Mechanisms of tropical precipitation biases in climate models
Descripción del Articulo
We investigate the possible causes for inter-model spread in tropical zonal-mean precipitation pattern, which is divided into hemispherically symmetric and anti-symmetric modes via empirical orthogonal function analysis. The symmetric pattern characterizes the leading mode and is tightly related to...
Autores: | , , , , |
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Formato: | artículo |
Fecha de Publicación: | 2020 |
Institución: | Servicio Nacional de Meteorología e Hidrología del Perú |
Repositorio: | SENAMHI-Institucional |
Lenguaje: | inglés |
OAI Identifier: | oai:repositorio.senamhi.gob.pe:20.500.12542/481 |
Enlace del recurso: | https://hdl.handle.net/20.500.12542/481 https://doi.org/10.1007/s00382-020-05325-z |
Nivel de acceso: | acceso abierto |
Materia: | Tropical Precipitation Model Uncertainty Double ITCZ Problem Modelos Zona Tropical Energy Flow Precipitación Climatología http://purl.org/pe-repo/ocde/ford#1.05.00 precipitacion - Clima y Eventos Naturales |
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dc.title.en_US.fl_str_mv |
Mechanisms of tropical precipitation biases in climate models |
title |
Mechanisms of tropical precipitation biases in climate models |
spellingShingle |
Mechanisms of tropical precipitation biases in climate models Kim, Hanjun Tropical Precipitation Model Uncertainty Double ITCZ Problem Modelos Zona Tropical Energy Flow Precipitación Climatología http://purl.org/pe-repo/ocde/ford#1.05.00 precipitacion - Clima y Eventos Naturales |
title_short |
Mechanisms of tropical precipitation biases in climate models |
title_full |
Mechanisms of tropical precipitation biases in climate models |
title_fullStr |
Mechanisms of tropical precipitation biases in climate models |
title_full_unstemmed |
Mechanisms of tropical precipitation biases in climate models |
title_sort |
Mechanisms of tropical precipitation biases in climate models |
author |
Kim, Hanjun |
author_facet |
Kim, Hanjun Kang, Sarah M. Takahashi, Ken Donohoe, Aaron Pendergrass, Angeline G. |
author_role |
author |
author2 |
Kang, Sarah M. Takahashi, Ken Donohoe, Aaron Pendergrass, Angeline G. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Kim, Hanjun Kang, Sarah M. Takahashi, Ken Donohoe, Aaron Pendergrass, Angeline G. |
dc.subject.en_US.fl_str_mv |
Tropical Precipitation Model Uncertainty Double ITCZ Problem Modelos Zona Tropical Energy Flow |
topic |
Tropical Precipitation Model Uncertainty Double ITCZ Problem Modelos Zona Tropical Energy Flow Precipitación Climatología http://purl.org/pe-repo/ocde/ford#1.05.00 precipitacion - Clima y Eventos Naturales |
dc.subject.es_PE.fl_str_mv |
Precipitación Climatología |
dc.subject.ocde.es_PE.fl_str_mv |
http://purl.org/pe-repo/ocde/ford#1.05.00 |
dc.subject.sinia.es_PE.fl_str_mv |
precipitacion - Clima y Eventos Naturales |
description |
We investigate the possible causes for inter-model spread in tropical zonal-mean precipitation pattern, which is divided into hemispherically symmetric and anti-symmetric modes via empirical orthogonal function analysis. The symmetric pattern characterizes the leading mode and is tightly related to the seasonal amplitude of maximum precipitation position. The energetic constraints link the symmetric pattern to the seasonal amplitude in cross-equatorial atmospheric energy transport AET0 and the annual-mean equatorial net energy input NEI0. Decomposition of AET0 into the energetics variables indicates that the inter-model spread in symmetric precipitation pattern is correlated with the inter-model spread in clear-sky atmospheric shortwave absorption, which most likely arises due to differences in radiative transfer parameterizations rather than water vapor patterns. Among the components that consist NEI0, the inter-model spread in symmetric precipitation pattern is mostly associated with the inter-model spread in net surface energy flux in the equatorial region, which is modulated by the strength of cooling by equatorial upwelling. Our results provide clues to understand the mechanism of tropical precipitation bias, thereby providing guidance for model improvements. |
publishDate |
2020 |
dc.date.accessioned.none.fl_str_mv |
2020-10-29T17:03:52Z |
dc.date.available.none.fl_str_mv |
2020-10-29T17:03:52Z |
dc.date.issued.fl_str_mv |
2020-10-27 |
dc.type.es_PE.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.sinia.es_PE.fl_str_mv |
text/publicacion cientifica |
format |
article |
dc.identifier.citation.es_PE.fl_str_mv |
Kim, H., Kang, S.M., Takahashi, K. et al. (2020) Mechanisms of tropical precipitation biases in climate models . Clim Dyn. https://doi.org/10.1007/s00382-020-05325-z |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12542/481 |
dc.identifier.isni.none.fl_str_mv |
0000 0001 0746 0446 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1007/s00382-020-05325-z |
dc.identifier.journal.es_PE.fl_str_mv |
Climate Dynamics |
dc.identifier.url.none.fl_str_mv |
https://hdl.handle.net/20.500.12542/481 https://hdl.handle.net/20.500.12542/481 https://hdl.handle.net/20.500.12542/481 https://hdl.handle.net/20.500.12542/481 |
identifier_str_mv |
Kim, H., Kang, S.M., Takahashi, K. et al. (2020) Mechanisms of tropical precipitation biases in climate models . Clim Dyn. https://doi.org/10.1007/s00382-020-05325-z 0000 0001 0746 0446 Climate Dynamics |
url |
https://hdl.handle.net/20.500.12542/481 https://doi.org/10.1007/s00382-020-05325-z |
