Extended Reconstruction of Global Sea Surface Temperatures Based on COADS Data (1854–1997)
Extended Reconstruction of Global Sea Surface Temperatures Based on COADS Data (1854–1997)
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DOI：
10.1175/1520-0442-16.10.1495
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发表时间：
2003-05
影响因子：
4.9
通讯作者：
Thomas M. Smith;R. Reynolds
中科院分区：
文献类型：
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作者：
Thomas M. Smith;R. Reynolds
基于综合海洋-大气数据集（COADS）第2期1854 - 1997年的观测资料，生成了全球海温的逐月扩展重建（ERSST）。改进来自使用更新的COADS观测资料和新的质量控制程序以及改进的重建方法。此外，还计算了误差估计，其中包括采样误差和分析误差的不确定性。使用这种方法，在19世纪80年代之前几乎无法重建全球方差，因为该时期的数据过于稀疏，无法解析足够的模态。误差估计表明，除北大西洋外，1880年以前的erst值有限，当时近全球平均的不确定性几乎与信号一样大。在大多数区域，不确定性在大部分时间内减小，在1950年之后最小。ERSST的大尺度变化与英国气象局哈德利中心全球海冰和海面温度（HadISST）重建的结果大致一致。由于使用不同的历史偏差校正以及不同的数据和分析程序，存在差异，但这些差异不会改变海温变化的总体特征。与HadISST相比，这里使用的程序产生更平滑的分析。更平滑的ERSST的优点是可以滤除更多的噪声，但代价可能是在采样稀疏时滤除一些真实的变化。对erst异常进行旋转EOF分析表明，主要的变化模态包括ENSO和与趋势相关的模态。将HadISST数据投影到旋转的特征向量上产生的时间序列与ERSST相似，表明两者的主要变化模式是一致的。
A monthly extended reconstruction of global SST (ERSST) is produced based on Comprehensive Ocean‐ Atmosphere Data Set (COADS) release 2 observations from the 1854‐1997 period. Improvements come from the use of updated COADS observations with new quality control procedures and from improved reconstruction methods. In addition error estimates are computed, which include uncertainty from both sampling and analysis errors. Using this method, little global variance can be reconstructed before the 1880s because data are too sparse to resolve enough modes for that period. Error estimates indicate that except in the North Atlantic ERSST is of limited value before 1880, when the uncertainty of the near-global average is almost as large as the signal. In most regions, the uncertainty decreases through most of the period and is smallest after 1950. The large-scale variations of ERSST are broadly consistent with those associated with the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) reconstruction produced by the Met Office. There are differences due to both the use of different historical bias corrections as well as different data and analysis procedures, but these differences do not change the overall character of the SST variations. Procedures used here produce a smoother analysis compared to HadISST. The smoother ERSST has the advantage of filtering out more noise at the possible cost of filtering out some real variations when sampling is sparse. A rotated EOF analysis of the ERSST anomalies shows that the dominant modes of variation include ENSO and modes associated with trends. Projection of the HadISST data onto the rotated eigenvectors produces time series similar to those for ERSST, indicating that the dominant modes of variation are consistent in both.