Extended Reconstructed Sea Surface Temperature Version 6 (ERSSTv6): An Artificial Neural Network Approach

NOAA’s Extended Reconstructed Sea Surface Temperature (SST; ERSST) is an operational global SST product based on in situ observations, which has been widely used in monitoring and assessing global ocean climate particularly El Niño and Southern Oscillation (ENSO) events. ERSSTv5 and its predecessors, however, suffered from 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. These problems have been mitigated in ERSSTv6 by applying an interpolation method using artificial neural network (ANN). The improvements of ANN method over 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 over 1850-2022). The increase of SCC and decrease of RMSD are more pronounced in the tropical Pacific and the Southern Hemisphere oceans between 60°S and 30°S.