Indo-Pacific Warm Pool (IPWP) region is a key region which is sensitive to climate change. Therefore a comprehensive knowledge of the variability of sea surface salinity (SSS) for the entire IPWP region on decadal time scale is of great importance. This study mainly focuses on the spatiotemporal variability of SSS in the IPWP region, using conventional empirical orthogonal function (EOF) analysis, the lead–lag correlation analysis and long-term trend analysis. Barnett and Preisendorfer’s improved Canonical Correlation Analysis (BPCCA) is also applied to examine the covariation of SSS and freshwater flux (F) under climate change of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). We found that SSS in most eastern Indian Ocean regions show obviously decreasing trends, whereas slightly increasing trends are found in most western Pacific Ocean regions. The leading two EOF modes both have a strong correlation with MEI and lead MEI by approximately 5 and 2 months, respectively. The spatial distribution of the canonical modes for SSS and F are very similar, except for slightly zonal deviation of the anomaly center between SSS and F, which can be explained by horizontal advection.
Aquarius is a satellite designed for measuring sea surface salinity (SSS). The Aquarius measurements may be influenced by marine environmental factors. The result is the inconsistency of data quality under different conditions. Although the data qualities have been considered in some previous studies, they have only been used for data screening. Base on this, a quality weighting method is proposed in this paper. The key differences between our method and traditional method, is way of weighting the data. In the present paper, both distance and data quality are considered in the weighting process. After the weight is determined, the weighted average fitting (WAF) method is used to calculate the grid SSS value. Then weekly 0.25°×0.25°gridded SSS fields between 40°S and 40°N are generated, covering the period from September 2011 through May 2015. The error statistics are calculated and the result shows that the root-mean squared difference (RMSD) is about 0.18 psu, which can improve the accuracy by about 31%. Therefore the method proposed in this paper could improve the precise of SSS filed with higher temporal and spatial resolution significantly.
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