A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

Adjoua Moise Famien,Benjamin Sultan,Serge Janicot,Thomas Noël,Dimitri Defrance,Mathieu Vrac,Abe Delfin Ochou

doi:10.5194/esd-9-313-2018

Abstract

Abstract. The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950–2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.

Highlights

Global and regional climate models (GCMs and RCMs) are used to produce projections of future climates driven by various types of greenhouse gas emission scenarios
The objectives of this paper are to present and evaluate bias-corrected global climate model (GCM) data obtained by performing the cumulative distribution function transform (CDF-t) method over Africa to quantify the sensitivity of the bias-corrected data to different reference datasets and to illustrate this in terms of simulated crop yields
We are presenting results based on some of these metrics related to the three variables, pr, near-surface air temperature, and surface downwelling shortwave radiation

Summary

Introduction

Global and regional climate models (GCMs and RCMs) are used to produce projections of future climates driven by various types of greenhouse gas emission scenarios. Warmer-than-normal sea surface temperatures in the equatorial Atlantic lead to a too southern location of the Inter-Tropical Convergence Zone (ITCZ) in boreal summer over West Africa. This bias has not been reduced between CMIP3 and CMIP5 GCM simulations (see Roehrig et al, 2013). This too southern ITCZ location over West Africa leads to too weak precipitation over the Sahel and too weak crop yields whose values cannot be used as relevant information for stakeholders and farmers

Objectives

Results

Conclusion