EC-Earth3气候模式对北极雪冰的模拟及预测

杨歆蕊; 赵杰臣; 王世柱; 许明环; 张自轩; 陈禹汗; 王晶净; 姜晨

doi:10.12284/hyxb2025003

EC-Earth3气候模式对北极雪冰的模拟及预测

doi: 10.12284/hyxb2025003

杨歆蕊^{1, 2,},
赵杰臣^{2, 3, 4, ,},
王世柱⁴,
许明环^{1, 2},
张自轩^{1, 2},
陈禹汗^{1, 2},
王晶净^{1, 2},
姜晨^{1, 2}

1.
哈尔滨工程大学青岛创新发展基地，山东青岛 266000
2.
青岛海洋科技中心区域海洋动力学与数值模拟功能实验室，山东青岛，266000
3.
联合国海洋十年海洋与气候协作中心（DCC-OCC），山东青岛 266000
4.
自然资源部第一海洋研究所，山东青岛 266061

基金项目: 国家自然科学基金（42376241）；中央级公益性科研院所基本科研业务费专项资金（2023Q01）；国家自然科学基金（42276251, 42211530033）；山东省泰山学者工程（2023）。

详细信息

作者简介:
杨歆蕊（2002—），女，黑龙江省绥化市人，主要从事极地海洋和气候变化研究。E-mail：yangxinrui@hrbeu.edu.cn

通讯作者:
赵杰臣，副教授，主要从事极地海洋环境研究。E-mail: zhaojiechen@hrbeu.edu.cn

中图分类号: P731.15
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- 文章访问数: 3
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- 被引次数: 0
出版历程
- 收稿日期: 2024-09-10
- 修回日期: 2024-12-02
- 刊出日期: 2025-02-28

Simulation and projection of Arctic snow ice by the EC-Earth3 climate model

Yang Xinrui^{1, 2
,},
Zhao Jiechen^{2, 3, 4
, ,},
Wang Shizhu⁴,
Xu Minghuan^{1, 2},
Zhang Zixuan^{1, 2},
Chen Yuhan^{1, 2},
Wang Jingjing^{1, 2},
Jiang Chen^{1, 2}

1.
Qingdao Innovation and Development Base of Harbin Engineering University, Qingdao 266000, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center, Qingdao 266000, China
3.
UN Decade Collaborative Centre on Ocean-Climate Nexus and Coordination Amongst Decade Implementing Partners in P.R.China, Qingdao 266000, China
4.
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China

摘要

摘要: 雪冰是雪转化为海冰的产物，对北极海冰结构有重要作用。研究雪冰的时空变化能够深度揭示“雪−冰”转化过程的细节，并帮助理解海冰的演变规律及极地气候变化。本文基于EC-Earth3模式，分析了历史情景模拟实验（1990−2014年）和共享社会经济路径SSP245实验（2015−2100年）中的雪冰及其影响因素的变化。通过集合平均、回归分析、Mann-Kendall趋势检验等统计方法，研究了雪冰生长量在历史时期和未来时期的时空演变过程。与美国国家冰雪数据中心的卫星观测海冰密集度数据对比表明，EC-Earth3模式较好地重建了历史时期海冰演变，为预测未来海冰变化提供了信心。分析显示，雪冰主要在冬季和春季生成，分布在戴维斯海峡、北欧海及巴伦支海北部海域。历史时期雪冰生长量全北极平均减少趋势为7.4 × 10⁸ kg/a；弗拉姆海峡等地区雪冰在春季和冬季年变化呈现约1 kg/m²·a的增加趋势；雪冰占冰厚的比例最高在格陵兰岛东南侧海域，平均约为2%，降雪降雨的增加以及温度升高是促进雪冰生成的重要因素。未来预估显示，雪冰生成仍主要集中在春季和冬季，雪冰生长量全北极平均将减少2.6 × 10⁸ kg/a；受降水增加和温度升高影响，研究区域3月份的雪冰年变化增加趋势最大呈0.7 kg/m²·a，雪冰占冰厚的比例逐年有所增加。未来情景实验结果分析对于北极航道开发利用和破冰船能力设计都具有重要的科学参考价值。
- 北极海冰 /
- 雪冰演变 /
- EC-Earth3模式 /
- 历史时期 /
- 未来情景
Abstract: Snow ice is the product of the transformation from snow into sea ice, which plays an important role in the change of sea ice structure. Studying the spatial and temporal variations of snow ice can provide deep insights into the “snow-ice” transformation process and help understand the evolution of sea ice and polar climate changes. This paper utilizes the EC-Earth3 model to analyze snow ice and its influencing factors in both historical simulations (1990−2014) and Shared Socioeconomic Pathways SSP245 projections (2015−2100). The spatiotemporal evolution of snow ice growth in historical and future periods was investigated by statistical methods such as ensemble averaging, regression analysis, and Mann-Kendall trend test. Compared with the satellite observation sea ice density data of the National Ice and Snow Data Center, the results indicate that the EC-Earth3 model performs well in reconstructing the observed sea ice, and hence provides confidence in projecting the future ice variation. Snow ice primarily forms in winter and spring, with distribution in the Davis Strait, the Nordic Seas, and the northern Barents Sea. The average decrease trend of snow ice growth is 7.4 × 10⁸ kg/a; the change of the average sea ice outer edge line is about 1 kg/m² in spring and winter; the highest proportion of snow ice is in the southeast of Greenland with an average of about 2%. Increased snowfall, rainfall and rising temperatures are important factors affecting snow ice formation. Future projections suggest that the generation of snow ice is still mainly concentrated in spring and winter, and the total amount of snow ice growth will decrease by 2.6 × 10⁸ kg/a on average; due to the increase of precipitation and temperature increase, the maximum increase trend of snow ice annual in March in the study area is 0.7 kg/m², and the proportion of snow ice in ice thickness increases year by year. The analysis of future scenario experiment results has important scientific reference value for the development and utilization of Arctic waterway and the design of icebreaker capacity.
- Arctic sea ice /
- snow ice evolution /
- EC-Earth3 model /
- historical period /
- future scenario

