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Title: Statistically Steady State Large-Eddy Simulations Forced By an Idealized GCM: 1. Forcing Framework and Simulation Characteristics

Abstract

Using large-eddy simulations (LES) systematically has the potential to inform parameterizations of subgrid-scale (SGS) processes in general circulation models (GCMs), such as turbulence, convection, and clouds. Here we show how LES can be run to emulate grid columns of GCMs to generate a library of LES across a cross-section of dynamical regimes. The LES setup replicates the thermodynamic and water budgets in GCM grid columns. Resolved horizontal and vertical transports of heat and water and large-scale pressure gradients from the GCM are prescribed as forcing in the LES. The LES satisfies the same (slab-ocean) surface boundary conditions as the GCM, leaving the LES temperatures free to adjust. Radiative transfer is treated in a unied but highly idealized manner (a semi-gray atmosphere without cloud radiative effects) in both the GCM and LES. We show that the LES with these forcing and boundary conditions reaches statistically steady states without nudging to reference profiles. These steady states provide a training dataset for developing GCM parameterizations. The same LES setup also provides a good basis for studying the cloud response to global warming.

Authors:
 [1];  [2];  [3];  [1]
  1. California Institute of Technology
  2. BATTELLE (PACIFIC NW LAB)
  3. University of Chicago
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1605554
Report Number(s):
[PNNL-SA-144935]
Grant/Contract Number:  
[AC05-76RL01830]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
[ Journal Volume: 12; Journal Issue: 2]
Country of Publication:
United States
Language:
English

Citation Formats

Shen, Zhaoyi, Pressel, Kyle G., Tan, Zhihong, and Schneider, Tapio. Statistically Steady State Large-Eddy Simulations Forced By an Idealized GCM: 1. Forcing Framework and Simulation Characteristics. United States: N. p., 2020. Web. doi:10.1029/2019MS001814.
Shen, Zhaoyi, Pressel, Kyle G., Tan, Zhihong, & Schneider, Tapio. Statistically Steady State Large-Eddy Simulations Forced By an Idealized GCM: 1. Forcing Framework and Simulation Characteristics. United States. doi:10.1029/2019MS001814.
Shen, Zhaoyi, Pressel, Kyle G., Tan, Zhihong, and Schneider, Tapio. Mon . "Statistically Steady State Large-Eddy Simulations Forced By an Idealized GCM: 1. Forcing Framework and Simulation Characteristics". United States. doi:10.1029/2019MS001814.
@article{osti_1605554,
title = {Statistically Steady State Large-Eddy Simulations Forced By an Idealized GCM: 1. Forcing Framework and Simulation Characteristics},
author = {Shen, Zhaoyi and Pressel, Kyle G. and Tan, Zhihong and Schneider, Tapio},
abstractNote = {Using large-eddy simulations (LES) systematically has the potential to inform parameterizations of subgrid-scale (SGS) processes in general circulation models (GCMs), such as turbulence, convection, and clouds. Here we show how LES can be run to emulate grid columns of GCMs to generate a library of LES across a cross-section of dynamical regimes. The LES setup replicates the thermodynamic and water budgets in GCM grid columns. Resolved horizontal and vertical transports of heat and water and large-scale pressure gradients from the GCM are prescribed as forcing in the LES. The LES satisfies the same (slab-ocean) surface boundary conditions as the GCM, leaving the LES temperatures free to adjust. Radiative transfer is treated in a unied but highly idealized manner (a semi-gray atmosphere without cloud radiative effects) in both the GCM and LES. We show that the LES with these forcing and boundary conditions reaches statistically steady states without nudging to reference profiles. These steady states provide a training dataset for developing GCM parameterizations. The same LES setup also provides a good basis for studying the cloud response to global warming.},
doi = {10.1029/2019MS001814},
journal = {Journal of Advances in Modeling Earth Systems},
number = [2],
volume = [12],
place = {United States},
year = {2020},
month = {2}
}

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