极地研究

极地研究 ›› 2022, Vol. 34 ›› Issue (2): 177-188.DOI: 10.13679/j.jdyj.20210006

• 研究论文 • 上一篇    下一篇

春季云属性的空间分布特征及其对北极海冰减退的影响

李妍星1,常亮1,2,3,张春玲1   

  1. 1上海海洋大学, 海洋科学学院, 上海 201306;
    2自然资源部第二海洋研究所, 卫星海洋环境动力学国家重点实验室, 杭州310012;
    3自然资源部极地科学重点实验室, 中国极地研究中心, 上海200136
  • 收稿日期:2021-01-07 修回日期:2021-03-12 出版日期:2022-06-30 发布日期:2022-06-15
  • 通讯作者: 常亮
  • 基金资助:
    国家自然科学基金(42174016, 42076240)、上海市浦江人才计划(19PJ1404300)、自然资源部极地科学重点实验室开放研究基金(KP201701)和自然资源部卫星海洋环境动力学国家重点实验室开放研究基金(QNHX2122)

Spatial distribution of cloud attributes in spring and its influence on Arctic sea ice decline

Li Yanxing1, Chang Liang1,2,3, Zhang Chunling1   

  1. 1College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
    2State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources,
     Hangzhou 310012, China;
    3Key Laboratory of Polar Science, MNR, Polar Research Institute of China, Shanghai 200136, China
  • Received:2021-01-07 Revised:2021-03-12 Online:2022-06-30 Published:2022-06-15

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摘要： 春季云属性的分布和变化对北极9月的海冰变化具有重要的预调节作用。但是, 在北极及全球气候变暖加剧的背景下, 春季云和9月海冰之间的关系及其在北极不同海域的特征需要进一步更新。本文基于ERA5云辐射、MODIS云量和云水路径以及美国国家冰雪中心的海冰密集度(sea ice concentration, SIC)数据, 首先分析了2000—2017年北极地区春季云量、云水路径两种云宏观属性与云长波辐射效应、云短波辐射效应两种云辐射属性气候学尺度的空间分布特征, 然后探讨了云宏观属性和辐射属性的相关关系, 以及在不同感兴趣区(region of interest, ROI)内的海冰变化对云属性的响应特征。结果表明, 在北极的春季, 云量的分布随海冰密集度的升高而递减, 云水路径的分布随纬度的升高而增大。云长波辐射效应的分布不连续, 未见显著规律, 在巴伦支海和东格陵兰海域及其以北的北冰洋海域(ROI2)以外的其他区域, 云短波辐射效应差异较小。北极云量和云水路径与云长波辐射效应主要存在正相关关系, 与云短波辐射效应存在负相关关系, 但在相关性的强度和范围上云水路径均不如云量。在冰边缘区占比最高的拉普捷夫海和喀拉海域及其以北的北冰洋海域(ROI1)和波弗特、楚科奇、东西伯利亚海域及其以北的北冰洋海域(ROI4), 春季云长波辐射效应倾向于增强9月海冰的融化, 云短波辐射效应作用相反, 这种响应的滞后时长约为4个月。在多元回归模型中, 决定系数可用于表征自变量对因变量的解释程度, 决定系数的结果表明, ROI1的云量变率占海冰退化成因的约18.53%, ROI4的云量和云水路径的重要性在海冰减退机制中没有得到体现。

关键词: 北极, 云属性, 云辐射效应, 海冰

Abstract:

The distribution and variation of cloud attributes in spring play important roles in preconditioning September Arctic sea ice change. However, given the background of increased warming both globally and in the Arctic, the characteristics of potential connections between springtime clouds and September sea ice in different areas of the Arctic Sea should be updated. In this paper, we analyze the impact of springtime clouds on September Arctic sea ice using ERA5 radiation data, MODIS cloud fraction (CF) and cloud water path data, and sea ice concentration (SIC) data from the National Snow and Ice Data Center (USA). First, the climatological spatial distribution characteristics of cloud microphysics properties (i.e., CF and total water path (TWP)) and cloud radiation properties (i.e., longwave cloud radiation effects (LWCRE) and shortwave cloud radiation effects (SWCRE)) in spring (2000–2017) in the Arctic region are presented. Then, the correlation between cloud macroscopic properties and cloud radiation is presented, and the response of sea ice to springtime cloud properties in different areas of interest is discussed. Results show that the CF distribution decreases as the SIC increases, and that the TWP distribution increases as the latitude increases. The distribution of LWCRE is discontinuous over the Arctic Ocean and no significant regularity is observed. In areas other than the Barents and eastern Greenland seas and the Arctic Ocean to the north, the difference in SWCRE is small. Additionally, Arctic CF and TWP are correlated positively (negatively) with LWCRE (SWCRE). The correlation (both positive and negative) between TWP and cloud radiation effects is not as notable as that with CF in terms of significance and range. Over areas with a higher proportion of the marginal ice zone, i.e., the Laptev and Kara seas and the Arctic Ocean to the north (ROI1), and the Beaufort, Chukchi, and eastern Siberia seas and the Arctic Ocean to the north (ROI4), the warming effect of spring LWCRE tends to enhance sea ice melting in September, whereas the cooling effect of SWCRE exhibits the opposite effect on September sea ice with a time lag of approximately 4 months. The coefficient of determination in the multiple regression model, which can be used to characterize the degree of explanation of the independent variable to the dependent variable, indicates that CF variability of ROI1 significantly explains 18.53% of the cause of sea ice loss. In ROI4, no significant connection is found between springtime CF (and TWP) and sea ice loss in September.

Key words: Arctic, cloud properties, cloud radiation effects, sea ice