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Abstract
The relative importance of various sources of imbalance in analyses produced by multivariate optimal interpolation is determined. The experimental design uses the shallow-water equations and nonlinear normal mode initialization to define the correct balanced reference atmospheric state and thus restricts this study to horizontal aspects of the problem. The experiments show that the analysis procedure itself introduces systematic imbalances in lows due to the use of the geostrophic relationship to determine the height–wind covariances from the height–height covariances. Random observational errors introduce imbalances but not out of proportion to the observational errors themselves. Data-void areas are responsible for a region of imbalance with width approximately equal to the maximum radius of influence of the analysis on the data-void side of the data-void/data-rich boundary. Model errors in the form of equivalent depth errors do not introduce large imbalances.
Abstract
The relative importance of various sources of imbalance in analyses produced by multivariate optimal interpolation is determined. The experimental design uses the shallow-water equations and nonlinear normal mode initialization to define the correct balanced reference atmospheric state and thus restricts this study to horizontal aspects of the problem. The experiments show that the analysis procedure itself introduces systematic imbalances in lows due to the use of the geostrophic relationship to determine the height–wind covariances from the height–height covariances. Random observational errors introduce imbalances but not out of proportion to the observational errors themselves. Data-void areas are responsible for a region of imbalance with width approximately equal to the maximum radius of influence of the analysis on the data-void side of the data-void/data-rich boundary. Model errors in the form of equivalent depth errors do not introduce large imbalances.
Abstract
Environment Canada meteorological station hourly sampled air temperatures T air at four stations in the southwest Yukon were used to identify cloud contamination in the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra clear-sky daytime land surface temperature (LST) and emissivity daily level-3 global 1-km grid product (MOD11A1, Collection 5) that is not flagged by the MODIS quality algorithm as contaminated. The additional cloud masking used qualitative ground-based sky condition observations, collected at two of the four stations, and coincident MODIS quality flag information. The results indicate that air temperature observed at a variety of discrete spatial locations having different land cover is highly correlated with MODIS LST collected at 1-km grid spacing. Quadratic relationships between LST and air temperature, constrained by ground observations of “clear” sky conditions, show less variability than relationships found under “mainly clear” and “mostly cloudy” sky conditions, and the more clouds observed in the sky coincides with a decreasing y intercept. Analysis of MODIS LST and its associated quality flags show a cold bias (<0°C) in the assignment of the ≤3-K-average LST error, indicating MODIS LST has a maximum average error of ≤2 K over a warm surface (>0°C). Analysis of two observation stations shows that unidentified clouds in MODIS LST are between 13% and 17%, a result that agrees well with previous studies. Analysis of daytime values is important because many processes are dependent on daylight and maximum temperature. The daytime clear-sky LST–T air relationship observed for the good-quality confirmed cloud-free-sky MODIS LST quality flag can be used to discriminate cloud-contaminated grid cells beyond the standard MODIS cloud mask.
Abstract
Environment Canada meteorological station hourly sampled air temperatures T air at four stations in the southwest Yukon were used to identify cloud contamination in the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra clear-sky daytime land surface temperature (LST) and emissivity daily level-3 global 1-km grid product (MOD11A1, Collection 5) that is not flagged by the MODIS quality algorithm as contaminated. The additional cloud masking used qualitative ground-based sky condition observations, collected at two of the four stations, and coincident MODIS quality flag information. The results indicate that air temperature observed at a variety of discrete spatial locations having different land cover is highly correlated with MODIS LST collected at 1-km grid spacing. Quadratic relationships between LST and air temperature, constrained by ground observations of “clear” sky conditions, show less variability than relationships found under “mainly clear” and “mostly cloudy” sky conditions, and the more clouds observed in the sky coincides with a decreasing y intercept. Analysis of MODIS LST and its associated quality flags show a cold bias (<0°C) in the assignment of the ≤3-K-average LST error, indicating MODIS LST has a maximum average error of ≤2 K over a warm surface (>0°C). Analysis of two observation stations shows that unidentified clouds in MODIS LST are between 13% and 17%, a result that agrees well with previous studies. Analysis of daytime values is important because many processes are dependent on daylight and maximum temperature. The daytime clear-sky LST–T air relationship observed for the good-quality confirmed cloud-free-sky MODIS LST quality flag can be used to discriminate cloud-contaminated grid cells beyond the standard MODIS cloud mask.
