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Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations
Quantifying the urbanization effect on station and regional surface air temperature (SAT) trends is a prerequisite for monitoring and detecting long‐term climate change. Based on the data set of satellite visible spectral remote sensing, a new method is developed to determine the urbanization level...
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Published in: | Journal of geophysical research. Atmospheres 2019-10, Vol.124 (20), p.10646-10661 |
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container_title | Journal of geophysical research. Atmospheres |
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creator | Tysa, Suonam Kealdrup Ren, Guoyu Qin, Yun Zhang, Panfeng Ren, Yuyu Jia, Wenqian Wen, Kangmin |
description | Quantifying the urbanization effect on station and regional surface air temperature (SAT) trends is a prerequisite for monitoring and detecting long‐term climate change. Based on the data set of satellite visible spectral remote sensing, a new method is developed to determine the urbanization level around observational sites on varied spatial scales and to classify the sites into different categories of stations (U1, U2, …, U6) with U1 the least and U6 the largest affected by urbanization. Urbanization effect on SAT anomaly series of urban and national stations are then evaluated for the periods of 1980–2015 and 1960–2015. Results show that the percentage of built‐up area in different circumferences of the observational sites can be considered as a good indicator of comprehensive urbanization level of station and can be used to classify stations and to determine reference stations; the largest increase in annual mean SAT (Tmean) during 1980–2015 occurred at U6 stations, and U1 stations registered the weakest annual mean warming. The urbanization level is significantly positively correlated to the linear trends of annual mean Tmean and minimum SAT (Tmin) and significantly negatively correlated to the diurnal temperature range (DTR) change. The data sets of the national reference climate station network and basic meteorological station network show large urbanization effect and contribution, with the annual mean urbanization contributions reaching 28.7% and 25.8% for the periods 1960–2015 and 1980–2015, respectively. For all the national stations (2,286 in total), the urbanization contributions are 17.1% and 14.6% for the two same periods, respectively.
Key Points
A new method to determine urbanization level around stations is presented, which shows the applicability to classify stations for assessing urbanization effect
Also provided is the further evidence that the urbanization has significantly contributed to the observed warming trendsover the last decades in mainland China
Urbanization effect shows notable difference for different data sets of national stations and for different timeperiods of 1960–2015 and 1980–2015 |
doi_str_mv | 10.1029/2019JD030948 |
format | article |
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Key Points
A new method to determine urbanization level around stations is presented, which shows the applicability to classify stations for assessing urbanization effect
Also provided is the further evidence that the urbanization has significantly contributed to the observed warming trendsover the last decades in mainland China
Urbanization effect shows notable difference for different data sets of national stations and for different timeperiods of 1960–2015 and 1980–2015</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1029/2019JD030948</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Air temperature ; China ; Classification ; Climate change ; Daily temperature range ; Daily temperatures ; Datasets ; Diurnal ; Geophysics ; linear trend ; Remote sensing ; station classification ; Stations ; Surface temperature ; Surface-air temperature relationships ; Temperature effects ; Trends ; Urbanization ; urbanization effect ; Weather stations</subject><ispartof>Journal of geophysical research. Atmospheres, 2019-10, Vol.124 (20), p.10646-10661</ispartof><rights>2019. