Monthly extreme temperature trends in CMIP5 hindcast/prediction simulations

Stegall, Steve T.; Kunkel, Kenneth E.

doi:10.1175/JAMC-D-16-0281.1

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Monthly extreme temperature trends in CMIP5 hindcast/prediction simulations

2017
By Stegall, Steve T. ; Kunkel, Kenneth E.
Source: Journal of Applied Meteorology and Climatology, 56(4), 1141-1154

[PDF-1.52 MB]

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Journal Title:

Journal of Applied Meteorology and Climatology
Personal Author:

Stegall, Steve T. ; Kunkel, Kenneth E.

Stegall, Steve T. ; Kunkel, Kenneth E. Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) ; CICSP (Cooperative Institute for Climate Science)

NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) ; CICSP (Cooperative Institute for Climate Science) Less -
Description:

A simple index of extreme surface (2 m) monthly temperature was analyzed over the conterminous United States for 13 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) hindcast (1981–2010) and prediction (2006–35) datasets as well as the U.S. climate division dataset, version 2 (nClimDiv), as observations for 1981–2010. Results are analyzed for regions defined in the recent Third U.S. National Climate Assessment. There is good agreement between models and observations for all regions for the annual warm and cold indices except for the warm index in the Northwest. For seasonal values of the temperature index, model simulations generally agree with the sign of the observed seasonal trends in all regions except for the Northwest and a few seasons in the “warming hole” areas of the central and southeastern United States. Most individual ensemble member simulations agree with the sign of the observed trend. However, in all regions and seasons, some simulations, in the range of 10%–40% of all ensemble members, show opposite signs, indicating that even overall skillful projections can have substantial uncertainty. These results indicate that there is potential skill in use of GCMs to provide projections of hot and cold extremes on the 30-yr time scale. However, it is important to note that natural variability is comparable to the forced signal on this time scale and thus introduces uncertainty. Analysis of the future simulations (2006–35) indicates that warm extremes increase rapidly while cold extremes become substantially more rare.
Keywords:

[+]

Climatology CMIP5 Earth Temperature
Source:

Journal of Applied Meteorology and Climatology, 56(4), 1141-1154
DOI:

https://doi.org/10.1175/JAMC-D-16-0281.1
Document Type:

Journal Article
Funding:

Grant no. NA14NES4320003
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Other
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Submitted
Main Document Checksum:

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urn:sha256:a05caf32ff1d8b7cd0e8b518b6f20513a1a08ff44448c537172436a1e3c55fe2
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https://repository.library.noaa.gov/view/noaa/45300/noaa_45300_DS1.pdf
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Monthly extreme temperature trends in CMIP5 hindcast/prediction simulations

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