Abstract
The dynamical simulation of the standard configuration of the latest version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) is examined, including the seasonal variation of its mean state and its intraseasonal and interannual variability. A 15-yr integration in which the model is forced with observed monthly varying sea surface temperatures (SSTs) since 1979 is compared to coexisting observations. Results show that the most serious systematic errors in previous NCAR CCM versions have either been eliminated or substantially reduced.
At sea level, CCM3 reproduces the basic observed patterns of the pressure field very well. Simulated surface pressures are higher than observed over the subtropics, however, an error consistent with an easterly bias in the simulated trade winds and low-latitude surface wind stress. Amplitude errors and phase shifts of the subpolar low pressure centers over both hemispheres during winter produce the largest regional errors, which are on the order of 5 mb. In the upper troposphere, both the amplitude and location of the major circulation centers are very well captured by the model, in agreement with relatively small regional biases in the simulated winds. Errors in the zonal wind component at 200 mb are most notable between 40° and 50° lat of both hemispheres, where the modeled westerlies are stronger than observed especially over the Southern Hemisphere during winter. A ∼50% reduction in the magnitude of the zonally averaged westerly bias in the equatorial upper troposphere that plagued previous CCM versions can be attributed to a significantly improved tropical hydrologic cycle and reduced Walker circulation.
Over middle latitudes, the CCM3 realistically depicts the main storm tracks, although the transient kinetic energy is generally underestimated, especially over the summer hemispheres. Over lower latitudes, the model simulates tropical intraseasonal oscillations with marked seasonality in their occurrence. Typical periodicities, however, are near 20–30 days, which are shorter than observed, and the simulated amplitudes are weaker than in both observations and previous versions of the model. The simulated response to interannual variations in tropical SSTs is also realistic in CCM3. A simulated index of the Southern Oscillation agrees well with the observed, and the model captures the overall structure and magnitude of observed shifts in tropical and subtropical convergence zones and monthly rainfall anomalies associated with the tropical SST changes.
Abstract
The dynamical simulation of the standard configuration of the latest version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) is examined, including the seasonal variation of its mean state and its intraseasonal and interannual variability. A 15-yr integration in which the model is forced with observed monthly varying sea surface temperatures (SSTs) since 1979 is compared to coexisting observations. Results show that the most serious systematic errors in previous NCAR CCM versions have either been eliminated or substantially reduced.
At sea level, CCM3 reproduces the basic observed patterns of the pressure field very well. Simulated surface pressures are higher than observed over the subtropics, however, an error consistent with an easterly bias in the simulated trade winds and low-latitude surface wind stress. Amplitude errors and phase shifts of the subpolar low pressure centers over both hemispheres during winter produce the largest regional errors, which are on the order of 5 mb. In the upper troposphere, both the amplitude and location of the major circulation centers are very well captured by the model, in agreement with relatively small regional biases in the simulated winds. Errors in the zonal wind component at 200 mb are most notable between 40° and 50° lat of both hemispheres, where the modeled westerlies are stronger than observed especially over the Southern Hemisphere during winter. A ∼50% reduction in the magnitude of the zonally averaged westerly bias in the equatorial upper troposphere that plagued previous CCM versions can be attributed to a significantly improved tropical hydrologic cycle and reduced Walker circulation.
Over middle latitudes, the CCM3 realistically depicts the main storm tracks, although the transient kinetic energy is generally underestimated, especially over the summer hemispheres. Over lower latitudes, the model simulates tropical intraseasonal oscillations with marked seasonality in their occurrence. Typical periodicities, however, are near 20–30 days, which are shorter than observed, and the simulated amplitudes are weaker than in both observations and previous versions of the model. The simulated response to interannual variations in tropical SSTs is also realistic in CCM3. A simulated index of the Southern Oscillation agrees well with the observed, and the model captures the overall structure and magnitude of observed shifts in tropical and subtropical convergence zones and monthly rainfall anomalies associated with the tropical SST changes.
