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

William Gregory, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft, Laure Zanna

Research output: Contribution to journalArticlepeer-review


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, 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.

Original languageEnglish (US)
Article numbere2023GL106776
JournalGeophysical Research Letters
Issue number3
StatePublished - Feb 16 2024


  • data assimilation
  • machine learning
  • modeling
  • neural networks
  • parameterization
  • sea ice

ASJC Scopus subject areas

  • Geophysics
  • General Earth and Planetary Sciences


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