Future Changes in Precipitation Extremes over East Africa Based on CMIP6 Models

A multi-criteria decision analysis approach for ranking the performance of CMIP6 models in reproducing precipitation patterns over Abaya-Chamo sub-basin, Ethiopia

The most suitable multi-model ensemble set of general circulation models is used to reduce the uncertainty associated with GCM selection and improve the accuracy of the model simulations. This study evaluated the performance of 20 global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing precipitation patterns over the Abaya-Chamo Sub-basin, Ethiopia. For the validation and selection of the models' capabilities, datasets from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) were used after comparing them with ground observational datasets. The objective was to identify the most suitable multi-model ensemble (MME) of a subset of CMIP6 GCMs to capture the rainfall for the 1981-2014 period over the region. Climate Data Operators (CDOs) were used in climate data processing and extraction, and the Mann-Kendall test and Theil-Sen slope estimator methods were utilized to analyze the trends of the CMIP6 simulations. Four statistical metrics (Nash-Sutcliffe coefficient, percent bias, normalized root mean square error, and Kling-Gupta efficiency) were used to further assess the performance of the models. A multi-criteria decision analysis approach, namely, the technique for order preferences by similarity to an ideal solution (TOPSIS) method, was used to obtain the overall ranks of CMIP6 models and to select the best-performing CMIP6 model in the region. The results indicated that CHIRPS and most of the CMIP6 simulations generally reproduced bimodal precipitation patterns over the region. The CESM2-WACCM, NorESM2-MM, NorESM2-LM, and NorESM2-LM models performed better than the other models in reproducing seasonal patterns for the winter, spring, summer, and autumn seasons, respectively. On the other hand, FGOALS-f3-L revealed the trends of the reference datasets for all seasons. In terms of the NSE, PB, NRMSE, and KGE metrics, EC-Earth3-C, EC-Earth3, EC-Earth3-C, and EC-Earth-C, respectively, were considered good at representing the observed features of precipitation over the region. EC-Earth3-C,EC-Earth3, EC-Earth3-Veg-LR, ACCESS-CM2, MPI-ESM1-2-HR, and CNRM-CM6-1-HR exhibited the best performances in the Abaya-Chamo Sub-basin.

Evaluation of CMIP6 model performance and extreme precipitation prediction in the Awash basin

Extreme rainfall and its accompanying hydrological extremes are happening more frequently as a result of global warming's alteration of regional and local weather patterns. This poses a serious risk to ecosystem, environment and the community livelihoods. The Awash basin in Ethiopia is especially vulnerable to these events, posing significant threats to the region. There are, however, limited information's available that could be used to characterize the condition of extreme precipitation in the basin. Therefore, this study aims to evaluate the performance of CMIP6 models in simulating extreme precipitation in the Awash basin. Additionally, the study calculated extreme precipitation using best-fit probability distribution functions (PDFs) for the period from 1985 to 2014. The Climate Hazards Group Infrared Precipitation with station data (CHIRPS) were used to evaluate the global climate models. Simulated data were interpolated using bilinear techniques. Four statistical indices (percentage of bias, root mean square error, mean absolute error, and Pearson correlation) assessed GCM performance in simulating precipitation extremes. Graphical approaches, numerical methods, and empirical distribution functions were employed to evaluate the performance of various probability distribution functions (PDFs). The study identified MIROC6, CESM2-WACCM, and Ensemble as well-performing models with PBIAS and RMSE of 6.6 %, -10.2 %, -17.2 %, and 11.5, 10, 9.7 respectively, while MPI-ESM1-2-HR and EC-Earth3 struggled with extreme rainfall simulation. The generalized extreme values distribution was found to be a good fit for extreme rainfall estimation. GFDL-ESM4 and BCC-CSM2-MR models estimated the highest extreme rainfall of 90 mm/day and 80 mm/day, respectively, however these models overestimated the return period. Conversely, MRI-ESM2-0, NorESM2-MM, ACCESS ESM1-5, and CMCC-ESM2 models underestimated the return periods. Spatially, GFDL-ESM4 and ACCESS-ESM1-5 models exhibited uniform peak rainfall values over a large area. Overall, the study suggests that employing the generalized extreme value distribution could effectively inform risk assessment and management of extreme events in the Awash basin.

Extreme precipitation indices over India using CMIP6: a special emphasis on the SSP585 scenario

In this study, a comparison of 17 Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets with Indian Meteorological Department (IMD) data in terms of finding out extreme precipitation indices obtained by the Expert Team on Climate Change Detection and Indices (ETCCDI) is done. The extreme indices considered were the consecutive dry days (CDD), consecutive wet days (CWD), maximum 1-day precipitation (RX1DAY), maximum 5-day precipitation (RX5DAY), precipitation > 2.5 mm (RR2.5), heavy precipitation > 10 mm (R10MM), very heavy precipitation > 20 mm (R20MM), and simple daily intensity (SDII) that have been calculated for 17 CMIP6 datasets from 1950 to 2014 with IMD-gridded precipitation datasets over India. Rankings were assigned using the TOPSIS method with evaluation metrics as CC and RMSE as conditions. Almost all datasets performed well for indices, i.e., CWD, R10MM, R20MM, and RR2.5. The top 5 performing models are EC-Earth3, EC-Earth3-Veg, MRI-ESM2-0, GFDL-ESM4, and MIROC6 which were ensembled and projected for future periods in the near (2015-2040), middle (2041-2070), and far future (2070-2100), and extreme indices were calculated under Shared Socioeconomic Pathways 126 (SSP126), SSP245, SSP370, and SSP585 scenarios. The ensemble mean shows that RX1DAY, RX5DAY, R10MM, R20MM, and CWD are observed to increase in western ghats and northeastern regions of India. Central India exhibits a dynamic influence on precipitation indices in different climate change scenarios. The temporal variation for the SSP585 scenario predicts significant increases of about 45.41%, 149.40%, 52.26%, and 45.92% in R10MM, R20MM, RX1DAY, and RX5DAY over the current climate. Future extreme precipitation indices help flood modelers and hydrologists with watershed management.

Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models

The ongoing global warming has caused unprecedented changes in the climate system, leading to an increase in the intensity and frequency of weather and climate extremes. This study uses the sixth phase of Coupled Model Intercomparison Project (CMIP6) data to investigate projected changes in drought events over East Africa (EA) under four Shared Socioeconomic Pathway (SSP) emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-4.0, and SSP5-8.5). The CMIP6 data are bias-corrected using a quantile mapping method, with the Climatic Research Unit's precipitation dataset as reference. Drought is quantified using the standardized precipitation index and different measures of drought are estimated: drought duration, drought frequency, drought severity, and drought intensity. Evaluating the accuracy and reliability of historical data before and after bias correction demonstrates the importance of the approach. The overall distribution after bias correction depicts a close agreement with observation. Moreover, the multi-model ensemble mean demonstrate superiority over individual Global Circulation Models. Projected future changes show enhancement in precipitation over most parts of EA in the far future under different SSP scenarios. However, the arid and semi-arid regions are expected to receive less amount of precipitation, whereas the highlands and lake regions are expected to receive a larger amount of precipitation increase. Furthermore, the dry areas of EA are likely to experience more frequent drought events with longer duration, stronger intensity, and severity in the far future. Overall, this study identifies possible drought hotspots over EA, enabling early preparation for such events.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11069-022-05341-8.