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1. Predictability of El Niño Duration Based on the Onset Timing

   https://doi.org/10.1175/JCLI-D-19-0963.1  

   Wu, Xian ; Okumura, Yuko M. ; DiNezio, Pedro N. ( February 2021 , Journal of Climate)

   null (Ed.)

   Abstract Analysis of observational data and a long control simulation of the Community Earth System Model, version 1 (CESM1), shows that El Niño events developing in boreal spring to early summer usually terminate after peaking in winter, whereas those developing after summer tend to persist into the second year. To test the predictability of El Niño duration based on the onset timing, perfect model predictions were conducted for three El Niño events developing in April or September in the CESM1 control simulation. For each event, 30-member ensemble simulations are initialized with the same oceanic conditions in the onset month but with slightly different atmospheric conditions and integrated for 2 years. The CESM1 successfully predicts the termination of El Niño after the peak in 95% of the April-initialized simulations and the continuation of El Niño into the second year in 83% of the September-initialized simulations. The predictable component of El Niño duration arises from the initial oceanic conditions that affect the timing and magnitude of negative feedback within the equatorial Pacific, as well as from the Indian and Atlantic Oceans. The ensemble spread of El Niño duration, on the other hand, originates from surface wind variability over the western equatorial Pacific in spring following the peak. The wind variability causes a larger spread in the September-initialized than the April-initialized ensemble simulations due to weaker negative feedback in spring. These results indicate potential predictability of El Niño events beyond the current operational forecasts by 1 year. 

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2. What Controls the Duration of El Niño and La Niña Events?

   https://doi.org/10.1175/JCLI-D-18-0681.1  

   Wu, Xian ; Okumura, Yuko M. ; DiNezio, Pedro N. ( September 2019 , Journal of Climate)

   The temporal evolution of El Niño and La Niña varies greatly from event to event. To understand the dynamical processes controlling the duration of El Niño and La Niña events, a suite of observational data and a long control simulation of the Community Earth System Model, version 1, are analyzed. Both observational and model analyses show that the duration of El Niño is strongly affected by the timing of onset. El Niño events that develop early tend to terminate quickly after the mature phase because of the early arrival of delayed negative oceanic feedback and fast adjustments of the tropical Atlantic and Indian Oceans to the tropical Pacific Ocean warming. The duration of La Niña events is, on the other hand, strongly influenced by the amplitude of preceding warm events. La Niña events preceded by a strong warm event tend to persist into the second year because of large initial discharge of the equatorial oceanic heat content and delayed adjustments of the tropical Atlantic and Indian Oceans to the tropical Pacific cooling. For both El Niño and La Niña, the interbasin sea surface temperature (SST) adjustments reduce the anomalous SST gradient toward the tropical Pacific and weaken surface wind anomalies over the western equatorial Pacific, hastening the event termination. Other factors external to the dynamics of El Niño–Southern Oscillation, such as coupled variability in the tropical Atlantic and Indian Oceans and atmospheric variability over the North Pacific, also contribute to the diversity of event duration. 

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3. Effective ENSO Amplitude Forecasts Based on Oceanic and Atmospheric Preconditions

   https://doi.org/10.1175/JCLI-D-21-0383.1  

   Xuan, Zhuolin ; Zhang, Wenjun ; Jiang, Feng ; Jin, Fei-Fei ( June 2022 , Journal of Climate)

   Abstract Current climate models have relatively high skills in predicting El Niño–Southern Oscillation (ENSO) phase (i.e., El Niño, neutral, and La Niña), once leaping over the spring predictability barrier. However, it is still a big challenge to realistically forecast the ENSO amplitude, for instance, whether a predicted event will be strong, moderate, or weak. Here we demonstrate that the accumulated westerly wind events (WWEs)/easterly wind surges (EWSs) and oceanic recharged/discharged states are both of importance in accurate ENSO amplitude forecasts. El Niño and La Niña events exhibit asymmetric temporal and spatial features in the atmospheric and oceanic preconditions. El Niño amplitude at the peak season is closely associated with the accumulated WWEs over the eastern equatorial Pacific from the previous December to May and the recharged state in the western equatorial Pacific during February. In contrast, the amplitude of La Niña events is sensitive to the accumulated EWSs over the equatorial western Pacific from the previous November to April and the discharged state extending from the equatorial western to central Pacific during February. Considering these asymmetric atmospheric and oceanic preconditions of El Niño and La Niña cases, a statistical model is established to accurately forecast the ENSO amplitude at its mature phase during 1982–2018, which is validated to be robust based on a 1-yr cross-validation and independent sample tests. The feasibility and the limitation of the established statistical model are also discussed by examining its practical utility. 

