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Remaining error sources in bias-corrected climate model outputs

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Abstract

Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a “pseudo-reality” approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability.

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Acknowledgments

The authors would like to acknowledge the contribution of the World Climate Research Program Working Group on Coupled Modeling, and to thank the climate modeling groups for making available their respective model outputs.

Funding

This work was partially supported by the National Natural Science Foundation of China (Grant No. 51779176; 51539009), the National Key Research and Development Program of China (No. 2017YFA0603704), the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) funded by Ministry of Education of China and State Administration of Foreign Experts Affairs P.R. China (Grant No. B18037), and the Thousand Youth Talents Plan from the Organization Department of CCP Central Committee (Wuhan University, China), the Natural Science and Engineering Research Council of Canada (NSERC), Hydro-Québec and the Ouranos Consortium on Regional Climatology and Adaption to Climate Change.

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Authors and Affiliations

  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, Hubei, China

    Jie Chen

  2. Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan, China

    Jie Chen

  3. École de technologie supérieure, Université du Québec, 1100 Notre-Dame Street West, Montreal, QC, H3C 1K3, Canada

    François P. Brissette & Daniel Caya

  4. Centre ESCER, Université du Québec à Montréal, Case postale 8888, succursale Centre-Ville,, Montreal, QC, H3C 3P8, Canada

    Daniel Caya

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  1. Jie Chen
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  2. François P. Brissette
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Correspondence to Jie Chen.

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Chen, J., Brissette, F.P. & Caya, D. Remaining error sources in bias-corrected climate model outputs. Climatic Change 162, 563–582 (2020). https://doi.org/10.1007/s10584-020-02744-z

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