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Glen E. Liston

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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.

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Glen E. Liston

Abstract

To improve the depiction of autumn through spring land–atmosphere interactions and feedbacks within regional and global weather, climate, and hydrologic models, a Subgrid SNOW Distribution (SSNOWD) submodel that explicitly includes subgrid snow-depth and snow-cover variability has been developed. From both atmospheric and hydrologic perspectives, the subgrid snow-depth distribution is an important quantity to account for within large-scale models. In the natural system, these subgrid snow-depth distributions are largely responsible for the mosaic of snow-covered and snow-free areas that develop as the snow melts, and the impacts of these fractional areas must be quantified in order to realistically simulate grid-averaged surface fluxes. SSNOWD's formulation incorporates observational studies showing that snow distributions can be described by a lognormal distribution and the snow-depth coefficient of variation. Using an understanding of the physical processes that lead to these observed snow-depth variations, a global distribution of nine subgrid snow-depth-variability categories was developed, and coefficient-of-variation values were assigned to each category based on published measurements. In addition, SSNOWD adopts the physically realistic approach of performing separate surface-energy-balance calculations over the snow-covered and snow-free portions of each model grid cell and weighing the resulting fluxes according to these fractional areas. Using a climate version of the Regional Atmospheric Modeling System (ClimRAMS) over a North American domain, SSNOWD was compared with a snow-cover formulation similar to those currently used in most general circulation models. The simulations indicated that accounting for snow-distribution variability has a significant impact on snow-cover evolution and associated energy and moisture fluxes.

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Glen E. Liston

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.

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Glen E. Liston and Kelly Elder

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SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.

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Glen E. Liston and Kelly Elder

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An intermediate-complexity, quasi–physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature–elevation, wind–topography, humidity–cloudiness, and radiation–cloud–topography relationships.

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Glen E. Liston and Matthew Sturm

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A blowing-snow model (SnowTran-3D) was combined with field measurements of end-of-winter snow depth and density to simulate solid (winter) precipitation, snow transport, and sublimation distributions over a 20 000-km2 arctic Alaska domain. The domain included rolling uplands and a flat coastal plain. Simulations were produced for the winters of 1994/95, 1995/96, and 1996/97. The model, which accounts for spatial and temporal variations in blowing-snow sublimation, as well as saltation and turbulent-suspended transport, was driven with interpolated fields of observed temperature, humidity, and wind speed and direction. Model outputs include local (a few hundreds of meters) to regional (several tens of kilometers) distributions of winter snow-water-equivalent depths and blowing-snow sublimation losses, from which the regional winter precipitation distributions are computed. At regional scales, the end-of-winter snow depth is largely equal to the difference between winter precipitation and moisture loss due to sublimation. While letting SnowTran-3D simulate the blowing-snow sublimation fluxes, the precipitation fields were determined by forcing the regional variation in model-simulated snow depths to match measured values. Averaged over the entire domain and the three simulation years, the winter precipitation was 17.6 cm, with uplands values averaging 19.0 cm and coastal values averaging 15.3 cm. On average, 21% of the precipitation was returned to the atmosphere by blowing-snow sublimation, while in the windier coastal regions 34% of the winter precipitation sublimated.

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Ulrich Strasser, Michael Warscher, and Glen E. Liston

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Snow interception in a coniferous forest canopy is an important hydrological feature, producing complex mass and energy exchanges with the surrounding atmosphere and the snowpack below. Subcanopy snowpack accumulation and ablation depends on the effects of canopy architecture on meteorological conditions and on interception storage by stems, branches, and needles. Mountain forests are primarily composed of evergreen conifer species that retain their needles throughout the year and hence intercept snow efficiently during winter. Canopy-intercepted snow can melt, fall to the ground, and/or sublimate into the air masses above and within the canopy. To improve the understanding of snow–canopy interception processes and the associated influences on the snowpack below, a series of model experiments using a detailed, physically based snow–canopy and snowpack evolution model [Alpine Multiscale Numerical Distributed Simulation Engine (AMUNDSEN)] driven with observed meteorological forcing was conducted. A cone-shaped idealized mountain covered with a geometrically regular pattern of coniferous forest stands and clearings was constructed. The model was applied for three winter seasons with different snowfall intensities and distributions. Results show the effects of snow–canopy processes and interactions on the pattern of ground snow cover, its duration, and the amount of meltwater release, in addition to showing under what conditions the protective effect of a forest canopy overbalances the reduced accumulation of snow on the ground. The simulations show considerable amounts of canopy-intercepted snowfall can sublimate, leading to reduced snow accumulation beneath the forest canopy. In addition, the canopy produces a shadowing effect beneath the trees that leads to reduced radiative energy reaching the ground, reduced below-canopy snowmelt rates, and increased snow-cover duration relative to nonforested areas. During snow-rich winters, the shadowing effect of the canopy dominates and snow lasts longer inside the forest than in the open, but during winters with little snow, snow sublimation losses dominate and snow lasts longer in the open areas than inside the forest. Because of the strong solar radiation influence on snowmelt rates, the details of these relationships vary for northern and southern radiation exposures and time of year. In early and high winter, the radiation protection effect of shadowing by the canopy is small. If little snow is available, an intermittent melt out of the snow cover inside the forest can occur. In late winter and spring, the shadowing effect becomes more efficient and snowmelt is delayed relative to nonforested areas.

