Representation of U.S. warm temperature extremes in global climate model ensembles

Emily Hogan, Robert E. Nicholas, Klaus Keller, Stephanie Eilts, Ryan L. Sriver

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.

Original languageEnglish (US)
Pages (from-to)2591-2603
Number of pages13
JournalJournal of Climate
Volume32
Issue number9
DOIs
StatePublished - May 1 2019

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Fingerprint Dive into the research topics of 'Representation of U.S. warm temperature extremes in global climate model ensembles'. Together they form a unique fingerprint.

  • Cite this