Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system

Ajin Cho,Hee-Sook Ji,Yu-Kyung Hyun,Sang-Wook Yeh,Yong-Jin Tak,Sang-Min Lee,Hajoon Song,Soon-Il An

doi:10.1007/s00382-022-06228-x

Abstract

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluated the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast, particularly focusing on the western North Pacific (WNP), where the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface net heat flux. At the beginning of model integration, the ocean receives excessive heat from the atmosphere because of the predominant positive bias in the downward shortwave radiation (SW), which rapidly decreased within a few days as cloud cover builds. Then, the negative bias in the latent heat (LH) flux increases over time and induces a negative bias in the surface net heat flux. Although the magnitude of the negative bias in LH flux gradually decreases, it remains the most significant contributor to the negative bias in the net heat flux bias for more than 250 days. Uncoupled ocean model experiments showed that the ocean model is unlikely to be the primary source of the SST bias.

Highlights

Seasonal forecast systems (SFSs) aim to provide an overview of the atmospheric and oceanic conditions a few months in advance (Shukla et al, 2000; Troccoli, 2010)
To confirm that the surface forcing is the main source of the sea surface temperature (SST) bias in the western North Pacific (WNP) in Global Seasonal forecast system version 5 (GloSea5), we evaluate the SST of the uncoupled ocean model, NEMO, integrated from the same initial conditions as for GloSea5
GloSea5 is a seasonal forecast system used in many institutes, and the diagnostics of the SST bias and possible errors in the system are critical as they can influence air-sea interactions and seasonal prediction

Summary

Introduction

Seasonal forecast systems (SFSs) aim to provide an overview of the atmospheric and oceanic conditions a few months in advance (Shukla et al, 2000; Troccoli, 2010). The SST with a lead time of six months, which is used for the seasonal prediction, has an overall cold bias in the WNP and a warm bias in the northeast Pacific, and the magnitude of the bias exceeds 1.0 ◦ C in some areas (Fig. 1(b)).

Results

Conclusion