Evaluating parameterisations of subgrid-scale variability

Parameterisations of fractional cloudiness in large-scale atmospheric models rely on information about the subgrid-scale variablity of the total water specific humidity, qt , provided in form of a probability density function (PDF). In this contribution, four different approaches to evaluate such total-water PDFs are discussed: (i) Satellite spectroradiometers with high spatial resolution allow to construct at the scale of model grid boxes a histogram, and subsequently to derive the moments of the PDF, of the vertical integral of qt . This can be compared to the same quantity diagnosed from the model parameterisation. Although the vertical integral mostly focuses on the boundary layer, and involves issues in grid-boxes with orographic variability, it allowed nevertheless in the example presented to pinpoint deficiencies of a model parameterisation. (ii) Assuming a simple PDF shape and saturation within clouds, the simple “critical relative humidity” metric can be derived from infrared sounders and/or cloud lidar in combination with reanalysis data with a vertical resolution. It allows to evaluate the underlying PDF of any cloud scheme, but is sensitive to the assumptions. (iii) Supersites with a combination of ground-based lidar, radar and microwave data provide high-resolution high-quality reference data. In a “virtual reality” framework, we showed, however, that it is difficult to evaluate higher moments of a spatial PDF with this temporally-varying data. (iv) From a hierarchy of models from general circulation models to direct numerical simulations, we find that the variance of the qt follows a power-law scaling with an exponent of about -2. This information is very useful to improve the parameterisations

Scale dependency of total water variance and its implication for cloud parameterizations: Scale dependency of total water variance and its implication for cloudparameterizations

The scale dependency of variance of total water mixing ratio is explored by analyzing data from a general circulation model (GCM), a numerical weather prediction model (NWP), and large-eddy simulations (LESs). For clarification, direct numerical simulation (DNS) data are additionally included, but the focus is placed on defining a general scaling behavior for scales ranging from global down to cloud resolving. For this, appropriate power-law exponents are determined by calculating and approximating the power density spectrum. The large-scale models (GCM and NWP) show a consistent scaling with a power-law exponent of approximately 22. For the high-resolution LESs, the slope of the power density spectrum shows evidence of being somewhat steeper, although the estimates are more uncertain. Also the transition between resolved and parameterized scales in a current GCM is investigated. Neither a spectral gap nor a strong scale break is found, but a weak scale break at high wavenumbers cannot be excluded. The evaluation of the parameterized total water variance of a state-of-the-art statistical scheme shows that the scale dependency is underestimated by this parameterization. This study and the discovered general scaling behavior emphasize the need for a development of scale-dependent parameterizations

Evaluating statistical cloud schemes: what can we gain from ground-based remote sensing?

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

Scale dependency of total water variance and its implication for cloud parameterizations: Scale dependency of total water variance and its implication for cloudparameterizations

The scale dependency of variance of total water mixing ratio is explored by analyzing data from a general circulation model (GCM), a numerical weather prediction model (NWP), and large-eddy simulations (LESs). For clarification, direct numerical simulation (DNS) data are additionally included, but the focus is placed on defining a general scaling behavior for scales ranging from global down to cloud resolving. For this, appropriate power-law exponents are determined by calculating and approximating the power density spectrum. The large-scale models (GCM and NWP) show a consistent scaling with a power-law exponent of approximately 22. For the high-resolution LESs, the slope of the power density spectrum shows evidence of being somewhat steeper, although the estimates are more uncertain. Also the transition between resolved and parameterized scales in a current GCM is investigated. Neither a spectral gap nor a strong scale break is found, but a weak scale break at high wavenumbers cannot be excluded. The evaluation of the parameterized total water variance of a state-of-the-art statistical scheme shows that the scale dependency is underestimated by this parameterization. This study and the discovered general scaling behavior emphasize the need for a development of scale-dependent parameterizations

Scale dependency of total water variance and its implication for cloud parameterizations: Scale dependency of total water variance and its implication for cloudparameterizations

The scale dependency of variance of total water mixing ratio is explored by analyzing data from a general circulation model (GCM), a numerical weather prediction model (NWP), and large-eddy simulations (LESs). For clarification, direct numerical simulation (DNS) data are additionally included, but the focus is placed on defining a general scaling behavior for scales ranging from global down to cloud resolving. For this, appropriate power-law exponents are determined by calculating and approximating the power density spectrum. The large-scale models (GCM and NWP) show a consistent scaling with a power-law exponent of approximately 22. For the high-resolution LESs, the slope of the power density spectrum shows evidence of being somewhat steeper, although the estimates are more uncertain. Also the transition between resolved and parameterized scales in a current GCM is investigated. Neither a spectral gap nor a strong scale break is found, but a weak scale break at high wavenumbers cannot be excluded. The evaluation of the parameterized total water variance of a state-of-the-art statistical scheme shows that the scale dependency is underestimated by this parameterization. This study and the discovered general scaling behavior emphasize the need for a development of scale-dependent parameterizations

Evaluating statistical cloud schemes: what can we gain from ground-based remote sensing?

