DETERMINISM AND NOISE IN SURFACE-TEMPERATURE TIME-SERIES

The extent to which surface temperature data can be deterministic is i nvestigated on a time series of hourly temperatures recorded at the Na tional Observatory of Athens, Greece, over a period of 7 years (1984-1 990). Using the Grassberger-Procaccia method, we measure, for the full time series, a correlation dimension v almost-equal-to 3.8, and a pos itive largest Liapounov exponent. Our series passes also a number of o ther tests, proposed in the recent literature, for low-dimensional cha os, yet shows evidence of fractral noise in the high-frequency part of its power spectrum. This is explained by showing that our data fails to pass the test of time-differencing, in which the series {DELTAT(t(i )) = T(t(i)) - T(t(i-1))} is clearly seen to behave like random noise. Theiler's test of time separation also shows that the low-dimensional , deterministic features of our series {T(t(i))} may be due to tempora l (rather than spatial) correlations. Still, the presence and signific ance of these deterministic features is supported by singular value de composition (SVD) analysis and successful prediction results.