Simulated Snow Water Equivalent Research Articles

Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance

AbstractThe forcing irradiances (downwelling shortwave and longwave irradiances) are the primary drivers of snowmelt; however, in complex terrain, few observations, the use of estimated irradiances, and the influence of topography and elevation all lead to uncertainties in these radiative fluxes. The impact of uncertainties in the forcing irradiances on simulations of snow is evaluated in idealized modeling experiments. Two snow models of contrasting complexity, the Utah Energy Balance Model (UEB) and the Snow Thermal Model (SNTHERM), are forced with irradiances with prescribed errors of the structure and magnitude representative of those found in methods for estimating the downwelling irradiances. Relatively modest biases have substantial impacts on simulated snow water equivalent (SWE) and surface temperature (Ts) across a range of climates, whereas random noise at the daily scale has a negligible effect on modeled SWE and Ts. Shortwave biases have a smaller SWE impact, due to the influence of albedo, and Ts impact, due to their diurnal cycle, compared to equivalent longwave biases. Warmer sites exhibit greater sensitivity to errors when evaluated using SWE, while colder sites exhibit more sensitivity as evaluated using Ts. The two models displayed different sensitivity and responses to biases. The stability feedback in the turbulent fluxes explains differences in Ts between models in the negative longwave bias scenarios. When the models diverge during melt events, differences in the turbulent fluxes and internal energy change of the snow are found to be responsible. From this analysis, we suggest model evaluations use Ts in addition to SWE.

Monitoring Groundwater Variations from Satellite Gravimetry and Hydrological Models: A Comparison with in-situ Measurements in the Mid-Atlantic Region of the United States

Aimed at mapping time variations in the Earth’s gravity field, the Gravity Recovery and Climate Experiment (GRACE) satellite mission is applicable to access terrestrial water storage (TWS), which mainly includes groundwater, soil moisture (SM), and snow. In this study, SM and accumulated snow water equivalent (SWE) are simulated by the Global Land Data Assimilation System (GLDAS) land surface models (LSMs) and then used to isolate groundwater anomalies from GRACE-derived TWS in Pennsylvania and New York States of the Mid-Atlantic region of the United States. The monitoring well water-level records from the U.S. Geological Survey Ground-Water Climate Response Network from January 2005 to December 2011 are used for validation. The groundwater results from different combinations of GRACE products (from three institutions, CSR, GFZ and JPL) and GLDAS LSMs (CLM, NOAH and VIC) are compared and evaluated with in-situ measurements. The intercomparison analysis shows that the solution obtained through removing averaged simulated SM and SWE of the three LSMs from the averaged GRACE-derived TWS of the three centers would be the most robust to reduce the noises, and increase the confidence consequently. Although discrepancy exists, the GRACE-GLDAS estimated groundwater variations generally agree with in-situ observations. For monthly scales, their correlation coefficient reaches 0.70 at 95% confidence level with the RMSE of the differences of 2.6 cm. Two-tailed Mann-Kendall trend test results show that there is no significant groundwater gain or loss in this region over the study period. The GRACE time-variable field solutions and GLDAS simulations provide precise and reliable data sets in illustrating the regional groundwater storage variations, and the application will be meaningful and invaluable when applied to the data-poor regions.

Modeling seasonal snowpack evolution in the complex terrain and forested Colorado Headwaters region: A model intercomparison study

