Parameterizing deep convection using the assumed probability density function method

Storer, R. L.; Griffin, Brian M.; Hoft, Jan; Weber, J. K.; Raut, E.; Larson, Vincent E.; Wang, Minghuai; Rasch, Philip J.

doi:10.5194/gmd-8-1-2015

Cited by 44 publications

(74 citation statements)

References 63 publications

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“…Second, SILHS feeds within-cloud variability directly and consistently into microphysics, ensuring that the same marginal PDF that is used to diagnose cloud water content is also used to diagnose autoconversion. Third, assumptions about subgrid variability, such as those regarding vertical overlap of condensate and vapor, are removed from the microphysics scheme and instead embedded in SILHS Storer et al, 2015). This facil-itates the implementation of subgrid assumptions that are more general.…”

Section: K Thayer-calder Et Al: Unified Convection Using Clubb and mentioning

confidence: 99%

“…It shuts off the Zhang and McFarlane (1995) parameterization of deep convection and instead uses CLUBB to parameterize deep cumulus, shallow cumulus, stratiform liquid clouds, and stratiform ice clouds. SILHS is used in order to feed samples of the subgrid variability into a microphysics scheme, following the approach of Storer et al (2015). A single microphysics scheme is used in all cloud types.…”

Section: K Thayer-calder Et Al: Unified Convection Using Clubb and mentioning

confidence: 99%

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A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

et al. 2015

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Abstract.Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations.Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysics scheme.This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Model computational expense is estimated, and sensitivity to the number of subcolumns is investigated. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation.

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Section: K Thayer-calder Et Al: Unified Convection Using Clubb and mentioning

confidence: 99%

Section: K Thayer-calder Et Al: Unified Convection Using Clubb and mentioning

confidence: 99%

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

et al. 2015

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“…12 and 13 of Thayer-Calder et al, 2015). The extra cost of computing four samples is (1.89-1.69) / 1.69 = 16 % (see Table 2 of Thayer-Calder et al, 2015).…”

Section: Computational Cost Of the Ncat Methodsmentioning

confidence: 99%

“…These costs may be compared with other costs in global climate simulations. To this end, Thayer-Calder et al (2015) tested the cost of SILHS in the Community Atmosphere Model (Neale et al, 2012). They show that an adequate cloud climatology can be obtained with as few as four sample points (see Figs.…”

Section: Computational Cost Of the Ncat Methodsmentioning

confidence: 99%

“…For instance, a prior version of the Subgrid Importance Latin Hypercube Sampler (SILHS) placed sample points preferentially in cloud, and also stratified the within-cloud sample points using Latin hypercube sampling Larson and Schanen, 2013;Storer et al, 2015;Thayer-Calder et al, 2015). SILHS similarly stratified the points out of cloud.…”

Section: E K Raut and V E Larson: A Flexible Importance Sampling mentioning

confidence: 99%

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A flexible importance sampling method for integrating subgrid processes

Raut

Larson

2016

Geosci. Model Dev.

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Abstract. Numerical models of weather and climate need to compute grid-box-averaged rates of physical processes such as microphysics. These averages are computed by integrating subgrid variability over a grid box. For this reason, an important aspect of atmospheric modeling is spatial integration over subgrid scales.The needed integrals can be estimated by Monte Carlo integration. Monte Carlo integration is simple and general but requires many evaluations of the physical process rate. To reduce the number of function evaluations, this paper describes a new, flexible method of importance sampling. It divides the domain of integration into eight categories, such as the portion that contains both precipitation and cloud, or the portion that contains precipitation but no cloud. It then allows the modeler to prescribe the density of sample points within each of the eight categories.The new method is incorporated into the Subgrid Importance Latin Hypercube Sampler (SILHS). The resulting method is tested on drizzling cumulus and stratocumulus cases. In the cumulus case, the sampling error can be considerably reduced by drawing more sample points from the region of rain evaporation.

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Evaluating the relationship between topography and groundwater using outputs from a continental‐scale integrated hydrology model

Condon

Maxwell

2015

Water Resources Research

147

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We study the influence of topography on groundwater fluxes and water table depths across the contiguous United States (CONUS). Groundwater tables are often conceptualized as subdued replicas of topography. While it is well known that groundwater configuration is also controlled by geology and climate, nonlinear interactions between these drivers within large real-world systems are not well understood and are difficult to characterize given sparse groundwater observations. We address this limitation using the fully integrated physical hydrology model ParFlow to directly simulate groundwater fluxes and water table depths within a complex heterogeneous domain that incorporates all three primary groundwater drivers. Analysis is based on a first of its kind, continental-scale, high-resolution (1 km), groundwater-surface water simulation spanning more than 6.3 million km 2 . Results show that groundwater fluxes are most strongly driven by topographic gradients (as opposed to gradients in pressure head) in humid regions with small topographic gradients or low conductivity. These regions are generally consistent with the topographically controlled groundwater regions identified in previous studies. However, we also show that areas where topographic slopes drive groundwater flux do not generally have strong correlations between water table depth and elevation. Nonlinear relationships between topography and water table depth are consistent with groundwater flow systems that are dominated by local convergence and could also be influenced by local variability in geology and climate. One of the strengths of the numerical modeling approach is its ability to evaluate continental-scale groundwater behavior at a high resolution not possible with other techniques.

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Parameterizing deep convection using the assumed probability density function method

Cited by 44 publications

References 63 publications

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

A flexible importance sampling method for integrating subgrid processes

Evaluating the relationship between topography and groundwater using outputs from a continental‐scale integrated hydrology model

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