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Title: Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [1] ;  [1]
  1. Univ. of Wisconsin, Madison, WI (United States). Center for Climate Research and Dept. of Atmospheric and Oceanic Sciences
  2. Peking Univ., Beijing (China). Lab. of Ocean, Atmospheric Studies
  3. Princeton Univ., NJ (United States). Search Results NOAA Geophysical Fluid Dynamics Lab.
  4. Chinese Academy of Meteorological Sciences, Beijing (China)
  5. Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 27; Journal Issue: 11; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; optimization; statistical techniques; climate models; data assimilation; ensembles
OSTI Identifier:
1395035

Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., and Lu, F.. Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method. United States: N. p., Web. doi:10.1175/JCLI-D-13-00091.1.
Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., & Lu, F.. Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method. United States. doi:10.1175/JCLI-D-13-00091.1.
Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., and Lu, F.. 2014. "Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method". United States. doi:10.1175/JCLI-D-13-00091.1. https://www.osti.gov/servlets/purl/1395035.
@article{osti_1395035,
title = {Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method},
author = {Liu, Y. and Liu, Z. and Zhang, S. and Rong, X. and Jacob, R. and Wu, S. and Lu, F.},
abstractNote = {Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.},
doi = {10.1175/JCLI-D-13-00091.1},
journal = {Journal of Climate},
number = 11,
volume = 27,
place = {United States},
year = {2014},
month = {5}
}