中国近海和西北太平洋温跃层时空变化分析、模拟及预报
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摘要
温度跃层是反映海洋温度场的重要物理特性指标,对水下通讯、潜艇活动及渔业养殖、捕捞等有重要影响。本文利用中国科学院海洋研究所“中国海洋科学数据库”在中国近海及西北太平洋(110oE-140oE,10oN-40oN)的多年历史资料(1930-2002年,510143站次),基于一种改进的温跃层判定方法,分析了该海域温跃层特征量的时空分布状况。同时利用Princeton Ocean Model(POM),对中国近海,特别是东南沿海的水文结构进行了模拟,研究了海洋水文环境对逆温跃层的影响。最后根据历史海温观测资料,利用EOF分解统计技术,提出了一种适于我国近海及毗邻海域,基于现场有限层实测海温数据,快速重构海洋水温垂直结构的统计预报方法,以达到对现场温跃层的快速估计。
历史资料分析结果表明,受太阳辐射和风应力的影响,20°N以北研究海域,温跃层季节变化明显,夏季温跃层最浅、最强,冬季相反,温跃层厚度的相位明显滞后于其他变量,其在春季最薄、秋季最厚。12月份到翌年3月份,渤、黄及东海西岸,呈无跃层结构,西北太平洋部分海域从1月到3月份,也基本无跃层结构。在黄海西和东岸以及台湾海峡附近的浅滩海域,由于风力搅拌和潮混合作用,温跃层出现概率常年较低。夏季,海水层化现象在近海陆架海域得到了加强,陆架海域温跃层强度季节性变化幅度(0.31°C/m)明显大于深水区(约0.05°C/m),而前者温跃层深度和厚度的季节性变化幅度小于后者。20°N以南研究海域,温跃层季节变化不明显。逆温跃层主要出现在冬、春季节(10月-翌年5月)。受长江冲淡水和台湾暖流的影响,东南沿海区域逆温跃层持续时间最长,出现概率最大,而在山东半岛北及东沿岸、朝鲜半岛西及北岸,逆温跃层消长过程似乎和黄海暖流有关。多温跃层结构常年出现于北赤道流及对马暖流区。在黑潮入侵黄、东、南海的区域,多温跃层呈现明显不同的季节变化。在黄海中部,春季多温跃层发生概率高于夏季和秋季,在东海西部,多跃层主要出现在夏季,在南海北部,冬季和春季多温跃层发生概率大于夏季和秋季。这些变化可能主要受海表面温度变化和风力驱动的表层流的影响。
利用Princeton Ocean Model(POM),对中国东南沿海逆温跃层结构进行了模拟,模拟结果显示,长江冲淡水的季节性变化以及夏季转向与实际结果符合较好,基本再现了渤、黄、东海海域主要的环流、温盐场以及逆温跃层的分布特征和季节变化。通过数值实验发现,若无长江、黄河淡水输入,则在整个研究海域基本无逆温跃层出现,因此陆源淡水可能是河口附近逆温跃层出现的基本因素之一。长江以及暖流(黑潮和台湾暖流)流量的增加,均可在不同程度上使逆温跃层出现概率及强度、深度和厚度增加,且暖流的影响更加明显。长江对东南沿海逆温跃层的出现,特别是秋季到冬季初期,有明显的影响,使长江口海域逆温跃层位置偏向东南。暖流对于中国东南沿海的逆温跃层结构,特别是初春时期,有较大影响,使长江口海域的逆温跃层位置向东北偏移。
通过对温跃层长期变化分析得出,黄海冷水团区域,夏季温跃层强度存在3.8年左右的年际变化及18.9年左右的年代际变化,此变化可能主要表现为对当年夏季和前冬东亚地区大气气温的热力响应。东海冷涡区域,夏季温跃层强度存在3.7年的年际变化,在El Nino年为正的强度异常,其可能主要受局地气旋式大气环流变异所影响。谱分析同时表明,该海域夏季温跃层强度还存在33.2年的年代际变化,上世纪70年代中期,温跃层强度由弱转强,而此变化可能与黑潮流量的年代际变化有关。
海洋水温垂直结构的统计预报结果显示,EOF分解的前四个主分量即能够解释原空间点温度距平总方差的95%以上,以海洋表层附近观测资料求解的特征系数推断温度垂直结构分布的结果最稳定。利用东海陆架区、南海深水区和台湾周边海域三个不同区域的实测CTD样本廓线资料,对重构模型的检验结果表明,重构与实测廓线的相关程度超过95%的置信水平。三个区重构与实测温度廓线值的平均误差分别为0.69℃,0.52℃,1.18℃,平均重构廓线误差小于平均气候偏差,统计模式可以很好的估算温度廓线垂直结构。东海陆架海区温度垂直重构廓线与CTD观测廓线获得的温跃层结果对比表明,重构温跃层上界、下界深度和强度的平均绝对误差分别为1.51m、1.36m和0.17℃/m,它们的平均相对误差分别为24.7%、8.9%和22.6%,虽然温跃层深度和强度的平均相对误差较大,但其绝对误差量值较小。而在南海海区,模型重构温跃层上界、下界和强度的平均绝对预报误差分别为4.1m、27.7m和0.007℃/m,它们的平均相对误差分别为16.1%、16.8%和9.5%,重构温跃层各特征值的平均相对误差都在20%以内。虽然南海区温跃层下界深度平均绝对预报误差较大,但相对于温跃层下界深度的空间尺度变化而言(平均温跃层下界深度为168m),平均相对误差仅为16.8%。因此说模型重构的温度廓线可以达到对我国陆架海域、深水区温跃层的较好估算。
基于对历史水文温度廓线观测资料的分析及自主温跃层统计预报模型,研制了实时可利用微机简单、快捷地进行温跃层估算及查询的可视化系统,这是迄今进行大范围海域温跃层统计与实时预报研究的较系统成果。
The thermocline reflects the ocean temperature field's important physics characteristics, and has important influence on underwater communication, submarine activity as well as fishery farming and fishing. In this study, dstributions and seasonal variations of the thermocline in the China Seas and Northwestern Pacific Ocean (NWP) (110oE-140o E, 10oN-40oN) were studied with an improved method for identifying the thermocline from the historical data from 1930 through 2002 (totally 510143 profiles).Meanwhile,based on the Princeton Ocean Model (POM), hydrologic- al structure