Recent climate trends over Greece

Tzanis, Chris G.; Koutsogiannis, Ioannis; Philippopoulos, Kostas; Deligiorgi, Despina

doi:10.1016/j.atmosres.2019.104623

Cited by 26 publications

(16 citation statements)

References 45 publications

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“…From Figure 2b, it can be observed that h(q) depends on q. This also reveals the multifractal character of CH 4 . In addition, h(q) > 0.5 for all moments q, and, therefore, long-range positive correlations are identified in the CH 4 time series.…”

Section: Lower Tropospherementioning

confidence: 60%

“…In this section, the results of the application of the MF-DCCA procedure are presented. MF-DCCA was initially applied to reveal the multifractal structure of the cross-correlations between global CH 4 and temperature anomalies of the global LT. Figure 3a portrays the way that log(F q (s)) changes with log(s) for a multitude of moments q. It can be observed from the plot that log(F q (s)) increases linearly with log(s).…”

Section: Lower Tropospherementioning

confidence: 99%

“…Atmospheric temperature and methane (CH 4 ) are two of the most important parameters examined in climate research. Air temperature variability and trends at various atmospheric levels represent a crucial indicator of the warming or cooling of the Earth's atmosphere both at a global and regional scale [1][2][3][4]. In addition, adequate and in-depth knowledge of temperature fluctuations is essential for validating the accuracy of climate models' simulations [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature

et al. 2020

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Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) was applied to time series of global methane concentrations and remotely-sensed temperature anomalies of the global lower and mid-troposphere, with the purpose of investigating the multifractal characteristics of their cross-correlated time series and examining their interaction in terms of nonlinear analysis. The findings revealed the multifractal nature of the cross-correlated time series and the existence of positive persistence. It was also found that the cross-correlation in the lower troposphere displayed more abundant multifractal characteristics when compared to the mid-troposphere. The source of multifractality in both cases was found to be mainly the dependence of long-range correlations on different fluctuation magnitudes. Multifractal Detrended Fluctuation Analysis (MF-DFA) was also applied to the time series of global methane and global lower and mid-tropospheric temperature anomalies to separately study their multifractal properties. From the results, it was found that the cross-correlated time series exhibit similar multifractal characteristics to the component time series. This could be another sign of the dynamic interaction between the two climate variables.

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Section: Lower Tropospherementioning

confidence: 60%

Section: Lower Tropospherementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature

et al. 2020

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“…The air quality monitoring sites, from which the data were derived, are located at the metropolitan city of Athens, in Greece. As part of the southeastern Mediterranean region, Athens climate is defined by dry summers (long periods, during which the temperatures are considerably high) and wet winters (these periods are usually short) [9]. The basin is bounded by mounts Parnitha, Pentelikon, Hymmetus and Aigaleo to the north, northeast, east-central and west respectively.…”

Section: Datamentioning

confidence: 99%

Application of a Machine Learning Methodology for Data Implementation

Tzanis

Alimissis

Koutsogiannis

2020

The 3rd International Electronic Conference on Atmospheric Sciences

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An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air quality monitoring networks that operate in urban areas provide data of the most important pollutants, which via environmental statistics can be used for the development of continuous surfaces of pollutants concentrations. Generating ambient air quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air quality distributions. This study uses point monitoring data from the network of stations that operates in Athens. A machine learning scheme will be applied as a method to spatially estimate pollutants' concentrations and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes.

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“…Tzanis et al [8] have also applied the Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) [9] in order to investigate the multifractal structure of the cross-correlation between global methane and temperature. In this work, our scope is to study the multifractal characteristics and the scaling behavior of daily relative humidity time series [10] from meteorological stations at different locations within the Greek region.…”

Section: Introductionmentioning

confidence: 99%

Multifractal detrended fluctuation analysis of relative humidity over Greece

Tzanis

Koutsogiannis

Alimissis

2020

Proceedings of the 3rd International Electronic Conference on Atmospheric Sciences

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Water, in its various forms, is considered a key parameter in climate change studies. Water vapor is recognized as the most important natural greenhouse gas playing a vital role in the hydrological cycle. Thus, studying air humidity fluctuations may contribute towards a deeper understanding of the radiative and thermodynamic processes that take part in the Earth's atmosphere. Traditional statistical analysis is not always efficient to describe complex physical processes with high temporal variability. In addition, a more thorough study of the variations of climatic parameters requires examination of their time series fluctuations over multiple time scales. Fractal theory offers robust solutions that satisfy the above requirements. In this work, the Multifractal Detrended Fluctuation Analysis (MF-DFA) is used in order to investigate the intrinsic dynamics of daily relative humidity time series over the Greek region from a nonlinear perspective. The scaling properties and the multifractal structure of the time series are studied by examining the fluctuation function, the multifractal spectrum and the Hurst exponent.

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Recent climate trends over Greece

Cited by 26 publications

References 45 publications

Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature

Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature

Application of a Machine Learning Methodology for Data Implementation

Multifractal detrended fluctuation analysis of relative humidity over Greece

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