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El Niño–Southern Oscillation (ENSO) events tend to peak at the end of the calendar year, a phenomenon called ENSO phase locking. This phase locking is a fundamental ENSO property that is determined by its basic dynamics. The conceptual ENSO recharge oscillator (RO) model is adopted to examine the ENSO phase-locking behavior in terms of its peak time, strength of phase locking, and asymmetry between El Niño and La Niña events. The RO model reproduces the main phase-locking characteristics found in observations, and the results show that the phase locking of ENSO is mainly dominated by the seasonal modulation of ENSO growth/decay rate. In addition, the linear/nonlinear mechanism of ENSO phase preference/phase locking is investigated using RO model. The difference between the nonlinear phase-locking mechanism and linear phase-preference mechanism is largely smoothed out in the presence of noise forcing. Further, the impact on ENSO phase locking from annual cycle modulation of the growth/decay rate, stochastic forcing, nonlinearity, and linear frequency are examined in the RO model. The preferred month of ENSO peak time depends critically on the phase and strength of the seasonal modulation of the ENSO growth/decay rate. Furthermore, the strength of phase locking is mainly controlled by the linear growth/decay rate, the amplitude of seasonal modulation of growth/decay rate, the amplitude of noise, the SST-dependent factor of multiplicative noise, and the linear frequency. The asymmetry of the sharpness of ENSO phase locking is induced by the asymmetric effect of state-dependent noise forcing in El Niño and La Niña events.

The characteristics of El Niño–Southern Oscillation (ENSO) phase-locking in observations and CMIP5 and CMIP6 models are examined in this study. Two metrics based on the peaking month histogram for all El Niño and La Niña events are adopted to delineate the basic features of ENSO phase-locking in terms of the preferred calendar month and strength of this preference. It turns out that most models are poor at simulating the ENSO phase-locking, either showing little peak strength or peaking at the wrong seasons. By deriving ENSO’s linear dynamics based on the conceptual recharge oscillator (RO) framework through the seasonal linear inverse model (sLIM) approach, various simulated phase-locking behaviors of CMIP models are systematically investigated in comparison with observations. In observations, phase-locking is mainly attributed to the seasonal modulation of ENSO’s SST growth rate. In contrast, in a significant portion of CMIP models, phase-locking is codetermined by the seasonal modulations of both SST growth and phase transition rates. Further study of the joint effects of SST growth and phase transition rates suggests that for simulating realistic winter peak ENSO phase-locking with the right dynamics, climate models need to have four key factors in the right combination: 1) correct phase of SST growth rate modulation peaking at the fall, 2) large-enough amplitude for the annual cycle in growth rate, 3) small amplitude of semiannual cycle in growth rate, and 4) small amplitude of seasonal modulation in SST phase transition rate.

Previous studies suggested that fast‐decay El Niño events are more favorable in generating the western North Pacific anticyclone (WNPAC) in the decaying summer. However, we found that this is not the case for all fast‐decay El Niño events. By comparing two groups of fast‐decay El Niño events with significant and insignificant WNPAC in the following summer, we found that the westward extension of the equatorial Pacific cold sea surface temperature anomalies (SSTA) and the subtropical central‐north Pacific cold SSTA play important roles in the generation and intensification of the WNPAC during decaying summer. Further analyses indicated that the internal atmospheric mode—North Pacific Oscillation during boreal spring can affect the formation of the cold SSTA over the subtropical central‐north Pacific and the westward extension of the equatorial Pacific cold SSTA during summer. Additional effects of tropical Indian and Atlantic forcing on the maintenance of the WNPAC are also shown. Plain Language Summary The boreal summer western North Pacific anticyclone (WNPAC) is an important low‐level circulation over the western Pacific, which can have great impacts on the East Asia climate. Previous studies pointed out that the fast‐decay El Niño events are favorable in inducing the WNPAC in the decaying summer. However, our observational and modeling results suggested that not all the fast‐decay El Niño events can generate WNPAC in the decaying summer. By dividing fast‐decay El Niño events into two groups, one with significant WNPAC in the following summer and the other without, we found that the difference in the equatorial Pacific cold sea surface temperature anomalies (SSTA) westward extension, the subtropical central‐northern Pacific cold SSTA and the tropical Indian/Atlantic warm SSTA are important in causing the diverse response of the WNPAC to the fast‐decay El Niño events during decaying summer. Further analyses indicated that the internal atmospheric mode—North Pacific Oscillation during boreal spring can affect the cold SSTA over the subtropical central‐north Pacific and the westward extension of the equatorial Pacific cold SSTA during boreal summer. The tropical Indian and Atlantic warming, can also directly influence the WNPAC intensity or feedback to the equatorial and subtropical Pacific cooling to influence the WNPAC. Key Points Fast‐decay El Niño is more favorable in generating western North Pacific anticyclone (WNPAC) in the decaying summer but this is not the case for all fast‐decay El Niño Equatorial Pacific, subtropical Pacific cold sea surface temperature anomalies (SSTA) and tropical Indian/Atlantic warm SSTA in summer are key to the diverse response of WNPAC The spring North Pacific Oscillation is important in causing the diverse equatorial and subtropical Pacific cold SSTA forcing in summer

