Warm bias of cold sea surface temperatures in the East Sea (Japan Sea)

Abstract

The East/Japan Sea (ES) is regarded as a natural laboratory for predicting future changes in the global Meridional Overturning Circulation (MOC) under warming climates, as the ES MOC (EMOC) changes rapidly in comparison with the global MOC. Specifically, intermediate and deep-water masses of the ES are formed in its northern reaches via wind-driven subduction of surface water, and convection from the surface to deep layers during the winter. Accordingly, it is important to investigate the variation of winter sea surface temperatures (SSTs) for characterizing and predicting the EMOC; however, global SST products must be corrected and optimized for the ES, as they fail to incorporate the local marginal sea conditions. Here, a warm bias in cold SST was identified for three SST products, such as optimally interpolated sea surface temperatures (OISSTs), microwave SSTs, and operational SST and sea ice analysis products, suggesting the potential usefulness of a correction method incorporating Argo float data. When comparing OISSTs with 5 m temperature estimates from Argo float data during 2000–2020, under the assumption that the mixed layer depth is deeper than 8 m, a nearly normalized histogram of biases was produced, and the robust warm bias (mean = 0.9°C) was detected in the range of relatively cold SSTs (-2°C to 10°C), yet no significant bias in warm SSTs (> 10°C) was found. To minimize the warm bias in cold SSTs, OISSTs were corrected with an inverse 4th-order polynomial fitting method. Subsequently, the mean bias between the corrected SSTs and top depth temperatures of Argo float data was significantly reduced to less than 0.1°C. Moreover, the warm bias of cold SSTs resulted in severe underestimations of the outcropping area colder than 1°C over the northern region, as well as the occurrence period of 1°C to 5°C SSTs in the north-western ES. These results highlight the importance of local bias correction for SST products, and it is expected that the newly suggested correction method will improve model predictions of EMOC change by enhancing SST data quality in the northern ES.