Multi-model remote sensing assessment of primary production in the subtropical gyres

10 pages, 8 figures, 5 tables, 1 appendix

Detalhes bibliográficos
Autores: Regaudie de Gioux, Aurore, Huete-Ortega, María, Sobrino, Cristina, López-Sandoval, Daffne, González, Natalia, Fernández-Carrera, Ana, Vidal, Montserrat, Marañón, Emilio, Cermeño, Pedro, Latasa, Mikel, Agustí, Susana, Duarte, Carlos M.
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::9cca28268af72f35d7f796bd30b78a41
Acesso em linha:http://hdl.handle.net/10261/189755
Access Level:acceso abierto
Palavra-chave:Remote PP model
Skills
Subtropical gyre
Primary production
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spelling Multi-model remote sensing assessment of primary production in the subtropical gyresRegaudie de Gioux, AuroreHuete-Ortega, MaríaSobrino, CristinaLópez-Sandoval, DaffneGonzález, NataliaFernández-Carrera, AnaVidal, MontserratMarañón, EmilioCermeño, PedroLatasa, MikelAgustí, SusanaDuarte, Carlos M.Remote PP modelSkillsSubtropical gyrePrimary production10 pages, 8 figures, 5 tables, 1 appendixThe subtropical gyres occupy about 70% of the ocean surface. While primary production (PP) within these oligotrophic regions is relatively low, their extension makes their total contribution to ocean productivity significant. Monitoring marine pelagic primary production across broad spatial scales, particularly across the subtropical gyre regions, is challenging but essential to evaluate the oceanic carbon budget. PP in the ocean can be derived from remote sensing however in situ depth-integrated PP (IPPis) measurements required for validation are scarce from the subtropical gyres. In this study, we collected >120 IPPis measurements from both northern and southern subtropical gyres that we compared to commonly used primary productivity models (the Vertically Generalized Production Model, VGPM and six variants; the Eppley-Square-Root model, ESQRT; the Howard–Yoder–Ryan model, HYR; the model of MARRA, MARRA; and the Carbon-based Production Model, CbPM) to predict remote PP (PPr) in the subtropical regions and explored possibilities for improving PP prediction. Our results showed that satellite-derived PP (IPPsat) estimates obtained from the VGPM1, MARRA and ESQRT provided closer values to the IPPis (i.e., the difference between the mean of the IPPsat and IPPis was closer to 0; |Bias| ~ 0.09). Model performance varied due to differences in satellite predictions of in situ parameters such as chlorophyll a (chl-a) concentration or the optimal assimilation efficiency of the productivity profile (PBopt) in the subtropical region. In general, model performance was better for areas showing higher IPPis, highlighting the challenge of PP prediction in the most oligotrophic areas (i.e. PP < 300 mg C m−2 d−1). The use of in situ chl-a data, and PBopt as a function of sea surface temperature (SST) and the mixed layer depth (MLD) from gliders and floats in PPr models would improve their IPP predictions considerably in oligotrophic oceanic regions such as the subtropical gyres where MLD is relatively low (<60 m) and cloudiness may bias satellite input dataThis work is a contribution to the Malaspina Circumnavigation Expedition 2010, funded by the INGENIO 2010 CONSOLIDER program (ref. CDS2008-00077) of the Spanish Ministry of Economy and CompetitivenessPeer ReviewedElsevierMinisterio de Economía y Competitividad (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2019201920192019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/189755reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1016/j.jmarsys.2019.03.007Síinfo:eu-repo/semantics/openAccessoai:dnet:digitalcsic_::9cca28268af72f35d7f796bd30b78a412026-05-22T06:33:51Z
dc.title.none.fl_str_mv Multi-model remote sensing assessment of primary production in the subtropical gyres
title Multi-model remote sensing assessment of primary production in the subtropical gyres
spellingShingle Multi-model remote sensing assessment of primary production in the subtropical gyres
Regaudie de Gioux, Aurore
Remote PP model
Skills
Subtropical gyre
Primary production
title_short Multi-model remote sensing assessment of primary production in the subtropical gyres
title_full Multi-model remote sensing assessment of primary production in the subtropical gyres
title_fullStr Multi-model remote sensing assessment of primary production in the subtropical gyres
title_full_unstemmed Multi-model remote sensing assessment of primary production in the subtropical gyres
title_sort Multi-model remote sensing assessment of primary production in the subtropical gyres
dc.creator.none.fl_str_mv Regaudie de Gioux, Aurore
Huete-Ortega, María
Sobrino, Cristina
López-Sandoval, Daffne
González, Natalia
Fernández-Carrera, Ana
Vidal, Montserrat
Marañón, Emilio
Cermeño, Pedro
Latasa, Mikel
Agustí, Susana
Duarte, Carlos M.
author Regaudie de Gioux, Aurore
author_facet Regaudie de Gioux, Aurore
Huete-Ortega, María
Sobrino, Cristina
López-Sandoval, Daffne
González, Natalia
Fernández-Carrera, Ana
Vidal, Montserrat
Marañón, Emilio
Cermeño, Pedro
Latasa, Mikel
Agustí, Susana
Duarte, Carlos M.
author_role author
author2 Huete-Ortega, María
Sobrino, Cristina
López-Sandoval, Daffne
González, Natalia
Fernández-Carrera, Ana
Vidal, Montserrat
Marañón, Emilio
Cermeño, Pedro
Latasa, Mikel
Agustí, Susana
Duarte, Carlos M.
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Remote PP model
Skills
Subtropical gyre
Primary production
topic Remote PP model
Skills
Subtropical gyre
Primary production
description 10 pages, 8 figures, 5 tables, 1 appendix
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
2019
2019
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/10261/189755
url http://hdl.handle.net/10261/189755
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1016/j.jmarsys.2019.03.007
Sí
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
_version_ 1869410438796541952
score 15,81155

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