Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change

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Abstract
Anthropogenic climate change poses a significant threat to both natural and social systems worldwide. In this study, we aim to identify regions most impacted by climate change using the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR) reanalysis of near-surface daily air temperature data spanning 73 years (1948–2020). We develop a novel climate network framework to identify “hot spots”, regions that exhibit significant impact or impacted characteristics. Specifically, we use the node degree, a fundamental feature of the network, to measure the influence of each region and analyze its trend over time using the Mann–Kendall test. Our findings reveal that the majority of land areas experiencing increasing degrees are more closely connected to other regions, while the ocean shows the opposite trend due to weakened oceanic circulations. In particular, the degree in the central Pacific Ocean’s El Niño region is significantly reduced. Notably, we identify three “hot spots” in East Asia, South America, and North Africa, respectively, with intensive increasing network degree fields. Additionally, we find that the hot spot in East Asia is teleconnected to remote regions, such as the South Pacific, Siberia, and North America, with stronger teleconnections in recent years. This provides a new perspective for assessing the planetary impacts of anthropogenic global warming. By using a novel climate network framework, our study highlights regions that are most vulnerable to the effects of climate change and emphasizes the importance of understanding network structures to assess the global impacts of anthropogenic climate change.
Funding Information
  • Fundamental Research Program of Yunnan Province (CB22052C173A, 61573173, 12205025, 12275020, 12135003, 202005AF150025)
  • National Natural Science Foundation of China (CB22052C173A, 61573173, 12205025, 12275020, 12135003, 202005AF150025)
  • Yunnan Academician Workstation of Wang Jingxiu (CB22052C173A, 61573173, 12205025, 12275020, 12135003, 202005AF150025)

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