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Title: Spatial patterns of historical temperature variability: Global correlations using spectral and wavelet techniques

Conference ·
OSTI ID:549164

In order to assess man`s impact on global climate, we need to understand natural climate variability more fully. Using 100 years of global temperature data, we have developed time-series methods that identify coherent spatio-temporal {open_quotes}modes{close_quotes} of temperature variability e.g., El Nino-Southern Oscillation (ENSO) cycles. Methods based on multiple-taper spectral analysis estimate the correlated temperature variability within narrow frequency bands. Methods based on a multiple wavelet analysis identify short-term global temperature {open_quotes}events{close_quotes} on a range of time scales. We assess the statistical significance of narrow-band and event correlations from Monte Carlo confidence limits, which are derived from stochastic variations of uncorrelated white-noise time series. Significant patterns of variability with 2.8 to 5.7 year duration exhibit the characteristic ENSO pattern: warming in the tropics, followed by temperature excursions in middle latitudes. An interdecadal mode (15-18 years) appears to represent long-term ENSO variability, an interpretation supported by the persistence of warm Pacific Ocean surface water in the decade after the large 1982-3 El Nino episode. The interdecadal mode appears to explain much of the anomalous global warmth of the 1980s. North Atlantic variability dominates quasi-biennial (2.2 years) and decadal (7-12 years) modes.

OSTI ID:
549164
Report Number(s):
CONF-950264-; TRN: 95:006094-0033
Resource Relation:
Conference: American Association for the Advancement of Science (AAAS) annual meeting and science innovation exposition, Atlanta, GA (United States), 16-21 Feb 1995; Other Information: PBD: 1995; Related Information: Is Part Of 1995 AAAS annual meeting and science innovation exposition: Unity in diversity; Strauss, M.S. [ed.]; Heasley, C.; PB: 337 p.
Country of Publication:
United States
Language:
English