A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: Preliminary results

By M. Khairoutdinov, David A. Randall. 2001

511 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ffea2fc85f45746bfbab059578ba38c522711326

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Citations in the corpus

High-Resolution Simulation of Shallow-to-Deep Convection Transition over Land (2006, background): “The relatively new " superparameterization " approach to general circulation modeling (Grabowski 2001; Khairoutdinov and Randall 2001; Khairoutdinov et al. 2005) has the advantage of explicitly including the sub-GCM/grid-scale dynamics by replacing the cloud parameterization with a cloud-resolving…”
Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part II: Case Study Comparisons with Observations and Other Schemes (2015, methodology): “Climate modeling is also now performed at these scales, using global ‘‘cloud resolving’’ models (CRMs) (e.g.,Miura etal.2007)or by embeddingCRMsineachgrid cell of traditional general circulation models (i.e., ‘‘super-parameterization’’) (e.g., Grabowski 2001; Khairoutdinov and Randall 2001; Tao et a”
Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation (2015, methodology): “…convection parameterizations are replaced with cloud-system-resolving models (CSRMs) embedded in each GCM grid column, so that convective temperature and humidity tendencies and cloud and rainfall statistics are generated by explicit simulation [Khairoutdinov and Randall, 2001; Grabowski, 2001].”
MJO-like Coherent Structures: Sensitivity Simulations Using the Cloud-Resolving Convection Parameterization (CRCP) (2003, unknown): “The cost of running a large-scale model featuring CRCP is almost entirely due to cloud-resolving calculations (see also a discussion in Khairoutdinov and Randall 2001).”
Structure of the Madden-Julian Oscillation in the Superparameterized CAM (2009, background): “One approach to bypassing this limitation is to replace certain conventional parameterizations with cloud-resolving models (CRMs; Grabowski and Smolarkiewicz 1999; Grabowski 2001), a technique termed ‘‘superparameterization’’ (Khairoutdinov and Randall 2001).”
A Cumulus Parameterization with State-Dependent Entrainment Rate. Part I: Description and Sensitivity to Temperature and Humidity Profiles (2010, methodology): “…models (CRMs) embedded in AGCM grids and a global CRM began to be used recently thanks to the rapid progress of computer technology (Grabowski 2001; Khairoutdinov and Randall 2001; Satoh et al. 2008), such models are very expensive to run and require a huge amount of storages for the output.”
Madden‐Julian Oscillation (2005, methodology): “This ‘‘superparameterization’’ approach has proven capable of producing MJO-like signals in a full GCM that showed no such capability when its original cumulus parameterizations were used [Khairoutdinov and Randall, 2001; Randall et al., 2003].”
A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, methodology): “This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.”
Evaluation of the Simulated Interannual and Subseasonal Variability in an AMIP-Style Simulation Using the CSU Multiscale Modeling Framework (2008, methodology): “In a new type of general circulation model (GCM), many processes that unresolved on GCM grid are incorporated using a cloud-resolving model (CRM; Grabowski and Smolarkiewicz 1999; Grabowski 2001; Khairoutdinov and Randall 2001; Khairoutdinov et al.2005).”
Comparison of Bulk and Bin Warm-Rain Microphysics Models Using a Kinematic Framework (2007, methodology): “The bin and bulk microphysics schemes were implemented in a 2D kinematic modeling framework, presented in Szumowski et al. (1998) and subsequently applied in Grabowski (1998, 1999).”
Stochastic generation of subgrid‐scale cloudy columns for large‐scale models (2004, background): “Aircraft observations show that there is often a clear relation between cloud liquid-water content wl and cloud droplet effective radius re (e.g. Korolev et al. 2001).”
The Role of Convective Moistening in the Madden–Julian Oscillation (2009, methodology): “…Research Community Atmosphere Model (CAM) version 3.0, and the second is a superparameterized version of the CAM (SP-CAM) in which the convective parameterizations have been replaced with a two-dimensional cloud-resolving model (CRM) in each GCM grid column (Khairoutdinov and Randall 2001).”
A global cloud‐resolving simulation: Preliminary results from an aqua planet experiment (2005, background): “These uncertainties are reduced in ‘‘the cloud-resolving convection parameterization’’ (or ‘‘super-parameterization’’) [Grabowski, 2001] that embeds a two-dimensional (2D) cloud-resolving model (CRM) at each grid box, which interacts with large-scale motions in the AGCM [Randall et al., 2003; Khair”
Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization (2015, background): “The need to improve the representation of convection in large-scalemodels has led to the idea of the “superparameterization” of small-scale and mesoscale processes in large-scale models and GCMs [Grabowski and Smolarkiewicz, 1999; Grabowski, 2001; Khairoutdinov and Randall, 2001; Khairoutdinov et al”