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

Abstract

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.

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

“…Duan and Wu (2014) identified a “summer prediction barrier” in Pacific decadal oscillation‐related SST anomalies, emphasizing the challenges in mid‐latitude predictions from the perspective of initial error growth in perfect model predictability experiments. The seasonal predictability of SST anomalies in the KOE also exhibited seasonal dependence (Wu & Duan, 2018; Wu et al., 2016). The predictability of North Pacific SST requires further study.…”

Evaluation of FIO-ESM v1.0 Seasonal Prediction Skills Over the North Pacific

“…In the western central North Pacific, initial error growth also exhibits a distinctive seasonal dependence. The prediction skill is lowest in summer, giving rise to the summer predictability barrier (Zhao et al, 2012;Duan and Wu, 2014;Wu et al, 2016). Previous researches suggested that a shallow mixed-layer depth in the North Pacific accompanied by strong oceanic stratification in summer could result in a relatively weak correlation between SSTAs in the summer and temperature in the following winter (Alexander, 1999;Jacox et al, 2019), which could lead to poor prediction of SSTAs.…”