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16 October 2013 Recent 10-year changes and the prediction of Arctic sea ice: a multivariate SARIMA approach
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The environment of Arctic is very important for the global environment and human society because it is sensitive as sea ice changes and keeps the Earth’s cool or warm climate. So we need continuous monitoring of Arctic sea ice to understand and predict the process of climate changes. Satellite remote sensing is a useful tool for monitoring sea ice. Thus, this study analyzed the time-series of Arctic sea ice changes using satellite remote sensing data with a time-series statistical method for last ten years from 2003 and predicted the sea ice extent in the near future. Especially, we used the Multivariate SARIMA(Seasonal Autoregressive Integrated Moving Average) model that reflects multiple meteorological variables and seasonality. It was carried out to convert daily to monthly data of sea ice products because optical sensors have high spatial and temporal resolution than passive microwave sensors, but have difficulty observing the sea ice because of clouds. The result showed that minimum area of sea ice was a decrease trend during the study period and the explanatory power of the constructed Multivariate SARIMA model was about 0.71. It is thought of as a remarkable outcome because there are no studies for the Multivariate SARIMA analysis showing high explanatory power for the changes of sea ice extent. To improve the explanatory power of our model, it will be necessary as a future work to set the optimal thresholds of algorithm for estimating monthly sea ice extent and to increase the accuracy of climate factors data.
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Jihye Ahn and Yang Won Lee "Recent 10-year changes and the prediction of Arctic sea ice: a multivariate SARIMA approach", Proc. SPIE 8888, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2013, 888803 (16 October 2013);


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