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

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Constantin Bône, Guillaume Gastineau, Sylvie Thiria, Patrick Gallinari, Carlos Mejia
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2024
• Subjects: climate change; U‐Net; noise to noise; climate model; internal and forced variability; artificial intelligence; Physical geography; GB3-5030; Oceanography; GC1-1581; North Atlantic
• Online Access: https://doi.org/10.1029/2023MS003964; https://doaj.org/article/2aa31b66929846b887c30b9387f47c5f

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.