Abstract
Forecasts of southeast Pacific stratocumulus at 20°S and 85°W during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 are examined with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized from ECMWF analyses and each model is run for 3–5 days to determine the differences with the EPIC field observations.
Observations during the EPIC cruise show a well-mixed boundary layer under a sharp inversion. The inversion height and the cloud layer have a strong and regular diurnal cycle. A key problem common to the models is that the planetary boundary layer (PBL) depth is too shallow when compared to EPIC observations. However, it is suggested that improved PBL depths are achieved with more physically realistic PBL schemes: at one end, CAM uses a dry and surface-driven PBL scheme and produces a very shallow PBL, while the ECWMF model uses an eddy-diffusivity/mass-flux approach and produces a deeper and better-mixed PBL. All the models produce a strong diurnal cycle in the liquid water path (LWP), but there are large differences in the amplitude and phase when compared to the EPIC observations. This, in turn, affects the radiative fluxes at the surface and the surface energy budget. This is particularly relevant for coupled simulations as this can lead to a large SST bias.
Abstract
Forecasts of southeast Pacific stratocumulus at 20°S and 85°W during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 are examined with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized from ECMWF analyses and each model is run for 3–5 days to determine the differences with the EPIC field observations.
Observations during the EPIC cruise show a well-mixed boundary layer under a sharp inversion. The inversion height and the cloud layer have a strong and regular diurnal cycle. A key problem common to the models is that the planetary boundary layer (PBL) depth is too shallow when compared to EPIC observations. However, it is suggested that improved PBL depths are achieved with more physically realistic PBL schemes: at one end, CAM uses a dry and surface-driven PBL scheme and produces a very shallow PBL, while the ECWMF model uses an eddy-diffusivity/mass-flux approach and produces a deeper and better-mixed PBL. All the models produce a strong diurnal cycle in the liquid water path (LWP), but there are large differences in the amplitude and phase when compared to the EPIC observations. This, in turn, affects the radiative fluxes at the surface and the surface energy budget. This is particularly relevant for coupled simulations as this can lead to a large SST bias.
Abstract
Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic energy was higher over the urban domain mainly because of the shear production of building surfaces and building wakes. Direct transport of turbulent momentum flux from the LLJ to the urban street level was very small because of the relatively high elevation of the jet. However, since the LLJ dominated the mean wind in the boundary layer, the turbulence kinetic energy in the urban domain is correlated directly with the LLJ maximum speed and inversely with its height. The results indicate that the jet Richardson number is a reasonably good indicator for turbulent kinetic energy over the urban domain in the LLJ-dominated atmospheric boundary layer.
Abstract
Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic energy was higher over the urban domain mainly because of the shear production of building surfaces and building wakes. Direct transport of turbulent momentum flux from the LLJ to the urban street level was very small because of the relatively high elevation of the jet. However, since the LLJ dominated the mean wind in the boundary layer, the turbulence kinetic energy in the urban domain is correlated directly with the LLJ maximum speed and inversely with its height. The results indicate that the jet Richardson number is a reasonably good indicator for turbulent kinetic energy over the urban domain in the LLJ-dominated atmospheric boundary layer.
Abstract
Cloud effects have repeatedly been pointed out as the leading source of uncertainty in projections of future climate, yet clouds remain poorly understood and simulated in climate models. Aquaplanets provide a simplified framework for comparing and understanding cloud effects, and how they are partitioned as a function of regime, in large-scale models. This work uses two climate models to demonstrate that aquaplanets can successfully predict a climate model’s sensitivity to an idealized climate change. For both models, aquaplanet climate sensitivity is similar to that of the realistic configuration. Tropical low clouds appear to play a leading role in determining the sensitivity. Regions of large-scale subsidence, which cover much of the tropics, are most directly responsible for the differences between the models. Although cloud effects and climate sensitivity are similar for aquaplanets and realistic configurations, the aquaplanets lack persistent stratocumulus in the tropical atmosphere. This, and an additional analysis of the cloud response in the realistically configured simulations, suggests the representation of shallow (trade wind) cumulus convection, which is ubiquitous in the tropics, is largely responsible for differences in the simulated climate sensitivity of these two models.
