Machine learning for the physics of climate

Climate science has been revolutionized by the combined effects of an exponential growth in computing power, which has enabled more sophisticated and higher-resolution simulations to be made of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit. Big data and associated algorithms, coalesced under the field of machine learning (ML), offer the opportunity to study the physics of the climate system in ways, and with an amount of detail, that were previously infeasible. Additionally, ML can ask causal questions to determine whether one or more variables cause or affect one or more outcomes and improve prediction skills beyond classical limits. Furthermore, when paired with modelling experiments or robust research on model parameterizations, ML can accelerate computations, increasing accuracy and generating very large ensembles with a fraction of the computational cost of traditional systems. In this Review, we outline the accomplishments of ML in climate physics. We discuss how ML has been used to tackle long-standing problems in the reconstruction of observational data, representation of sub-grid-scale phenomena and climate (and weather) prediction. Finally, we consider the benefits and major challenges of exploiting ML in studying complex systems. Artificial intelligence techniques, specifically machine learning, are being increasingly applied to climate physics owing to the growing availability of big data and increasing computational power. This Review focuses on key results obtained with machine learning in reconstruction, sub-grid-scale parameterization, and weather or climate prediction. Advances in machine learning for climate physics have extended observational data records in time, space and observables, making them longer, more global and more complete. Innovative approaches that use machine learning to learn parameterizations from data or high-resolution simulations could contribute to hybrid models that will be able to provide more detailed, physically consistent simulations of the climate system. The use of machine learning has enabled classical predictability barriers to be broken in forecasts ranging from weather to phenomena at interannual scales such as the El Niño Southern Oscillation, leading to higher forecast skill at larger lead times using orders-of-magnitude less computing resource. Advances in machine learning for climate physics have extended observational data records in time, space and observables, making them longer, more global and more complete. Innovative approaches that use machine learning to learn parameterizations from data or high-resolution simulations could contribute to hybrid models that will be able to provide more detailed, physically consistent simulations of the climate system. The use of machine learning has enabled classical predictability barriers to be broken in forecasts ranging from weather to phenomena at interannual scales such as the El Niño Southern Oscillation, leading to higher forecast skill at larger lead times using orders-of-magnitude less computing resource.