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Glen E. Liston and Sebastian H. Mernild

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A gridded linear-reservoir runoff routing model (HydroFlow) was developed to simulate the linkages between runoff production from land-based snowmelt and icemelt processes and the associated freshwater fluxes to downstream areas and surrounding oceans. HydroFlow was specifically designed to account for glacier, ice sheet, and snow-free and snow-covered land applications. Its performance was verified for a test area in southeast Greenland that contains the Mittivakkat Glacier, the local glacier in Greenland with the longest observed time series of mass-balance and ice-front fluctuations. The time evolution of spatially distributed gridcell runoffs required by HydroFlow were provided by the SnowModel snow-evolution modeling system, driven with observed atmospheric data, for the years 2003 through 2010. The spatial and seasonal variations in HydroFlow hydrographs show substantial correlations when compared with observed discharge coming from the Mittivakkat Glacier area and draining into the adjacent ocean. As part of its discharge simulations, HydroFlow creates a flow network that links the individual grid cells that make up the simulation domain. The collection of networks that drain to the ocean produced a range of runoff values that varied most strongly according to catchment size and percentage and elevational distribution of glacier cover within each individual catchment. For 2003–10, the average annual Mittivakkat Glacier region runoff period was 200 ± 20 days, with a significant increase in annual runoff over the 8-yr study period, both in terms of the number of days (30 days) and in volume (54.9 × 106 m3).

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Glen E. Liston and Christopher A. Hiemstra

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A vegetation-protruding-above-snow parameterization for earth system models was developed to improve energy budget calculations of interactions among vegetation, snow, and the atmosphere in nonforested areas. These areas include shrublands, grasslands, and croplands, which represent 68% of the seasonally snow-covered Northern Hemisphere land surface (excluding Greenland). Snow depth observations throughout nonforested areas suggest that mid- to late-winter snowpack depths are often comparable or lower than the vegetation heights. As a consequence, vegetation protruding above the snow cover has an important impact on snow-season surface energy budgets. The protruding vegetation parameterization uses disparate energy balances for snow-covered and protruding vegetation fractions of each model grid cell, and fractionally weights these fluxes to define grid-average quantities. SnowModel, a spatially distributed snow-evolution modeling system, was used to test and assess the parameterization. Simulations were conducted during the winters of 2005/06 and 2006/07 for conditions of 1) no protruding vegetation (the control) and 2) with protruding vegetation. The spatial domain covered Colorado, Wyoming, and portions of the surrounding states; 81% of this area is nonforested. The surface net radiation, energy, and moisture fluxes displayed considerable differences when protruding vegetation was included. For shrubs, the net radiation, sensible, and latent fluxes changed by an average of 12.7, 6.9, and −22.7 W m−2, respectively. For grass and crops, these fluxes changed by an average of 6.9, −0.8, and −7.9 W m−2, respectively. Daily averaged flux changes were as much as 5 times these seasonal averages. As such, the new parameterization represents a major change in surface flux calculations over more simplistic and less physically realistic approaches.

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Brian Taras, Matthew Sturm, and Glen E. Liston

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Air and snow–ground interface temperatures were measured during two winters at 33 stations spanning the 180-km-long Kuparuk basin in arctic Alaska. Interface temperatures averaged 7.5°C higher than air temperatures and varied in a manner that was more complex, and on a spatial scale more than 100 times smaller, than the air temperature. Within the basin, two distinct thermal regimes could be identified, with the division at the boundary between coastal and uplands provinces. When each station was classified into one of three snow exposure classes (exposed, intermediate, or sheltered), accounting for variations in snow depth and thermal properties, 87% of the variation in the average interface temperature could be predicted from air temperature. Individual station interface temperature records were fit using a beta curve that captured the slow decrease in autumn and the rapid rise in spring. Beta curves were specified by three parameters (α, β, and γ) that could be predicted if province and snow exposure class were known. A model based on the beta curves was developed and applied over the basin to predict interface temperatures in both time and space. Tests of the model against data from within the domain and from arctic Alaskan locations outside the domain suggest an accuracy of ±2°C when simulating average winter interface temperatures, and ±3°C when simulating daily interface temperatures.

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