  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.
