Doubling of U.S. Population Exposure to Climate Extremes by 2050

Fulden Batibeniz, Moetasim Ashfaq, Noah S. Diffenbaugh, Kesondra Key, Katherine J. Evans, Ufuk Utku Turuncoglu, Barış Önol

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

We quantify historical and projected trends in the population exposure to climate extremes as measured by the United States National Center for Environmental Information Climate Extremes Index (CEI). Based on the analyses of the historical observations, we find that the U.S. has already experienced a rise in the occurrence of aggregated extremes in recent decades, consistent with the climate response to historical increases in radiative forcing. Additionally, we find that exposure can be expected to intensify under the Representative Concentration Pathway 8.5, with all counties permanently exceeding the baseline variability in the occurrence of extreme hot days, warm nights, and drought conditions by 2050. As a result, every county in the U.S. is projected to permanently exceed the historical CEI variability (as measured by one standard deviation during the 1981–2005 period). Based on the current population distribution, this unprecedented change implies a yearly exposure to extreme conditions for one in every three people. We find that the increasing trend in exposure to the aggregated extremes is already detectable over much of the U.S., and particularly in the central and eastern U.S. The high correspondence between the pattern of trends in our simulations and observations increases confidence in the projected amplification of population exposure to unprecedented combinations of extreme climate conditions, should greenhouse gas concentrations continue to escalate along their current trajectory.

Original languageEnglish
Article numbere2019EF001421
JournalEarth's Future
Volume8
Issue number4
DOIs
StatePublished - Apr 1 2020

Funding

This study was partly funded by the Regional and Global Climate Modeling Program within the Office of Science of the U.S. Department of Energy (DOE). Support for model simulations, data storage and analyses are provided by the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory (ORNL). M. A. was supported by the National Climate-Computing Research Center, which is located within the National Center for Computational Sciences at the ORNL and supported under a Strategic Partnership Project, 2316-T849–08, between DOE and NOAA. UT was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. The population data can be obtained from Socioeconomic Data and Application Center (SDAC; https://sedac.ciesin.columbia.edu/). The regional climate model code is publicly available at https://github.com/ictp-esp/RegCM, and the GCMs forcing are available at https://esgf-node.llnl.gov/search/cmip5/. This manuscript has been co-authored by employees of Oak Ridge National Laboratory, managed by UT Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This study was partly funded by the Regional and Global Climate Modeling Program within the Office of Science of the U.S. Department of Energy (DOE). Support for model simulations, data storage and analyses are provided by the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory (ORNL). M. A. was supported by the National Climate‐Computing Research Center, which is located within the National Center for Computational Sciences at the ORNL and supported under a Strategic Partnership Project, 2316‐T849–08, between DOE and NOAA. UT was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. The population data can be obtained from Socioeconomic Data and Application Center (SDAC; https://sedac.ciesin.columbia.edu/ ). The regional climate model code is publicly available at https://github.com/ictp‐esp/RegCM , and the GCMs forcing are available at https://esgf‐node.llnl.gov/search/cmip5/ . This manuscript has been co‐authored by employees of Oak Ridge National Laboratory, managed by UT Battelle, LLC, under contract DE‐AC05‐00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the United States Government retains a non‐exclusive, paid‐up, irrevocable, world‐wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe‐public‐access‐plan ).

FundersFunder number
DOE Public Access Plan
National Climate-Computing Research Center
Socioeconomic Data and Application Center
United States Government
National Science Foundation
U.S. Department of Energy2316‐T849–08
Directorate for Geosciences1852977
National Oceanic and Atmospheric Administration
National Center for Atmospheric Research
Oak Ridge National Laboratory
UT-BattelleDE‐AC05‐00OR22725

    Keywords

    • Climate Extremes Index
    • Detection and Attribution
    • Multivariate Extremes
    • Population Exposure
    • Time of Emergence
    • Weather and Climate

    Fingerprint

    Dive into the research topics of 'Doubling of U.S. Population Exposure to Climate Extremes by 2050'. Together they form a unique fingerprint.

    Cite this