Assessing uncertainties in the regional projections of precipitation in CORDEX-AFRICA

Adeline Bichet,Benoit Hingray,Kouakou Kouadio,Arona Diedhiou,Guillaume Evin,Klutse Nana Ama Browne,N’Datchoh Evelyne Touré

doi:10.1007/s10584-020-02833-z

Abstract

Over the past decades, large variations of precipitation were observed in Africa, which often led to dramatic consequences for local society and economy. To avoid such disasters in the future, it is crucial to better anticipate the expected changes, especially in the current context of climate change and population growth. To this date, however, projections of precipitation over Africa are still associated with very large uncertainties. To better understand how this uncertainty can be reduced, this study uses an advanced Bayesian analysis of variance (ANOVA) method to characterize, for the first time in the regional climate projections of CORDEX-AFRICA, the different sources of uncertainty associated with the projections of precipitation over Africa.By 2090, the ensemble mean precipitation is projected to increase over the Horn of Africa from September to May and over the eastern Sahel and Guinea Coast from June to November. It is projected to decrease over the northern coast and southern Africa all year long, over western Sahel from March to August, and over the Sahel and Guinea Coast from March to May. Most of these projections however are not robust, i.e., the magnitude of change is smaller than the associated uncertainty. Over time, the relative contribution of internal variability (excluding interannual variability) to total uncertainty is moderate and quickly falls below 10%. By 2090, it is found that over the Horn of Africa, northern coast, southern Africa, and Sahel, most of the uncertainty results from a large dispersion across the driving Global Climate Models (in particular MIROC, CSIRO, CCCma, and IPSL), whereas over the tropics and parts of eastern Africa, most of the uncertainty results from a large dispersion across Regional Climate Models (in particular CLMcom).

Highlights

Global warming is expected to have substantial consequences on precipitation and its variability, especially extreme events, potentially leading to more frequent and severe droughts and flood episodes in both, the tropics and the subtropical regions (Zwiers et al 2013; Giorgi et al 2014)
In agreement with previous global and regional climate projections (e.g., Niang et al 2014; Sylla et al 2016; Todzo et al 2020), we find that in the twenty-first century, mean precipitation is projected to increase over the Horn of Africa from September to May and over the Guinea Coast in JJA, September–October– November (SON), and the annual means, but decrease over the Ethiopian Highlands in March– April–May (MAM), the northern part of the Great Lakes area in JJA, the northern coast and southern Africa all year long, and the Sahel in JJA and annual means
In agreement with Hawkins and Sutton (2009 and 2011), our results show that total uncertainty increases with time and that the contribution from internal variability falls very rapidly compared to model uncertainty

Summary

Introduction

Global warming is expected to have substantial consequences on precipitation and its variability, especially extreme events, potentially leading to more frequent and severe droughts and flood episodes in both, the tropics and the subtropical regions (Zwiers et al 2013; Giorgi et al 2014). Over western Africa and parts of eastern Africa (including Ethiopian Highlands, Great Lakes), no clear trend is detected and the confidence in projections is extremely low (Niang et al 2014). These uncertainties and large ensemble spread in the projections of precipitation by CMIP5 Global Climate Models (GCMs) have been reported by Diedhiou et al (2018) when investigating changes in climate extremes over West and Central Africa at 1.5 °C and 2 °C global warming. Uncertainties in land surface processes and interactions with atmosphere contribute to a large spread of GCM projections in regions where the vegetation and soil moisture are strongly interlinked with precipitation and temperature (Koster et al 2004; He et al 2017; Donat et al 2018)

Methods

Results

Discussion

Conclusion