We examined the effect of snow-ice formation on SnowModel-LG snow depth and density products. We coupled SnowModel-LG, a modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D thermodynamic sea ice model, to create SnowModel-LG_HS. Pan-Arctic model simulations spanned from 1 August 1980 through 31 July 2022. In SnowModel-LG_HS, domain average snow depth decreased by 20%, and snow density increased by 2% when compared to SnowModel-LG, with largest differences in the Atlantic sector. Averaged across the CryoSat-2 era (2011–2022), domain average April sea ice thickness retrievals from CryoSat-2 decreased by 7.7% when snow-ice was accounted for. Evaluation of SnowModel-LG HS against snow depth, snow-ice, and sea ice thickness observations highlighted the importance of snow redistribution over deformed sea ice. The findings suggest that neglecting snow and sea ice interactions in models can lead to substantial overestimation of snow depth over level ice.

This study quantified the effect of snow-ice formation on SnowModel-LG snow depth and density products. We coupled SnowModel-LG, a modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. We compared snow depth and density from the coupled product (SnowModel-LG_HS) to the original outputs of SnowModel-LG. In SnowModel-LG_HS, domain average snow depth decreased by 22%, and snow density increased by 2% when compared to SnowModel-LG. The differences were much larger in the Atlantic sector. Our simulations suggest that when snow-on-sea-ice models account for snow-ice formation, snow depth can be remarkably reduced. Sea ice thickness retrievals from CryoSat-2 were guided by both snow products. Averaged across the CryoSat-2 era (2011-2022), domain average sea ice thickness retrievals decreased by 10% when snow-ice was accounted for.

With a unique biogeophysical signature relative to other freshwater sources, meltwater from glaciers plays a crucial role in the hydrological and ecological regime of high latitude coastal areas. Today, as glaciers worldwide exhibit persistent negative mass balance, glacier runoff is changing in both magnitude and timing, with potential downstream impacts on infrastructure, ecosystems, and ecosystem resources. However, runoff trends may be difficult to detect in coastal systems with large precipitation variability. Here, we use the coupled energy balance and water routing model SnowModel-HydroFlow to examine changes in timing and magnitude of runoff from the western Juneau Icefield in Southeast Alaska between 1980 to 2016. We find that under sustained glacier mass loss (-0.57 +/-0.12 m w.e. a-1), several hydrological variables related to runoff show increasing trends. This includes annual and spring glacier ice melt volumes (+10% and +16% decade-1) which, because of higher proportions of precipitation, translate to smaller increases in glacier runoff (+3% and +7% decade-1) and total watershed runoff (+1.4% and +3% decade-1). These results suggest that the western Juneau Icefield watersheds are still in an increasing glacier runoff period prior to reaching ‘peak water.’ In terms of timing, we find that maximum glacier ice melt is occurring earlier (2.5 days decade-1), indicating a change in the source and quality of freshwater being delivered downstream in the early summer. Our findings highlight that even in maritime climates with large precipitation variability, high latitude coastal watersheds are experiencing hydrological regime change driven by ongoing glacier mass loss.

We evaluate the effects of rapidly changing Arctic sea ice conditions on sea salt aerosol (SSA) produced by oceanic wave-breaking and the sublimation of wind-lofted salty blowing snow on sea ice. We use the GEOS-Chem chemical transport model to assess the influence of changing extent of the open ocean, multi-year sea ice, first-year sea ice (FYI), and snow depths on SSA emissions for 1980-2017. We combine snow depths from the Lagrangian snow-evolution model (SnowModel-LG) together with an empirically-derived snow salinity function of snow depth to derive spatially and temporally varying snow surface salinity over Arctic FYI. We find that snow surface salinity on Arctic sea ice is increasing at a rate of ~30% decade-1 and SSA emissions are increasing at a rate of 7-9% decade-1 during the cold season (November – April). As a result, simulated SSA mass concentrations over the Arctic increased by 8-12% decade-1 in the cold season for 1980-2017. Blowing snow SSA accounts for more than 75% of this increase. During the warm season (May – October), sea ice loss results in a 12-14% decade-1 increase in SSA emissions due to increasing open ocean emissions. Observations of SSA mass concentrations at Alert, Canada display positive trends during the cold season (10-12% decade-1), consistent with our pan-Arctic simulations. During fall, Alert observations show a negative trend (-18% decade-1), due to locally decreasing wind speeds and thus lower open ocean emissions. These significant changes in SSA concentrations could potentially affect past and future bromine explosions and Arctic climate feedbacks.