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Optimal Ensemble Size for Climate Analysis: Balancing Utility and Cost

This study explores the ideal number of models in an ensemble to analyze climate variability and change, focusing on return level curves and uncertainties in extreme precipitation and temperature. By comparing single-model and multi-model ensembles, the analysis delves into heat waves, precipitation intensity, extreme temperature range, warm nights, and maximum 5-day precipitation. It also examines the trade-off between marginal utility and cost of additional ensemble members, including insights on creating "super ensembles" with varying model contributions. For more information, visit http://www.isse.ucar.edu/extremevalues/evtk.html.

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Optimal Ensemble Size for Climate Analysis: Balancing Utility and Cost

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  1. What is the optimal number in an ensemble? Joint Meeting of the Climate Variability and Climate Change Working Groups

  2. Return level curves and their uncertaintyPrecipitation Extremes http://www.isse.ucar.edu/extremevalues/evtk.html

  3. Return level curves and their uncertaintyTemperature Extremes http://www.isse.ucar.edu/extremevalues/evtk.html

  4. Single-model ensembles and multi-model ensemblesHeat Wave index

  5. Single-model ensembles and multi-model ensemblesHeat Wave index

  6. Single-model ensembles and multi-model ensemblesMax 5 Day Precipitation index

  7. Single-model ensembles and multi-model ensemblesMax 5 Day Precipitation index

  8. Single-model ensembles and multi-model ensemblesExtreme Temperature Range index

  9. Single-model ensembles and multi-model ensemblesExtreme Temperature Range index

  10. Single-model ensembles and multi-model ensemblesWarm Nights index

  11. Single-model ensembles and multi-model ensemblesWarm Nights index

  12. Single-model ensembles and multi-model ensemblesPrecipitation Intensity index

  13. Single-model ensembles and multi-model ensemblesPrecipitation Intensity index

  14. Marginal utility vs. marginal cost of additional ensemble members • Single-model ensembles vs. multi-model ensembles • Analyzing “super ensembles” where different models provide different numbers of ensemble members

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