Network analysis reveals strongly localized impacts of El Niño
Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on n...
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| Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2017-07, Vol.114 (29), p.7543-7548 |
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| container_title | Proceedings of the National Academy of Sciences - PNAS |
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| creator | Fan, Jingfang Meng, Jun Ashkenazy, Yosef Havlin, Shlomo Schellnhuber, Hans Joachim |
| description | Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network “in”-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger in-weighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables. |
| doi_str_mv | 10.1073/pnas.1701214114 |
| format | Article |
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Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network “in”-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger in-weighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables.</description><identifier>ISSN: 0027-8424</identifier><identifier>EISSN: 1091-6490</identifier><identifier>DOI: 10.1073/pnas.1701214114</identifier><identifier>PMID: 28674008</identifier><language>eng</language><publisher>United States: National Academy of Sciences</publisher><subject>Agricultural industry ; Air temperature ; Climate ; Climatic conditions ; Community structure ; El Nino ; Impact analysis ; La Nina ; Network analysis ; Ocean currents ; Physical Sciences</subject><ispartof>Proceedings of the National Academy of Sciences - PNAS, 2017-07, Vol.114 (29), p.7543-7548</ispartof><rights>Volumes 1–89 and 106–114, copyright as a collective work only; author(s) retains copyright to individual articles</rights><rights>Copyright National Academy of Sciences Jul 18, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c509t-3b7e7f23521f4597f15d256e0a8e5aa21e1d9fd6683b57130e0854bf3a4459ee3</citedby><cites>FETCH-LOGICAL-c509t-3b7e7f23521f4597f15d256e0a8e5aa21e1d9fd6683b57130e0854bf3a4459ee3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26486657$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26486657$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,723,776,780,799,881,27901,27902,53766,53768,57992,58225</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28674008$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fan, Jingfang</creatorcontrib><creatorcontrib>Meng, Jun</creatorcontrib><creatorcontrib>Ashkenazy, Yosef</creatorcontrib><creatorcontrib>Havlin, Shlomo</creatorcontrib><creatorcontrib>Schellnhuber, Hans Joachim</creatorcontrib><title>Network analysis reveals strongly localized impacts of El Niño</title><title>Proceedings of the National Academy of Sciences - PNAS</title><addtitle>Proc Natl Acad Sci U S A</addtitle><description>Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network “in”-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger in-weighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables.</description><subject>Agricultural industry</subject><subject>Air temperature</subject><subject>Climate</subject><subject>Climatic conditions</subject><subject>Community structure</subject><subject>El Nino</subject><subject>Impact analysis</subject><subject>La Nina</subject><subject>Network analysis</subject><subject>Ocean currents</subject><subject>Physical Sciences</subject><issn>0027-8424</issn><issn>1091-6490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNpd0ctuEzEUBmALUdFQWLMCjcSGzTTn-D4bEIrSi1SVDawtZ-ZMmeCMgz0pCm_FM_BiTJS2aVl5cT7_ss_P2BuEUwQjpuve51M0gBwlonzGJggVllpW8JxNALgpreTymL3MeQkAlbLwgh1zq40EsBP26ZqGXzH9KHzvwzZ3uUh0Sz7kIg8p9jdhW4RY-9D9pqboVmtfD7mIbTEPxXX39098xY7aUdPru_OEfTubf51dlFdfzi9nn6_KWkE1lGJhyLRcKI6tVJVpUTVcaQJvSXnPkbCp2kZrKxbKoAACq-SiFV6OnEicsI_73PVmsaKmpn5IPrh16lY-bV30nXs66bvv7ibeOqUEaC3HgA93ASn-3FAe3KrLNYXge4qb7LBCZS23Ekf6_j-6jJs07menpJZKGNwFTveqTjHnRO3DYxDcrhy3K8cdyhlvvHv8hwd_38YI3u7BMg8xHeZaWq2VEf8AKW6VDg</recordid><startdate>20170718</startdate><enddate>20170718</enddate><creator>Fan, Jingfang</creator><creator>Meng, Jun</creator><creator>Ashkenazy, Yosef</creator><creator>Havlin, Shlomo</creator><creator>Schellnhuber, Hans Joachim</creator><general>National Academy of Sciences</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TK</scope><scope>7TM</scope><scope>7TO</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170718</creationdate><title>Network analysis reveals strongly localized impacts of El Niño</title><author>Fan, Jingfang ; Meng, Jun ; Ashkenazy, Yosef ; Havlin, Shlomo ; Schellnhuber, Hans Joachim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c509t-3b7e7f23521f4597f15d256e0a8e5aa21e1d9fd6683b57130e0854bf3a4459ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Agricultural industry</topic><topic>Air temperature</topic><topic>Climate</topic><topic>Climatic conditions</topic><topic>Community structure</topic><topic>El Nino</topic><topic>Impact analysis</topic><topic>La Nina</topic><topic>Network analysis</topic><topic>Ocean currents</topic><topic>Physical Sciences</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Jingfang</creatorcontrib><creatorcontrib>Meng, Jun</creatorcontrib><creatorcontrib>Ashkenazy, Yosef</creatorcontrib><creatorcontrib>Havlin, Shlomo</creatorcontrib><creatorcontrib>Schellnhuber, Hans Joachim</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Proceedings of the National Academy of Sciences - PNAS</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fan, Jingfang</au><au>Meng, Jun</au><au>Ashkenazy, Yosef</au><au>Havlin, Shlomo</au><au>Schellnhuber, Hans Joachim</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network analysis reveals strongly localized impacts of El Niño</atitle><jtitle>Proceedings of the National Academy of Sciences - PNAS</jtitle><addtitle>Proc Natl Acad Sci U S A</addtitle><date>2017-07-18</date><risdate>2017</risdate><volume>114</volume><issue>29</issue><spage>7543</spage><epage>7548</epage><pages>7543-7548</pages><issn>0027-8424</issn><eissn>1091-6490</eissn><abstract>Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network “in”-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger in-weighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. 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| source | Jstor Complete Legacy; PubMed Central; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry |
| subjects | Agricultural industry Air temperature Climate Climatic conditions Community structure El Nino Impact analysis La Nina Network analysis Ocean currents Physical Sciences |
| title | Network analysis reveals strongly localized impacts of El Niño |
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