Wing shape and echolocation are two novel adaptations in the Chiroptera and are strongly influenced by environmental conditions. Wing shape is influenced by environmental clutter. Shorter, broader wings allow for more maneuverable flight, and are advantageous for bats living in highly cluttered environments. Longer, narrower wings help bats to increase flight speed, and are best suited for bats living in more open environments. It is likely that wing shape is also influenced by temperature, given the potential for wings to act as thermoregulatory appendages. Wings provide a thermal gradient across their surfaces, dissipating excess heat from the body. However, the importance in thermoregulation in determining wing size is unknown. If thermoregulation is a strong selective pressure, bats in hotter, more arid regions should have larger wings. Environmental conditions also influence echolocation pulse design. Echolocation pulses must successfully reach a target and generate an audible echo despite atmospheric attenuation. High-duty cycle (HDC) pulses, calls with longer durations than the interval between them, are particularly useful in environments with high amount of environmental clutter. HDC echolocators use an acoustic fovea and Doppler shift compensation to detect the fluttering wings of insect prey in dense vegetation. However, the flexibility of these pulses is limited by the bat's acoustic fovea. Wing shape and echolocation combined form an adaptive complex, providing bats with a highly specialized system of foraging. Climate change poses an enormous risk to a bat's foraging success, because rising ambient temperatures are likely to change the selective pressures on wing size (due to the potential thermoregulatory benefits) as well as prey detection volumes of the bat's echolocation (because sound propagation is influenced by temperature). As an adaptive complex any selection on either wings or echolocation is likely to influence changes in the other, with consequences for the foraging efficiency of bats. The potential impact of climate change on the foraging efficiency of bats can be gauged by the bats' adaptive responses to different climatic conditions over their geographic range. I examined these two traits in different localities across the geographic range of the Cape horseshoe bat, R. capensis to determine if wing and echolocation parameters are adapted to current climatic conditions. I measured wing area and echolocation parameters at sites within the distribution of R. capensis that were representative of the different climates across its range. I measured wing areas using digital image analysis software, and I measured echolocation parameters using a microphone array system. Temperature was a predictor in the top fitting linear mixed effects models for both wing area and prey detection volume. For differences in wing area, body mass was the only significant explanatory variable. However, body mass may itself be influenced by environmental conditions. NDVI, latitude, and average winter minimum temperature significantly related to differences in prey detection volume. My results indicate geographic variation in both wing area and prey detection volume, an indication that these traits are adapted to local climate conditions. Geographic variation in wing area is a consequence of body mass, which may or may not be a function of climate. However, geographic variation in prey detection volume is directly influenced by the environment. Therefore, increases in ambient temperature due to human-induced climate change are likely to have an effect on the foraging efficiency of R. capensis.

San Salvador Island is a small Bahamian island located in the subtropics just north of the Tropic of Cancer. Due to its subtropical location, the island is influenced by both mid-latitude and tropical weather patterns. These weather patterns vary in scale from localized convective uplift to synoptic-scale systems. This study compares satellite-derived estimates of precipitation and rain gauge observations from June 2019 through September 2021 to evaluate the relationship between the two datasets. This study then uses the satellite-derived estimates of precipitation over a 20-year period to quantify annual and seasonal variability in precipitation on San Salvador. Corroborating past research, the island exhibits a bimodal pattern of precipitation during the year, but rainfall is highly variable across seasons and between years. Atmospheric fields from a reanalysis dataset indicate the North Atlantic subtropical high influences summertime rainfall, but a relationship between upper-level wind patterns and rainfall is less clear.

Wet Bulb Globe Temperature (WBGT) is used to assess categorical heat stress risk factors for individuals working in outdoor environments and to provide guidelines for work/rest ratios and water intake. The variables required to compute WBGT are used by meteorologists for many purposes and are available through the Copernicus Program which was implemented by European Centre for Medium-Range Weather Forecasts (ECMWF) to provide users access to ECMWF Re-Analysis (ERA) historical database. WBGT values for Tennessee during National Weather Service (NWS) issued excessive heat warnings were calculated to determine the need for additional advisories based on WBGT guidelines. Monthly average WBGT values suggest work/rest ratio thresholds were exceeded in areas with no active heat warnings during the same time. Site assessment for an improved weather station infrastructure was conducted to determine favorability for placement of additional instrumentation to benefit forecasters and general public with greater data availability on a temporal scale.

Polar mesoscale cyclones (PMCs) are low-pressure systems that form in polar regions. Their small size and short lifespan pose challenges for coarse resolution models in capturing PMCs and their associated air-sea mass and energy transfer. To address the influence of resolution on PMCs, a comparison between a higher resolution dataset (CARRA with 2.5 km resolution) and a lower resolution reanalysis dataset (ERA5 with 30km resolution) over a 10-year period in the North Atlantic is conducted by employing an automatic algorithm. The results revealed that CARRA detected a greater number of PMC activities, highlighting the benefits of higher resolution data in reducing uncertainties during tracking. PMCs identified using CARRA exhibited smaller vortex sizes, but higher relative vorticity and faster movement speeds compared to ERA5 results. Notably, the typical vortex diameters derived from ERA5 and CARRA were 80 km and 120 km, respectively. Furthermore, the climatology of PMCs in the North Atlantic is presented. Monthly variations indicated that the majority of PMCs occurred during winter, with only a few cases developing in summer. The spatial distribution exhibits that the highest density of PMCs was observed in the Irminger Sea and the Norwegian Sea. It also shows that the presence of sea ice can influence the PMC density, as a significant number of cases formed near the sea ice edge. The comparison results indicate that the current coarse climate simulation may underestimate the influence of PMCs in the Arctic due to the misrepresentation of them, thus introducing uncertainties in the climate prediction.

