A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate

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 DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with no smoothing or drift. DLESyM 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 6th Climate Model Intercomparison Project. DLESyM, 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.

33 Pages, 14 figures

References in corpus (9)

• A ConvNet for the 2020s
• Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench
• Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
• AIFS -- ECMWF's data-driven forecasting system
• WeatherBench 2: A benchmark for the next generation of data-driven global weather models
• ACE: A fast, skillful learned global atmospheric model for climate prediction
• Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
• Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
• ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO${}_2$