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Spatio-temporal patterns of hot extremes in China based on complex network analysis

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Abstract

In the face of escalating frequency and severity of hot extreme (HE) events worldwide, understanding their spatio-temporal characteristics and hazard patterns has become crucial. This study employs a complex network (CN) approach, specifically using visibility graph and similarity network analysis, to investigate HEs. According to the HE network, we have successfully identified anomalous years, divided stages of change, selected representative cities, and zoned spatial hazard patterns of HE. Results reveal that 85% of cities in China experienced varying degrees of increasing HEs, with the highest increase observed as 63 times. The HE networks in China exhibit small-world characteristics, allowing the classification of HE changes into 5–8 stages and 10 types. Hefei emerges as the most representative city in this context. Additionally, the hazard of HE in China can be divided into four grades, with a gradual increase from north to south. This study sheds light on the intensifying hot extreme events in China and establishes a connection between CN and HE analysis, offering innovative ideas and methods for studying climate extremes.

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Data availability

The temperature data used in this study are from the China Meteorological Science Data Centre (https://data.cma.cn/). Administrative divisions of municipal units in China are from the Resource and Environmental Science and Data Centre (RESTEC) 2015 China Municipal Administrative Boundary Data (https://www.resdc.cn/).The hot extreme processing data and codes that support the findings in this study are available from the corresponding author upon request. The parameters and similarity data of HE-VGNs for different cities are available in the supplementary file.

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Acknowledgements

This work was supported by the National Climate Centre, acknowledges funding from the National Key R&D Program of China (Grant No. 2020YFA0608200), National Natural Science Foundation of China (Grant No. 32171574), Science and Technology Major Project of Tibet (Grant Nos. XZ202101ZD0007G, XZ202201ZD0005G05), Foreign Experts Program of Tibet (Grant No.2020WZ00330.22), Lhasa Key R&D Program (Grant No. LSKJ202316).

Funding

National Key R&D Program of China (Grant No. 2020YFA0608200), National Natural Science Foundation of China (Grant No. 32171574), Science and Technology Major Project of Tibet (Grant Nos. XZ202101ZD0007G, XZ202201ZD0005G05), Foreign Experts Program of Tibet (Grant No.2020WZ00330.22), Lhasa Key R&D Program (Grant No. LSKJ202316).

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Authors and Affiliations

  1. Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Peng Zhang, Erfu Dai & Chunsheng Wu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Peng Zhang, Erfu Dai & Chunsheng Wu

  3. School of Economics and Management, Fuzhou University, Fuzhou, 350108, China

    Jun Hu

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  1. Peng Zhang
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  4. Jun Hu
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Contributions

Peng Zhang: Conceptualization, Data treating, Formal analysis, Writing–original draft, Visualization. Erfu Dai: Conceptualization, Methodology, Formal analysis, Writing–review & editing. Chunsheng Wu: Formal analysis, Writing–review & editing. Jun Hu: Formal analysis, Visualization.

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Correspondence to Erfu Dai.

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Zhang, P., Dai, E., Wu, C. et al. Spatio-temporal patterns of hot extremes in China based on complex network analysis. Clim Dyn 62, 841–860 (2024). https://doi.org/10.1007/s00382-023-06947-9

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