1887
PDF

Abstract

Summary

The main principles of the methodology for assessing the influence of height above sea level and geographic coordinates (latitude and longitude) on the values of annual and average monthly temperature in Ukraine for the period 1991–2020 are proposed. Thus, the annual surface air temperature decreases by an average: of 0.69°C per 100 m of height above sea level, of 0.51°C with a shift of one degree of latitude to the north, and of 0.08°C with a shift of one degree of longitude to the East. The altitudinal and latitudinal gradients of temperature have the greatest spatiotemporal variability and the longitudinal gradient has the smallest one. These gradients generate «microclimatic noise» of temperature. On their basis, a regional semi-empirical model of the spatiotemporal distribution of the average monthly temperature for the plain part of Ukraine for the period 1991–2020 was worked out. A comparison of values of model average annual and monthly temperature for 58 meteostations in Ukraine with their actual values showed a statistically significant correlation. Additionally, validation of semi-empirical model average monthly temperature for 10 other stations was carried out.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2022580191
2022-11-15
2026-01-17
Loading full text...

Full text loading...

/deliver/fulltext/2214-4609/2022/monitoring'2022/Mon-22-191.html?itemId=/content/papers/10.3997/2214-4609.2022580191&mimeType=html&fmt=ahah

References

  1. Boychenko, S. (2008). Semi-Empirical Models and Scenarios of Global and Regional Changes of Climate. Voloshchuk, V. (Ed.). Kyiv: Naukova Dumka, 310 p. Available online: https://www.researchgate.net/publication/321301027
    [Google Scholar]
  2. Boychenko, S.; Serdyuchenko, N. (2005). Assessment of the dependence of the parameters of regional climatic fields on the height above sea level. Geofiz. Zhurnal, 5, 858–867. https://www.researchgate.net/publication/342081987
    [Google Scholar]
  3. Boychenko, S.; VoloshchukV.M. (2007). Stochastic semi-empirical model of spatio-temporal transformation of the modern climate of Ukraine. Reports of the National Academy of Sciences of Ukraine, 1, 105–111. https://www.researchgate.net/publication/321269048
    [Google Scholar]
  4. CGO: Central Geophysical Observatory of empirical data. (2021). Retrieved from http://cgo-sreznevskyi.kyiv.ua/index.php?lang=en&fn=u_klimat&f=ukraine&p=l
    [Google Scholar]
  5. Daly, Ch., GibsonW., Taylor, G, Johnson, G, PasterisP. (2002). A knowledge-based approach to the statistical mapping of climate. Climate Research, 22, 99–113. https://doi.org/10.3354/cr022099
    [Google Scholar]
  6. LookingbillT.R., UrbanD.L. (2003). Spatial estimation of air temperature differences for landscape-scale studies in montane environments. Agricult. Forrest. Meteorological rol, 114, 141–151. https://doi.org/10.1016/S0168-1923(02)00196-X
    [Google Scholar]
  7. Voloshchuk, V.; Boychenko, S. (2003). Scenarios of possible changes of climate of Ukraine in 21th century (under influence of global anthropogenic warming). In The Climate of Ukraine; Lipinskyy, V., Dyachuk, V., Babichenko, V. (Eds.). Kiev: Raevsky Publishing, pp. 308–331.
    [Google Scholar]
/content/papers/10.3997/2214-4609.2022580191
Loading
/content/papers/10.3997/2214-4609.2022580191
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error