Projections of precipitation extremes based on bias‐corrected Coupled Model Intercomparison Project phase 6 models ensemble over southern Africa

Abstract

The increasing awareness of climate change requires accurate, reliable and timely information on possible precipitation (PRE) changes to build climate resilience. This study uses the Coupled Model Intercomparison Project phase 6 (CMIP6) data to examine the effectiveness of bias correction on simulated historical mean and extreme PRE, and investigates the projected changes in extreme PRE events over southern Africa (SAF). Quantile mapping on a gamma distribution bias correction method is applied to the historical and projected CMIP6 data, with the Global Precipitation Climatology Centre (GPCC) PRE dataset as reference data. The projection analyses are conducted for the wet (December-March) and dry (May-October) seasons for two shared socioeconomic pathway (SSP) scenarios: SSP2-4.5 and SSP5-8.5. The results affirm that the bias correction significantly reduces (by more than 90%) the biases in the modelled mean and extreme PRE. The projected extreme PRE shows a general drying pattern over SAF. In the wet season, wet days (R1mm) are projected to decrease by 1 and 2 days by the end of the 21st century for SSP2-4.5 and SSP5-8.5, respectively, while very heavy PRE days (R20mm) show a slight upward pattern over SAF, reaching 0.1 and 0.4 days for the two scenarios. In contrast, the dry season exhibits a more pronounced drying tendency, with a consistent decrease in R1mm of 2 and 0.1 days by 2100 for SSP2-4.5 and SSP5-8.5, respectively. R20mm is projected to decrease by 4 and 0.2 days for SSP2-4.5

Projected changes in extreme rainfall and temperature events and possible implications for Cameroon's socio‐economic sectors

“…We further investigated whether the good representation of the mean climatology would be reflected in their representation of extreme events, especially those considered in the present study. This is because a good/poor representation of the mean climatology does not necessarily imply a good/poor simulation of extreme events (Akinsanola & Zhou, 2018; Akinsanola, Kooperman, Pendergrass, et al, 2020;Dosio et al, 2015;Lim Kam Sian et al, 2022). We summarized in Figure 3 the statistical performance measures of individual RCM experiments and for the EnsMean-RCMs in simulating all indices.…”

Exploring the Influence of Improved Horizontal Resolution on Extreme Precipitation in Southern Africa Major River Basins: Insights from CMIP6 HighResMIP Simulations

“…The limited performance of CMIP6 simulations for extreme wet days has also been reported in earlier studies. For example, Ayugi et al (2022) reported that CMIP6 models are unable to simulate R95p over East Africa. The spatial distribution of Rx1day is well reproduced by both the HR and LR models across the basins.…”

“…Although several recent studies (Dosio et al 2021b;Lim Kam Sian et al 2021;Lim Kam Sian et al 2022;Karypidou et al 2022;Samuel et al 2021) have evaluated how well CMIP6 models reproduce observed precipitation characteristics in southern Africa, the influence of spatial resolution on the performance of CMIP6 models in simulating extreme precipitation events in the region has not been adequately explored. To the best of our knowledge, no research has specifically examined how horizontal resolution influences the ability of CMIP6 models to accurately simulate precipitation characteristics across major river basins in southern Africa.…”

Dominant modes of precipitation over Africa, and their associated atmospheric circulations from observations

“…The GPCC dataset is chosen because it includes a large number of station data and has a long temporal coverage (38 years). A number of studies have ascertained the robustness of the GPCC product to reproduce observed precipitation climatology and extreme events over most regions in Africa (e.g., Dosio et al, 2021; Lim Kam Sian et al, 2022; Ongoma & Chen, 2017).…”