Classification of the Earth's surface points based on the properties of the yearly course of variables characterizing a state of the near-surface layer

Abstract

An approach to assessing seasonality is proposed based on the
properties of the yearly course of variables characterizing a state of the near-surface
layer. The factual basis of this assessment is an array of monthly values of a
variable of the state of the near-surface layer (temperature, total precipitation, etc.)
over a climatically significant time period (for example, 1, 2 or 3 decades). It is
assumed that within this period a linear trend of the variable is possible, but the
nature of non-directional variability does not change. As a result of the analysis of
these data, for any year within the time period under consideration, the yearly
course parameters, i.e. seasonal systematic deviations from the annual mean, as
well as their standard deviations are estimated. Further, for each year, any month is
assigned with a number R equal to 1, (-1) or 0, depending on whether the
corresponding deviation is positive, negative or uncertain with respect to the sign.
This R-sequence is used to characterize seasonality. For an example of classifying
points of geographic space using seasonality, the year corresponding to the center
of the time period under consideration was chosen in this paper. For two points in
geographic space, seasonality in relation to the variable being studied is considered
to be identical, if their R-sequences, consisting of 12 numbers, coincide under
cyclic permutation of months. This approach is applied to the global analysis of
seasonal temperature variability. In this case, data from the NOAA-CIRES-DOE
20th Century Reanalysis V3 reanalysis for 1981-2010 were employed. The
resulting classification of seasonality is presented in the form of schematic maps
and provided with appropriate geographical comments.