Abstract
The paper continues the discussion concerning the computational decision making on evolution of local climate dynamics taking into account inevitable nonlinear nature of such systems and deficiency of reliable data on its dynamics. Here we focus on seasonality in the context of bifurcation phenomena described by the model of the hysteresis regulator with double synchronization (so-called HDS-model). From this conception, the method of structuring and analysis of meteorological data (method of relative scales) is proposed, where new useful information on local seasonal evolution becomes available. First of all, it concerns increase in analytical resolution (daily description in a climate scale). The key procedures of this method provide building the specialized seasonal structures in relative time scales. Advantages are illustrated in comparison with the traditional processing the time series of temperature observations on daily mean surface air temperature over last century. We believe that the results could be interesting in order to increase the confidence of estimations on coming climate changes.
Highlights
Per se, it seems to be impossible to imagine the contemporary climatology without IT-support in relation to collecting, digitizing, processing, transferring, storing, transmitting, converting and estimating climate data series [1]
The instrumental measurements, temperature observations remain the main variable for analytics [5, 29, 30], and we restrict our discussion by the land surface air temperature
That is why we develop the tools for climate dynamics analytics in the context of bifurcation phenomena described by the model of the hysteresis regulator with double synchronization (HDS-model)
Summary
It seems to be impossible to imagine the contemporary climatology without IT-support in relation to collecting, digitizing, processing, transferring, storing, transmitting, converting and estimating climate data series [1]. Any new model and method of meteorological data analytics will need in IT-support in order to realize its verification at least; and such support supposes the specialized software, the main purpose of which is aimed at formalization of expert solutions. From this viewpoint we focus hereafter on how to estimate local seasonal evolution. This model provides unique chance to make daily descriptions of peculiarities of local annual temperature variation which are appropriated for heterogeneous local climate processes [23, 24, 25] It supposes reconsidering the traditional viewpoint [15]. The instrumental measurements, temperature observations remain the main variable for analytics [5, 29, 30], and we restrict our discussion by the land surface air temperature
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