An online air-sea exchange model framework for trace gases powered by machine- learning

Autores
Wang, Siyuan; Emmons, Louisa K.; Tilmes, Simone; Kinnison, Douglas E.; Long, Mateo C.; Lamarque, Jean Francoise; Apel, Eric C.; Hornbrook, Rebecca S.; Montzka, Stephen; Saiz López, Alfonso; Fernandez, Rafael Pedro
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The ocean emits a wide range of trace gases, such as volatile organic compounds, or sulfur-,nitrogen-, and halogen-containing compounds. Many of these gases play critical roles in the atmosphere, including aerosol and cloud formation, tropospheric and stratospheric ozone budget, as well as the self-cleaning capacity of the atmosphere. Most chemistry-climate models use prescribed oceanic emissions (often derived from observations). These prescribed (offline) emissions generally do not respond to changes in local conditions. A process-level representation of the bi-directional oceanic emissions of trace gases remains challenging, mainly because the ocean biogeochemical
processes controlling the natural synthesis of these compounds in the seawater remain poorly understood. We present a new online air-sea exchange framework for the NCAR CESM2, with an observationally trained machine-learning emulator to couple the ocean biogeochemistry with the air-sea exchange. This machine-learning based approach so far has been tested for a number of important trace gases, including dimethyl sulfide (DMS), acetone, bromoform (CHBr 3 ), and dibromomethane (CH 2 Br 2 ), and the preliminary results are evaluated with observations around the globe. This new model framework is more skillful than the widely used top-down approaches for representing the seasonal/spatial variations and the annual means of atmospheric concentrations. The new approach improves the model predictability for the coupled earth system model, and can be used as a basis for investigating the future ocean emissions and feedbacks under climate change.
Fil: Wang, Siyuan. National Center for Atmospheric Research; Estados Unidos
Fil: Emmons, Louisa K.. National Center for Atmospheric Research; Estados Unidos
Fil: Tilmes, Simone. National Center for Atmospheric Research; Estados Unidos
Fil: Kinnison, Douglas E.. National Center for Atmospheric Research; Estados Unidos
Fil: Long, Mateo C.. National Center for Atmospheric Research; Estados Unidos
Fil: Lamarque, Jean Francoise. National Center for Atmospheric Research; Estados Unidos
Fil: Apel, Eric C.. National Oceanic & Atmospheric Administration, Esrl; Estados Unidos
Fil: Hornbrook, Rebecca S.. Centro Nacional de Investigación Atmosférica; Estados Unidos
Fil: Montzka, Stephen. National Ocean And Atmospheric Administration; Estados Unidos
Fil: Saiz López, Alfonso. Consejo Superior de Investigaciones Científicas; España
Fil: Fernandez, Rafael Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina
American Geophysical Union Fall Meeting
San Francisco
Estados Unidos
American Geophysical Union
Materia
SEA-AIR EXCHANGE
VSL HALOGENS
CAM-CHEM
MACHINE LEARNING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/215122
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/215122
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An online air-sea exchange model framework for trace gases powered by machine- learningWang, SiyuanEmmons, Louisa K.Tilmes, SimoneKinnison, Douglas E.Long, Mateo C.Lamarque, Jean FrancoiseApel, Eric C.Hornbrook, Rebecca S.Montzka, StephenSaiz López, AlfonsoFernandez, Rafael PedroSEA-AIR EXCHANGEVSL HALOGENSCAM-CHEMMACHINE LEARNINGhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The ocean emits a wide range of trace gases, such as volatile organic compounds, or sulfur-,nitrogen-, and halogen-containing compounds. Many of these gases play critical roles in the atmosphere, including aerosol and cloud formation, tropospheric and stratospheric ozone budget, as well as the self-cleaning capacity of the atmosphere. Most chemistry-climate models use prescribed oceanic emissions (often derived from observations). These prescribed (offline) emissions generally do not respond to changes in local conditions. A process-level