Search Results
You are looking at 21 - 27 of 27 items for
- Author or Editor: Eugene S. Takle x
- Refine by Access: All Content x
Abstract
Precipitation intensity spectra for a central U.S. region in a 10-yr regional climate simulation are compared to corresponding observed spectra for precipitation accumulation periods ranging from 6 h to 10 days. Model agreement with observations depends on the length of the precipitation accumulation period, with similar results for both warm and cold halves of the year. For 6- and 12-h accumulation periods, simulated and observed spectra show little overlap. For daily and longer accumulation periods, the spectra are similar for moderate precipitation rates, though the model produces too many low-intensity precipitation events and too few high-intensity precipitation events for all accumulation periods. The spatial correlation of simulated and observed precipitation events indicates that the model's 50-km grid spacing is too coarse to simulate well high-intensity events. Spatial correlations with and without very light precipitation indicate that coarse resolution is not a direct cause of excessive low-intensity events. The model shows less spread than observations in its pattern of spatial correlation versus distance, suggesting that resolved model circulation patterns producing 6-hourly precipitation are limited in the range of precipitation patterns they can produce compared to the real world. The correlations also indicate that replicating observed precipitation intensity distributions for 6-h accumulation periods requires grid spacing smaller than about 15 km, suggesting that models with grid spacing substantially larger than this will be unable to simulate the observed diurnal cycle of precipitation.
Abstract
Precipitation intensity spectra for a central U.S. region in a 10-yr regional climate simulation are compared to corresponding observed spectra for precipitation accumulation periods ranging from 6 h to 10 days. Model agreement with observations depends on the length of the precipitation accumulation period, with similar results for both warm and cold halves of the year. For 6- and 12-h accumulation periods, simulated and observed spectra show little overlap. For daily and longer accumulation periods, the spectra are similar for moderate precipitation rates, though the model produces too many low-intensity precipitation events and too few high-intensity precipitation events for all accumulation periods. The spatial correlation of simulated and observed precipitation events indicates that the model's 50-km grid spacing is too coarse to simulate well high-intensity events. Spatial correlations with and without very light precipitation indicate that coarse resolution is not a direct cause of excessive low-intensity events. The model shows less spread than observations in its pattern of spatial correlation versus distance, suggesting that resolved model circulation patterns producing 6-hourly precipitation are limited in the range of precipitation patterns they can produce compared to the real world. The correlations also indicate that replicating observed precipitation intensity distributions for 6-h accumulation periods requires grid spacing smaller than about 15 km, suggesting that models with grid spacing substantially larger than this will be unable to simulate the observed diurnal cycle of precipitation.
Perturbations of mean and turbulent wind characteristics by large wind turbines modify fluxes between the vegetated surface and the lower boundary layer. While simulations have suggested that wind farms could significantly change surface fluxes of heat, momentum, momentum, moisture, and CO2 over hundreds of square kilometers, little observational evidence exists to test these predictions. Quantifying the influences of the “turbine layer” is necessary to quantify how surface fluxes are modified and to better forecast energy production by a wind farm. Changes in fluxes are particularly important in regions of intensely managed agriculture where crop growth and yield are highly dependent on subtle changes in moisture, heat, and CO2. Furthermore, speculations abound about the possible mesoscale consequences of boundary layer changes that are produced by wind farms. To address the lack of observations to answer these questions, we developed the Crop Wind Energy Experiment (CWEX) as a multiagency, multiuniversity field program in central Iowa. Throughout the summer of 2010, surface fluxes were documented within a wind farm test site and a 2-week deployment of a vertically pointing lidar quantified wind profiles. In 2011, we expanded measurements at the site by deploying six flux stations and two wind-profiling lidars to document turbine wakes. The results provide valuable insights into the exchanges over a surface that has been modified by wind turbines and a basis for a more comprehensive measurement program planned for the summer in 2014.
Perturbations of mean and turbulent wind characteristics by large wind turbines modify fluxes between the vegetated surface and the lower boundary layer. While simulations have suggested that wind farms could significantly change surface fluxes of heat, momentum, momentum, moisture, and CO2 over hundreds of square kilometers, little observational evidence exists to test these predictions. Quantifying the influences of the “turbine layer” is necessary to quantify how surface fluxes are modified and to better forecast energy production by a wind farm. Changes in fluxes are particularly important in regions of intensely managed agriculture where crop growth and yield are highly dependent on subtle changes in moisture, heat, and CO2. Furthermore, speculations abound about the possible mesoscale consequences of boundary layer changes that are produced by wind farms. To address the lack of observations to answer these questions, we developed the Crop Wind Energy Experiment (CWEX) as a multiagency, multiuniversity field program in central Iowa. Throughout the summer of 2010, surface fluxes were documented within a wind farm test site and a 2-week deployment of a vertically pointing lidar quantified wind profiles. In 2011, we expanded measurements at the site by deploying six flux stations and two wind-profiling lidars to document turbine wakes. The results provide valuable insights into the exchanges over a surface that has been modified by wind turbines and a basis for a more comprehensive measurement program planned for the summer in 2014.
Abstract
Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.
Abstract
Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.
The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II.
This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.
The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II.
This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.
Abstract
This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.
Abstract
This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.
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.
