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Title: Expanding the computational frontier of multi-scale atmospheric simulation to advance understanding of low cloud / climate feedbacks: Final Technical Report

Abstract

Marine boundary layer clouds are a challenge for climate models. They cover much of the oceans, but they are driven by small-scale turbulent eddies only a few hundred meters across, which in turn respond to the cloud formation. Global climate models have a grid spacing that is far too large to simulate such fluid motions, so they parameterize these cloud formation processes. Different climate modeling groups have designed different parameterizations in which the clouds turn out to be differently sensitive to a warming climate. This drives uncertainties in our best guess at the sensitivity of global warming to greenhouse gas increases. If these cloud-forming eddies could be directly simulated using the well-known equations of fluid motion, they would no longer need to be parameterized, removing a major source of climate modeling uncertainty. In this project, we overcame software engineering challenges to successfully implemented ‘ultraparameterization’ (UP), the first global model that does this, and we tested how well it works. We simulated five-year periods with present-day temperatures and with a warmer climate, and we investigated how the UP-simulated clouds responded to climate – the ‘cloud feedback’ problem. We found little response of clouds at all latitudes to the imposed climate change,more » which is within the range of predictions of conventional global climate models. UP is a variation on superparameterization, in which small cloud-resolving models (CRMs) are embedded in each column of the global model. In UP, the CRM grid is fine enough (250 m horizontal × 20 m vertical) to explicitly capture boundary-layer turbulent eddies and associated clouds. Because only one small columnar patch is simulated within each climate model grid cell, this is a million-fold more efficient than simulating the entire globe on this same CRM grid., but achieves much of the same effect for the cloud properties. It doesn’t work perfectly. For instance, like conventional climate models, UP simulates too little subtropical stratocumulus cloud, a bias that we are continuing to work to reduce. However, because it directly simulates the turbulent cloud-forming processes, UP is inherently more plausible for simulating how clouds will change in a perturbed climate. In future, we hope to apply UP to another key climate modeling issue: cloud-aerosol interaction and the effect of human-produced aerosols on the climate change we have already experienced and that which is likely to come.« less

Authors:
ORCiD logo [1]
  1. Univ. of Washington, Seattle, WA (United States). Dept. of Atmospheric Sciences
Publication Date:
Research Org.:
Univ. of Washington, Seattle, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21). Scientific Discovery through Advanced Computing (SciDAC)
Contributing Org.:
Univ. of California, Irvine, CA (United States); Stony Brook Univ., Stony Brook, NY (United States)
OSTI Identifier:
1493449
Report Number(s):
DOE_UWASHINGTON_12451
DOE Contract Number:  
SC0012451
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; climate modeling; cloud feedbacks; high performance computing; boundary-layer clouds

Citation Formats

Bretherton, Christopher. Expanding the computational frontier of multi-scale atmospheric simulation to advance understanding of low cloud / climate feedbacks: Final Technical Report. United States: N. p., 2019. Web. doi:10.2172/1493449.
Bretherton, Christopher. Expanding the computational frontier of multi-scale atmospheric simulation to advance understanding of low cloud / climate feedbacks: Final Technical Report. United States. doi:10.2172/1493449.
Bretherton, Christopher. Tue . "Expanding the computational frontier of multi-scale atmospheric simulation to advance understanding of low cloud / climate feedbacks: Final Technical Report". United States. doi:10.2172/1493449. https://www.osti.gov/servlets/purl/1493449.
@article{osti_1493449,
title = {Expanding the computational frontier of multi-scale atmospheric simulation to advance understanding of low cloud / climate feedbacks: Final Technical Report},
author = {Bretherton, Christopher},
abstractNote = {Marine boundary layer clouds are a challenge for climate models. They cover much of the oceans, but they are driven by small-scale turbulent eddies only a few hundred meters across, which in turn respond to the cloud formation. Global climate models have a grid spacing that is far too large to simulate such fluid motions, so they parameterize these cloud formation processes. Different climate modeling groups have designed different parameterizations in which the clouds turn out to be differently sensitive to a warming climate. This drives uncertainties in our best guess at the sensitivity of global warming to greenhouse gas increases. If these cloud-forming eddies could be directly simulated using the well-known equations of fluid motion, they would no longer need to be parameterized, removing a major source of climate modeling uncertainty. In this project, we overcame software engineering challenges to successfully implemented ‘ultraparameterization’ (UP), the first global model that does this, and we tested how well it works. We simulated five-year periods with present-day temperatures and with a warmer climate, and we investigated how the UP-simulated clouds responded to climate – the ‘cloud feedback’ problem. We found little response of clouds at all latitudes to the imposed climate change, which is within the range of predictions of conventional global climate models. UP is a variation on superparameterization, in which small cloud-resolving models (CRMs) are embedded in each column of the global model. In UP, the CRM grid is fine enough (250 m horizontal × 20 m vertical) to explicitly capture boundary-layer turbulent eddies and associated clouds. Because only one small columnar patch is simulated within each climate model grid cell, this is a million-fold more efficient than simulating the entire globe on this same CRM grid., but achieves much of the same effect for the cloud properties. It doesn’t work perfectly. For instance, like conventional climate models, UP simulates too little subtropical stratocumulus cloud, a bias that we are continuing to work to reduce. However, because it directly simulates the turbulent cloud-forming processes, UP is inherently more plausible for simulating how clouds will change in a perturbed climate. In future, we hope to apply UP to another key climate modeling issue: cloud-aerosol interaction and the effect of human-produced aerosols on the climate change we have already experienced and that which is likely to come.},
doi = {10.2172/1493449},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {2}
}

Works referenced in this record:

The Sensitivity of Numerical Simulations of Cloud‐Topped Boundary Layers to Cross‐Grid Flow
journal, February 2018

  • Wyant, Matthew C.; Bretherton, Christopher S.; Blossey, Peter N.
  • Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 2
  • DOI: 10.1002/2017MS001241

Insensitivity of the Cloud Response to Surface Warming Under Radical Changes to Boundary Layer Turbulence and Cloud Microphysics: Results From the Ultraparameterized CAM
journal, December 2018

  • Parishani, Hossein; Pritchard, Michael S.; Bretherton, Christopher S.
  • Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 12
  • DOI: 10.1029/2018MS001409

Mean-state acceleration of cloud-resolving models and large eddy simulations: CRM AND LES MEAN-STATE ACCELERATION
journal, October 2015

  • Jones, C. R.; Bretherton, C. S.; Pritchard, M. S.
  • Journal of Advances in Modeling Earth Systems, Vol. 7, Issue 4
  • DOI: 10.1002/2015MS000488

Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence: LOW-CLOUD ULTRAPARAMETERIZATION
journal, July 2017

  • Parishani, Hossein; Pritchard, Michael S.; Bretherton, Christopher S.
  • Journal of Advances in Modeling Earth Systems, Vol. 9, Issue 3
  • DOI: 10.1002/2017MS000968

Cloud and circulation feedbacks in a near‐global aquaplanet cloud‐resolving model
journal, May 2017

  • Narenpitak, Pornampai; Bretherton, Christopher S.; Khairoutdinov, Marat F.
  • Journal of Advances in Modeling Earth Systems, Vol. 9, Issue 2
  • DOI: 10.1002/2016MS000872