3.9 Article

Intercomparison of coral oxygen isotope data and historical sea surface temperature (SST): Potential for coral-based SST field reconstructions

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PALEOCEANOGRAPHY
卷 15, 期 5, 页码 551-563

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2000PA000498

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We examine the extent to which the large-scale features of the sea surface temperature (SST) anomaly field are represented by a sparse observational network of coral oxygen isotope (delta(18)O) time series. Regression of annually averaged delta(18)O data against gridded estimates of local SST anomaly within the period 1856-1990 confirm the literature regression of delta(18)O anomaly on SST anomaly for Indo-Pacific corals. However, while interannual SST variability is generally well represented by individual coral time series, observed decadal and secular variability does not always display a linear relationship to local SST anomaly. Instead, many records appear to better recover nonlocal, large-scale phenomena, which in turn are related to either the coral local SST or SST covariant changes in seawater delta(18)O. We employ empirical orthogonal function (EOF) analysis to identify common patterns of variability in the coral data. We find two significant patterns which are interpretable as the oceanographic signature of the El Nino-Southern Oscillation (ENSO) and as a near-global warming in which the eastern equatorial Pacific cools. A third pattern weakly resembles the Pacific Decadal Oscillation. These modes are seen more clearly in a singular Vector decomposition (SVD) of the covariance between the coral data and the dominant patterns of large-scale historical SST variability. The results are consistent with those found in EOF and SVD analyses of SST data from the coral locations. As additional coral-based proxy estimates become available, they will improve the resolution of the patterns recovered. These results suggest that a sparse network of coral data may be used to reconstruct interannual, secular, and decadal SST variability for preinstrumental periods, albeit with large uncertainty.

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