Evaluation of Snow Depth and Soil Temperatures Predicted by the Hydro–Thermodynamic Soil–Vegetation Scheme Coupled with the Fifth-Generation Pennsylvania State University–NCAR Mesoscale Model (original) (raw)
Related papers
1997
Snow cover is one of the most important variables affecting agriculture, hydrology, and climate, but detailed measurements are not widely available. Therefore, the effectiveness and validity of snow schemes in general circulation models have been difficult to assess. Using long-term snow cover data from the former Soviet Union, this paper focuses on the validation of the snow submodel in the Biosphere-Atmosphere Transfer Scheme (BATS) using 6 years of data (1978-83) at six stations. Fundamental uncertainties in the datasets limit the accuracy of our assessment of the model's performance. In the absence of a wind correction for the gauge-measured precipitation and with the standard rain-snow transition criterion (2.2ЊC), the model gives reasonable simulations of snow water equivalent and surface temperature for all of the six stations and the six winters examined. In particular, the time of accumulation and the end of ablation and the alteration due to aging are well captured. With some simple modifications of the code, the model can also reproduce snow depth, snow cover fraction, and surface albedo. In view of the scheme's simplicity and efficiency, these results are encouraging. However, if a wind correction is applied to the gauge-measured precipitation, the model shows increased rootmean-square errors in snow water equivalent for all six stations except Tulun. Perhaps, the better agreement without wind correction means that the snow has blown beyond the area of snow measurement, as might be accounted for only by a detailed regional snow-wind distribution model. This study underlines four aspects that warrant special attention: (i) estimation of the downward longwave radiation, (ii) separation of the aging processes for snowpack density and snow surface albedo, (iii) parameterization of snow cover fraction, and (iv) choice of critical temperature for rain-snow transition.
Atmospheric Research, 2002
A snow model is developed, coupled to and tested within the framework of the meso-h/g-scale non-hydrostatic model, Geesthacht's simulation model of the atmosphere (GESIMA). An evaluation of the snow model is conducted both in a stand-alone version and within GESIMA. In the stand-alone mode, it is evaluated at local scales using data routinely observed at Brandis (51.32jN, 12.62jE, 133 m NN, Saxony) between 1993 and 1997. The snow model reproduces reasonably the temporal evolution of the snow depth; however, it slightly underestimates snow depth, on average. In the coupled mode, simulations are performed with and without the snow model for a winter-storm snow event and a melt period in East Germany to examine the influence of explicitly modeled snow metamorphism on the simulated microclimate. The snow model reasonably predicts the effects typically associated with snow cover. Accuracy of predicted snow depth and extension depends on the lateral boundary conditions and snow prediction by the host model. Evaluation of the simulated air temperatures as well as humidity shows that the inclusion of the snow model improves the model performance as compared to the simulations without snow model. The results show that changing only the values of albedo and emissivity to those typical for snow, as often done in meso-h/g-scale modeling of snow events, can even lead to opposite effects in simulated latent heat fluxes, ground heat fluxes, soil-and near-surface air temperatures than those typically associated with a snow cover. A rigorous evaluation of the snow simulations in coupled meso-h/g-scale non-hydrostatic models requires datasets of snow properties (e.g., albedo and emissivity, snow cover extent, snow depth, snow water equivalent, snow temperature) in a high quality and resolution for the region under study. The available datasets are not yet ready to fulfil this objective.
The knowledge of certain snow indices such as the number of snow days, maximum snow depth and snow water equivalent or the date of snow disappearance is important for many economical and ecological applications. However, snow data are frequently not available at the required locations and therefore have to be modelled. In this study we analyse the performance of the physically based snow model SNOWPACK to calculate the snow cover evolution with input data commonly available from automatic weather stations. We validated the model over several years at three very diverse stations in Switzerland: Weissfluhjoch (2540 m a.s.l.), Davos (1590 m a.s.l.) and Payerne (490 m a.s.l.), where snow depth and the full radiation balance are measured in order to assess the uncertainties induced by the parameterizations of radiation fluxes and by the use of uncorrected precipitation measurements. In addition, we analysed the snow water equivalent at the high-alpine station Weissfluhjoch. The results demonstrate that the radiation balance, which is often measured incompletely, can successfully be parameterized and has an unexpectedly small impact on the modelled snow depth. A detailed analysis demonstrates that an adequate precipitation correction decreases the mean absolute percentage error by 14% for snow depth at the alpine and high-alpine stations and by 19% for snow water equivalent at Weissfluhjoch. The low altitude station Payerne (ephemeral snow conditions) revealed a high sensitivity with regard to the temperature threshold to distinguish solid from liquid precipitation. The analysis further suggested a high sensitivity to ground heat fluxes for ephemeral snow covers. Overall, the daily snow depth could be modelled with a mean bias error of less than − 8 cm at all sites, whereas the mean bias error for the snow water equivalent was less than −55 mm w.e. at Weissfluhjoch.
Remote Sensing of Environment, 2006
The snowpack is a key variable of the hydrological cycle. In recent years, numerous studies have demonstrated the importance of long-term monitoring of the Siberian snowpack on large spatial scales owing to evidence of increased river discharge, changes in snow fall amount and alterations with respect to the timing of ablation. This can currently only be accomplished using remote sensing methods. The main objective of this study is to take advantage of a new land surface forcing and simulation database in order to both improve and evaluate the snow depths retrieved using a dynamic snow depth retrieval algorithm. The dynamic algorithm attempts to account for the spatial and temporal internal properties of the snow cover. The passive microwave radiances used to derive snow depth were measured by the Special Sensor Microwave/ Imager (SSM/I) data between July 1987 and July 1995.
