Bias correction of CORDEX-Africa regional climate model simulations for trend analysis in northeastern Lake Chad: Comparison of three bias correction methods
Keywords:
Bias correction, regional climate models, modified MannKendall test, trend analysis, northeastern Lake Chad
Abstract
In order to better adapt to the consequences of climate change, regional climate models (RCMs) have been set up for simulations. However, these simulations are often subject to biases, making it difficult to use them directly in studies of the impact of climate change. It is therefore necessary to use bias correction methods to reduce discrepancies between observed data and the data simulated by RCMs. The aim of this study is to analyse the results of three bias correction techniques (scaling, EQM and GQM) applied to rainfall data and mean minimum and maximum temperatures from CORDEX-Africa Regional Climate Models (RCMs), specifically in the north-eastern region of Lake Chad. Various statistical measures such as Pbiais, RMSE, R2 and EAM were used to assess the performance of each bias correction method in this study. In addition, the adjusted Mann-Kendall test and the Sen slope estimator were used to examine trends and their magnitude over the recent (1975-2004) and future (2021-2050) periods with a significance level of 5%. Overall, based on the statistical measures evaluating the effectiveness of the bias correction techniques, this study shows that all the methods tested were able to reduce the biases of the RCM outputs satisfactorily. In particular, the linear scaling approach proved to be more effective in correcting biases than the EQM and GQM methods. Therefore, an analysis of future trends in mean annual precipitation and temperature (minimum and maximum) was carried out for the RCP4.5 and RCP8.5 scenarios using the linear scaling method to correct for data biases. An increase in precipitation and temperature was observed in the study area over the recent period. The results of multi-model averaging of regional climate change for the RCP4.5 and RCP8.5 scenarios indicate a significant increase in mean annual temperatures (minimum and maximum) in the future. As far as annual precipitation is concerned, only an increase is forecast under the RCP4.5 scenarios. Under the RCP8.5 scenarios, a trend towards stable precipitation is predominant, with the exception of the south of the zone, where an increase has been observed. In the light of these results, it is clear that the impact of climate change will intensify in the region studied in the future.
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References
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6. Boé, J., Terray, L., Habets, F., & Martin, E. (2007). Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies. International Journal of Climatology, 27(12), 1643‑1655. https://doi.org/10.1002/joc.1602
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8. Caya, D., Laprise, R., Giguière, M., Blanchet, J. P., Stocks, B. J., Boergeron, G. J., Boer, G. J., & Mcfarlane, N. A. (1995). Description of the Canadian regional climate model. Water, Air and Soil Pollution, 82, 477‑482.
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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. 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
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/c
16. Fotso-Nguemo, T. C., Vondou, D. A., Pokam, W. M., Djomou, Z. Y., Diallo, I., Haensler, A., Tchotchou, L. A. D., Kamsu-Tamo, P. H., Gaye, A. T., & Tchawoua, C. (2017). On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa. Climate Dynamics, 49(11‑12), 3813‑3838. https://doi.org/10.1007/s00382-017-3547-7
17. Fowler, H. J., Kilsby, C. G., & Stunell, J. (2007). Modelling the impacts of projected future climate change on water resources in north-west England. Hydrology and Earth System Sciences, 11(3), 1115‑1126. https://doi.org/10.5194/hess-11-1115-2007
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Published
2024-07-29
How to Cite
Dingamadji, M., Adounkpe, J., Abderamane, H., & Abdallah, M. N. (2024). Bias correction of CORDEX-Africa regional climate model simulations for trend analysis in northeastern Lake Chad: Comparison of three bias correction methods. European Scientific Journal, ESJ, 31, 549. Retrieved from https://eujournal.org/index.php/esj/article/view/18380
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ESI Preprints
Copyright (c) 2024 Mardochee Dingamadji, Julien Adounkpe, Hamit Abderamane, Mahamat Nour Abdallah
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