in the China Seas, particularly along the southeast Chinese coast, is simulated to find the affection of the ocean hydrological environment to the inversion thermocline. Empirical Orthogonal Function (EOF) analyse was used to get the main eigenvector fields of historical temperature for the China Seas and NWP, then the nice temperature profile is reconstructed based on the in situ temperature data and the thermocline is rapidly estimated.
The main results of analyseing the historical data can be summarized as follows: The thermocline has obvious seasonal variations in the study area north of 20°N influenced by the strong annual cycles of SST and wind. The thermocline is basically shallower and stronger in summer while deeper and weaker in winter. However, it is thinner in spring and thicker in autumn. There is not the thermocline along the western coasts of the Bohai Sea (BS), Yellow Sea (YS) and northern East China Sea (ECS) from December to March, and in some areas of NWP from January to March. The thermocline is infrequent along the shoal areas of the western and eastern YS and in the vicinity of the Taiwan Strait affected by the wind and tidal mixing. Seasonal variation of the thermocline strength is generally enhanced in the shelf area with averaged seasonal amplitude of about 0.31°C/m that is much larger than that in the deep area (about 0.05°C/m). While the amplitudes of seasonal variations of the thermocline depth and thickness are larger in the deeper area than those in the shelf area. It reveals little seasonal variations in the study area south of 20°N. The inversion thermocline is found in winter and spring (Oct.-May). It appears in the southeast Chinese coast with the longest lasting period and the highest occurrence probability resulting from the Yangtze River diluted water and the Taiwan Warm Current (TWC). In the areas west and south of Korean Peninsula and the northern coast of the Shandong Peninsula, the emergence and disappearance of the inversion thermocline there are all accorded with the onset and decay of the YS Warm Current (YSWC). The multi-thermocline exists in the North Equatorial Current (NEC) and the Tsushima Current (TC) areas all the year round. The multi-thermocline reveals obvious seasonal variations in the branches of the Kuroshio in the YS, ECS and the South China Sea (SCS). In the central YS, the occurrence probability is higher in spring than in summer and autumn. In the western ECS, the multi-thermocline mainly appears in summer. The occurrence probability of the multi-thermocline is larger in winter and spring than in summer and autumn in the northern SCS. These different seasonal variations are mainly influenced by the variations of the SST and the horizontal advection of the surface warm water carried by the wind-driven currents.
