%0 Journal Article %1 gebrechorkos2019regional %A Gebrechorkos, Solomon H %A Hülsmann, Stephan %A Bernhofer, Christian %D 2019 %I IOP Publishing %J Environmental Research Letters %K africa, climate\_change, rcm %N 4 %P 044031 %R 10.1088/1748-9326/ab055a %T Regional climate projections for impact assessment studies in East Africa %U https://doi.org/10.1088%2F1748-9326%2Fab055a %V 14 %X In order to overcome limitations of climate projections from Global Climate Models (GCMs), such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models. We focus on East Africa, a region known to be highly vulnerable to climate change and at the same time facing challenges concerning availability and accessibility of climate data. SDSM is first calibrated and validated using observed daily precipitation, (T-max, and T-min) from 214 stations and predictors derived from the reanalysis data of the National Centers for Environmental Prediction. For projection (2006–2100), the same predictors derived from the second generation Canadian Earth System Model (CanESM2) are used. SDSM projections show an increase in precipitation during the short-rain season (October–December) in large parts of the region in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). During the long-rain season (March–May (MAM)) precipitation is expected to increase (up to 680 mm) in Ethiopia, mainly in the western part, and Kenya and decrease (up to −500 mm) in Tanzania in the 2020s, 2050s, and 2080s. However, the western part of Ethiopia will be much drier than the baseline period (1961–1990) during June–September (JJAS) in the 2020s, 2050s, and 2080s, which indicates a shift in precipitation from JJAS to MAM. Annually, precipitation, T-max, and T-min will be higher than during the baseline period throughout the 21 century in large parts of the region. The projection based on SDSM is in line with the direction of CMIP5 GCMs but differs in magnitude, particularly for T-max and T-min. Overall, we conclude that the downscaled data allow for much more fine-scaled adaptation plans and ultimately better management of the impacts of projected climate in basins of Ethiopia, Kenya, and Tanzania.

@article{gebrechorkos2019regional, abstract = {In order to overcome limitations of climate projections from Global Climate Models (GCMs), such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models. We focus on East Africa, a region known to be highly vulnerable to climate change and at the same time facing challenges concerning availability and accessibility of climate data. SDSM is first calibrated and validated using observed daily precipitation, (T-max, and T-min) from 214 stations and predictors derived from the reanalysis data of the National Centers for Environmental Prediction. For projection (2006–2100), the same predictors derived from the second generation Canadian Earth System Model (CanESM2) are used. SDSM projections show an increase in precipitation during the short-rain season (October–December) in large parts of the region in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). During the long-rain season (March–May (MAM)) precipitation is expected to increase (up to 680 mm) in Ethiopia, mainly in the western part, and Kenya and decrease (up to −500 mm) in Tanzania in the 2020s, 2050s, and 2080s. However, the western part of Ethiopia will be much drier than the baseline period (1961–1990) during June–September (JJAS) in the 2020s, 2050s, and 2080s, which indicates a shift in precipitation from JJAS to MAM. Annually, precipitation, T-max, and T-min will be higher than during the baseline period throughout the 21 century in large parts of the region. The projection based on SDSM is in line with the direction of CMIP5 GCMs but differs in magnitude, particularly for T-max and T-min. Overall, we conclude that the downscaled data allow for much more fine-scaled adaptation plans and ultimately better management of the impacts of projected climate in basins of Ethiopia, Kenya, and Tanzania.}, added-at = {2019-04-26T13:50:37.000+0200}, author = {Gebrechorkos, Solomon H and Hülsmann, Stephan and Bernhofer, Christian}, biburl = {https://www.bibsonomy.org/bibtex/2f34b19d30bc2f0ae8c1c35b9909e959b/rutgerdankers}, doi = {10.1088/1748-9326/ab055a}, interhash = {dc0027078d9c10b8e3ee20c4374529ae}, intrahash = {f34b19d30bc2f0ae8c1c35b9909e959b}, journal = {Environmental Research Letters}, keywords = {africa, climate\_change, rcm}, month = apr, number = 4, pages = 044031, publisher = {{IOP} Publishing}, timestamp = {2019-04-26T13:50:37.000+0200}, title = {Regional climate projections for impact assessment studies in East Africa}, url = {https://doi.org/10.1088%2F1748-9326%2Fab055a}, volume = 14, year = 2019 }