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Previous studies suggested that tropical sea surface salinity (SSS) can influence tropical Pacific sea surface temperature (SST) through mixing and entrainment and thus it may be a signal for El Niño‐Southern Oscillation (ENSO) prediction. This paper explores the influence of SSS on ENSO spring predictability barrier (SPB) using an empirical dynamic model ‐ Linear Inverse Model (LIM). By coupling and decoupling SSS in the LIM, we find that tropical Pacific SSS plays a significant role in weakening both Central‐Pacific and Eastern‐Pacific ENSO SPB. The evolution of optimal initial structure also shows the importance of SSS dynamics in ENSO. We found an SSS mode that plays the dominant role in SSS impacting ENSO prediction. By the analysis of lead‐lag correlation, we find that this mode can induce easterlies during the spring, which finally leads to a La Niña‐like SST pattern in the winter through zonal advective and thermocline feedbacks. Plain Language Summary: The spring predictability barrier (SPB) is a phenomenon of forecast skill reduction of the El Niño‐Southern Oscillation (ENSO) when it comes to the boreal spring, regardless of the initial month. Sea surface salinity (SSS) can influence sea surface temperature by altering sea surface density and thus it may be a signal for ENSO prediction. Using a linear dynamical model, we find that SSS plays an important role in improving the forecast skill of ENSO and weakening SPB. We further find that SSS can induce the easterlies during the spring, which finally leads to a La Niña‐like SST pattern. Our study suggests that SSS can be used to predict ENSO about 1 year later. Key Points: A linear dynamical model suggests that taking sea surface salinity (SSS) into consideration can strongly weaken Central‐Pacific and Eastern‐Pacific El Niño‐Southern Oscillation (ENSO) spring predictability barrier (SPB)A new SSS mode is found to be important for weakening ENSO SPBThe SSS mode can predict ENSO events about 1 year earlier by inducing sea surface temperature and wind anomalies in the early spring [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We develop a theory in the a seasonal stochastic climate model for understanding the general features of a seasonal persistence predictability barrier (PB), which is characterized by a band of maximum persistence decline in autocorrelation function (ACF) phase-locked to a particular season. The model is the Langevin equation with a seasonal cycle incorporated in the damping rate and the magnitude of noise forcing. The theory determines the forcing threshold, the timing and intensity of the seasonal PB as a function of the damping rate and the magnitude of seasonal forcing. It is found that a seasonal PB is an intrinsic feature of a seasonal stochastic climate system forced by either a seasonal growthrate or seasonal noise forcing. A seasonal PB emerges after the magnitude of the seasonal forcing, relative to the damping rate, exceeds a modest threshold. The timing of the seasonal PB is determined by the decline of the seasonal forcing as well as the delay associated with the damping rate. As such, for realistically weak damping, the PB season is locked close to the minimum SST variance under the growthrate forcing, butand after the minimum SST variance under the noise forcing. The intensity of the PB is determined mainly by the amplitude of the seasonal cycle of the forcing. The theory is further found to be able to explain the general features of the seasonal PB of the observed SST variability over the world ocean. Our theory provides a general framework and a null hypothesis for the understanding of the seasonal PB of climate variability in general. [ABSTRACT FROM AUTHOR]