Based on the Princeton Ocean Model (POM), hydrological structure in the China Seas, particularly along the southeast Chinese coast, is simulated. The results show that the seasonal changes and expansion of the Yangtze River diluted water in summer better meet the actual results. The main distributions and seasonal variations of the circulation, temperature, salt and the inversion thermocline in the BS, YS and ECS are well simulated. There are no inversion themocine in the entire study area without Yangtze River and Yellow River; therefor the diluted water plays a leading role to the formulation of the inversion thermocline. Meanwhile the increasing discharges of the Yangtze River, TWC and Kuroshio make the occurrence probability, gradient and depth of the inversion themocline in the southeast China Seas increase too. The Yangtze River plays the major role to the formulation of the inversion themocline in autumn and winter, and make the inversion thermocline transfer southeastward, however the TWC and Kuroshio make it transfer northwestward in early spring.
By analyzing the long period cycle of the strong thermocline in the YSCWM and ECSCE, we can find that there are 3.8yrs and 18.9yrs period oscillations of the thermocline gradient in the YSCWM area in summer. These changes were mainly the responses to the variations of the atmospheric temperature in the summer and former winter in the East Asian. In the ECSCE area, the interannual oscillation of the thermocline gradient with about 3.7-yr period in summer (stronger in El Nino yrs) is well correlated with that of local wind stress. The transition from weak to strong thermocline gradient in the ECSCE during the mid-1970s is consistent with the change of the Kuroshio volume transport in summer.
The results of the prediction of vertical temperature structure show that the accumulative variance of the first four eigenvector fields reaches 95%, and the vertical distribution of the reconstructed temperature is most stable using the in situ temperature near the surface to get the coefficient. The model test is operated using the CTD data from the ECS, SCS and the areas around Taiwan Island. The reconstructed profiles have a high correlation with the observed ones and reaching the 95% confidence level. The average error between the reconstructed profiles in these three area and the observed ones were 0.69℃, 0.52℃, 1.18℃. This shows this statistical model can estimate the temperature profile vertical structure well. Comparing the thermocline characteristics from the reconstructed profiles and historical data respectively, the results in the ECS show that the upper thermocline boundary, lower thermocline boundary and the gradient average absolute errors are 1.51m, 1.36m and 0.17℃/m respectively, and the average relative errors of them are 24.7%, 8.9% and 22.6% respectively. Although the relative errors of thermocline depth and gradient there are large, the average error is small. In the SCS, average absolute errors of the upper thermocline boundary, lower thermocline boundary and the gradient are 4.1m, 27.7m and 0.007℃/m respectively, while the average relative errors of them are 16.1%, 16.8 and 9.5% respectively that are all <20%. Although the average absolute error of the thermocline lower bound is larger, but contrast to the spatial scale of average depth of the thermocline lower bound (168 m), the average relative error is only 16.8%. Therefor the model can estimate the thermocline well.
