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Abstract
Detailed observations of stratiform boundary layer clouds on 12 days are examined with specific reference to drizzle formation processes. The clouds differ considerably in mean thickness, liquid water path (LWP), and droplet concentration. Cloud-base precipitation rates differ by a factor of 20 between cases. The lowest precipitation rate is found in the case with the highest droplet concentration even though this case had by far the highest LWP, suggesting that drizzle can be severely suppressed in polluted clouds.
The vertical and horizontal structure of cloud and drizzle liquid water and bulk microphysical parameters are examined in detail. In general, the highest concentration of r > 20 μm drizzle drops is found toward the top of the cloud, and the mean volume radius of the drizzle drops increases monotonically from cloud top to base. The resulting precipitation rates are largest at the cloud base but decrease markedly only in the upper third of the cloud. Below cloud, precipitation rates decrease markedly with distance below base due to evaporation, and are broadly consistent in most cases with the results from a simple sedimentation–evaporation model. Evidence is presented that suggests evaporating drizzle is cooling regions of the subcloud layer, which could result in dynamical feedbacks. A composite power spectrum of the horizontal spatial series of precipitation rate is found to exhibit a power-law scaling from the smallest observable scales to close to the maximum observable scale (∼30 km). The exponent is considerably lower (1.1–1.2) than corresponding exponents for LWP variability obtained in other studies (∼1.5–2), demonstrating that there is relatively more variability of drizzle on small scales. Singular measures analysis shows that drizzle fields are much more intermittent than the cloud liquid water content fields, consistent with a drizzle production process that depends strongly upon liquid water content. The adiabaticity of the clouds, which can be modeled as a simple balance between drizzle loss and turbulent replenishment, is found to decrease if the time scale for drizzle loss is shorter than roughly 5–10 eddy turnover time scales. Finally, the data are compared with three simple scalings derived from recent observations of drizzle in subtropical stratocumulus clouds.
Abstract
Detailed observations of stratiform boundary layer clouds on 12 days are examined with specific reference to drizzle formation processes. The clouds differ considerably in mean thickness, liquid water path (LWP), and droplet concentration. Cloud-base precipitation rates differ by a factor of 20 between cases. The lowest precipitation rate is found in the case with the highest droplet concentration even though this case had by far the highest LWP, suggesting that drizzle can be severely suppressed in polluted clouds.
The vertical and horizontal structure of cloud and drizzle liquid water and bulk microphysical parameters are examined in detail. In general, the highest concentration of r > 20 μm drizzle drops is found toward the top of the cloud, and the mean volume radius of the drizzle drops increases monotonically from cloud top to base. The resulting precipitation rates are largest at the cloud base but decrease markedly only in the upper third of the cloud. Below cloud, precipitation rates decrease markedly with distance below base due to evaporation, and are broadly consistent in most cases with the results from a simple sedimentation–evaporation model. Evidence is presented that suggests evaporating drizzle is cooling regions of the subcloud layer, which could result in dynamical feedbacks. A composite power spectrum of the horizontal spatial series of precipitation rate is found to exhibit a power-law scaling from the smallest observable scales to close to the maximum observable scale (∼30 km). The exponent is considerably lower (1.1–1.2) than corresponding exponents for LWP variability obtained in other studies (∼1.5–2), demonstrating that there is relatively more variability of drizzle on small scales. Singular measures analysis shows that drizzle fields are much more intermittent than the cloud liquid water content fields, consistent with a drizzle production process that depends strongly upon liquid water content. The adiabaticity of the clouds, which can be modeled as a simple balance between drizzle loss and turbulent replenishment, is found to decrease if the time scale for drizzle loss is shorter than roughly 5–10 eddy turnover time scales. Finally, the data are compared with three simple scalings derived from recent observations of drizzle in subtropical stratocumulus clouds.
