Prediction Beyond the Medium Range With an Atmosphere‐Ocean Model That Combines Physics‐Based Modeling and Machine Learning

Abstract This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics‐based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022, https://doi.org/10.1029/2021ms002712 ), which tested the approach for short‐ and medium‐range weather prediction, and the work of Arcomano et al. (2023, https://doi.org/10.1029/2022gl102649 ), which investigated its potential for climate modeling. The hybrid model used for the forecast experiments of the paper is based on the low‐resolution, simplified parameterization atmospheric general circulation model SPEEDY. In addition to the hybridized prognostic variables of SPEEDY, the model has three purely ML‐based prognostic variables: the 6 hr cumulative precipitation, the sea surface temperature, and the heat content of the top 300 m deep layer of the ocean (a new addition compared to the model used in Arcomano et al., 2023, https://doi.org/10.1029/2022gl102649 ). The model has skill in predicting the El Niño cycle and its global teleconnections with precipitation for 3–7 months depending on the season. The model captures equatorial variability of the precipitation associated with Kelvin and Rossby waves and MJO. Predictions of the precipitation in the equatorial region have skill for 15 days in the East Pacific and 11.5 days in the West Pacific. Though the model has low spatial resolution, for these tasks it has prediction skill comparable to what has been published for high‐resolution, purely physics‐based, conventional, operational forecast models.