Regional assessments of droughts are limited and meticulous assessment of droughts over larger spatial scales are often not substantial. Understanding drought variability on a regional scale is crucial for enhancing resiliency and adaptive ability of water supply and distribution systems. Moreover, it can be essential for appraising the dynamics and predictability of droughts based on regional climate across various spatial and temporal scales. The drought analysis of the past was carried out with the development of a high-resolution dataset (1km×1km) for three drought-prone regions of India between 1950 and 2016. In the study the monthly values of self-calibrating Palmer Drought Severity Index (scPDSI), incorporating Penman–Monteith (PM) approximation, which is physically based on potential evapotranspiration. Climate data were statistically downscaled using the delta downscaling method and was formulated to form a timeline for characterizing major drought events that occurred in the past. The downscaled climate data were validated with the station observations. Major severe drought events that occurred between 1950 and 2016 were identified and studied with greater emphasis to the drought situation in smaller spatial extent such as districts, villages or localities. A timeline of drought events within the period of study was also prepared to have an understanding of the severity of drought in all three regions.Likewise, the future drought durations are projected for droughts of different levels of severity and assessed in the same regions of India. Coupled Model Intercomparison Project Phase 6 (CMIP6) simulated precipitation and climate data were used for near‐future (2015–2044) for different shared socio-economic pathways (SSPs). scPDSI, was used again based on its fairness in identifying drought conditions which accounts for the temperature as well. Gridded rainfall and temperature data of spatial resolution of 1km were used to bias correct the multi-model ensemble (MME) mean of 7 Global Climatic Models (GCMs) from CMIP6 project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data and were corrected at the monthly scale. The downscaled climate data exhibited good statistical agreement with station data with correlation coefficient (R) ranging from 0.83 to 0.93 for both precipitation and temperature. Drought analysis indicated several major incidences over the analysis time period considered in this work, which truly adheres to the droughts recorded in qualitative reports of meteorological institutions in those regions. The drought study of the past helped to understand the situation in local levels and understand the necessities that can be opted for the future by proper management of water resources. While the outcome of the future prediction of drought duration suggests multiple severe to extreme drought events in all three study areas of appreciable durations mostly during the mid-2030s under the SSP2-4.5 scenario. The severe drought durations under the SSP2-4.5 scenario were found to be ranging around 25 to 30 months in 30 years period of the near future. The high-resolution drought index proved to be key to assess the drought situation for both the past and the future in three different drought-prone regions of India.

Hydrological extremes associated with climate change are becoming an increasing concern all over the world. Frequent flooding, one of the extremes, needs to be analyzed while considering climate change to mitigate flood risk. This study forecasted streamflow and evaluated the risk of flooding in the Neuse River, North Carolina considering future climatic scenarios, and comparing them with an existing Federal Emergency Management Agency (FEMA) flood insurance study (FIS) report. The cumulative distribution function transformation (CDF-t) method was adopted for bias correction to reduce the uncertainty present in the Coupled Model Intercomparison Project Phase 6 (CMIP6) streamflow data. To calculate 100-year and 500-year flood discharges, the Generalized Extreme Value (GEV) (L-Moment) was utilized on bias-corrected multimodel ensemble data with different climate projections. The delta change method was applied for the quantification of flows, utilizing the future 100-year peak flow and FEMA 100-year peak flows. Out of all projections, shared socio-economic pathways (SSP)5-8.5 exhibited the maximum design streamflow, which was routed through a hydraulic model, the Hydrological Engineering Center’s River Analysis System (HEC-RAS), to generate flood inundation and risk maps. The result indicates an increase in flood inundation extent compared to the existing study, depicting a higher flood hazard and risk in the future. This study highlights the importance of forecasting future flood risk and utilizing the projected climate data to obtain essential information to determine effective strategic plans for future floodplain management.

Changes in precipitation patterns, temperature, and other climatic variables have been shown to modify thehydrological cycle and hydrological systems, potentially resulting in a shift in river runoff behavior and an increasedrisk of floods. There have been several instances of devastating floods throughout Europe’s history, which haveresulted in devastation and enormous economic losses. As a result of the effects of climate change, floods areoccurring more frequently in Sweden as well as across Europe. Research on the subject of flood prediction has beengoing on for decades, where particularly data-driven models have advanced in recent years. This study examinedtwo different machine learning (data-driven) models for forecasting river discharge in the Nissan River: Linearregression and Random Forrest regression (RFR), with the use of ECMWF Reanalysis v5 ( ERA5 ) data and historicaldischarge data. The Linear regression model yielded a r2 score of 0.45 and could not be considered an acceptablemodel. The RFR model had a r2 score of 0.71. This implies, given ERA5 reanalysis data, that one might generatea moderately performing machine learning model for Nissan river. An additional investigation was carried out,to see if the trained model could be used with EC-EARTH CMIP6 future projection. The findings resulting fromapplying the EC-EARTH CMIP6 future data on the trained RFR indicated too many uncertainties, necessitatingmore investigation before any conclusions can be drawn.

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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

Do changes in how cross-equatorial energy transport is partitioned between the ocean and atmosphere impact the hemispheric climate response to forcing? To find out, we alter the cross-equatorial ocean heat transport in a state-of-the-art GCM and ascertain how changes in energy transport and its partitioning impact hemispheric climate and precipitation sensitivity following abrupt CO2-doubling. We further evaluate the applicability our results in CMIP6-class ESMs, where AMOC facilitates the northward cross-equatorial ocean heat transport. In our experiments, changes in ocean cross-equatorial energy transport trigger compensating changes in atmospheric energy transport through changes in the Hadley cells and a shift in the Intertropical Convergence Zone. However, the climate sensitivity in each hemisphere is linearly related to the ocean heat transport convergence, not atmospheric energy transport convergence, due to the impact of ocean heating on evaporation and atmospheric specific humidity. Similarly, we also find that ocean heat transport convergence controls the hemispheric precipitation sensitivity through the impact of ocean heating on surface evaporation. This relationship is also evident in CMIP6 models, where we find differences in hemispheric precipitation sensitivity to be related to the Atlantic Meridional Overturning Circulation (AMOC). Changes in the AMOC control hemispheric differences in upper ocean heat content, which then affect how the hydrologic cycle responds to CO2 forcing in each hemisphere. These results suggest that ocean dynamics impact the hemispheric climate response to CO2 forcing, particularly how much regional precipitation changes with warming. / Graduate