气候变化领域集成服务门户(CCPortal): Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

CCPortal

DOI	10.5194/tc-14-1083-2020
	Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
	Jun Kim Y.; Kim H.-C.; Han D.; Lee S.; Im J.
发表日期	2020
ISSN	19940416
起始页码	1083
结束页码	1104
卷号	14 期号:3
英文摘要	Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics. © 2020 Copernicus GmbH. All rights reserved.
英文关键词	artificial neural network; correlation; error analysis; fishing industry; marginal ice zone; persistence; prediction; satellite altimetry; sea ice; snow cover; temperature anomaly; Arctic
语种	英语
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202122
作者单位	School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea; Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute, Incheon, South Korea; Centre for Polar Observation and Modelling, University College London, London, United Kingdom
推荐引用方式 GB/T 7714	Jun Kim Y.,Kim H.-C.,Han D.,et al. Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks[J],2020,14(3).
APA	Jun Kim Y.,Kim H.-C.,Han D.,Lee S.,&Im J..(2020).Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks.Cryosphere,14(3).
MLA	Jun Kim Y.,et al."Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks".Cryosphere 14.3(2020).