Exploring Non-Gaussian Sea Ice Characteristics via Observing System Simulation Experiments

Abstract. The Arctic is warming at a faster rate compared to the globe on average, commonly referred to as Arctic amplification. Sea ice has been linked to Arctic amplification and 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 Gaussian assumptions built into DA algorithms. This study configures different observing system simulated experiments (OSSEs) to find the optimal sea ice and snow observation subset for assimilation to produce the most accurate analyses and forecasts. Findings indicate that not assimilating sea ice concentration observations while assimilating snow depth observation produced the best sea ice and snow forecasts. 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-parametric DA method does not alleviate the non-Gaussian effects of the sea ice concentration observations, and assimilating sea ice surface temperatures have a positive impact on snow updates. Lastly, it is shown that perturbed sea ice model parameters, used to create additional ensemble spread in the free forecasts, lead to a year-long negative snow volume bias.