representation of the bi-directional oceanic emissions of trace gases remains challenging, mainly because the ocean biogeochemical<br />processes controlling the natural synthesis of these compounds in the seawater remain poorly understood. We present a new online air-sea exchange framework for the NCAR CESM2, with an observationally trained machine-learning emulator to couple the ocean biogeochemistry with the air-sea exchange. This machine-learning based approach so far has been tested for a number of important trace gases, including dimethyl sulfide (DMS), acetone, bromoform (CHBr 3 ), and dibromomethane (CH 2 Br 2 ), and the preliminary results are evaluated with observations around the globe. This new model framework is more skillful than the widely used top-down approaches for representing the seasonal/spatial variations and the annual means of atmospheric concentrations. The new approach improves the model predictability for the coupled earth system model, and can be used as a basis for investigating the future ocean emissions and feedbacks under climate change.Fil: Wang, Siyuan. National Center for Atmospheric Research; Estados UnidosFil: Emmons, Louisa K.. National Center for Atmospheric Research; Estados UnidosFil: Tilmes, Simone. National Center for Atmospheric Research; Estados UnidosFil: Kinnison, Douglas E.. National Center for Atmospheric Research; Estados UnidosFil: Long, Mateo C.. National Center for Atmospheric Research; Estados UnidosFil: Lamarque, Jean Francoise. National Center for Atmospheric Research; Estados UnidosFil: Apel, Eric C.. National Oceanic & Atmospheric Administration, Esrl; Estados UnidosFil: Hornbrook, Rebecca S.. Centro Nacional de Investigación Atmosférica; Estados UnidosFil: Montzka, Stephen. National Ocean And Atmospheric Administration; Estados UnidosFil: Saiz López, Alfonso. Consejo Superior de Investigaciones Científicas; EspañaFil: Fernandez, Rafael Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; ArgentinaAmerican Geophysical Union Fall MeetingSan FranciscoEstados UnidosAmerican Geophysical UnionAmerican Geophysical Union2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectReuniónBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/215122An online air-sea exchange model framework for trace gases powered by machine- learning; American Geophysical Union Fall Meeting; San Francisco; Estados Unidos; 2019; 1-1CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/510957Internacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:18:33Zoai:ri.conicet.gov.ar:11336/215122instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:18:33.84CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An online air-sea exchange model framework for trace gases powered by machine- learning
title An online air-sea exchange model framework for trace gases powered by machine- learning
spellingShingle An online air-sea exchange model framework for trace gases powered by machine- learning
Wang, Siyuan
SEA-AIR EXCHANGE
VSL HALOGENS
CAM-CHEM
MACHINE LEARNING
title_short An online air-sea exchange model framework for trace gases powered by machine- learning
title_full An online air-sea exchange model framework for trace gases powered by machine- learning
title_fullStr An online air-sea exchange model framework for trace gases powered by machine- learning
title_full_unstemmed An online air-sea exchange model framework for trace gases powered by machine- learning
title_sort An online air-sea exchange model framework for trace gases powered by machine- learning
dc.creator.none.fl_str_mv Wang, Siyuan
Emmons, Louisa K.
Tilmes, Simone
Kinnison, Douglas E.
Long, Mateo C.
Lamarque, Jean Francoise
Apel, Eric C.
Hornbrook, Rebecca S.
Montzka, Stephen
Saiz López, Alfonso
Fernandez, Rafael Pedro
author Wang, Siyuan
author_facet Wang, Siyuan
Emmons, Louisa K.
Tilmes, Simone
Kinnison, Douglas E.
Long, Mateo C.
Lamarque, Jean Francoise
Apel, Eric C.
Hornbrook, Rebecca S.
Montzka, Stephen
Saiz López, Alfonso
Fernandez, Rafael Pedro
author_role author
author2 Emmons, Louisa K.
Tilmes, Simone
Kinnison, Douglas E.
Long, Mateo C.
Lamarque, Jean Francoise
Apel, Eric C.
Hornbrook, Rebecca S.
