Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979-2012

Abstract: Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS) regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA) models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying

• Publication year

  2014

• Venue

  Remote Sensing

• Publication date

  2014-06-16

• Fields of study

  Geology, Computer Science, Environmental Science

• Identifiers
  DOI 10.3390/RS6065520
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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