Abstract
This is the second of two observational papers examining drizzle in stratiform boundary layer clouds. Part I details the vertical and horizontal structure of cloud and drizzle parameters, including some bulk microphysical variables. In this paper, the focus is on the in situ size-resolved microphysical measurements, particularly of drizzle drops (r > 20 μm). Layer-averaged size distributions of drizzle drops within cloud are shown to be well represented using either a truncated exponential or a truncated lognormal size distribution. The size-resolved microphysical measurements are used to estimate autoconversion and accretion rates by integration of the stochastic collection equation (SCE). These rates are compared with a number of commonly used bulk parameterizations of warm rain formation. While parameterized accretion rates agree well with those derived from the SCE initialized with observed spectra, the autoconversion rates seriously disagree in some cases. These disagreements need to be addressed in order to bolster confidence in large-scale numerical model predictions of the aerosol second indirect effect. Cloud droplet coalescence removal rates and mass and number fall rate relationships used in the bulk microphysical schemes are also compared, revealing some potentially important discrepancies. The relative roles of autoconversion and accretion are estimated by examination of composite profiles from the 12 flights. Autoconversion, although necessary for the production of drizzle drops, is much less important than accretion throughout the lower 80% of the cloud layer in terms of the production of drizzle liquid water. The SCE calculations indicate that the autoconversion rate depends strongly upon the cloud droplet concentration Nd such that a doubling of Nd would lead to a reduction in autoconversion rate of between 2 and 4.
Radar reflectivity–precipitation rate (Z–R) relationships suitable for radar use are derived and are shown to be significantly biased in some cases by the undersampling of large (r > 200 μm) drops with the 2D-C probe. A correction based upon the extrapolation to larger sizes using the exponential size distribution changes the Z–R relationship, leading to the conclusion that consideration should be given to sampling issues when examining higher moments of the drop size distribution in drizzling stratiform boundary layer clouds.
Abstract
This is the second of two observational papers examining drizzle in stratiform boundary layer clouds. Part I details the vertical and horizontal structure of cloud and drizzle parameters, including some bulk microphysical variables. In this paper, the focus is on the in situ size-resolved microphysical measurements, particularly of drizzle drops (r > 20 μm). Layer-averaged size distributions of drizzle drops within cloud are shown to be well represented using either a truncated exponential or a truncated lognormal size distribution. The size-resolved microphysical measurements are used to estimate autoconversion and accretion rates by integration of the stochastic collection equation (SCE). These rates are compared with a number of commonly used bulk parameterizations of warm rain formation. While parameterized accretion rates agree well with those derived from the SCE initialized with observed spectra, the autoconversion rates seriously disagree in some cases. These disagreements need to be addressed in order to bolster confidence in large-scale numerical model predictions of the aerosol second indirect effect. Cloud droplet coalescence removal rates and mass and number fall rate relationships used in the bulk microphysical schemes are also compared, revealing some potentially important discrepancies. The relative roles of autoconversion and accretion are estimated by examination of composite profiles from the 12 flights. Autoconversion, although necessary for the production of drizzle drops, is much less important than accretion throughout the lower 80% of the cloud layer in terms of the production of drizzle liquid water. The SCE calculations indicate that the autoconversion rate depends strongly upon the cloud droplet concentration Nd such that a doubling of Nd would lead to a reduction in autoconversion rate of between 2 and 4.
Radar reflectivity–precipitation rate (Z–R) relationships suitable for radar use are derived and are shown to be significantly biased in some cases by the undersampling of large (r > 200 μm) drops with the 2D-C probe. A correction based upon the extrapolation to larger sizes using the exponential size distribution changes the Z–R relationship, leading to the conclusion that consideration should be given to sampling issues when examining higher moments of the drop size distribution in drizzling stratiform boundary layer clouds.
