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Yishuai Jin and Zhengyu Liu

Abstract

In this paper, we investigate the role of the period of El Niño–Southern Oscillation (ENSO) in the spring persistence barrier (SPB), mainly using the neutral recharge oscillator (NRO) model both analytically and numerically. It is suggested that a shorter ENSO period strengthens the SPB. Moreover, in contrast to the strict phase locking of the SPB in the Langevin equation, the phase of the SPB is no longer locked exactly to a particular time of the calendar year in the NRO model. Instead, the phases of the SPB for different initial months shift earlier with lag months of maximum persistence decline. In particular, the phase of the SPB will be shifted from the early summer to early spring, corresponding to the initial months of the early half year and later half year. This feature demonstrates that for the later half year, ENSO predictability decreases as the presence of ENSO period. For realistic parameters, the range of the phase change is modest, smaller than 2–3 months. A similar phase shift is also identified for the SPB in the damped ENSO regime, unstable ENSO regime, and observations. Our theory provides a null hypothesis for the role of ENSO period with regard to the SPB.

Open access
Zhengyu Liu, Yishuai Jin, and Xinyao Rong

Abstract

A theory is developed in a stochastic climate model for understanding the general features of the seasonal predictability barrier (PB), which is characterized by a band of maximum decline in autocorrelation function phase-locked to a particular season. Our theory determines the forcing threshold, timing, and intensity of the seasonal PB as a function of the damping rate and seasonal forcing. A seasonal PB is found to be an intrinsic feature of a stochastic climate system forced by either seasonal growth rate or seasonal noise forcing. A PB is generated when the seasonal forcing, relative to the damping rate, exceeds a modest threshold. Once generated, all the PBs occur in the same calendar month, forming a seasonal PB. The PB season is determined by the decline of the seasonal forcing as well as the delayed response associated with damping. As such, for a realistic weak damping, the PB season is locked close to the minimum SST variance under the seasonal growth-rate forcing, but after the minimum SST variance under the seasonal noise forcing. The intensity of the PB is determined mainly by the amplitude of the seasonal forcing. The theory is able to explain the general features of the seasonal PB of the observed SST variability over the world. In the tropics, a seasonal PB is generated mainly by a strong seasonal growth rate, whereas in the extratropics a seasonal PB is generated mainly by a strong seasonal noise forcing. Our theory provides a general framework for the understanding of the seasonal PB of climate variability.

Open access
Yishuai Jin, Zhengyu Liu, Chengfei He, and Yuchu Zhao

Abstract

The mechanism of the seasonal persistence barrier (SPB) is studied in the framework of an autoregressive (AR) model. In contrast to the seasonal variance, whose minimum is modulated mainly by the minimum growth rate or noise forcing, the SPB is caused primarily by the declining growth rate or increasing noise forcing, instead of the minimum/maximum of the growth rate or noise forcing. In other words, the SPB is caused by the declining signal-to-noise ratio (SNR) rather than the weakest SNR. In a weakly damped system, the phase of the SPB is delayed from that of declining SNR by about a season. The mechanism is further applied to explain the observed SST variability in the tropical and North Pacific. For the tropical Pacific, the spring SPB could be caused by the decreasing growth rate from September to March and weak annual mean damping rate, instead of the minimum growth rate in spring. Over the North Pacific, the increasing noise forcing from March to June may lead to the summer SPB. Our mechanism provides a null hypothesis for understanding the SPB of climate variability.

Open access
Yuchu Zhao, Zhengyu Liu, Fei Zheng, and Yishuai Jin

Abstract

We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

Open access