Brief communication: Drought likelihood for East Africa

Hui Yang,Chris Huntingford

doi:10.5194/nhess-18-491-2018

Abstract

Abstract. The East Africa drought in autumn of year 2016 caused malnutrition, illness and death. Close to 16 million people across Somalia, Ethiopia and Kenya needed food, water and medical assistance. Many factors influence drought stress and response. However, inevitably the following question is asked: are elevated greenhouse gas concentrations altering extreme rainfall deficit frequency? We investigate this with general circulation models (GCMs). After GCM bias correction to match the climatological mean of the CHIRPS data-based rainfall product, climate models project small decreases in probability of drought with the same (or worse) severity as 2016 ASO (August to October) East African event. This is by the end of the 21st century compared to the probabilities for present day. However, when further adjusting the climatological variability of GCMs to also match CHIRPS data, by additionally bias-correcting for variance, then the probability of drought occurrence will increase slightly over the same period.

Highlights

To assess any influence of increasing atmospheric greenhouse gas concentrations, we use monthly rainfall data from 37 general circulation model (GCM) simulations for the historical period and for the high-emission future scenario RCP8.5
A bias correction with two postprocessing steps is applied to the GCM precipitation estimates
The drought event of 2016 is defined as the three consecutive months of August to the end of October (ASO), and noting rainfall in that year is below blue shading in these months. (c) CMIP5-based probability density functions (PDFs; binned to 5 mm month−1 intervals) of mean ASO rainfall for periods 1861–1891, 2001–2031, 2035–2065 and 2070–2100

Summary

Introduction

To assess any influence of increasing atmospheric greenhouse gas concentrations, we use monthly rainfall data from 37 general circulation model (GCM) simulations for the historical period and for the high-emission future scenario RCP8.5. We first calculate modelled and CHIRPS-based mean ASO rainfall estimates over the East Africa region (set as within black rectangle, Fig. 1a) and during the period 1981– 2015. The GCM precipitation mean ASO estimates, both past and future, are corrected for each model year by a GCMspecific mean correction factor.

Results

Conclusion