AbstractCorrectly modeling snow is critical for climate models and for hydrologic applications. Snowpack simulated by six land surface models (LSM: Noah, Variable Infiltration Capacity, snow‐atmosphere‐soil transfer, Land Ecosystem‐Atmosphere Feedback, Noah with Multiparameterization, and Community Land Model) were evaluated against 1 year snow water equivalent (SWE) data at 112 Snow Telemetry (SNOTEL) sites in the Colorado River Headwaters region and 4 year flux tower data at two AmeriFlux sites. All models captured the main characteristics of the seasonal SWE evolution fairly well at 112 SNOTEL sites. No single model performed the best to capture the combined features of the peak SWE, the timing of peak SWE, and the length of snow season. Evaluating only simulated SWE is deceiving and does not reveal critical deficiencies in models, because the models could produce similar SWE for starkly different reasons. Sensitivity experiments revealed that the models responded differently to variations of forest coverage. The treatment of snow albedo and its cascading effects on surface energy deficit, surface temperature, stability correction, and turbulent fluxes was a major intermodel discrepancy. Six LSMs substantially overestimated (underestimated) radiative flux (heat flux), a crucial deficiency in representing winter land‐atmosphere feedback in coupled weather and climate models. Results showed significant intermodel differences in snowmelt efficiency and sublimation efficiency, and models with high rate of snow accumulation and melt were able to reproduce the observed seasonal evolution of SWE. This study highlights that the parameterization of cascading effects of snow albedo and below‐canopy turbulence and radiation transfer is critical not only for SWE simulation but also for correctly capturing the winter land‐atmosphere interactions.

Changes in Snow Storage in the Upper Hron River Basin (Slovakia)

Abstract An evaluation of changes in the snow cover in mountainous basins in Slovakia and a validation of MODIS satellite images are provided in this paper. An analysis of the changes in snow cover was given by evaluating changes in the snow depth, the duration of the snow cover, and the simulated snow water equivalent in a daily time step using a conceptual hydrological rainfall-runoff model with lumped parameters. These values were compared with the available measured data at climate stations. The changes in the snow cover and the simulated snow water equivalent were estimated by trend analysis; its significance was tested using the Mann-Kendall test. Also, the satellite images were compared with the available measured data. From the results, it is possible to see a decrease in the snow depth and the snow water equivalent from 1961-2010 in all the months of the winter season, and significant decreasing trends were indicated in the months of December, January and February

Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

Interpreting observed northern hemisphere snow trends with large ensembles of climate simulations

Simulated variability and trends in Northern Hemisphere seasonal snow cover are analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiative forcings are generated, one coupled to a dynamical ocean and the other driven by observed sea surface temperatures (SSTs) over the period 1981–2010. The simulations reproduce many aspects of the observed climatology and variability of snow cover extent as characterized by the NOAA snow chart climate data record. Major features of the simulated snow water equivalent (SWE) also agree with observations (GlobSnow Northern Hemisphere SWE data record), although with a lesser degree of fidelity. Ensemble spread in the climate response quantifies the impact of natural climate variability in the presence and absence of coupling to the ocean. Both coupled and uncoupled ensembles indicate an overall decrease in springtime snow cover that is consistent with observations, although springtime trends in most climate realizations are weaker than observed. In the coupled ensemble, a tendency towards excessive warming in wintertime leads to a strong wintertime snow cover loss that is not found in observations. The wintertime warming bias and snow cover reduction trends are reduced in the uncoupled ensemble with observed SSTs. Natural climate variability generates widely different regional patterns of snow trends across realizations; these patterns are related in an intuitive way to temperature, precipitation and circulation trends in individual realizations. In particular, regional snow loss over North America in individual realizations is strongly influenced by North Pacific SST trends (manifested as Pacific Decadal Oscillation variability) and by sea level pressure trends in the North Pacific/North Atlantic sectors.

A regional climate model hindcast for Siberia: analysis of snow water equivalent

Abstract. This study analyzes the added value of a regional climate model hindcast with respect to snow water equivalent (SWE) for Siberia when compared to SWE estimates from forcing NCEP-R. In addition, we examine the discrepancies of simulated SWE to several recent reanalysis products (NCEP-R2 NCEP-CFSR, ERA-Interim). We apply the regional climate model COSMO-CLM (CCLM) to a 50 km grid spacing using NCEP-R1 as driving force to obtain a 63 yr (1948 to 2010) gridded dataset of historical SWE. Simulated regional climate data is necessary because of the absence of station data in that region. To perform large-scale assessments we use the satellite-derived daily SWE product of ESA DUE GlobSnow from 1987 to 2010. Russian station SWE data is used for cross-checking the findings. In January (mid-winter), the SWE hindcast is in good agreement with GlobSnow, whereas it overestimates SWE during the melting season. CCLM shows a clear added value in providing realistic SWE information compared to the driving reanalysis. The temporal consistency of CCLM is higher than that presented by ERA-Interim and NCEP-R2.

