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Abstract
Global climate change due to increasing concentrations of greenhouse gases has stimulated numerous studies and discussions about its possible impacts on water resources. Climate scenarios generated by climate models at spatial resolutions ranging from about 50 km to 400 km may not provide enough spatial specificity for use in impact assessment. In Parts I and II of this paper, the spatial specificity issue is addressed by examining what information on mesoscale and small-scale spatial features can be gained by using a regional climate model with a subgrid parameterization of orographic precipitation and land surface cover, driven by a general circulation model. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO2 concentration. This paper describes and contrasts the large-scale and mesoscale features of the greenhouse warming climate signals simulated by the general circulation model and regional climate model over the Pacific Northwest.
Results indicate that changes in the large-scale circulation exhibit strong seasonal variability. There is an average warming of about 2°C, and precipitation generally increases over the Pacific Northwest and decreases over California. The precipitation signal over the Pacific Northwest is only statistically significant during spring, when both the change in the large-scale circulation and increase in water vapor enhance the moisture convergence toward the north Pacific coast. The combined effects of surface temperature and precipitation changes are such that snow cover is reduced by up to 50% on average, causing large changes in the seasonal runoff. This paper also describes the high spatial resolution (1.5 km) climate signals simulated by the regional climate model. Reductions in snow cover of 50%–90% are found in areas near the snow line of the control simulation. Analyses of the variations of the climate signals with surface elevation ranging from sea level to 4000 m over two mountain ranges in the Pacific Northwest show that because of changes in the alitude of the freezing level, strong elevation dependency is found in the surface temperature, rainfall, snowfall, snow cover, and runoff signals.
Abstract
Global climate change due to increasing concentrations of greenhouse gases has stimulated numerous studies and discussions about its possible impacts on water resources. Climate scenarios generated by climate models at spatial resolutions ranging from about 50 km to 400 km may not provide enough spatial specificity for use in impact assessment. In Parts I and II of this paper, the spatial specificity issue is addressed by examining what information on mesoscale and small-scale spatial features can be gained by using a regional climate model with a subgrid parameterization of orographic precipitation and land surface cover, driven by a general circulation model. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO2 concentration. This paper describes and contrasts the large-scale and mesoscale features of the greenhouse warming climate signals simulated by the general circulation model and regional climate model over the Pacific Northwest.
Results indicate that changes in the large-scale circulation exhibit strong seasonal variability. There is an average warming of about 2°C, and precipitation generally increases over the Pacific Northwest and decreases over California. The precipitation signal over the Pacific Northwest is only statistically significant during spring, when both the change in the large-scale circulation and increase in water vapor enhance the moisture convergence toward the north Pacific coast. The combined effects of surface temperature and precipitation changes are such that snow cover is reduced by up to 50% on average, causing large changes in the seasonal runoff. This paper also describes the high spatial resolution (1.5 km) climate signals simulated by the regional climate model. Reductions in snow cover of 50%–90% are found in areas near the snow line of the control simulation. Analyses of the variations of the climate signals with surface elevation ranging from sea level to 4000 m over two mountain ranges in the Pacific Northwest show that because of changes in the alitude of the freezing level, strong elevation dependency is found in the surface temperature, rainfall, snowfall, snow cover, and runoff signals.
Abstract
The global performance of a new two-moment cloud microphysics scheme for a general circulation model (GCM) is presented and evaluated relative to observations. The scheme produces reasonable representations of cloud particle size and number concentration when compared to observations, and it represents expected and observed spatial variations in cloud microphysical quantities. The scheme has smaller particles and higher number concentrations over land than the standard bulk microphysics in the GCM and is able to balance the top-of-atmosphere radiation budget with 60% the liquid water of the standard scheme, in better agreement with retrieved values. The new scheme diagnostically treats both the mixing ratio and number concentration of rain and snow, and it is therefore able to differentiate the two key regimes, consisting of drizzle in shallow, warm clouds and larger rain drops in deeper cloud systems. The modeled rain and snow size distributions are consistent with observations.
Abstract
The global performance of a new two-moment cloud microphysics scheme for a general circulation model (GCM) is presented and evaluated relative to observations. The scheme produces reasonable representations of cloud particle size and number concentration when compared to observations, and it represents expected and observed spatial variations in cloud microphysical quantities. The scheme has smaller particles and higher number concentrations over land than the standard bulk microphysics in the GCM and is able to balance the top-of-atmosphere radiation budget with 60% the liquid water of the standard scheme, in better agreement with retrieved values. The new scheme diagnostically treats both the mixing ratio and number concentration of rain and snow, and it is therefore able to differentiate the two key regimes, consisting of drizzle in shallow, warm clouds and larger rain drops in deeper cloud systems. The modeled rain and snow size distributions are consistent with observations.
