Improvement of snowpack simulations in a regional climate model (original) (raw)
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Noah LSM Snow Model Diagnostics and Enhancements
Journal of Hydrometeorology, 2010
A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model's albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales.
Analysis of the Impact of Snow on Daily Weather Variability in Mountainous Regions Using MM5
Journal of Hydrometeorology, 2007
The impacts of snow on daily weather variability, as well as the mechanisms of snowmelt over the Sierra Nevada, California-Nevada, mountainous region, were studied using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) forced by 6-h reanalysis data from the National Centers for Environmental Prediction. The analysis of two-way nested 36-12-km MM5 simulations during the 1998 snowmelt season (April-June) shows that the snow water equivalent (SWE) is underestimated when there are conditions with higher temperature and greater precipitation than observations. An observed daily SWE dataset derived from the snow telemetry network was assimilated into the Noah land surface model within MM5. This SWE assimilation reduces the warm bias. The reduction of the warm bias results from suppressed upward sensible heat flux caused by the decreased skin temperature. This skin temperature reduction is the result of the longer assimilated snow duration than in the model run without SWE assimilation. Meanwhile, the cooled surface leads to a more stable atmosphere, resulting in a decrease in the exaggerated precipitation. Additionally, the detailed analysis of the snowmelt indicates that the absence of vegetation fraction in the most sophisticated land surface model (Noah) in the MM5 package results in an overestimation of solar radiation reaching the snow surface, giving rise to heavier snowmelt. An underestimated surface albedo also weakly contributes to the stronger snowmelt. The roles of the vegetation fraction and albedo in snowmelt are further verified by an additional offline simulation from a more realistic land surface model with advanced snow and vegetation schemes forced by the MM5 output. An improvement in SWE description is clearly seen in this offline simulation over the Sierra Nevada region.
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.
Journal of Geophysical Research, 2003
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 SNOTEL network for a 3-year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic negative bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000mm. 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 2000mm 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.
Evaluating the Utah Energy Balance (UEB) snow model in the Noah land-surface model
Hydrology and Earth System Sciences, 2014
Noah (version 2.7.1), the community landsurface model (LSM) of National Centers for Environmental Predictions-National Center for Atmospheric Research (NCEP-NCAR), which is widely used to describe the landsurface processes either in stand-alone or in coupled landatmospheric model systems, is recognized to underestimate snow-water equivalent (SWE). Noah's SWE bias can be attributed to its simple snow sub-model, which does not effectively describe the physical processes during snow accumulation and melt period. To improve SWE simulation in the Noah LSM, the Utah Energy Balance (UEB) snow model is implemented in Noah to test alternate snow surface temperature and snowmelt outflow schemes. Snow surface temperature was estimated using the force-restore method and snowmelt event is regulated by accounting for the internal energy of the snowpack. The modified Noah's SWE simulations are compared with the SWE observed at California's NRCS SNOTEL stations for 7 water years: 2002-2008, while the model's snow surface temperature is verified with observed surface-temperature data at an observation site in Utah. The experiments show that modification in Noah's snow process substantially reduced SWE estimation bias while keeping the simplicity of the Noah LSM. The results suggest that the model did not benefit from the alternate temperature representation but primary improvement can be attributed to the substituted snowmelt process.
Journal of Geophysical Research, 2003
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 SNOTEL network for a 3-year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic negative bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000mm. 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 2000mm at certain stations.
Simulating cold season snowpack: Impacts of snow albedo and multi-layer snow physics
Climatic Change, 2011
This study used numerical experiments to investigate two important concerns in simulating the cold season snowpack: the impact of the alterations of snow albedo due to anthropogenic aerosol deposition on snowpack and the treatment of snow physics using a multi-layer snow model. The snow albedo component considered qualitatively future changes in anthropogenic emissions and the subsequent increase or decrease of black carbon deposition on the Sierra Nevada snowpack by altering the prescribed snow albedo values. The alterations in the snow albedo primarily affect the snowpack via surface energy budget with little impact on precipitation. It was found that a decrease in snow albedo (by as little as 5-10% of the reference values) due to an increase in local emissions enhances snowmelt and runoff (by as much as 30-50%) in the early part of a cold season, resulting in reduced snowmelt-driven runoff (by as much as 30-50%) in the later part of the cold season, with the greatest impacts at higher elevations. An increase in snow albedo associated with reduced anthropogenic emissions results in the opposite effects. Thus, the most notable impact of the decrease in snow albedo is to enhance early-season snowmelt and to reduce late-season snowmelt, resulting in an adverse impact on warm season water resources in California. The timing of the sensitivity of snow water equivalent (SWE), snowmelt, and runoff vary systematically according to terrain elevation; as terrain elevation increases, the peak response of these fields occurs later in the cold season. The response of SWE and surface energy budget to the alterations in snow albedo found in this study shows that the effects of snow albedo on snowpack are further enhanced via local snow-albedo Climatic Change (2011) 109 (Suppl 1):S95-S117 feedback. Results from this experiment suggest that a reduction in local emissions, which would increase snow albedo, could alleviate the early snowmelt and reduced runoff in late winter and early spring caused by global climate change, at least partially. The most serious uncertainties associated with this part of the study are a quantification of the