Abstract

A parameterization scheme is proposed for the subgrid-scale transport of hydrometeors in an assumed probability density function (PDF) scheme. Joint distributions of vertical velocity and hydrometeor mixing ratios are typically unknown, but marginal (1D) PDFs of these variables are available. The parameterization is developed using high-resolution simulations of continental and tropical deep convection. A 3D cloud-resolving model (CRM) providing benchmark solutions has a horizontal grid spacing of 250 m and employs the Morrison microphysics scheme, which treats prognostically mass and number mixing ratios for four types of precipitating hydrometeors (rain, graupel, snow, and ice) as well as cloud droplet number mixing ratio. The subgrid-scale hydrometeor transport scheme assumes input given in the form of marginal PDFs of vertical velocity and hydrometeor mixing ratios; in this study, these marginal distributions are provided by the cloud-resolving model. Conditional sampling and scaling are then applied to the marginal distributions to account for subplume correlations. The parameterized fluxes tested for four episodes of deep convection show good agreement with benchmark fluxes computed directly from the CRM output. The results demonstrate the potential use of the subgrid-scale hydrometeor transport scheme in an assumed PDF scheme to parameterize the covariances of vertical velocity and hydrometeor mixing ratios.

Abstract

Two-moment autoconversion parameterizations as compared to accretion parameterizations exhibit significant errors suggesting that additional moments are needed to increase their accuracy. We develop a three-moment autoconversion parameterization using output from an LES model with size-resolved microphysics. Adding the third moment decreases the errors of parameterization and improves precipitation prediction. However, the errors are still significantly larger than errors of accretion rate. An analysis of the cloud drop size distributions (DSDs) in the simulated tropical convective cloud system reveals that most DSDs have a significant fraction of cloud liquid water content q _c in the midsize droplet range (radii from 20 to 40 μm). Our data indicate that more than 30% of DSDs have over half of q _c contained in the midsize range and about 60% of spectra have, at least, one-third of q _c in this range. Even when the rain/drizzle mode is small (radar reflectivity Z < −10 dBZ), there is a significant number of spectra in which fraction of q _c in the midsize range is as large as 60%. These DSDs are more complex than the frequently used gamma or lognormal distributions, which exhibit a single mode and can be defined by three microphysical moments. The need to define DSDs by more than three moments explains the large errors in the three-moment autoconversion parameterization. The limitation of three-parameter gamma or lognormal distributions should be kept in mind when applying them in precipitating shallow cumulus clouds.

Abstract

Potential ways of parameterizing vertical turbulent fluxes of hydrometeors are examined using a high-resolution simulation of continental deep convection. The cloud-resolving model uses a double-moment microphysics scheme that contains prognostic variables for four hydrometeor types: rain, graupel, cloud ice, and snow. The benchmark simulation with a horizontal grid spacing of 250 m is analyzed to evaluate three different ways of parameterizing the turbulent vertical fluxes of hydrometeors: an eddy-diffusion approximation, a quadrant-based decomposition, and a scaling method that accounts for within-quadrant (subplume) correlations. Results show that the downgradient nature of the eddy-diffusion approximation enforces transport of mass away from concentrated regions, whereas the benchmark simulation indicates that the vertical transport often moves mass from below the level of maximum concentration to aloft. Unlike the eddy-diffusion approach, the quadrimodal decomposition is able to capture the signs of the flux gradient but underestimates the magnitudes. The scaling approach, which accounts empirically for within-quadrant correlations, improves the representation of the vertical fluxes for all hydrometeors except snow. A sensitivity study is performed to illustrate how vertical transport effects on the vertical distribution of hydrometeors are compounded by accompanying changes in microphysical process rates. Results from the sensitivity tests show that suppressing rain or graupel transport drastically alters vertical profiles of cloud ice and snow through changes in the distribution of cloud water, which in turn governs the production of cloud ice and snow aloft. Last, a viable subgrid-scale hydrometeor transport scheme in an assumed probability density function parameterization is discussed.

Abstract

Many present-day numerical weather prediction (NWP) models are run at resolutions that permit deep convection. In these models, however, the boundary layer turbulence and boundary layer cloud features are still grossly underresolved. Underresolution is also present in climate models that use a multiscale modeling framework (MMF), in which a convection-permitting model is run in each grid column of a global general circulation model.

To better represent boundary layer clouds and turbulence in convection-permitting models, a parameterization was developed that models the joint probability density function (PDF) of vertical velocity, heat, and moisture. Although PDF-based parameterizations are more complex and computationally expensive than many other parameterizations, in principle PDF parameterizations have several advantages. For instance, they ensure consistency of liquid (cloud) water and cloud fraction; they avoid using separate parameterizations for different cloud types such as cumulus and stratocumulus; and they have an appropriate formulation in the “terra incognita” in which updrafts are marginally resolved.

In this paper, an implementation of a PDF parameterization is tested to see whether it improves the simulations of a state-of-the-art convection-permitting model. The PDF parameterization used is the Cloud Layers Unified By Binormals (CLUBB) parameterization. The host cloud-resolving model used is the System for Atmospheric Modeling (SAM). SAM is run both with and without CLUBB implemented in it. Simulations of two shallow cumulus (Cu) cases and two shallow stratocumulus (Sc) cases are run in a 3D configuration at 2-, 4-, and 16-km horizontal grid spacings.

