Identifying climate extremes in Southern Africa through advanced bias correction of climate projections

Abstract

This study presents a novel bias correction methodology for enhancing the reliability of climate projections, particularly in regions vulnerable to climate change impacts, such as the Southern African Development Community (SADC). This methodology is here applied jointly with a downscaling method to correct systematic distributional biases and refine the spatial resolution of climate model outputs, with the aim of improving the identification of extreme weather events. Our work builds upon a unique bias correction approach that enhances the quantile mapping (QM) technique by fitting a generalised extreme value distribution (GEV) to the tails of the distribution. This approach addresses the limitations of traditional methods when dealing with extreme events, which are often tied to the scarcity of data for such high-impact, yet low-probability occurrences. Moreover, we extend the applicability of our methodology to climate projections by integrating an approach designed to preserve relative trends in climate data. The technique is then applied to the daily mean temperature data of long-term climate projections (until 2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The bias correction of historical simulations produces an increase in the ensemble forecast members predicting temperature extremes in agreement with the reference dataset (i.e., the reanalysis ERA5-Land). When analysing future scenarios, there is an expected increase in both the intensity and frequency of extreme events, especially in more pessimistic scenarios, such as SSP585. However, these results vary depending on the specific climate model used. Our results suggest that considering multiple climate models of varying resolutions and comparing their outputs against observations and other climate datasets might be key to a comprehensive understanding of extreme event trends in climate scenarios. This approach accounts for each model’s strengths and limitations, providing a robust analysis of changes in extreme weather phenomena.

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