A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields

By Robert Pincus, H. Barker, J. Morcrette. 2003

412 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/6640b947393da18b88e2f278a15d1541cd94f4be

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Citations in the corpus

Habitable Moist Atmospheres on Terrestrial Planets near the Inner Edge of the Habitable Zone around M Dwarfs (2017, methodology): “Cloud overlap is treated using the Monte Carlo Independent Column Approximation (MCICA), assuming maximum-random overlap (Pincus et al. 2003).”
The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies (2018, methodology): “We continue to use the Monte Carlo Independent Column Approximation [McICA, [Pincus et al., 2003] as in AM3, which treats subgrid scale cloud variability by creating sub-columns with consistent cloud properties and vertical structure [Pincus et al.”
Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2 (2019, methodology): “10.1029/2018MS001400 PSrad treats fractional cloudiness using the Monte Carlo Independent Column Approximation (see Pincus et al., 2003).”
Spatial variability of liquid cloud and rain: observations and microphysical effects (2014, background): “Most operational GCMs include some estimate of how subgrid variability affects radiative transfer, either via scaling of the cloud water content (e.g. Cahalan et al., 1994) or Monte Carlo methods (e.g. Pincus et al., 2003), but few do for microphysical process rates.”
A Flexible and Efficient Radiation Scheme for the ECMWF Model (2018, methodology): “…BOZZO 1990 This was followed in 2007 by a major upgrade of the radiation package to McRad, which involved not only incorporation of the shortwave RRTM-G model but also the McICA scheme of Pincus et al. (2003) to represent realistic cloud heterogeneity and overlap via a stochastic cloud generator.”
Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models (2008, methodology): “RRTMG_LW and SW with the addition of McICA, the Monte Carlo Independent Column Approximation [Barker et al., 2002; Pincus et al., 2003], which is a statistical technique for representing subgrid-scale cloud variability including cloud overlap.”
The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations (2004, methodology): “…• replacement of random cloud overlap with by a resolution-invariant overlap scheme, which will be ac- complished by a stochastic treatment of clouds (Pincus et al. 2003); also under consideration is the replacement of the prognostic cloud fraction scheme (Tiedtke 1993) with a statistical…”
The ERA5 global reanalysis (2020, methodology): “It incorporates the MCICA (Pincus et al., 2003) for representing subgrid cloud structure and overlap, and the short-wave RRTMG (Iacono et al.”
Stochastic generation of subgrid‐scale cloudy columns for large‐scale models (2004, background): “As a radical alternative, Barker et al. (2002) and Pincus et al. (2003) proposed the Monte Carlo Independent Column Approximation (McICA) for estimating domain averages.”
Global indirect aerosol effects: a review (2004, neutral): “transfer through inhomogeneous cloud fields by Pincus et al. (2003). Such a treat-”
ICON‐A, the Atmosphere Component of the ICON Earth System Model: I. Model Description (2018, methodology): “In the ICON implementation of PSrad cloud effects are treated by sampling subcolumns, similar to Räisänen and Barker (2004) using a generalization of the Monte Carlo Independent Column Approximation (McICA; Pincus et al., 2003).”
Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements (2005, neutral): “[Pincus et al., 2003], the model produces slightly differ-”
Impact of a New Radiation Package, McRad, in the ECMWF Integrated Forecasting System (2008, background): “As discussed in Räisänen and Barker (2004), McICA’s incomplete pairing of subcolumns and spectral intervals ensures that its solution will contain random, but unbiased, errors.”
Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator (2012, neutral): “…the likeliest cause for the reduction of optically thick cloud is the introduction of the Monte Carlo Independent Column Approximation (McICA) [Pincus et al., 2003], which affects a model’s radiation budget by removing biases in the treatment of subgrid scale variability in cloud optical…”
Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model (2011, methodology): “…starting the optimization process, we compared two different radiation schemes, RRTMG (Rapid Radiative Transfer Model for GCMs, Barker et al., 2003; Pincus et al., 2003) vs. CAM (Community Atmosphere Model 3.0, Collins et al., 2004), and two different microphysics schemes, WSM6 (WRF Single-Moment…”
The University of Washington Shallow Convection and Moist Turbulence Schemes and Their Impact on Climate Simulations with the Community Atmosphere Model (2009, background): “One possible approach is to assign separate subcolumns to cumulus clouds independent of those used for stratiform clouds (i.e., subcolumn approach, Pincus et al. 2003).”