The Effect of Successive Correction on Variability Estimates for Climatological Datasets

Abstract

The effects on a dataset of smoothing by successive correction have been investigated. The resulting spatial resolution is estimated using a distribution of ship reports from a sample month. Although the smoothing uses the same characteristic radii over the whole globe, the resulting resolution is spatially variable and, in data-sparse regions, will show large month-to-month variability with changes in the distribution of the ship tracks. The climatological dataset, which is gridded at 1°, is shown to have a typical resolution of 3°. In some regions the resolution is much coarser. Using sea surface temperature as an example, it is shown that the successive correction procedure as used, for example, in a recent climatological dataset, is not successful in removing all of the noise in data-sparse regions. Additionally, the well-defined intermonthly variability in the main shipping lanes, where there are many observations, is degraded by the influence of poorer-quality data in the surrounding regions. This typically increases the intermonthly variability estimates in the shipping lanes by a factor of 2. Further, the reduction of intermonthly variability, by up to a factor of 6, in highly variable regions such as the Gulf Stream, is greater than can be accounted for by noise in the individual ship reports. This reduction is due to the removal of small-scale variability by the smoothing process. Removal of coherent and persistent small-scale variability has an effect on the temporal and spatial characteristics of the data. It is suggested that smoothing by successive correction, although commonly used, is poorly suited to such spatially inhomogenous data as those from the merchant ships. However, the effect of successive correction on variability analysis using empirical orthogonal functions (EOFs) is shown to be small for the most significant modes of variability identified in the Gulf Stream region. This is because the EOF analysis picks out the large-scale variability in the highest-order modes. However, too large a fraction of the total variance explained is ascribed to the large-scale modes of variability. Variability with small spatial scales is more likely to be significant if raw data are used in the EOF analysis. Little significance should be given to EOF modes with spatial scales similar to the size of gaps between shipping lanes; this varies from region to region.