Sensitivity of model parameterizations for simulated latent heat flux at the snow surface for complex mountain sites

The snowcover energy balance is typically dominated by net radiation and sensible and latent heat fluxes. Validation of the two latter components is rare and often difficult to undertake at complex mountain sites. Latent heat flux, the focus of this paper, is the primary coupling mechanism between the snow surface and the atmosphere. It accounts for the critical exchange of mass (sublimation or condensation), along with the associated snowcover energy loss or gain. Measured and modelled latent heat fluxes at a wind-exposed and wind-sheltered site were compared to evaluate variability in model parameters. A well-tested and well-validated snowcover energy balance model, Snobal, was selected for this comparison because of previously successful applications of the model at these sites and because of the adjustability of the parameters specific to latent heat transfer within the model. Simulated latent heat flux and snow water equivalent (SWE) were not sensitive to different formulations of the stability profile functions associated with heat transfer calculations. The model parameters of snow surface roughness length and active snow layer thickness were used to improve latent heat flux simulations while retaining accuracy in the simulation of the SWE at an exposed and sheltered study site. Optimal parameters for simulated latent heat flux and SWE were found at the exposed site with a shorter roughness length and thicker active layer, and at the sheltered site with a longer roughness length and thinner active layer. These findings were linked to physical characteristics of the study sites and will allow for adoption into other snow models that use similar parameters. Physical characteristics of wind exposure and cover could also be used to distribute critical parameters in a spatially distributed modelling domain and aid in parameter selection for application to other watersheds where detailed information is not available. Copyright © 2012 John Wiley & Sons, Ltd.

Alpine snow cover in a changing climate: a regional climate model perspective

An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951–2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971–2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40–80 % by mid century relative to 1971–2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000–2,500 m, SWE reductions amount to 10–60 % by mid century and to 30–80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.

Projection of snow cover changes over China under RCP scenarios

Snow cover changes in the middle (2040–2059) and end (2080–2099) of the twenty-first century over China were investigated with a regional climate model, nested within the global model BCC_CSM1.1. The simulations had been conducted for the period of 1950–2099 under the RCP4.5 and RCP8.5 scenarios. Results show that the model perform well in representing contemporary (1986–2005) spatial distributions of snow cover days (SCDs) and snow water equivalent (SWE). However, some differences between observation and simulation were detected. Under the RCP4.5 scenarios, SCDs are shortened by 10–20 and 20–40 days during the middle and end of the twenty-first century, respectively. Whereas simulated SWE is lowered by 0.1–10 mm in most areas over the Tibetan Plateau (TP). On the other hand, the spatial distributions of SWE are reversed between the middle and end terms in the northeast China. Furthermore, compared with the changes of RCP4.5 scenario, SCDs are reduced by 5–20 days in the middle period under RCP8.5 scenario with even larger decreasing amplitude in the end term. SWE was lowered by 0.1–2.5 mm in most areas except the northeast of China in middle term under RCP8.5 scenario. The great center of SCDs and SWE changes are always located over TP. The regional mean of SCDs and SWE for the TP and for China display a declining trend from 2006 to 2099 with more pronounced changes in the TP than in China as a whole. Under the RCP8.5 scenario, the changes are enhanced compared to those under RCP4.5.

Evaluation of Snowpack Simulations over the Canadian Rockies with an Experimental Hydrometeorological Modeling System

Abstract To improve the representation of the land surface in their operational numerical weather prediction (NWP) models, the Meteorological Research Division of Environment Canada (EC) is developing an external hydrometeorological modeling and data assimilation system. The objective of this study is to verify the improvement in simulating snow cover extent (SCE) and snow water equivalent (SWE) over the Canadian Rockies with this new modeling system. This study will be an important first step in determining the optimal configuration of the land surface model and atmospheric forcing for a future operational implementation. Simulated SCE is compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) analysis, while simulated SWE values are verified against a series of manual snow survey sites located within the Canadian Rockies. Results show that land surface model simulations of SCE and SWE were sensitive to precipitation forcing. Simulations at both low and high resolution forced with EC’s experimental precipitation analysis were found to underestimate SCE and SWE values. Mountain snowpack retreated too early during the spring melt period. Precipitation forcing derived from EC’s short-range NWP model resulted in improved values for both SCE and SWE, which also contributed to higher contributions to streamflow. Terrain adjusting the atmospheric forcing data was found to be important for properly modeling local extreme SWE values. A comparison with available precipitation observations over the Canadian Rockies region found EC’s experimental precipitation analysis to possess a negative precipitation bias that increases with increasing elevation.

