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    Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations

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    Author
    Cheung, Anson H.
    Mann, Michael E.
    Steinman, Byron A.
    Frankcombe, Leela M.
    England, Matthew H.
    Miller, Sonya K.
    Affiliation
    Univ Arizona, Dept Geosci
    Issue Date
    2017-06
    
    Metadata
    Show full item record
    Publisher
    AMER METEOROLOGICAL SOC
    Citation
    Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations 2017, 30 (12):4763 Journal of Climate
    Journal
    Journal of Climate
    Rights
    © 2017 American Meteorological Society.
    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
    Low-frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semiempirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semiempirical method as well as from analysis of ICV in CMIP5 preindustrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal time scales (>20 yr). Analysis of the amplitude of total variability and the ICV in the models and instrumental data indicates that the models underestimate ICV amplitude on low-frequency time scales (>20 yr in the NA; >40 yr in the NP), while agreement is found in the NH variability. A multiple linear regression analysis of ICV in the instrumental record shows that variability in the NP drives decadal-to-interdecadal variability in the NH, whereas the NA drives multidecadal variability in the NH. Analysis of the CMIP5 historical simulations does not reveal such a relationship, indicating model limitations in simulating ICV. These findings demonstrate the need to better characterize low-frequency ICV, which may help improve attribution and decadal prediction.
    Note
    6 month embargo; Published Online: 13 March 2017
    ISSN
    0894-8755
    1520-0442
    DOI
    10.1175/JCLI-D-16-0712.1
    Version
    Final published version
    Sponsors
    U.S. National Science Foundation [AGS-1263225]; Australian Research Council
    Additional Links
    http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0712.1
    ae974a485f413a2113503eed53cd6c53
    10.1175/JCLI-D-16-0712.1
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