Showing papers by "Thomas M. Smith published in 1999"

Covariability of Aspects of North American Climate with Global Sea Surface Temperatures on Interannual to Interdecadal Timescales

Abstract: Rotated canonical correlation analysis between seasonal- and longer-mean global SSTs and either U.S. surface temperatures or 700-hPa heights in the Pacific–North America region have led to decompositions into three distinct signals. One of these represents the interannual variability of ENSO and a second is related to the North Atlantic oscillation and exhibits considerable variability on interdecadal timescales. In contrast the temporal behavior of the third, which is referred to here as the global signal, is mostly characterized by a steady trend since the late 1960s. The robustness of this time series to variations in the analyses, as well as the robustness of the spatial structure of the SST pattern accompanying it, suggests that the decomposition represents a successful separation of the climate signal from the climate noise. When viewed in the context of other recent work, the global signal cannot be discounted as a “fingerprint” of global warming. Finally, calculations that exploit ensembl...

GCM Systematic Error Correction and Specification of the Seasonal Mean Pacific–North America Region Atmosphere from Global SSTs

Abstract: Specifications of 1- and 3-month mean Pacific–North America region 700-hPa heights and U.S. surface temperatures and precipitation, from global sea surface temperatures (SSTs) and the ensemble average output of multiple runs of a general circulation model with the same SSTs prescribed, were explored with canonical correlation analysis. In addition to considerable specification skill, the authors found that 1) systematic errors in SST-forced model variability had substantial linear parts, 2) use of both predictor fields usually enhanced specification performance for the U.S. fields over that for just one of the predictor fields, and 3) skillful specification and model correction of the heights and temperatures were also possible for nonactive or transitional El Nino–Southern Oscillation situations.

Considerations for Use of the Barnett and Preisendorfer (1987) Algorithm for Canonical Correlation Analysis of Climate Variations

Abstract: Bretherton et al. (1992) have pointed out that for most problems involving interseasonal and longer timescale variability of the atmosphere and oceans, prefiltering data is necessary for canonical correlation analysis (CCA) to perform comparably to certain other multivariate statistical schemes like singular value decomposition. This approach to CCA for climate problems was first suggested by Barnett and Preisendorfer (1987, hereafter BP), who filtered multiple fields of time series by replacing them with a truncated set of their principal components (PCs). Subsequent to its introduction, the BP method was applied extensively to linear climate prediction at the National Weather Service (Barnston 1994), which in turn has led to several further applications, both published (Barnston and Smith 1996, hereafter BS; He and Barnston 1996; Barnston and He 1996) and in progress. The CCA procedure produces an orthogonal hierarchy of predictor–predictand pattern pairs that represent in turn the maximum amount of cross correlation between the predictor and predictand fields, the second largest portion, etc. The transformation matrix relating the ordered predictor and predictand patterns can be used to optimally linearly specify the latter from the former. Its use (specifically the BP method) was adopted by us to address variants of the specification (i.e., simultaneous relationships) problems studied by BS (see Smith and Livezey 1998, hereafter SL, and Livezey and Smith 1998, hereafter LS2). In the course of this work it became apparent that the application of PC prefiltering