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4165-7604f74654415d5049fab5872f1d6d2c68e45540e3599464a698ec80e3d42ded3</citedby><cites>FETCH-LOGICAL-c4165-7604f74654415d5049fab5872f1d6d2c68e45540e3599464a698ec80e3d42ded3</cites><orcidid>0000-0002-9351-4179 ; 0000-0001-6084-9231 ; 0000-0002-7291-0981</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Tysa, Suonam Kealdrup</creatorcontrib><creatorcontrib>Ren, Guoyu</creatorcontrib><creatorcontrib>Qin, Yun</creatorcontrib><creatorcontrib>Zhang, Panfeng</creatorcontrib><creatorcontrib>Ren, Yuyu</creatorcontrib><creatorcontrib>Jia, Wenqian</creatorcontrib><creatorcontrib>Wen, Kangmin</creatorcontrib><title>Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations</title><title>Journal of geophysical research. Atmospheres</title><description>Quantifying the urbanization effect on station and regional surface air temperature (SAT) trends is a prerequisite for monitoring and detecting long‐term climate change. Based on the data set of satellite visible spectral remote sensing, a new method is developed to determine the urbanization level around observational sites on varied spatial scales and to classify the sites into different categories of stations (U1, U2, …, U6) with U1 the least and U6 the largest affected by urbanization. Urbanization effect on SAT anomaly series of urban and national stations are then evaluated for the periods of 1980–2015 and 1960–2015. Results show that the percentage of built‐up area in different circumferences of the observational sites can be considered as a good indicator of comprehensive urbanization level of station and can be used to classify stations and to determine reference stations; the largest increase in annual mean SAT (Tmean) during 1980–2015 occurred at U6 stations, and U1 stations registered the weakest annual mean warming. The urbanization level is significantly positively correlated to the linear trends of annual mean Tmean and minimum SAT (Tmin) and significantly negatively correlated to the diurnal temperature range (DTR) change. The data sets of the national reference climate station network and basic meteorological station network show large urbanization effect and contribution, with the annual mean urbanization contributions reaching 28.7% and 25.8% for the periods 1960–2015 and 1980–2015, respectively. For all the national stations (2,286 in total), the urbanization contributions are 17.1% and 14.6% for the two same periods, respectively.
Key Points
A new method to determine urbanization level around stations is presented, which shows the applicability to classify stations for assessing urbanization effect
Also provided is the further evidence that the urbanization has significantly contributed to the observed warming trendsover the last decades in mainland China
Urbanization effect shows notable difference for different data sets of national stations and for different timeperiods of 1960–2015 and 1980–2015</description><subject>Air temperature</subject><subject>China</subject><subject>Classification</subject><subject>Climate change</subject><subject>Daily temperature range</subject><subject>Daily temperatures</subject><subject>Datasets</subject><subject>Diurnal</subject><subject>Geophysics</subject><subject>linear trend</subject><subject>Remote sensing</subject><subject>station classification</subject><subject>Stations</subject><subject>Surface temperature</subject><subject>Surface-air temperature relationships</subject><subject>Temperature effects</subject><subject>Trends</subject><subject>Urbanization</subject><subject>urbanization effect</subject><subject>Weather stations</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhhdRsNTe_AEBr1aT7CS7OWpbq1IQ-gHelnR3UlP2oyZbpP56U1fEk3OZmXceXpg3ii4ZvWGUq1tOmXoe05gqSE-iHmdSDVOl5OnvnLyeRwPvtzRUSmMQ0IvqlVvr2n7q1jY1mRiDeUtsTea4CYIuyRKrHTrd7h2SBTqLntxrjwUJuA5Y1bTHQ-1tvSGjUntvjc07u0X-hhWSxpBF-634i-jM6NLj4Kf3o9XDZDl6HM5epk-ju9kwBybFMJEUTAJSADBRCArK6LVIE25YIQueyxRBCKAYC6VAgpYqxTwNewG8wCLuR1ed784173v0bbZt9i784zMehzSAAZeBuu6o3DXeOzTZztlKu0PGaHYMNfsbasDjDv-wJR7-ZbPn6XwsRCJF_AWDeXd_</recordid><startdate>20191027</startdate><enddate>20191027</enddate><creator>Tysa, Suonam Kealdrup</creator><creator>Ren, Guoyu</creator><creator>Qin, Yun</creator><creator>Zhang, Panfeng</creator><creator>Ren, Yuyu</creator><creator>Jia, Wenqian</creator><creator>Wen, Kangmin</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-9351-4179</orcidid><orcidid>https://orcid.org/0000-0001-6084-9231</orcidid><orcidid>https://orcid.org/0000-0002-7291-0981</orcidid></search><sort><creationdate>20191027</creationdate><title>Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations</title><author>Tysa, Suonam Kealdrup ; Ren, Guoyu ; Qin, Yun ; Zhang, Panfeng ; Ren, Yuyu ; Jia, Wenqian ; Wen, Kangmin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4165-7604f74654415d5049fab5872f1d6d2c68e45540e3599464a698ec80e3d42ded3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Air temperature</topic><topic>China</topic><topic>Classification</topic><topic>Climate change</topic><topic>Daily temperature range</topic><topic>Daily temperatures</topic><topic>Datasets</topic><topic>Diurnal</topic><topic>Geophysics</topic><topic>linear trend</topic><topic>Remote sensing</topic><topic>station classification</topic><topic>Stations</topic><topic>Surface temperature</topic><topic>Surface-air temperature relationships</topic><topic>Temperature effects</topic><topic>Trends</topic><topic>Urbanization</topic><topic>urbanization effect</topic><topic>Weather stations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tysa, Suonam Kealdrup</creatorcontrib><creatorcontrib>Ren, Guoyu</creatorcontrib><creatorcontrib>Qin, Yun</creatorcontrib><creatorcontrib>Zhang, Panfeng</creatorcontrib><creatorcontrib>Ren, Yuyu</creatorcontrib><creatorcontrib>Jia, Wenqian</creatorcontrib><creatorcontrib>Wen, Kangmin</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of geophysical research. Atmospheres</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tysa, Suonam Kealdrup</au><au>Ren, Guoyu</au><au>Qin, Yun</au><au>Zhang, Panfeng</au><au>Ren, Yuyu</au><au>Jia, Wenqian</au><au>Wen, Kangmin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><date>2019-10-27</date><risdate>2019</risdate><volume>124</volume><issue>20</issue><spage>10646</spage><epage>10661</epage><pages>10646-10661</pages><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>Quantifying the urbanization effect on station and regional surface air temperature (SAT) trends is a prerequisite for monitoring and detecting long‐term climate change. Based on the data set of satellite visible spectral remote sensing, a new method is developed to determine the urbanization level around observational sites on varied spatial scales and to classify the sites into different categories of stations (U1, U2, …, U6) with U1 the least and U6 the largest affected by urbanization. Urbanization effect on SAT anomaly series of urban and national stations are then evaluated for the periods of 1980–2015 and 1960–2015. Results show that the percentage of built‐up area in different circumferences of the observational sites can be considered as a good indicator of comprehensive urbanization level of station and can be used to classify stations and to determine reference stations; the largest increase in annual mean SAT (Tmean) during 1980–2015 occurred at U6 stations, and U1 stations registered the weakest annual mean warming. The urbanization level is significantly positively correlated to the linear trends of annual mean Tmean and minimum SAT (Tmin) and significantly negatively correlated to the diurnal temperature range (DTR) change. The data sets of the national reference climate station network and basic meteorological station network show large urbanization effect and contribution, with the annual mean urbanization contributions reaching 28.7% and 25.8% for the periods 1960–2015 and 1980–2015, respectively. For all the national stations (2,286 in total), the urbanization contributions are 17.1% and 14.6% for the two same periods, respectively.
Key Points
A new method to determine urbanization level around stations is presented, which shows the applicability to classify stations for assessing urbanization effect
Also provided is the further evidence that the urbanization has significantly contributed to the observed warming trendsover the last decades in mainland China
Urbanization effect shows notable difference for different data sets of national stations and for different timeperiods of 1960–2015 and 1980–2015</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2019JD030948</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-9351-4179</orcidid><orcidid>https://orcid.org/0000-0001-6084-9231</orcidid><orcidid>https://orcid.org/0000-0002-7291-0981</orcidid></addata></record> |
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source | Wiley; Alma/SFX Local Collection |
subjects | Air temperature China Classification Climate change Daily temperature range Daily temperatures Datasets Diurnal Geophysics linear trend Remote sensing station classification Stations Surface temperature Surface-air temperature relationships Temperature effects Trends Urbanization urbanization effect Weather stations |
title | Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations |
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