An improved snow scheme for the ECMWF land surface model: description and offline validation
Journal of Hydrometeorology, 2010
A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the sub-grid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and temporal scales) includes simulations for several observation sites from the Snow Models Intercomparison Project-2 (SnowMIP2), global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2), and by ECMWF ERA-Interim re-analysis. This snow scheme was introduced in the ECMWF operational forecast system in September 2009 (CY35R3). SnowMIP2 simulations revealed that the original snow scheme had a systematic early and late prediction of the final ablation in forest and open sites, respectively. The NEW scheme reduces the negative timing bias in forest plots from 15 to 1 day and the positive bias in open plots from 11 to 2 days. The new snow density parameterization has a good agreement with observations, resulting in an augmented insulation effect of the snowpack. The increased insulation and the new exposed and shaded albedo change the surface energy fluxes. There is a reduction of the basal heat flux that reduces the cooling of the underlying soil, which is warmer in NEW than in CTR (old scheme) during the cold season. Thus, reduced soil freezing decreased the surface runoff and increased soil water storage. The mean annual cycles of runoff and TSWV (terrestrial water storage) analyzed for the Ob and Mackenzie basins are closer to the observations in NEW. In ten Northern hemisphere basins, there is an average reduction of the monthly runoff RMSE from 0.75 to 0.51 mm day-1 when comparing CTR and NEW, respectively. These results illustrate the importance of the snow insulation on the hydrological cycle, even at regional scales. On a hemispheric scale, the new snow scheme reduces the negative bias of snow-covered area, especially during spring. On a daily scale, using NOAA/NESDIS snow cover data, the early ablation in CTR is reduced by a factor of two in some identified regions over the Northern Hemisphere. The changes in snow-covered area are closely related with the changes in surface albedo. The original snow scheme had a systematic negative bias in surface albedo, when compared against MODIS remote sensing data. The new scheme reduced the albedo bias, consequently reducing the spatial (only over snow covered area) and time (October to November) averaged surface net shortwave radiation bias from +7.1 W m-2 in CTR to -1.8 W m-2 in NEW. For each validation dataset, sensitivity experiments were performed to assess the impact of the new components of the presented snow scheme. Prognostic and diagnostic SLW (snow liquid water) representations display similar skill in SnowMIP2 (RMSE of SWE) and GSWP2 (RMSE of basin runoff) simulations. Simulated improvements of SWE in SnowMIP2 locations were mainly due to SLW representation on forest sites and due to the new exposed albedo on open sites. The increased snow insulation effect, due to the new snow density parameterization, had an important role on the basins water balance. Impacts of the new snow cover fraction and exposed and shaded albedo parameterizations were evident when validating against remotely sensed data. Sensitivity tests highlight the role of the different components of the snow scheme with the behavior conditioned by the climate and vegetation conditions of each site. Thus, a robust verification of a LSM model should include a variety of different (and independent) validation datasets.
Evaluation of the snow regime in dynamic vegetation land surface models using field measurements
The Cryosphere Discussions, 2013
An increasing number of studies have demonstrated significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled Earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both system processes and its initial state. This study focuses on snow-related variables and makes extensive use of a historical data set of field snow measurements acquired across the extent of the former Soviet Union to evaluate a range of simulated snow metrics produced by several land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific failings in simulating snowpack properties such as magnitude, inter-annual variability, timings of snow water equivalent and evolution of snow density. We develop novel and model-independent methodologies that use the field snow measurements to extract the values of fresh snow density and snowpack sublimation, and exploit them to assess model outputs. By directly forcing the surface heat exchange formulation of a land surface model with field data on snow depth and snow density, we evaluate how inaccuracies in simulating snow metrics affect soil temperature, thaw depth and soil carbon decomposition. We also show how field data can be assimilated into models using optimization techniques in order to identify model defects and improve model performance.
Comparative Analyses of Physically Based Snowmelt Models for Climate Simulations
Journal of Climate, 1999
A comparative study of three snow models with different complexities was carried out to assess how a physically detailed snow model can improve snow modeling within general circulation models. The three models were (a) the U.S. Army Cold Regions Research and Engineering Laboratory Model (SNTHERM), which uses the mixture theory to simulate multiphase water and energy transfer processes in snow layers; (b) a simplified three-layer model, Snow-Atmosphere-Soil Transfer (SAST), which includes only the ice and liquid-water phases; and (c) the snow submodel of the Biosphere-Atmosphere Transfer Scheme (BATS), which calculates snowmelt from the energy budget and snow temperature by the force-restore method. Given the same initial conditions and forcing of atmosphere and radiation, these three models simulated time series of snow water equivalent, surface temperature, and fluxes very well, with SNTHERM giving the best match with observations and SAST simulation being close. BATS captured the major processes in the upper portion of a snowpack where solar radiation provides the main energy source and gave satisfying results for seasonal periods. Some biases occurred in BATS surface temperature and energy exchange due to its neglecting of liquid water and underestimating snow density. Ice heat conduction, meltwater heat transport, and the melt-freeze process of snow exhibit strong diurnal variations and large gradients at the uppermost layers of snowpacks. Using two layers in the upper 20 cm and one deeper layer at the bottom to simulate the multiphase snowmelt processes, SAST closely approximated the performance of SNTHERM with computational requirements comparable to those of BATS.
Development of a land surface model with coupled snow and frozen soil physics
Water Resources Research, 2017
Snow and frozen soil are important factors that influence terrestrial water and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameteri-zation. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.
The Representation of Snow in Land Surface Schemes: Results from PILPS 2(d)
Journal of Hydrometeorology, 2001
Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra-and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models' snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.