Abstract
The aim of this work is to study the effects induced by climate changes in the framework of the stochastic resonance approach. First, a wavelet cross-correlation analysis on Earth temperature data concerning the last 5,500,000 years is performed; this analysis confirms a correlation between the planet’s temperature and the 100,000, 41,000, and 23,000-year periods of the Milankovitch orbital cycles. Then, the stochastic resonance model is invoked. Specific attention is given to the study of the impact of the registered global temperature increase within the stochastic model. Further, a numerical simulation has been performed, based on: (1) A double-well potential, (2) an external periodic modulation, corresponding to the orbit eccentricity cycle, and (3) an increased value of the global Earth temperature. The effect of temperature increase represents one of the novelties introduced in the present study and is determined by downshifting the interaction potential used within the stochastic resonance model. The numeric simulation results show that, for simulated increasing values of the global temperature, the double-well system triggers changes, while at higher temperatures (as in the case of the absence of a global temperature increase although with a different threshold) the system goes into a chaotic regime. The wavelet analysis allows characterization of the stochastic resonance condition through the evaluation of the signal-to-noise ratio. On the basis of the obtained findings, we hypothesize that the global temperature increase can suppress, on a large time scale corresponding to glacial cycles, the external periodic modulation effects and, hence, the glacial cycles.
Highlights
In the following we will present at first the outputs of a numerical simulation investigation for a system characterized by a double well potential to which is added a small periodic contribution; the Climate study2019, is performed by taking into account different weights for the noise
8 of 16 the effects of the temperature rise in the framework of the stochastic resonance approach, we will show the results of a simulation for a system characterized by a down-shifted double well potential
Climate change phenomena connected to the registered global increase of temperature within the framework of the stochastic resonance” (SR) approach are analysed by means of XWT and numeric simulations and discussed
Summary
Climate occurs as a result of processes operating on multiple scales, some of which are slow, as in the Earth’s glacial cycles, and others of which are fast, such as daily weather fluctuations [1].Glacial cycles, on the order of tens of thousands of years, start with a gradual temperature decrease, which gives rise to an increase of sea ice, polar cap volume, and total global area occupied by ice, increasing Earth’s overall albedo and the amount of the sun’s energy reflected away from the Earth.With less energy entering the Earth’s system, temperatures decrease further, creating positive feedback.as more ocean water is converted to ice, the overall water volume of the oceans decreases, allowing continental submerged portions to emerge [2,3,4,5]. Climate occurs as a result of processes operating on multiple scales, some of which are slow, as in the Earth’s glacial cycles, and others of which are fast, such as daily weather fluctuations [1]. On the order of tens of thousands of years, start with a gradual temperature decrease, which gives rise to an increase of sea ice, polar cap volume, and total global area occupied by ice, increasing Earth’s overall albedo and the amount of the sun’s energy reflected away from the Earth. With less energy entering the Earth’s system, temperatures decrease further, creating positive feedback. The equilibrium global average temperature reached by this process represents a lower stationary regime of the overall climate during that period. Glacial ages end with a temperature increase reversing the water transfer from the cryosphere back to Climate 2019, 7, 21; doi:10.3390/cli7020021 www.mdpi.com/journal/climate
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