Abstract

Abstract NOAA’s Extended Reconstructed Sea Surface Temperature (SST; ERSST) is operational global SST product based on in situ observations, which has been widely used monitoring and assessing global ocean climate particularly El Niño and Southern Oscillation (ENSO) events. ERSSTv5 and its predecessors, however, encountered two shortcomings: (1) low SST spatial-variabilities in the data sparse regions before the 1950s and (2) low performance scores against in situ observations after the 1970s. The first problem has been mitigated in this Part study of ERSSTv6 by removing a 3-month running average and applying an interpolation method using an artificial neural network (ANN). The improvements of ANN method over an empirical orthogonal teleconnection (EOT) method used in previous versions were assessed against validation, testing, and observation datasets. In comparison with ERSSTv5, the spatial correlation coefficient (SCC) with reference to observations increases by 5%, and root-mean-square-difference (RMSD) with reference to observations decreases by 0.03°C in ERSSTv6. The improvements of SCC and RMSD are more pronounced in the tropical Pacific and the Southern Hemisphere oceans between 60°S and 30°S. The second problem has been addressed separately in our Part II study.