Several warm season, late-afternoon precipitation events are simulated over the Chesapeake Bay watershed using the Weather Research and Forecasting (WRF) model at three different resolutions. The onset and peak of surface-based convection are predicted to occur prematurely when two popular cumulus parameterization schemes (Betts-Miller-Janji and Kain-Fritsch) are used. Rainfall predictions are significantly improved with explicit convection. The early bias appears to be associated with the inadequacy in representing convective inhibition (CIN) or negative buoyancy in the trigger for moist convection. In particular, both schemes have weak constraints for the negative buoyancy above cloud base and below the level of free convection, leading to premature rainfall. Satellite-derived soundings suggest that, even with extremely favorable conditions, negative buoyancy in this layer may delay the onset of surface-based convection. Other factors, such as enhanced mixing due to overactive shallow convection, also appear to contribute to the early rainfall bias through the premature removal of CIN during the day.

Land cover changes alter the near surface weather and climate. Changes in land surface properties such as albedo, roughness length, stomatal resistance, and leaf area index alter the surface energy balance, leading to differences in near surface temperatures. This study utilized a newly developed land cover data set for the eastern United States to examine the influence of historical land cover change on June temperatures and precipitation. The new data set contains representations of the land cover and associated biophysical parameters for 1650, 1850, 1920, and 1992, capturing the clearing of the forest and the expansion of agriculture over the eastern United States from 1650 to the early twentieth century and the subsequent forest regrowth. The data set also includes the inferred distribution of potentially water‐saturated soils at each time slice for use in the sensitivity tests. The Regional Atmospheric Modeling System, equipped with the Land Ecosystem‐Atmosphere Feedback (LEAF‐2) land surface parameterization, was used to simulate the weather of June 1996 using the 1992, 1920, 1850, and 1650 land cover representations. The results suggest that changes in surface roughness and stomatal resistance have caused present‐day maximum and minimum temperatures in the eastern United States to warm by about 0.3°C and 0.4°C, respectively, when compared to values in 1650. In contrast, the maximum temperatures have remained about the same, while the minimums have cooled by about 0.1°C when compared to 1920. Little change in precipitation was found.

Invasive shrubs and soot pollution both have the potential to alter the surface energy balance and timing of snow melt in the Arctic. Shrubs reduce the amount of snow lost to sublimation on the tundra during the winter leading to a deeper end‐of‐winter snowpack. The shrubs also enhance the absorption of energy by the snowpack during the melt season by converting incoming solar radiation to longwave radiation and sensible heat. Soot deposition lowers the albedo of the snow, allowing it to more effectively absorb incoming solar radiation and thus melt faster. This study uses the Colorado State University Regional Atmospheric Modeling System version 4.4 (CSU‐RAMS 4.4), equipped with an enhanced snow model, to investigate the effects of shrub encroachment and soot deposition on the atmosphere and snowpack in the Kuparuk Basin of Alaska during the May–June melt period. The results of the simulations suggest that a complete invasion of the tundra by shrubs leads to a 2.2°C warming of 3 m air temperatures and a 108 m increase in boundary layer depth during the melt period. The snow‐free date also occurred 11 d earlier despite having a larger initial snowpack. The results also show that a decrease in the snow albedo of 0.1, owing to soot pollution, caused the snow‐free date to occur 5 d earlier. The soot pollution caused a 1.0°C warming of 3 m air temperatures and a 25 m average deepening of the boundary layer.

The presence of snow and its relationship to surrounding vegetation significantly impacts the surface energy balance. For accurate atmospheric model simulations, the degree to which a snowpack can cover vegetation must be realistically represented. Both vegetation height and snow depth must be reasonably known to determine the amount of masking.The Regional Atmospheric Modeling System/Land Ecosystem - Atmosphere Feedback, version two (RAMS/ LEAF-2) snow model was modified to simulate snow depth in addition to snow water equivalent and was driven offline with observed atmospheric forcing data. The model was run for five of the Boreal Ecosystem - Atmosphere Study (BOREAS) surface mesonet stations over the 1995/96 winter. The time evolution of simulated snow depth was compared with the observed snow depth. Averaged over the winter, the modeled snow depth at the four low-wind stations was within 0.09 m of the observations, and the average percent error was 27%, while the one wind-blown station was considerably worse. The average depth error at all five stations was +/- 0.08 m. This is shown to be sufficient to reasonably account for the surface energy balance effects of vegetation protruding through the snow.

Shrublands cover a large fraction of western North America and are often partially masked by snow during the winter months. Due to the prevalence of this landscape it is important for weather and climate models to accurately simulate the exchange of energy between snow-covered shrublands and the atmosphere. In this study we test the ability of a modified version of the LEAF-2 (Land EcosystemAtmosphere Exchange Feedback) model (Walko et al. 2000) to simulate sensible and latent heat fluxes from a snow-covered mixture of shrubs and grass at North Park Colorado. The fluxes computed by the model using similarity theory are compared to those measured by tower-mounted eddy-covariance instruments that were installed at North Park during the 2002/2003 winter for the FLuxes Over Snow Surfaces (FLOSS) project.

Snow cover can significantly suppress daytime temperatures by increasing the surface albedo and limiting the surface temperature to 0degreesC. The strength of this effect is dependent upon how well the snow can cover, or mask, the underlying surface. In regions where tall vegetation protrudes through a shallow layer of snow, the temperature-reducing effects of the snow will be suppressed since the protruding vegetation will absorb solar radiation and emit an upward turbulent heat flux. This means that an atmospheric model must have a reasonable representation of the land cover, as well as be able to correctly calculate snow depth, if an accurate simulation of surface heat fluxes, air temperatures, and boundary layer structure is to be made. If too much vegetation protrudes through the snow, then the surface sensible heat flux will be too large and the air temperatures will be too high.In this study four simulations are run with the Regional Atmospheric Modeling System (RAMS 4.30) for a snow event that occurred in 1988 over the Texas Panhandle. The first simulation, called the control, is run with the most realistic version of the current land cover and the results verified against both ground stations and aircraft data. Simulations 2 and 3 use the default methods of specifying land cover in RAMS 4.29 and RAMS 4.30, respectively. The significance of these variations in land-cover definition is then examined by comparing with the control run. Finally, the last simulation is run with the land cover defined as all short grass, the natural cover for the region. The results of this study indicate that variations in the land-cover specification can lead to differences in sensible heat flux over snow as large as 80 W m(-2). These differences in sensible heat flux can then lead to differences in daytime temperatures of as much as 6degreesC. Also, the height of the afternoon boundary layer can vary by as much as 200-300 m.In addition, the results suggest that daytime temperatures are cooler over snow in the regions where short grass has been converted to cropland, while they appear to be warmer over regions where shrubs have increased.