Transfer learning-based hybrid machine learning in single-column model of AFES v4

Abstract. The validity of a transfer learning-based hybrid machine learning (ML) in single-column model (SCM) of Atmospheric general circulation For the Earth Simulator (AFES) version 4 is examined. The results of the SCM with and without hybrid ML using transfer learning (i.e., the original and hybrid models) are compared against observational datasets and they are evaluated in tropical and midlatitude intensive observation periods. The hybrid model produces better results compared with the original model in all experiments, even when the period of training data is shifted from the target period. However, seasonality is more important for the midlatitude cases than the tropical cases, i.e., training data from the same month is necessary, even though the year of training data is different. The ML component of the hybrid model successfully corrects the model’s bias, but the correction for temperature is greater than that for humidity, especially in the amplitude rather than in the phase. If the temporal and spatial variability is significant, the ML component fails to correct the biases. Analysis of the bias components reveals that the hybrid model can reduce the mean state bias, but it cannot reduce the high-frequency components of the biases. The hybrid model slightly improves precipitation depending on the cases but does not improve surface heat fluxes that cause biases in the low-level. This implies that further synchronisation is needed for surface heat fluxes. In conclusion, transfer learning-based hybrid ML can better simulate atmospheric variability by reducing mean state bias when the appropriate training data are used. Due to this advantage, the model has the potential to improve the prediction skill of numerical models over longer periods with limited training data.