By Renata Dmowska, Barry Saltzman
This sequence presents a venue for longer stories of present advances in geophysics. Written at a degree available to graduate scholars, the articles serve to expand wisdom of assorted fields and will be precious in classes and seminars.
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Extra resources for Advances in Geophysics, Vol. 41
These signals include a 15- to 18-year time-scale oscillatory pattern exhibiting important tropical and extratropical features, and a secular ‘‘multidecadal’’ variation associated with a single cycle of warming and cooling global in extent but most pronounced in the North Atlantic. To obtain a more direct picture of the possible dynamical processes governing such signals, we analyze spatiotemporal variability jointly between surface temperature and sea-level pressure ŽSLP. records that are available for nearly a century covering much of the Northern Hemisphere Žsee Mann and Park, 1996b..
The case 0 corresponds to a pure white-noise sequence. 9999 1 nearly a nonstationary random walk Žthe reader is referred back to Fig. 2.. We applied the MTM-SVD methodology with K 3 and NW 2 to estimate the LFV spectrum for each spatiotemporal noise realization, estimating signiﬁcance levels from the bootstrap procedure described above. The estimated null distribution, the reader might note, will be independent of the value of owing to the whitening nature of the bootstrap. Of interest, then, is whether this independence holds up, at least under reasonable degrees of redness, for the observed distributions of LFV spectra for the red-noise processes themselves.
1 where n in this context runs over the N distinct frequencies of the discrete Fourier transform of a data set of length N samples. The empirical orthogonal function ŽEOF. vm describes the complex spatial pattern in amplitude and phase of the nth eigenvector, and the N-vector u m now describes the combinations of pure harmonic components of variability that describe the relatively smooth time-evolution of the nth eigenvector. The eigenvalue Žthe square of the singular value. m , as before, describes the associated fraction of data variance.