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Title: Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles

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 tomore » extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.« less

Authors:
 [1];  [2];  [3];  [1];  [1]
  1. Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois
  2. Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, Pennsylvania
  3. Earth and Environmental Systems Institute, and Department of Geosciences, The Pennsylvania State University, University Park, Pennsylvania
Publication Date:
Research Org.:
Stanford Univ., CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1508202
Alternate Identifier(s):
OSTI ID: 1610814
Grant/Contract Number:  
SC0005171; SC0016162
Resource Type:
Published Article
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Name: Journal of Climate Journal Volume: 32 Journal Issue: 9; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences; Extreme events; Climate change; Climate variability; Temperature; Climate models; Model output statistics

Citation Formats

Hogan, Emily, Nicholas, Robert E., Keller, Klaus, Eilts, Stephanie, and Sriver, Ryan L. Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles. United States: N. p., 2019. Web. https://doi.org/10.1175/JCLI-D-18-0075.1.
Hogan, Emily, Nicholas, Robert E., Keller, Klaus, Eilts, Stephanie, & Sriver, Ryan L. Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles. United States. https://doi.org/10.1175/JCLI-D-18-0075.1
Hogan, Emily, Nicholas, Robert E., Keller, Klaus, Eilts, Stephanie, and Sriver, Ryan L. Mon . "Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles". United States. https://doi.org/10.1175/JCLI-D-18-0075.1.
@article{osti_1508202,
title = {Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles},
author = {Hogan, Emily and Nicholas, Robert E. and Keller, Klaus and Eilts, Stephanie and Sriver, Ryan L.},
abstractNote = {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.},
doi = {10.1175/JCLI-D-18-0075.1},
journal = {Journal of Climate},
number = 9,
volume = 32,
place = {United States},
year = {2019},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1175/JCLI-D-18-0075.1

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Cited by: 1 work
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