Statistical analysis of air and sea temperature anomalies Metadata

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Title

  • Main Title Statistical analysis of air and sea temperature anomalies

Creator

  • Author: Scafetta, Nicola
    Creator Type: Personal
    Creator Info: Duke University; University of North Texas
  • Author: Imholt, Timothy
    Creator Type: Personal
    Creator Info: University of North Texas
  • Author: Grigolini, Paolo
    Creator Type: Personal
    Creator Info: University of North Texas; Università di Pisa; Istituto di Biofisica
  • Author: Roberts, James A.
    Creator Type: Personal
    Creator Info: University of North Texas

Date

  • Creation: 2013-03-11

Language

  • English

Description

  • Physical Description: 19 p.: ill.
  • Content Description: Paper discussing the statistical analysis of air and sea temperature anomalies.

Subject

  • Keyword: diffusion entropy analysis
  • Keyword: standard deviation analysis
  • Keyword: oceanic physics
  • Keyword: atmospheric physics

Source

  • Website: arXiv: cond-mat/0208117

Collection

  • Name: UNT Scholarly Works
    Code: UNTSW

Institution

  • Name: UNT College of Arts and Sciences
    Code: UNTCAS

Rights

  • Rights Access: public

Resource Type

  • Paper

Format

  • Text

Identifier

  • Archival Resource Key: ark:/67531/metadc174686

Degree

  • Academic Department: Physics
  • Academic Department: Center for Nonlinear Science

Note

  • Display Note: This is the pre-print version of the paper.
  • Display Note: Abstract: This paper presents a global air and sea temperature anomalies analysis based upon a combination of the wavelet multiresolution analysis and the scaling analysis methods of a time series. The wavelet multiresolution analysis decomposes the two temperature signals on a scale-by-scale basis. The scale-by-scale smooth and detail curves are compared and the correlation coefficients between each couple of correspondent sets of data evaluated. The scaling analysis is based upon the study of the spreading and the entropy of the diffusion generated by the temperature signals. Therefore, we jointly adopt two distinct methods: the Diffusion Entropy Analysis (DEA) and the Standard Deviation Analysis (SDA). The joint use of these two methods allows us to establish with more confidence the nature of the signals, as well as their scaling, and it yields the discovery of a slight Lévy component in the two temperature data sets. Finally, the DEA and SDA are used to study the wavelet residuals of the two temperature anomalies. The temporal regions of persistence and antipersistence of the signals are determined and the non-stationary effect of the 10-11 year solar cycle upon the temperature is studied. The temperature monthly data cover the period from 1860 to 2000 A. D. E.
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