Analysis of Climate Dynamics Across a European Transect Using a Multifractal Method

Krzyszczak, Jaromir; Baranowski, Piotr; Hoffmann, Holger; Zubik, Monika; Sławiński, Cezary

doi:10.1007/978-3-319-55789-2_8

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…As it has already been shown by Kantelhardt et al (2006), MF-DFA can be applied to detect non-stationarities and overcoming trends in the noisy data at all timescales. It was confirmed that numerous processes in the soilatmosphere system expressed through respective long time series exhibit multifractality, including wind speed (Kavasseri and Nagarajan 2005;Feng et al 2009), relative air humidity (Baranowski et al 2015), precipitation (de Lima and de Lima 2009;Gemmer et al 2010;Valencia Delfa et al 2010;Yonghe et al 2013), temperature fluctuations of the oceanic waters (Fraedrich and Blender 2003), mean and extreme values of air temperature (Koscielny-Bunde et al 1998; Bartos and Jánosi 2006;Yuan et al 2012;Krzyszczak et al 2017a), or temperatures of the soil in the soil profile (Jiang et al 2013). Yu et al (2014) applied multifractal formalism to investigate whether any relation between rainfall variability and the lay of land can be found.…”

Section: Introductionmentioning

confidence: 94%

Multifractal characterization and comparison of meteorological time series from two climatic zones

Krzyszczak

Baranowski

Zubik

et al. 2018

Theor Appl Climatol

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The results of the multifractal analysis performed for meteorological time series coming from four stations in Poland and Bulgaria located in varying climatic zones are presented. To assess climatic shift response (in 2001/2002), the analysis was conducted separately for two subsets. To analyze long-distance power-law correlations within the studied time series and evaluate the differences in dynamics of the climate between the analyzed sites and periods of time, the multifractal detrended fluctuation analysis methodology (MF-DFA) was proposed. It was revealed that the multifractal properties of precipitation differ considerably from other analyzed quantities. The singularity spectra were susceptible to climatic shift, what was indicated by the changes of spectra parameters. It was especially apparent for asymmetry, which changed from being right-to left-skewed, implying the occurrence of more extreme events. Similarities in the dynamics of meteorological processes for each of the climatic zones were proven by the close relation of respective multifractal spectra parameters coming from closely spatially related localizations. Highlights • Meteorological time series exhibit multifractality. • Multifractality source for meteorological time series was evaluated. • Spectra of precipitation differ from the other climate variables spectra. • The singularity spectra were sensitive to climatic shift. • Country-level similarity of the multifractal spectra parameters was observed.

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Section: Introductionmentioning

confidence: 94%

Multifractal characterization and comparison of meteorological time series from two climatic zones

Krzyszczak

Baranowski

Zubik

et al. 2018

Theor Appl Climatol

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“…Recent progress in the modeling of paleo ecological processes has been made using advanced mathematical tools and sophisticated models of statistical physics developed based on long-term biological data [5][6][7]. Natural time and multifractal analyses are core concepts for studies aimed at building nowcasting models [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A New Climate Nowcasting Tool Based on Paleoclimatic Data

et al. 2020

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Atmospheric pollutants and environmental indicators are often used to reconstruct historic atmospheric pollution from peat, as it accumulates over time by decomposing plant material, thus recording a history of air pollution. In the present study, three key parameters related to the peat bogs’ surface wetness dynamics in European Russia during the Holocene were investigated using modern statistical analysis. These parameters are: (i) the water table depth (WTD) in relation to the surface, which is reconstructed based on the community structure of the subfossil testate amoeba assemblages; (ii) the peat humification estimated as absorption of alkaline extract that directly reflects moisture at which the peat was formed; (iii) the Climate Moisture Index (CMI) and the Aridity Index derived from pollen-based reconstructions of the mean annual temperature and precipitation and classifying moisture conditions as the ratio between available annual precipitation and potential land surface evapotranspiration. All these parameters provide useful information about the paleoclimate (atmospheric moisture component) dynamics. High values of WTD and peat humification appear to comply with Gutenberg–Richter law. It is noteworthy that this law also seems to reproduce the high values of the modeled climate moisture and aridity indices. The validity of this new result is checked by replacing “conventional time” with “natural time”. On this basis, a new nowcasting tool is developed to more accurately estimate the average waiting time for the extreme values of these climate parameters. This will help to understand climate variability better to address emerging development needs and priorities by implementing empirical studies of the interactions between climatic effects, mitigation, adaptation, and sustainable growth.

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“…The dynamics of meteorological elements can be evaluated using multifractal analysis (Kantelhardt et al ., 2002), which enables the detection of long‐range correlations in noisy, nonstationary time series of respective variables (Krzyszczak et al ., 2019). The multifractality of meteorological elements has already been extensively studied, indicating strongly self‐similar properties in most climate variables, including precipitation (de Lima and de Lima, 2009; Gemmer et al ., 2010; Baranowski et al ., 2015), ozone concentration (Jiménez‐Hornero et al ., 2009; Pavon‐Dominguez et al ., 2013), air and surface temperature (Yuan et al ., 2012; Jiang et al ., 2013; Burgueño et al ., 2014), or wind speed (Krzyszczak et al ., 2017a; Laib et al ., 2018). Additionally, the spatio‐temporal variabilities of multifractal parameters of meteorological elements have been recognized at various scales (Hoffmann et al ., 2017; Krzyszczak et al ., 2017b).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland

Gos

Baranowski

Krzyszczak

et al. 2020

Intl Journal of Climatology

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Analysis of Climate Dynamics Across a European Transect Using a Multifractal Method

Cited by 7 publications

References 34 publications

Multifractal characterization and comparison of meteorological time series from two climatic zones

Multifractal characterization and comparison of meteorological time series from two climatic zones

A New Climate Nowcasting Tool Based on Paleoclimatic Data

Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland

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