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

TitleRepresentation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Publication TypeJournal Article
Year of Publication2019
JournalJournal of Climate
Volume32
Number9
Pages2591-2603
Date Published05/2019
Abstract / Summary

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.

URLhttp://dx.doi.org/10.1175/jcli-d-18-0075.1
DOI10.1175/jcli-d-18-0075.1
Funding Program: 
Journal: Journal of Climate
Year of Publication: 2019
Volume: 32
Number: 9
Pages: 2591-2603
Date Published: 05/2019

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
Citation:
Hogan, E, R Nicholas, K Keller, S Eilts, and R Sriver.  2019.  "Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles."  Journal of Climate 32(9): 2591-2603.  https://doi.org/10.1175/jcli-d-18-0075.1.