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Abstract
Cloud horizontal size distributions from near-global satellite data, from aircraft, and from a global high-resolution numerical weather prediction model, are presented for the scale range 0.1–8000 km and are shown to be well-represented using a single power-law relationship with an exponent of β = 1.66 ±0.04 from 0.1 to 1500 km or more. At scales longer than 1500 km, there is a statistically significant scale break with fewer very large clouds than expected from the power law. The size distribution is integrated to determine the contribution to cloud cover and visible reflectance from clouds larger than a given size. Globally, clouds with a horizontal dimension of 200 km or more constitute approximately 50% of the cloud cover and 60% of the reflectance, and this result is not sensitive to the minimum size threshold assumed in the integral assuming that the power law can be extrapolated below 100-m scale. The result is also not sensitive to whether the size distribution is determined using cloud segment length or cloud area. This emphasizes the great role played by large cloud sheets in determining the earth’s albedo. On the other hand, some 15% of global cloud cover comes from clouds smaller than 10 km, thus emphasizing the broad range of cloud sizes that contribute significantly to the earth’s radiation budget. Both of these results stem from the fact that β is slightly less than 2. The data are further divided and geographical and seasonal variations in the cloud size L 50 for which clouds larger than L 50 constitute 50% of the cloud cover are determined. The largest clouds (L 50 > 300 km) are found over the midlatitude oceans, particularly in summer, and over the tropical convective regions of the west Pacific and Indian Oceans and the monsoon-influenced landmasses. The smallest clouds (L 50 < 100 km) are found over the trade wind regions of the tropics/subtropics and over arid land areas. Small variations in the exponent β contribute significantly to the variations in L50. Finally, it is shown that a bounded cascade model can faithfully simulate the observed cloud size distributions and use this to examine the effects of limiting sensor resolution (pixel size) and domain size (number of pixels across image). Sensor resolution is not found to strongly impact the cloud size distribution provided the ratio of the domain to pixel size remains greater than ~1000. Thus, previous studies with small domain–pixel size ratios may provide biased information about the true cloud size distribution, and should be interpreted with caution.
Abstract
Cloud horizontal size distributions from near-global satellite data, from aircraft, and from a global high-resolution numerical weather prediction model, are presented for the scale range 0.1–8000 km and are shown to be well-represented using a single power-law relationship with an exponent of β = 1.66 ±0.04 from 0.1 to 1500 km or more. At scales longer than 1500 km, there is a statistically significant scale break with fewer very large clouds than expected from the power law. The size distribution is integrated to determine the contribution to cloud cover and visible reflectance from clouds larger than a given size. Globally, clouds with a horizontal dimension of 200 km or more constitute approximately 50% of the cloud cover and 60% of the reflectance, and this result is not sensitive to the minimum size threshold assumed in the integral assuming that the power law can be extrapolated below 100-m scale. The result is also not sensitive to whether the size distribution is determined using cloud segment length or cloud area. This emphasizes the great role played by large cloud sheets in determining the earth’s albedo. On the other hand, some 15% of global cloud cover comes from clouds smaller than 10 km, thus emphasizing the broad range of cloud sizes that contribute significantly to the earth’s radiation budget. Both of these results stem from the fact that β is slightly less than 2. The data are further divided and geographical and seasonal variations in the cloud size L 50 for which clouds larger than L 50 constitute 50% of the cloud cover are determined. The largest clouds (L 50 > 300 km) are found over the midlatitude oceans, particularly in summer, and over the tropical convective regions of the west Pacific and Indian Oceans and the monsoon-influenced landmasses. The smallest clouds (L 50 < 100 km) are found over the trade wind regions of the tropics/subtropics and over arid land areas. Small variations in the exponent β contribute significantly to the variations in L50. Finally, it is shown that a bounded cascade model can faithfully simulate the observed cloud size distributions and use this to examine the effects of limiting sensor resolution (pixel size) and domain size (number of pixels across image). Sensor resolution is not found to strongly impact the cloud size distribution provided the ratio of the domain to pixel size remains greater than ~1000. Thus, previous studies with small domain–pixel size ratios may provide biased information about the true cloud size distribution, and should be interpreted with caution.
Abstract
Composite mean fields and probability distribution functions (PDFs) of rain rate, cloud type and cover, cloud-top temperature, surface wind velocity, and water vapor path (WVP) are constructed using satellite observations of midlatitude cyclones from four oceanic regions (i.e., the North Pacific, South Pacific, North Atlantic, and South Atlantic). Reanalysis surface pressure fields are used to ascertain the locations of the cyclone centers, onto which the satellite fields are interpolated to give a database of ∼1500 cyclones from a two-year period (2003–04). Cyclones are categorized by their strength, defined here using surface wind speed, and by their WVP, and it is found that these two measures can explain a considerable amount of the intercyclone variability of other key variables. Composite cyclones from each of the four ocean basins exhibit similar spatial structure for a given strength and WVP. A set of nine composites is constructed from the database using three strength and three WVP ranges and is used to demonstrate that the mean column relative humidity of these systems varies only slightly (0.58–0.62) for a doubling in WVP (or equivalently a 7-K rise in sea surface temperature) and a 50% increase in cyclone strength. However, cyclone-mean rain rate increases markedly with both cyclone strength and WVP, behavior that is explained with a simple warm conveyor belt model. Systemwide high cloud fraction (tops above 440 hPa) increases from 0.23 to 0.31 as cyclone strength increases by 50%, but does not vary systematically with WVP. It is suggested that the composite fields constitute useful diagnostics for evaluating the behavior of large-scale numerical models, and may provide insight into how precipitation and clouds in midlatitude cyclones respond under a changed climate.
