The paper presents the method developed for spatial generalization of surface air temperature and precipitation applicable to GIS and a reanalysis. By the example of the Altai-Sayan mountain country, it was shown that relative variations in surface air temperature and precipitation expressed in percent of average long-term values were uniform throughout. The forecast of climate change was performed for the mountain country up to 2030. Temperature decrease in January (~ 20%) with its increase in March and April (> 20%) are expected. In the rest months, temperature will remain approximately the same. The predicted changes in precipitation will vary depending on months.
N
Weather station
WMO index 52° 41' Longitude 84° 56' Altitude a.s.l, m 222
51° 09' 53° 49' 127 2601 293 324 218 125 409 977 482 Note: 1 – the plains adjacent to the Altai Mountains; 2 – The Altai mountains; 3 – the Kuznetsk intermountain basin.
Temperature and precipitation for each month of each year were expressed in percent of their
average long-term value for a certain month [
Results and discussion. Cold and warm periods of the year can be specified by average
longterm values of mean monthly air temperature (Tab. 2). Taking into account “the effect of altitude”
determined by temperature inversions in winter and atmospheric circulation processes in summer,
we attributed 10-12, 1-4 months of the year to the cold period, while 5-9 – to the warm one [
On the territory of Russia, the secular climate cycle having three phases (1918-1950, 19511983, 1984-2016) with certain statistical regularities of interannual changes in temperature and precipitation [3] was formed. Influenced by anthropogenic factors, meso- and macro-scale processes of moisture and heat transfer in the atmosphere become less stable. Therefore, it is reasonable to use the third 33-year phase of the cycle – the closest to the forecast period of 2019-2030. The figure presents air temperature and precipitation trends obtained for this phase in the just terminated secular climate cycle.
The traditional description of long-term changes in air temperature and precipitation in the mountains is rather imprecise. We give a more adequate description of climatic trends, when changes in average monthly air temperature and monthly precipitation for cold and warm periods of the year are expressed in percent of their in-situ average long-term values [4-7]. These trends shown in the figure can be used in long-term forecasts. The greatest variation in temperature is expected in January (cooling by ~ 20% by 2030) and March-April (warming >20%). Precipitation will be changed markedly and variously in most months of the year.
The method for spatial generalization of meteorological factors allows to solve the second
part of the problem, i.e. their reanalysis for the mountains. One can easily perform the reanalysis for
mean monthly temperature and monthly precipitation through their calculation in percentage of
insitu mean monthly values. For transition to the generally accepted units of measurements (°C, mm),
one needs just January/July mean long-term values of temperature and precipitation for the study
area. This method also makes it possible to reconstruct the missing data in the long-term series of
meteorological observations much better as compared to their substitution by appropriate
meanlong-term values [
Conclusion. The method proposed for spatial generalization of climatic factors is applicable to plains [4-7] and mountain areas as well. The method-based prediction of relative changes in surface air temperature and precipitation for the Altai-Sayan mountain country was made up to 2030. The novelty of this method is in calculations made in percent of mean long-term values for the reference months (January and July). The defined long-term dynamics of climatic factors is uniform throughout the analyzed territory, regardless of its orographic and climatic heterogeneity.
The work was performed at the financial support of RFBR (grant No. 18-45-220019 r_a).