I would like to show the change in temperatures since 1960 until today. I have thought about creating two time blocks, e.g. from 1960-1970 and from 2010-2020! Then I want to average the data of these two time blocks (i.e. the data from 1960-1970 and from 2010-2020) and then calculate the difference between the two time blocks. Now I wonder if there are rules in the geosciences about how large the time periods should be, because I simply assumed the 10 years. The question for me is what period can be assumed as the minimum to prevent extreme years (either extremely hot or extremely cold) from having a major influence?
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1$\begingroup$ Generally climate sets use 30 year averages in atmospheric sciences to reduce the impact of extreme years/shortterm fluctuations. That's the standard the NWS uses to calculate longterm means, updating each 10 years. $\endgroup$– JeopardyTempestJun 27, 2022 at 20:52
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$\begingroup$ @JeopardyTempest thank you very much! $\endgroup$– WeissJun 28, 2022 at 8:18
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1$\begingroup$ Does your title refer to Milankovich cycles? If that is the case I'd like to give a warning. Whatever you will find it won't show any trend induced by the periodic changes of earth relative to the sun. $\endgroup$– Joscha FreginJun 28, 2022 at 9:37
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$\begingroup$ Oh I think it is called this way! Is there a nouther way to call this type of time seperated analysis! $\endgroup$– WeissJun 28, 2022 at 9:39
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The most common period to use is 30-years, which comes from the idea of climate normals used in NWP. Using only 10 years even global mean temperature will see the influence of a single large El Nino or prolonged La Nina events, which probably isn't what you're after. If you were writing a paper that used 30-years means, the reviewer probably wouldn't question it; if you used 20-year means they might ask you to justify it; if you used 10-year means you'd probably be criticized for it.
Regardless of what period you pick, it's always a good idea to also calculate confidence intervals for your means, which can give you a sanity check on whether differences between periods are really notable. That's sort of one step short of doing formal statistical hypothesis testing.
From the IPCC AR6 glossary, for reference:
Climate: Climate in a narrow sense is usually defined as the average weather, or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization (WMO). The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system.
and,
Global warming: Global warming refers to the increase in global surface temperature relative to a baseline reference period, averaging over a period sufficient to remove interannual variations (e.g., 20 or 30 years). A common choice for the baseline is 1850-1900 (the earliest period of reliable observations with sufficient geographic coverage), with more modern baselines used depending upon the application.
