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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Considerations for parameter optimization and sensitivity in climate models.

J David Neelin1, Annalisa Bracco, Hao Luo

  • 1Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA.

Proceedings of the National Academy of Sciences of the United States of America
|December 1, 2010
PubMed
Summary

Climate model simulations face challenges in accurately predicting regional precipitation. This study introduces a novel multiobjective optimization approach using metamodels to improve climate model parameterization and accuracy.

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Area of Science:

  • Climate modeling
  • Atmospheric science
  • Geophysics

Background:

  • Climate models struggle with regional precipitation prediction due to high dimensionality and computational costs.
  • Existing methods face challenges in constraining regional climatology and choosing objective functions.

Purpose of the Study:

  • To develop a multiobjective optimization approach for climate model parameterization.
  • To improve the accuracy of regional climate simulations and intercompare model sensitivities.

Main Methods:

  • Utilized an atmospheric General Circulation Model (GCM) coupled with observed sea surface temperature or a mixed-layer ocean.
  • Employed low-order polynomial fits to create metamodels of GCM output spatial fields.
  • Applied multiobjective optimization to identify optimal parameter values.

Main Results:

  • Metamodels successfully approximated GCM outputs, facilitating optimization.
  • Identified tradeoffs between different climate variables' optimal parameters.
  • Highlighted key parameterization aspects, such as convection-water vapor interaction, at the feasible parameter range limits.

Conclusions:

  • The developed approach enhances climate model parameter choice and intercomparison.
  • Metamodels offer a computationally efficient way to navigate complex climate model parameter spaces.
  • Improved understanding of regional climate prediction challenges and potential solutions.