4430 JOURNAL OF CLIMATE VOLUME 13 The Timescale, Power Spectra, and Climate Noise Properties of Teleconnection Patterns
This study uses NCEP–NCAR reanalysis data covering the boreal winters of 1958–97 to examine the power spectral, timescale, and climate noise properties of the dominant atmospheric teleconnection patterns. The patterns examined include the North Atlantic oscillation (NAO), the Pacific–North American...
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| Format: | Text |
| Language: | English |
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1999
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.4540 http://www.meteo.psu.edu/~sbf1/papers/tele.2000.pdf |
| Summary: | This study uses NCEP–NCAR reanalysis data covering the boreal winters of 1958–97 to examine the power spectral, timescale, and climate noise properties of the dominant atmospheric teleconnection patterns. The patterns examined include the North Atlantic oscillation (NAO), the Pacific–North American (PNA), and west Pacific (WP) teleconnections, and a spatial pattern associated with ENSO. The teleconnection patterns are identified by applying a rotated principal component analysis to the daily unfiltered 300-mb geopotential height field. The NAO and PNA were found to be the two dominant patterns on all timescales. The main finding is that the temporal evolution of the NAO, PNA, and WP teleconnections can be interpreted as being a stochastic (Markov) process with an e-folding timescale between 7.4 and 9.5 days. The time series corresponding to the ENSO spatial pattern did not match that of a Markov process, and thus a well-defined timescale could not be specified. The shortness of the above timescales indicates that the excitation of these teleconnection patterns is limited to a period of time less than a few days. These findings also suggest that in order to improve our understanding of the growth and decay mechanisms of teleconnection patterns, it is best to use daily, unfiltered data, rather than monthly or seasonally averaged data. The signal (interannual variance due to external forcing) to noise (interannual variance from stochastic processes) ratios were also examined. For the NAO (PNA), the signal-to-noise ratio is 0.09 (1.11). This indicates that the interannual variability of the NAO (PNA) arises primarily from climate noise (both from climate noise and external forcing). An explanation for why the NAO and PNA dominate on interannual timescales is also presented. 1. |
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