Spatial-temporal variability of ice cover of the Bering sea

Abstract In this paper, we study the spatial and temporal variability of the Bering sea ice cover. The Bering sea is a major transportation importance as a link in the Northern sea route in turn every activity at sea, meeting the challenges of hydro-meteorological research and predictions in this re...

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Bibliographic Details
Published in:IOP Conference Series: Earth and Environmental Science
Main Authors: Martyn, I, Petrov, Y, Stepanov, S, Sidorenko, A
Format: Article in Journal/Newspaper
Language:unknown
Published: IOP Publishing 2020
Subjects:
Online Access:http://dx.doi.org/10.1088/1755-1315/539/1/012198
https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012198/pdf
https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012198
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Summary:Abstract In this paper, we study the spatial and temporal variability of the Bering sea ice cover. The Bering sea is a major transportation importance as a link in the Northern sea route in turn every activity at sea, meeting the challenges of hydro-meteorological research and predictions in this region largely depend on the knowledge about ice conditions. Observing changes in the ice environment can also serve to assess climate change. For the study, the methods of mathematical statistics applied to data on the amount of ice as a percentage for the Bering sea area in the 1x1 degree grid for the period November 1981goda-April 2014. In the course of the work, the trend component of the time variability of sea ice was identified, spectral and harmonic analysis was performed, and autocorrelation analysis was performed. As a result, for long-term variability, the existence of a trend in the development of ice conditions was revealed, and the presence of a linear trend is unlikely. Analysis of local trends showed a decrease in ice cover in the period 1992-2003, after 2003 there was a large positive trend. Spectral analysis 4 peaks of the spectrum for each were calculated characteristics of harmonics. The sum of harmonics gives a good result when combined with the original data. Long-term variability is low-inertia. when performing an auto forecast for average annual values with a 10-year lead time, it was noted that the prognostic model does not give good results.