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

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

doi:10.1029/2023gl106776

i

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

Supporting Files

2024
By Gregory, William ; Bushuk, Mitchell ; Zhang, Yongfei ; ...

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Journal Title:

Geophysical Research Letters
Personal Author:

Gregory, William ; Bushuk, Mitchell ; Zhang, Yongfei ; Adcroft, Alistair ; Zanna, Laure
NOAA Program & Office:

GFDL (Geophysical Fluid Dynamics Laboratory) ; OAR (Oceanic and Atmospheric Research)
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.
Keywords:

General Earth And Planetary Sciences Geophysics General Earth And Planetary Sciences General Earth And Planetary Sciences
Source:

Geophysical Research Letters, 51(3)
DOI:

https://doi.org/10.1029/2023gl106776
ISSN:

0094-8276 ; 1944-8007
Format:

PDF
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
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urn:sha-512:55791a670ec0a5479a35fd064b19915ad0f49fe739059adae41b4ce4fbc1c5defa795512dab82f7c26920d25be5a4a6b73a485248cad9edb584ad2213227728f
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https://repository.library.noaa.gov/view/noaa/63993/noaa_63993_DS1.pdf
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