Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends

This paper provides a solution to the problem of estimating the mean value of near-land-surface temperature over a relatively large area (here, by way of example, applied to mainland Spain covering an area of around half a million square kilometres) from a limited number of weather stations covering...

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Detalles Bibliográficos
Autores: Wang, Hong, Pardo-Igúzquiza, Eulogio, Dowd, Peter A., Yang, Yongguo
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/277175
Acceso en línea:http://hdl.handle.net/10261/277175
https://doi.org/10.1016/j.cageo.2017.06.002
Access Level:acceso abierto
Palabra clave:Constrained spatial clustering
Temperature-altitude correlation
Regression kriging
Time series
Temperature trend detection
Global warming
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spelling Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trendsWang, HongPardo-Igúzquiza, EulogioDowd, Peter A.Yang, YongguoConstrained spatial clusteringTemperature-altitude correlationRegression krigingTime seriesTemperature trend detectionGlobal warmingThis paper provides a solution to the problem of estimating the mean value of near-land-surface temperature over a relatively large area (here, by way of example, applied to mainland Spain covering an area of around half a million square kilometres) from a limited number of weather stations covering a non-representative (biased) range of altitudes. As evidence mounts for altitude-dependent global warming, this bias is a significant problem when temperatures at high altitudes are under-represented. We correct this bias by using altitude as a secondary variable and using a novel clustering method for identifying geographical regions (clusters) that maximize the correlation between altitude and mean temperature. In addition, the paper provides an improved regression kriging estimator, which is optimally determined by the cluster analysis. The optimal areal values of near-land-surface temperature are used to generate time series of areal temperature averages in order to assess regional changes in temperature trends. The methodology is applied to records of annual mean temperatures over the period 1950–2011 across mainland Spain. The robust non-parametric Theil-Sen method is used to test for temperature trends in the regional temperature time series. Our analysis shows that, over the 62-year period of the study, 78% of mainland Spain has had a statistically significant increase in annual mean temperature.School of Resources and Geosciences, China University of Mining and Technology, ChinaInstituto Geológico y Minero de España, EspañaUniversity of Adelaide, AustraliaAustralian Research Council, AustraliaElsevierMinistry of Science and Technology of the People's Republic of ChinaChina Scholarship CouncilMinisterio de Economía, Industria y Competitividad (España)202220222017info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/277175https://doi.org/10.1016/j.cageo.2017.06.002reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#NSFC41672324NSFC41430317CGL2015-71510-RDP110104766https://www.sciencedirect.com/science/article/pii/S0098300417301310?via%3Dihubinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2771752026-05-22T06:33:51Z
dc.title.none.fl_str_mv Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
title Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
spellingShingle Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
Wang, Hong
Constrained spatial clustering
Temperature-altitude correlation
Regression kriging
Time series
Temperature trend detection
Global warming
title_short Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
title_full Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
title_fullStr Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
title_full_unstemmed Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
title_sort Optimal estimation of areal values of near-land-surface temperatures for testing global and local spatio-temporal trends
dc.creator.none.fl_str_mv Wang, Hong
Pardo-Igúzquiza, Eulogio
Dowd, Peter A.
Yang, Yongguo
author Wang, Hong
author_facet Wang, Hong
Pardo-Igúzquiza, Eulogio
Dowd, Peter A.
Yang, Yongguo
author_role author
author2 Pardo-Igúzquiza, Eulogio
Dowd, Peter A.
Yang, Yongguo
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministry of Science and Technology of the People's Republic of China
China Scholarship Council
Ministerio de Economía, Industria y Competitividad (España)
dc.subject.none.fl_str_mv Constrained spatial clustering
Temperature-altitude correlation
Regression kriging
Time series
Temperature trend detection
Global warming
topic Constrained spatial clustering
Temperature-altitude correlation
Regression kriging
Time series
Temperature trend detection
Global warming
description This paper provides a solution to the problem of estimating the mean value of near-land-surface temperature over a relatively large area (here, by way of example, applied to mainland Spain covering an area of around half a million square kilometres) from a limited number of weather stations covering a non-representative (biased) range of altitudes. As evidence mounts for altitude-dependent global warming, this bias is a significant problem when temperatures at high altitudes are under-represented. We correct this bias by using altitude as a secondary variable and using a novel clustering method for identifying geographical regions (clusters) that maximize the correlation between altitude and mean temperature. In addition, the paper provides an improved regression kriging estimator, which is optimally determined by the cluster analysis. The optimal areal values of near-land-surface temperature are used to generate time series of areal temperature averages in order to assess regional changes in temperature trends. The methodology is applied to records of annual mean temperatures over the period 1950–2011 across mainland Spain. The robust non-parametric Theil-Sen method is used to test for temperature trends in the regional temperature time series. Our analysis shows that, over the 62-year period of the study, 78% of mainland Spain has had a statistically significant increase in annual mean temperature.
publishDate 2017
dc.date.none.fl_str_mv 2017
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/277175
https://doi.org/10.1016/j.cageo.2017.06.002
url http://hdl.handle.net/10261/277175
https://doi.org/10.1016/j.cageo.2017.06.002
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
NSFC41672324
NSFC41430317
CGL2015-71510-R
DP110104766
https://www.sciencedirect.com/science/article/pii/S0098300417301310?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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