / Um das Erdsystem angesichts des anthropogenen Klimawandels zu stabilisieren, sind Maßnahmen auf Basis wissenschaftlicher Erkenntnisse dringend erforderlich. Insbesondere muss die drastisch Überschreitung der planetaren Grenze ‘Integrität der Biosphäre’ angegangen werden. Bisher haben aber Modelle des Erdsystems Schwierigkeiten, regionale bis lokale Klimamerkmale und Aspekte der Biodiversität genau abzubilden. Aktuelle Entwicklungen ermöglichen es, diese Herausforderungen mithilfe von Künstlicher Intelligenz anzugehen. Diese Dissertation konzentriert sich auf zwei Hauptaspekte: (i) die Ableitung von Klimadaten mit hoher räumlicher und zeitlicher Auflösung aus groberen Modellen und (ii) die Integration von Klimadaten mit hoher zeitlicher Auflösung in Modelle zur Artverbreitung. Es wurden drei konkrete Ziele definiert: Ziel1 Verbesserung von Perfect Prognosis – Statistische Downscaling-Methoden durch moderne Deep Learning-Algorithmen Ziel2 Downscaling eines hochauflösenden multivariaten Klimaensembles Ziel3 Verwendung des resultierenden Datensatzes zur Verbesserung von Prognosen in Modellen zur Artverbreitung Diese Ziele werden in drei wissenschaftlichen Artikeln beantwortet, auf die diese kumulative Dissertation sich stützt. Der Anwendungsbereich erstreckt sich auf den Freistaat Sachsen, Deutschland, wo lokale hochauflösende Klimadaten und hochwertige Beobachtungen gefährdeter Gefäßpflanzenarten verwendet wurden. In einer breiteren Perspektive tragen diese Bemühungen dazu bei, die Kluft zwischen regionalen sowie zeitlichen Skalen der Artverbreitung und Klimamodellen zu überbrücken und gleichzeitig Open-Source-, reproduzierbare und skalierbare containerisierte Frameworks zu etablieren. Aktuelle Deep Learning-Algorithmen wurden eingesetzt, um Hauptaspekt (i) zu erreichen. Die vorgeschlagenen Frameworks verbessern die bisherige Leistung von Perfect Prognosis – Statistische Downscaling-Ansätzen und gewährleisten gleichzeitig die Wiederholbarkeit. Die wichtigsten bodennahen Variablen, die berücksichtigt werden, sind Niederschlag, Wasserdampfdruck, Strahlung, Windgeschwindigkeit sowie Maximal-, Durchschnitts- und Minimaltemperatur. Die Annahmen, die den Perfect Prognosis-Ansatz unterstützen, wurden analysiert und bestätigen die Robustheit der Methoden. Das downscaled Ensemble weist eine neuartige Auflösung von 1 km auf Tagesbasis auf, welches als Grundlage für mehrere Studien zu den Auswirkungen des Klimawandels dienen kann, insbesondere für Entscheidungsfindung auf lokaler Ebene und in topografisch komplexen Regionen. Es wurden umfassende methodische Implementierungen vorgeschlagen und analysiert, um Hauptaspekt (ii) zu erreichen. Trotz großer Einschränkungen werden Modelle zur Artverbreitung häufig in der Klimaschutzplanung eingesetzt. Daher könnten aktuelle Entwicklungen in der Klimadatenauflösung deren Nützlichkeit und Zuverlässigkeit verbessern, die bisher auf grobe zeitliche Aggregatformen im Projektionsbereich beschränkt waren. Das vorgestellte Framework bietet feingliedrige Prognosen zur Eignung von Arten und zufriedenstellende räumlich-zeitliche Übertragbarkeit, trotz besorgniserregender Trends. Diese verbesserten Prognosen sind ein Schritt in Richtung maßgeschneiderter Naturschutzmaßnahmen. Einschränkungen von Machine Learning-Methoden und Modellen zur Artverbreitung werden untersucht. Substanzielle Möglichkeiten zur zukünftigen Verbesserung werden ausführlich erörtert. Da die Ergebnisse darauf hinweisen, dass geeignete Lebensräume weiter abnehmen, wird erneut zum schnellen Handeln in Richtung kohlenstoffarmer Gesellschaften aufgerufen. Dies erfordert die Maximierung von Maßnahmen zur Bekämpfung des Klimawandels und zur Anpassung, zusammen mit einem raschen Übergang von kurzfristig Profitorientierten Politiken zu langfristiger nachhaltiger Entwicklung, aber vor allem zu einem kollektiven Bewusstseinswandel von anthropozentrischen Positionen zu ökozentrischen Politiken und Gesellschaften.