GAP: a unified deep generative framework for emulating weather and climate

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models often suffer from long-term error accumulation, limiting their applicability to seasonal predictions and climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP delivers probabilistic weather forecasts competitive with state-of-the-art forecasting systems, while using its own assimilated initial states from a small fraction of observations. Also, it provides seasonal predictions with skill comparable to leading operational system. Finally, GAP produces stable millennial-scale climate simulations that capture variability from daily weather fluctuations to decadal oscillations.