El Niño–Southern Oscillation (ENSO), the dominant mode of interannual variability in the tropical Pacific, is well known to affect the extratropical climate via atmospheric teleconnections. Extratropical atmospheric variability may in turn influence the occurrence of ENSO events. The winter North Pacific Oscillation (NPO), as the secondary dominant mode of atmospheric variability over the North Pacific, has been recognized as a potential precursor for ENSO development. This study demonstrates that the preexisting winter NPO signal is primarily excited by sea surface temperature (SST) anomalies in the equatorial western–central Pacific. During ENSO years with a preceding winter NPO signal, which accounts for approximately 60% of ENSO events observed in 1979–2021, significant SST anomalies emerge in the equatorial western–central Pacific in the preceding autumn and winter. The concurrent presence of local convection anomalies can act as a catalyst for NPO-like atmospheric circulation anomalies. In contrast, during other ENSO years, significant SST anomalies are not observed in the equatorial western–central Pacific during the preceding winter, and correspondingly, the NPO signal is absent. Ensemble simulations using an atmospheric general circulation model driven by observed SST anomalies in the tropical western–central Pacific can well reproduce the interannual variability of observed NPO. Therefore, an alternative explanation for the observed NPO–ENSO relationship is that the preceding winter NPO is a companion to ENSO development, driven by the precursory SST signal in the equatorial western–central Pacific. Our results suggest that the lagged relationship between ENSO and the NPO involves a tropical–extratropical two-way coupling rather than a purely stochastic forcing of the extratropical atmosphere on ENSO.

The decay pace of El Niño can significantly modify its impacts on the Asian climate during the post‐El Niño summer. Hence, accurately reproducing the observed decay pace in state‐of‐art coupled models is essential for realistic climate simulations. In the Coupled Model Intercomparison Project models, El Niño decays slower than observed. This slower decay can be attributed to weaker‐than‐observed air‐sea coupling in the models that causes a weaker atmospheric convective response and smaller westerly anomalies along the equatorial Pacific during the El Niño life cycle. The smaller westerly anomalies result in a slower discharge of equatorial ocean heat, weaker negative/positive thermocline anomalies along/off the equator and thus a weaker meridional gradient of the thermocline anomalies. This weakens the easterly current anomalies, diminishes the zonal advection feedback, and ultimately slows the decay pace of El Niño in the models. Plain Language Summary El Niño is a crucial air‐sea coupled phenomenon in the tropical Pacific, significantly impacting weather and climate worldwide. According to the seminal recharge‐discharge oscillator theory, El Niño occurs when the tropical Pacific contains higher‐than‐normal heat content, which is then discharged, reaching a minimum at the end of El Niño life cycle. Our study shows that in state‐of‐the‐art Coupled Model Intercomparison Project (CMIP6) coupled models, El Niño decays slower than observed. This is primarily caused by the weaker‐than‐observed air‐sea coupling in the models that leads to a weaker surface westerly response to the sea surface temperature anomaly. As the ocean heat discharge is driven by the surface wind curl, the weaker surface westerly response results in slower heat discharge and thus a slower decay of El Niño in the models compared to observations. Key Points El Niño decays slower in the Coupled Model Intercomparison Project models compared to observations primarily due to weaker zonal advection feedback This weaker zonal advection feedback is caused by the unrealistic simulation of zonal current anomalies due to a slower discharging process The slower discharging process results from a weaker westerly response to the sea surface temperature anomaly

