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Machine Learning for Online Sea Ice Bias Correction Within Global Ice‐Ocean Simulations
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2024
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Source: Geophysical Research Letters, 51(3)
[PDF-17.74 MB]
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Journal Title:Geophysical Research Letters
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Description: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.
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Source:Geophysical Research Letters, 51(3)
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DOI:
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ISSN:0094-8276;1944-8007;
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Rights Information:CC BY
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Compliance:Library
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