Abstract
Previous development of the Pacific Northwest National Laboratory’s regional climate model has focused on representing orographic precipitation using a subgrid parameterization where subgrid variations of surface elevation are aggregated to a limited number of elevation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys. This paper describes further testing and evaluation of this subgrid parameterization. Building upon this modeling framework, a subgrid vegetation scheme has been developed based on statistical relationships between surface elevation and vegetation. By analyzing high-resolution elevation and vegetation data, a dominant land cover is defined for each elevation band of each model grid cell to account for the subgrid heterogeneity in vegetation. When larger lakes are present, they are distinguished from land within elevation bands and a lake model is used to simulate the thermodynamic properties. The use of the high-resolution vegetation data and the subgrid vegetation scheme has resulted in an improvement in the model’s representation of surface cover over the western United States. Simulation using the new vegetation scheme yields a 1°C cooling when compared with a simulation where vegetation was derived from a 30-min global vegetation dataset without subgrid vegetation treatment; this cooling helps to reduce the warm bias previously found in the regional climate model. A 3-yr simulation with the subgrid parameterization in the climate model is compared with observations.
Abstract
Previous development of the Pacific Northwest National Laboratory’s regional climate model has focused on representing orographic precipitation using a subgrid parameterization where subgrid variations of surface elevation are aggregated to a limited number of elevation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys. This paper describes further testing and evaluation of this subgrid parameterization. Building upon this modeling framework, a subgrid vegetation scheme has been developed based on statistical relationships between surface elevation and vegetation. By analyzing high-resolution elevation and vegetation data, a dominant land cover is defined for each elevation band of each model grid cell to account for the subgrid heterogeneity in vegetation. When larger lakes are present, they are distinguished from land within elevation bands and a lake model is used to simulate the thermodynamic properties. The use of the high-resolution vegetation data and the subgrid vegetation scheme has resulted in an improvement in the model’s representation of surface cover over the western United States. Simulation using the new vegetation scheme yields a 1°C cooling when compared with a simulation where vegetation was derived from a 30-min global vegetation dataset without subgrid vegetation treatment; this cooling helps to reduce the warm bias previously found in the regional climate model. A 3-yr simulation with the subgrid parameterization in the climate model is compared with observations.
Abstract
A model nesting approach has been used to simulate the regional climate over the Pacific Northwest. The present-day global climatology is first simulated using the NCAR Community Climate Model (CCM3) driven by observed sea surface temperature and sea ice distribution at T42 (2.8°) resolution. This large-scale simulation is used to provide lateral boundary conditions for driving the Pacific Northwest National Laboratory Regional Climate Model (RCM). One notable feature of the RCM is the use of subgrid parameterizations of orographic precipitation and vegetation cover, in which subgrid variations of surface elevation and vegetation are aggregated to a limited number of elevation–vegetation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys.
The 7-yr climatologies as simulated by CCM3 and RCM are evaluated and compared in terms of large-scale spatial patterns and regional means. Biases are found in the simulation of large-scale circulations, which also affect the regional model simulation. Therefore, the regional simulation is not very different from the CCM3 simulation in terms of large-scale features. However, the regional model greatly improves the simulation of precipitation, surface temperature, and snow cover at the local scales. This is shown by improvements in the spatial correlation between the observations and simulations. The RCM simulation is further evaluated using station observations of surface temperature and precipitation to compare the simulated and observed relationships between surface temperature–precipitation and altitude. The model is found to correctly capture the surface temperature–precipitation variations as functions of surface topography over different mountain ranges, and under different climate regimes.
Abstract
A model nesting approach has been used to simulate the regional climate over the Pacific Northwest. The present-day global climatology is first simulated using the NCAR Community Climate Model (CCM3) driven by observed sea surface temperature and sea ice distribution at T42 (2.8°) resolution. This large-scale simulation is used to provide lateral boundary conditions for driving the Pacific Northwest National Laboratory Regional Climate Model (RCM). One notable feature of the RCM is the use of subgrid parameterizations of orographic precipitation and vegetation cover, in which subgrid variations of surface elevation and vegetation are aggregated to a limited number of elevation–vegetation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys.
The 7-yr climatologies as simulated by CCM3 and RCM are evaluated and compared in terms of large-scale spatial patterns and regional means. Biases are found in the simulation of large-scale circulations, which also affect the regional model simulation. Therefore, the regional simulation is not very different from the CCM3 simulation in terms of large-scale features. However, the regional model greatly improves the simulation of precipitation, surface temperature, and snow cover at the local scales. This is shown by improvements in the spatial correlation between the observations and simulations. The RCM simulation is further evaluated using station observations of surface temperature and precipitation to compare the simulated and observed relationships between surface temperature–precipitation and altitude. The model is found to correctly capture the surface temperature–precipitation variations as functions of surface topography over different mountain ranges, and under different climate regimes.
