Analyses of global sea surface temperature 1856-1991
Analyses of global sea surface temperature 1856-1991
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DOI：
10.1029/97jc01736
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发表时间：
1998-08-15
影响因子：
3.6
通讯作者：
Rajagopalan, B
中科院分区：
文献类型：
--
作者：
Kaplan, A;Cane, MA;Rajagopalan, B
利用最优平滑（OS）、卡尔曼滤波（KF）和最优插值（OI）三种基于统计的方法，对1856 - 1991年全球月海表温度（SST）异常进行了分析。每一个都伴随着对所分析的场的误差协方差的估计。这些方法需要的空间协方差函数是从现有数据中估计出来的；时间推进模型是一阶自回归模型。用于分析的输入数据来自英国气象局历史海表温度数据集（MOHSST5） [Parker et al.， 1994]和全球海表温度图集（GOSTA） [Bottomley et al.， 1990]的月度异常。这些分析相互比较，与GOSTA进行比较，并与Smith等人[1996]中通过在一组经验正交函数上的投影(P)生成的分析进行比较。理论上，分析的质量应该按照OS、KF、OI、P和GOSTA的顺序排列。研究发现，在数据丰富的时期（1951-1991），前四种方法给出了可比较的结果，但有时当数据稀疏时，前三种方法与P和GOSTA有显著差异。在这些时候，后两者往往有极端和波动的值，初步证据的错误。统计方案也会根据任何分析中未使用的数据（来自珊瑚的替代记录和来自沿海和岛屿站的气温记录）进行核实。我们也提出证据，分析误差估计确实是指示产品的质量。在大多数情况下，OS和KF产品与OI产品接近，但是在覆盖率特别低的情况下，它们对其他时间的信息的使用是有利的。这些方法似乎可以从非常稀疏的数据中重建全球海温场的主要特征。与厄尔尼诺-南方涛动周期的其他迹象比较表明，这些分析提供了早在19世纪60年代的年际变化的有用信息。
Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kalman filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the time-marching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) [Parker et al., 1994] of the Global Ocean Surface Temperature Atlas (GOSTA) [Bottomley et al., 1990].These analyses are compared with each other, with GOSTA, and with an analysis generated by projection (P) onto a set of empirical orthogonal functions las in Smith et al. [1996]). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and GOSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and GOSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous.The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the El Nino - Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.