relationship between the amount of black carbon deposition and snow albedo-a subject of future study. The comparison of the spring snowpack simulated with a single-and multi-layer snow model during the spring of 1998 shows that a more realistic treatment of snow physics in a multi-layer snow model could improve snowpack simulations, especially during spring when snow ablation is significant, or in conjunction with climate change projections. Fig. 1 The time series of the annual-mean (a) surface air temperature and (b) SWE normalized by the 20th century ensemble mean value (i.e.,% of the 20th century mean SWE) over the regions corresponding to the southern/central California and the Sierra Nevada, respectively, during the 20th and 21st centuries simulated by the 16 GCMs including BCC, BCCR, CGCM, CNRM, CSIRO, GFDL, GISS, FGOALS, INM, IPSL, MIROC3.2, ECHAM5, MRI, CCSM3, HadCM3, and HadGEM1, contributing to the IPCC Assessment Report 4. The thick black line in (b) represents the average over all GCMs S96 Climatic Change (2011) 109 (Suppl 1):S95-S117 Climatic Change (2011) 109 (Suppl 1):S95-S117 S97 S98 Climatic Change (2011) 109 (Suppl 1):S95-S117
Evaluation of snow extent and its variability in the Atmospheric Model Intercomparison Project
Journal of Geophysical Research: Atmospheres, 1998
Simulations of monthly mean northern hemisphere snow extent from 27 atmospheric general circulation models (GCMs), run under the auspices of the Atmospheric Model Intercomparison Project (AMIP), are compared to observations. AMIP model runs have common values for sea surface temperatures specified from observations for the decade 1979 through 1988. Here AMIP GCMs are evaluated in terms of their simulations of (1) snow extent over northern hemisphere lands and (2) synoptic conditions associated with extremes in snow extent over particular regions. Observations of snow extent are taken from digitized charts of remotely sensed snow extent from visible imagery provided by the National Oceanic and Atmospheric Administration. In general, AMIP models reproduce a seasonal cycle of snow extent similar to the observed cycle. However, GCMs tend to underestimate fall and winter snow extent (especially over North America) and overestimate spring snow extent (especially over Eurasia). The majority of models display less than half of the observed interannual variability. No temporal correlation is found between simulated and observed snow extent, even when only months with extremely high or low values are considered. These poor correlations indicate that in the models, interannual fluctuations of snow extent are not driven by sea surface temperatures. GCMs are inconsistent in their abilities to simulate synoptic-scale tropospheric circulation patterns associated with extreme snow extent over North American regions, although some models are able to capture many of the observed teleconnection patterns. concentration and solar insolation [Gates, 1992]. Since boundary conditions are largely specified, model results are comparable to each other. In addition, since the boundary conditions are representative of specific calendar years, model results can be directly compared to observations. Hydrological processes in AMIP GCMs have been evaluated by Lau et al. [1996], who found that models generally simulate global precipitation to within 10%-20%. Heavy precipitation associated with deep convection is reasonably estimated, but the
Erratum to: Simulating cold season snowpack: Impacts of snow albedo and multi-layer snow physics
2013
This study used numerical experiments to investigate two important concerns in simulating the cold season snowpack: the impact of the alterations of snow albedo due to anthropogenic aerosol deposition on snowpack and the treatment of snow physics using a multi-layer snow model. The snow albedo component considered qualitatively future changes in anthropogenic emissions and the subsequent increase or decrease of black carbon deposition on the Sierra Nevada snowpack by altering the prescribed snow albedo values. The alterations in the snow albedo primarily affect the snowpack via surface energy budget with little impact on precipitation. It was found that a decrease in snow albedo (by as little as 5-10% of the reference values) due to an increase in local emissions enhances snowmelt and runoff (by as much as 30-50%) in the early part of a cold season, resulting in reduced snowmelt-driven runoff (by as much as 30-50%) in the later part of the cold season, with the greatest impacts at higher elevations. An increase in snow albedo associated with reduced anthropogenic emissions results in the opposite effects. Thus, the most notable impact of the decrease in snow albedo is to enhance early-season snowmelt and to reduce late-season snowmelt, resulting in an adverse impact on warm season water resources in California. The timing of the sensitivity of snow water equivalent (SWE), snowmelt, and runoff vary systematically according to terrain elevation; as terrain elevation increases, the peak response of these fields occurs later in the cold season. The response of SWE and surface energy budget to the alterations in snow albedo found in this study shows that the effects of snow albedo on snowpack are further enhanced via local snow-albedo Climatic Change (2011) 109 (Suppl 1):S95-S117 feedback. Results from this experiment suggest that a reduction in local emissions, which would increase snow albedo, could alleviate the early snowmelt and reduced runoff in late winter and early spring caused by global climate change, at least partially. The most serious uncertainties associated with this part of the study are a quantification of the relationship between the amount of black carbon deposition and snow albedo-a subject of future study. The comparison of the spring snowpack simulated with a single-and multi-layer snow model during the spring of 1998 shows that a more realistic treatment of snow physics in a multi-layer snow model could improve snowpack simulations, especially during spring when snow ablation is significant, or in conjunction with climate change projections. Fig. 1 The time series of the annual-mean (a) surface air temperature and (b) SWE normalized by the 20th century ensemble mean value (i.e.,% of the 20th century mean SWE) over the regions corresponding to the southern/central California and the Sierra Nevada, respectively, during the 20th and 21st centuries simulated by the 16 GCMs including BCC, BCCR, CGCM, CNRM, CSIRO, GFDL, GISS, FGOALS, INM, IPSL, MIROC3.2, ECHAM5, MRI, CCSM3, HadCM3, and HadGEM1, contributing to the IPCC Assessment Report 4. The thick black line in (b) represents the average over all GCMs S96 Climatic Change (2011) 109 (Suppl 1):S95-S117 Climatic Change (2011) 109 (Suppl 1):S95-S117 S97 S98 Climatic Change (2011) 109 (Suppl 1):S95-S117