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Remaining error sources in bias-corrected climate model outputs

Author

Listed:
  • Jie Chen

    (Wuhan University
    Wuhan University)

  • François P. Brissette

    (Université du Québec)

  • Daniel Caya

    (Université du Québec
    Université du Québec à Montréal)

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.

Suggested Citation

  • Jie Chen & François P. Brissette & Daniel Caya, 2020. "Remaining error sources in bias-corrected climate model outputs," Climatic Change, Springer, vol. 162(2), pages 563-582, September.
    • Handle: RePEc:spr:climat:v:162:y:2020:i:2:d:10.1007_s10584-020-02744-z
    DOI: 10.1007/s10584-020-02744-z
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    References listed on IDEAS

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    1. Jie Chen & François P. Brissette & Xunchang J. Zhang & Hua Chen & Shenglian Guo & Yan Zhao, 2019. "Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology," Climatic Change, Springer, vol. 153(3), pages 361-377, April.
    2. Jie Chen & François Brissette & Robert Leconte, 2012. "Coupling statistical and dynamical methods for spatial downscaling of precipitation," Climatic Change, Springer, vol. 114(3), pages 509-526, October.
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    Citations

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    Cited by:

    1. Jie Chen & Xunchang John Zhang, 2021. "Challenges and potential solutions in statistical downscaling of precipitation," Climatic Change, Springer, vol. 165(3), pages 1-19, April.
    2. Mingcong Lv & Zhongmei Wang, 2024. "Research on Meteorological Drought Risk Prediction in the Daqing River Basin Based on HADGEM3-RA," Agriculture, MDPI, vol. 14(10), pages 1-20, October.
    3. Omid Alizadeh, 2022. "Advances and challenges in climate modeling," Climatic Change, Springer, vol. 170(1), pages 1-26, January.
    4. Yusuke Satoh & Kei Yoshimura & Yadu Pokhrel & Hyungjun Kim & Hideo Shiogama & Tokuta Yokohata & Naota Hanasaki & Yoshihide Wada & Peter Burek & Edward Byers & Hannes Müller Schmied & Dieter Gerten & S, 2022. "The timing of unprecedented hydrological drought under climate change," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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