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Yves Delage and Diana Verseghy

Abstract

A new land surface scheme developed for the Canadian general circulation model has been introduced into the Canadian global forecast model and tested for a summer case. It features three soil layers, a snow layer, and a vegetation layer; its behavior is compared with that of the current operational force–restore scheme, in which the evaporation is a prescribed function of the climatological soil moisture content. The most noticeable effects of replacing the operational scheme by the new scheme are a reduction of the evaporation and an increase of the sensible heat flux at the surface, a result that has also been found in other models with similar schemes. In this study, we additionally examine the impact of changing the initial soil moisture (ISM): this quantity proves to be of primary importance in setting the amplitude of the Bowen ratio for several days after the beginning of a forecast. The study also points out problems in the Canadian global forecast model that are not caused by the land surface schemes but that do have an impact on their performance, in particular, a cold and moist bias in the lower troposphere, and an excess of solar radiation at the surface. The high sensitivity of the temperature and humidity forecasts to ISM enabled the construction of a hypothetical ISM field based on forecast errors. When this field is used instead of a climatological estimate to initiate the forecast, the standard deviation of the temperature error is reduced by 20%. This suggests that national meteorological centers should produce soil moisture analyses to initiate their weather forecasts.

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Diana Verseghy, Ross Brown, and Libo Wang

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.

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Diana L. Verseghy and Murray D. MacKay

Abstract

The Canadian Small Lake Model (CSLM), version 2, was run with the Canadian Land Surface Scheme (CLASS), version 3.6.1, in an offline regional test over western Canada. Forcing data were derived from ERA-Interim and downscaled using the fifth-generation Canadian Regional Climate Model (CRCM5). The forcing precipitation field was adjusted using monthly data from the Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) observation-based dataset. The modeled surface air temperature was evaluated against CANGRD data, the modeled albedo against MODIS data, and the modeled snow water equivalent against Canadian Meteorological Centre (CMC) and Global Snow Monitoring for Climate Research (GlobSnow) data. The lake simulation itself was evaluated using the Along Track Scanning Radiometer (ATSR) Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) dataset. Summer surface lake temperatures and the lake ice formation and breakup periods were well simulated, except for slight warm/cold summer/fall surface temperature biases, early ice breakup, and early ice formation, consistent with warm/cold biases in the climate simulation. Tests were carried out to investigate the sensitivity of the CSLM simulation to the default values assigned to the shortwave extinction coefficient and the average lake depth, and changing the former from 0.5 to 2.0 m−1 and the latter from 10.0 to 50.0 or 5.0 m had minimal effects on the simulation. Comparisons of the average annual variations of the simulated net shortwave radiation, turbulent fluxes, snowpack, and maximum and minimum daily surface temperatures between the land and the lake fractions for tundra, boreal, and southern regions showed patterns consistent with those expected. Finally, a test of the CSLM over the large resolved lakes in the model domain demonstrated a performance comparable to that for subgrid lakes.

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Murray D. MacKay, Diana L. Verseghy, Vincent Fortin, and Michael D. Rennie

Abstract

A one-dimensional mixed layer dynamic lake model is enhanced with snow and ice physics for an examination of processes governing ice cover and phenology in a small boreal lake. The complete snowpack physics module of the Canadian Land Surface Scheme along with a new snow-ice parameterization have been added to the Canadian Small Lake Model, and detailed meteorological and temperature profile data have been acquired for the forcing and evaluation of two wintertime simulations. During the first winter, simulated ice-on and ice-off biases were −3 and −5 days, respectively. In the second winter simulation, ice-on bias was larger, likely due to the absence of a frazil ice scheme in the model, and simulated ice-off was 6 days late, evidently due to insufficient convective mixing beneath the ice in the weeks leading up to ice-off. Ice cover was simulated about 25% too thin between January and March for this year, though late January simulated snow and snow-ice amounts were close to observed. The impact of snow-ice production on simulated ice cover and phenology was found to be dramatic for this lake. In the absence of this process, January snow was more than twice as deep as observed and March ice thickness was less than one-third of that observed. Without snow-ice production, a reasonable simulation of ice cover could only be restored if 62% of snowfall was removed ad hoc (e.g., through blowing snow redistribution)—an excessive amount for a small, sheltered boreal lake.