Abstract
Cloud effects have repeatedly been pointed out as the leading source of uncertainty in projections of future climate, yet clouds remain poorly understood and simulated in climate models. Aquaplanets provide a simplified framework for comparing and understanding cloud effects, and how they are partitioned as a function of regime, in large-scale models. This work uses two climate models to demonstrate that aquaplanets can successfully predict a climate model’s sensitivity to an idealized climate change. For both models, aquaplanet climate sensitivity is similar to that of the realistic configuration. Tropical low clouds appear to play a leading role in determining the sensitivity. Regions of large-scale subsidence, which cover much of the tropics, are most directly responsible for the differences between the models. Although cloud effects and climate sensitivity are similar for aquaplanets and realistic configurations, the aquaplanets lack persistent stratocumulus in the tropical atmosphere. This, and an additional analysis of the cloud response in the realistically configured simulations, suggests the representation of shallow (trade wind) cumulus convection, which is ubiquitous in the tropics, is largely responsible for differences in the simulated climate sensitivity of these two models.
Abstract
This study examines the sensitivity of a number of important archetypical tracer problems to the numerical method used to solve the equations of tracer transport and atmospheric dynamics. The tracers' scenarios were constructed to exercise the model for a variety of problems relevant to understanding and modeling the physical, dynamical, and chemical aspects of the climate system. The use of spectral, semi-Lagrangian, and finite volume (FV) numerical methods for the equations is explored. All subgrid-scale physical parameterizations were the same in all model simulations.
The model behavior with a few short simulations with passive tracers is explored, and with much longer simulations of radon, SF6, ozone, a tracer designed to mimic some aspects of a biospheric source/sink of CO2, and a suite of tracers designed around the conservation laws for thermodynamics and mass in the model.
Large differences were seen near the tropopause in the model, where the FV core shows a much reduced level of vertical and meridional mixing. There was also evidence that the subtropical subsidence regions are more isolated from Tropics and midlatitudes in the FV core than seen in the other model simulations. There are also big differences in the stratosphere, particularly for age of air in the stratosphere and ozone. A comparison with estimated age of air from CO2 and SF6 measurements in the stratosphere suggest that the FV core is behaving most realistically.
A neutral biosphere (NB) test case is used to explore issues of diurnal and seasonal rectification of a tracer with sources and sinks at the surface. The sources and sinks have a zero annual average, and the rectification is associated with temporal correlations between the sources and sinks, and transport. The test suggests that the rectification is strongly influenced by the resolved-scale dynamics (i.e., the dynamical core) and that the numerical formulation for dynamics and transport still plays a critical role in the distribution of NB-like species. Since the distribution of species driven by these processes have a strong influence on the interpretation of the “missing sink” for CO2 and the interpretation of climate change associated with anthropogenic forcing herein, these issues should not be neglected.
The spectral core showed the largest departures from the predicted nonlinear relationship required by the equations for thermodynamics and mass conservations. The FV and semi-Lagrangian dynamics (SLD) models both produced errors a factor of 2 lower. The SLD model shows a small but systematic bias in its ability to maintain this relationship that was not present in the FV simulation.
The results of the study indicate that for virtually all of these problems, the model numerics still have a large role in influencing the model solutions. It was frequently the case that the differences in solutions resulting from varying the numerics still exceed the differences in the simulations resulting from significant physical perturbations (like changes in greenhouse gas forcing). This does not mean that the response of the system to physical changes is not correct. When results are consistent using different numerical formulations for dynamics and transport it lends confidence to one's conclusions, but it does indicate that some caution is required in interpreting the results.
The results from this study favor use of the FV core for tracer transport and model dynamics. The FV core is, unlike the others, conservative, less diffusive (e.g., maintains strong gradients better), and maintains the nonlinear relationships among variables required by thermodynamic and mass conservation constraints more accurately.
