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

1 Scopus citations

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.

Original languageEnglish (US)
Article numbere2023GL106776
JournalGeophysical Research Letters
Volume51
Issue number3
DOIs
StatePublished - Feb 16 2024

All Science Journal Classification (ASJC) codes

  • Geophysics
  • General Earth and Planetary Sciences

Keywords

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

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