Comprehensive assessment of climate extremes in high-resolution CMIP6 projections for Ethiopia - Rettie, Fasil M.; Gayler, Sebastian; Weber, Tobias K. D.; Tesfaye, Kindie; Streck, Thilo (Frontiers in Environmental Science, 2023)
Climate extremes have more far-reaching and devastating effects than the mean climate shift, particularly on the most vulnerable societies. Due to its low economic adaptive capacity, Ethiopia has been facing recurrent climate extreme effects for a long period usually resulting in devastating impacts and in consequence leaving millions of people within acute food shortages. In face of ongoing climate change, the frequency and intensity of climate extreme events are expected to continue to increase in the foreseeable future. This study provides an overview of projected changes in climate extremes indices derived from downscaled high-resolution (i.e., 10x10 km^2) daily climate data derived from global climate models (GCMs). The magnitude and spatial patterns of trends in the projected climate extreme indices were explored under a range of emission scenarios called Shared Socioeconomic Pathways (SSPs). The performance of the GCMs to reproduce the observed climate extreme trends in the base period (1983-2012) was evaluated and the uncertainties quantified. Overall, results show largely significant and spatially consistent trends in the projected temperature-derived extreme indices with acceptable model performance in the base period. The projected changes are dominated by the uncertainties in the GCMs at the beginning of the projection period while by the end of the century proportional uncertainties arise both from the GCMs and SSPs. The results for precipitation-related extreme indices are heterogeneous in terms of spatial distribution, magnitude, and statistical significance coverage. Unlike the temperature-related indices, the uncertainty from internal climate variability constitutes a considerable proportion of the total uncertainty in the projected trends. Our work provides a comprehensive insight into the projected changes in climate extremes at relatively high spatial resolution and related uncertainties.