Predictability of the recent slowdown and subsequent recovery of large-scale surface warming using statistical methods
AuthorMann, Michael E.
Steinman, Byron A.
Miller, Sonya K.
Frankcombe, Leela M.
England, Matthew H.
Cheung, Anson H.
AffiliationUniv Arizona, Dept Geosci
MetadataShow full item record
PublisherAMER GEOPHYSICAL UNION
CitationPredictability of the recent slowdown and subsequent recovery of large-scale surface warming using statistical methods 2016, 43 (7):3459 Geophysical Research Letters
JournalGeophysical Research Letters
Rights©2016. American Geophysical Union. All Rights Reserved
Collection InformationThis 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 firstname.lastname@example.org.
AbstractThe 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.
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VersionFinal published version
SponsorsAll 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.