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4. Extended-range statistical ENSO prediction through operator-theoretic techniques for nonlinear dynamics

   https://doi.org/10.1038/s41598-020-59128-7  

   Wang, Xinyang ; Slawinska, Joanna ; Giannakis, Dimitrios ( February 2020 , Scientific Reports)

   Forecasting the El Niño-Southern Oscillation (ENSO) has been a subject of vigorous research due to the important role of the phenomenon in climate dynamics and its worldwide socioeconomic impacts. Over the past decades, numerous models for ENSO prediction have been developed, among which statistical models approximating ENSO evolution by linear dynamics have received significant attention owing to their simplicity and comparable forecast skill to first-principles models at short lead times. Yet, due to highly nonlinear and chaotic dynamics (particularly during ENSO initiation), such models have limited skill for longer-term forecasts beyond half a year. To resolve this limitation, here we employ a new nonparametric statistical approach based on analog forecasting, called kernel analog forecasting (KAF), which avoids assumptions on the underlying dynamics through the use of nonlinear kernel methods for machine learning and dimension reduction of high-dimensional datasets. Through a rigorous connection with Koopman operator theory for dynamical systems, KAF yields statistically optimal predictions of future ENSO states as conditional expectations, given noisy and potentially incomplete data at forecast initialization. Here, using industrial-era Indo-Pacific sea surface temperature (SST) as training data, the method is shown to successfully predict the Niño 3.4 index in a 1998–2017 verification period out to a 10-month lead, which corresponds to an increase of 3–8 months (depending on the decade) over a benchmark linear inverse model (LIM), while significantly improving upon the ENSO predictability “spring barrier”. In particular, KAF successfully predicts the historic 2015/16 El Niño at initialization times as early as June 2015, which is comparable to the skill of current dynamical models. An analysis of a 1300-yr control integration of a comprehensive climate model (CCSM4) further demonstrates that the enhanced predictability afforded by KAF holds over potentially much longer leads, extending to 24 months versus 18 months in the benchmark LIM. Probabilistic forecasts for the occurrence of El Niño/La Niña events are also performed and assessed via information-theoretic metrics, showing an improvement of skill over LIM approaches, thus opening an avenue for environmental risk assessment relevant in a variety of contexts.

    

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5. The Equatorial Pacific Cold Tongue Bias in CESM1 and Its Influence on ENSO Forecasts

   https://doi.org/10.1175/JCLI-D-21-0470.1  

   Wu, Xian ; Okumura, Yuko M. ; DiNezio, Pedro N. ; Yeager, Stephen G. ; Deser, Clara ( June 2022 , Journal of Climate)

   The mean-state bias and the associated forecast errors of the El Niño–Southern Oscillation (ENSO) are investigated in a suite of 2-yr-lead retrospective forecasts conducted with the Community Earth System Model, version 1, for 1954–2015. The equatorial Pacific cold tongue in the forecasts is too strong and extends excessively westward due to a combination of the model’s inherent climatological bias, initialization imbalance, and errors in initial ocean data. The forecasts show a stronger cold tongue bias in the first year than that inherent to the model due to the imbalance between initial subsurface oceanic states and model dynamics. The cold tongue bias affects not only the pattern and amplitude but also the duration of ENSO in the forecasts by altering ocean–atmosphere feedbacks. The predicted sea surface temperature anomalies related to ENSO extend to the far western equatorial Pacific during boreal summer when the cold tongue bias is strong, and the predicted ENSO anomalies are too weak in the central-eastern equatorial Pacific. The forecast errors of pattern and amplitude subsequently lead to errors in ENSO phase transition by affecting the amplitude of the negative thermocline feedback in the equatorial Pacific and tropical interbasin adjustments during the mature phase of ENSO. These ENSO forecast errors further degrade the predictions of wintertime atmospheric teleconnections, land surface air temperature, and rainfall anomalies over the Northern Hemisphere. These mean-state and ENSO forecast biases are more pronounced in forecasts initialized in boreal spring–summer than other seasons due to the seasonal intensification of the Bjerknes feedback.

    

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