Bias correction of CORDEX-Africa regional climate model simulations for climate change projections in northeastern Lake Chad: Comparative analysis of three bias correction methods
Keywords:
Bias correction, MCR, climate change projections, bias correction methods, and Northeastern Lake Chad
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.
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References
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9. Centre du climat. (2022). Fiche d’information Climat.
10. Christensen, B., Martin, D., & Jens, H. C. (2007). The HIRHAM Regional Climate Model. Version 5 (beta) (Technical Report 06‑17). Danish Climate Centre, DMI. http://www.dmi.dk/dmi/tr06-17
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12. Ezéchiel, O., Eric, A. A., Josué, Z. E., Eliézer, B. I., & Amédée, C. (2016). Comparative study of seven bias correction methods applied to three Regional Climate Models in Mekrou catchment (Benin, West Africa). International Journal of Current Engineering and Technology, 6(5), 1831‑1840.
13. Fang, G. H., Yang, J., Chen, Y. N., & Zammit, C. (2015). Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19(6), 2547‑2559. https://doi.org/10.5194/hess-19-2547-2015
14. Fita, E. D., Ombolo, A., Fotso-Nguemo, T. C., Saïdou, D. B., Daïka, A., Chouto, S., & Mbele, F. A. (2024). Analysing the Potential Impact of Climate Change on the Hydrological Regime of the Upper Benue River Basin (North Cameroon). Journal of Water Resource and Protection, 16(08), 569‑583. https://doi.org/10.4236/jwarp.2024.168032
15. Fotso‐Nguemo, T. C., Chamani, R., Yepdo, Z. D., Sonkoué, D., Matsaguim, C. N., Vondou, D. A., & Tanessong, R. S. (2018). Projected trends of extreme rainfall events from CMIP5 models over Central Africa. Atmospheric Science Letters, 19(2), e803. https://doi.org/10.1002/asl.803
16. Fotso-Nguemo, T. C., Chamani, R., Yepdo, Z. D., Sonkoué, D., Matsaguim, C. N., Vondou, D. A., & Tanessong, R. S. (2018). Projected trends of extreme rainfall events from CMIP5 models over Central Africa. Atmospheric Science Letters, 19(2), e803. https://doi.org/c
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Published
2024-10-31
How to Cite
Dingamadji, M., Adounkpe, J., Abderamane, H., & Abdallah, M. N. (2024). Bias correction of CORDEX-Africa regional climate model simulations for climate change projections in northeastern Lake Chad: Comparative analysis of three bias correction methods. European Scientific Journal, ESJ, 20(30), 204. https://doi.org/10.19044/esj.2024.v20n30p204
Section
ESJ Natural/Life/Medical Sciences
Copyright (c) 2024 Mardochee Dingamadji, Julien Adounkpe, Hamit Abderamane, Mahamat Nour Abdallah
This work is licensed under a Creative Commons Attribution 4.0 International License.
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