Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change
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) reanaly...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/4/692 |
| _version_ | 1827745899178098688 |
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| author | Haiming Kuai Ping Yu Wenqi Liu Yongwen Zhang Jingfang Fan |
| author_facet | Haiming Kuai Ping Yu Wenqi Liu Yongwen Zhang Jingfang Fan |
| author_sort | Haiming Kuai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:14:59Z |
| format | Article |
| id | doaj.art-6841ac2b6541450797ec92e508afb56c |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T05:14:59Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-6841ac2b6541450797ec92e508afb56c2023-11-17T18:17:25ZengMDPI AGAtmosphere2073-44332023-04-0114469210.3390/atmos14040692Climate Network Analysis Detects Hot Spots under Anthropogenic Climate ChangeHaiming Kuai0Ping Yu1Wenqi Liu2Yongwen Zhang3Jingfang Fan4Data Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaData Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaData Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaData Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Systems Science, Institute of Nonequilibrium Systems, Beijing Normal University, Beijing 100875, ChinaAnthropogenic 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.https://www.mdpi.com/2073-4433/14/4/692climate changeclimate networkcomplexity scienceMann–Kendall testteleconnections |
| spellingShingle | Haiming Kuai Ping Yu Wenqi Liu Yongwen Zhang Jingfang Fan Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change Atmosphere climate change climate network complexity science Mann–Kendall test teleconnections |
| title | Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change |
| title_full | Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change |
| title_fullStr | Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change |
| title_full_unstemmed | Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change |
| title_short | Climate Network Analysis Detects Hot Spots under Anthropogenic Climate Change |
| title_sort | climate network analysis detects hot spots under anthropogenic climate change |
| topic | climate change climate network complexity science Mann–Kendall test teleconnections |
| url | https://www.mdpi.com/2073-4433/14/4/692 |
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