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- Author or Editor: Roger A. Pielke Sr. x
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Abstract
Before European settlement, the Great Plains of the United States contained vast herds of bison. These bison altered the landscape through their grazing. Measurement data of the disturbance that such grazing could produce, when scaled for the large population of bison, were used with a coupled atmospheric–ecosystem model to evaluate the likely effect that this grazing had on the growing season weather in the Great Plains. A dynamically coupled meteorological and plant growth model was used to investigate the regional atmospheric conditions over a single growing season. A 50-km horizontal mesh was implemented, covering the central plains of the United States. The modeling system was then integrated, with a time step of 90 s, for a period covering 1 April 1989 through 31 August 1989 using boundary conditions obtained from an objective analysis of gridded archive data. This integration was performed with and without grazing to assess the effects on regional atmospheric and biological processes. The grazing algorithm was employed to represent presettlement North American bison and was switched on and off for different simulations. The results indicated a cooling response in daily maximum temperatures to removal of grazing. The opposite trends were found for the minimum daily temperature. It was also found that grazing produced significant perturbations in the hydrological cycle.
Abstract
Before European settlement, the Great Plains of the United States contained vast herds of bison. These bison altered the landscape through their grazing. Measurement data of the disturbance that such grazing could produce, when scaled for the large population of bison, were used with a coupled atmospheric–ecosystem model to evaluate the likely effect that this grazing had on the growing season weather in the Great Plains. A dynamically coupled meteorological and plant growth model was used to investigate the regional atmospheric conditions over a single growing season. A 50-km horizontal mesh was implemented, covering the central plains of the United States. The modeling system was then integrated, with a time step of 90 s, for a period covering 1 April 1989 through 31 August 1989 using boundary conditions obtained from an objective analysis of gridded archive data. This integration was performed with and without grazing to assess the effects on regional atmospheric and biological processes. The grazing algorithm was employed to represent presettlement North American bison and was switched on and off for different simulations. The results indicated a cooling response in daily maximum temperatures to removal of grazing. The opposite trends were found for the minimum daily temperature. It was also found that grazing produced significant perturbations in the hydrological cycle.
Abstract
Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.
Abstract
Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.
Abstract
Snow cover can significantly suppress daytime temperatures by increasing the surface albedo and limiting the surface temperature to 0°C. 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 6°C. 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.
Abstract
Snow cover can significantly suppress daytime temperatures by increasing the surface albedo and limiting the surface temperature to 0°C. 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 6°C. 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.
Abstract
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.
Abstract
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.
Abstract
The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such “shifting mean models” are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River.
Abstract
The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such “shifting mean models” are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River.
Abstract
Thirteen regional climate model (RCM) simulations of June–July 1993 were compared with each other and observations. Water vapor conservation and precipitation characteristics in each RCM were examined for a 10° × 10° subregion of the upper Mississippi River basin, containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports.
All RCMs produced positive precipitation minus evapotranspiration (P − E > 0), though most RCMs produced P − E below the observed range. RCM recycling ratios were within the range estimated from observations. No evidence of common errors of E was found. In contrast, common dry bias of P was found in the simulations.
Daily cycles of terms in the water vapor conservation equation were qualitatively similar in most RCMs. Nocturnal maximums of P and C (convergence) occurred in 9 of 13 RCMs, consistent with observations. Three of the four driest simulations failed to couple P and C overnight, producing afternoon maximum P. Further, dry simulations tended to produce a larger fraction of their 60-day accumulated precipitation from low 3-h totals.
In station reports, accumulation from high (low) 3-h totals had a nocturnal (early morning) maximum. This time lag occurred, in part, because many mesoscale convective systems had reached peak intensity overnight and had declined in intensity by early morning. None of the RCMs contained such a time lag. It is recommended that short-period experiments be performed to examine the ability of RCMs to simulate mesoscale convective systems prior to generating long-period simulations for hydroclimatology.
Abstract
Thirteen regional climate model (RCM) simulations of June–July 1993 were compared with each other and observations. Water vapor conservation and precipitation characteristics in each RCM were examined for a 10° × 10° subregion of the upper Mississippi River basin, containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports.
All RCMs produced positive precipitation minus evapotranspiration (P − E > 0), though most RCMs produced P − E below the observed range. RCM recycling ratios were within the range estimated from observations. No evidence of common errors of E was found. In contrast, common dry bias of P was found in the simulations.
Daily cycles of terms in the water vapor conservation equation were qualitatively similar in most RCMs. Nocturnal maximums of P and C (convergence) occurred in 9 of 13 RCMs, consistent with observations. Three of the four driest simulations failed to couple P and C overnight, producing afternoon maximum P. Further, dry simulations tended to produce a larger fraction of their 60-day accumulated precipitation from low 3-h totals.
In station reports, accumulation from high (low) 3-h totals had a nocturnal (early morning) maximum. This time lag occurred, in part, because many mesoscale convective systems had reached peak intensity overnight and had declined in intensity by early morning. None of the RCMs contained such a time lag. It is recommended that short-period experiments be performed to examine the ability of RCMs to simulate mesoscale convective systems prior to generating long-period simulations for hydroclimatology.
