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

• Published in: Geophysical research letters Vol. 51; no. 3
• Main Authors: Gregory, William, Bushuk, Mitchell, Zhang, Yongfei, Adcroft, Alistair, Zanna, Laure
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 16.02.2024; Wiley
• Subjects: Artificial neural networks; Atmosphere; Atmospheric models; Bias; Climate; Climate models; Data assimilation; Data augmentation; Data collection; Errors; Fluid dynamics; Geophysical fluids; Hydrodynamics; Ice conditions; Ice forecasting; Ice observations; Initial conditions; Learning algorithms; Machine learning; modeling; Neural networks; Ocean models; Ocean temperature; Oceans; parameterization; Sea ice; Sea ice concentrations; Sea ice conditions; Sea ice data; Sea ice forecasting; Training

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 o...