Learning bias corrections for climate models using deep neural operators

Aniruddha Bora, Khemraj Shukla, Shixuan Zhang, Bryce Harrop, Ruby Leung, George Em Karniadakis

Keywords: neural operator, nudging, E3SM

Abstract:

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-encoderdecoder
(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.