위성 및 기후자료를 이용한 북극권 해빙면적비 변화의 통계모델링

Abstract

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. 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 relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. We extended our method by using the climate data provided by general circulation models (GCM) and selected for climate factors such as surface skin temperature/SST, surface specific humidity, total precipitation, surface downwelling shortwave radiation. Finally, future sea ice concentration in the Barents and Kara Seas was forecasted by the OLS regression and ARIMA models. Our study may be improved when using additional explanatory variables related to albedo and surface roughness. In addition, an investigation into the time-lag between explanatory variables and sea ice concentration is necessary for improving the time-series modeling.