Abstract
A 6.25-km resolution dataset of meteorology, vegetation type, and soil type for a domain covering a typical global climate model grid cell is used to drive a land surface physics model for a period of 6 months. Additional simulations are performed driving the land surface physics model by spatially averaged meteorology, spatially averaged vegetation characteristics, spatially averaged soil properties, and spatially averaged meteorology, vegetation characteristics, and soil properties. By comparing the simulated water balance for the whole domain for each simulation, the relative influence of subgrid variability in meteorology, vegetation, and soil are assessed. Subgrid variability in summertime precipitation is found to have the largest effect on the surface hydrology, with a nearly twofold increase on surface runoff and a 15% increase in evapotranspiration. Subgrid variations in vegetation and soil properties also increase surface runoff and reduce evapotranspiration, so that surface runoff is 2.75 times as great with subgrid variability than without and evapotranspiration is 19% higher with subgrid variability than without.
Abstract
A 6.25-km resolution dataset of meteorology, vegetation type, and soil type for a domain covering a typical global climate model grid cell is used to drive a land surface physics model for a period of 6 months. Additional simulations are performed driving the land surface physics model by spatially averaged meteorology, spatially averaged vegetation characteristics, spatially averaged soil properties, and spatially averaged meteorology, vegetation characteristics, and soil properties. By comparing the simulated water balance for the whole domain for each simulation, the relative influence of subgrid variability in meteorology, vegetation, and soil are assessed. Subgrid variability in summertime precipitation is found to have the largest effect on the surface hydrology, with a nearly twofold increase on surface runoff and a 15% increase in evapotranspiration. Subgrid variations in vegetation and soil properties also increase surface runoff and reduce evapotranspiration, so that surface runoff is 2.75 times as great with subgrid variability than without and evapotranspiration is 19% higher with subgrid variability than without.
Abstract
The authors have decomposed the anthropogenic aerosol radiative forcing into direct contributions from each aerosol species to the planetary energy balance through absorption and scattering of solar radiation, indirect effects of anthropogenic aerosol on solar and infrared radiation through droplet and crystal nucleation on aerosol, and semidirect effects through the influence of solar absorption on the distribution of clouds. A three-mode representation of the aerosol in version 5.1 of the Community Atmosphere Model (CAM5.1) yields global annual mean radiative forcing estimates for each of these forcing mechanisms that are within 0.1 W m−2 of estimates using a more complex seven-mode representation that distinguishes between fresh and aged black carbon and primary organic matter. Simulating fresh black carbon particles separately from internally mixed accumulation mode particles is found to be important only near fossil fuel sources. In addition to the usual large indirect effect on solar radiation, this study finds an unexpectedly large positive longwave indirect effect (because of enhanced cirrus produced by homogenous nucleation of ice crystals on anthropogenic sulfate), small shortwave and longwave semidirect effects, and a small direct effect (because of cancelation and interactions of direct effects of black carbon and sulfate). Differences between the three-mode and seven-mode versions are significantly larger (up to 0.2 W m−2) when the hygroscopicity of primary organic matter is decreased from 0.1 to 0 and transfer of the primary carbonaceous aerosol to the accumulation mode in the seven-mode version requires more hygroscopic material coating the primary particles. Radiative forcing by cloudborne anthropogenic black carbon is only −0.07 W m−2.
Abstract
The authors have decomposed the anthropogenic aerosol radiative forcing into direct contributions from each aerosol species to the planetary energy balance through absorption and scattering of solar radiation, indirect effects of anthropogenic aerosol on solar and infrared radiation through droplet and crystal nucleation on aerosol, and semidirect effects through the influence of solar absorption on the distribution of clouds. A three-mode representation of the aerosol in version 5.1 of the Community Atmosphere Model (CAM5.1) yields global annual mean radiative forcing estimates for each of these forcing mechanisms that are within 0.1 W m−2 of estimates using a more complex seven-mode representation that distinguishes between fresh and aged black carbon and primary organic matter. Simulating fresh black carbon particles separately from internally mixed accumulation mode particles is found to be important only near fossil fuel sources. In addition to the usual large indirect effect on solar radiation, this study finds an unexpectedly large positive longwave indirect effect (because of enhanced cirrus produced by homogenous nucleation of ice crystals on anthropogenic sulfate), small shortwave and longwave semidirect effects, and a small direct effect (because of cancelation and interactions of direct effects of black carbon and sulfate). Differences between the three-mode and seven-mode versions are significantly larger (up to 0.2 W m−2) when the hygroscopicity of primary organic matter is decreased from 0.1 to 0 and transfer of the primary carbonaceous aerosol to the accumulation mode in the seven-mode version requires more hygroscopic material coating the primary particles. Radiative forcing by cloudborne anthropogenic black carbon is only −0.07 W m−2.
