Analytic upscaling of a local microphysics scheme. Part I: Derivation

Larson, Vincent E.; Griffin, Brian M.

doi:10.1002/qj.1967

Cited by 46 publications

(66 citation statements)

References 22 publications

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“…Some studies represented it with the subcolumn method, which statistically reconstructs the internal cloud structure by using large-scale cloud fraction information and overlapping assumption Klein, 1999, 2000;Jess et al, 2011;Pincus and Stevens, 2009;Pincus et al, 2003Pincus et al, , 2006Raisanen et al, 2004]. Other studies sought to specify the assumed probability density function (PDF) of cloud quantities diagnostically or prognostically [Bogenschutz and Krueger, 2013;Golaz et al, 2002;Larson and Golaz, 2005;Norris et al, 2008;Tompkins, 2002] so that the subgrid variability effect can be accounted by integrating the cloud PDF for microphysics [Larson and Griffin, 2013;Pincus and Klein, 2000;Weber and Quaas, 2012;Wood et al, 2002] and radiation calculations Hogan, 2008, 2010;. Oreopoulos and Cahalan [2005] gave a very comprehensive survey for the 1°× 1°global cloud inhomogeneity climatology including latitudinal, seasonal, and diurnal changes using Moderate Resolution Imaging Spectroradiometer (MODIS) data.…”

Section: Introductionmentioning

confidence: 99%

Scale‐aware parameterization of liquid cloud inhomogeneity and its impact on simulated climate in CESM

Xie

Zhang

2015

JGR Atmospheres

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Using long-term radar-based ground measurements from the Atmospheric Radiation Measurement Program, we derive the inhomogeneity of cloud liquid water as represented by the shape parameter of a gamma distribution. The relationship between the inhomogeneity and the model grid size as well as atmospheric condition is presented. A larger grid scale and more unstable atmosphere are associated with larger inhomogeneity that is described by a smaller shape parameter. This relationship is implemented as a scale-aware parameterization of the liquid cloud inhomogeneity in the Community Earth System Model (CESM) in which the shape parameter impacts the cloud microphysical processes. When used in the default CESM1 with the finite-volume dynamic core where a constant liquid inhomogeneity parameter was assumed, it reduces the cloud inhomogeneity in high latitudes and increases it in low latitudes. This is due to both the smaller (larger) grid size in high (low) latitudes in the longitude-latitude grid setting of CESM and the more stable (unstable) atmosphere. The single-column model and general circulation model sensitivity experiments show that the new parameterization increases the cloud liquid water path in polar regions and decreases it in low latitudes. An advantage of the parameterization is that it can recognize the spatial resolutions of the CESM without special tuning of the cloud water inhomogeneity parameter.

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Section: Introductionmentioning

confidence: 99%

Scale‐aware parameterization of liquid cloud inhomogeneity and its impact on simulated climate in CESM

Xie

Zhang

2015

JGR Atmospheres

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“…Part I (Larson and Griffin, 2012) discussed the theory underlying the upscaling method. It involved analytically integrating the local microphysical formulas over the probability density function (PDF) of spatial variability within a grid box.…”

Section: Discussionmentioning

confidence: 99%

“…In Part I of this two-part series (Larson and Griffin, 2012), we upscale the Khairoutdinov and Kogan (2000) microphysics scheme (hereafter, KK scheme) by use of the assumed probability density function (PDF) method. In the present paper, we apply the upscaled microphysics to a simulation of a drizzling marine stratocumulus cloud.…”

Section: Introductionmentioning

confidence: 99%

Analytic upscaling of a local microphysics scheme. Part II: Simulations

Griffin

Larson

2012

Quart J Royal Meteoro Soc

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Part I of this two-part series presents a method to account for the effects of subgrid variability on average microphysical process rates. The method involves upscaling a local microphysics scheme, that is, computing a grid-box average by integrating over an assumed probability density function (PDF). In this paper (Part II), the method is tested. The test case is based on research flight two (RF02) of the second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) field experiment. The upscaled microphysics scheme is incorporated into a single-column model (SCM). Then two SCM simulations of the RF02 test case are performed. They are identical except that one upscales the local microphysics scheme and one does not. Both SCM simulations are compared with a benchmark large-eddy simulation (LES) of the same test case using the same local microphysics scheme.Compared to the local microphysics, the representation of variability inherent in the upscaled microphysics increases autoconversion of cloud droplets to raindrops and accretion (i.e. collection) of cloud droplets onto raindrops. The combined effect is that upscaling the microphysics in the RF02 case leads to significantly more rainwater at the ocean surface, in closer agreement to the LES.

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“…For example, we set r 2 r /r r 2 equal to a constant everywhere throughout the simulation. The values of the constants of proportionality that we choose, for the marine stratocumulus case discussed below, are listed in Table 1 of Larson and Griffin (2013). These values are based on a large-eddy simulation.…”

Section: Drawing a Sample Of Subcolumns From The Subgrid Pdfmentioning

confidence: 99%

“…Other papers have used analytic integration in order to upscale microphysical process rates (e.g., Zhang et al, 2002;Morrison and Gettelman, 2008;Cheng and Xu, 2009;Larson and Griffin, 2013;Griffin and Larson, 2013;Weber and Quaas, 2012).…”

Section: Introductionmentioning

confidence: 99%

The Subgrid Importance Latin Hypercube Sampler (SILHS): a multivariate subcolumn generator

Larson

Schanen

2013

Geosci. Model Dev.

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Abstract. Coarse-resolution climate and weather forecast models cannot accurately parameterize small-scale, nonlinear processes without accounting for subgrid-scale variability. To do so, some models integrate over the subgrid variability analytically. Although analytic integration methods are attractive, they can be used only with physical parameterizations that have a sufficiently simple functional form. Instead, this paper introduces a method to integrate subgrid variability using a type of Monte Carlo integration. The method generates subcolumns with suitable vertical correlations and feeds them into a microphysics parameterization. The subcolumn methodology requires little change to the parameterization source code and can be used with a wide variety of physical parameterizations.Our subcolumn generator is multivariate, which is important for physical processes that involve two or more hydrometeor species, such as accretion of cloud droplets by rain drops. In order to reduce sampling noise in the integrations, our subcolumn generator employs two variance-reduction methods, namely importance and stratified (Latin hypercube) sampling. For this reason, we name the subcolumn generator the Subgrid Importance Latin Hypercube Sampler (SILHS).This paper tests SILHS in interactive, single-column simulations of a marine stratocumulus case and a shallow cumulus case. The paper then compares simulations that use SILHS with those that use analytic integration. Although the SILHS solutions exhibit considerable noise from time step to time step, the noise is greatly damped in most of the timeaveraged profiles.

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Analytic upscaling of a local microphysics scheme. Part I: Derivation

Cited by 46 publications

References 22 publications

Scale‐aware parameterization of liquid cloud inhomogeneity and its impact on simulated climate in CESM

Scale‐aware parameterization of liquid cloud inhomogeneity and its impact on simulated climate in CESM

Analytic upscaling of a local microphysics scheme. Part II: Simulations

The Subgrid Importance Latin Hypercube Sampler (SILHS): a multivariate subcolumn generator

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