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Comparison of conventional and machine learning methods for bias correcting CMIP6 rainfall and temperature in Nigeria

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Abstract

This research assesses the efficacy of thirteen bias correction methods, including traditional and machine learning-based approaches, in downscaling four chosen GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in Nigeria. The 0.5° resolution gridded rainfall, maximum temperature (Tmx), and minimum temperature (Tmn) of the Climate Research Unit (CRU) for the period 1975 − 2014 was used as the reference. The Compromise Programming Index (CPI) was used to assess the performance of bias correction methods based on three statistical metrics. The optimal bias-correction technique was employed to rectify bias to project the spatiotemporal variations in rainfall, Tmx, and Tmn over Nigeria for two distinct future timeframes: the near future (2021–2059) and the distant future (2060–2099). The study's findings indicate that the Random Forest (RF) machine learning technique better corrects the bias of all three climate variables for the chosen GCMs. The CPI of RF for rainfall, Tmx, and Tmn were 0.62, 0.0, and 0.0, followed by the Power Transformation approach with CPI of 0.74, 0.36, and 0.29, respectively. The geographic distribution of rainfall and temperatures significantly improved compared to the original GCMs using RF. The mean bias-corrected projections from the multimodel ensemble of the GCMs indicated a rainfall increase in the near future, particularly in the north by 2.7–12.7%, while a reduction in the south in the far future by -3.3% to -10% for different SSPs. The temperature projections indicated a rise in the Tmx and Tm from 0.71 °C and 0.63 °C for SSP126 to 2.71 °C and 3.13 °C for SSP585. This work highlights the significance of comparing bias correction approaches to determine the most suitable approach for adjusting biases in GCM estimations for climate change research.

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Data availability

All the data are available in the public domain at the links provided in the texts.

Code availability

The codes used for data processing can be provided on request to the corresponding author.

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Funding

The authors received no financial support for this research, authorship, or publication.

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Authors and Affiliations

  1. Department of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria, Nigeria

    Bashir Tanimu, Al-Amin Danladi Bello, Sule Argungu Abdullahi & Morufu A. Ajibike

  2. Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia

    Bashir Tanimu, Mohd Khairul Idlan bin Muhammad & Shamsuddin Shahid

  3. Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia

    Zaher Mundher Yaseen

  4. Farm Machinery and Postharvest Technology Division, Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh

    Mohammad Kamruzzaman

  5. Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia

    Zaher Mundher Yaseen

  6. Environmental and Atmospheric Sciences Research Group, Scientific Research Center, Al-Ayen University, 64001, Thi-Qar, Nasiriyah,, Iraq

    Shamsuddin Shahid

Authors
  1. Bashir Tanimu
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  2. Al-Amin Danladi Bello
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  3. Sule Argungu Abdullahi
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  4. Morufu A. Ajibike
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  5. Zaher Mundher Yaseen
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  6. Mohammad Kamruzzaman
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  7. Mohd Khairul Idlan bin Muhammad
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  8. Shamsuddin Shahid
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Contributions

All the authors participated in the conceptualization of the research. B.T., A.D.B. and S.A.A. gathered data. M.K. and M.K.I.M. pre-processed the data. S.S. developed the code for data analysis. B.T., M.A.A. and Z.M.Y. generated results. B.T. and M.K. analyzed the results. B.T. and S.S. prepared the first draft of the article. All the authors contributed to revising and editing the draft. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shamsuddin Shahid.

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Tanimu, B., Bello, AA.D., Abdullahi, S.A. et al. Comparison of conventional and machine learning methods for bias correcting CMIP6 rainfall and temperature in Nigeria. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04888-9

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