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Statistical Variability and Persistence Change in Daily Air Temperature Time Series from High Latitude Arctic Stations

Authors
  • Suteanu, Cristian
Type
Published Article
Journal
Pure and Applied Geophysics
Publisher
Springer Basel
Publication Date
Jul 03, 2014
Volume
172
Issue
7
Pages
2057–2073
Identifiers
DOI: 10.1007/s00024-014-0878-8
Source
Springer Nature
Keywords
License
Yellow

Abstract

In the last decades, Arctic communities have been reporting that weather conditions are becoming less predictable. Most scientific studies have not been able to consistently confirm such a trend. The question regarding the possible increase in weather variability was addressed here based on daily minimum and maximum surface air temperature time series from 15 high latitude Arctic stations from Canada, Norway, and the Russian Federation. A range of analysis methods were applied, distinguished mainly by the way in which they treat time scale. Statistical L-moments were determined for temporal windows of different lengths. While the picture provided by L-scale and L-kurtosis is not consistent with an increasing variability, L-skewness was found to change towards more positive values, reflecting an enhancement of warm spells. Haar wavelet analysis was applied both to the entire time series and to running windows. Persistence diagrams were generated based on running windows advancing through time and on local slopes of Haar analysis graphs; they offer a more nuanced view on variability by reflecting its change over time on a range of temporal scales. Local increases in variability could be identified in some cases, but no consistent change was detected in any of the stations over the studied temporal scales. The possibility for other intervals of temporal scale (e.g., days, hours, minutes) to potentially reveal a different situation cannot be ruled out. However, in the light of the results presented here, explanations for the discrepancy between variability perception and results of pattern analysis might have to be explored using an integrative approach to weather variables such as air temperature, cloud cover, precipitation, wind, etc.

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