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冬春季黄海温度锋面的多时间尺度变化及主控因素分析

任春宇 高建华 刘焘 石勇 徐笑梅 杨光

任春宇,高建华,刘焘,等. 冬春季黄海温度锋面的多时间尺度变化及主控因素分析[J]. 海洋学报,2023,45(4):31–45 doi: 10.12284/hyxb2023023
引用本文: 任春宇,高建华,刘焘,等. 冬春季黄海温度锋面的多时间尺度变化及主控因素分析[J]. 海洋学报,2023,45(4):31–45 doi: 10.12284/hyxb2023023
Ren Chunyu,Gao Jianhua,Liu Tao, et al. Multi-timescale variation of temperature fronts in the Yellow Sea during winter and spring and its main controlling factors analysis[J]. Haiyang Xuebao,2023, 45(4):31–45 doi: 10.12284/hyxb2023023
Citation: Ren Chunyu,Gao Jianhua,Liu Tao, et al. Multi-timescale variation of temperature fronts in the Yellow Sea during winter and spring and its main controlling factors analysis[J]. Haiyang Xuebao,2023, 45(4):31–45 doi: 10.12284/hyxb2023023

冬春季黄海温度锋面的多时间尺度变化及主控因素分析

doi: 10.12284/hyxb2023023
基金项目: 国家自然科学基金项目( 42276170,42106158)
详细信息
    作者简介:

    任春宇(2001-),男,黑龙江省萝北县人,主要从事物理海洋学研究。E-mail:191830117@smail.nju.edu.cn

    通讯作者:

    高建华(1973-),教授,主要从事海洋沉积动力学研究。E-mail: jhgao@nju.edu.cn

  • 中图分类号: P731.11

Multi-timescale variation of temperature fronts in the Yellow Sea during winter and spring and its main controlling factors analysis

  • 摘要: 海洋锋面强度变化对陆源物质输运和全球物质循环有重要作用。冬春季节,中国东部陆架区西太平洋边界流分支与沿岸流之间形成了海洋温度锋。为探究冬季风暴和陆架环流双重影响下温度锋面的多时间尺度变化及主控因素,本文以黄海为研究区,分别在年代际尺度和天气尺度,利用信号分解和可解释深度学习方法,研究了低纬度驱动的环流系统和高纬度驱动的冬季风暴对锋面变化的耦合作用。在年代际尺度,通过使用经验正交函数分解和集合经验模态分解的方法,将北黄海的温度变化与黄海暖流强度相联系。研究结果表明,黄海的海表温度经验正交函数(EOF)分解第一模态的空间分布有明显的黄海暖流—沿岸流体系特征;海表温度EOF第一模态时间序列与黄海暖流强度指标的相关性良好,且受低频率厄尔尼诺‒南方涛动信号调控。在天气尺度,对卷积神经网络−长短时记忆网络(CNN-LSTM)模型进行训练并使用可解释性指标进行分析,结果发现无风或弱风条件下,海洋锋面主要由压力梯度力和科里奥利力的地转平衡维持;但在冬季风暴条件下,受开尔文波传播和切变锋破碎的影响,流场的低频波动成为导致锋面强度变化的主因。本文研究结果表明,大数据及机器学习方法是在众多海洋参数间建立联系,并发现一些独特物理海洋过程的重要手段,具有广阔的应用前景。
  • 图  1  研究区域冬季(12月至翌年3月)平均海表温度(a)和冬季平均温度梯度(b)

    a中箭头代表研究区域冬季平均20 m水深流场

    Fig.  1  Mean sea surface temperature (a) and gradient (b) in the study domain during winter (december to march )

    The arrows in a represent the mean 20 m water depth flow field during winter

    图  2  海表温度经验正交函数(EOF)分解第一模态空间分布(a),以及海表温度EOF分解第一模态(相反数)和黄海暖流强度指标第六、第七本征模态(IMF 6+7)时间序列变化(b)

    Fig.  2  The first-mode spatial distribution pattern of sea surface temperature empirical orthogonal function (EOF) decomposition (a); time series of the first mode decomposed by EOF (opposite number) and sum of the sixth and seventh intrinsic mode functions of the Yellow Sea Warm Current intensity indicator (b)

    图  3  空间相关性关系

    a. 海表温度(SST)与纬向风场;b. SST与经向风场;c. 经向风场与$\Delta $SST;d. SST与表层纬向流场;e. SST与表层经向流场;f. SST与40 m水深纬向流场;g. SST与40 m水深经向流场;h. 经向风场与20 m水深位温变化量

