Abstract. This paper presents a simple snow model for climate studies. There are three prognostic variables in the model: specific enthalpy, snow water equivalent, and snow depth. This model is developed on the basis of up-to-date comprehensive and complex snow schemes but with substantial simplification and improvement. The effects of vapor on snow processes have been analyzed in the paper. On the basis of the analysis, vapor's contribution in the mass equation is eliminated, and an effective conductivity coefficient, which includes a simple parameterization for vapor diffusion effect, is used to describe its contribution in the energy equation to simplify the computation. Specific enthalpy is used in the energy balance equation. Using enthalpy rather than temperature greatly simplifies the computational procedure for the phase change calculation in the snow process. This approach, along with a one-step test scheme that avoids iterations, saves computational time, which is important for general circulation model (GCM) simulations. The layering scheme is a critical part in the model. After many tests, it is found that three layers with an appropriate layering scheme are adequate for most cases. Preliminary testing using Russian and French snow data shows that the three-layer model is able to produce reasonable and consistent results.

[1] A series of numerical experiments have been designed to understand the physics at the soil-vegetation-snow-atmosphere interface and to find the major parameterizations/ parameters, which are crucial to simulate cold season processes. Observational data sets from Col de Porte of France, Ovre Lansjarv of Sweden, and Gander of Canada were used to help interpret the results. This study shows that snow layering and compaction are among the most important factors affecting proper simulations of snow depth, snow water equivalent (SWE), surface temperature, and surface runoff. Fixed snow density could produce as high as 100 percent error in estimating snow depth and could cause significant biases in SWE simulation during the melting period. Furthermore, with a bulk snow/soil layer, the simulated surface temperature would persistently be close to the freezing point with substantially hampered variability, and the variability and the amplitude of the runoff during the snow-melting season could also be severely underestimated. The experiments also show that proper snow albedo is crucial during the ablation period and affects the magnitude and timing in both SWE and runoff simulations. Furthermore, this study indicates that the parameterizations in the surface aerodynamic resistance in the stable regime play an important role in determining the sensible and latent heat fluxes during the winter season in the Arctic region and then affect the snow depth simulations and prediction of snow melting as well as runoff timing. Although the snow may fully cover the ground in cold regions during the winter, numerical experiments in this study show the vegetation still exerts a substantial influence in the snow depth and runoff simulations. Numerical experimentation shows that less downward sensible heat on the bare ground produces thick snow cover and extremely high peak runoff, which leads to a typical deforestation scenario in cold regions.

[1] Numerous frozen soil models currently in use differ in the complexity of their governing equations or/and in the processes being considered. It is important to comprehensively examine and categorize these on the basis of physical principles, assumptions, and relationship to each other. In this paper frozen soil models are classified into different levels according to the complexity of the governing equations. On the basis of scale analysis, models with different levels of complexity were derived from the most complicated frozen soil model. Rationales for the simplification of models at different levels are discussed. To overcome the difficulties in achieving numerical solutions, a new method of substituting soil enthalpy and total water mass for soil temperature and volumetric liquid water content in governing equations is introduced for each level of the frozen soil models. Models with different complexity levels are assessed with observational data. The preliminary monthly and seasonal evaluation shows that the results from the models with different complexity are generally similar but with substantial differences at the Tibetan D66 site during the melting and freezing period. The model including the contribution of vapor flux due to the matric potential gradient to the water balance performs the best at the D66 site. Compared to the corresponding original models, the frozen soil model versions with enthalpy and total water mass for governing equations appear to produce consistently better performance. Furthermore, the rationale of different methods for the freezing-melting process in frozen soil is discussed. It has been noted that the model derived from the freezing point depression equation and the soil matric potential equation is supported by both thermodynamic equilibrium theory and the simulation results.Citation: Li, Q., S. Sun, and Y. Xue (2010), Analyses and development of a hierarchy of frozen soil models for cold region study,

Abstract. Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400 ppm CO 2 ) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that warm extremes largely change in accordance with mean shifts in the distribution of summertime temperatures. Cold extremes warm more than mean shifts in the distribution of wintertime temperatures, but changes in GEV location parameters are generally well explained by the combination of mean shifts and reduced wintertime temperature variability. For cold extremes at inland locations, return levels at long recurrence intervals show additional effects related to changes in the spread and shape of GEV distributions. We then examine uncertainties that result from using shorter model runs. In theory, the GEV distribution can allow prediction of infrequent events using time series shorter than the recurrence interval of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.