Evaluation of the Performance of CMIP6 Models in Reproducing Rainfall Patterns over North Africa

Hassen Babaousmail,Moses Ojara,Hamida Ngoma,Victor Ongoma,Rizwan Karim,Brian Ayugi,Adharsh Rajasekar,Rongtao Hou

doi:10.3390/atmos12040475

Abstract

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

Highlights

Climate change is a major concern globally, in the arid and semi-arid regions such as North Africa
The performance of Coupled Model Intercomparison Project phase 6 (CMIP6) models is examined based on their capability to mimic regional mean annual climatology, trends, and inter-annual variability
The results show that the Root Mean Squared Error (RMSE) of most CMIP6 models varies in performance with variations to different climatic features and topography

Summary

Introduction

Climate change is a major concern globally, in the arid and semi-arid regions such as North Africa. The changes in climate directly affect many socio-economic activities. It affects agriculture, water availability and quality, energy, and food security, limiting socioeconomic growth [1]. According to several studies [2,3,4], the. North African region is regarded as one of the climate change hotspots. Limited gauge stations hinder quantification and assessment of impacts of climate change over the region. To better understand climate change and its effects on the past, current, and future environments, several tools have been employed to reproduce the climate patterns. One of the main tools is Global Circulation Models (GCMs) of the Coupled Model Inter-comparison

Methods

Results

Conclusion