Enhancing climate predictions with combination of dynamical model and
artificial neural network
Abstract
Dynamical models used in climate prediction often suffer from systematic
errors that can deteriorate their predictions. We propose a hybrid model
that combines both dynamical model and artificial neural network (ANN)
correcting model errors to improve climate predictions. We conducted a
series of experiments using the Modular Arbitrary-Order Ocean-Atmosphere
Model (MAOOAM) and trained the ANN with input from both atmospheric and
oceanic variables and output from analysis increments. Our results
demonstrate that the hybrid model outperforms the dynamical model in
terms of prediction skill for both atmospheric and oceanic variables
across different lead times. Furthermore, we conducted additional
experiments to identify the key factors influencing the prediction skill
of the hybrid model. We found that correcting both atmospheric and
oceanic errors yields the highest prediction skill while correcting only
atmospheric or oceanic errors has limited improvement.