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Using Generative Art to Convey Past and Future Climate Transitions
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Selecting global climate models for regional climate change studies.

David W Pierce1, Tim P Barnett, Benjamin D Santer

  • 1Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography, La Jolla, CA 92093, USA. dpierce@ucsd.edu

Proceedings of the National Academy of Sciences of the United States of America
|May 15, 2009
PubMed
Summary

Choosing global climate models for regional studies is crucial. Ensemble averages outperform individual models, highlighting the need to account for internal climate variability in detection and attribution analysis.

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Area of Science:

  • Climate Science
  • Atmospheric Science
  • Environmental Science

Background:

  • Regional climate change studies rely on downscaling global climate models.
  • Selecting appropriate global models is critical for accurate regional analysis.
  • Detection and attribution (D&A) studies assess climate change drivers.

Purpose of the Study:

  • To investigate the impact of global climate model selection on regional D&A studies.
  • To evaluate performance metrics for regional climate models.
  • To assess the influence of internal climate variability and model ensembles.

Main Methods:

  • Developed 42 performance metrics for seasonal temperature, precipitation, ENSO, and PDO.
  • Applied metrics to 21 global climate models for a western U.S. D&A study.
  • Compared individual model performance, multimodel ensemble averages (MM), and randomly selected models.

Main Results:

  • Model performance metrics showed no strong correlation with D&A results.
  • Ensembles of model runs are essential to mitigate internal climate variability.
  • The multimodel ensemble average (MM) proved superior to individual models, likely due to error cancellation.
  • D&A results confirmed anthropogenic forcing of January-February-March (JFM) temperature changes in the western U.S.
  • Future temperature projections diverged based on model performance only after the 2080s.

Conclusions:

  • Model selection based on regional performance metrics is less important than using multimodel ensembles.
  • Accounting for internal climate variability through ensembles is key for robust regional D&A.
  • Multimodel averages reduce uncertainty and improve the reliability of regional climate change assessments.