Analysis of simulated and spaceborne passive microwave brightness temperatures using in situ measurements of snow and vegetation properties

Variations in snow cover and vegetation in the Province of Quebec, Canada, were characterized for a transect spanning from 50°N to 60°N during the International Polar Year field campaign of February 2008. The main objective of this study was to compare measured (AMSR-E) and modeled (MEMLS) brightness temperature (Tb) and to analyze differences in the in situ measurement of snow water equivalent (SWE) and vegetation. Sampling involved detailed snow measurements on the ground in four different ecological environments. Measured and modeled SWE were compared using a thermodynamic multilayered snow model (SNOWPACK) driven with North American Regional Reanalysis (NARR) data. The root mean square error (RMSE) of modeled data compared with measurements was 63 mm (30%). The simulated SWE was generally underestimated throughout the transect but stayed within the large standard deviation observed for measured SWE. In situ snow measurements were used as input to a microwave emission model (MEMLS) to simulate Tb. An innovative approach using calibrated near-infrared reflectance photographs was used to characterize the effective snow grain-size parameter needed for the radiative transfer model. Although some results provided Tb predictions similar to AMSR-E data for certain areas, large differences remained for the majority of sampling sites. The derived RMSE of 16 K and 32 K, respectively, for 18.7 and 36.5 GHz (vertical polarization) throughout the transect cannot be explained solely in terms of grain-size variations introduced into the simulations. Local variability in snow structure and thickness produced large variability (up to 60 K within one AMSR-E pixel) compared with AMSR-E Tb throughout the transect (15 K for 18.7 GHz and 35 K for 36.5 GHz).

Use of distributed snow measurements to test and improve a snowmelt model for predicting the effect of forest clear-cutting

Summary Modeling the effect of forest clear-cutting with a distributed hydrological model can be used to detect hydrologic changes as an alternative to paired-catchment studies, and also to estimate the hydrologic sensitivity of a catchment to assist in forest management decisions. To model the effect of clear-cutting in a snow dominated forested watershed, a model needs to be able to simulate effects of all of the main controls on snow accumulation and melt. However, most studies that used a distributed modeling approach relied on one or a few sites for model testing. In this study, we employ a stratified nested sample layout that was specifically designed to test distributed models, involving measurement of snow water equivalent (SWE) under forest and in clearcuts over a range of elevations and aspects. To test the ability of a model to simulate the main controls on the spatial distribution of SWE, spatial gradients of observed and simulated SWE in relation to topographic and vegetation controls are computed using regression analysis. Comparison of observed and simulated gradients helps to highlight model weaknesses. The approach is applied to evaluate the snow algorithms in the distributed hydrology soil and vegetation model (DHSVM) using data collected in Cotton Creek, a snow dominated forested watershed in south-eastern British Columbia. SWE measurements were made from 2005 to 2008, covering peak snow accumulation and snow melt. Albedo decay and canopy transmittance were found to be the two processes that DSHVM version 3.0 did not simulate well enough to predict basin average differences between forests and clearcuts properly. After replacing the internal albedo decay functions with functions obtained from snow albedo measurements and changing the canopy transmittance function in the model, DHSVM was able to reproduce the major spatial patterns derived from snow surveys. Model performance is better during winter up to the peak snow accumulation than during snow melt. Spatial patterns of peak snow accumulation in the snow-rich year 2006 can be modeled better than those after the warm winter of 2005. The influence of aspect on snow accumulation and snowmelt is underestimated by DHSVM. While we have focused specifically on DHSVM, the methods developed in this study should be generally useful for model testing purposes and important in the context of interpretation of modeling results, in particular when dealing with large spatial datasets.