Montzka, Stephen
Saiz López, Alfonso
Fernandez, Rafael Pedro
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv SEA-AIR EXCHANGE
VSL HALOGENS
CAM-CHEM
MACHINE LEARNING
topic SEA-AIR EXCHANGE
VSL HALOGENS
CAM-CHEM
MACHINE LEARNING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The ocean emits a wide range of trace gases, such as volatile organic compounds, or sulfur-,nitrogen-, and halogen-containing compounds. Many of these gases play critical roles in the atmosphere, including aerosol and cloud formation, tropospheric and stratospheric ozone budget, as well as the self-cleaning capacity of the atmosphere. Most chemistry-climate models use prescribed oceanic emissions (often derived from observations). These prescribed (offline) emissions generally do not respond to changes in local conditions. A process-level representation of the bi-directional oceanic emissions of trace gases remains challenging, mainly because the ocean biogeochemical<br />processes controlling the natural synthesis of these compounds in the seawater remain poorly understood. We present a new online air-sea exchange framework for the NCAR CESM2, with an observationally trained machine-learning emulator to couple the ocean biogeochemistry with the air-sea exchange. This machine-learning based approach so far has been tested for a number of important trace gases, including dimethyl sulfide (DMS), acetone, bromoform (CHBr 3 ), and dibromomethane (CH 2 Br 2 ), and the preliminary results are evaluated with observations around the globe. This new model framework is more skillful than the widely used top-down approaches for representing the seasonal/spatial variations and the annual means of atmospheric concentrations. The new approach improves the model predictability for the coupled earth system model, and can be used as a basis for investigating the future ocean emissions and feedbacks under climate change.
Fil: Wang, Siyuan. National Center for Atmospheric Research; Estados Unidos
Fil: Emmons, Louisa K.. National Center for Atmospheric Research; Estados Unidos
Fil: Tilmes, Simone. National Center for Atmospheric Research; Estados Unidos
Fil: Kinnison, Douglas E.. National Center for Atmospheric Research; Estados Unidos
Fil: Long, Mateo C.. National Center for Atmospheric Research; Estados Unidos
Fil: Lamarque, Jean Francoise. National Center for Atmospheric Research; Estados Unidos
Fil: Apel, Eric C.. National Oceanic & Atmospheric Administration, Esrl; Estados Unidos
Fil: Hornbrook, Rebecca S.. Centro Nacional de Investigación Atmosférica; Estados Unidos
Fil: Montzka, Stephen. National Ocean And Atmospheric Administration; Estados Unidos
Fil: Saiz López, Alfonso. Consejo Superior de Investigaciones Científicas; España
Fil: Fernandez, Rafael Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina
American Geophysical Union Fall Meeting
San Francisco
Estados Unidos
American Geophysical Union
description The ocean emits a wide range of trace gases, such as volatile organic compounds, or sulfur-,nitrogen-, and halogen-containing compounds. Many of these gases play critical roles in the atmosphere, including aerosol and cloud formation, tropospheric and stratospheric ozone budget, as well as the self-cleaning capacity of the atmosphere. Most chemistry-climate models use prescribed oceanic emissions (often derived from observations). These prescribed (offline) emissions generally do not respond to changes in local conditions. A process-level representation of the bi-directional oceanic emissions of trace gases remains challenging, mainly because the ocean biogeochemical<br />processes controlling the natural synthesis of these compounds in the seawater remain poorly understood. We present a new online air-sea exchange framework for the NCAR CESM2, with an observationally trained machine-learning emulator to couple the ocean biogeochemistry with the air-sea exchange. This machine-learning based approach so far has been tested for a number of important trace gases, including dimethyl sulfide (DMS), acetone, bromoform (CHBr 3 ), and dibromomethane (CH 2 Br 2 ), and the preliminary results are evaluated with observations around the globe. This new model framework is more skillful than the widely used top-down approaches for representing the seasonal/spatial variations and the annual means of atmospheric concentrations. The new approach improves the model predictability for the coupled earth system model, and can be used as a basis for investigating the future ocean emissions and feedbacks under climate change.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Reunión
Book
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/215122
An online air-sea exchange model framework for trace gases powered by machine- learning; American Geophysical Union Fall Meeting; San Francisco; Estados Unidos; 2019; 1-1
CONICET Digital
CONICET
url http://hdl.handle.net/11336/215122
identifier_str_mv An online air-sea exchange model framework for trace gases powered by machine- learning; American Geophysical Union Fall Meeting; San Francisco; Estados Unidos; 2019; 1-1
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/510957
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv American Geophysical Union
publisher.none.fl_str_mv American Geophysical Union
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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