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图 1 北极地区海冰范围月平均变化的历史时期EC-Earth3、NSIDC对比（a）和全时期EC-Earth3模拟（b）

图a中蓝线为EC-Earth3，红线为NSIDC，虚线为线性回归；图b中红线为历史时期，蓝线为未来时期，虚线为线性回归

Fig. 1 Monthly mean change of Arctic sea ice extent compared by EC-Earth3 and NSIDC data in historical period (a), and simulated by EC-Earth3 in the entire period (b)

In figure a, blue line is EC-Earth3, red line is NSIDC and dashed line is linear regression. In figure b, red line is historical period, blue line is future period and dashed line is linear regression

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图 2 历史时期模式的月平均海冰密集度模拟情况

EC-Earth3模拟（a），EC-Earth3与NSIDC数据的差异（b）；黑色线和蓝色线分别为 EC-Earth3 与 NSIDC 的海冰外缘线

Fig. 2 Simulations of monthly sea ice concentration for model during the historical period

EC-Earth3 simulation (a), differences between EC-Earth3 and NSIDC data (b). The black line and blue line are the sea ice edges of EC-Earth3 and NSIDC

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图 3 雪冰生长量在历史时期的平均分布（a）和年变化趋势（b）

黑色线为平均海冰外缘线，阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 3 Average distribution (a) and annual variation trends (b) of snow ice growth mass in historical periods

The black lines are the average sea ice edges, and shaded areas represent passing 99% M-K significance test

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图 4 模式历史时期海冰密集度（a）和海冰厚度（b）的年变化趋势

阴影区域代表通过99%的M-K显著性检验

Fig. 4 Annual variation trends of sea ice concentration (a) and sea ice thickness (b) in the historical period of model simulations

Shaded areas represent passing 99% M-K significance test

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图 5 雪冰生长厚度与平均冰厚之比在历史时期的平均分布（a）和年变化趋势（b）

阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 5 Average distribution (a) and annual variation trends (b) of the ratio of snow ice growth thickness to average ice thickness in historical periods

The shaded areas represent passing 99% M-K significance test

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图 6 研究区域雪冰生长厚度（a）、雪冰月生长总质量（b）、平均海冰厚度（c）和雪冰厚度与冰厚之比（d）的时间序列

红线为历史时期，蓝线为未来时期

Fig. 6 Time series of snow ice growth thickness (a), total snow ice growth mass (b), average sea ice thickness (c), and ratio of snow ice thickness to ice thickness (d) in the study area

The red line is the historical period, and the blue line is the future period

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图 7 全时期的平均雪冰生长厚度和平均海冰厚度的时间序列

红线为历史时期，蓝线为未来时期

Fig. 7 Time series of average snow ice growth thickness and average sea ice thickness in full periods

The red line is the historical period, and the blue line is the future period

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图 8 未来时期模式海冰密集度平均分布（a）和海冰厚度年变化趋势（b）

阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 8 Sea ice concentration average distribution (a) andsea ice thickness annual variation trends (b) in the future period

The shaded areas represent passing 99% M-K significance test

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图 9 雪冰生长量在未来时期的平均分布（a）和年变化趋势（b）

阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 9 Average distribution (a) and annual variation trends (b) of snow ice growth mass in future periods

The shaded areas represent passing 99% M-K significance test

下载: 全尺寸图片幻灯片

图 10 雪冰生长厚度与冰厚之比在未来时期的平均分布（a）和年变化趋势（b）

阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 10 Average distribution (a) and annual variation trends (b) of the radio of snow ice growth thickness to ice thickness in future periods

The shaded areas represent passing 99% M-K significance test

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图 11 降雪量在历史时期的平均分布（a）、年变化趋势（b）和在未来时期的平均分布（c）、年变化趋势（d）

阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 11 Of snowfall, average distribution (a) and annual variation trends (b) in historical periods, and average distribution (c) and annual variations trends (d) in future periods

The shaded areas represent passing 99% M-K significance test

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图 12 液态降水在历史时期的平均分布（a）、年变化趋势（b）和在未来时期的平均分布（c）、年变化趋势（d）

阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 12 Of liquid precipitation, average distribution (a) and annual variation trends (b) in historical periods, and average distribution (c) and annual variations trends (d) in future periods

The shaded areas represent passing 99% M-K significance test

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图 13 近地表2 m气温在历史时期的平均分布（a）、年变化趋势（b）和在未来时期的平均分布（c）、年变化趋势（d）

阴影区域代表通过 99% 的 M-K 显著性检验

Fig. 13 Of near-surface air temperature, average distribution (a) and annual variation trends (b) in historical periods, and average distribution (c) and annual variations trends (d) in future periods

The shaded areas represent passing 99% M-K significance test

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表 1 本文使用的EC-Earth3模式数据

Tab. 1 The EC-Earth3 model data used in this paper

实验筛选	两个实验的变量	水平网格数（经度 × 纬度）
历史实验（1990−2014年）、共享社会经济路径（2015−2100年）	海冰密集度（siconc）	362 × 292
	海冰厚度（sithick）	362 × 292
	雪冰生长量（sidmasssi）	362 × 292
	降水通量（pr）	512 × 256
	降雪通量（prsn）	512 × 256
	近地表2 m气温（tas）	512 × 256
	海洋格点面积（areacello）	362 × 292

下载: 导出CSV

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