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Abstract
The impact of the observed sea surface temperature (SST) frequency in the model initialization on the prediction of the boreal summer intraseasonal oscillation (BSISO) over the Western North Pacific (WNP) is investigated using the Beijing Climate Center Climate System Model. Three sets of hindcast experiments initialized by the observed monthly, weekly and daily SST data (referred to as the Exp_MSST, Exp_WSST and Exp_DSST, respectively) are conducted with 3-month integration starting from the 1st, 11th, and 21st day of each month in June–August during 2000–2014, respectively. The results show that the useful prediction skill of BSISO index reaches out to about 10 days in the Exp_MSST, and further increases by 1–2 days in the Exp_WSST and Exp_DSST. The skill differences among various hindcast experiments are especially apparent during the forecast time of 6–20 days. Focusing on the strong BSISO cases in this period, the BSISO activity and its related moist static energy (MSE) characteristics over the WNP are further diagnosed. It is found that from the Exp_MSST to the Exp_WSST and Exp_DSST, the enhanced BSISO prediction skill is associated with the more realistic variations of intraseasonal MSE and its tendency. Among the various budget terms that dominate the MSE tendency, the surface latent heat flux and MSE advection are evidently improved, with reduction of mean biases by more than 21% and 10%, respectively. Therefore, the better reproduced MSE variation may contribute to the more skillful BSISO forecast through improving the surface evaporation as well as atmospheric convergence and divergence that related to the BSISO activity. Our findings suggest the necessity of increasing the observed SST frequency (i.e., from monthly to weekly or daily) in the initialization process of coupled models to enhance the actual BSISO predictability, since some current subseasonal forecast operations and researches still use relatively low-frequency SST observations for the model initialization.
Highlights
The boreal summer intraseasonal oscillation (BSISO) is an essential mode of atmospheric variability with a period of 10–60 days over the Asian summer monsoon region (Yasunari 1980; Zhu and Wang 1993)
Based on the prediction of three sets of hindcast experiments with BCC-CSM2 initialized by sea surface temperature (SST) observations of different temporal frequencies, this study examines the impact of observed SST frequency in the model initialization on the prediction of BSISO over the Western North Pacific (WNP)
A 15-year free run simulation shows that the model itself can reasonably reproduce the spatial structure of the BSISO variability and BSISO mode, the northward propagation of intraseasonal anomalies of precipitation, circulation and SST over the WNP, as well as the convection-circulation phase relationship
Summary
The boreal summer intraseasonal oscillation (BSISO) is an essential mode of atmospheric variability with a period of 10–60 days over the Asian summer monsoon region (Yasunari 1980; Zhu and Wang 1993). Despite great progress in the climate model development, the capability in BSISO simulation and prediction is still limited (Waliser et al 2003; Sobel et al 2008; Fang et al 2016). In the latest Subseasonal to Seasonal (S2S) Prediction Project, most state-of-the-art operational models exhibit useful BSISO forecast skill of about 2 weeks in advance (Jie et al 2017). This skill is much lower than the potential BSISO predictability limit of about 5 weeks (Ding et al 2011). Using the hindcasts by several models in the Intraseasonal Variability Hindcast Experiment (ISVHE) project, Lee et al (2015) found that the multi-model mean actual prediction skill is clearly lower than the theoretical predictability of BSISO, indicating that there is large room to improve the BSISO prediction
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