Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique

In this study we assess the suitability of a recently introduced analog-based Model Output Statistics (MOS) downscaling method (referred to as MOS-Analog) for climate change studies and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider da...

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Detalles Bibliográficos
Autores: Turco, Marco, Llasat, Maria C., Herrera, Sixto, Gutiérrez, José Manuel|||0000-0002-2766-6297
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/103466
Acceso en línea:https://hdl.handle.net/2117/103466
https://dx.doi.org/10.1002/2016JD025724
Access Level:acceso abierto
Palabra clave:Climate--Research
Climate change
Precipitation anomalies
Model Output Statistics (MOS)
Bias correction
Clima--Observacions
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria biomèdica
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oai_identifier_str oai:upcommons.upc.edu:2117/103466
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
title Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
spellingShingle Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
Turco, Marco
Climate--Research
Climate change
Precipitation anomalies
Model Output Statistics (MOS)
Bias correction
Climate change
Clima--Observacions
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria biomèdica
title_short Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
title_full Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
title_fullStr Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
title_full_unstemmed Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
title_sort Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog technique
dc.creator.none.fl_str_mv Turco, Marco
Llasat, Maria C.
Herrera, Sixto
Gutiérrez, José Manuel|||0000-0002-2766-6297
author Turco, Marco
author_facet Turco, Marco
Llasat, Maria C.
Herrera, Sixto
Gutiérrez, José Manuel|||0000-0002-2766-6297
author_role author
author2 Llasat, Maria C.
Herrera, Sixto
Gutiérrez, José Manuel|||0000-0002-2766-6297
author2_role author
author
author
dc.subject.none.fl_str_mv Climate--Research
Climate change
Precipitation anomalies
Model Output Statistics (MOS)
Bias correction
Climate change
Clima--Observacions
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria biomèdica
topic Climate--Research
Climate change
Precipitation anomalies
Model Output Statistics (MOS)
Bias correction
Climate change
Clima--Observacions
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria biomèdica
description In this study we assess the suitability of a recently introduced analog-based Model Output Statistics (MOS) downscaling method (referred to as MOS-Analog) for climate change studies and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis-driven Regional Climate Model (RCM) data provide the historical data (with day-to-day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) global climate model (GCM)-driven RCM values. First, we show that the MOS-Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971–2000) for all considered regions and precipitation indices, although for the worst-performing models the method is less effective. Second, we show that the MOS-Analog method broadly preserves the original RCM climate change signal for different future periods (2011–2040, 2041–2070, 2071–2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS-Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-03-06
2017
2017-04-10
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/103466
https://dx.doi.org/10.1002/2016JD025724
url https://hdl.handle.net/2117/103466
https://dx.doi.org/10.1002/2016JD025724
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 CGL2015-66583-R ETODOS DE DOWNSCALING ESTADISTICO MULTIVARIADOS (ESPACIALES Y MULTI-VARIABLE): CONTRIBUCION A LAS INICIATIVAS INTERNACIONALES Y AL PROGRAMA NACIONAL ESCENARIOS-PNACC
Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 CGL2014-52571-R ANALISIS HOLISTICO DEL IMPACTO DE LAS PRECIPITACIONES EXTREMAS E INUNDACIONES Y SU INTRODUCCION EN ESCENARIOS FUTUROS. APLICACION A LAS ESTRATEGIAS DE ADAPTACION Y RESILIENCIA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Geophysical Union (AGU)
publisher.none.fl_str_mv American Geophysical Union (AGU)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406354601410560
spelling Bias correction and downscaling of future RCM precipitation projections using a MOS-Analog techniqueTurco, MarcoLlasat, Maria C.Herrera, SixtoGutiérrez, José Manuel|||0000-0002-2766-6297Climate--ResearchClimate changePrecipitation anomaliesModel Output Statistics (MOS)Bias correctionClimate changeClima--ObservacionsCanvis climàticsÀrees temàtiques de la UPC::Enginyeria biomèdicaIn this study we assess the suitability of a recently introduced analog-based Model Output Statistics (MOS) downscaling method (referred to as MOS-Analog) for climate change studies and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis-driven Regional Climate Model (RCM) data provide the historical data (with day-to-day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) global climate model (GCM)-driven RCM values. First, we show that the MOS-Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971–2000) for all considered regions and precipitation indices, although for the worst-performing models the method is less effective. Second, we show that the MOS-Analog method broadly preserves the original RCM climate change signal for different future periods (2011–2040, 2041–2070, 2071–2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS-Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.This work was partially supported by the strategic action for energy and climate change by the Spanish R+D 2008–2011 Program ESTCENA (code 200800050084078), the project MULTI-SDM (CGL2015-66583- R, MINECO/FEDER), the Italian project of Interest NextData of the Italian Ministry for Education, University and Research, and by the European Science Foundation within the framework of COST ES1102 (Validating and integrating downscaling methods for climate change research). This paper has also been written under the framework of the International HYMEX project and the Spanish HOPE (CGL2014-52571-R) project. We also acknowledge the ENSEMBLES project (funded by the European Commission’s 6th Framework Programme through contract GOCE-CT-2003-505539) for the RCM data used in this work (http://ensemblesrt3.dmi.dk/). The authors thank AEMET and UC for the data provided for this work (Spain02 gridded precipitation data set, www.meteo.unican.es/es/datasets/spain02). Special thanks to the authors of the MeteoLab-Toolbox (www.meteo.unican.es/software/meteolab) which helped us to postprocess the data and to validate the method. Finally, we thank the anonymous referees for their useful comments.Peer ReviewedAmerican Geophysical Union (AGU)20172017-03-0620172017-04-10journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/103466https://dx.doi.org/10.1002/2016JD025724reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 CGL2015-66583-R ETODOS DE DOWNSCALING ESTADISTICO MULTIVARIADOS (ESPACIALES Y MULTI-VARIABLE): CONTRIBUCION A LAS INICIATIVAS INTERNACIONALES Y AL PROGRAMA NACIONAL ESCENARIOS-PNACCMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 CGL2014-52571-R ANALISIS HOLISTICO DEL IMPACTO DE LAS PRECIPITACIONES EXTREMAS E INUNDACIONES Y SU INTRODUCCION EN ESCENARIOS FUTUROS. APLICACION A LAS ESTRATEGIAS DE ADAPTACION Y RESILIENCIAopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1034662026-05-27T15:37:01Z
score 15,300724

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