Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction— Part 2: Representation of Extreme Precipitation DOI Open Access
Amarech Alebie Addisuu, Gizaw Mengistu Tsidu, Lenyeletse Vincent Basupi

et al.

Climate, Journal Year: 2025, Volume and Issue: 13(5), P. 93 - 93

Published: May 2, 2025

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness three bias correction techniques—scaled distribution mapping (SDM), quantile (QDM), QDM with a focus on above below 95th percentile (QDM95)—and daily outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation Stations (CHIRPS) dataset was served reference. bias-corrected native were evaluated against observational datasets—the CHIRPS, Multi-Source Weighted Ensemble (MSWEP), Global Climatology Center (GPCC) datasets—for period 1982–2014, focusing December-January-February season. ability generate eight indices developed by Expert Team Change Detection Indices (ETCCDI) evaluated. results show that captured similar spatial patterns precipitation, but there significant changes amount episodes. While generally improved representation its varied depending reference used, particularly maximum one-day (Rx1day), consecutive wet days (CWD), dry (CDD), extremely (R95p), simple intensity index (SDII). In contrast, total rain (RR1), heavy (R10mm), (R20mm) showed consistent improvement across all observations. All techniques enhanced accuracy indices, demonstrated higher pattern correlation coefficients, Taylor skill scores (TSSs), reduced root mean square errors, fewer biases. ranking using comprehensive rating (CRI) indicates no single model consistently outperformed others relative GPCC, MSWEP datasets. Among methods, SDM QDM95 variety criteria. strategies, best-performing EC-Earth3-Veg, EC-Earth3, MRI-ESM2, multi-model ensemble (MME). These findings demonstrate efficiency improving modeling extremes ultimately boosting assessments.

Language: Английский

Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction— Part 2: Representation of Extreme Precipitation DOI Open Access
Amarech Alebie Addisuu, Gizaw Mengistu Tsidu, Lenyeletse Vincent Basupi

et al.

Climate, Journal Year: 2025, Volume and Issue: 13(5), P. 93 - 93

Published: May 2, 2025

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness three bias correction techniques—scaled distribution mapping (SDM), quantile (QDM), QDM with a focus on above below 95th percentile (QDM95)—and daily outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation Stations (CHIRPS) dataset was served reference. bias-corrected native were evaluated against observational datasets—the CHIRPS, Multi-Source Weighted Ensemble (MSWEP), Global Climatology Center (GPCC) datasets—for period 1982–2014, focusing December-January-February season. ability generate eight indices developed by Expert Team Change Detection Indices (ETCCDI) evaluated. results show that captured similar spatial patterns precipitation, but there significant changes amount episodes. While generally improved representation its varied depending reference used, particularly maximum one-day (Rx1day), consecutive wet days (CWD), dry (CDD), extremely (R95p), simple intensity index (SDII). In contrast, total rain (RR1), heavy (R10mm), (R20mm) showed consistent improvement across all observations. All techniques enhanced accuracy indices, demonstrated higher pattern correlation coefficients, Taylor skill scores (TSSs), reduced root mean square errors, fewer biases. ranking using comprehensive rating (CRI) indicates no single model consistently outperformed others relative GPCC, MSWEP datasets. Among methods, SDM QDM95 variety criteria. strategies, best-performing EC-Earth3-Veg, EC-Earth3, MRI-ESM2, multi-model ensemble (MME). These findings demonstrate efficiency improving modeling extremes ultimately boosting assessments.

Language: Английский

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