Separation of Internal and Forced Variability of Climate Using a U‐Net

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Bône, Constantin, Gastineau, Guillaume, Thiria, Sylvie, Gallinari, Patrick, Mejia, Carlos
• Other Authors: 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
• Language: English
• Published: HAL CCSD 2024
• Subjects: [SDU]Sciences of the Universe [physics]; North Atlantic
• 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

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.