Seasonal predictability of sea surface temperature anomalies over the Kuroshio-Oyashio Extension: Low in summer and high in winter

The seasonal predictability of sea surface temperature anomalies (SSTA) in the Kuroshio-Oyashio Extension (KOE) is explored by performing perfect model predictability experiments from the viewpoint of initial error growth in a global coupled model. It is found that prediction errors of KOE-SSTA always increase in the boreal summer and decrease in the boreal winter. This leads to smaller (larger) prediction errors and higher (lower) prediction skills in boreal winter (summer). This seasonal characteristic of the KOE-SSTA error growth implies a season-dependent predictability that is lower in summer and higher in winter. The mechanism responsible for error growth associated with seasonal predictability is also explored. The error increase in summer and error decrease in winter in the KOE-SSTA are both largely attributed to the seasonal evolution of latent heat flux error and mean temperature advection by vertical current error in the KOE region, both of which are forced by the prediction error of 1 month leading zonal wind stress per unit mass for the mixed layer over the KOE region. The shallowest (deepest) mixed layer in summer (winter) amplifies (reduces) the forcing of zonal wind stress errors on the error growth of KOE-SSTA, thereby causing the seasonal evolution of prediction errors of KOE-SSTA and ultimately resulting in the season-dependent predictability of the KOE-SSTA, i.e., low in summer and high in winter.