Machine Learning for Online Sea Ice Bias Correction Within Global Ice‐Ocean Simulations

• Published in: Geophysical Research Letters
• Main Authors: William Gregory, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft, Laure Zanna
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2024
• Subjects: sea ice; machine learning; data assimilation; modeling; parameterization; neural networks; Geophysics. Cosmic physics; QC801-809; Sea ice
• Online Access: https://doi.org/10.1029/2023GL106776; https://doaj.org/article/c1f2f3c4aff14bcaac00ef9cebfba463

Abstract In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice‐ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023, https://doi.org/10.1029/2023ms003757) 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 novel sea ice data augmentation approach. This approach applies sequential CNN and DA corrections to a new simulation over the training period, which then provides a new training data set 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.