Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments

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

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynamical model. In this study, we use convolutional neural networks (CNNs) to learn a mapping from model state variables to analysis increments, in order to showcase the feasibility of a data-driven model parameterization which can predict state-dependent model errors. We undertake this problem using an ice-ocean data assimilation system within the Seamless system for Prediction and EArth system Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory, which assimilates satellite observations of sea ice concentration every 5 days between 1982 and 2017. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are states and tendencies of sea ice concentration, sea-surface temperature, ice velocities, ice thickness, net shortwave radiation, ice-surface skin temperature, sea-surface salinity, as well as a land-sea mask. We find the CNN is able to make skillful predictions of the increments in both the Arctic and Antarctic and across all seasons, with skill that consistently exceeds that of a climatological increment prediction. This suggests that the CNN could be used to reduce sea ice biases in free-running SPEAR simulations, either as a sea ice parameterization or an online bias correction tool for numerical sea ice forecasts.

Original languageEnglish (US)
Article numbere2023MS003757
JournalJournal of Advances in Modeling Earth Systems
Issue number10
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences


  • climate modeling
  • data assimilation
  • machine learning
  • parameterization
  • prediction
  • sea ice


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