A Prognostic Parameterization for the Subgrid-Scale Variability of Water Vapor and Clouds in Large-Scale Models and Its Use to Diagnose Cloud Cover

By Adrian M. Tompkins. 2002

446 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3325c8495aa5cf4806862064d493c65673d56612

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Hugh Morrison
2. Andrew Gettelman
3. J. Golaz
4. Bjorn Stevens
5. V. Larson
6. D. Hartmann
7. C. Hoose
8. R. Wood

In Global and Planetary Change

1. Hugh Morrison
2. Andrew Gettelman
3. Bjorn Stevens
4. J. Golaz
5. D. Hartmann
6. V. Larson
7. C. Hoose
8. R. Wood

Citations in the corpus

• Tropical and Subtropical Cloud Transitions in Weather and Climate Prediction Models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI) (2011, background): “…advances in the development of cloud and cloudy boundary layer parameterizations during the last several years (e.g., Tiedtke 1993; Del Genio et al. 1996; Fowler et al. 1996; Rasch and Kristjánsson 1998; Lock et al. 2000; Lock 2001; Bony and Emanuel 2001; Teixeira and Hogan 2002; Tompkins 2002).”
• Tropospheric water vapor, convection, and climate (2010, background): “Clouds are generally found where environmental relative humidity is sufficiently high, long part of the basis for their parameterization in models [e.g., Sundqvist, 1978; Tompkins, 2002].”
• The atmospheric general circulation model ECHAM6 - Model description (2013, methodology): “The statistical cloud cover scheme (Tompkins, 2002) used in ECHAM5 is available on request (see Roeckner et al.”
• Spatial variability of liquid water path in marine low cloud : The importance of mesoscale cellular convection (2006, methodology): “There has been considerable recent focus upon designing cloud schemes that predict the cloud fraction (CF) and internal variability using a saturation deficit probability distribution approach (e.g., Smith 1990; Tompkins 2002 ).”
• The aerosol-climate model ECHAM5-HAM (2004, background): “Cloud cover is predicted with a prognostic-statistical scheme solving equations for the distribution moments of total water (Tompkins, 2002).”
• Impact of a New Radiation Package, McRad, in the ECMWF Integrated Forecasting System (2008, methodology): “…connect the radiation transfer calculations with cloud information derived from probability density function (PDF)-based cloud schemes [(as that of Tompkins (2002); thanks to the McICA approach] and from observations of the vertical profiles of the condensed water, made available from…”
• A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields (2003, background): “Statistical cloud schemes [e.g., Tompkins, 2002] could generate columns consistent with the probability distribution of condensate predicted within each model layer, with overlap relationships incorporated by making the probability of condensate values for one layer conditional on other layers.”
• Interpreting the cloud cover – aerosol optical depth relationship found in satellite data using a general circulation model (2009, methodology): “The cloud cover and large-scale condensation are diagnosed from a prognostic subgrid-scale distribution of total water mixing ratio (Tompkins, 2002).”
• Using Probability Density Functions to Derive Consistent Closure Relationships among Higher-Order Moments (2005, background): “The new closures have shortcomings, but nevertheless are superior to the double or triple delta closures in most of the cases tested.”
• Simulations of midlatitude frontal clouds by single-column and cloud--resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period (2005, unknown): “scheme [Tompkins, 2002] modified from Tiedtke [1989];”
• The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations (2004, methodology): “…is the replacement of the prognostic cloud fraction scheme (Tiedtke 1993) with a statistical cloud scheme with prognostic higher-order moments similar to Tompkins (2002); • addition of more vertical levels at the top of the model to better simulate the stratosphere and its coupling with the…”
• Unified treatment of dry convective and stratocumulus‐topped boundary layers in the ECMWF model (2011, methodology): “At the beginning of the BL parametrization time step, the Tiedtke cloud variables, qv, qc and fc are converted to qt, and σqt, assuming a beta distribution as used by Tompkins (2002).”
• Sensitivity of Simulated Climate to Horizontal and Vertical Resolution in the ECHAM5 Atmosphere Model (2006, methodology): “The cloud scheme consists of prognostic equations for the water phases (vapor, liquid, solid), bulk cloud microphysics (Lohmann and Roeckner 1996), and a statistical cloud cover scheme with prognostic equations for the distribution moments (Tompkins 2002).”
• A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests (2008, background): “As described by Tompkins (2002), a wide variety of probability density functions (PDFs) has been used in GCMs to represent cloud variability because of the difficulty in obtaining this information directly from observations at small scales.”
• Small-Scale and Mesoscale Variability in Cloudy Boundary Layers: Joint Probability Density Functions (2002, methodology): “The present paper differs from prior studies of onedimensional PDFs of thermodynamic variables such as total water content (Bougeault 1981, 1982; Lewellen and Yoh 1993; Xu and Randall 1996; Larson et al. 2001b; Price 2001; Tompkins 2002).”