RCM 자료와 기계학습을 이용한 북극권 카라-바렌츠 해역의 해빙면적비 예측
Prediction of Arctic Sea Ice Concentration of Kara-Barents Seas Using RCM Data with Machine Learning
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Arctic sea ice as an indicator of climate change plays an important role in controlling global climate system. Thus, accurate observation and prediction of Sea Ice Concentration (SIC) is essential for understanding global climate change. In this study, we aim to improve the prediction accuracy of SIC by using machine learning and Regional Climate Model (RCM) data for a more robust method and a higher spatial resolution. Using the CORDEX RCM and NASA SIC data between January 1981 and December 2015, we developed three statistical models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Deep Neural Network (DNN) which can deal with the non-linearity problem, respectively. The DNN model showed the best performance among the three models with the significant correlation between the predictive and observed SIC (r=0.811, p-value < 0.01)and the Root Mean Square Error (RMSE) of 0.258. With deeper considerations of the polar fronts and the characteristics of ocean current and tide, the DNN model can be applied for near future prediction of Arctic sea ice changes.
Abstract
Arctic sea ice as an indicator of climate change plays an important role in controlling global climate system. Thus, accurate observation and prediction of Sea Ice Concentration (SIC) is essential for understanding global climate change. In this study, we aim to improve the prediction accuracy of SIC by using machine learning and Regional Climate Model (RCM) data for a more robust method and a higher spatial resolution. Using the CORDEX RCM and NASA SIC data between January 1981 and December 2015, we developed three statistical models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Deep Neural Network (DNN) which can deal with the non-linearity problem, respectively. The DNN model showed the best performance among the three models with the significant correlation between the predictive and observed SIC (r=0.811, p-value < 0.01)and the Root Mean Square Error (RMSE) of 0.258. With deeper considerations of the polar fronts and the characteristics of ocean current and tide, the DNN model can be applied for near future prediction of Arctic sea ice changes.
참고문헌 (15)
- Ahn, J., "Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979-2012", Remote Sensing, 6(6), pp.5520~5540 (2014)
- Blanchard-Wrigglesworth, E., "Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations", Journal of Climate, 24(1), pp.231~250 (2011)
- Deser, C., "Arctic sea ice variability in the context of recent atmospheric circulation trends", Journal of Climate, 13(3), pp.617~633 (2000)
- Sheldon D. Drobot, "A long-range forecast of Arctic summer sea-ice minimum extent", Geophysical Research Letters, 33(10) (2006), Wiley-Blackwell[DOI]
- Kim, J. H., "Mid-long term prediction of Arctic Sea Ice Concentration using statistical method", Proceedings of the Spring Meeting of Korean Meteorological Society, pp.142~143 (2017)
- Lubinski, D. J., "Freshwater Atlantic water inflows to the deep northern Barents and Kara seas since ca 13 14C ka: foraminifera and stable isotopes", Quaternary Science Reviews, 20(18), pp.1851~1879 (2001)
- "Arctic Sea Ice News & Analysis" (2017)[URL]
- Park, D. S., "Analysis of factors of Arctic sea ice decline in Winter", Proceedings of the Autumn Meeting of Korean Meteorological Society, pp.543~544 (2015)
- Pfirman, S. L., "The northern Barents Sea: water mass distribution and modification", The polar oceans and their role in shaping the global environment, pp.77~94 (1994)
- Schauer, U., "Atlantic water flow through the Barents and Kara Seas", Deep Sea Research Part I: Oceanographic Research Papers, 49(12), pp.2281~2298 (2002)
- Smedsrud, L. H., "The role of the Barents Sea in the Arctic climate system", Reviews of Geophysics, 51(3), pp.415~449 (2013)
- Sorteberg, A., "Atmospheric forcing on the Barents Sea winter ice extent", Journal of Climate, 19(19), pp.4772~4784 (2006)
- Stroeve, J., "Arctic sea-ice variability revisited", Annals of Glaciology, 48(1), pp.71~81 (2008)
- Tivy, A., "Long-range prediction of the shipping season in Hudson Bay: A statistical approach", Weather and Forecasting, 22(5), pp.1063~1075 (2007)