Sea Ice Albedo Bounded Data Assimilation and Its Impact on Modeling: A Regional Approach

Abstract. We conducted a perfect model experiment using Icepack, a one-dimensional single-column sea ice model, to assess the potential of data assimilation (DA) to improve predictions of the mean sea ice state through the incorporation of sea ice albedo (SIAL) observations. One ensemble member is designated as the TRUTH, and synthetic observations drawn from it are assimilated into the remaining ensemble members. DA is carried out using the Data Assimilation Research Testbed (DART) with a bounded Quantile Conserving Ensemble Filtering Framework (QCEFF), which accounts for the bounded nature of sea ice variables. Icepack ensembles were spun-up for four Arctic locations based on small perturbations to atmospheric forcing. Results show that assimilating SIAL yields comparable or superior performance to more commonly assimilated observables such as sea ice concentration (SIC) and thickness (SIT) in three-quarters of the Arctic regions studied, and across all regions when observational uncertainty in SIAL is reduced below estimates from the current literature. These findings underscore the value of leveraging existing SIAL observations and expanding their temporal and spatial coverage in the Arctic. Furthermore, the study highlights the critical need to better constrain the observational uncertainty of SIAL. Enhanced observational networks would provide the necessary validation data, enabling more accurate uncertainty characterization and improved sea ice forecasts in a rapidly evolving polar climate.