The impact of high latitude climate warming on Arctic snow cover and its insulating properties has key implications for the surface and soil energy balance. Few studies have investigated specific trends in Arctic snowpack properties because there is a lack of long-term in situ observations and current detailed snow models fail to represent the main traits of Arctic snowpacks. This results in high uncertainty in modeling snow feedbacks on ground thermal regime due to induced changes in snow insulation. To better simulate Arctic snow structure and snow thermal properties, we implemented new parameterizations of several snow physical processes-including the effect of Arctic low vegetation and wind on snowpack-in the Crocus detailed snowpack model. Significant improvements compared to standard Crocus snow simulations and ERA-Interim (ERAi) reanalysis snow outputs were observed for a large set of in-situ snow data over Siberia and North America. Arctic Crocus simulations produced improved Arctic snow density profiles over the initial Crocus version, leading to a soil surface temperature bias of -0.5 K with RMSE of 2.5 K. We performed Crocus simulations over the past 39 years (1979-2018) for circumpolar taiga (open forest) and pan-Arctic areas at a resolution of 0.5 degrees, driven by ERAi meteorological data. Snowpack properties over that period feature significant increase in spring snow bulk density (mainly in May and June), a downward trend in snow cover duration and an upward trend in wet snow (mainly in spring and fall). The pan-Arctic maximum snow water equivalent shows a decrease of -0.33 cm dec(-1). With the ERAi air temperature trend of +0.84 K dec(-1) featuring Arctic winter warming, these snow property changes have led to an upward trend in soil surface temperature (Tss) at a rate of +0.41 K dec(-1) in winter. We show that the implemented snowpack property changes increased the Tss trend by 36% compared to the standard simulation. Winter induced changes in Tss led to a significant increase of 16% (+4 cm dec(-1)) in the estimated active layer thickness (ALT) over the past 39 years. An increase in ALT could have a significant impact on permafrost evolution, Arctic erosion and hydrology.

We extend a stochastic aerosol-snow albedo model to explicitly simulate dust internally/externally mixed with snow grains of different shapes and for the first time quantify the combined effects of dust-snow internal mixing and snow nonsphericity on snow optical properties and albedo. Dust-snow internal/external mixing significantly enhances snow single-scattering coalbedo and absorption at wavelengths of <1.0 mu m, with stronger enhancements for internal mixing (relative to external mixing) and higher dust concentrations but very weak dependence on snow size and shape variabilities. Compared with pure snow, dust-snow internal mixing reduces snow albedo substantially at wavelengths of <1.0 mu m, with stronger reductions for higher dust concentrations, larger snow sizes, and spherical (relative to nonspherical) snow shapes. Compared to internal mixing, dust-snow external mixing generally shows similar spectral patterns of albedo reductions and effects of snow size and shape. However, relative to external mixing, dust-snow internal mixing enhances the magnitude of albedo reductions by 10%-30% (10%-230%) at the visible (near-infrared) band. This relative enhancement is stronger as snow grains become larger or nonspherical, with comparable influences from snow size and shape. Moreover, for dust-snow external and internal mixing, nonspherical snow grains have up to similar to 45% weaker albedo reductions than spherical grains, depending on snow size, dust concentration, and wavelength. The interactive effect of dust-snow mixing state and snow shape highlights the importance of accounting for these two factors concurrently in snow modeling. For application to land/climate models, we develop parameterizations for dust effects on snow optical properties and albedo with high accuracy.

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow-Atmosphere-Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of mid-winter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17 7, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to be primary factors affecting the implementation of an energy balance model in operational streamflow prediction. (C) 2008 Elsevier B.V. All rights reserved.

Borehole temperature-depth profiles contain a record of surface ground temperature (SGT) changes with time and complement surface air temperature (SAT) analysis to infer climate change over multiple centuries. Ground temperatures are generally warmer than air temperatures due to solar radiation effects in the summer and the insulating effect of snow cover during the winter. The low thermal diffusivity of snow damps surface temperature variations; snow effectively acts as an insulator of the ground during the coldest part of the year. A numerical model of snow-ground thermal interactions is developed to investigate the effect of seasonal snow cover on annual ground temperatures. The model is parameterized in terms of three snow event parameters: onset time of the annual snow event, duration of the event, and depth of snow during the event. These parameters are commonly available from meteorological and remotely sensed data making the model broadly applicable. The model is validated using SAT, subsurface temperature from a depth of 10 cm, and snow depth data from the 6 years of observations at Emigrant Pass climate observatory in northwestern Utah and 217 station years of National Weather Service data from sites across North America. Measured subsurface temperature-time series are compared to changes predicted by the model. The model consistently predicts ground temperature changes that compare well with those observed. Sensitivity analysis of the model leads to a nonlinear relationship between the three snow event parameters (onset, duration, and depth of the annual snow event) and the influence snow has on mean annual SGT.