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
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2023GL106776

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. Plain Language Summary Climate models contain errors which often lead to predictions which are consistently out of agreement with what we observe in reality. In some cases we know the origin of these errors, for example, predicting too much sea ice as a result of consistently cool ocean temperatures. In reality, however, there are typically numerous model errors interacting across the atmosphere, ocean and sea ice, and to manually parse through large volumes of climate model data in an attempt to isolate these errors in time and space is highly impractical. Machine learning on the other hand is a framework which is well‐suited to this task. In this work we take a machine learning model which, at any given moment, ingests information about a climate model's atmosphere, ocean and sea ice conditions, and predicts how much error there is in the climate model's representation of sea ice, without seeing any actual sea ice observations. We use this to adjust the sea ice conditions in one particular climate model as it is running forward in time making predictions, and we find that this significantly reduces the model's sea ice errors globally. Key Points We use a convolutional neural network (CNN) to perform online sea ice bias correction within global ice‐ocean simulations The CNN systematically reduces the free‐running model bias in both the Arctic and Antarctic The online performance can be improved by combining CNN and data assimilation corrections in order to iteratively augment the training data