Youmin Tang

Reliable seasonal predictions of Atlantic hurricanes heavily rely on the predictions of sea surface temperature anomalies (SSTA) in the main development region (SSTA _MDR ) and its relative changes against the SSTA over the whole tropical region (RSST). By analyzing the prediction skill of Community Earth System Model ensemble mean hindcasts, we identified significant multidecadal variations in the seasonal prediction skill of both SSTA _MDR and RSST during hurricane seasons. Both RSST and SSTA _MDR are more predictable in the beginning and ending periods of the 20th century and less predictable in between. The multidecadal variations of the seasonal prediction skill of RSST are dominated by that of SSTA _MDR , which can be explained mostly by its variability changes in July. Further, mixed layer heat budget analyses based on SODA subsurface oceanic temperature and NOAA‐20C reanalysis data show that the multidecadal variations of the seasonal prediction skill of SSTA _MDR and its variability changes in July are closely linked to the mixed layer depth fluctuations, which is caused by turbulent heat flux changes induced by surface wind anomalies.

The Benguela Niño/Niña events manifest the anomalous signals of sea surface temperature (SST) in the upwelling region off the west coast of Africa. These events are triggered by the interannual modulation of either equatorial waves or local atmospheric forcing. In the present study, the mechanism that equatorial waves induce the coastal SST anomaly is investigated in terms of the transfer episodes of wave energy by both numerical experiments and reanalysis data. The result of numerical experiments suggests that most of the coastal events can be reproduced by subseasonal wind forcing with an interannual modulation that excites oceanic waves of the first three baroclinic modes. The transfer routes of wave energy illustrate the role of wave dynamics that explains how the interannual variability of SST in the equatorial Atlantic is connected with that in coastal regions. The linearly superposed sign-indefinite potential energy flux owing to waves manifests its capability of sufficiently displacing the thermocline so as to trigger the coastal events. The diagnosis of wave energy for reanalysis data further confirms that there are clear wave energy routes from the equatorial Atlantic to the coastal region, along which different source regions for waves in the first-four modes are found, jointly contributing to the 2019 Niño event.

This study studies the predictability of the sea surface temperature anomaly (SSTA) for the eastern Indian Ocean (EIO) over the past 142 years from 1880–2021 by exploring its fastest error growth. This is implemented by applying the climate‐relevant singular vector (CSV) analysis onto a tropical intermediate coupled model. Emphasis places on identifying the optimal perturbation that can result in the fastest prediction error growth of the EIO SSTA. It is found that, with the 6‐month lead prediction, the spatial pattern of the optimal initial perturbations is characterized by a negative SSTA pole with the diverged surface wind in the southern Indian Ocean and a positive SSTA pole with the converged surface wind in the eastern Pacific. The growth rate of optimal initial perturbation, as measured by the leading singular value, exhibits significant interannual variation consistent with the variation of the SSTA in EIO. Specifically, when the SSTA in the EIO is colder, the prediction error grows more rapidly, and conversely, when the SSTA is warmer, the error grows more slowly. The results of the dynamical analysis show that a cold SSTA background strengthens Ekman feedback through both atmospheric and oceanic responses, leading to faster growth of prediction errors. This study enhances our understanding to the predictability in the Indian Ocean, in particular the role of EIO in the Indian Ocean dipole predictability.

The model‐dependency has been a challenging issue for traditional data assimilation‐based targeted observational method. This study developed a new strategy to address this challenge using multiple‐model prediction ensemble. It was found that while the ensemble size reaches a sufficiently large number the optimal observational sites detected tend to stable and model‐independent. This new finding answers the long‐standing challenge question on the model dependence in targeted observational analysis, offering an efficient and objective way to identify optimal observational sites. With this strategy, we designed an optimal observational array in the tropical Pacific for the El Niño‐Southern Oscillation (ENSO) prediction using the multiple historical simulation data sets from Coupled Model Intercomparison Project Phase 6 and reanalysis data sets. Sensitive experiments show that while number of data sets reaches 12, a robust optimal observational array is obtained. The first 10 optimal observational sites, mostly located in the equatorial central eastern Pacific, can reduce initial uncertainties by 67%. This was further confirmed by the observation system simulation experiments, which is implemented by the Ensemble Adjustment Kalman Filter assimilation system developed in the Community Earth System Model. This newly developed model‐independent strategy makes it feasible to design a robust oceanic observational network for ENSO prediction even using the current targeted observational algorithm, well serving the goal of international Tropical Pacific Observation System 2020 project.