Modeling snow accumulation and ablation processes in forested environments

The effects of forest canopies on snow accumulation and ablation processes can be very important for the hydrology of midlatitude and high‐latitude areas. A mass and energy balance model for snow accumulation and ablation processes in forested environments was developed utilizing extensive measurements of snow interception and release in a maritime mountainous site in Oregon. The model was evaluated using 2 years of weighing lysimeter data and was able to reproduce the snow water equivalent (SWE) evolution throughout winters both beneath the canopy and in the nearby clearing, with correlations to observations ranging from 0.81 to 0.99. Additionally, the model was evaluated using measurements from a Boreal Ecosystem‐Atmosphere Study (BOREAS) field site in Canada to test the robustness of the canopy snow interception algorithm in a much different climate. Simulated SWE was relatively close to the observations for the forested sites, with discrepancies evident in some cases. Although the model formulation appeared robust for both types of climates, sensitivity to parameters such as snow roughness length and maximum interception capacity suggested the magnitude of improvements of SWE simulations that might be achieved by calibration.

Mesocell Study Area Snow Distributions for the Cold Land Processes Experiment (CLPX)

AbstractThe Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can distribute meteorological forcings, simulate snowpack accumulation and ablation processes, and assimilate snow-related observations. A SnowModel was developed and used to simulate winter snow accumulation across three 30 km × 30 km domains, enveloping the CLPX mesocell study areas (MSAs) in Colorado. The three MSAs have distinct topography, vegetation, meteorological, and snow characteristics. Simulations were performed using a 30-m grid increment and spanned the snow accumulation season (1 October 2002–1 April 2003). Meteorological forcing was provided by 27 meteorological stations and 75 atmospheric analyses grid points, distributed using a meteorological model (MicroMet). The simulations included a data assimilation model (SnowAssim) that adjusted simulated snow water equivalent (SWE) toward ground-based and airborne SWE observations. The observations consisted of SWE over three 1 km × 1 km intensive study areas (ISAs) for each MSA and a collection of 117 airborne gamma observations, each integrating area 10 km long by 300 m wide. Simulated SWE distributions displayed considerably more spatial heterogeneity than the observations alone, and the simulated distribution patterns closely fit the current understanding of snow evolution processes and observed snow depths. This is the result of the MicroMet/SnowModel’s relatively finescale representations of orographic precipitation, elevation-dependant snowmelt, wind redistribution, and snow–vegetation interactions.

A Simple Data Assimilation System for Complex Snow Distributions (SnowAssim)

Abstract A methodology for assimilating ground-based and remotely sensed snow data within a snow-evolution modeling system (SnowModel) is presented. The data assimilation scheme (SnowAssim) is consistent with optimal interpolation approaches in which the differences between the observed and modeled snow values are used to constrain modeled outputs. The calculated corrections are applied retroactively to create improved fields prior to the assimilated observations. Thus, one of the values of this scheme is the improved simulation of snow-related distributions throughout the entire snow season, even when observations are only available late in the accumulation and/or ablation periods. Because of this, the technique is particularly applicable to reanalysis applications. The methodology includes the ability to stratify the assimilation into regions where either the observations and/or model has unique error properties, such as the differences between forested and nonforested snow environments. The methodologies are introduced using synthetic data and a simple simulation domain. In addition, the model is applied over NASA’s Cold Land Processes Experiment (CLPX), Rabbit Ears Pass, Colorado, observation domain. Simulations using the data assimilation scheme were found to improve the modeled snow water equivalent (SWE) distributions, and simulated SWE displayed considerably more realistic spatial heterogeneity than that provided by the observations alone.

