Abstract
Abstract. We have designed a method for testing the quality of multidecadal analyses of sea surface temperature (SST) in regional seas by using a set of high-quality local SST observations. In recognizing that local data may reflect local effects, we focus on the dominant empirical orthogonal functions (EOFs) of the local data and of the localized data of the gridded SST analyses. We examine the patterns, variability, and trends of the principal components. This method is applied to examine three different SST analyses, i.e., HadISST1, ERSST, and COBE SST. They have been assessed using a newly constructed high-quality dataset of SST at 26 coastal stations along the Chinese coast in 1960–2015, which underwent careful examination with respect to quality and a number of corrections for inhomogeneities. The three gridded analyses perform generally well from 1960 to 2015, in particular since 1980. However, for the pre-satellite period prior to the 1980s, the analyses differ among each other and show some inconsistencies with the local data, such as artificial break points, periods of bias, and differences in trends. We conclude that gridded SST analyses need improvement in the pre-satellite period (prior to the 1980s) by reexamining in detail archives of local quality-controlled SST data in many data-sparse regions of the world.
Highlights
Sea surface temperature (SST) is a key parameter for climate change assessments
In order to test the validity of these gridded SST datasets along the coast of China, SST records for the period of 1960–2015 at a total of 26 Chinese coastal hydrological stations coast are used
The SST series are fully consistent with these surface air temperature (SAT) series. When this exercise is repeated with Climate Research Unit (CRU) TS 3.24.01 instead of the in situ SAT series, we find a similar consistency
Summary
Sea surface temperature (SST) is a key parameter for climate change assessments. It is significantly associated with many atmospheric and oceanographic modes, such as the Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD) (Saji et al, 1999; Mantua and Hare, 2002; Yeh and Kim, 2010). There are some differences in quality control and gap-filling choices regarding when and where observations are sparse, in early record periods and coastal areas (Huang et al, 2015; Li et al, 2017) These differences indicate some uncertainties in these SST analyses. In order to test the validity of these gridded SST datasets along the coast of China, SST records for the period of 1960–2015 at a total of 26 Chinese coastal hydrological stations coast are used All of these in situ SST data from 1960 to 2015 are provided by the National Marine Data and Information Service (NMDIS) of China and have been quality-controlled and homogenized by Li et al (2018).
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