Abstract

A finescale model of blowing snow is used to simulate the characteristics of snow cover in a low-Arctic catchment with moderate topography and partial shrub cover. The influence of changing shrub characteristics is investigated by performing a sequence of simulations with varying shrub heights and coverage. Increasing shrub height gives an increase in snow depth within the shrub-covered areas, up to a limit determined by the supply of falling and blowing snow, but increasing shrub coverage gives a decrease in snow depths within shrubs as the supply of blowing snow imported from open areas is reduced. A simulation of snow redistribution over the existing topography without any shrub cover gives much greater accumulations of snow on slopes in the lee of the prevailing wind than on windward slopes; in contrast, shrubs are able to trap snow on both lee and windward slopes. A spatially aggregated, or tiled, model is developed in which snow is relocated by wind transport from sparsely vegetated tiles to more densely vegetated tiles. The vegetation distribution is not specified, but the simulation is parameterized using average fetch lengths along the major transport axis. The aggregated model is found to be capable of matching the average snow accumulation in shrub and open areas predicted by the distributed model reasonably well but with much less computational cost.

Abstract

Improved representations of snow interception by coniferous forest canopies and sublimation of intercepted snow are implemented in a land surface model. Driven with meteorological observations from forested sites in Canada, the United States, and Sweden, the modified model is found to give reduced sublimation, better simulations of snow loads on and below canopies, and improved predictions of snowmelt runoff. When coupled to an atmospheric model in a GCM, however, drying and warming of the air because of the reduced sublimation provides a feedback that limits the impact of the new canopy snow model on the predicted sublimation. There is little impact on the average annual snowmelt runoff in the GCM, but runoff is delayed and peak runoff increased by the introduction of the canopy snow model.

Abstract

A number of general circulation model (GCM) experiments have shown that changes in vegetation in the Sahel can cause substantial reductions in rainfall. In some studies, the climate sensitivity is large enough to trigger drought of the severity observed since the late 1960s. The extent and intensity of vegetation changes are crucial in determining the magnitude of the atmospheric response in the models. However, there is no accurate historical record of regional vegetation changes extending back to before the drought began. One important driver of vegetation change is land use practice. In this paper the hypothesis that recent changes in land use have been large enough to cause the observed drought is tested. Results from a detailed land use model are used to generate realistic maps of vegetation changes linked to land use. The land use model suggests that cropland coverage in the Sahel has risen from 5% to 14% in the 35 yr prior to 1996. It is estimated that this process of agricultural extensification, coupled with deforestation and other land use changes, translates to a conversion of 4% of the land from tree cover to bare soil over this period. The model predicts further changes in the composition of the land surface by 2015 based on changes in human population (rural and urban), livestock population, rainfall, cereals imports, and farming systems.

The impact of land use change on Sahelian climate is assessed using a GCM, forced by the estimates of land use in 1961, 1996, and 2015. Relative to 1961 conditions, simulated rainfall decreases by 4.6% (1996) and 8.7% (2015). The decreases are closely linked to a later onset of the wet season core during July. Once the wet season is well developed, however, the sensitivity of total rainfall to the land surface is greatly reduced, and depends on the sensitivity of synoptic disturbances to the land surface. The results suggest that while the climate of the region is rather sensitive to small changes in albedo and leaf area index, recent historical land use changes are not large enough to have been the principal cause of the Sahel drought. However, the climatic impacts of land use change in the region are likely to increase rapidly in the coming years.

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.

The Northern Hemisphere has large areas that are forested and seasonally snow covered. Compared with open areas, forest canopies strongly influence interactions between the atmosphere and snow on the ground by sheltering the snow from wind and solar radiation and by intercepting falling snow; these influences have important consequences for the meteorology, hydrology, and ecology of forests. Many of the land surface models used in meteorological and hydrological forecasting now include representations of canopy snow processes, but these have not been widely tested in comparison with observations. Phase 2 of the Snow Model Intercomparison Project (SnowMIP2) was therefore designed as an intercomparison of surface mass and energy balance simulations for snow in forested areas. Model forcing and calibration data for sites with paired forested and open plots were supplied to modeling groups. Participants in 11 countries contributed output from 33 models, and the results are published here for sites in Canada, the United States, and Switzerland. On average, the models perform fairly well in simulating snow accumulation and ablation, although there is a wide intermodal spread and a tendency to underestimate differences in snow mass between open and forested areas. Most models capture the large differences in surface albedos and temperatures between forest canopies and open snow well. There is, however, a strong tendency for models to underestimate soil temperature under snow, particularly for forest sites, and this would have large consequences for simulations of runoff and biological processes in the soil.

Abstract

The spatial variation of melt energy can influence snow cover depletion rates and in turn be influenced by the spatial variability of shortwave irradiance to snow. The spatial variability of shortwave irradiance during melt under uniform and discontinuous evergreen canopies at a U.S. Rocky Mountains site was measured, analyzed, and then compared to observations from mountain and boreal forests in Canada. All observations used arrays of pyranometers randomly spaced under evergreen canopies of varying structure and latitude. The spatial variability of irradiance for both overcast and clear conditions declined dramatically, as the sample averaging interval increased from minutes to 1 day. At daily averaging intervals, there was little influence of cloudiness on the variability of subcanopy irradiance; instead, it was dominated by stand structure. The spatial variability of irradiance on daily intervals was higher for the discontinuous canopies, but it did not scale reliably with canopy sky view. The spatial variation in irradiance resulted in a coefficient of variation of melt energy of 0.23 for the set of U.S. and Canadian stands. This variability in melt energy smoothed the snow-covered area depletion curve in a distributed melt simulation, thereby lengthening the duration of melt by 20%. This is consistent with observed natural snow cover depletion curves and shows that variations in melt energy and snow accumulation can influence snow-covered area depletion under forest canopies.

Abstract

This study investigates the dependence of net radiation at snow surfaces under forest canopies on the overlying canopy density. The daily sum of positive values of net radiation is used as an index of the snowmelt rate. Canopy cover is represented in terms of shortwave transmissivity and sky-view factor. The cases studied are a spruce forest in the Wolf Creek basin, Yukon Territory, Canada, and a pine forest near Fraser, Colorado. Of particular interest are the atmospheric conditions that favor an offset between shortwave energy attenuation and longwave irradiance enhancement by the canopy, such that net radiation does not decrease with increasing forest density. Such an offset is favored in dry climates and at high altitudes, where atmospheric emissivities are low, and in early spring when snow albedos are high and solar elevations are low. For low snow albedos, a steady decrease in snowmelt energy with increasing canopy cover is found, up to a forest density close to the actual densities of mature spruce forests. Snowmelt rates for high albedos are either insensitive or increase with increasing canopy cover. At both sites, foliage area indices close to 2 are associated with a minimum in net radiation, independent of snow albedo or cloud cover. However, these results are more uncertain for open forests because solar heating of trees may invalidate the longwave assumptions, increasing the longwave irradiance.

Abstract

Solar radiation beneath a forest canopy can have large spatial variations, but this is frequently neglected in radiative transfer models for large-scale applications. To explicitly model spatial variations in subcanopy radiation, maps of canopy structure are required. Aerial photography and airborne laser scanning are used to map tree locations, heights, and crown diameters for a lodgepole pine forest in Colorado as inputs to a spatially explicit radiative transfer model. Statistics of subcanopy radiation simulated by the model are compared with measurements from radiometer arrays, and scaling of spatial statistics with temporal averaging and array size is discussed. Efficient parameterizations for spatial averages and standard deviations of subcanopy radiation are developed using parameters that can be obtained from the model or hemispherical photography.

Abstract

Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.