Including CLUBB in the simulations improves some of the simulated fields—such as vertical velocity variance, horizontal wind fields, cloud water content, and drizzle water content—especially in the two Cu cases. Implementing CLUBB in SAM improves the simulations slightly at 2-km horizontal grid spacing, significantly at 4-km grid spacing, and greatly at 16-km grid spacing. Furthermore, the simulations that include CLUBB exhibit a reduced sensitivity to horizontal grid spacing.

Abstract

Atmospheric properties in a convective boundary layer vary over a wide range of spatial scales and are commonly studied using large-eddy simulations (LES) in various configurations. We examine how the boundary layer depth and distribution of variability across scales are affected by LES grid spacing, domain size, inhomogeneity of surface properties, and external forcing. Two different setups of the Weather Research and Forecasting (WRF) Model are analyzed. A semi-idealized configuration uses a periodic domain, flat surface, prescribed homogeneous surface heat fluxes, and horizontally uniform profiles of large-scale advective tendencies. A nested LES setup employs a larger domain and realistic initial and boundary conditions, including an interactive land surface model with representative topography and vegetation and soil types. Subdomains of identical size are analyzed for all simulations. Characteristic structure sizes are quantified using the variability scales L ₅₀ and L ₉₅, defined such that features smaller than that contain 50% and 95% of the total variance, respectively. Progressive increase in L ₅₀ from vertical velocity to temperature and moisture structures is systematically reproduced in all simulation configurations. This dependence of L ₅₀ on the considered variable complicates the development of scale-aware parameterizations for models with grid spacing in the “terra incognita.” In simulations using a larger domain with heterogeneous surface properties, the development of internal mesoscale patterns significantly affects variance distributions inside analyzed subdomains. Sizes of boundary layer structures also strongly depend on the LES grid spacing and, in case of heterogeneous surface and topography, on location of the subdomain inside a larger computational domain.

Abstract

A comprehensive dataset of microphysical and radiative properties of aerosols and clouds in the boundary layer in the vicinity of Barrow, Alaska, was collected in April 2008 during the Indirect and Semi-Direct Aerosol Campaign (ISDAC). ISDAC's primary aim was to examine the effects of aerosols, including those generated by Asian wildfires, on clouds that contain both liquid and ice. ISDAC utilized the Atmospheric Radiation Measurement Pro- gram's permanent observational facilities at Barrow and specially deployed instruments measuring aerosol, ice fog, precipitation, and radiation. The National Research Council of Canada Convair-580 flew 27 sorties and collected data using an unprecedented 41 stateof- the-art cloud and aerosol instruments for more than 100 h on 12 different days. Aerosol compositions, including fresh and processed sea salt, biomassburning particles, organics, and sulfates mixed with organics, varied between flights. Observations in a dense arctic haze on 19 April and above, within, and below the single-layer stratocumulus on 8 and 26 April are enabling a process-oriented understanding of how aerosols affect arctic clouds. Inhomogeneities in reflectivity, a close coupling of upward and downward Doppler motion, and a nearly constant ice profile in the single-layer stratocumulus suggests that vertical mixing is responsible for its longevity observed during ISDAC. Data acquired in cirrus on flights between Barrow and Fairbanks, Alaska, are improving the understanding of the performance of cloud probes in ice. Ultimately, ISDAC data will improve the representation of cloud and aerosol processes in models covering a variety of spatial and temporal scales, and determine the extent to which surface measurements can provide retrievals of aerosols, clouds, precipitation, and radiative heating.

A supplement to this article is available online:

DOI: 10.1175/2010BAMS2935.2

Abstract

One of the most intense air mass transformations on Earth happens when cold air flows from frozen surfaces to much warmer open water in cold-air outbreaks (CAOs), a process captured beautifully in satellite imagery. Despite the ubiquity of the CAO cloud regime over high-latitude oceans, we have a rather poor understanding of its properties, its role in energy and water cycles, and its treatment in weather and climate models. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) was conducted to better understand this regime and its representation in models. COMBLE aimed to examine the relations between surface fluxes, boundary layer structure, aerosol, cloud, and precipitation properties, and mesoscale circulations in marine CAOs. Processes affecting these properties largely fall in a range of scales where boundary layer processes, convection, and precipitation are tightly coupled, which makes accurate representation of the CAO cloud regime in numerical weather prediction and global climate models most challenging. COMBLE deployed an Atmospheric Radiation Measurement Mobile Facility at a coastal site in northern Scandinavia (69°N), with additional instruments on Bear Island (75°N), from December 2019 to May 2020. CAO conditions were experienced 19% (21%) of the time at the main site (on Bear Island). A comprehensive suite of continuous in situ and remote sensing observations of atmospheric conditions, clouds, precipitation, and aerosol were collected. Because of the clouds’ well-defined origin, their shallow depth, and the broad range of observed temperature and aerosol concentrations, the COMBLE dataset provides a powerful modeling testbed for improving the representation of mixed-phase cloud processes in large-eddy simulations and large-scale models.