Climate classification in the Northern hemisphere using phases of temperature signals

The results of structuring surface temperatures in the Northern hemisphere for the period of modern climate changes are presented. The main idea of the classification suggested is the geographic conditionality of the phase modulation of the temperature signal. The consistency, namely, phasing of the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Atmospheric and oceanic optics 2017, Vol.30 (1), p.63-69
Hauptverfasser: Cheredko, N. N., Tartakovsky, V. A., Krutikov, V. A., Volkov, Yu. V.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 69
container_issue 1
container_start_page 63
container_title Atmospheric and oceanic optics
container_volume 30
creator Cheredko, N. N.
Tartakovsky, V. A.
Krutikov, V. A.
Volkov, Yu. V.
description The results of structuring surface temperatures in the Northern hemisphere for the period of modern climate changes are presented. The main idea of the classification suggested is the geographic conditionality of the phase modulation of the temperature signal. The consistency, namely, phasing of the temperature oscillations in certain geographic areas, serves the criterion. We believe that changes in the synchronization modes of climatic processes under changing climate lead to transformations of the spatial structure of the temperature field due to the system transition to a new state. The temperature series are represented as phase-modulated oscillations. External and internal factors that disturb the climate system form a complicated phase modulation, which partly corresponds to these disturbances. The initial space of 818 temperature series is structured into 17 regional clusters, where the temperature changes synchronously. Properties of the resulting clusters and their compliance with the known climate classifications are discussed. The classifying algorithm allows the researchers to choose the degree of differentiation of the field under study depending on the task. The phase modulation indices were estimated to identify manifestations of the external forcing in the surface temperature. Inconsistency of the indices with those in the case of the harmonic phase modulation allows the role of the regional climate factors to be assessed for each class. Modulation, which is the closest to the harmonic one, was found in the North Atlantic thermohaline conveyor. During the study of the climate change, the approach suggested can be used as an analytical framework on any spatial scale, based on only data on the surface temperature, and with predetermined level of quality. The search for synchronization in nonlinear chaotic systems may be one of the promising ways to optimize the predictive models.
doi_str_mv 10.1134/S1024856017010043
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1880752244</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1880752244</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-fcd8a4e910ef03dc273f545212eef0629a948017f65909028aae51215d4e61e3</originalsourceid><addsrcrecordid>eNp1UEtLw0AQXkTBWv0B3hY8R2f2kcdRii8oerCew5LOtlvaJO4kB_-9G-pBEE_fMN-DmU-Ia4RbRG3u3hGUKW0OWAACGH0iZgoKyEBX-lTMJjqb-HNxwbwDyG1lcSY-FvtwcAPJZu-Ygw-NG0LXytDKYUvytYsJYiu3dAjcp5HkyKHdyH7rmFh2Xg506Cm6YUwch03r9nwpznwCuvrBuVg9PqwWz9ny7ellcb_MGo35kPlmXTpDFQJ50OtGFdpbYxUqSotcVa4yZfrIp2OhAlU6RxYV2rWhHEnPxc0xto_d50g81LtujNMBNZYlFFYpY5IKj6omdsyRfN3H9HP8qhHqqbz6T3nJo44eTtp2Q_FX8r-mbyRjcJI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1880752244</pqid></control><display><type>article</type><title>Climate classification in the Northern hemisphere using phases of temperature signals</title><source>SpringerLink Journals - AutoHoldings</source><creator>Cheredko, N. N. ; Tartakovsky, V. A. ; Krutikov, V. A. ; Volkov, Yu. V.</creator><creatorcontrib>Cheredko, N. N. ; Tartakovsky, V. A. ; Krutikov, V. A. ; Volkov, Yu. V.</creatorcontrib><description>The results of structuring surface temperatures in the Northern hemisphere for the period of modern climate changes are presented. The main idea of the classification suggested is the geographic conditionality of the phase modulation of the temperature signal. The consistency, namely, phasing of the temperature oscillations in certain geographic areas, serves the criterion. We believe that changes in the synchronization modes of climatic processes under changing climate lead to transformations of the spatial structure of the temperature field due to the system transition to a new state. The temperature series are represented as phase-modulated oscillations. External and internal factors that disturb the climate system form a complicated phase modulation, which partly corresponds to these disturbances. The initial space of 818 temperature series is structured into 17 regional clusters, where the temperature changes synchronously. Properties of the resulting clusters and their compliance with the known climate classifications are discussed. The classifying algorithm allows the researchers to choose the degree of differentiation of the field under study depending on the task. The phase modulation indices were estimated to identify manifestations of the external forcing in the surface temperature. Inconsistency of the indices with those in the case of the harmonic phase modulation allows the role of the regional climate factors to be assessed for each class. Modulation, which is the closest to the harmonic one, was found in the North Atlantic thermohaline conveyor. During the study of the climate change, the approach suggested can be used as an analytical framework on any spatial scale, based on only data on the surface temperature, and with predetermined level of quality. The search for synchronization in nonlinear chaotic systems may be one of the promising ways to optimize the predictive models.