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 language | English (US) |
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Article number | e2023GL106776 |
Journal | Geophysical Research Letters |
Volume | 51 |
Issue number | 3 |
DOIs | |
State | Published - 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