El Niño is generally phase‐locked to the boreal winter but displays significant variability in its onset timing, contributing to its diverse climate impacts. The physical mechanisms driving this variability remain inadequately understood. This study demonstrates that onset of El Niño events can occur over a broad range of months from March to September, with its onset timing closely linked to the precondition of oceanic recharged state and the occurrence of westerly wind bursts (WWBs) in the preceding spring. A stronger recharged state and increased frequency of WWBs promote earlier onset by efficiently transporting warm water to the equatorial eastern Pacific. Supporting evidence from MIROC6 simulations and a conceptual model underscores the crucial roles of both the recharged state and WWBs in determining the timing of El Niño onset. These results enhance our understanding of El Niño dynamics and hold important implications for seasonal climate prediction. Plain Language Summary El Niño, a significant climate phenomenon, arises from interactions between the ocean and atmosphere in the tropical Pacific, profoundly affecting global weather patterns. Currently, predicting the precise timing of the El Niño onset remains a scientific challenge. In this study, we find that the onset of El Niño varies considerably, ranging from March to September. This variability is strongly linked to the precondition of oceanic heat buildup (recharged state) and the occurrence of westerly wind bursts (WWBs). A stronger recharged state and increased frequency of WWBs in the preceding spring can lead to an earlier El Niño onset by accelerating the eastward movement of warm water toward the central‐eastern equatorial Pacific. These findings deepen our understanding of El Niño dynamics and offers valuable insights for improving seasonal climate predictions. Key Points El Niño exhibits considerable variation in its onset timing, ranging from March to September The onset of El Niño is closely linked to the preconditioned oceanic recharge state and the occurrence of westerly wind bursts MIROC6 simulations and a conceptual model support the curial roles of recharge conditions and WWBs in determining El Niño onset timing

El Niño-Southern Oscillation (ENSO) is the dominant interseasonal–interannual variability in the tropical Pacific and substantial efforts have been dedicated to predicting its occurrence and variability because of its extensive global impacts. However, ENSO predictability has been reduced in the 21 st century, and the impact of extratropical atmosphere on the tropics has intensified during the past 2 decades, making the ENSO more complicated and harder to predict. Here, by combining tropical preconditions/ocean–atmosphere interaction with extratropical precursors, we provide a novel approach to noticeably increase the ENSO prediction skill beyond the spring predictability barrier. The success of increasing the prediction skill results mainly from the longer lead-time of the extratropical–tropical ocean-to-atmosphere interaction process, especially for the first 2 decades of the 21 st century.

Accurately predicting the El Niño–Southern Oscillation (ENSO) remains a key challenge in climate science. An evaluation of the International Research Institute for Climate and Society (IRI) real‐time ENSO forecast system reveals an asymmetry in prediction skill linked to consecutive La Niña events. ENSO forecasts show consistently high skill for El Niño events, whereas La Niña forecasts exhibit greater uncertainty. Specifically, first‐year La Niña events demonstrate prediction skill comparable to that of El Niño events; however, most consecutive La Niña events display much lower predictability. This asymmetry is related to differences in ENSO dynamics. El Niño and first‐year La Niña events typically follow the linear recharge–discharge oscillator framework, supporting their high predictability. In contrast, consecutive La Niña events tend to deviate from this framework, likely due to enhanced nonlinear processes that constrain their forecast skill. Improved representation of these nonlinear processes may help enhance prediction skill for consecutive La Niña events.

This study examines historical simulations of ENSO in the E3SM-1-0, CESM2, and GFDL-CM4 climate models, provided by three leading U.S. modeling centers as part of the Coupled Model Intercomparison Project phase 6 (CMIP6). These new models have made substantial progress in simulating ENSO's key features, including amplitude, time scale, spatial patterns, phase-locking, the spring persistence barrier, and recharge oscillator dynamics. However, some important features of ENSO are still a challenge to simulate. In the central and eastern equatorial Pacific, the models' weaker-than-observed subsurface zonal current anomalies and zonal temperature gradient anomalies serve to weaken the nonlinear zonal advection of subsurface temperatures, leading to insufficient warm/cold asymmetry of ENSO's sea surface temperature anomalies (SSTA). In the western equatorial Pacific, the models' excessive simulated zonal SST gradients amplify their zonal temperature advection, causing their SSTAto extend farther west than observed. The models underestimate both ENSO's positive dynamic feedbacks (due to insufficient zonal wind stress responses to SSTA) and its thermodynamic damping (due to insufficient convective cloud shading of eastern Pacific SSTA during warm events); compensation between these biases leads to realistic linear growth rates for ENSO, but for somewhat unrealistic reasons. The models also exhibit stronger-than-observed feedbacks onto eastern equatorial Pacific SSTAs from thermocline depth anomalies, which accelerates the transitions between events and shortens the simulated ENSO period relative to observations. Implications for diagnosing and simulating ENSO in climate models are discussed.

Smart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational resources. This paper proposes a recurrent probabilistic neural network (RPNN) for toothbrush posture recognition that demonstrates the advantages of low computational resources as a requirement, along with high recognition accuracy and efficiency. The RPNN model is trained for toothbrush posture recognition and brushing position and then monitors the correctness and integrity of the Bass Brushing Technique. Compared to conventional deep learning models, the recognition accuracy of RPNN is 99.08% in our experiments, which is 16.2% higher than that of the Convolutional Neural Network (CNN) and 21.21% higher than the Long Short-Term Memory (LSTM) model. The model we used can greatly reduce the computing power of hardware devices, and thus, our system can be used directly on smartphones.