Abstract
Studies of climate change by means of optimal detection of faint signals embedded in natural variability have been underway for several decades now (1⇓–3), and some more recent ones are referred to in refs. 4⇓⇓–7. These studies are also sometimes referred to as “fingerprint” studies. The idea is that if the space–time pattern of response to one or more external stimuli, such as greenhouse gas increases, is known from, for example, a model simulation or analytical model, then there is an optimal way of weighting the observed data stream over the same space–time domain in such a way as to determine whether the response is really in the data stream. It boils down to construction of a statistical model in the framework of which some kind of statistical significance test can be performed.
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