Spatial organization of regional mesoclimate

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Abstract

In this article the method of derivation of the hierarchical levels of organization of climatic variables or regional scale is considered. Based on the Worldclim database, the main integral factors reflecting the variation of climatic variables are identified, and then decomposed into hierarchical levels with different linear dimensions of oscillations. Hierarchical levels are distinguished through the study of the fractal dimensions of different parts of the spectrum of the obtained factors and the isolation of subharmonics on the basis of an analysis of the residues of the fractal model. The analysis shows the existence of a complex hierarchical organization of the region's mesoclimate. The approach makes it possible to identify the most significant scales and amplitudes of fluctuations in climatic variables, both for natural and for agricultural ecosystems. Differentiation of the variation of climatic variables at different spatial scales and the influence of these elements on a specific ecosystem object creates a basis for constructing statistical models of ecosystem processes or yield patterns of various agricultural crops. The possibilities of visualization of climate variation at different hierarchical levels and reflection of equilibrium (normative) relations between the studied ecosystem processes and the current state of climate in the region are shown.

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About the authors

A. N. Krenke

Institute of Geography, Russian Academy of Sciences

Author for correspondence.
Email: Krenke-igras@yandex.ru
Russian Federation, Moscow

Yu. G. Puzachenko

A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences

Email: Krenke-igras@yandex.ru
Russian Federation, Moscow

M. Yu. Puzachenko

Institute of Geography, Russian Academy of Sciences

Email: Krenke-igras@yandex.ru
Russian Federation, Moscow

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Supplementary files

Supplementary Files
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2. Fig. 1. Assessment of the dimension of climate space by the method of “falling debris”.

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3. Fig. 2. Spatial variation of the six main components (order parameters), describing 86.69% variation of 60 variables.

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4. Fig. 3. The two-dimensional spectrum of the first component.

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5. Fig. 4. The residuals from the linear dependence of the logarithm of the spectrum power on the period and their polynomial approximation.

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6. Fig. 5. Two hierarchical levels of spatial variation of the first component.

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7. Fig. 6. Reproduction of subharmonic oscillations in the residuals from the seven-fractal model by two nonlinear self-oscillatory processes. R2 = 0.26. The root mean square error is 0.08. The points on the top graph are the remains of the spectrum. The lower graph is two subharmonics with periods generating oscillations of 205.4 and 62.2 km.

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8. Fig. 7. The full range of subharmonics generated by six independent self-oscillatory systems (the numbers near the peaks are the initial frequency of the subharmonics).

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9. Fig. 8. Linear relationship between the harmonic number and the maximum (minimum) frequency.

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10. Fig. 9. Hierarchical organization of the first climate component.

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11. Fig. 10. The dependence of the logarithm of the spectrum power on the period with the fractal dimension at the level of black noise (the third component) and the dimension is substantially less than 2 (the fifth component) for a period less than 10 km.

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12. Fig. 11. Spatial variation of temperature in January at the first (1) and second (2) levels and in July (3 - the first level, 4 - the second level).

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13. Fig. 12. Spatial variation of precipitation amount in January at the first (1) and second (2) levels and in July (3 - the first level, 4 - the second level).

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