The concept of 'return period' (= 'average recurrence interval', or ARI) is widely used in hydrology, and to a much lesser extent in geology, geomorphology, geophysics, etc. Whatever the application, the traditional analysis is predicated upon 'stationarity of the data-set'. However, in this new era of climate change the hydrologic data set is, by definition, no longer 'stationary'. That is, the underlying statistical parameters are no longer consistent. Therefore, a new methodology is required to take account of the drift in climatic means, extremes, revised frequencies, etc. Are there any first attempts to deal with this problem?
One possible example could be the study of ice cores in Antarctica where scientists are trying to determine changes in concentrations of gases like carbon dioxide over a period of time . Such variations serve as indicators for other Climate related factors such as Temperature.
This record was a key contribution to climate science. One, it revealed how past CO2 levels compared to past temperatures. And since the data was directly derived from air bubbles trapped beneath hundreds of feet of ice, it also provided us with an exact measure for past atmospheres.
Secondly, and perhaps much more ominously, it showed us how very far beyond any climate comparable to that great span of time we’d already come.
102 ppm higher than at any time in the last 800,000 years
Humans have now pushed the CO2 boundary 102 parts per million higher than the context provided by the last 800,000 years. It’s kind of a big deal when you consider that a mere fluctuation of about 100 parts per million CO2 was enough, when combined with changes in orbital forcing, to set off feedbacks resulting in a 4 C temperature change globally (8 C change for the Antarctic environment) as ice age proceeded to interglacial and back.