A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.

Winter climate variability and snowpack in the West

Snowpack is an important source of water supply in the western United States. This study examines the seasonal variability of an extensive history of snow observations over the western United States and Canada. It links variations in snowpack to variations in atmospheric circulation, surface temperature, and precipitation.

Satellite passive microwave observations of the upper Colorado River snowpack

Seasonal snow cover in the mountains of the Upper Colorado River Basin is a major source of water for a large portion of the southwestern United States. The extent and amount of this snowpack not only reflects changes in weather patterns and climate but also influences the general circulation through modification of the energy exchange between land and atmosphere. ... Satellite observations and remote sensing techniques can enhance the standard snowpack observations to provide the temporal and spatial measurements required for understanding the role of snow in the surface energy balance and improving the management of water resources.

The radiative effect of a fir canopy on a snowpack

Models of snow processes in areas of possible large-scale change need to be site independent and physically based. Here, the accumulation and ablation of the seasonal snow cover beneath a fir canopy has been simulated with a new physically based snow-soil vegetation-atmosphere transfer scheme (Snow-SVAT) called SNOWCAN. The model was formulated by coupling a canopy optical and thermal radiation model to a physically based multilayer snow model. Simple representations of other forest effects were included. These include the reduction of wind speed and hence turbulent transfer beneath the canopy, sublimation of intercepted snow, and deposition of debris on the surface. This paper tests this new modeling approach fully at a fir site within Reynolds Creek Experimental Watershed, Idaho. Model parameters were determined at an open site and subsequently applied to the fir site. SNOWCAN was evaluated using measurements of snow depth, subcanopy solar and thermal radiation, and snowpack profiles of temperature, density, and grain size. Simulations showed good agreement with observations (e.g., fir site snow depth was estimated over the season with r(2) = 0.96), generally to within measurement error. However, the simulated temperature profiles were less accurate after a melt-freeze event, when the temperature discrepancy resulted from underestimation of the rate of liquid water flow and/or the rate of refreeze. This indicates both that the general modeling approach is applicable and that a still more complete representation of liquid water in the snowpack will be important.

Modeling postglacial vegetation dynamics of temperate forests on the Olympic Peninsula (WA, USA) with special regard to snowpack

Past and future forest composition and distribution in temperate mountain ranges is strongly influenced by temperature and snowpack. We used LANDCLIM, a spatially explicit, dynamic vegetation model, to simulate forest dynamics for the last 16,000 years and compared the simulation results to pollen and macrofossil records at five sites on the Olympic Peninsula (Washington, USA). To address the hydrological effects of climate-driven variations in snowpack on simulated forest dynamics, we added a simple snow accumulation-and-melt module to the vegetation model and compared simulations with and without the module. LANDCLIM produced realistic present-day species composition with respect to elevation and precipitation gradients. Over the last 16,000 years, simulations driven by transient climate data from an atmosphere-ocean general circulation model (AOGCM) and by a chironomid-based temperature reconstruction captured Late-glacial to Late Holocene transitions in forest communities. Overall, the reconstruction-driven vegetation simulations matched observed vegetation changes better than the AOGCM-driven simulations. This study also indicates that forest composition is very sensitive to snowpack-mediated changes in soil moisture. Simulations without the snow module showed a strong effect of snowpack on key bioclimatic variables and species composition at higher elevations. A projected upward shift of the snow line and a decrease in snowpack might lead to drastic changes in mountain forests composition and even a shift to dry meadows due to insufficient moisture availability in shallow alpine soils.

(Table 1) Moisture content of buried snowpack deposit samples from Pearse Valley, McMurdo Dry Valleys

Buried snowpack deposits are found within the McMurdo Dry Valleys of Antarctica, which offers the opportunity to study these layered structures of sand and ice within a polar desert environment. Four discrete buried snowpacks are studied within Pearse Valley, Antarctica, through in situ observations, sample analyses, O-H isotope measurements and numerical modelling of snowpack stability and evolution. The buried snowpack deposits evolve throughout the year and undergo deposition, melt, refreeze, and sublimation. We demonstrate how the deposition and subsequent burial of snow can preserve the snowpacks in the Dry Valleys. The modelled lifetimes of the buried snowpacks are dependent upon subsurface stratigraphy but are typically less than one year if the lag thickness is less than c. 7 cm and snow thickness is less than c. 10 cm, indicating that some of the Antarctic buried snowpacks form annually. Buried snowpacks in the Antarctic polar desert may serve as analogues for similar deposits on Mars and may be applicable to observations of the north polar erg, buried ice at the Mars Phoenix landing site, and observations of buried ice throughout the martian Arctic. Numerical modelling suggests that seasonal snows and subsequent burial are not required to preserve the snow and ice on Mars.

Snowpack-runoff relationships for mid-elevation snowpacks on the Workman Creek Watersheds of central Arizona /

"July 2002."

Spatialisation du modèle de couvert nival SNOWPACK dans le Nord canadien pour l’étude de l’accès à la nourriture du caribou de Peary

Le caribou de Peary est l’unité désignable du caribou la plus septentrionale ; sa population a chuté d’environ 70% au cours des trois dernières générations. Le Comité sur la situation des espèces en péril au Canada (COSEPAC) identifie les conditions difficiles d’accès à la nourriture à travers le couvert nival comme le facteur le plus influant contribuant à ce déclin. Cette étude se concentre sur l’établissement d’un outil spatial de caractérisation des conditions nivales pour l’accès à la nourriture du caribou de Peary dans le Nord canadien, utilisant des simulations du couvert nival générées avec le logiciel suisse SNOWPACK à partir des données du Modèle Régional Climatique Canadien. Le cycle de vie du caribou de Peary a été divisé en trois périodes critiques : la période de mise bas et de migration printanière (avril – juin), celle d’abondance de nourriture et de rut (juillet – octobre) et celle de migration automnale et de survie des jeunes caribous (novembre – mars). Les conditions nivales sont analysées et les simulations du couvert nival comparées aux comptes insulaires de caribous de Peary pour identifier un paramètre nival qui agirait comme prédicateur des conditions d’accès à la nourriture et expliquerait les fluctuations des comptes de caribous. Cette analyse conclue que ces comptes sont affectés par des densités de neige au-dessus de 300 kg/m³. Un outil logiciel cartographiant à une échelle régionale (dans l’archipel arctique canadien) les conditions d’accès à la nourriture possiblement favorables et non favorables basées sur la neige est proposé. Des exemples spécifiques de sorties sont données pour montrer l’utilité de l’outil, en cartographiant les pixels selon l’épaisseur cumulée de neige au-dessus de densités de 300 kg/m³, où des épaisseurs cumulées au-dessus de 7000 cm par hiver sont considérées comme non favorables pour le caribou de Peary.