Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations

Abstract

We present a dataset of daily, bias-adjusted temperature and precipitation projections for continental Africa based on a large ensemble of regional climate model simulations, which can be useful for climate change impact studies in several sectors. We provide guidance on the benefits and caveats of using the dataset by investigating the effect of bias-adjustment on impact-relevant indices (both their future absolute value and change). Extreme threshold-based temperature indices show large differences between original and bias-adjusted values at the end of the century due to the general underestimation of temperature in the present climate. These results indicate that when biases are accounted for, projected risks of extreme temperature-related hazards are higher than previously found, with possible consequences for the planning of adaptation measures. Bias-adjusted results for precipitation indices are usually consistent with the original results, with the median change preserved for most regions and indices. The interquartile and full range of the original model ensemble is usually well preserved by bias-adjustment, with the exception of maximum daily precipitation, whose range is usually greatly reduced by the bias-adjustment. This is due to the poor simulation and extremely large model range for this index over the reference period; when the bias is reduced, most models converge in projecting a similar change. Finally, we provide a methodology to select a small subset of simulations that preserves the overall uncertainty in the future projections of the large model ensemble. This result can be useful in practical applications when process-based impact models are too expensive to be run with the full ensemble of model simulations.

Change in Climate Indices Using Bias-Adjusted CMIP5 Models: The Case Study of the Fatick Region, Senegal

“…Compared to the uncorrected ones, bias-adjustment affected mainly the magnitude of the climate change signal, which was lower in the bias-adjusted data. However, Dosio et al (2022) found that bias-adjusted results of CORDEX-Africa RCM for precipitation indices are usually consistent with the original results. The decrease of the rainfall found over the Senegal could probably be induced in part by a weakening of the humidity from local sources, which slows down the hydrological cycle (Diallo et al, 2016).…”

Dynamic downscaling of climate simulations and projected changes in tropical South America using RegCM4.7

“…In the context of climate change, from the publication of the Fifth Assessment Report (AR5) it appears that the number of studies of regional climate modelling in different continental areas of the world has increased (Flato et al, 2013; IPCC, 2021; Le Treut et al, 2007), as the RCM generate additional detail compared to GCM in regional climate change projections (Di Virgilio et al, 2020; Dosio et al, 2022; Fotso‐Nguemo et al, 2017). However, compared to the current climate for which observations exist, in the context of climate change the performance of regional modelling is more complex to assess (Rummukainen, 2016).…”

Record-breaking and unprecedented compound hot and dry summers in Europe under different emission scenarios

“…These model simulations were bias-adjusted with a method developed by Lange (2019) and applied to regional climate simulations by e.g. Dosio et al (2022). The bias-adjustment is a trend preserving, parametric quantile mapping, designed to robustly adjust biases in all percentiles.…”