Model Biases in Simulating Extreme Sea Ice Loss Associated With the Record January 2022 Arctic Cyclone

In January 2022, the strongest Arctic cyclone on record resulted in a record weekly loss in sea ice cover in the Barents‐Kara‐Laptev seas. While ECMWF operational forecasts skillfully predicted the cyclone, the loss in sea ice was poorly predicted. We explore the ocean's response to the cyclone using observations from an Argo float that was profiling in the region, and investigate model biases in simulating the observed sea ice loss in a fully coupled GCM. The observations showed changes over the whole ocean column in the Barents Sea after the passage of the storm, cooling and mixing with enough implied heat release to melt roughly 1 m of sea ice. We replicate the observed cyclone in the GCM by nudging the model's winds to observations above the boundary layer. In these simulations, the associated loss of sea ice is only about 10%–15% of the observed loss, and the ocean exhibits very small changes in response to the cyclone. With the use of a simple 1‐D ice‐ocean model, we find that the overly strong ocean stratification in the GCM may be a significant source of model bias in its simulated response to the cyclone. However, even initialized with observed stratification profiles, the 1‐D model also underestimated mixing and sea ice melt relative to the observations. Plain Language Summary Extreme storms in the Arctic can significantly impact the ocean and sea ice state. In January 2022, the strongest Arctic storm on record resulted in a record loss of sea ice. The storm was well predicted by the ECMWF operational forecasts, yet the loss of sea ice was not. Here we further study the impact that the storm had on the ocean, and how well a fully coupled global climate model simulates the observed response in sea ice and ocean to the storm. We do this by nudging the winds in the model to observations. In observations, the ocean responded to the storm by cooling and mixing to full depth in the Barents Sea, releasing enough heat to melt a significant amount of sea ice. In contrast, the model's simulated sea ice and ocean response to the storm is much smaller than estimated in observations. The model's ocean stratification prior to the storm is significantly stronger than observed and is likely a source of bias, which we confirm with the use of a simple one dimensional model. Key Points An Argo float showed cooling and mixing in the Barents Sea during a record Arctic cyclone, accounting for the associated record sea ice loss A coupled GCM with winds nudged to observ