Statistical analysis of air and sea temperature anomalies

PDF Version Also Available for Download.

Description

Paper discussing the statistical analysis of air and sea temperature anomalies.

Physical Description

19 p.: ill.

Creation Information

Scafetta, Nicola; Imholt, Timothy; Grigolini, Paolo & Roberts, James A. March 11, 2013.

Context

This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Arts and Sciences to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 94 times . More information about this paper can be viewed below.

Who

People and organizations associated with either the creation of this paper or its content.

Authors

Rights Holders

For guidance see Citations, Rights, Re-Use.

  • Unknown

Provided By

UNT College of Arts and Sciences

The UNT College of Arts and Sciences educates students in traditional liberal arts, performing arts, sciences, professional, and technical academic programs. In addition to its departments, the college includes academic centers, institutes, programs, and offices providing diverse courses of study.

Contact Us

What

Descriptive information to help identify this paper. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

Paper discussing the statistical analysis of air and sea temperature anomalies.

Physical Description

19 p.: ill.

Notes

This is the pre-print version of the paper.

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.

Source

  • arXiv: cond-mat/0208117

Language

Item Type

Collections

This paper is part of the following collection of related materials.

UNT Scholarly Works

The Scholarly Works Collection is home to materials from the University of North Texas community's research, creative, and scholarly activities and serves as UNT's Open Access Repository. It brings together articles, papers, artwork, music, research data, reports, presentations, and other scholarly and creative products representing the expertise in our university community.** Access to some items in this collection may be restricted.**

What responsibilities do I have when using this paper?

When

Dates and time periods associated with this paper.

Creation Date

  • March 11, 2013

Added to The UNT Digital Library

  • July 24, 2013, 1:20 p.m.

Description Last Updated

  • July 15, 2014, 4:20 p.m.

Usage Statistics

When was this paper last used?

Yesterday: 0
Past 30 days: 1
Total Uses: 94

Interact With This Paper

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Scafetta, Nicola; Imholt, Timothy; Grigolini, Paolo & Roberts, James A. Statistical analysis of air and sea temperature anomalies, paper, March 11, 2013; (digital.library.unt.edu/ark:/67531/metadc174686/: accessed March 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.