The Community Earth System Model (CESM) is a flexible and extensible community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales. This global coupled model significantly extends its predecessor, the Community Climate System Model, by incorporating new Earth system simulation capabilities. These comprise the ability to simulate biogeochemical cycles, including those of carbon and nitrogen, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere. These and other new model capabilities are enabling investigations into a wide range of pressing scientific questions, providing new foresight into possible future climates and increasing our collective knowledge about the behavior and interactions of the Earth system. Simulations with numerous configurations of the CESM have been provided to phase 5 of the Coupled Model Intercomparison Project (CMIP5) and are being analyzed by the broad community of scientists. Additionally, the model source code and associated documentation are freely available to the scientific community to use for Earth system studies, making it a true community tool. This article describes this Earth system model and its various possible configurations, and highlights a number of its scientific capabilities.
The Community Earth System Model (CESM) is a flexible and extensible community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales. This global coupled model significantly extends its predecessor, the Community Climate System Model, by incorporating new Earth system simulation capabilities. These comprise the ability to simulate biogeochemical cycles, including those of carbon and nitrogen, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere. These and other new model capabilities are enabling investigations into a wide range of pressing scientific questions, providing new foresight into possible future climates and increasing our collective knowledge about the behavior and interactions of the Earth system. Simulations with numerous configurations of the CESM have been provided to phase 5 of the Coupled Model Intercomparison Project (CMIP5) and are being analyzed by the broad community of scientists. Additionally, the model source code and associated documentation are freely available to the scientific community to use for Earth system studies, making it a true community tool. This article describes this Earth system model and its various possible configurations, and highlights a number of its scientific capabilities.
Abstract
A modest operational program of systematic aircraft measurements can resolve key satellite aerosol data record limitations. Satellite observations provide frequent global aerosol amount maps but offer only loose aerosol property constraints needed for climate and air quality applications. We define and illustrate the feasibility of flying an aircraft payload to measure key aerosol optical, microphysical, and chemical properties in situ. The flight program could characterize major aerosol airmass types statistically, at a level of detail unobtainable from space. It would 1) enhance satellite aerosol retrieval products with better climatology assumptions and 2) improve translation between satellite-retrieved optical properties and species-specific aerosol mass and size simulated in climate models to assess aerosol forcing, its anthropogenic components, and other environmental impacts. As such, Systematic Aircraft Measurements to Characterize Aerosol Air Masses (SAM-CAAM) could add value to data records representing several decades of aerosol observations from space; improve aerosol constraints on climate modeling; help interrelate remote sensing, in situ, and modeling aerosol-type definitions; and contribute to future satellite aerosol missions. Fifteen required variables are identified and four payload options of increasing ambition are defined to constrain these quantities. “Option C” could meet all the SAM-CAAM objectives with about 20 instruments, most of which have flown before, but never routinely several times per week, and never as a group. Aircraft integration and approaches to data handling, payload support, and logistical considerations for a long-term, operational mission are discussed. SAM-CAAM is feasible because, for most aerosol sources and specified seasons, particle properties tend to be repeatable, even if aerosol loading varies.
Abstract
A modest operational program of systematic aircraft measurements can resolve key satellite aerosol data record limitations. Satellite observations provide frequent global aerosol amount maps but offer only loose aerosol property constraints needed for climate and air quality applications. We define and illustrate the feasibility of flying an aircraft payload to measure key aerosol optical, microphysical, and chemical properties in situ. The flight program could characterize major aerosol airmass types statistically, at a level of detail unobtainable from space. It would 1) enhance satellite aerosol retrieval products with better climatology assumptions and 2) improve translation between satellite-retrieved optical properties and species-specific aerosol mass and size simulated in climate models to assess aerosol forcing, its anthropogenic components, and other environmental impacts. As such, Systematic Aircraft Measurements to Characterize Aerosol Air Masses (SAM-CAAM) could add value to data records representing several decades of aerosol observations from space; improve aerosol constraints on climate modeling; help interrelate remote sensing, in situ, and modeling aerosol-type definitions; and contribute to future satellite aerosol missions. Fifteen required variables are identified and four payload options of increasing ambition are defined to constrain these quantities. “Option C” could meet all the SAM-CAAM objectives with about 20 instruments, most of which have flown before, but never routinely several times per week, and never as a group. Aircraft integration and approaches to data handling, payload support, and logistical considerations for a long-term, operational mission are discussed. SAM-CAAM is feasible because, for most aerosol sources and specified seasons, particle properties tend to be repeatable, even if aerosol loading varies.
