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Title: Prognostic Validation of a Neural Network Unified Physics Parameterization

Abstract

Weather and climate models approximate diabatic and sub-grid-scale processes in terms of grid-scale variables using parameterizations. Current parameterizations are designed by humans based on physical understanding, observations, and process modeling. As a result, they are numerically efficient and interpretable, but potentially oversimplified. However, the advent of global high-resolution simulations and observations enables a more robust approach based on machine learning. In this letter, a neural network-based parameterization is trained using a near-global aqua-planet simulation with a 4-km resolution (NG-Aqua). The neural network predicts the apparent sources of heat and moisture averaged onto (160 km)2 grid boxes. A numerically stable scheme is obtained by minimizing the prediction error over multiple time steps rather than single one. In prognostic single-column model tests, this scheme matches both the fluctuations and equilibrium of NG-Aqua simulation better than the Community Atmosphere Model does.

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Univ. of Washington, Seattle, WA (United States)
Publication Date:
Research Org.:
Univ. of Washington, Seattle, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1539747
Alternate Identifier(s):
OSTI ID: 1457061
Grant/Contract Number:  
SC0012451; SC00164
Resource Type:
Accepted Manuscript
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Volume: 45; Journal Issue: 12; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Geology

Citation Formats

Brenowitz, N. D., and Bretherton, C. S. Prognostic Validation of a Neural Network Unified Physics Parameterization. United States: N. p., 2018. Web. doi:10.1029/2018gl078510.
Brenowitz, N. D., & Bretherton, C. S. Prognostic Validation of a Neural Network Unified Physics Parameterization. United States. doi:10.1029/2018gl078510.
Brenowitz, N. D., and Bretherton, C. S. Wed . "Prognostic Validation of a Neural Network Unified Physics Parameterization". United States. doi:10.1029/2018gl078510. https://www.osti.gov/servlets/purl/1539747.
@article{osti_1539747,
title = {Prognostic Validation of a Neural Network Unified Physics Parameterization},
author = {Brenowitz, N. D. and Bretherton, C. S.},
abstractNote = {Weather and climate models approximate diabatic and sub-grid-scale processes in terms of grid-scale variables using parameterizations. Current parameterizations are designed by humans based on physical understanding, observations, and process modeling. As a result, they are numerically efficient and interpretable, but potentially oversimplified. However, the advent of global high-resolution simulations and observations enables a more robust approach based on machine learning. In this letter, a neural network-based parameterization is trained using a near-global aqua-planet simulation with a 4-km resolution (NG-Aqua). The neural network predicts the apparent sources of heat and moisture averaged onto (160 km)2 grid boxes. A numerically stable scheme is obtained by minimizing the prediction error over multiple time steps rather than single one. In prognostic single-column model tests, this scheme matches both the fluctuations and equilibrium of NG-Aqua simulation better than the Community Atmosphere Model does.},
doi = {10.1029/2018gl078510},
journal = {Geophysical Research Letters},
number = 12,
volume = 45,
place = {United States},
year = {2018},
month = {6}
}

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Works referenced in this record:

Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean
journal, March 2013

  • Hwang, Y. -T.; Frierson, D. M. W.
  • Proceedings of the National Academy of Sciences, Vol. 110, Issue 13
  • DOI: 10.1073/pnas.1213302110

Cloud Resolving Modeling of the ARM Summer 1997 IOP: Model Formulation, Results, Uncertainties, and Sensitivities
journal, February 2003


Impact of Physics Parameterization Ordering in a Global Atmosphere Model
journal, February 2018

  • Donahue, Aaron S.; Caldwell, Peter M.
  • Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 2
  • DOI: 10.1002/2017MS001067

Parameterizing Convective Organization to Escape the Entrainment Dilemma: PARAMETERIZING CONVECTIVE ORGANIZATION
journal, February 2011

  • Mapes, Brian; Neale, Richard
  • Journal of Advances in Modeling Earth Systems, Vol. 3, Issue 2
  • DOI: 10.1029/2011MS000042

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

Convective self-aggregation feedbacks in near-global cloud-resolving simulations of an aquaplanet: AGGREGATION FEEDBACKS IN NEAR-GLOBAL CRM
journal, November 2015

  • Bretherton, Christopher S.; Khairoutdinov, Marat F.
  • Journal of Advances in Modeling Earth Systems, Vol. 7, Issue 4
  • DOI: 10.1002/2015MS000499

Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model
journal, January 2013

  • Krasnopolsky, Vladimir M.; Fox-Rabinovitz, Michael S.; Belochitski, Alexei A.
  • Advances in Artificial Neural Systems, Vol. 2013
  • DOI: 10.1155/2013/485913

Pareto-Optimal Estimates of California Precipitation Change: PARETO-OPTIMAL PRECIPITATION CONSTRAINTS
journal, December 2017

  • Langenbrunner, Baird; Neelin, J. David
  • Geophysical Research Letters, Vol. 44, Issue 24
  • DOI: 10.1002/2017GL075226

Determination of Bulk Properties of Tropical Cloud Clusters from Large-Scale Heat and Moisture Budgets
journal, May 1973


New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model
journal, May 2005

  • Krasnopolsky, Vladimir M.; Fox-Rabinovitz, Michael S.; Chalikov, Dmitry V.
  • Monthly Weather Review, Vol. 133, Issue 5
  • DOI: 10.1175/MWR2923.1

Metrics for the Diurnal Cycle of Precipitation: Toward Routine Benchmarks for Climate Models
journal, June 2016

  • Covey, Curt; Gleckler, Peter J.; Doutriaux, Charles
  • Journal of Climate, Vol. 29, Issue 12
  • DOI: 10.1175/JCLI-D-15-0664.1

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations: EARTH SYSTEM MODELING 2.0
journal, December 2017

  • Schneider, Tapio; Lan, Shiwei; Stuart, Andrew
  • Geophysical Research Letters, Vol. 44, Issue 24
  • DOI: 10.1002/2017GL076101

The Cumulus Parameterization Problem: Past, Present, and Future
journal, July 2004


Single-Column Models and Cloud Ensemble Models as Links between Observations and Climate Models
journal, August 1996


El Niño-Southern Oscillation sensitivity to cumulus entrainment in a coupled general circulation model: ENSO SENSITIVE TO CONVECTION SCHEME
journal, November 2011

  • Kim, Daehyun; Jang, Yeon-Soo; Kim, Dong-Hoon
  • Journal of Geophysical Research: Atmospheres, Vol. 116, Issue D22
  • DOI: 10.1029/2011JD016526

Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I
journal, April 1974


On large-scale circulations in convecting atmospheres
journal, July 1994

  • Emanuel, Kerry A.; David Neelin, J.; Bretherton, Christopher S.
  • Quarterly Journal of the Royal Meteorological Society, Vol. 120, Issue 519
  • DOI: 10.1002/qj.49712051902

Thermal Equilibrium of the Atmosphere with a Convective Adjustment
journal, July 1964


    Works referencing / citing this record:

    Deep learning to represent subgrid processes in climate models
    journal, September 2018

    • Rasp, Stephan; Pritchard, Michael S.; Gentine, Pierre
    • Proceedings of the National Academy of Sciences, Vol. 115, Issue 39
    • DOI: 10.1073/pnas.1810286115