Climate models capture key features of extreme precipitation probabilities across regions
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Climate models capture key features of extreme precipitation probabilities across regions

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  • Journal Title:
    Environmental Research Letters
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    Quantitative simulation of precipitation in current climate has been an ongoing challenge for global climate models. Despite serious biases in correctly simulating probabilities of extreme rainfall events, model simulations under global warming scenarios are routinely used to provide estimates of future changes in these probabilities. To minimize the impact of model biases, past literature tends to evaluate fractional (instead of absolute) changes in probabilities of precipitation extremes under the assumption that fractional changes would be more reliable. However, formal tests for the validity of this assumption have been lacking. Here we evaluate two measures that address properties important to the correct simulation of future fractional probability changes of precipitation extremes, and that can be assessed with current climate data. The first measure tests climate model performance in simulating the characteristic shape of the probability of occurrence of daily precipitation extremes and the second measure tests whether the key parameter governing the scaling of this shape is well reproduced across regions and seasons in current climate. Contrary to concerns regarding the reliability of global models for extreme precipitation assessment, our results show most models lying within the current range of observational uncertainty in these measures. Thus, most models in the Coupled Model Intercomparison Project Phase 6 ensemble pass two key tests in current climate that support the usefulness of fractional measures to evaluate future changes in the probability of precipitation extremes.
  • Keywords:
  • Source:
    Environmental Research Letters, 16(2), 024017
  • DOI:
  • ISSN:
    1748-9326
  • Format:
    PDF
  • Publisher:
    IOP Publishing
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA18OAR4310280
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
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