Over the past two decades, forecasting the El Niño–Southern Oscillation has become increasingly challenging, primarily due to the inherent difficulty in predicting Central Pacific (CP) El Niño events. In this study, we present a detailed analysis by explicitly quantifying the role of the tropical Indian Ocean (IO) on the initial uncertainty of CP-El Niño predictions. We identify optimal observation sites within the tropical IO that significantly reduce this uncertainty and validate their efficacy through observing system simulation experiments within a fully coupled climate prediction system. Our results demonstrate a remarkable achievement in the root mean squared errors of the prediction initial conditions using Community Earth System Model. This study not only enriches our understanding of the interplay between the tropical IO and CP-El Niño but also offers a concrete strategy for enhancing CP-El Niño forecasts, advancing seasonal prediction capabilities, and bolstering global resilience against the escalating frequency of CP-El Niño events.

In this study, we employ the Model‐based Analog Forecast approach to conduct Indian Ocean Dipole (IOD) hindcasts from 1982 to 2017, using 18 CMIP6 models. We focus on the skill diversity among different climate models, with particular attention to how the cold tongue bias affects IOD predictions. Our findings reveal a significant diversity in IOD prediction skills across the CMIP6 models. Skillful predictions are observed for lead times ranging from 1 to 4 months, depending on the model. Also, this study identifies a direct relationship between cold tongue bias and IOD prediction skills. Models that exhibit a more pronounced cold tongue bias tend to show weaker El Niño‐Southern Oscillation influences over the tropical Indian Ocean, which in turn leads to a reduction in IOD prediction skills. This study provides valuable insights into the factors driving the diversity in IOD predictions and highlights the potential for improving IOD forecasting skills.

Sea surface temperature anomalies (SSTAs) over the North Atlantic (NA) have a significant impact on the weather and climate in both local and remote regions. This study first evaluated the seasonal prediction skill of NA SSTA using the North American multi-model ensemble and found that its performance is limited across various regions and seasons. Therefore, this study constructs models based on the long short-term memory (LSTM) network machine learning method to improve the seasonal prediction of NA SSTA. Results show that the seasonal prediction skill can be significantly improved by LSTM models since they show higher capability to capture nonlinear processes such as the impact of El Niño-Southern Oscillation on NA SSTA. This study shows the great potential of the LSTM model on the seasonal prediction of NA SSTA and provides new clues to improve the seasonal predictions of SSTA in other regions.

Utilizing ensemble hindcast data from the Community Earth System Model (CESM) spanning the years 1900–2014, the multidecadal changes in the seasonal potential predictability of the winter Pacific–North American (PNA) teleconnection pattern and associated circulation anomalies have been investigated by using an information-based metric of relative entropy and the method of the most predictable component analysis. Results show that the seasonal potential predictability of winter PNA has significant multidecadal changes, with values much higher at the two ends of the twentieth century and much lower in between particularly in the 1930s and 1940s. The changes in the seasonal potential predictability of winter PNA are mostly reflected by the temporal evolutions of PNA rather than the location changes of active centers. Further, the changes are mostly contributed by the external forcing of El Niño–Southern Oscillation (ENSO)-related sea surface temperature anomalies in tropical central and eastern Pacific. In particular, the combined effects of lower amplitudes, reduced persistence, and a more eastward shift in warming centers lead to the reduced seasonal potential predictability of PNA and associated circulation changes in the 1930s and 1940s. Significance Statement: Seasonal prediction of the winter Pacific–North American (PNA) teleconnection pattern and associated circulation anomalies is very important due to its profound climate impacts. Understanding the multidecadal fluctuations and its driving sources of the potential predictability of winter PNA and associated circulation anomalies are meaningful for skillful seasonal prediction of winter PNA and circulation anomalies as well as related climate variations. This study for the first time shows that the multidecadal fluctuations of the potential predictability of winter PNA are quite significant and the changes are mostly reflected by its temporal evolutions rather than spatial shifts of active centers. Furthermore, this study shows that the strength, persistence, and warming center locations of ENSO-related sea surface temperatures in tropical Pacific play a crucial role on the multidecadal changes of the potential predictability of winter PNA and associated circulation anomalies.