dc.language.iso.es_PE.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.none.fl_str_mv |
urn:issn:1432-0894 |
dc.relation.uri.none.fl_str_mv |
https://par.nsf.gov/biblio/10298218-mechanisms-tropical-precipitation-biases-climate-models |
dc.rights.es_PE.fl_str_mv |
info:eu-repo/semantics/openAccess Reconocimiento - No comercial - Sin obra derivada (CC BY-NC-ND) |
dc.rights.uri.es_PE.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
Reconocimiento - No comercial - Sin obra derivada (CC BY-NC-ND) https://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.format.es_PE.fl_str_mv |
application/pdf |
dc.publisher.es_PE.fl_str_mv |
Springer |
dc.source.es_PE.fl_str_mv |
Repositorio Institucional - SENAMHI Servicio Nacional de Meteorología e Hidrología del Perú |
dc.source.none.fl_str_mv |
reponame:SENAMHI-Institucional instname:Servicio Nacional de Meteorología e Hidrología del Perú instacron:SENAMHI |
instname_str |
Servicio Nacional de Meteorología e Hidrología del Perú |
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SENAMHI |
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SENAMHI-Institucional |
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SENAMHI-Institucional |
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Kim, HanjunKang, Sarah M.Takahashi, KenDonohoe, AaronPendergrass, Angeline G.2020-10-29T17:03:52Z2020-10-29T17:03:52Z2020-10-27Kim, H., Kang, S.M., Takahashi, K. et al. (2020) Mechanisms of tropical precipitation biases in climate models . Clim Dyn. https://doi.org/10.1007/s00382-020-05325-zhttps://hdl.handle.net/20.500.12542/4810000 0001 0746 0446https://doi.org/10.1007/s00382-020-05325-zClimate Dynamicshttps://hdl.handle.net/20.500.12542/481https://hdl.handle.net/20.500.12542/481https://hdl.handle.net/20.500.12542/481https://hdl.handle.net/20.500.12542/481We investigate the possible causes for inter-model spread in tropical zonal-mean precipitation pattern, which is divided into hemispherically symmetric and anti-symmetric modes via empirical orthogonal function analysis. The symmetric pattern characterizes the leading mode and is tightly related to the seasonal amplitude of maximum precipitation position. The energetic constraints link the symmetric pattern to the seasonal amplitude in cross-equatorial atmospheric energy transport AET0 and the annual-mean equatorial net energy input NEI0. Decomposition of AET0 into the energetics variables indicates that the inter-model spread in symmetric precipitation pattern is correlated with the inter-model spread in clear-sky atmospheric shortwave absorption, which most likely arises due to differences in radiative transfer parameterizations rather than water vapor patterns. Among the components that consist NEI0, the inter-model spread in symmetric precipitation pattern is mostly associated with the inter-model spread in net surface energy flux in the equatorial region, which is modulated by the strength of cooling by equatorial upwelling. Our results provide clues to understand the mechanism of tropical precipitation bias, thereby providing guidance for model improvements.Por paresapplication/pdfengSpringerurn:issn:1432-0894https://par.nsf.gov/biblio/10298218-mechanisms-tropical-precipitation-biases-climate-modelsinfo:eu-repo/semantics/openAccessReconocimiento - No comercial - Sin obra derivada (CC BY-NC-ND)https://creativecommons.org/licenses/by-nc-nd/4.0/Repositorio Institucional - SENAMHIServicio Nacional de Meteorología e Hidrología del Perúreponame:SENAMHI-Institucionalinstname:Servicio Nacional de Meteorología e Hidrología del Perúinstacron:SENAMHITropical PrecipitationModel UncertaintyDouble ITCZ ProblemModelosZona TropicalEnergy FlowPrecipitaciónClimatologíahttp://purl.org/pe-repo/ocde/ford#1.05.00precipitacion - Clima y Eventos NaturalesMechanisms of tropical precipitation biases in climate modelsinfo:eu-repo/semantics/articletext/publicacion cientificaCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811http://repositorio.senamhi.gob.pe/bitstream/20.500.12542/481/1/license_rdf9868ccc48a14c8d591352b6eaf7f6239MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.senamhi.gob.pe/bitstream/20.500.12542/481/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52ORIGINALMechanisms-tropical-precipitation-biases-climate models_2020.pdfMechanisms-tropical-precipitation-biases-climate models_2020.pdfTexto Completoapplication/pdf24208308http://repositorio.senamhi.gob.pe/bitstream/20.500.12542/481/3/Mechanisms-tropical-precipitation-biases-climate%20models_2020.pdf7c26618fa3f6b929f0df87e2d51cfa9dMD53TEXTMechanisms-tropical-precipitation-biases-climate models_2020.pdf.txtMechanisms-tropical-precipitation-biases-climate models_2020.pdf.txtExtracted texttext/plain11http://repositorio.senamhi.gob.pe/bitstream/20.500.12542/481/4/Mechanisms-tropical-precipitation-biases-climate%20models_2020.pdf.txtb308b7fe5c1c2bbdc0cb686d451b84aaMD54THUMBNAILMechanisms-tropical-precipitation-biases-climate models_2020.pdf.jpgMechanisms-tropical-precipitation-biases-climate models_2020.pdf.jpgGenerated Thumbnailimage/jpeg6686http://repositorio.senamhi.gob.pe/bitstream/20.500.12542/481/5/Mechanisms-tropical-precipitation-biases-climate%20models_2020.pdf.jpgdcd6688a11a063f831a80074bbdfb211MD5520.500.12542/481oai:repositorio.senamhi.gob.pe:20.500.12542/4812024-12-18 18:00:30.26Repositorio Institucional SENAMHIrepositorio@senamhi.gob.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 |
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13.95948 |
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).