Search Results
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
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
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.
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.
