Separation of Internal and Forced Variability of Climate Using a U‐Net
International audience Abstract The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system....
Published in: | Journal of Advances in Modeling Earth Systems |
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Main Authors: | , , , , |
Other Authors: | , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2024
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Online Access: | https://hal.science/hal-04650147 https://hal.science/hal-04650147/document https://hal.science/hal-04650147/file/J%20Adv%20Model%20Earth%20Syst%20-%202024%20-%20B%C3%B4ne%20-%20Separation%20of%20Internal%20and%20Forced%20Variability%20of%20Climate%20Using%20a%20U%E2%80%90Net.pdf https://doi.org/10.1029/2023ms003964 |
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English |
topic |
[SDU]Sciences of the Universe [physics] |
spellingShingle |
[SDU]Sciences of the Universe [physics] Bône, Constantin Gastineau, Guillaume Thiria, Sylvie Gallinari, Patrick Mejia, Carlos Separation of Internal and Forced Variability of Climate Using a U‐Net |
topic_facet |
[SDU]Sciences of the Universe [physics] |
description |
International audience Abstract The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term. |
author2 |
Océan et variabilité du climat (VARCLIM) Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN) Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X) Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X) Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) Criteo AI Lab Criteo Paris ANR-20-THIA-0003,SCAI Doctoral Programme,Programme Doctoral SCAI(2020) ANR-17-EURE-0006,IPSL-CGS,IPSL Climate graduate school(2017) ANR-19-JPOC-0003,ROADMAP,The Role of ocean dynamics and Ocean-Atmosphere interactions in Driving cliMAte variations and future Projections of impact-relevant extreme events(2019) |
format |
Article in Journal/Newspaper |
author |
Bône, Constantin Gastineau, Guillaume Thiria, Sylvie Gallinari, Patrick Mejia, Carlos |
author_facet |
Bône, Constantin Gastineau, Guillaume Thiria, Sylvie Gallinari, Patrick Mejia, Carlos |
author_sort |
Bône, Constantin |
title |
Separation of Internal and Forced Variability of Climate Using a U‐Net |
title_short |
Separation of Internal and Forced Variability of Climate Using a U‐Net |
title_full |
Separation of Internal and Forced Variability of Climate Using a U‐Net |
title_fullStr |
Separation of Internal and Forced Variability of Climate Using a U‐Net |
title_full_unstemmed |
Separation of Internal and Forced Variability of Climate Using a U‐Net |
title_sort |
separation of internal and forced variability of climate using a u‐net |
publisher |
HAL CCSD |
publishDate |
2024 |
url |
https://hal.science/hal-04650147 https://hal.science/hal-04650147/document https://hal.science/hal-04650147/file/J%20Adv%20Model%20Earth%20Syst%20-%202024%20-%20B%C3%B4ne%20-%20Separation%20of%20Internal%20and%20Forced%20Variability%20of%20Climate%20Using%20a%20U%E2%80%90Net.pdf https://doi.org/10.1029/2023ms003964 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISSN: 1942-2466 Journal of Advances in Modeling Earth Systems https://hal.science/hal-04650147 Journal of Advances in Modeling Earth Systems, 2024, 16 (6), ⟨10.1029/2023ms003964⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1029/2023ms003964 hal-04650147 https://hal.science/hal-04650147 https://hal.science/hal-04650147/document https://hal.science/hal-04650147/file/J%20Adv%20Model%20Earth%20Syst%20-%202024%20-%20B%C3%B4ne%20-%20Separation%20of%20Internal%20and%20Forced%20Variability%20of%20Climate%20Using%20a%20U%E2%80%90Net.pdf doi:10.1029/2023ms003964 WOS: 001241769600001 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1029/2023ms003964 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
16 |
container_issue |
6 |
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1810463955674464256 |
spelling |
ftuniparissaclay:oai:HAL:hal-04650147v1 2024-09-15T18:23:42+00:00 Separation of Internal and Forced Variability of Climate Using a U‐Net Bône, Constantin Gastineau, Guillaume Thiria, Sylvie Gallinari, Patrick Mejia, Carlos Océan et variabilité du climat (VARCLIM) Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN) Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X) Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X) Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)) Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) Criteo AI Lab Criteo Paris ANR-20-THIA-0003,SCAI Doctoral Programme,Programme Doctoral SCAI(2020) ANR-17-EURE-0006,IPSL-CGS,IPSL Climate graduate school(2017) ANR-19-JPOC-0003,ROADMAP,The Role of ocean dynamics and Ocean-Atmosphere interactions in Driving cliMAte variations and future Projections of impact-relevant extreme events(2019) 2024-06-10 https://hal.science/hal-04650147 https://hal.science/hal-04650147/document https://hal.science/hal-04650147/file/J%20Adv%20Model%20Earth%20Syst%20-%202024%20-%20B%C3%B4ne%20-%20Separation%20of%20Internal%20and%20Forced%20Variability%20of%20Climate%20Using%20a%20U%E2%80%90Net.pdf https://doi.org/10.1029/2023ms003964 en eng HAL CCSD American Geophysical Union info:eu-repo/semantics/altIdentifier/doi/10.1029/2023ms003964 hal-04650147 https://hal.science/hal-04650147 https://hal.science/hal-04650147/document https://hal.science/hal-04650147/file/J%20Adv%20Model%20Earth%20Syst%20-%202024%20-%20B%C3%B4ne%20-%20Separation%20of%20Internal%20and%20Forced%20Variability%20of%20Climate%20Using%20a%20U%E2%80%90Net.pdf doi:10.1029/2023ms003964 WOS: 001241769600001 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 1942-2466 Journal of Advances in Modeling Earth Systems https://hal.science/hal-04650147 Journal of Advances in Modeling Earth Systems, 2024, 16 (6), ⟨10.1029/2023ms003964⟩ [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2024 ftuniparissaclay https://doi.org/10.1029/2023ms003964 2024-08-30T01:48:44Z International audience Abstract The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term. Article in Journal/Newspaper North Atlantic Archives ouvertes de Paris-Saclay Journal of Advances in Modeling Earth Systems 16 6 |