Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the “perfect model approach.” This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme.

A simple way to diagnose fractional cloud cover in general circulation models is to relate it to the simulated relative humidity, and allowing for fractional cloud cover above a “critical relative humidity” of less than 100%. In the formulation chosen here, this is equivalent to assuming a uniform “top-hat” distribution of subgrid-scale total water content with a variance related to saturation. Critical relative humidity has frequently been treated as a “tunable” constant, yet it is an observable. Here, this parameter, and its spatial distribution, is examined from Atmospheric Infrared Sounder (AIRS) satellite retrievals, and from a combination of relative humidity from the ECMWF Re-Analyses (ERA-Interim) and cloud fraction obtained from CALIPSO lidar satellite data. These observational data are used to evaluate results from different simulations with the ECHAM general circulation model (GCM). In sensitivity studies, a cloud feedback parameter is analyzed from simulations applying the original parameter choice, and applying parameter choices guided by the satellite data. Model sensitivity studies applying parameters adjusted to match the observations show larger positive cloud-climate feedbacks, increasing by up to 30% compared to the standard simulation.

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the “perfect model approach.” This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme.

A simple way to diagnose fractional cloud cover in general circulation models is to relate it to the simulated relative humidity, and allowing for fractional cloud cover above a “critical relative humidity” of less than 100%. In the formulation chosen here, this is equivalent to assuming a uniform “top-hat” distribution of subgrid-scale total water content with a variance related to saturation. Critical relative humidity has frequently been treated as a “tunable” constant, yet it is an observable. Here, this parameter, and its spatial distribution, is examined from Atmospheric Infrared Sounder (AIRS) satellite retrievals, and from a combination of relative humidity from the ECMWF Re-Analyses (ERA-Interim) and cloud fraction obtained from CALIPSO lidar satellite data. These observational data are used to evaluate results from different simulations with the ECHAM general circulation model (GCM). In sensitivity studies, a cloud feedback parameter is analyzed from simulations applying the original parameter choice, and applying parameter choices guided by the satellite data. Model sensitivity studies applying parameters adjusted to match the observations show larger positive cloud-climate feedbacks, increasing by up to 30% compared to the standard simulation.