Decadal potential predictability of upper ocean heat content over the twentieth century
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Decadal potential predictability of upper ocean heat content over the twentieth century

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Details:

  • Journal Title:
    Climate Dynamics
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    The statistical method, Average Predictability Time (APT) decomposition, is used in the present paper to estimate the decadal predictability of upper ocean heat content over the global ocean, North Pacific and North Atlantic, respectively. The twentieth century simulations from CMIP5 outputs are the main data sources in this study. On global scale, the leading predictable component is characterized by a warming trend over the majority of oceans, which is related to the anthropogenic forced response. The second predictable component has significant loadings in the North Atlantic, especially in the subtropical region, which originates from the Atlantic Multidecadal Oscillation (AMO) predictability. To separate interactions among different ocean basins, we further maximize APT in individual North Pacific and North Atlantic oceans. It is found that the second and the third predictable component in North Pacific are significantly correlated with the well-known North Pacific Gyre Oscillation mode and the Pacific Decadal Oscillation respectively. Upper limit prediction skill of these two components are on the order of 6 years. In contrast, the most predictable component derived from the North Atlantic features an AMO-like spatial structure with its prediction skill up to 18 years, while the basin mode due to global warming only exists as the third component. This indicates the interdecadal variability in the North Atlantic is strong enough to mask the anthropogenic climate signals. Furthermore, predictability in the real world is also investigated and compared with model results by using observation-based data.
  • Source:
    Climate Dynamics, 49(9-10), 3293-3307
  • DOI:
  • ISSN:
    0930-7575;1432-0894;
  • Format:
    PDF
  • Publisher:
    Springer Science and Business Media LLC
  • Document Type:
    Journal Article
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    Submitted
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