Improving snow depth simulations on Arctic Sea ice by assimilating a passive microwave-derived record

Abstract

The snow depth can modulate sea ice changes and is a necessary input parameter to obtain altimeter-derived sea ice thickness values. First, we determine the optimal model parameters for simulating snow depth based on a two-layer snow depth model. Then, we use the particle filter (PF) approach to assimilate satellite-derived snow depth observations to improve the snow depth simulations. Next, we generate daily snow depth estimates from 2012 to 2020 at a 50-km resolution. With the use of three airborne Operation IceBridge (OIB) datasets (i.e., the NSIDC OIB quick look product, NSIDC OIB L4 product and NOAA OIB product), this work reveals that compared to the original simulated snow depths, the snow depth estimates are improved, with root mean square error (RMSE) decreases of 0.8 cm (12%), 1.3 cm (22%) and 1.1 cm (15%), respectively, corresponding to the three OIB products. With the use of Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy and Snow and Ice Mass Balance Array (SIMBA) buoy data, it can be verified that the application of PF improves snow depth simulations in the Central Arctic. The variations in the monthly and seasonal snow depth estimates retrieved from the proposed method agree well with those in the estimates retrieved from two other existing algorithms. Based on the presented snow depth estimates, we can perform long-term snow depth and sea ice analysis. Snow depth estimates improve the understanding of Arctic environmental change and promote the future development of sea ice models.