Unresolved spatial variability and microphysical process rates in large‐scale models

By Robert Pincus, Stephen A. Klein. 2000

172 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/15567b61432b44d0d36ca5e07a1fb9f5ee3765b2

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Citations in the corpus

A double-moment parameterization for simulating autoconversion, accretion and selfcollection (2001, background): “Further investigations point out that simulation of autoconversion also needs modifications due to scale-related problems (Rotstayn, 2000; Pincus and Klein, 2000).”
Cloud tuning in a coupled climate model: Impact on 20th century warming (2013, background): “…clouds forms when the maximum mean volume droplet radius exceeds 10 m. Attempts to explain reduced values in large-scale models have invoked the neglect of in-cloud subgrid-scale variability [Pincus and Klein, 2000; Larson et al., 2001] and the dispersion effect [Rotstayn and Liu, 2005].”
The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations (2004, background): “The value used in AM2–LM2 is perhaps close enough to realistic values that the lack of subgrid-scale variability to cloud water in microphysical calculations may be the reason that large-scale models tune this parameter (Rotstayn 2000; Pincus and Klein 2000).”
Drizzle in Stratiform Boundary Layer Clouds. Part II: Microphysical Aspects. (2005, background): “Because autoconversion rates are significant only in a thin layer of cloud, its parameterization in numerical models with coarse vertical resolution is likely to be particularly problematic and may suggest why some of the commonly used parameterizations require considerable tuning when used in such ”
Spatial variability of liquid water path in marine low cloud : The importance of mesoscale cellular convection (2006, background): “2002), and in controlling the precipitation rate (Rotstayn 2000; Pincus and Klein 2000; Larson et al. 2001b; Wood et al. 2002; Stevens et al. 2005).”
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): “For example, as pointed out by Pincus and Klein (2000), Rotstayn et al. (2000), and Larson et al. (2001), precipitation formation is highly nonlinear, resulting in potentially substantial biases if only the mean in-cloud cloud water is considered.”
A Prognostic Parameterization for the Subgrid-Scale Variability of Water Vapor and Clouds in Large-Scale Models and Its Use to Diagnose Cloud Cover (2002, methodology): “Knowing the inhomogeneity of clouds also permits a more accurate derivation of microphysical processes such as precipitation formation, which are highly nonlinear, resulting in potentially substantial biases if the grid mean value is used (Pincus and Klein 2000; Rotstayn 2000; Larson et al. 2001b).”
Small-Scale and Mesoscale Variability in Cloudy Boundary Layers: Joint Probability Density Functions (2002, background): “Second, predicting the PDF allows one to remove certain systematic biases that occur in the microphysics of models that ignore subgrid variability (Rotstayn 2000; Pincus and Klein 2000; Larson et al. 2001a,b).”
Global indirect aerosol effects: a review (2004, unknown): “9.5 Subgrid-scale variability and radiative transfer A new approach to account for unresolved spatial variability and microphysical process rates is to consider probability distribution functions of the respective quantities (Pincus and Klein, 2000; Tompkins, 2002).”
A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields (2003, background): “Unresolved subgridscale variability impacts radiative fluxes as well as microphysical process rates [Pincus and Klein, 2000], so cloud schemes are now emerging that address this structure either parametrically [Cusack et al., 1999; Tompkins, 2002] or explicitly [Grabowski and Smolarkiewicz, 1999;…”
Optical properties of terrestrial clouds (2004, background): “Then one can obtain for the reflection function of a pixel in the framework of this approximation (Pinkus and Klein, 2000):”
Aerosol indirect effects in a multi-scale aerosol-climate model PNNL-MMF (2011, background): “To take account of the strongly non-linear dependence of autoconversion rate on subgrid cloud liquid water, most conventional GCMs either derive the autoconversion rate based on an assumed probability distribution of subgrid cloud liquid water or simply tune the autoconversion rate to produce reason”
Drizzle in Stratiform Boundary Layer Clouds. Part I: Vertical and Horizontal Structure (2005, background): “The spatial variability occurs on horizontal scales that are smaller than the current state of the art general circulation model and must therefore be parameterized (e.g., Cahalan et al. 1994; Oreopoulos and Davies 1998; Pincus and Klein 2000; Rotstayn 2000; Larson et al. 2001; Wood et al. 2002).”
A PDF-Based Model for Boundary Layer Clouds. Part I: Method and Model Description (2002, background): “In other types of closure, even when cloud fraction is taken into account, systematic biases in cloud and radiative properties remain, due to the neglect of subgrid-scale variability (Cahalan et al. 1994; Rotstayn 2000; Pincus and Klein 2000; Larson et al. 2001b).”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “…coupling with the microphysics because grid‐averaged microphysical process rates (and cloud‐radiative effects) generally have a nonlinear dependence on cloud and precipitation properties like bulk water content (e.g., Larson et al., 2005; Pincus & Klein, 2000), but this is not a focus of the paper.”