Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria

Mohammed Sanusi Shiru,Eun-Sung Chung

doi:10.1007/s00704-021-03746-2

Mohammed Sanusi Shiru, Eun-Sung Chung

Open Access

https://doi.org/10.1007/s00704-021-03746-2

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Abstract

This study assessed the performances of 13 global climate models (GCMs) of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984–2014 period in order to identify the best GCMs for multi-model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product version 6 of the Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of the Climatic Research Unit as reference data. The study applied five statistical indices, namely, normalized root mean square error, percentage of bias, Nash–Sutcliffe efficiency, coefficient of determination, and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest, while AWI-CM-1–1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.

Highlights

There have been many studies on the impacts of climate change resulting from increasing temperature and erratic precipitations in many parts of the globe (Iqbal et al, 2019; Khan et al., 2020a; Salman et al, 2020; Shiru et al, 2018)
They are expected to increase in the future under different emission scenarios of generations of global climate models (GCM) (Homsi et al, 2020; Sa’adi et al, 2019; Shiru et al, 2020c; Tan et al, 2020); which are the primary tools for climate predictions and future climate projections
Though the ideal values vary from GCM to GCM, the overall ranking indicates that a GCM which may have its value as the most ideal for more metrics may not necessarily rank best

Summary

Introduction

There have been many studies on the impacts of climate change resulting from increasing temperature and erratic precipitations in many parts of the globe (Iqbal et al, 2019; Khan et al., 2020a; Salman et al, 2020; Shiru et al, 2018). It has been commonly concluded that disaster frequencies, severities and risks, relating to droughts and floods have increased (Alamgir et al, 2019; Asdak and Supian, 2018; Ayugi et al, 2020; Manawi et al, 2020). They are expected to increase in the future under different emission scenarios of generations of global climate models (GCM) (Homsi et al, 2020; Sa’adi et al, 2019; Shiru et al, 2020c; Tan et al, 2020); which are the primary tools for climate predictions and future climate projections. There has been development of many GCMs for the different scenarios of the IPCC assessment reports including the coupled model inter-comparison project (CMIP)

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