    Fig.  3  Spatial correlations

    a. SST and zonal wind; b. SST and meridional wind; c. meridional wind field and $\Delta $SST; d. SST and surface zonal flow; e. SST and surface meridional flow; f. SST and zonal flow at 40 m water depth; g. SST and meridional flow at 40 m water depth; h. meridional wind and 20 m water depth potential temperature change

    S1  CNN-LSTM模型均方误差

    S1  Mean square error of CNN-LSTM model

    图  4  纬向(a)和经向(b)流场平均积分梯度空间分布

    Fig.  4  Zonal (a) and meridional (b) spatial pattern of the flow field integrated gradient

    图  5  2019年12月至2020年3月研究区域各要素日均变化

    a. 风向序列;b. 鲁北锋面强度;c. 鲁北锋面磷酸盐内外浓度差;d. 辽南、朝鲜半岛表层锋面强度

    Fig.  5  Oceanographic daily average of study area during December 2019 to March 2020

    a. Wind sequence; b. strength of the Lubei front; c. difference between internal and external phosphate concentrations at the Lubei front; d. strength of surface fronts in Liaonan and Korea Peninsula

    图  6  黄海暖流强度与锋面强度散点图(a)及Niño3与黄海暖流强度指标时间序列(b)

    Fig.  6  Scatterplot of Yellow Sea Warm Current (YSWC) strength versus frontal strength (a), and the time series of Niño3 and YSWC strength index (b)

    图  7  2020年2月13‒20日风序列(a),3条主要锋面强度(b),锋面内3点(P1、P3、P5)海表高度变化(c)及神经网络9个输入变量平均积分梯度序列(d)

    Fig.  7  Wind sequence for February 13‒20, 2020 (a), intensity of the three main fronts (b), sea surface height variation at three points (P1, P3, P5) within the front (c), and neural network mean integral gradient series of nine input variables (d)

    图  8  海表高度异常值(a‒f)和积分梯度场(g‒j)

    SSH:海表高度;V:经向流速

    Fig.  8  Sea surface height anomalies (a‒f), and integrated gradient field (g‒j)

    SSH: sea surface heigh; V: meridional velocity

    图  9  122°E断面流速(a, b)和37°N断面流速(c, d)

    Fig.  9  Flow veloctiy at Section 122°E (a, b) and Section 37°N (c, d)

    图  10  科里奥利力和压强梯度力计算结果

    Fig.  10  Calculated Coriolis and pressure gradient forces

    S2  研究区域37°N断面平均位温(a),黄海暖流指标EEMD分解结果(仅展示分析使用的6~9本征模态函数,b‒e)

    S2  Mean potential temperature at the Section 37°N in the study area (a), EEMD decomposition results for the Yellow Sea Warm Current indicator (only the 6‒9 IMFs used in the analysis are shown, b‒e)

    S1  CNN-LSTM模型架构

    S1  CNN-LSTM model structure

    层序号神经网络架构
    类型输出参数参数
    1输入层85×121×9核尺寸:(1,1)
    外缘填充:0
    2卷积层85×121×32核尺寸:(1,1)
    外缘填充:0
    3卷积层85×121×64核尺寸:(3,3)
    外缘填充:1
    4卷积层85×121×128核尺寸:(1,1)
    外缘填充:0
    5卷积层85×121×64核尺寸:(1,1)
    外缘填充:0
    6卷积层85×121×32核尺寸:(1,1)
    外缘填充:0
    7卷积层85×121×10核尺寸:(1,1)
    外缘填充:0
    8卷积层85×121×8核尺寸:(3,3)
    外缘填充:1
    9卷积层85×121×3核尺寸:(1,1)
    外缘填充:0
    10LSTM层85×121×1
    11全连接层10285×1
    12全连接层10285×1
    13全连接层10285×1
    14全连接层10285×1
    下载: 导出CSV

    S2  CNN-LSTM模型9个输入变量的平均积分梯度

    S2  The mean integrated gradient of nine kinds of parameters which is input to the CNN-LSTM model

    纬向流速/10−5经向流速/10−5海表高度/10−5合计/10−5
    当天1.6831.7251.8875.295
    1天前2.3871.1540.8574.398
    2天前0.7252.5580.8574.140
    合计4.7955.4373.60113.833
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-18
  • 修回日期:  2022-09-22
  • 网络出版日期:  2023-02-02
  • 刊出日期:  2023-03-31

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