A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate
Nathaniel Cresswell‐Clay, Bowen Liu, Dale R. Durran, Zihui Liu, Zachary I. Espinosa, Raul A. Moreno, Matthias Karlbauer
Abstract
Abstract A key challenge for computationally intensive state‐of‐the‐art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce Deep Learning Earth System Model (DL ESy M), a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000‐year periods with minimal smoothing and no drift. DL ESy M simulations equal or exceed key metrics of seasonal and interannual variability—such as tropical cyclogenesis over the range of observed intensities, the cycle of the Indian Summer monsoon, and the climatology of mid‐latitude blocking events—when compared to historical simulations from four leading models from the sixth Climate Model Intercomparison Project. DL ESy M, trained on both historical reanalysis data and satellite observations, is an accurate, highly efficient model of the coupled Earth system, empowering long‐range sub‐seasonal and seasonal forecasts while using a fraction of the energy and computational time required by traditional models.