A Parameterization of the Cloudiness Associated with Cumulus Convection; Evaluation Using TOGA COARE Data

By Sandrine Bony, Kerry A. Emanuel. 2001

262 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/08aedb4ab57100953f9c611b5a9720bf98f321c8

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

In Global and Planetary Change

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).”
Using Probability Density Functions to Derive Consistent Closure Relationships among Higher-Order Moments (2005, methodology): “The new closures have shortcomings, but nevertheless are superior to the double or triple delta closures in most of the cases tested.”
Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data (2009, methodology): “The LMDZ model described in Hourdin et al. (2006) includes a cloud scheme based on a log-normal PDF representation of the cloud cover and the water content (Bony and Emanuel, 2001).”
Influence of convective processes on the isotopic composition (δ18O and δD) of precipitation and water vapor in the tropics: 2. Physical interpretation of the amount effect (2008, unknown): “[5] For this purpose, isotopes were introduced in the Emanuel convective parameterization [Emanuel, 1991; Emanuel and Zivkovic-Rothman, 1999] and in a single column model of radiative-convective equilibrium including this parameterization as well as parameterizations of clouds [Bony and Emanuel, 200”
Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model (2020, methodology): “Cloud cover and cloud water content are computed using a statistical scheme using a lognormal function for deep convection (Bony & Emanuel, 2001) and a bigaussian function for shallow cumulus (Jam et al., 2013).”
Process-evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopologues: 1. Comparison between models and observations (2012, methodology): “The physical package includes the Emanuel convective scheme [Emanuel, 1991; Emanuel and Zivkovic-Rothman, 1999] and a statistical cloud scheme [Bony and Emanuel, 2001].”
Ice supersaturation in the ECMWF integrated forecast system (2007, background): “Smith, 1990; Bony and Emanuel, 2001; Tompkins, 2002). However, most GCMs that do not have explicit representation of subgrid variability are still able to provide some information, the most basic of which is the provision of a fractional cloud cover, which determines exactly what portion of the grid”
LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection (2013, methodology): “Either we use the Bony and Emanuel (2001) procedure to invert the width parameter ξ from the knowledge of the condensed water computed in the thermal plumes (like what is done for deep convection) or we use a new statistical cloud scheme proposed by Jam et al (2011) in which the sub-grid scale…”
On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles (2006, methodology): “In convective areas the cloud scheme is coupled to the cumulus convection scheme after Bony and Emanuel (2001). The slab model version submitted to CFMIP uses climatological sea ice.”
Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements (2005, methodology): “For instance, the LMD model, whose cloud scheme is physically coupled to the convection scheme and uses both large-scale and subgrid-scale predictors to diagnose the total water distribution [Bony and Emanuel, 2001], does not overestimate the occurrence of optically thick clouds as much as most othe”
Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5 (2013, methodology): “Cloud cover and cloud water content are computed using a statistical scheme (Bony and Emanuel 2001).”
Interpretation of the positive low-cloud feedback predicted by a climate model under global warming (2013, background): “…convective situations, the statistical moments of this PDF are diagnosed from the in-cloud water content predicted in convective updrafts by the Emanuel parameterization (Emanuel 1991), modified by Grandpeix et al. (2004) and from the large-scale relative humidity field (Bony and Emanuel 2001).”
On the Role of Moist Processes in Tropical Intraseasonal Variability: Cloud–Radiation and Moisture–Convection Feedbacks (2005, methodology): “Recent experiments conducted with the Massachusetts Institute of Technology (MIT) GCM, using a parameterization of the cloudiness coupled to the convection and carefully evaluated against Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) data (Bony and Emanue”