On the “tuning” of autoconversion parameterizations in climate models

By Leon D. Rotstayn. 2000

144 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a8ed8f78d062279b1962dabf7986184f65aed0c7

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Citations in the corpus

Parameterization of the Autoconversion Process. Part I: Analytical Formulation of the Kessler-Type Parameterizations (2004, methodology): “…is important for understanding the interactions between cloud microphysics and cloud dynamics (e.g., Chen and Cotton 1987), for the forecasting of the freezing drizzle formation and aircraft icing (Rasmussen et al. 2002), and for improving the treatment of clouds in climate models (Rotstayn 2000).”
Drizzle in Stratiform Boundary Layer Clouds. Part II: Microphysical Aspects. (2005, unknown): “…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 models (Pincus and Klein, 2000; Rotstayn, 2000).”
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).”
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).”
Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges (2016, methodology): “A widely used methodology to adapt parameterizations to changing environments and scales is to adjust (or ‘‘tune’’) parameters to get a more ‘‘realistic’’ match to available local data (e.g., Rotstayn 2000).”
Spatial variability of liquid cloud and rain: observations and microphysical effects (2014, methodology): “If the Gamma or log-normal PDFs were a perfect fit to the data, these lines would lie exactly at 1. In both cases they lie slightly below 1, showing that the correction applied via these equations is slightly too large. The reason for this can be seen from the example PDF in Figure 8. Both PDF fits ”
Cloud tuning in a coupled climate model: Impact on 20th century warming (2013, background): “Indeed, numerous previous studies have documented a strong sensitivity of the radiative forcing from aerosol indirect effects to details of warm rain cloud processes [e.g., Rotstayn, 2000; Menon et al., 2002; Posselt and Lohmann, 2009; Wang et al., 2012].”
Radiative forcing of climate change (2002, background): “Using meteorological and air traffic data scaled to regional observations of contrail cover, Sausen et al. (1998) estimated the present day global mean cover by line-shaped contrails to be about 0.”
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).”
First interactive simulations of cirrus clouds formed by homogeneous freezing in the ECHAM general circulation model (2002, methodology): “This is permitted because all cloud microphysics parameterization are very uncertain when applied to climate model grid scales [see Rotstayn, 2000].”
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).”
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).”
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”
An observational study of drizzle formation in stratocumulus clouds for general circulation model (GCM) parameterizations (2003, methodology): “…1996; Ghan et al., 1997; Rotstayn, 1997; Wilson and Ballard, 1999], although a GCM is limited to a horizontal resolution of 50 to 200 km and a vertical resolution of typically 100 m. [4] The GCM schemes are generally tuned against global monitoring of clouds and precipitations [Rotstayn, 2000].”
The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3 (2011, methodology): “The critical droplet radius for auto-conversion is 8.2 m m. Autoconversion thresholds as low as 4.5 m m have been used in GCMs. [See Rotstayn (2000) for a detailed discussion].”
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).”