Abstract
Composite mean fields and probability distribution functions (PDFs) of rain rate, cloud type and cover, cloud-top temperature, surface wind velocity, and water vapor path (WVP) are constructed using satellite observations of midlatitude cyclones from four oceanic regions (i.e., the North Pacific, South Pacific, North Atlantic, and South Atlantic). Reanalysis surface pressure fields are used to ascertain the locations of the cyclone centers, onto which the satellite fields are interpolated to give a database of ∼1500 cyclones from a two-year period (2003–04). Cyclones are categorized by their strength, defined here using surface wind speed, and by their WVP, and it is found that these two measures can explain a considerable amount of the intercyclone variability of other key variables. Composite cyclones from each of the four ocean basins exhibit similar spatial structure for a given strength and WVP. A set of nine composites is constructed from the database using three strength and three WVP ranges and is used to demonstrate that the mean column relative humidity of these systems varies only slightly (0.58–0.62) for a doubling in WVP (or equivalently a 7-K rise in sea surface temperature) and a 50% increase in cyclone strength. However, cyclone-mean rain rate increases markedly with both cyclone strength and WVP, behavior that is explained with a simple warm conveyor belt model. Systemwide high cloud fraction (tops above 440 hPa) increases from 0.23 to 0.31 as cyclone strength increases by 50%, but does not vary systematically with WVP. It is suggested that the composite fields constitute useful diagnostics for evaluating the behavior of large-scale numerical models, and may provide insight into how precipitation and clouds in midlatitude cyclones respond under a changed climate.
Abstract
Numerical simulations are used to investigate the impact on the stable boundary layer of moderate topography (with hill heights in some cases comparable to the undisturbed boundary layer depth). Area-averaged properties of the resulting boundary layers, which are often highly inhomogeneous, are diagnosed. The presence of the hills leads to enhanced turbulence and drag, and a deepening of the area-averaged boundary layers (over and above that due to a simple displacement effect). The ability of well-established formulas for the depth of the boundary layer over homogeneous terrain to predict this deepening is investigated. Finally, the implications of the results for the use in large-scale weather and climate prediction models of effective roughness length parameterizations of the effects of hills are discussed. While not capturing some of the more detailed effects, the simplest approach of using a roughness length independent of stability is found to perform reasonably well in predicting the total surface drag.
Abstract
Numerical simulations are used to investigate the impact on the stable boundary layer of moderate topography (with hill heights in some cases comparable to the undisturbed boundary layer depth). Area-averaged properties of the resulting boundary layers, which are often highly inhomogeneous, are diagnosed. The presence of the hills leads to enhanced turbulence and drag, and a deepening of the area-averaged boundary layers (over and above that due to a simple displacement effect). The ability of well-established formulas for the depth of the boundary layer over homogeneous terrain to predict this deepening is investigated. Finally, the implications of the results for the use in large-scale weather and climate prediction models of effective roughness length parameterizations of the effects of hills are discussed. While not capturing some of the more detailed effects, the simplest approach of using a roughness length independent of stability is found to perform reasonably well in predicting the total surface drag.
Abstract
Relationships among total water, condensed water, and cloud fraction in boundary layer and cold tropospheric stratiform clouds are investigated using a large observational dataset collected by the U.K. Met. Office C-130 aircraft. Values of the above parameters are estimated using horizontal aircraft runs ranging from 40 to 80 km in length. Boundary layer (warm cloud) data were taken from the Atlantic Stratocumulus Transition Experiment (ASTEX) and First International Satellite Cloud Climatology Project (ISCCP) Research Experiment (FIRE). Free tropospheric (cold cloud) data were taken from the European Cloud and Radiation Experiment (EUCREX). In both warm and cold cloud a single reasonably well-defined relationship exists between the cloud fraction and the total water content (vapor + condensate) when normalized with the saturation specific humidity. A relationship exists between the condensed water content and the cloud fraction when appropriately scaled with the saturation specific humidity. Functional forms fitted to the data are used as comparators to test three existing diagnostic cloud fraction parameterization schemes.
