Seasonal forecasting using the GenCast probabilistic machine learning model

Abstract

Machine-learnt weather prediction (MLWP) models are now well established as being competitive with conventional numerical weather prediction (NWP) models in the medium range. However, there is still much uncertainty as to how this performance extends to longer timescales, where interactions with slower components of the earth system become important. We take GenCast, a state-of-the-art probabilistic MLWP model, and apply it to the task of seasonal forecasting with prescribed sea surface temperature (SST), by providing anomalies persisted over climatology (GenCast-Persisted) or forcing with observations (GenCast-Forced). The forecasts are compared to the European Centre for Medium-Range Weather Forecasts seasonal forecasting system, SEAS5. Our results indicate that, despite being trained at short timescales, GenCast-Persisted produces much of the correct precipitation patterns in response to El Ni\~{n}o and La Ni\~{n}a events, with several erroneous patterns in GenCast-Persisted corrected with GenCast-Forced. The uncertainty in precipitation response, as represented by the ensemble, compares favourably to SEAS5. Whilst SEAS5 achieves superior skill in the tropics for 2-metre temperature and mean sea level pressure (MSLP), GenCast-Persisted achieves significantly higher skill in some areas in higher latitudes, including mountainous areas, with notable improvements for MSLP in particular; this is reflected in a higher correlation with the observed NAO index. Reliability diagrams indicate that GenCast-Persisted is overconfident compared to SEAS5, whilst GenCast-Forced produces well-calibrated seasonal 2-metre temperature predictions. These results provide an indication of the potential of MLWP models similar to GenCast for the `full' seasonal forecasting problem, where the atmospheric model is coupled to ocean, land and cryosphere models.