Exploring non-Gaussian sea ice characteristics via observing system simulation experiments

The Arctic is warming at a faster rate compared to the globe on average, a phenomenon commonly referred to as Arctic amplification. Sea ice has been linked to Arctic amplification and has gathered attention recently due to the decline in summer sea ice extent. Data assimilation (DA) is the act of combining observations with prior forecasts to obtain a more accurate model state. Sea ice poses a unique challenge for DA because sea ice variables have bounded distributions, leading to non-Gaussian distributions. The non-Gaussian nature violates the Gaussian assumptions built into DA algorithms. This study presents different observing system simulation experiments (OSSEs), which will provide a data assimilating testing framework through experimental observation networks and synthetic observations. The OSSE framework will help determine the best data assimilation configuration for assimilating sea ice and snow observations. Findings indicate that assimilating both sea ice thickness and snow depth observations while omitting sea ice concentration observations produced the best sea ice and snow forecasts in our idealized experimental setup. A simplified DA experiment helped demonstrate that the DA solution is biased when assimilating sea ice concentration observations. The biased DA solution is related to the observation error distribution being a truncated normal distribution, and the assumed observation likelihood is normal for the DA method. Additional OSSEs show that using a non-Gaussian DA method does not alleviate the non-Gaussian effects of sea ice concentration observations, and assimilating sea ice surface temperatures has a positive impact on snow updates. Finally, it is shown that the perturbed sea ice model parameters used to create additional ensemble spread in the free forecasts lead to a year-long negative snow volume bias.