scholarly journals Evaluation of Snow Water Equivalent in NARCCAP Simulations, Including Measures of Observational Uncertainty

2017 ◽  
Vol 18 (9) ◽  
pp. 2425-2452 ◽  
Author(s):  
Rachel R. McCrary ◽  
Seth McGinnis ◽  
Linda O. Mearns
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Dominant Role ◽  
Regional Climate ◽  
Land Surface Model ◽  
Surface Model ◽  
Observational Uncertainty ◽  
Snow Water ◽  
Noah Land Surface Model

Abstract This study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs’ ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. HadRM3 (HRM3) underestimates SWE because of warm temperature biases, while in the version of WRF using the Grell scheme (WRFG) and ECPC-RSM (ECP2), the misrepresentation of snow in the Noah land surface model plays the dominant role in SWE bias, particularly in ECP2 where sublimation is too high.

Evaluation of Temperature Simulation over Northern Thailand from Regional Climate Model Coupled with Land Surface Model

2017 ◽  
Vol 866 ◽  
pp. 108-111
Author(s):  
Theerapan Saesong ◽  
Pakpoom Ratjiranukool ◽  
Sujittra Ratjiranukool
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Global Analysis ◽  
Regional Climate ◽  
Land Surface Model ◽  
Analysis Data ◽  
Surface Model ◽  
Northern Thailand ◽  
Weather Model

Numerical Weather Model called The Weather Research and Forecasting model, WRF, developed by National Center for Atmospheric Research (NCAR) is adapted to be regional climate model. The model is run to perform the daily mean air surface temperatures over northern Thailand in 2010. Boundery dataset provided by National Centers for Environmental Prediction, NCEP FNL, (Final) Operational Global Analysis data which are on 10 x 10. The simulated temperatures by WRF with four land surface options, i.e., no land surface scheme (option 0), thermal diffusion (option 1), Noah land-surface (option 2) and RUC land-surface (option 3) were compared against observational data from Thai Meteorological Department (TMD). Preliminary analysis indicated WRF simulations with Noah scheme were able to reproduce the most reliable daily mean temperatures over northern Thailand.

scholarly journals Evaluation of CLASS Snow Simulation over Eastern Canada

2017 ◽  
Vol 18 (5) ◽  
pp. 1205-1225 ◽  
Author(s):  
Diana Verseghy ◽  
Ross Brown ◽  
Libo Wang
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern Canada ◽  
Fractional Snow Cover ◽  
Forest Regions ◽  
Snow Water ◽  
Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010

2015 ◽  
Vol 28 (20) ◽  
pp. 8037-8051 ◽  
Author(s):  
L. R. Mudryk ◽  
C. Derksen ◽  
P. J. Kushner ◽  
R. Brown
Keyword(s):  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Temporal Consistency ◽  
Meteorological Forcing ◽  
Temporal Correlations ◽  
Snow Water

Abstract Five, daily, gridded, Northern Hemisphere snow water equivalent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and temporal consistency of satellite retrievals, land surface assimilation systems, physical snow models, and reanalyses. While the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the datasets by as much as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good temporal correlations (between 0.60 and 0.85) on both interannual and intraseasonal time scales. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among the five datasets but vary in strength by a factor of 2–3. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and alpine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlated with one another and show better correlations with the satellite product [the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow)] than do those using older reanalyses. Finally, a comparison of eight reanalysis datasets over the 2001–10 period shows that land surface model differences control the majority of spread in the climatological value of SWM, while meteorological forcing differences control the majority of the spread in temporal correlations of SWM anomalies.

scholarly journals Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model

2009 ◽  
Vol 10 (1) ◽  
pp. 130-148 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Matthew Rodell
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Radiation Budget ◽  
Surface Model ◽  
Energy Fluxes ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Abstract Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring.

scholarly journals Simulation of Northern Eurasian Local Snow Depth, Mass, and Density Using a Detailed Snowpack Model and Meteorological Reanalyses

