The decline of Svalbard land-fast sea ice extent as a result of climate change

IF 2.6 3区 地球科学 Q2 OCEANOGRAPHY
Jacek A. Urbański , Dagmara Litwicka
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引用次数: 7

Abstract

The Svalbard Archipelago has experienced some of the most severe temperature increases in the Arctic in the last three decades. This temperature rise has accelerated sea-ice melting along the coast of the archipelago, thus bringing changes to the local environment. In view of the importance of the near-future distribution of land-fast sea ice along the Svalbard coast, the available observation data on the ice extent between 1973 and 2018 are used herein to create a random forest (RF) model for predicting the daily ice extent and its spatial distribution according to the cumulative number of freezing and thawing degree days and the duration of the ice season. Two RF models are constructed by using either regression or classification algorithms. The regression model makes it possible to estimate the extent of land-fast ice with a root mean square error (RMSE) of 800 km2, while the classification model creates a cluster of submodels in order to forecast the spatial distribution of land-fast ice with less than 10% error. The models also enable the reconstruction of the past ice extent, and the prediction of the near-future extent, from standard meteorological data, and can even analyze the real-time spatial variability of land-fast ice. On average, the minimum two-monthly extent of land-fast sea ice along the Svalbard coast was about 12,000 km2 between 1973 and 2000. In 2005–2019, however, the ice extent declined to about 6,000 km2. A further increase in mean winter air temperatures by two degrees, which is forecast in 10 to 20 years, will result in a minimum two-monthly land-fast ice extent of about 1,500 km2, thus indicating a trend of declining land-fast ice extent in this area.

由于气候变化,斯瓦尔巴群岛陆上海冰面积下降
在过去的三十年里,斯瓦尔巴群岛经历了北极地区最严重的温度上升。气温上升加速了群岛沿岸的海冰融化,从而给当地环境带来了变化。考虑到斯瓦尔巴群岛海岸陆速海冰近未来分布的重要性,本文利用1973 - 2018年的冰面观测数据,根据冻融日数和冰期持续时间,建立随机森林(RF)模型,预测日冰面及其空间分布。采用回归算法和分类算法分别构建了两个射频模型。回归模型能以800 km2的均方根误差(RMSE)估计陆固冰的范围,而分类模型能以小于10%的误差建立一组子模型来预测陆固冰的空间分布。这些模式还可以根据标准气象数据重建过去的冰范围,预测近未来的冰范围,甚至可以分析陆地冰的实时空间变异性。平均而言,1973年至2000年间,斯瓦尔巴群岛沿岸陆上海冰的最小两个月面积约为12,000平方公里。然而,在2005年至2019年期间,冰面积减少到约6000平方公里。据预测,在未来10至20年内,冬季平均气温若再升高2摄氏度,将导致至少两个月约1500平方公里的陆禁冰面积,从而表明该地区陆禁冰面积呈下降趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Oceanologia
Oceanologia 地学-海洋学
CiteScore
5.30
自引率
6.90%
发文量
63
审稿时长
146 days
期刊介绍: Oceanologia is an international journal that publishes results of original research in the field of marine sciences with emphasis on the European seas.
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