Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization

Main modes of atmospheric variability exert a significant influence on weather and climate at local and regional scales on all time scales. However, their past changes and variability over the instrumental record are not well constrained due to limited availability of observations, particularly over...

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Detalhes bibliográficos
Autores: Jaume Santero, Fernando, Barriopedro Cepero, David, García Herrera, Ricardo Francisco, Luterbacher, Jürg
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/72939
Acesso em linha:https://hdl.handle.net/20.500.14352/72939
Access Level:acceso abierto
Palavra-chave:52
Winter precipitation
Field reconstruction
Experimental-design
Subtropical highs
East Atlantic
Icelandic low
Nao index
Oscillation
Temperature
Climate
Física atmosférica
2501 Ciencias de la Atmósfera
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oai_identifier_str oai:docta.ucm.es:20.500.14352/72939
network_acronym_str ES
network_name_str España
repository_id_str
spelling Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimizationJaume Santero, FernandoBarriopedro Cepero, DavidGarcía Herrera, Ricardo FranciscoLuterbacher, Jürg52Winter precipitationField reconstructionExperimental-designSubtropical highsEast AtlanticIcelandic lowNao indexOscillationTemperatureClimateFísica atmosférica2501 Ciencias de la AtmósferaMain modes of atmospheric variability exert a significant influence on weather and climate at local and regional scales on all time scales. However, their past changes and variability over the instrumental record are not well constrained due to limited availability of observations, particularly over the oceans. Here we couple a reconstruction method with an evolutionary algorithm to yield a new 1° × 1° optimized reconstruction of monthly North Atlantic sea level pressure since 1750 from a network of meteorological land and ocean observations. Our biologically inspired optimization technique finds an optimal set of weights for the observing network that maximizes the reconstruction skill of sea level pressure fields over the North Atlantic Ocean, bringing significant improvements over poorly sampled oceanic regions, as compared to non-optimized reconstructions. It also reproduces realistic variations of regional climate patterns such as the winter North Atlantic Oscillation and the associated variability of the subtropical North Atlantic high and the subpolar low pressure system, including the unprecedented strengthening of the Azores high in the second half of the twentieth century. We find that differences in the winter North Atlantic Oscillation indices are partially explained by disparities in estimates of its Azores high center. Moreover, our reconstruction also shows that displacements of the summer Azores high center toward the northeast coincided with extremely warm events in western Europe including the anomalous summer of 1783. Overall, our results highlight the importance of improving the characterization of the Azores high for understanding the climate of the Euro-Atlantic sector and the added value of artificial intelligence in this avenue.American Meteorological SocietyUniversidad Complutense de Madrid20222022-06-0120222022-06-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/72939reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/729392026-06-02T12:44:21Z
dc.title.none.fl_str_mv Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
title Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
spellingShingle Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
Jaume Santero, Fernando
52
Winter precipitation
Field reconstruction
Experimental-design
Subtropical highs
East Atlantic
Icelandic low
Nao index
Oscillation
Temperature
Climate
Física atmosférica
2501 Ciencias de la Atmósfera
title_short Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
title_full Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
title_fullStr Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
title_full_unstemmed Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
title_sort Monthly North Atlantic Sea level pressure reconstruction back to 1750 CE using artificial intelligence optimization
dc.creator.none.fl_str_mv Jaume Santero, Fernando
Barriopedro Cepero, David
García Herrera, Ricardo Francisco
Luterbacher, Jürg
author Jaume Santero, Fernando
author_facet Jaume Santero, Fernando
Barriopedro Cepero, David
García Herrera, Ricardo Francisco
Luterbacher, Jürg
author_role author
author2 Barriopedro Cepero, David
García Herrera, Ricardo Francisco
Luterbacher, Jürg
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 52
Winter precipitation
Field reconstruction
Experimental-design
Subtropical highs
East Atlantic
Icelandic low
Nao index
Oscillation
Temperature
Climate
Física atmosférica
2501 Ciencias de la Atmósfera
topic 52
Winter precipitation
Field reconstruction
Experimental-design
Subtropical highs
East Atlantic
Icelandic low
Nao index
Oscillation
Temperature
Climate
Física atmosférica
2501 Ciencias de la Atmósfera
description Main modes of atmospheric variability exert a significant influence on weather and climate at local and regional scales on all time scales. However, their past changes and variability over the instrumental record are not well constrained due to limited availability of observations, particularly over the oceans. Here we couple a reconstruction method with an evolutionary algorithm to yield a new 1° × 1° optimized reconstruction of monthly North Atlantic sea level pressure since 1750 from a network of meteorological land and ocean observations. Our biologically inspired optimization technique finds an optimal set of weights for the observing network that maximizes the reconstruction skill of sea level pressure fields over the North Atlantic Ocean, bringing significant improvements over poorly sampled oceanic regions, as compared to non-optimized reconstructions. It also reproduces realistic variations of regional climate patterns such as the winter North Atlantic Oscillation and the associated variability of the subtropical North Atlantic high and the subpolar low pressure system, including the unprecedented strengthening of the Azores high in the second half of the twentieth century. We find that differences in the winter North Atlantic Oscillation indices are partially explained by disparities in estimates of its Azores high center. Moreover, our reconstruction also shows that displacements of the summer Azores high center toward the northeast coincided with extremely warm events in western Europe including the anomalous summer of 1783. Overall, our results highlight the importance of improving the characterization of the Azores high for understanding the climate of the Euro-Atlantic sector and the added value of artificial intelligence in this avenue.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-06-01
2022
2022-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/72939
url https://hdl.handle.net/20.500.14352/72939
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
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
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Meteorological Society
publisher.none.fl_str_mv American Meteorological Society
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869419210226008064
score 15,300724

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