Machine learning for online sea ice bias correction within global ice-ocean simulations

TL;DR

This paper presents a machine learning approach using CNNs for online bias correction of sea ice concentration in global ice-ocean models, showing systematic improvements and potential for real-time forecasting.

Contribution

It introduces an iterative data augmentation method to refine CNN-based bias correction in sea ice modeling, enhancing accuracy over previous approaches.

Findings

Systematic bias reduction in sea ice concentration

Significant improvement with data augmentation

Potential for real-time sea ice forecast correction

Abstract

In this study we perform online sea ice bias correction within a GFDL global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023) for the purpose of predicting sea ice concentration (SIC) data assimilation (DA) increments. An initial implementation of the CNN shows systematic improvements in SIC biases relative to the free-running model, however large summertime errors remain. We show that these residual errors can be significantly improved with a data augmentation approach, in which sequential CNN and DA corrections are applied to a new simulation over the training period. This then provides a new training data set with which to refine the weights of the initial network. We propose that this machine-learned correction scheme could be utilized for generating improved initial conditions, and also for real-time sea ice bias correction within seasonal-to-subseasonal sea ice forecasts.