Bias correction of CORDEX-Africa regional climate model simulations for climate change projections in northeastern Lake Chad: Comparative analysis of three bias correction methods

Climate model simulations are frequently affected by biases, which makes it difficult to incorporate them directly into analyses of the impact of climate change. It is therefore essential to use bias correction methods to minimize discrepancies between real data and that generated by Regional Climate Models (RCMs). This study aims to analyze the results of three bias correction methods (LS, MQE, and MQG) applied to the processing of mean rainfall and temperature data from CORDEX-Africa's Regional Climate Models (RCMs), specifically in the north-eastern region of Lake Chad. Four statistical measures (bias, RMSE, r2, and MEA) were used to assess the effectiveness of each bias correction method. In addition, adjusted Mann-Kendall analysis and the Sen slope estimation method were applied to study trends and their magnitude over the recent (1975-2020) and future (2021-2050) periods, using a 5% significance level. The results highlight the existence of significant biases between the uncorrected RCM outputs and the observed data. After applying the bias correction, significant reductions in bias and comparable performance between the different bias correction methods were observed, with the LS method performing slightly better in correcting biases in monthly mean precipitation and temperature. Consequently, the LS method was selectively applied to correct the biases in the RCM monthly mean precipitation and temperature projections for the 2021-2050 period under the RCP4.5 and RCP8.5 scenarios using the 1975-2004 reference period. The results of multi-model averaging of RCMs under the RCP4.5 and RCP8.5 scenarios indicate a significant increase in mean annual temperatures over the period 2021-2050. As far as annual precipitation is concerned, only an increase is forecast under the RCP4.5 scenario. Under the RCP8.5 scenario, the absence of a precipitation trend is predominant, with the exception of the south of the zone, where an increasing trend has been observed. In light of these results, it is clear that the impact of climate change will intensify in the study area in the future. It is imperative to develop strategies to adapt and reduce the impacts in order to manage the availability of water resources efficiently.

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Publication Details

Journal
European Scientific Journal ESJ
Published
2024-10-31
DOI
https://doi.org/10.19044/esj.2024.v20n30p204
Citations
2
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.57

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article

Bias correction of CORDEX-Africa regional climate model simulations for climate change projections in northeastern Lake Chad: Comparative analysis of three bias correction methods

Julien Adounkpè, Abdallah Mahamat Nour, Abderamane Hamit, Mardochee Dingamadji
2 citations
European Scientific Journal ESJ
Climate variability and models
0.57
article

Bias correction of CORDEX-Africa regional climate model simulations for climate change projections in northeastern Lake Chad: Comparative analysis of three bias correction methods

Julien Adounkpè, Abdallah Mahamat Nour, Abderamane Hamit, Mardochee Dingamadji
article en
2 citations

Abstract

Climate model simulations are frequently affected by biases, which makes it difficult to incorporate them directly into analyses of the impact of climate change. It is therefore essential to use bias correction methods to minimize discrepancies between real data and that generated by Regional Climate Models (RCMs). This study aims to analyze the results of three bias correction methods (LS, MQE, and MQG) applied to the processing of mean rainfall and temperature data from CORDEX-Africa's Regional Climate Models (RCMs), specifically in the north-eastern region of Lake Chad. Four statistical measures (bias, RMSE, r2, and MEA) were used to assess the effectiveness of each bias correction method. In addition, adjusted Mann-Kendall analysis and the Sen slope estimation method were applied to study trends and their magnitude over the recent (1975-2020) and future (2021-2050) periods, using a 5% significance level. The results highlight the existence of significant biases between the uncorrected RCM outputs and the observed data. After applying the bias correction, significant reductions in bias and comparable performance between the different bias correction methods were observed, with the LS method performing slightly better in correcting biases in monthly mean precipitation and temperature. Consequently, the LS method was selectively applied to correct the biases in the RCM monthly mean precipitation and temperature projections for the 2021-2050 period under the RCP4.5 and RCP8.5 scenarios using the 1975-2004 reference period. The results of multi-model averaging of RCMs under the RCP4.5 and RCP8.5 scenarios indicate a significant increase in mean annual temperatures over the period 2021-2050. As far as annual precipitation is concerned, only an increase is forecast under the RCP4.5 scenario. Under the RCP8.5 scenario, the absence of a precipitation trend is predominant, with the exception of the south of the zone, where an increasing trend has been observed. In light of these results, it is clear that the impact of climate change will intensify in the study area in the future. It is imperative to develop strategies to adapt and reduce the impacts in order to manage the availability of water resources efficiently.

European Scientific Journal ESJVol. 20(30)
Université d'Abomey-Calavi (BJ)
Agence Française de Développement
Climate action
Openalex Percentile: Top 33%
Climate variability and models
0.57
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