Scanning multichannel microwave radiometer snow water equivalent assimilation

Accurate prediction of snowpack status is important for a range of environmental applications, yet model estimates are typically poor and in situ measurement coverage is inadequate. Moreover, remote sensing estimates are spatially and temporally limited due to complicating effects, including distance to open water, presence of wet snow, and presence of thick snow. However, through assimilation of remote sensing estimates into a land surface model, it is possible to capitalize on the strengths of both approaches. In order to achieve this, reliable estimates of the uncertainty in both remotely sensed and model simulated snow water equivalent (SWE) estimates are critical. For practical application, the remotely sensed SWE retrieval error is prescribed with a spatially constant but monthly varying value, with data omitted for (1) locations closer than 200 km to significant open water, (2) times and locations with model‐predicted presence of liquid water in the snowpack, and (3) model SWE estimates greater than 100 mm. The model error is estimated using standard error propagation with a calibrated spatially and temporally constant model error contribution. A series of tests have been performed to assess the assimilation algorithm performance. Multiyear model simulations with and without remotely sensed SWE assimilation are presented and evaluated with in situ SWE observations. The SWE estimates from assimilation were found to be superior to both the model simulation and remotely sensed estimates alone, except when model SWE estimates rapidly and erroneously crossed the 100‐mm SWE cutoff early in the snow season.

Modified snow algorithms in the Canadian land surface scheme: Model runs and sensitivity analysis at three boreal forest stands

Version 3.1 of the Canadian Land Surface Scheme (CLASS) contains a number of new algorithms of significance for snow simulations in the boreal forest. In particular, mixed precipitation is now modelled, the density of fresh snow varies with temperature and the maximum snowpack density varies with snow depth. A model for canopy interception and unloading of snow developed in the Canadian boreal forest has also been implemented. In this paper, nine‐month column runs of CLASS 3.1 are compared with CLASS 2.7, the current operational version. The model runs span the winter of 2002–03 at three boreal forest sites: a mature aspen stand, a mature jack pine stand and a mature black spruce stand, all located in central Saskatchewan. The focus is on the winter performance and the representation of snow. More accurate (lower) values of modelled snow density improve the modelled snowpack depth. The accuracy of the canopy interception algorithm could not be tested directly with respect to measured interception, but results suggest that the ability to unload intercepted snow is important for accurate estimates of sublimation loss, and that simulated snow water equivalent is sensitive to perceived canopy gap fraction, interception capacity, and unloading rate. Underestimation of the canopy gap fraction increases canopy interception and sublimation losses, and decreases snow water equivalent in the snowpack, and vice versa. Employing modified gap fraction values improved the modelled snow water equivalent at two of the sites. Modifications to the model are suggested to allow the total albedo to respond to changes in the modelled sub‐canopy albedo.

Updating a Land Surface Model with MODIS-Derived Snow Cover

Abstract A simple scheme for updating snow-water storage in a land surface model using snow cover observations is presented. The scheme makes use of snow cover observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites. Simulated snow-water equivalent is adjusted when and where the model and MODIS observation differ, following an internal accounting of the observation quality, by either removing the simulated snow or adding a thin layer. The scheme is tested in a 101-day global simulation of the Mosaic land surface model driven by the NASA/NOAA Global Land Data Assimilation System. Output from this simulation is compared to that from a control (not updated) simulation, and both are assessed using a conventional snow cover product and data from ground-based observation networks over the continental United States. In general, output from the updated simulation displays more accurate snow coverage and compares more favorably with in situ snow time series. Both the control and updated simulations have serious deficiencies on occasion and in certain areas when and where the precipitation and/or surface air temperature forcing inputs are unrealistic, particularly in mountainous regions. Suggestions for developing a more sophisticated updating scheme are presented.

Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent

This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's Snowpack Telemetry (SNOTEL) network for a 3‐year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic low bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000 mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000 mm at certain stations. Experiments with the VIC model indicated that most of the bias in SWE is removed by scaling the precipitation by a regional factor based on the regression of the NLDAS and SNOTEL precipitation. Individual station errors may be reduced further still using precipitation scaled to the local station SNOTEL record. Furthermore, the NLDAS air temperature is shown to be generally colder in winter months and biased warmer in spring and summer when compared to the SNOTEL record, although the level of bias is regionally dependent. Detailed analysis at a selected station indicate that errors in the air temperature forcing may cause the partitioning of precipitation into snowfall and rainfall by the models to be incorrect and thus may explain some of the remaining errors in the simulated SWE.