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?

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    $\begingroup$ You'd probably have to look at different literature for each type of event. Stationarity is fairly hard to show even with stationary datasets, and any calculation of a return period of an event is going to be highly dependent on the dataset(s) used for the analysis. $\endgroup$
    – naught101
    Commented Nov 3, 2015 at 2:09
  • $\begingroup$ I'm not sure if return period and average recurrence interval are truly equatable. As far as I understand return period relates to the chance an event has of occurring in any given year, which is subtly different. As for whether such concepts are stationary, you'd have to look into the scientific consensus as to how much regional and global climates have changed since such concepts were formalised. Personally at present I think with regards to insurance industry use of the terms, any climate change value change will be massively dwarfed by the increased value of property which is now insured. $\endgroup$
    – Siv
    Commented Nov 4, 2015 at 20:47
  • $\begingroup$ See engg.ronjie.com/2009/04/engineering-misconceptions-part-1.html $\endgroup$
    – Siv
    Commented Nov 4, 2015 at 20:58
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    $\begingroup$ This is a big controversial topic in hurricane season prediction. Sometimes, when environmental conditions are in flux, there is simply no answer. IOW, statistical return period can not be calculated to better than a factor of a few. Almost all climate predictions, based on records of occurance, are up in the air. We no longer have a good statistical handle on El Niño versus La Niña, basic predictions for the coming season, the jet stream, even monsoons in some locations. There is no consensus on how to deal with this. Models now carry more weight than past statistics. $\endgroup$
    – Eubie Drew
    Commented Nov 5, 2015 at 1:42
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    $\begingroup$ @Aabaakawad I suspect that the last sentence of your comment may be the core of a good answer. If you'd care to expand, I'd certainly upvote it :) $\endgroup$ Commented Jan 10, 2016 at 12:39

1 Answer 1


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.

enter image description here

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.

source : World CO2 Averages Touch 402.2 PPM Daily Values

Further read : Links between atmospheric carbon dioxide, the land carbon reservoir and climate over the past millennium

  • $\begingroup$ This info is useful but doesn't clearly tie back in to the original question about return periods. If you'd clarify how the carbon & temperature fluctuations we're observing through ice core sampling relate to return periods and how we're updating them, if at all, that would make this a much better answer to this question. $\endgroup$
    – cr0
    Commented Jan 11, 2016 at 0:55

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