Learning bias corrections for climate models using deep neural operators
This work addresses efficiency and accuracy improvements in climate simulations for researchers, though it appears incremental as it builds on existing nudging methods with a neural operator approach.
The study tackled the computational burden of climate modeling by replacing the traditional relaxation-based nudging correction with a Deep Operator Network (DeepONet) combined with an auto-encoder-decoder to learn bias corrections from low-resolution simulations to reanalyzed data, showing good agreement with the E3SMv2 model.
Numerical simulation for climate modeling resolving all important scales is a computationally taxing process. Therefore, to circumvent this issue a low resolution simulation is performed, which is subsequently corrected for bias using reanalyzed data (ERA5), known as nudging correction. The existing implementation for nudging correction uses a relaxation based method for the algebraic difference between low resolution and ERA5 data. In this study, we replace the bias correction process with a surrogate model based on the Deep Operator Network (DeepONet). DeepONet (Deep Operator Neural Network) learns the mapping from the state before nudging (a functional) to the nudging tendency (another functional). The nudging tendency is a very high dimensional data albeit having many low energy modes. Therefore, the DeepoNet is combined with a convolution based auto-encoder-decoder (AED) architecture in order to learn the nudging tendency in a lower dimensional latent space efficiently. The accuracy of the DeepONet model is tested against the nudging tendency obtained from the E3SMv2 (Energy Exascale Earth System Model) and shows good agreement. The overarching goal of this work is to deploy the DeepONet model in an online setting and replace the nudging module in the E3SM loop for better efficiency and accuracy.