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Identification and characterization of global compound heat wave: comparison from four datasets of ERA5, Berkeley Earth, CHIRTS and CPC

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Abstract

Compound heat wave (CompoundHW) has attracted extensive attention for its prolonged extreme heat from daytime to nighttime during its process. However, the performance of identifying and characterizing CompoundHW across different datasets has not been systematically evaluated. Here, we compared the similarities and differences of the ERA5, Berkeley Earth, CHIRTS and CPC datasets in identifying and characterizing CompoundHW. Results showed that the match of CompoundHW identification between datasets was consistent in both temporal and spatial dimensions, with the highest match observed between the ERA5 and CHIRTS datasets. Match of CompoundHW identification exhibited significant correlation with the density of observation stations, with matching rates above 50% in regions with dense observation networks, but extremely low match in regions with sparse data coverage. The rising trends of the CompoundHW metrics were captured by all datasets, especially in parts of North America, Europe, western Russia and Asia. Despite differences in the amplitude of CompoundHW changes across the four datasets, over 42% of global regions concurred on the changes in CompoundHW frequency, duration, and magnitude, and more than 27% agreed on the changes in the proportion of CompoundHW occurrences. Inconsistencies of CompoundHW changes were predominantly observed in regions with low matching rates, indicating that precise identification of CompoundHW is the basis for characterizing the changes in CompoudHW characteristics accurately. This study highlights the importance of multiple datasets comparison in heat wave research, especially in metrics defined by multiple climate variables and regions with sparse observational data.

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Data Availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Funding

This work was jointly supported by Central Guiding Local Science and Technology Development Fund of Shandong—Yellow River Basin Collaborative Science and Technology Innovation Special Project (No. YDZX2023019), The 2023 Zhonglou District of Changzhou City Science and Technology Research Project “Precision Monitoring Composite High Temperature with Drought and Warning Response System Based On AI and Space Technology (No. JBGS2023011)”, the CAS Strategic Priority Research Program (XDA19030402), and the National Natural Science Foundation of China (No. 42071425, No. 41871253).

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Authors and Affiliations

  1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Lijun Jiang & Jiahua Zhang

  2. International Research Center of Big Data for Sustainable Development Goals, Beijing, China

    Lijun Jiang & Jiahua Zhang

  3. University of Chinese Academy of Sciences, Beijing, China

    Lijun Jiang & Jiahua Zhang

  4. Research Center for Remote Sensing Information and Digital Earth, College of Compute Science and Technology, Qingdao University, Qingdao, China

    Jiahua Zhang & Shanshan Yang

  5. Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

    Xianglei Meng

  6. Center for Geospatial Information, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Jingwen Wang

  7. National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, China

    Lamei Shi

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  1. Lijun Jiang
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Contributions

Lijun Jiang: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Reviewing & Editing. Jiahua Zhang: Resources, Funding acquisition, Supervision, Writing - Reviewing & Editing. Xianglei Meng: Software, Formal analysis, Writing - Reviewing & Editing. Shanshan Yang: Formal analysis, Writing - Reviewing & Editing. Jingwen Wang: Formal analysis, Writing - Reviewing & Editing. Lamei Shi: Writing - Reviewing & Editing. All authors read and approved the final manuscript. All authors read and approved the final manuscript.

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Jiang, L., Zhang, J., Meng, X. et al. Identification and characterization of global compound heat wave: comparison from four datasets of ERA5, Berkeley Earth, CHIRTS and CPC. Clim Dyn 62, 631–648 (2024). https://doi.org/10.1007/s00382-023-06940-2

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