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127 / Acción urgente científicamente informada es necesaria para estabilizar el sistema terrestre en medio del cambio climático antropogénico. En particular, la notable transgresión del límite planetario de ’integridad de la biosfera’ debe abordarse. Los modernos modelos del sistema terrestre tienen dificultades para representar con precisión las características climáticas a escala regional y local, así como los aspectos de la biodiversidad. Desarrollos recientes permiten abordar estos problemas mediante la inteligencia artificial. Esta disertación se enfoca en dos aspectos principales: (i) derivar datos climáticos de alta resolución espacio-temporal a partir de modelos más gruesos; y (ii) integrar datos climáticos de alta resolución temporal en modelos de distribución de especies. Se definieron tres objetivos específicos: Obj1 Mejorar los métodos de pronóstico perfecto – reducción de escala estadística mediante algoritmos modernos de aprendizaje profundo. Obj2 Generar un conjunto climático multivariado de alta resolución. Obj3 Emplear el conjunto de datos resultante para mejorar las proyecciones de los modelos de distribución de especies. Los objetivos están vinculados a los tres artículos que respaldan esta disertación acumulativa. Su alcance se limita al Estado Libre de Sajonia, Alemania, donde se emplearon datos climáticos locales de alta resolución y observaciones de alta calidad de especies de plantas vasculares en peligro de extinción. Desde una perspectiva más amplia, estos esfuerzos deberían contribuir a la meta general de cerrar la brecha entre las escalas de la distribución de especies y los modelos climáticos, mientras que se establecen marcos de trabajo contenedorizados de código abierto, reproducibles y escalables. Algoritmos recientes de aprendizaje profundo fueron aprovechados para lograr (i). Los marcos de trabajo propuestos mejoran el rendimiento previo de los métodos de pronóstico perfecto – reducción de escala estadística, al tiempo que garantizan la repetibilidad. Las variables clave de la superficie cercana consideradas son precipitación, presión de vapor de agua, radiación, velocidad del viento, así como la temperatura máxima, media y mínima. Se examinaron meticulosamente las suposiciones que respaldan el método de pronóstico perfecto, confirmando la robustez de las propuestas. El conjunto reducido de escala exhibe una novedosa resolución diaria de 1 km, el cual puede servir como insumo para múltiples estudios de impacto climático, especialmente para la toma de decisiones a nivel local y en regiones topográficamente complejas. Se propusieron y analizaron minuciosamente considerables implementaciones metodológicas para lograr (ii). A pesar de sus notables limitaciones, los modelos de distribución de especies son utilizados con frecuencia en la planificación de la conservación debido al cambio climático. Por lo tanto, los desarrollos recientes en la resolución de datos climáticos podrían mejorar su utilidad y confiabilidad, ya que antes se limitaban a agregados temporales gruesos en el caso de las proyecciones. El marco de trabajo presentado proporciona proyecciones de idoneidad de especies detalladas y una transferibilidad espacio-temporal satisfactoria, aunque con tendencias preocupantes. Estas proyecciones mejoradas son un paso adelante en los esfuerzos de conservación a la medida. Se abordan las limitaciones de los métodos de aprendizaje automático y de los modelos de distribución de especies. Se discuten a fondo posibilidades sustanciales para futuras mejoras. Dado que los resultados sugieren una mayor reducción de hábitats adecuados, se hace otro llamado a la acción rápida hacia sociedades bajas en carbono. Esto requiere maximizar las medidas de mitigación y adaptación al cambio climático, junto con una transición rápida de políticas orientadas a beneficios a corto plazo hacia un desarrollo sostenible a largo plazo, pero principalmente, un cambio colectivo de conciencia, desde posiciones antropocéntricas hacia políticas y sociedades ecocéntricas.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127