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The BaSIC method: a new approach to quantitatively assessing the local predictability of extreme weather events

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Abstract

The attractor radius (AR) and global attractor radius (GAR) accurately characterize the intrinsic properties of chaotic systems and can be used to quantitatively estimate the practical and potential predictability of such systems. However, the AR and GAR fail to determine the local predictability of extreme events. In this study, the AR and GAR are first used to develop a new method of quantifying the local predictability of extreme events; i.e., backward searching for the initial condition (BaSIC). The BaSIC method is then used to quantitatively study the local predictability of the 2020/21 cold extremes that occurred in East Asia (EA). The EA regions have heterogeneous spatial distributions of practical predictability limits (PrPLs) of surface air temperature (SAT). The average PrPLs in December 2020 and January 2021 were 10 and 8 days, respectively, and the average potential predictability limits (PoPLs) of SAT exceeded 15 days for both months. Using the BaSIC method, the local PrPLs of three extreme cold events (ECEs) were quantitatively estimated to be 6, 8, and 6 days. By analyzing the dynamical growth of forecast errors, the forecast errors associated with these three ECEs were shown to have different spatial growth patterns. In addition, the northern regions of EA contributed significantly to the loss of local predictability for the three ECEs. Based on these results, the new method presented in this study (BaSIC) is a feasible and effective approach to investigating the local predictability of extreme events. It is expected to play a more important role in the fields of local predictability of extreme weather and climatic events in the future.

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Data availability

The TIGGE (The Interactive Grand Global Ensemble) (https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=pf/). The ERA5 and ERA-interim analysis is from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form and (https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/), respectively.

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Acknowledgements

We would like to thank Dr. Jie Feng for the discussion. This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 42005054 and 41975070) and China Postdoctoral Science Foundation (2020M681154).

Funding

This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 42005054 and 41975070) and China Postdoctoral Science Foundation (2020M681154).

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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China

    Xuan Li

  2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

    Ruiqiang Ding

  3. Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES)/Key Laboratory of Physical Oceanography, Academy of the Future Ocean/College of Oceanic and Atmospheric Science, Ocean University of China, Qingdao, China

    Jianping Li

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  1. Xuan Li
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The first draft of the manuscript was written by XL. RD and JL commented on initial versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ruiqiang Ding.

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Li, X., Ding, R. & Li, J. The BaSIC method: a new approach to quantitatively assessing the local predictability of extreme weather events. Clim Dyn 60, 3561–3576 (2023). https://doi.org/10.1007/s00382-022-06526-4

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  • DOI: https://doi.org/10.1007/s00382-022-06526-4

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