Sea ice albedo bounded data assimilation and its impact on modeling: a regional approach

Abstract. We conducted a series of perfect model experiments 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 in addition to sea ice concentration (SIC) and sea ice thickness (SIT) 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) Quantile Conserving Ensemble Filtering Framework (QCEFF), which accounts for the bounded nature of sea ice variables. Icepack ensembles were spun-up for five Arctic locations based on small perturbations to atmospheric forcing. Results show that assimilating SIAL has the potential to improve reanalysis products when concurrently assimilated with the more commonly assimilated observables SIC and SIT at three of the five discrete points examined in the Arctic Ocean, when observational uncertainty in SIAL is reduced below current literature estimates. 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 improving sea ice forecasts in a rapidly evolving polar climate.