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Abstract

Recurrence analysis was introduced to infer the degree separation between a
"control" and an "anomaly" ensemble of, say, seasonal means simulated in
General Circulation Model (GCM) experiments. The concept of recurrence
analysis is described as a particular application of a statistical technique
called multiple discriminant analysis (MDA). Using MDA, univariate recurrence
is easily generalized to multicomponent problems. Algorithms which can be used
to estimate the level of recurrence and tests which can be used to assess the
confidence in apriori specified levels of recurrence are presented.

Several of the techniques are used to reanalyze a series of El Nino
sensitivity experiments conducted with the Canadian Climate Centre GCM. The
simulated El Nifio responses in DJF mean SOO mb height are all estimated to be
more than 94% recurrent in the tropics and are estimated to be between 90 and
95% recurrent in the Northern Hemisphere between 20° and 60° north latitude.

Discrimination rules which can be used to classify individual realizations
of climate as members of the "control" or "experimental" ensemble are obtained
as a byproduct of the multiple recurrence analysis. We show that it is
possible to make reasonable inferences about the state of the eastern Pacific
sea surface temperature by classifying observed DJF 500 mb height fields with
discrimination rules derived from the GCM experiments.