Abstract

A numerical atmospheric boundary layer model, based on higher-order turbulence closure assumptions, is developed and used to simulate the local advection of momentum, heat, and moisture during the melt of patchy snow covers over a 10-km horizontal domain. The coupled model includes solution of the mass continuity equation, the horizontal and vertical momentum equations, an E−ε turbulence model, an energy equation, and a water vapor conservation equation. Atmospheric buoyancy is accounted for, and a land surface energy balance model is implemented at the lower boundary.

Model integrations indicate that advective processes occurring at local scales produce nonlinear horizontal variations in surface fluxes. Under conditions of the numerical experiments, the energy available to melt snow-covered regions has been found to increase by as much as 30% as the area of exposed vegetation increases upwind of the snow cover. The melt increase is found to vary in a largely linear fashion with decreasing snow-covered area for snow-covered areas greater than 25% and in a strongly nonlinear fashion below that value. Decreasing the ratio of patch size to total area, or increasing the patchiness, of the snow cover also leads to nonlinear increases in the energy available to melt the snow. In the limit of a snow cover composed of small patches, melt energy is found to increase linearly as the fractional snow-covered area decreases. In addition, for the purpose of computing grid-average surface fluxes during snowmelt in regional atmospheric models, the results of this study indicate that separate energy balance computations can be performed over the snow-covered and vegetation-covered regions, and the resulting fluxes can be weighted in proportion to the fractional snow cover to allocate the total energy flux partitioning within each surface grid cell.

Abstract

Local, regional, and global atmospheric, hydrologic, and ecologic models used to simulate weather, climate, land surface moisture, and vegetation processes all commonly represent their computational domains by a collection of finite areas or grid cells. Within each of these cells three fundamental features are required to describe the evolution of seasonal snow cover from the end of winter through spring melt. These three features are 1) the within-grid snow water equivalent (SWE) distribution, 2) the gridcell melt rate, and 3) the within-grid depletion of snow-covered area. This paper defines the exact mathematical interrelationships among these three features and demonstrates how knowledge of any two of them allows generation of the third. During snowmelt, the spatially variable subgrid SWE depth distribution is largely responsible for the patchy mosaic of snow and vegetation that develops as the snow melts. Applying the melt rate to the within-grid snow distribution leads to the exposure of vegetation, and the subgrid-scale vegetation exposure influences the snowmelt rate and the grid-averaged surface fluxes. By using the developed interrelationships, the fundamental subgrid-scale features of the seasonal snow cover evolution and the associated energy and moisture fluxes can be simulated using a combination of remote sensing products that define the snow-covered area evolution and a submodel that appropriately handles the snowmelt computation. Alternatively, knowledge of the subgrid SWE distribution can be used as a substitute for the snow-covered area information.

Abstract

In many applications, a realistic description of air temperature inversions is essential for accurate snow and glacier ice melt, and glacier mass-balance simulations. A physically based snow evolution modeling system (SnowModel) was used to simulate 8 yr (1998/99–2005/06) of snow accumulation and snow and glacier ice ablation from numerous small coastal marginal glaciers on the SW part of Ammassalik Island in SE Greenland. These glaciers are regularly influenced by inversions and sea breezes associated with the adjacent relatively low temperature and frequently ice-choked fjords and ocean. To account for the influence of these inversions on the spatiotemporal variation of air temperature and snow and glacier melt rates, temperature inversion routines were added to MircoMet, the meteorological distribution submodel used in SnowModel. The inversions were observed and modeled to occur during 84% of the simulation period. Modeled inversions were defined not to occur during days with strong winds and high precipitation rates because of the potential of inversion breakup. Field observations showed inversions to extend from sea level to approximately 300 m MSL, and this inversion level was prescribed in the model simulations. Simulations with and without the inversion routines were compared. The inversion model produced air temperature distributions with warmer lower-elevation areas and cooler higher-elevation areas than without inversion routines because of the use of cold sea-breeze-based temperature data from underneath the inversion. This yielded an up to 2 weeks earlier snowmelt in the lower areas and up to 1–3 weeks later snowmelt in the higher-elevation areas of the simulation domain. Averaged mean annual modeled surface mass balance for all glaciers (mainly located above the inversion layer) was −720 ± 620 mm w.eq. yr⁻¹ (w.eq. is water equivalent) for inversion simulations, and −880 ± 620 mm w.eq. yr⁻¹ without the inversion routines, a difference of 160 mm w.eq. yr⁻¹. The annual glacier loss for the two simulations was 50.7 × 10⁶ and 64.4 × 10⁶ m³ yr⁻¹ for all glaciers—a difference of ∼21%. The average equilibrium line altitude (ELA) for all glaciers in the simulation domain was located at 875 and 900 m MSL for simulations with or without inversion routines, respectively.

Abstract

Across the globe, wind speed trends have shown a slight decline for in situ meteorological datasets. Yet few studies have assessed long-term wind speed trends for alpine regions or how such trends could influence snow transport and distribution. Alpine-region meteorological stations are sparsely distributed, and their records are short. To increase spatial and temporal coverage, use of modeled data is appealing, but the level of agreement between modeled and in situ data is unknown for alpine regions. Data agreement, temporal trends, and the potential effects on snow distribution were evaluated using two in situ sites in an alpine region [Niwot Ridge in Colorado and the Glacier Lakes Ecological Experiments Station (GLEES) in Wyoming] and the corresponding grid cells of the North American Regional Reanalysis (NARR). Temperature, precipitation, and wind speed variables were used to assess blowing-snow trends at annual, seasonal, and daily scales. The correlation between NARR and in situ datasets showed that temperature data were correlated but that wind speed and precipitation were not. NARR wind speed data were systematically lower when compared with in situ data, yet the frequency of wind events was captured. Overall, there were not many significant differences between NARR and in situ wind speed trends at annual, seasonal, and daily scales, aside from GLEES daily values. This finding held true even when trends presented opposite signatures and slopes, which was likely a result of low trend slopes. The lack of agreement between datasets prohibited the use of NARR to broaden analyses for blowing-snow dynamics in alpine regions.