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Application-specific optimal model weighting of global climate models: A red tide example.

Ahmed Elshall1,2,3, Ming Ye1, Sven A Kranz1

  • 1Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA.

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Summary

Optimal model weighting for climate services can be improved by prescreening ensemble members. This method ensures robust models are not overlooked, enhancing the accuracy of predictions like red tide events.

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Area of Science:

  • Climate Science
  • Environmental Modeling
  • Oceanography

Background:

  • Global climate models (GCMs) and Earth system models (ESMs) are crucial for environmental climate services.
  • The Coupled Model Intercomparison Project Phase 6 (CMIP6) High Resolution Model Inter-comparison Project (HighResMIP) provides vital regional climate data.
  • Developing effective CMIP6 ensembles necessitates methods like model weighting and subset selection.

Purpose of the Study:

  • To evaluate application-specific optimal model weighting combined with prescreening-based subset selection for CMIP6 ensembles.
  • To assess the impact of these methods on predicting regional phenomena, using red tide events as a case study.
  • To highlight the strengths and limitations of optimal model weighting in climate service applications.

Main Methods:

  • Categorization, selection, and weighting of independent ensemble members based on their ability to reproduce specific, physically interpretable features.
  • Application of prescreening-based subset selection prior to optimal model weighting.
  • Case study analysis focusing on red tide prediction in the Gulf of Mexico (West Florida Shelf).

Main Results:

  • Prescreening-based subset selection is crucial because optimal model weighting can obscure robust ensemble members through error optimization.
  • This prescreening approach offers valuable insights into the reliability and validity of assigned model weights.
  • The study demonstrates the risks of employing non-representative models within optimal weighting schemes.

Conclusions:

  • Optimal model weighting, when used without prescreening, may lead to suboptimal ensemble performance by masking strong models.
  • Prescreening-based subset selection is essential for robust climate model ensembles and reliable climate services.
  • The findings are applicable to a wide range of climate services beyond red tide prediction.