南海放射虫与海表古温度转换函数的建立与检验  

RADIOLARIAN-BASED TRANSFER FUNCTIONS FOR ESTIMATING PALAEOTEMPERATURE IN THE SOUTH CHINA SEA

作　　者：陈木宏[1] 张兰兰[1] 张强[1] 向荣[1] 胡维芬[1] 

机构地区：[1]中国科学院边缘海地质重点实验室,中国科学院南海海洋研究所,广州510301

出　　处：《第四纪研究》2013年第6期1111-1121,共11页Quaternary Sciences

基　　金：国家重点基础研究发展规划项目(973项目)(批准号:2013CB956102);国家自然科学基金项目(批准号:41076026;91228207和41276051)共同资助

摘　　要：南海表层沉积物中放射虫分布与现代表层海水温度之间存在着较好的非线性关系。本文根据南海100个站位表层沉积中100个放射虫种类的丰度统计数据,采用主成分因子分析和多元线性与非线性回归方法,经数学分析筛选出29个站位和25个种类,用于探讨南海放射虫的古温度转换函数关系,分别建立了夏季和冬季的线性和非线性回归方程,并讨论它们的共同度、复相关系数、解释方差和估计误差。分析与检验结果显示,所建立的南海放射虫组合与冬季海水表层温度转换函数公式比夏季转换函数公式具有更好的参数指标与适用特征。文中对转换函数的建立方法与问题做了详细讨论。The South China Sea is a semienclosed marginal sea of the west Pacific with a regional range of near 20 latitudes,from 3°30.70＇N to 23 °29.00＇N. It is differentiated from the open ocean in oceanographic conditions with some special environmental factors. This is the first attempt to reconstruct a palaeoceanographic transfer function by using the information of 100 species radiolarian in 100 surface sediments with water depth of 36 -4410m from this sea area where covers various topographic units and distributing currents, only selecting 25 species and 29 sediments for final use after mathematic extraction. Analysis data of water temperatures were from sea surface （0m） of mean values of years, which are corresponding to each site of sediments collected. The summer temperatures use mean values of June - August and the winter temperatures of December ~ February. All temperatures use unit of ~C. The factor analysis of principal components and multicurvilinear regression by least-squares technique were used to establish the solution for summer and winter palaeotemperature. The factor analysis gives the communalitis of more than 0. 993990 in each species and an accumulating variance of 82. 0418% for six factors. Both linear and nonlinear equations have been derived and examined, showing the results as followings： 1 ）Equation for winter temperature by linear regression has the muhiple correlation coefficient of 0. 74296, average estimate error of 0. 45106℃ and 74% of variance explained： TWL =26. 12652-0. 46431F1 ＋0. 30851F2＋0. 28121F3-0. 07858F4＋0. 11594F5＋0. 03786F6 2） Equation for winter temperature by piecewise linear regression, with breakpoint at 26. 12652, has the multiple correlation coefficient of 0. 96101, average estimate error of 0. 186777℃ and 92. 354% of variance explained ： These equations and their relevant parameters show that nonlinear regression, especially for winter temperature, has a better result in criterion. Results from this study will probably provide a way fo

关 键 词：南海 放射虫 古温度 转换函数 古海洋学 

分 类 号：P722.7[天文地球—海洋科学] Q915.812[天文地球—古生物学与地层学]