Predictability of the recent slowdown and subsequent recovery of large-scale surface warming using statistical methods
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Mann_et_al-2016-Geophysical_Re ...
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Author
Mann, Michael E.Steinman, Byron A.
Miller, Sonya K.
Frankcombe, Leela M.
England, Matthew H.
Cheung, Anson H.
Affiliation
Univ Arizona, Dept GeosciIssue Date
2016-04-16
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AMER GEOPHYSICAL UNIONCitation
Predictability of the recent slowdown and subsequent recovery of large-scale surface warming using statistical methods 2016, 43 (7):3459 Geophysical Research LettersJournal
Geophysical Research LettersRights
© 2016. American Geophysical Union. All Rights Reserved.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The temporary slowdown in large-scale surface warming during the early 2000s has been attributed to both external and internal sources of climate variability. Using semiempirical estimates of the internal low-frequency variability component in Northern Hemisphere, Atlantic, and Pacific surface temperatures in concert with statistical hindcast experiments, we investigate whether the slowdown and its recent recovery were predictable. We conclude that the internal variability of the North Pacific, which played a critical role in the slowdown, does not appear to have been predictable using statistical forecast methods. An additional minor contribution from the North Atlantic, by contrast, appears to exhibit some predictability. While our analyses focus on combining semiempirical estimates of internal climatic variability with statistical hindcast experiments, possible implications for initialized model predictions are also discussed.Note
EMBARGO "Publisher's version/PDF must be used in Institutional Repository 6 months after publication."ISSN
00948276Version
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All raw data, (c) Matlab code, and results from our analysis are available at the supplementary website: http://www.meteo.psu.edu/~mann/supplements/GRL2016. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. B.A.S. acknowledges support by the U.S. National Science Foundation (EAR-1447048). M.H.E. and L.M.F. acknowledge support from the Australian Research Council (FL100100214). A.H.C. acknowledges support from the U.S. National Science Foundation (AGS-1263225). Kaplan SST V2 data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. HadISST data were provided by theMet Office Hadley Centre: www.metoffice.gov.uk/hadobs. ERSST data were provided by NOAA:www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v3b.Additional Links
http://doi.wiley.com/10.1002/2016GL068159ae974a485f413a2113503eed53cd6c53
10.1002/2016GL068159