</description><identifier>ISSN: 1024-8560</identifier><identifier>EISSN: 2070-0393</identifier><identifier>DOI: 10.1134/S1024856017010043</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Atmospheric Radiation ; Classification ; Climate ; Climate change ; Clusters ; Conveyors ; Differentiation ; Frameworks ; Lasers ; Mathematical models ; Nonlinear systems ; Northern Hemisphere ; Optical Devices ; Optical Weather ; Optics ; Oscillations ; Phase modulation ; Photonics ; Physics ; Physics and Astronomy ; Prediction models ; Regional analysis ; Sea surface temperature ; Surface temperature ; Synchronism ; Temperature ; Temperature distribution ; Temperature fields</subject><ispartof>Atmospheric and oceanic optics, 2017, Vol.30 (1), p.63-69</ispartof><rights>Pleiades Publishing, Ltd. 2017</rights><rights>Copyright Springer Science &amp; Business Media 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-fcd8a4e910ef03dc273f545212eef0629a948017f65909028aae51215d4e61e3</citedby><cites>FETCH-LOGICAL-c316t-fcd8a4e910ef03dc273f545212eef0629a948017f65909028aae51215d4e61e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1134/S1024856017010043$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1134/S1024856017010043$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Cheredko, N. N.</creatorcontrib><creatorcontrib>Tartakovsky, V. A.</creatorcontrib><creatorcontrib>Krutikov, V. A.</creatorcontrib><creatorcontrib>Volkov, Yu. V.</creatorcontrib><title>Climate classification in the Northern hemisphere using phases of temperature signals</title><title>Atmospheric and oceanic optics</title><addtitle>Atmos Ocean Opt</addtitle><description>The results of structuring surface temperatures in the Northern hemisphere for the period of modern climate changes are presented. The main idea of the classification suggested is the geographic conditionality of the phase modulation of the temperature signal. The consistency, namely, phasing of the temperature oscillations in certain geographic areas, serves the criterion. We believe that changes in the synchronization modes of climatic processes under changing climate lead to transformations of the spatial structure of the temperature field due to the system transition to a new state. The temperature series are represented as phase-modulated oscillations. External and internal factors that disturb the climate system form a complicated phase modulation, which partly corresponds to these disturbances. The initial space of 818 temperature series is structured into 17 regional clusters, where the temperature changes synchronously. Properties of the resulting clusters and their compliance with the known climate classifications are discussed. The classifying algorithm allows the researchers to choose the degree of differentiation of the field under study depending on the task. The phase modulation indices were estimated to identify manifestations of the external forcing in the surface temperature. Inconsistency of the indices with those in the case of the harmonic phase modulation allows the role of the regional climate factors to be assessed for each class. Modulation, which is the closest to the harmonic one, was found in the North Atlantic thermohaline conveyor. During the study of the climate change, the approach suggested can be used as an analytical framework on any spatial scale, based on only data on the surface temperature, and with predetermined level of quality. The search for synchronization in nonlinear chaotic systems may be one of the promising ways to optimize the predictive models.</description><subject>Atmospheric Radiation</subject><subject>Classification</subject><subject>Climate</subject><subject>Climate change</subject><subject>Clusters</subject><subject>Conveyors</subject><subject>Differentiation</subject><subject>Frameworks</subject><subject>Lasers</subject><subject>Mathematical models</subject><subject>Nonlinear systems</subject><subject>Northern Hemisphere</subject><subject>Optical Devices</subject><subject>Optical Weather</subject><subject>Optics</subject><subject>Oscillations</subject><subject>Phase modulation</subject><subject>Photonics</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>Prediction models</subject><subject>Regional analysis</subject><subject>Sea surface temperature</subject><subject>Surface temperature</subject><subject>Synchronism</subject><subject>Temperature</subject><subject>Temperature distribution</subject><subject>Temperature fields</subject><issn>1024-8560</issn><issn>2070-0393</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1UEtLw0AQXkTBWv0B3hY8R2f2kcdRii8oerCew5LOtlvaJO4kB_-9G-pBEE_fMN-DmU-Ia4RbRG3u3hGUKW0OWAACGH0iZgoKyEBX-lTMJjqb-HNxwbwDyG1lcSY-FvtwcAPJZu-Ygw-NG0LXytDKYUvytYsJYiu3dAjcp5HkyKHdyH7rmFh2Xg506Cm6YUwch03r9nwpznwCuvrBuVg9PqwWz9ny7ellcb_MGo35kPlmXTpDFQJ50OtGFdpbYxUqSotcVa4yZfrIp2OhAlU6RxYV2rWhHEnPxc0xto_d50g81LtujNMBNZYlFFYpY5IKj6omdsyRfN3H9HP8qhHqqbz6T3nJo44eTtp2Q_FX8r-mbyRjcJI</recordid><startdate>2017</startdate><enddate>2017</enddate><creator>Cheredko, N. N.</creator><creator>Tartakovsky, V. A.</creator><creator>Krutikov, V. A.</creator><creator>Volkov, Yu. V.