Abstract
This study examines the sensitivity of a number of important archetypical tracer problems to the numerical method used to solve the equations of tracer transport and atmospheric dynamics. The tracers' scenarios were constructed to exercise the model for a variety of problems relevant to understanding and modeling the physical, dynamical, and chemical aspects of the climate system. The use of spectral, semi-Lagrangian, and finite volume (FV) numerical methods for the equations is explored. All subgrid-scale physical parameterizations were the same in all model simulations.
The model behavior with a few short simulations with passive tracers is explored, and with much longer simulations of radon, SF6, ozone, a tracer designed to mimic some aspects of a biospheric source/sink of CO2, and a suite of tracers designed around the conservation laws for thermodynamics and mass in the model.
Large differences were seen near the tropopause in the model, where the FV core shows a much reduced level of vertical and meridional mixing. There was also evidence that the subtropical subsidence regions are more isolated from Tropics and midlatitudes in the FV core than seen in the other model simulations. There are also big differences in the stratosphere, particularly for age of air in the stratosphere and ozone. A comparison with estimated age of air from CO2 and SF6 measurements in the stratosphere suggest that the FV core is behaving most realistically.
A neutral biosphere (NB) test case is used to explore issues of diurnal and seasonal rectification of a tracer with sources and sinks at the surface. The sources and sinks have a zero annual average, and the rectification is associated with temporal correlations between the sources and sinks, and transport. The test suggests that the rectification is strongly influenced by the resolved-scale dynamics (i.e., the dynamical core) and that the numerical formulation for dynamics and transport still plays a critical role in the distribution of NB-like species. Since the distribution of species driven by these processes have a strong influence on the interpretation of the “missing sink” for CO2 and the interpretation of climate change associated with anthropogenic forcing herein, these issues should not be neglected.
The spectral core showed the largest departures from the predicted nonlinear relationship required by the equations for thermodynamics and mass conservations. The FV and semi-Lagrangian dynamics (SLD) models both produced errors a factor of 2 lower. The SLD model shows a small but systematic bias in its ability to maintain this relationship that was not present in the FV simulation.
The results of the study indicate that for virtually all of these problems, the model numerics still have a large role in influencing the model solutions. It was frequently the case that the differences in solutions resulting from varying the numerics still exceed the differences in the simulations resulting from significant physical perturbations (like changes in greenhouse gas forcing). This does not mean that the response of the system to physical changes is not correct. When results are consistent using different numerical formulations for dynamics and transport it lends confidence to one's conclusions, but it does indicate that some caution is required in interpreting the results.
The results from this study favor use of the FV core for tracer transport and model dynamics. The FV core is, unlike the others, conservative, less diffusive (e.g., maintains strong gradients better), and maintains the nonlinear relationships among variables required by thermodynamic and mass conservation constraints more accurately.
Abstract
The climate of high midlatitude mountains appears to be warming faster than the global average, but evidence for such elevation-dependent warming (EDW) at higher latitudes is presently scarce. Here, we use a comprehensive network of remote meteorological stations, proximal radiosonde measurements, downscaled temperature reanalysis, ice cores, and climate indices to investigate the manifestation and possible drivers of EDW in the St. Elias Mountains in subarctic Yukon, Canada. Linear trend analysis of comprehensively validated annual downscaled North American Regional Reanalysis (NARR) gridded surface air temperatures for the years 1979–2016 indicates a warming rate of 0.028°C a−1 between 5500 and 6000 m above mean sea level (MSL), which is ~1.6 times larger than the global-average warming rate between 1970 and 2015. The warming rate between 5500 and 6000 m MSL was ~1.5 times greater than the rate at the 2000–2500 m MSL bin (0.019°C a−1), which is similar to the majority of warming rates estimated worldwide over similar elevation gradients. Accelerated warming since 1979, measured by radiosondes, indicates a maximum rate at 400 hPa (~7010 m MSL). EDW in the St. Elias region therefore appears to be driven by recent warming of the free troposphere. MODIS satellite data show no evidence for an enhanced snow albedo feedback above 2500 m MSL, and declining trends in sulfate aerosols deposited in high-elevation ice cores suggest a modest increase in radiative forcing at these elevations. In contrast, increasing trends in water vapor mixing ratio at the 500-hPa level measured by radiosonde suggest that a longwave radiation vapor feedback is contributing to EDW.
