• Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality, and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev.,115, 1083–1126.

  • Cash, B. A., and S. Lee, 2000. Observed nonmodal growth of the Pacific–North American teleconnection pattern. J. Climate, in press.

  • Chao, B. F., 1988: Correlation of interannual length-of-day variation with El Niño/Southern Oscillation, 1972–1986. J. Geophys. Res.,93, 7709–7715.

  • Chatfield, C., 1989: The Analysis of Time Series: An Introduction. Chapman and Hall, 241 pp.

  • Dole, R. M., 1986: Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation: Structure. Mon. Wea. Rev.,114, 178–207.

  • Feldstein, S. B., 2000: Is interannual zonal mean flow variability simply climate noise? J. Climate,13, 2356–2362.

  • ——, and W. A. Robinson, 1994: Comments on ‘Spatial structure of ultra-low frequency variability of the flow in a simple atmospheric circulation model.’ Quart. J. Roy. Meteor. Soc.,120, 739–745.

  • ——, and S. Lee, 1998: Is the atmospheric zonal index driven by an eddy feedback? J. Atmos. Sci.,55, 3077–3086.

  • Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev.,109, 813–829.

  • Hurrell, J. W., and H. van Loon, 1997: Decadal variations in climate associated with the North Atlantic oscillation. Climate Change,36, 301–326.

  • Kestin, T. S., D. J. Karoly, J.-I. Yano, and N. A. Rayner, 1998: Time-frequency variability of ENSO and stochastic simulations. J. Climate,11, 2258–2272.

  • Leith, C. E., 1973: The standard error of time-averaged estimates of climatic means. J. Appl. Meteor.,12, 1066–1069.

  • Madden, R. A., 1976: Estimates of the natural variability of time-averaged sea-level pressure. Mon. Wea. Rev.,104, 942–952.

  • ——, and D. J. Shea, 1978: Estimates of the natural variability of time-averaged temperatures over the United States. Mon. Wea. Rev.,106, 1695–1703.

  • Mo, K. C., and R. E. Livezey, 1986: Tropical–extratropical geopotential height teleconnections during the Northern Hemisphere winter. Mon. Wea. Rev.,114, 2488–2515.

  • Nitsche, G., 1996: Some aspects of planetary-scale atmospheric variability in a low-resolution general circulation model. Ph.D. thesis, University of Washington, 208 pp. [Available from Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195-7940.].

  • Panofsky, H. A., and G. W. Brier, 1968: Some Applications of Statistics to Meteorology. Pennsylvania State University Press, 224 pp.

  • Rosen, R. D., D. A. Salstein, T. M. Eubanks, J. O. Dickey, and J. A. Steppe, 1984: An El Niño signal in atmospheric angular momentum and Earth rotation. Science,225, 411–414.

  • Thompson, D. W. J., and J. M. Wallace., 1998: The Arctic Oscillation signature in the wintertime geopotential height temperature fields. Geophys. Res. Lett.,25, 1297–1300.

  • ——, and ——, 2000a: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate,13, 1000–1016.

  • ——, and ——, 2000b: Annular modes in the extratropical circulation. Part II: Trends. J. Climate,13, 1018–1036.

  • Trenberth, K. E., 1984: Some effects of finite sample size and persistence on meteorological statistics. Part II: Potential predictability. Mon. Wea. Rev.,112, 2369–2379.

  • ——, 1985: Potential predictability of geopotential heights over the Southern Hemisphere. Mon. Wea. Rev.,113, 54–64.

  • von Storch, J.-S., 1999: The reddest atmospheric modes and the forcings of the spectra of these modes. J. Atmos. Sci.,56, 1614–1626.

  • Wallace, J. M., 2000: North Atlantic oscillation/annular mode: Two paradigms–one phenomenon. Quart. J. Roy. Meteor. Soc.,126, 791–804.

  • ——, and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev.,109, 784–812.

  • Wunsch, C., 1999: The interpretation of short climate records, with comments on the North Atlantic and Southern Oscillations. Bull. Amer. Meteor. Soc.,80, 245–255.

  • Zhang, X., J. Shiang, and A. Shabbar, 1998: Modes of interannual and interdecadal variability of Pacific SST. J. Climate,11, 2556–2569.

  • Zhang, Y., J. M. Wallace, and N. Iwasaka, 1996: Is climate variability over the North Pacific a linear response to ENSO? J. Climate,9, 1468–1478.

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The Timescale, Power Spectra, and Climate Noise Properties of Teleconnection Patterns

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  • 1 Earth System Science Center, The Pennsylvania State University, University Park, Pennsylavania
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Abstract

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.

Corresponding author address: Dr. Steven B. Feldstein, Earth System Science Center, The Pennsylvania State University, 0221 Deike Bldg., University Park, PA 16802.

Email: sbf@essc.psu.edu

Abstract

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.

Corresponding author address: Dr. Steven B. Feldstein, Earth System Science Center, The Pennsylvania State University, 0221 Deike Bldg., University Park, PA 16802.

Email: sbf@essc.psu.edu

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