Based on the analyses and prediction of the thermocline, a system that can estimate the thermocline quickly is developed, which is one of the most systematic research results about the thermocline analyses and prediction so far.
历史资料分析结果表明,受太阳辐射和风应力的影响,20°N以北研究海域,温跃层季节变化明显,夏季温跃层最浅、最强,冬季相反,温跃层厚度的相位明显滞后于其他变量,其在春季最薄、秋季最厚。12月份到翌年3月份,渤、黄及东海西岸,呈无跃层结构,西北太平洋部分海域从1月到3月份,也基本无跃层结构。在黄海西和东岸以及台湾海峡附近的浅滩海域,由于风力搅拌和潮混合作用,温跃层出现概率常年较低。夏季,海水层化现象在近海陆架海域得到了加强,陆架海域温跃层强度季节性变化幅度(0.31°C/m)明显大于深水区(约0.05°C/m),而前者温跃层深度和厚度的季节性变化幅度小于后者。20°N以南研究海域,温跃层季节变化不明显。逆温跃层主要出现在冬、春季节(10月-翌年5月)。受长江冲淡水和台湾暖流的影响,东南沿海区域逆温跃层持续时间最长,出现概率最大,而在山东半岛北及东沿岸、朝鲜半岛西及北岸,逆温跃层消长过程似乎和黄海暖流有关。多温跃层结构常年出现于北赤道流及对马暖流区。在黑潮入侵黄、东、南海的区域,多温跃层呈现明显不同的季节变化。在黄海中部,春季多温跃层发生概率高于夏季和秋季,在东海西部,多跃层主要出现在夏季,在南海北部,冬季和春季多温跃层发生概率大于夏季和秋季。这些变化可能主要受海表面温度变化和风力驱动的表层流的影响。
利用Princeton Ocean Model(POM),对中国东南沿海逆温跃层结构进行了模拟,模拟结果显示,长江冲淡水的季节性变化以及夏季转向与实际结果符合较好,基本再现了渤、黄、东海海域主要的环流、温盐场以及逆温跃层的分布特征和季节变化。通过数值实验发现,若无长江、黄河淡水输入,则在整个研究海域基本无逆温跃层出现,因此陆源淡水可能是河口附近逆温跃层出现的基本因素之一。长江以及暖流(黑潮和台湾暖流)流量的增加,均可在不同程度上使逆温跃层出现概率及强度、深度和厚度增加,且暖流的影响更加明显。长江对东南沿海逆温跃层的出现,特别是秋季到冬季初期,有明显的影响,使长江口海域逆温跃层位置偏向东南。暖流对于中国东南沿海的逆温跃层结构,特别是初春时期,有较大影响,使长江口海域的逆温跃层位置向东北偏移。
通过对温跃层长期变化分析得出,黄海冷水团区域,夏季温跃层强度存在3.8年左右的年际变化及18.9年左右的年代际变化,此变化可能主要表现为对当年夏季和前冬东亚地区大气气温的热力响应。东海冷涡区域,夏季温跃层强度存在3.7年的年际变化,在El Nino年为正的强度异常,其可能主要受局地气旋式大气环流变异所影响。谱分析同时表明,该海域夏季温跃层强度还存在33.2年的年代际变化,上世纪70年代中期,温跃层强度由弱转强,而此变化可能与黑潮流量的年代际变化有关。
海洋水温垂直结构的统计预报结果显示,EOF分解的前四个主分量即能够解释原空间点温度距平总方差的95%以上,以海洋表层附近观测资料求解的特征系数推断温度垂直结构分布的结果最稳定。利用东海陆架区、南海深水区和台湾周边海域三个不同区域的实测CTD样本廓线资料,对重构模型的检验结果表明,重构与实测廓线的相关程度超过95%的置信水平。三个区重构与实测温度廓线值的平均误差分别为0.69℃,0.52℃,1.18℃,平均重构廓线误差小于平均气候偏差,统计模式可以很好的估算温度廓线垂直结构。东海陆架海区温度垂直重构廓线与CTD观测廓线获得的温跃层结果对比表明,重构温跃层上界、下界深度和强度的平均绝对误差分别为1.51m、1.36m和0.17℃/m,它们的平均相对误差分别为24.7%、8.9%和22.6%,虽然温跃层深度和强度的平均相对误差较大,但其绝对误差量值较小。而在南海海区,模型重构温跃层上界、下界和强度的平均绝对预报误差分别为4.1m、27.7m和0.007℃/m,它们的平均相对误差分别为16.1%、16.8%和9.5%,重构温跃层各特征值的平均相对误差都在20%以内。虽然南海区温跃层下界深度平均绝对预报误差较大,但相对于温跃层下界深度的空间尺度变化而言(平均温跃层下界深度为168m),平均相对误差仅为16.8%。因此说模型重构的温度廓线可以达到对我国陆架海域、深水区温跃层的较好估算。
基于对历史水文温度廓线观测资料的分析及自主温跃层统计预报模型,研制了实时可利用微机简单、快捷地进行温跃层估算及查询的可视化系统,这是迄今进行大范围海域温跃层统计与实时预报研究的较系统成果。
The thermocline reflects the ocean temperature field's important physics characteristics, and has important influence on underwater communication, submarine activity as well as fishery farming and fishing. In this study, dstributions and seasonal variations of the thermocline in the China Seas and Northwestern Pacific Ocean (NWP) (110oE-140o E, 10oN-40oN) were studied with an improved method for identifying the thermocline from the historical data from 1930 through 2002 (totally 510143 profiles).Meanwhile,based on the Princeton Ocean Model (POM), hydrologic- al structure in the China Seas, particularly along the southeast Chinese coast, is simulated to find the affection of the ocean hydrological environment to the inversion thermocline. Empirical Orthogonal Function (EOF) analyse was used to get the main eigenvector fields of historical temperature for the China Seas and NWP, then the nice temperature profile is reconstructed based on the in situ temperature data and the thermocline is rapidly estimated.
