Observations-Based Computational Analytics on Local Climate Dynamics. Part 2: Seasonality

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.

Observations-Based Computational Analytics on Local Climate Dynamics. Part 3: Forecasting

“…The results presented here and in [23,24] represent a preview on the practice-oriented methods of the computer-based analytics of the temperature observations based on the contemporary scientific hypothesis, in accordance to which evolution of local climate dynamics is formed under the specific kind of feedbacks (hysteresis regulation with double synchronization). In this connection these methods are united by an attempt to find variants on how to apply the reconstructions of local climate behaviors from the corresponding conceptual model (HDSmodel) for real practical tasks.…”

“…As a result, the methods differ from the existing analogues in the combination of three important details: processing real observations over century and more; attracting the results of the bifurcation analysis; heightened time and space resolution of results (local daily means). This combination gives the following novel abilities: to reveal seasonal regularities of local climate dynamics with rather "weather resolution" in rather "climate scale" (here and in [24]); to derive constituents of regional (i.e. external) impacts on a local climate system by processing local temperature time series [23,24].…”

“…This combination gives the following novel abilities: to reveal seasonal regularities of local climate dynamics with rather "weather resolution" in rather "climate scale" (here and in [24]); to derive constituents of regional (i.e. external) impacts on a local climate system by processing local temperature time series [23,24].…”

“…2, c) determined by unique local H-limit, T ref -horizontal centerline and two synchronization surfaces (F 1 and F 2 ) with a half year shift. The last was discussed in detail in [23,24]. So, in the case of the ATV-model, a built-in regulator is absent and both time and temperature coordinates are fixed and adjusted (Fig.…”

“…Also, the HDS-model allows to derive some constituents of regional (i.e. external) impacts on a local climate system by analysis of its temperature observations, that could be promising to reconstruct missing data [23,24]. Since the HDSmodel presents local behaviors in terms of nonlinear dynamics, then we include brief comments to specify the differences of this viewpoint relatively the discussed subject (section 3).…”