Glen E. Liston

Blowing snow plays a key role in the surface mass and energy budgets of polar regions and can be a significant source of water vapor to the atmosphere. In this study, we optimize the algorithm for detecting blowing snow from NASA's Ice, Cloud, and land Elevation Satellite 2 (ICESat‐2) satellite for use over Arctic sea ice. We analyze six months (November 2019 through April 2020) of observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign together with 612 nearly coincident (within 100 km) ICESat‐2 overpasses to evaluate the ICESat‐2 detection algorithm and inferred blowing snow properties. Both ICESat‐2 and MOSAiC suggest a blowing snow occurrence frequency of 17% during the period of study. Blowing snow particle number and inferred from ICESat‐2 show broad agreement with in situ observations made at 10 m above the surface during MOSAiC but are often well below observations made at 8 cm. Within a 100 km radius around the MOSAiC observatory, we find a cumulative blowing snow sublimation of 2.38 cm snow‐water‐equivalent (SWE), comparable to MOSAiC (2.56 cm SWE) and SnowModel‐LG (2.35 cm SWE) estimates. This suggests that blowing snow sublimation removed 22%–33% of snowfall during MOSAiC. Across the central Arctic, ICESat‐2 and SnowModel‐LG indicate blowing snow occurrence frequencies as high as 18%–25%, with cumulative blowing snow sublimation fluxes (1.74–1.79 cm SWE) removing 16%–17% of snowfall. These findings highlight the importance of blowing snow sublimation for the Arctic snow on sea ice budget.

Blowing snow plays a key role in the surface mass and energy budgets of polar regions and can be a significant source of water vapor to the atmosphere. In this study, we optimize the algorithm for detecting blowing snow from NASA’s Ice, Cloud and land Elevation Satellite 2 (ICESat-2) satellite for use over Arctic sea ice. We analyze six months (November 2019 through April 2020) of observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign together with 155 nearly coincident (within 25 km) ICESat-2 overpasses to evaluate the ICESat-2 detection algorithm and inferred blowing snow properties. Both ICESat-2 and MOSAiC suggest a blowing snow occurrence frequency of 17-18% during the period of study. Blowing snow particle number (mass) inferred from ICESat-2 captures 60% (72%) of the spread in observations made at 10 m during MOSAiC. Based on daily ICESat-2 retrievals within a broader 200 km radius around MOSAiC, we find a cumulative blowing snow sublimation of 2.57 cm snow-water-equivalent (SWE), comparable to MOSAiC (2.38 cm SWE) and SnowModel-LG (2.40 cm SWE) estimates. This suggests that blowing snow sublimation removed 22-36% of snowfall during MOSAiC. Across the central Arctic, ICESat-2 and SnowModel-LG indicate blowing snow occurrence frequencies as high as 18-25%, with cumulative blowing snow sublimation fluxes (1.63-1.84 cm SWE) removing 16-21% of snowfall. These findings highlight the importance of blowing snow sublimation for the Arctic snow on sea ice budget.

For non-hibernating species within temperate climates, survival during severe winter weather often depends on individuals’ behavioral response and available refugia. Identifying refugia habitat that sustains populations during adverse winter conditions can be difficult and complex. This study provides an example of how modeled, biologically relevant snow and weather information can help identify important relationships between habitat selection and dynamic winter landscapes using greater sage-grouse (Centrocercus urophasianus, hereafter “sage-grouse”) as a model species. We evaluated whether sage-grouse responded to weather conditions in two ways: through (1) positive selection for refugia habitat to minimize adverse weather exposure, or (2) lowered activity level to minimize thermoregulation and locomotion expense. Our results suggested that sage-grouse respond to winter weather conditions by seeking refugia rather than changing daily activity levels. During periods of lower wind chill temperatures and greater wind speeds, sage-grouse selected areas with sheltered aspects and greater sagebrush (Artemisia spp.) cover. Broadly, sage-grouse selected winter home ranges in sagebrush shrublands characterized by higher wind chill temperatures, greater wind speeds, and greater blizzarding conditions. However, within these home ranges, sage-grouse specifically selected habitats with greater above-snow sagebrush cover, lower wind speeds, and lower blizzarding conditions. Our study underscores the importance of examining habitat selection at narrower temporal scales than entire seasons and demonstrates the value of incorporating targeted weather variables that wholistically synthesize winter conditions. This research allows identification of refugia habitat that sustain populations during winter disproportionate to their spatial extent or frequency of use, facilitating more targeted management and conservation efforts.

In order to study potential impacts arising from climate change, future projections of numerical model output often must be calibrated to be comparable to observations. Rather than calibrating the data values themselves, we propose a novel statistical calibration method for extremes that assumes there exists a linear relationship between parameters associated with model output and parameters associated with observations. This approach allows us to capture uncertainty in both parameter estimates and the linear calibration, which we achieve via bootstrap. To focus on extreme behavior, we assume both model output and observations have distributions composed of a mixture model combining a Weibull distribution with a generalized Pareto distribution for the tail. A simulation study shows good coverage rates. We apply the method to project future daily-averaged river runoff at the Purgatoire River in southeastern Colorado.

We examined snow sinks caused by snow and sea ice interactions (snow-ice formation and sub-parcel snow mass redistribution) on level sea ice in the Arctic. 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 SMLG_HS. Pan-Arctic model simulations spanned from 1 August 1980 through 31 July 2022. Evaluation of SMLG_HS against snow depth, snow-ice, and sea ice thickness observations highlighted the importance of snow mass changes due to snow redistribution processes. Without accounting for these processes, snow on level ice was overestimated, resulting in underestimation of level ice thickness and overestimation of snow-ice thickness. We show that snow depth on level ice needs to be reduced by 40 % to simulate both snow and level ice thicknesses realistically. Guided by this result, we re-evaluated snow-ice formation. Our findings suggest that, on average over the whole Arctic, snow sinks on level sea ice can reduce snow depth by 3.5 % and increase snow density by 2.3 %.

As Arctic sea ice and its overlying snow cover thin, more light penetrates into the ice and upper ocean, shifting the phenology of algal growth within the bottom of sea ice, with cascading impacts on higher trophic levels of the Arctic marine ecosystem. While field data or autonomous observatories provide direct measurements of the coupled sea ice‐algal system, they are limited in space and time. Satellite observations of key sea ice variables that control the amount of light penetrating through sea ice offer the possibility to map the under‐ice light field across the entire Arctic basin. This study provides the first satellite‐based estimates of potential sea ice‐associated algal bloom onset dates since the launch of CryoSat‐2 and explores how a changing snowpack may have shifted bloom onset timings over the last four decades.

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.

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r² values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r² 0.15 or lower. r² for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.