Adjusting for sampling density in grid box land and ocean surface temperature time series
Adjusting for sampling density in grid box land and ocean surface temperature time series
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DOI：
10.1029/2000jd900564
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发表时间：
2001-02
影响因子：
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通讯作者：
P. Jones;T. Osborn;K. Briffa;C. Folland;E. B. Horton;L. Alexander;D. Parker;N. Rayner
中科院分区：
文献类型：
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作者：
P. Jones;T. Osborn;K. Briffa;C. Folland;E. B. Horton;L. Alexander;D. Parker;N. Rayner
我们开发了调整网格箱平均温度时间序列的方法，用于改变贡献数据数量对方差的影响。由于数据的采样特征不同，我们在陆地和海洋上使用了不同的技术。其结果是通过与提供站点或观测的数量成反比的量来抑制网格框上的平均温度异常。方差校正影响所有格框时间序列，但对数据稀疏的海洋区域影响最大。经调整后，格框陆地和海洋表面温度数据集不受人为方差变化的影响，这些变化可能会影响极端值发生率的分析结果。我们结合调整后的陆地表面空气温度和海洋表面温度数据集，并应用有限的空间插值。我们的程序对半球和全球温度异常序列的影响很小。
We develop methods for adjusting grid box average temperature time series for the effects on variance of changing numbers of contributing data. Owing to the different sampling characteristics of the data, we use different techniques over land and ocean. The result is to damp average temperature anomalies over a grid box by an amount inversely related to the number of contributing stations or observations. Variance corrections influence all grid box time series but have their greatest effects over data sparse oceanic regions. After adjustment, the grid box land and ocean surface temperature data sets are unaffected by artificial variance changes which might affect, in particular, the results of analyses of the incidence of extreme values. We combine the adjusted land surface air temperature and sea surface temperature data sets and apply a limited spatial interpolation. The effects of our procedures on hemispheric and global temperature anomaly series are small.