2013 ◽  
Vol 14 (1) ◽  
pp. 203-219 ◽  
Author(s):  
Eric Brun ◽  
Vincent Vionnet ◽  
Aaron Boone ◽  
Bertrand Decharme ◽  
Yannick Peings ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Northern Eurasia ◽  
Surface Model ◽  
Monthly Precipitation ◽  
Blowing Snow ◽  
Snow Water

Abstract The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.

scholarly journals Assessment of the Urban Impact on Surface and Screen-Level Temperature in the ALADIN-Climate Driven SURFEX Land Surface Model for Budapest

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 709
Author(s):  
Gabriella Zsebeházi ◽  
Sándor István Mahó
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Land Surface Model ◽  
Surface Model ◽  
Urban Impact ◽  
Model Driven ◽  
The Impact

Land surface models with detailed urban parameterization schemes provide adequate tools to estimate the impact of climate change in cities, because they rely on the results of the regional climate model, while operating on km scale at low cost. In this paper, the SURFEX land surface model driven by the evaluation and control runs of ALADIN-Climate regional climate model is validated over Budapest from the aspect of urban impact on temperature. First, surface temperature of SURFEX with forcings from ERA-Interim driven ALADIN-Climate was compared against the MODIS land surface temperature for a 3-year period. Second, the impact of the ARPEGE global climate model driven ALADIN-Climate was assessed on the 2 m temperature of SURFEX and was validated against measurements of a suburban station for 30 years. The spatial extent of surface urban heat island (SUHI) is exaggerated in SURFEX from spring to autumn, because the urbanized gridcells are generally warmer than their rural vicinity, while the observed SUHI extent is more variable. The model reasonably simulates the seasonal means and diurnal cycle of the 2 m temperature in the suburban gridpoint, except summer when strong positive bias occurs. However, comparing the two experiments from the aspect of nocturnal UHI, only minor differences arose. The thorough validation underpins the applicability of SURFEX driven by ALADIN-Climate for future urban climate projections.

scholarly journals Land surface model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v3.9 model over North America

2020 ◽  
Vol 13 (11) ◽  
pp. 5345-5366
Author(s):  
Almudena García-García ◽  
Francisco José Cuesta-Valero ◽  
Hugo Beltrami ◽  
Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Temperature And Precipitation ◽  
Model Ensembles ◽  
Precipitation Events

Abstract. The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we analyze land–atmosphere interactions within four simulations performed by the WRF model from 1980 to 2012 over North America, using three different LSMs. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Our results show that the WRF simulation of the climatology of heat extremes can be 5 ∘C warmer and 6 d longer depending on the employed LSM component, and similarly for cold extremes and heavy precipitation events. Areas showing large uncertainty in WRF-simulated extreme events are also identified in a model ensemble from three different regional climate model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. Thus, studies based on multi-model ensembles and reanalyses should include a variety of LSM configurations to account for the uncertainty arising from this model component or to test the performance of the selected LSM component before running the whole simulation. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating extreme temperature and precipitation events, which in turn will help to reduce uncertainties in climate model projections.

Evaluation of the snow cover in the Soil, Vegetation, and Snow (SVS) land surface model

2021 ◽  
Author(s):  
Gonzalo Leonardini ◽  
François Anctil ◽  
Vincent Vionnet ◽  
Maria Abrahamowicz ◽  
Daniel F. Nadeau ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Late Winter ◽  
Surface Model ◽  
Lower Performance ◽  
Snow Water

AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at ten well-instrumented sites from the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force-restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

scholarly journals Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

2010 ◽  
Vol 11 (2) ◽  
pp. 352-369 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle ◽  
Paul R. Houser ◽  
Kristi R. Arsenault ◽  
Niko E. C. Verhoest ◽  
...  
Keyword(s):  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
A Priori ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Coarse Scale ◽  
Spatial Error ◽  
Snow Water

Abstract Four methods based on the ensemble Kalman filter (EnKF) are tested to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of passive microwave satellite retrievals) into finescale (1 km) land model simulations. Synthetic coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (regridding) to the finescale model resolution prior to data assimilation. In either case, observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated finescale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the finescale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

Sign in / Sign up

Export Citation Format

Share Document