Editorial Type:
Article
Article Type:
Research Article

A Trend Analysis of the 1930–2010 Extreme Heat Events in the Continental United States

Evan M. Oswald Atmospheric Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Richard B. Rood Atmospheric Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Print Publication:
01 Mar 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-071.1
Page(s):
565–582
Restricted access

Abstract

Extreme heat events (EHEs) are linked to mortality rates, making them an important research subject in both the climate and public health fields. This study evaluated linear trends in EHEs using the U.S. Historical Climatology Network (USHCN), version 2.0, dataset and quantified the longer-term EHE trends across the continental United States (CONUS). The USHCN-daily, version 1, dataset was integrated with the homogenized USHCN-monthly, version 2.0, dataset to create daily data for trend analysis. Time series and estimated trends in multiple characteristics of EHEs (number, total days, mean duration, etc.) were calculated as were the continental means and spatial maps. The differences between EHEs based on daily maximum temperatures, minimum temperatures, and both minimum and maximum temperatures were explored. To focus on warming and cooling periods, the trends were also estimated separately over the first half and second half of the study period (1930–2010). The results indicated that the trends for different EHE characteristics were coherent (e.g., temporally correlated, similar spatial pattern of trends). Maps indicated negative trends in the interior of the CONUS and positive trends in coastal and southern areas. Continental-scale increases between 1970 and 2010 were mostly offset by the decreases between 1930 and 1970. Several daily maximum (minimum) EHEs near the 1930s (2000s) led to 1930–2010 trends of daily maximum (minimum) EHEs decreasing (increasing). Last, the results suggest that linear trends depend on which daily temperature extreme is required to exceed the threshold.

Great Lakes Regional Integrated Sciences and Assessments Center Contribution Number 2013-4.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAMC-D-13-071.s1.

Corresponding author address: Evan Oswald, Geography Dept., University of Vermont, 94 University Place, Burlington, VT 05401. E-mail: eoswald@uvm.edu

Abstract

Extreme heat events (EHEs) are linked to mortality rates, making them an important research subject in both the climate and public health fields. This study evaluated linear trends in EHEs using the U.S. Historical Climatology Network (USHCN), version 2.0, dataset and quantified the longer-term EHE trends across the continental United States (CONUS). The USHCN-daily, version 1, dataset was integrated with the homogenized USHCN-monthly, version 2.0, dataset to create daily data for trend analysis. Time series and estimated trends in multiple characteristics of EHEs (number, total days, mean duration, etc.) were calculated as were the continental means and spatial maps. The differences between EHEs based on daily maximum temperatures, minimum temperatures, and both minimum and maximum temperatures were explored. To focus on warming and cooling periods, the trends were also estimated separately over the first half and second half of the study period (1930–2010). The results indicated that the trends for different EHE characteristics were coherent (e.g., temporally correlated, similar spatial pattern of trends). Maps indicated negative trends in the interior of the CONUS and positive trends in coastal and southern areas. Continental-scale increases between 1970 and 2010 were mostly offset by the decreases between 1930 and 1970. Several daily maximum (minimum) EHEs near the 1930s (2000s) led to 1930–2010 trends of daily maximum (minimum) EHEs decreasing (increasing). Last, the results suggest that linear trends depend on which daily temperature extreme is required to exceed the threshold.

Great Lakes Regional Integrated Sciences and Assessments Center Contribution Number 2013-4.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAMC-D-13-071.s1.

Corresponding author address: Evan Oswald, Geography Dept., University of Vermont, 94 University Place, Burlington, VT 05401. E-mail: eoswald@uvm.edu
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