the anomalously cold early 1900s, all of the oscillations were either decreasing or were at a minimum. These relationships are shown in Figure 12, where the four-waveform average is plotted with low-pass filtered actual and reconstructed warm-season temperatures since 1886. The similarity of the curves indicates that both unusually cool and unusually warm decadal-scale temperature anomalies over Tasmania during the twentieth century have been driven in part by long-term climatic oscillations.

This conclusion does not necessarily eliminate greenhouse warming as a possible contributor to the recent tem

Figure 12

Comparison of the four-oscillation waveform average with actual and reconstructed Tasmanian temperatures for the period 1886 to 1989. The waveform average is expressed in standard normal deviates, while the temperatures are expressed as anomalies. The temperatures have been low-pass filtered to a level comparable to the waveform average. Note the coincidence between the waveform minimum around 1905 and cold temperatures, and between the waveform maximum around 1975 and warm temperatures.

perature increase in Tasmania. Indeed, temperatures since 1985 appear to have increased sharply even as the waveform average has decreased. However, it would be premature to claim that this departure is a manifestation of the greenhouse effect. The average waveform shown in Figure 12 can be used only qualitatively at this time; it does not necessarily contain all of the important terms related to multi-year temperature fluctuations. Nonetheless, its agreement with instrumental temperature data indicates that oscillatory modes with time constants of decades to centuries are an important part of the SH climate system. Until this behavior is better understood, the existence of greenhouse warming in the SH will be difficult to prove.


We gratefully acknowledge the assistance of Trevor Bird (Tasmanian Trades and Labor Council, Forestry Unit) and Mike Peterson (Tasmanian Forestry Commission), who truly made this research possible. In addition, Roger Francey (C.S.I.R.O. Division of Atmospheric Research) and Mike Barbetti (University of Sydney) actively assisted us in our field work. We also thank the Tasmanian Forestry Commission and the C.S.I.R.O. Division of Forest Research in Hobart for local logistical support. Peter Kelly dated and measured the Huon pine stump wood, Rob Allan (C.S.I.R.O. Division of Atmospheric Research) provided the Hobart SLP data, and Tom Delworth at GFDL provided the model-generated SH temperature data. This research is supported by the National Science Foundation, Division of Earth Sciences, Geologic Record of Global Change Program, Grant EAR 91-04922. Lamont-Doherty Earth Observatory Contribution No. 5215.

Commentary on the Paper of Cook et al.


I should probably begin with the disclaimer that I am a forester, and by no means a statistician and number-cruncher in the sense that tree-ring people have become. But I strongly believe that one of our best sources of proxy data for the fairly recent past—several thousands of years—is tree-ring chronology. I am delighted to see people working hard and getting good sequences, and I really commend Ed Cook, Brendan Buckley, and Rosanne D'Arrigo for doing just that.

I always get nervous, however, at seeing these power spectra that have peaks with rather little power in them. And I notice too that often when one gets more data many of the relationships either diminish or disappear. I wonder, then, whether this very real problem might be solved with other, quite different data sets. For example, Pieter Grootes told us about the ability of the ice-core analyzers to get very high resolution, on the order of single years, in data from good locations. So to open the discussion, I would like to throw this question out to all of you: Has anyone really attempted to use other data sets to see whether the same peaks will occur in those data?

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