Abstract
The climate of high midlatitude mountains appears to be warming faster than the global average, but evidence for such elevation-dependent warming (EDW) at higher latitudes is presently scarce. Here, we use a comprehensive network of remote meteorological stations, proximal radiosonde measurements, downscaled temperature reanalysis, ice cores, and climate indices to investigate the manifestation and possible drivers of EDW in the St. Elias Mountains in subarctic Yukon, Canada. Linear trend analysis of comprehensively validated annual downscaled North American Regional Reanalysis (NARR) gridded surface air temperatures for the years 1979–2016 indicates a warming rate of 0.028°C a−1 between 5500 and 6000 m above mean sea level (MSL), which is ~1.6 times larger than the global-average warming rate between 1970 and 2015. The warming rate between 5500 and 6000 m MSL was ~1.5 times greater than the rate at the 2000–2500 m MSL bin (0.019°C a−1), which is similar to the majority of warming rates estimated worldwide over similar elevation gradients. Accelerated warming since 1979, measured by radiosondes, indicates a maximum rate at 400 hPa (~7010 m MSL). EDW in the St. Elias region therefore appears to be driven by recent warming of the free troposphere. MODIS satellite data show no evidence for an enhanced snow albedo feedback above 2500 m MSL, and declining trends in sulfate aerosols deposited in high-elevation ice cores suggest a modest increase in radiative forcing at these elevations. In contrast, increasing trends in water vapor mixing ratio at the 500-hPa level measured by radiosonde suggest that a longwave radiation vapor feedback is contributing to EDW.
To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework.
To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.
To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework.
To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.
Abstract
A new version of the Community Atmosphere Model (CAM) has been developed and released to the climate community. CAM Version 3 (CAM3) is an atmospheric general circulation model that includes the Community Land Model (CLM3), an optional slab ocean model, and a thermodynamic sea ice model. The dynamics and physics in CAM3 have been changed substantially compared to implementations in previous versions. CAM3 includes options for Eulerian spectral, semi-Lagrangian, and finite-volume formulations of the dynamical equations. It supports coupled simulations using either finite-volume or Eulerian dynamics through an explicit set of adjustable parameters governing the model time step, cloud parameterizations, and condensation processes. The model includes major modifications to the parameterizations of moist processes, radiation processes, and aerosols. These changes have improved several aspects of the simulated climate, including more realistic tropical tropopause temperatures, boreal winter land surface temperatures, surface insolation, and clear-sky surface radiation in polar regions. The variation of cloud radiative forcing during ENSO events exhibits much better agreement with satellite observations. Despite these improvements, several systematic biases reduce the fidelity of the simulations. These biases include underestimation of tropical variability, errors in tropical oceanic surface fluxes, underestimation of implied ocean heat transport in the Southern Hemisphere, excessive surface stress in the storm tracks, and offsets in the 500-mb height field and the Aleutian low.
Abstract
A new version of the Community Atmosphere Model (CAM) has been developed and released to the climate community. CAM Version 3 (CAM3) is an atmospheric general circulation model that includes the Community Land Model (CLM3), an optional slab ocean model, and a thermodynamic sea ice model. The dynamics and physics in CAM3 have been changed substantially compared to implementations in previous versions. CAM3 includes options for Eulerian spectral, semi-Lagrangian, and finite-volume formulations of the dynamical equations. It supports coupled simulations using either finite-volume or Eulerian dynamics through an explicit set of adjustable parameters governing the model time step, cloud parameterizations, and condensation processes. The model includes major modifications to the parameterizations of moist processes, radiation processes, and aerosols. These changes have improved several aspects of the simulated climate, including more realistic tropical tropopause temperatures, boreal winter land surface temperatures, surface insolation, and clear-sky surface radiation in polar regions. The variation of cloud radiative forcing during ENSO events exhibits much better agreement with satellite observations. Despite these improvements, several systematic biases reduce the fidelity of the simulations. These biases include underestimation of tropical variability, errors in tropical oceanic surface fluxes, underestimation of implied ocean heat transport in the Southern Hemisphere, excessive surface stress in the storm tracks, and offsets in the 500-mb height field and the Aleutian low.