</creator><general>Pleiades Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>2017</creationdate><title>Climate classification in the Northern hemisphere using phases of temperature signals</title><author>Cheredko, N. N. ; Tartakovsky, V. A. ; Krutikov, V. A. ; Volkov, Yu. V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-fcd8a4e910ef03dc273f545212eef0629a948017f65909028aae51215d4e61e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Atmospheric Radiation</topic><topic>Classification</topic><topic>Climate</topic><topic>Climate change</topic><topic>Clusters</topic><topic>Conveyors</topic><topic>Differentiation</topic><topic>Frameworks</topic><topic>Lasers</topic><topic>Mathematical models</topic><topic>Nonlinear systems</topic><topic>Northern Hemisphere</topic><topic>Optical Devices</topic><topic>Optical Weather</topic><topic>Optics</topic><topic>Oscillations</topic><topic>Phase modulation</topic><topic>Photonics</topic><topic>Physics</topic><topic>Physics and Astronomy</topic><topic>Prediction models</topic><topic>Regional analysis</topic><topic>Sea surface temperature</topic><topic>Surface temperature</topic><topic>Synchronism</topic><topic>Temperature</topic><topic>Temperature distribution</topic><topic>Temperature fields</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cheredko, N. N.</creatorcontrib><creatorcontrib>Tartakovsky, V. A.</creatorcontrib><creatorcontrib>Krutikov, V. A.</creatorcontrib><creatorcontrib>Volkov, Yu. V.</creatorcontrib><collection>CrossRef</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><jtitle>Atmospheric and oceanic optics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cheredko, N. N.</au><au>Tartakovsky, V. A.</au><au>Krutikov, V. A.</au><au>Volkov, Yu. V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Climate classification in the Northern hemisphere using phases of temperature signals</atitle><jtitle>Atmospheric and oceanic optics</jtitle><stitle>Atmos Ocean Opt</stitle><date>2017</date><risdate>2017</risdate><volume>30</volume><issue>1</issue><spage>63</spage><epage>69</epage><pages>63-69</pages><issn>1024-8560</issn><eissn>2070-0393</eissn><abstract>The results of structuring surface temperatures in the Northern hemisphere for the period of modern climate changes are presented. The main idea of the classification suggested is the geographic conditionality of the phase modulation of the temperature signal. The consistency, namely, phasing of the temperature oscillations in certain geographic areas, serves the criterion. We believe that changes in the synchronization modes of climatic processes under changing climate lead to transformations of the spatial structure of the temperature field due to the system transition to a new state. The temperature series are represented as phase-modulated oscillations. External and internal factors that disturb the climate system form a complicated phase modulation, which partly corresponds to these disturbances. The initial space of 818 temperature series is structured into 17 regional clusters, where the temperature changes synchronously. Properties of the resulting clusters and their compliance with the known climate classifications are discussed. The classifying algorithm allows the researchers to choose the degree of differentiation of the field under study depending on the task. The phase modulation indices were estimated to identify manifestations of the external forcing in the surface temperature. Inconsistency of the indices with those in the case of the harmonic phase modulation allows the role of the regional climate factors to be assessed for each class. Modulation, which is the closest to the harmonic one, was found in the North Atlantic thermohaline conveyor. During the study of the climate change, the approach suggested can be used as an analytical framework on any spatial scale, based on only data on the surface temperature, and with predetermined level of quality. The search for synchronization in nonlinear chaotic systems may be one of the promising ways to optimize the predictive models.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.1134/S1024856017010043</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1024-8560
ispartof Atmospheric and oceanic optics, 2017, Vol.30 (1), p.63-69
issn 1024-8560
2070-0393
language eng
recordid cdi_proquest_journals_1880752244
source SpringerLink Journals - AutoHoldings
subjects Atmospheric Radiation
Classification
Climate
Climate change
Clusters
Conveyors
Differentiation
Frameworks
Lasers
Mathematical models
Nonlinear systems
Northern Hemisphere
Optical Devices
Optical Weather
Optics
Oscillations
Phase modulation
Photonics
Physics
Physics and Astronomy
Prediction models
Regional analysis
Sea surface temperature
Surface temperature
Synchronism
Temperature
Temperature distribution
Temperature fields
title Climate classification in the Northern hemisphere using phases of temperature signals
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T22%3A05%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Climate%20classification%20in%20the%20Northern%20hemisphere%20using%20phases%20of%20temperature%20signals&rft.jtitle=Atmospheric%20and%20oceanic%20optics&rft.au=Cheredko,%20N.%20N.&rft.date=2017&rft.volume=30&rft.issue=1&rft.spage=63&rft.epage=69&rft.pages=63-69&rft.issn=1024-8560&rft.eissn=2070-0393&rft_id=info:doi/10.1134/S1024856017010043&rft_dat=%3Cproquest_cross%3E1880752244%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1880752244&rft_id=info:pmid/&rfr_iscdi=true