Abstract
Relationships among total water, condensed water, and cloud fraction in boundary layer and cold tropospheric stratiform clouds are investigated using a large observational dataset collected by the U.K. Met. Office C-130 aircraft. Values of the above parameters are estimated using horizontal aircraft runs ranging from 40 to 80 km in length. Boundary layer (warm cloud) data were taken from the Atlantic Stratocumulus Transition Experiment (ASTEX) and First International Satellite Cloud Climatology Project (ISCCP) Research Experiment (FIRE). Free tropospheric (cold cloud) data were taken from the European Cloud and Radiation Experiment (EUCREX). In both warm and cold cloud a single reasonably well-defined relationship exists between the cloud fraction and the total water content (vapor + condensate) when normalized with the saturation specific humidity. A relationship exists between the condensed water content and the cloud fraction when appropriately scaled with the saturation specific humidity. Functional forms fitted to the data are used as comparators to test three existing diagnostic cloud fraction parameterization schemes.
Abstract
Any population of cloud droplets forming on polydisperse condensation nuclei is thermodynamically unstable. There is no value of the supersaturation for which the growth rate of all the droplets is zero, so that if some droplets are in equilibrium, then some must have positive and some negative growth rates. Droplets with positive growth rates will continue to grow at the expense of those with negative growth rates. This effect has been termed the ripening process, and has been postulated to be a potential mechanism to explain broad droplet size distributions in stratiform clouds. In this paper multiple parcel trajectories are used, derived using a simple representation of the turbulent dynamics, to examine the time evolution of the droplet size distribution in a nonentraining stratiform cloud. It is shown that the magnitude of the effect is critically dependent upon the mean parcel in-cloud residence time. The simulations suggest that, for a stratiform clouds of h = 400 m thickness, and a vertical wind standard deviation of σ w = 0.6 m s–1 (typical for stratocumulus clouds in a fairly vigorous, well-mixed boundary layer), the ripening effect is negligible, in that the droplet size distribution changes little with time. However, clouds with low σ w = 0.2 m s–1 (typical of weaker stratus clouds) show a marked spectral ripening effect over a period of several hours. Ripening is observed in the numerical model in both clean and polluted aerosol distributions. Autoconversion rates calculated from the droplet size distributions increase markedly with time as ripening takes place. It is suggested that to accurately model droplet size distributions in stratus cloud, it may be necessary to take into account the distribution of in-cloud parcel residence time.
Abstract
Any population of cloud droplets forming on polydisperse condensation nuclei is thermodynamically unstable. There is no value of the supersaturation for which the growth rate of all the droplets is zero, so that if some droplets are in equilibrium, then some must have positive and some negative growth rates. Droplets with positive growth rates will continue to grow at the expense of those with negative growth rates. This effect has been termed the ripening process, and has been postulated to be a potential mechanism to explain broad droplet size distributions in stratiform clouds. In this paper multiple parcel trajectories are used, derived using a simple representation of the turbulent dynamics, to examine the time evolution of the droplet size distribution in a nonentraining stratiform cloud. It is shown that the magnitude of the effect is critically dependent upon the mean parcel in-cloud residence time. The simulations suggest that, for a stratiform clouds of h = 400 m thickness, and a vertical wind standard deviation of σ w = 0.6 m s–1 (typical for stratocumulus clouds in a fairly vigorous, well-mixed boundary layer), the ripening effect is negligible, in that the droplet size distribution changes little with time. However, clouds with low σ w = 0.2 m s–1 (typical of weaker stratus clouds) show a marked spectral ripening effect over a period of several hours. Ripening is observed in the numerical model in both clean and polluted aerosol distributions. Autoconversion rates calculated from the droplet size distributions increase markedly with time as ripening takes place. It is suggested that to accurately model droplet size distributions in stratus cloud, it may be necessary to take into account the distribution of in-cloud parcel residence time.