The permafrost region covers a vast area of land surface on the northern hemisphere,storing large amounts of carbon. Unfortunately, climate warming leads to permafrostthaw altering the hydrothermal state of permafrost soils. Due to the remoteness of thepermafrost region, access to field measurements is restricted. Therefore, remotesensing is an asset to study the permafrost region. Since permafrost is a sub-surfacephenomenon it cannot be directly observed from space. However, by using differenttypes of satellites the soil properties of the top soil layer, down to 10 cm depth, can beaccessed. To establish soil properties for the deeper soil layers modelling is required.The ERA5-Land (ERA5L) soil moisture is modelled based on climate reanalysis. Inthis study in-situ soil moisture data from the International Soil Moisture Network(ISMN) is used to evaluate the performance of the ERA5L soil moisture data withinthe permafrost region. The performance of the ERA5L soil moisture is found toperform best in soil layer 1 (0-7 cm depth) and worst in soil layer 3 (28-100 cm depth).For both soil layer 1 and 2 (0-7 and 7-28 cm depth) a moderate correlation(0.309 < R < 0.335) was found between ERA5L and in-situ soil moisture data, in Julyand August. The performance of the ERA5L soil moisture is best in Europe and worstin North-America. Compared to other evaluations of ERA5L soil moisture, within thepermafrost region, this study found a relatively low correlation. Therefore, this studyconcludes that on a global scale the ERA5L soil moisture is not ideal for directlyinforming permafrost research and decision making. However, integrating multisourcedatasets, resampled to a finer spatial resolution, could improve the performance ofERA5L soil moisture model on a global level. Moreover, on a local scale theapplication of a bias correction could also improve the performance of the ERA5L soilmoisture model.

The ECMWF Reanalysis v5 (ERA5), the New European Wind Atlas (NEWA), and the 3 km Norwegian Reanalysis (NORA3) are reference datasets that are available for industry and research. The resolution of 3km in both the NORA3 and NEWA datasets sets them apart, while ERA5, with its 31km resolution, continues to serve as a reliable data source that is widely used in the industry. The study offers a thorough analysis of three datasets from three research stations in Sweden, which are accessible through the Integrated Carbon Observation System (ICOS). It has been discovered that all three reference datasets exhibit a strong alignment with the measured data. However, NORA3 and ERA5 outperform NEWA in wind speed and direction estimation. The computation of Annual Energy Production (AEP) using WindPro is performed. We examine the representativeness of the correlation coefficient between the Weibull scale and shape parameters, the agreement of wind rose distributions, and the estimated AEP.

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.

Groundwater abstraction for irrigation use has steadily increased over the past decades, resulting in additional evaporation to the atmosphere, and increased precipitation. The precipitation stemming from groundwater irrigation (or Groundwater-dependent precipitation) has received little attention during recent years and is solely researched by the Eulerian model. This study aims to provide a supplement and improvement of the global fate of groundwater-dependent precipitation with the Lagrangian model outcome. The analysis combines the UTrack model output between 2008 to 2017, a global groundwater irrigation area map, groundwater abstraction from PCR-GLOBWB version 1, and groundwater irrigation efficiency to generate the global groundwater-dependent precipitation trajectory from 2001 to 2010. The primary assumption is that atmospheric factors do not change significantly in all pressure levels during 2001 – 2010 and 2008 – 2017. The simulation result shows that groundwater-dependent precipitation is generally more substantial in Asia than in other continents. Bhutan, Bangladesh, Nepal, India, Yemen, and Afghanistan are the top six countries receiving high groundwater-dependent precipitation contributions monthly and yearly. Moreover, groundwater-dependent precipitation in the continent and country shows a significant seasonal change in the monthly average. A country or continent with a high groundwater abstraction does not necessarily receive a massive amount of groundwater-dependent precipitation regardless of monthly and yearly scale. For instance, China has a yearly average groundwater abstraction of 100 km3 year-1 but receives less than 1% groundwater-dependent precipitation contribution per year. Approximately 75% of groundwater-dependent precipitation falls into the land, and 25% ends in the ocean from 2001 to 2010. The groundwater-dependent precipitation does not significantly contribute to land and ocean, with 0.16% and 0.015%, respectively. Consequently, the study suggests groundwater-dependent precipitation does not have a greater effect on downwind area precipitation on a yearly scale but a larger effect during a specific month. The highest monthly average groundwater-dependent contribution is 18% in January, whereas the highest yearly groundwater-dependent contribution is 2.5% in 2006. Major regions with high groundwater-dependent precipitation contributions are found along the Himalayas Range from January to April and moving eastward to Arabic Peninsula in July.

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