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Pablo F. Dornes, John W. Pomeroy, Alain Pietroniro, and Diana L. Verseghy

Abstract

Small-scale topography and snow redistribution have important effects on snow-cover heterogeneity and the timing, rate, and duration of spring snowmelt in mountain tundra environments. However, land surface schemes (LSSs) are usually applied as a means to provide large-scale surface states and vertical fluxes to atmospheric models and do not normally incorporate topographic effects or horizontal fluxes in their calculations

A study was conducted in Granger Creek, an 8-km2 catchment within Wolf Creek Research Basin in the Yukon Territory, Canada, to examine whether inclusion of the effects of wind redistribution of snow between landscape units, and slope and aspect in snowmelt calculations for tiles, could improve the simulation of snowmelt by an LSS.

Measured snow accumulation, reflecting overwinter wind redistribution of snow, was used to provide initial conditions for the melt simulation, and physically based algorithms from a small-scale hydrological model were used to calculate radiation on slopes during melt. Based on consideration of the spatial distribution of snow accumulation, topography, and shrub cover in the basin, it was divided into five landscapes units (tiles) for simulation of mass and energy balance using an LSS during melt. Effects of averaging initial conditions and forcing data on LSS model performance were contrasted against distributed simulations. Results showed that, in most of the cases, simulations using aggregated initial conditions and forcing data gave unsuccessful descriptions of snow ablation whereas the incorporation of both snow-cover redistribution and slope and aspect effects in an LSS improved the prediction of snowmelt rate, timing, and duration.

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James Foster, Glen Liston, Randy Koster, Richard Essery, Helga Behr, Lydia Dumenil, Diana Verseghy, Starly Thompson, David Pollard, and Judah Cohen

Abstract

Confirmation of the ability of general circulation models (GCMs) to accurately represent snow cover and snow mass distributions is vital for climate studies. There must be a high degree of confidence that what is being predicted by the models is reliable, since realistic results cannot be assured unless they are tested against results from observed data or other available datasets. In this study, snow output from seven GCMs and passive-microwave snow data derived from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) are intercompared. National Oceanic and Atmospheric Administration satellite data are used as the standard of reference for snow extent observations and the U.S. Air Force snow depth climatology is used as the standard for snow mass. The reliability of the SMMR snow data needs to be verified, as well, because currently this is the only available dataset that allows for yearly and monthly variations in snow depth. [The GCMs employed in this investigation are the United Kingdom Meteorological Office, Hadley Centre GCM, the Max Planck Institute for Meteorology/University of Hamburg (ECHAM) GCM, the Canadian Climate Centre GCM, the National Center for Atmospheric Research (GENESIS) GCM, the Goddard Institute for Space Studies GCM, the Goddard Laboratory for Atmospheres GCM and the Goddard Coupled Climate Dynamics Group (AIRES) GCM.] Data for both North America and Eurasia are examined in an effort to assess the magnitude of spatial and temporal variations that exist between the standards of reference, the models, and the passive microwave data. Results indicate that both the models and SMMR represent seasonal and year-to-year snow distributions fairly well. The passive microwave data and several of the models, however, consistently underestimate snow mass, but other models overestimate the mass of snow on the ground. The models do a better job simulating winter and summer snow conditions than in the transition months. In general, the underestimation by SMMR is caused by absorption of microwave energy by vegetation. For the GCMs, differences between observed snow conditions can be ascribed to inaccuracies in simulating surface air temperatures and precipitation fields, especially during the spring and fall.

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Sonia I. Seneviratne, Randal D. Koster, Zhichang Guo, Paul A. Dirmeyer, Eva Kowalczyk, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Keith W. Oleson, and Diana Verseghy

Abstract

Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment.

Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs’ behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation’s sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff’s sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.

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Zhichang Guo, Paul A. Dirmeyer, Randal D. Koster, Y. C. Sud, Gordon Bonan, Keith W. Oleson, Edmond Chan, Diana Verseghy, Peter Cox, C. T. Gordon, J. L. McGregor, Shinjiro Kanae, Eva Kowalczyk, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Ken Mitchell, Sergey Malyshev, Bryant McAvaney, Taikan Oki, Tomohito Yamada, Andrew Pitman, Christopher M. Taylor, Ratko Vasic, and Yongkang Xue

Abstract

The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.

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Randal D. Koster, Y. C. Sud, Zhichang Guo, Paul A. Dirmeyer, Gordon Bonan, Keith W. Oleson, Edmond Chan, Diana Verseghy, Peter Cox, Harvey Davies, Eva Kowalczyk, C. T. Gordon, Shinjiro Kanae, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Ken Mitchell, Sergey Malyshev, Bryant McAvaney, Taikan Oki, Tomohito Yamada, Andrew Pitman, Christopher M. Taylor, Ratko Vasic, and Yongkang Xue

Abstract

The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.

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