Simulations of the Atmospheric General Circulation Using a Cloud-Resolving Model as a Superparameterization of Physical Processes

By M. Khairoutdinov, David A. Randall, C. DeMott. 2005

419 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d3ee9b00c7fa26b4cb8dc5fa0b6c5c3e437a9c74

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Citations in the corpus

Could Machine Learning Break the Convection Parameterization Deadlock? (2018, methodology): “…approach has been to use cloud “superparameterization (SP)”, which computes the subgrid vertical heating and moistening profiles within a GCM grid cell by sampling a curtain of an embedded 2D CRM that uses convective permitting resolution [ Grabowski et al. 1999; Khairoutdinov et al. , 2005].”
Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time‐scales (2008, methodology): “Furthermore, the results suggest that it is possible to reasonably represent the atmospheric variability on a wide variety of time and space scales using a conventional set of physical parametrizations, without necessarily having to specifically use an explicit global representation of convection (e”
Structure of the Madden-Julian Oscillation in the Superparameterized CAM (2009, methodology): “2c and with the finding by Khairoutdinov et al. 2005 that boreal winter rainfall over eastern Indonesia and northern Australia, a sizable portion of which is related to MJO activity, is substantially overestimated by the SP-CAM.”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “been nested in small, usually two-dimensional subdomains of atmospheric grid columns, as a parameterization surrogate that explicitly resolves some aspects of cloud dynamics (e.g., Grabowski & Smolarkiewicz, 1999; Grabowski, 2001; Grabowski, 2016; Khairoutdinov & Randall, 2001; Khairoutdinov et al.,”
Moisture modes and the Madden-Julian oscillation. (2009, methodology): “Such a ‘‘superparameterization’’ was also used by Khairoutdinov et al. (2005) in place of the standard Zhang and McFarlane (1995) parameterization in the National Center for Atmospheric Research’s Community Atmosphere Model (CAM).”
Tropical and Subtropical Cloud Transitions in Weather and Climate Prediction Models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI) (2011, neutral): “…GEM Regional 0.58 3 0.58 53 Côté et al. (1998) CSU (US) BUGS Global 2.58 3 2.58 29 Colorado State University (2010) CSU (US) MMF Global/MMF T42 30 Khairoutdinov et al. (2005) DWD (Ger) GME Global 59.9 km 31 Majewski et al. (2002) ECMWF (UK) ECMWF Global T399 62 ECMWF (2006) ETH–MPI (Ger)…”
Revealing differences in GCM representations of low clouds (2011, background): “Khairoutdinov et al. 2005; Wyant et al. 2006b).”
The Role of Convective Moistening in the Madden–Julian Oscillation (2009, methodology): “This is consistent with the low tropical rain rate variability discussed in other studies (Khairoutdinov et al. 2005; Hack et al. 2006; Lin et al. 2006; Sun et al. 2006; DeMott et al. 2007), which generally conclude that the CAM convective parameterizations are triggered too often, resulting in…”
Cracking the MJO nut (2013, background): “, 2007] and superparameterization [Grabowski, 2001; Khairoutdinov et al., 2005].”
Challenges in Climate Science and Contemporary Applied Mathematics (2012, background): “This approach has been shown to be ideal for parallel computations on supercomputers and has yielded promising new results regarding tropical intraseasonal behavior [39, 40, 41, 65].”
Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization (2015, background): “…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., 2005; Grabowski, 2003, 2006; Tao et al., 2009a; Tao and Moncrieff, 2009].”
Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems. (2019, background): “0 [22] to simulate the climate for two years in aquaplanet configuration [23], where the surface temperatures are fixed with a realistic equator-to-pole gradient [24].”
Application of MJO Simulation Diagnostics to Climate Models (2009, methodology): “resolving models for these processes (Khairoutdinov et al. 2005).”
Multiscale Convective Organization and the YOTC Virtual Global Field Campaign (2012, methodology): “In superparameterization, cloud-system-resolving models are used in place of conventional parameterizations of convection and the planetary boundary layer in each grid cell of the parent global model (e.g., Grabowski 2001; Khairoutdinov et al. 2005; Tao et al. 2009).”
Moist Static Energy Budget of MJO-like Disturbances in the Atmosphere of a Zonally Symmetric Aquaplanet (2012, background): “SPCAM is a modified version of the Community Atmosphere Model (CAM) where a small domain two-dimensional Cloud System Resolving Model (CSRM) is embedded within each grid point of CAM (Khairoutdinov and Randall, 2001; Khairoutdinov et al., 2005).”