=Paper=
{{Paper
|id=Vol-2534/19_short_paper
|storemode=property
|title=The Forecast of Climate Changes in Altai-Sayan Mountain Country till 2030
|pdfUrl=https://ceur-ws.org/Vol-2534/19_short_paper.pdf
|volume=Vol-2534
|authors=Yury B. Kirsta,Olga V. Lovtskaya,Alexander V. Puzanov
}}
==The Forecast of Climate Changes in Altai-Sayan Mountain Country till 2030==
https://ceur-ws.org/Vol-2534/19_short_paper.pdf
The Forecast of Climate Changes
in Altai-Sayan Mountain Country till 2030
Yury B. Kirsta, Olga V. Lovtskaya, Alexander V. Puzanov
Institute for Water and Environmental Problems of Siberian Branch of the Russian Academy of
Sciences, Russia 656038 Barnaul
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 ex-
pressed 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.
Keywords: air temperature, precipitation, spatial generalization, mountain area, Altai, Sayan
Introduction. Mountains are characterized by complex vertical and horizontal differentiation
of climate. In this connection, the analysis of long-term changes in meteorological factors is labori-
ous and often impossible because of the lack of necessary observations. The method developed for
spatial generalization of average monthly temperature of the surface air layer and monthly precipi-
tation provides an adequate assessment of their monthly and long-term dynamics with due regard
for spatial differentiation of climate [1]. Such an assessment can be made for arbitrary parts of the
mountain territory, even at the absence of meteorological observations.
The Altai-Sayan mountain country selected as a test region consists of a fan-shaped system of
ridges stretching in the north-west direction. It is the highest mountain region of Siberia (300-4500
m a.s.l.). An important feature of the country is its landscape diversity (glacial-nival, tundra, alpine
and subalpine meadows, forest, steppe and semi-desert). It serves as a mountain catchment area for
such large rivers of Siberia as the Ob and the Irtysh.
The main objective of the research was a long-term forecasting the changes in air temperature
and precipitation throughout the study area. It was expected to perform the reanalysis having scarce
initial data on monthly and interannual dynamics of temperature and precipitation.
Source data and methodology. To assess the climatic situation in the Altai-Sayan mountain
country, 11 still operating weather stations were used (Tab. 1). The country’s climate, including av-
erage monthly air temperature and monthly precipitation, are largely formed by general atmospheric
circulation processes [2]. Average long-term values of the selected factors (1951-2017) for the
study territory are given in Tab. 2.
Table 1. The geographical location of reference weather stations in the Altai-Sayan mountain country.
N Weather station WMO index Latitude Longitude Altitude a.s.l, m
1 Biysk-Zonal 1
29939 52° 41' 84° 56' 222
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 Interna-
tional (CC BY 4.0).
2 Zmeinogorsk2 36038 51° 09' 82° 10' 354
3 Kamen’-on-Ob1 29822 53° 49' 81° 16' 127
4 Kara-Tyurek2 36442 50° 02' 86° 27' 2601
5 Kuzedeevo3 29849 53° 20' 87° 11' 293
6 Kyzyl-Ozek2 36055 51° 54' 86° 00' 324
7 Rebrikha1 29923 53° 05' 82° 20' 218
8 Slavgorod1 29915 52° 58' 78° 39' 125
9 Soloneshnoye2 36045 51° 38' 84° 20' 409
10 Ust-Koksa2 36229 50° 16' 85° 37' 977
11 Yaylu2 36064 51° 46' 87° 36' 482
Note: 1 – the plains adjacent to the Altai Mountains; 2 – The Altai mountains; 3 – the Kuznetsk
intermountain basin.
Table 2. Mean long-term values of air temperature and precipitation for the study area.
Climatic charac- Months
teristics 1 2 3 4 5 6 7 8 9 10 11 12
Temperature, °C –16,0 –14,6 –8,0 2,2 10,5 15,7 17,8 15,3 9,6 2,1 –7,3 –13,3
Precipitation, mm 21,9 19,5 22,3 40,7 58,3 71,1 81,9 73,0 50,9 49,4 39,5 30,6
Temperature and precipitation for each month of each year were expressed in percent of their
average long-term value for a certain month [1]; its choice was made using the least standard devia-
tion characterizing the scatter of the obtained values for all data. Then, temporal dynamics of fac-
tors (expressed in percent) was calculated for individual weather stations with further averaging the
data from all 11 stations for each month of each year. Thus, the long-term dynamics of air tempera-
ture and precipitation, which reflected the meteorological situation throughout the Altai-Sayan
mountain country during the observation period, was established. Microsoft Office Excel 2003 was
used to calculate long-term trends of both factors for each month of the year in order to perform a
multi-year forecast up to 2030.
Results and discussion. Cold and warm periods of the year can be specified by average long-
term 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 [1]. The
identification of such periods provides the best calculation accuracy and the possibility of forecast-
ing the monthly and interannual dynamics of the selected factors (expressed in percent). It also best
reflects the spatial uniformity of them throughout the mountain country due to atmospheric circula-
tion processes. January and July are taken as reference months distinguished by the best “meteoro-
logical” correlation with all months for cold and warm periods, correspondingly. Just these months
demonstrate the lowest dispersion in relative air temperature for all weather stations.
On the territory of Russia, the secular climate cycle having three phases (1918-1950, 1951-
1983, 1984-2016) with certain statistical regularities of interannual changes in temperature and pre-
cipitation [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 secu-
lar climate cycle.
Fig. Trends in relative air temperature (T) and precipitation (P) for 12 months of 1984-2016.
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 chang-
es 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 in-
situ 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 mean-
long-term values [1].
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 sur-
face 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 ref-
erence 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).
REFERENCES
[1] Kirsta Yu.B. Spatial generalization of climatic characteristics in mountain areas // World of Science,
Culture and Education (Mir Nauki, Kul’tury, Obrazovaniya). 2011. No.3 (28). P. 330-337.
[2] Modina T.D., Sukhova M.G. Klimat i Agroklimaticheskie Resursy Altaya (Climate and Agroclimatic
Resources of Altai). Novosibirsk: Universal book publishing house, 2007. 178 p.
[3] Kirsta Yu.B., Kirsta B.Yu. The Information-Physical Principle of Evolutionary Systems Formation.
System-Analytical Modeling of Ecosystems. Barnaul: Publishing house of Altai State University,
2014. 283 p.
[4] Kirsta Yu.B., Zinchenko G.S., Lovtskaya O.V., Belotoronina T.A. Estimate and prognosis of an agro-
climatic potential of Altai Territory in conditions of modern changes of a climate // Izvestiya of Altai
State University. 2002. No.3. P. 61-68.
[5] Kirsta Yu.B. System-analytical modelling – Part II: Wheat biotime run and yield formation. Agrocli-
matic potential, Le Chatelier principle, changes in agroclimatic potential and climate in Russia and the
U.S. // Ecol. Modelling. 2006. Vol. 191. P. 331-345.
[6] Kirsta Yu.B., Lovtskaya O.V. Forecast of climatic changes in grain-producing areas of Siberia and
Russia // World of Science, Culture and Education (Mir Nauki, Kul’tury, Obrazovaniya). 2009. No.7
(19). P. 9-13.
[7] Kirsta Yu.B., Kurepina N.Yu., Lovtskaya O.V. The forecast of climate and agroclimatic potential in
Altai Krai up to 2020 // Bulletin of Altai State Agricultural University. 2013. No. 1 (99). P. 27-32.