@article{9fe314b7c15049749dac739a528d3768,
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 to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.",
author = "Emily Hogan and Nicholas, {Robert E.} and Klaus Keller and Stephanie Eilts and Sriver, {Ryan L.}",
note = "Funding Information: Acknowledgments. This study was cosupported by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research Program, Integrated Assessment Research Program, Grant DE-SC0005171, the DOE Program on Coupled Human Earth Systems (PCHES) under DOE Cooperative Agreement DE-SC0016162, and the Penn State Center for Climate Risk Management. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding entities. Any errors and opinions are, of course, those of the authors. All results, model codes, analysis codes, data, and model outputs used for analysis are freely available from the corresponding author and are distributed under the GNU general public license. The datasets, software tools, and other resources are provided as is without warranty of any kind, express or implied. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability in connection with the use of these resources. We acknowledge the World Climate Research Programme{\textquoteright}s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in appendix A of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy{\textquoteright}s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We provide all source code (written in R) in the supplemental information. Publisher Copyright: {\textcopyright} 2019 American Meteorological Society.",
year = "2019",
month = may,
day = "1",
doi = "10.1175/JCLI-D-18-0075.1",
language = "English (US)",
volume = "32",
pages = "2591--2603",
journal = "Journal of Climate",
issn = "0894-8755",
publisher = "American Meteorological Society",
number = "9",
}