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

Evaluating parameterisations of subgrid-scale variability

Parameterisations of fractional cloudiness in large-scale atmospheric models rely on information about the subgrid-scale variablity of the total water specific humidity, qt , provided in form of a probability density function (PDF). In this contribution, four different approaches to evaluate such total-water PDFs are discussed: (i) Satellite spectroradiometers with high spatial resolution allow to construct at the scale of model grid boxes a histogram, and subsequently to derive the moments of the PDF, of the vertical integral of qt . This can be compared to the same quantity diagnosed from the model parameterisation. Although the vertical integral mostly focuses on the boundary layer, and involves issues in grid-boxes with orographic variability, it allowed nevertheless in the example presented to pinpoint deficiencies of a model parameterisation. (ii) Assuming a simple PDF shape and saturation within clouds, the simple “critical relative humidity” metric can be derived from infrared sounders and/or cloud lidar in combination with reanalysis data with a vertical resolution. It allows to evaluate the underlying PDF of any cloud scheme, but is sensitive to the assumptions. (iii) Supersites with a combination of ground-based lidar, radar and microwave data provide high-resolution high-quality reference data. In a “virtual reality” framework, we showed, however, that it is difficult to evaluate higher moments of a spatial PDF with this temporally-varying data. (iv) From a hierarchy of models from general circulation models to direct numerical simulations, we find that the variance of the qt follows a power-law scaling with an exponent of about -2. This information is very useful to improve the parameterisations

Evaluating parameterisations of subgrid-scale variability

Parameterisations of fractional cloudiness in large-scale atmospheric models rely on information about the subgrid-scale variablity of the total water specific humidity, qt , provided in form of a probability density function (PDF). In this contribution, four different approaches to evaluate such total-water PDFs are discussed: (i) Satellite spectroradiometers with high spatial resolution allow to construct at the scale of model grid boxes a histogram, and subsequently to derive the moments of the PDF, of the vertical integral of qt . This can be compared to the same quantity diagnosed from the model parameterisation. Although the vertical integral mostly focuses on the boundary layer, and involves issues in grid-boxes with orographic variability, it allowed nevertheless in the example presented to pinpoint deficiencies of a model parameterisation. (ii) Assuming a simple PDF shape and saturation within clouds, the simple “critical relative humidity” metric can be derived from infrared sounders and/or cloud lidar in combination with reanalysis data with a vertical resolution. It allows to evaluate the underlying PDF of any cloud scheme, but is sensitive to the assumptions. (iii) Supersites with a combination of ground-based lidar, radar and microwave data provide high-resolution high-quality reference data. In a “virtual reality” framework, we showed, however, that it is difficult to evaluate higher moments of a spatial PDF with this temporally-varying data. (iv) From a hierarchy of models from general circulation models to direct numerical simulations, we find that the variance of the qt follows a power-law scaling with an exponent of about -2. This information is very useful to improve the parameterisations

Trends in Upper-Tropospheric Humidity: Expansion of the Subtropical Dry Zones?

Subtropical dry zones, located in the Hadley cells' subsidence regions, strongly influence regional climate as well as outgoing longwave radiation. Changes in these dry zones could have significant impact on surface climate as well as on the atmospheric energy budget. This study investigates the behavior of upper-tropospheric dry zones in a changing climate, using the variable upper-tropospheric humidity (UTH), calculated from climate model experiment output as well as from radiances measured with satellite-based sensors. The global UTH distribution shows that dry zones form a belt in the subtropical winter hemisphere. In the summer hemisphere they concentrate over the eastern ocean basins, where the descent regions of the subtropical anticyclones are located. Recent studies with model and satellite data have found tendencies of increasing dryness at the poleward edges of the subtropical subsidence zones. However, UTH calculated from climate simulations with 25 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) shows these tendencies only for parts of the winter-hemispheric dry belts. In the summer hemisphere, even though differences exist between the simulations, UTH is increasing in most dry zones, particularly in the South and North Pacific Ocean. None of the summer dry zones is expanding in these simulations. Upper-tropospheric dry zones estimated from observational data do not show any robust signs of change since 1979. Apart from a weak drying tendency at the poleward edge of the southern winter-hemispheric dry belt in infrared measurements, nothing indicates that the subtropical dry belts have expanded poleward