Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Sonnewald, Maike, Lguensat, Redouane
• Other Authors: Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), ANR-17-MPGA-0010,HRMES,High-Resolution Modeling of the Earth System(2017)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2021
• Subjects: oceanography; transparent machine learning; climate modeling; explainable and interpretable AI; global heating; North Atlantic Ocean; [SDU]Sciences of the Universe [physics]; [SDU.STU]Sciences of the Universe [physics]/Earth Sciences; north atlantic current; North Atlantic
• Online Access: https://insu.hal.science/insu-03721909; https://insu.hal.science/insu-03721909/document; https://insu.hal.science/insu-03721909/file/J%20Adv%20Model%20Earth%20Syst%20-%202021%20-%20Sonnewald%20-%20Revealing%20the%20Impact%20of%20Global%20Heating%20on%20North%20Atlantic%20Circulation%20Using.pdf; https://doi.org/10.1029/2021MS002496

International audience The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning (ML) method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled data set is engineered using an explicitly interpretable equation transform and k-means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO 2 quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf Stream and an eastward shift in the North Atlantic Current. If CO 2 is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step toward trustworthy ML called for within oceanography and beyond, as its predictions are physically tractable.