The main results of analyseing the historical data can be summarized as follows: The thermocline has obvious seasonal variations in the study area north of 20°N influenced by the strong annual cycles of SST and wind. The thermocline is basically shallower and stronger in summer while deeper and weaker in winter. However, it is thinner in spring and thicker in autumn. There is not the thermocline along the western coasts of the Bohai Sea (BS), Yellow Sea (YS) and northern East China Sea (ECS) from December to March, and in some areas of NWP from January to March. The thermocline is infrequent along the shoal areas of the western and eastern YS and in the vicinity of the Taiwan Strait affected by the wind and tidal mixing. Seasonal variation of the thermocline strength is generally enhanced in the shelf area with averaged seasonal amplitude of about 0.31°C/m that is much larger than that in the deep area (about 0.05°C/m). While the amplitudes of seasonal variations of the thermocline depth and thickness are larger in the deeper area than those in the shelf area. It reveals little seasonal variations in the study area south of 20°N. The inversion thermocline is found in winter and spring (Oct.-May). It appears in the southeast Chinese coast with the longest lasting period and the highest occurrence probability resulting from the Yangtze River diluted water and the Taiwan Warm Current (TWC). In the areas west and south of Korean Peninsula and the northern coast of the Shandong Peninsula, the emergence and disappearance of the inversion thermocline there are all accorded with the onset and decay of the YS Warm Current (YSWC). The multi-thermocline exists in the North Equatorial Current (NEC) and the Tsushima Current (TC) areas all the year round. The multi-thermocline reveals obvious seasonal variations in the branches of the Kuroshio in the YS, ECS and the South China Sea (SCS). In the central YS, the occurrence probability is higher in spring than in summer and autumn. In the western ECS, the multi-thermocline mainly appears in summer. The occurrence probability of the multi-thermocline is larger in winter and spring than in summer and autumn in the northern SCS. These different seasonal variations are mainly influenced by the variations of the SST and the horizontal advection of the surface warm water carried by the wind-driven currents.
Based on the Princeton Ocean Model (POM), hydrological structure in the China Seas, particularly along the southeast Chinese coast, is simulated. The results show that the seasonal changes and expansion of the Yangtze River diluted water in summer better meet the actual results. The main distributions and seasonal variations of the circulation, temperature, salt and the inversion thermocline in the BS, YS and ECS are well simulated. There are no inversion themocine in the entire study area without Yangtze River and Yellow River; therefor the diluted water plays a leading role to the formulation of the inversion thermocline. Meanwhile the increasing discharges of the Yangtze River, TWC and Kuroshio make the occurrence probability, gradient and depth of the inversion themocline in the southeast China Seas increase too. The Yangtze River plays the major role to the formulation of the inversion themocline in autumn and winter, and make the inversion thermocline transfer southeastward, however the TWC and Kuroshio make it transfer northwestward in early spring.
By analyzing the long period cycle of the strong thermocline in the YSCWM and ECSCE, we can find that there are 3.8yrs and 18.9yrs period oscillations of the thermocline gradient in the YSCWM area in summer. These changes were mainly the responses to the variations of the atmospheric temperature in the summer and former winter in the East Asian. In the ECSCE area, the interannual oscillation of the thermocline gradient with about 3.7-yr period in summer (stronger in El Nino yrs) is well correlated with that of local wind stress. The transition from weak to strong thermocline gradient in the ECSCE during the mid-1970s is consistent with the change of the Kuroshio volume transport in summer.
The results of the prediction of vertical temperature structure show that the accumulative variance of the first four eigenvector fields reaches 95%, and the vertical distribution of the reconstructed temperature is most stable using the in situ temperature near the surface to get the coefficient. The model test is operated using the CTD data from the ECS, SCS and the areas around Taiwan Island. The reconstructed profiles have a high correlation with the observed ones and reaching the 95% confidence level. The average error between the reconstructed profiles in these three area and the observed ones were 0.69℃, 0.52℃, 1.18℃. This shows this statistical model can estimate the temperature profile vertical structure well. Comparing the thermocline characteristics from the reconstructed profiles and historical data respectively, the results in the ECS show that the upper thermocline boundary, lower thermocline boundary and the gradient average absolute errors are 1.51m, 1.36m and 0.17℃/m respectively, and the average relative errors of them are 24.7%, 8.9% and 22.6% respectively. Although the relative errors of thermocline depth and gradient there are large, the average error is small. In the SCS, average absolute errors of the upper thermocline boundary, lower thermocline boundary and the gradient are 4.1m, 27.7m and 0.007℃/m respectively, while the average relative errors of them are 16.1%, 16.8 and 9.5% respectively that are all <20%. Although the average absolute error of the thermocline lower bound is larger, but contrast to the spatial scale of average depth of the thermocline lower bound (168 m), the average relative error is only 16.8%. Therefor the model can estimate the thermocline well.
Based on the analyses and prediction of the thermocline, a system that can estimate the thermocline quickly is developed, which is one of the most systematic research results about the thermocline analyses and prediction so far.
引文
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