ENSO predictability over the past 137 years based on a CESM ensemble prediction system

In this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model to evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different timescales. To our knowledge, this is the first assessment of ENSO predictability using a long-term ensemble hindcast with a complicated coupled general circulation model (CGCM). Our results indicate that both the dispersion component (DC) and signal component (SC) contribute to the interannual variation of ENSO predictability (measured by relative entropy, RE). In detail, the SC is more important for ENSO events, whereas the DC is of comparable important for short lead times and in weak ENSO signal years. The SC dominates the seasonal variation of ENSO predictability, and an abrupt decrease in signal intensity results in the spring predictability barrier feature of ENSO. At the interdecadal scale, the SC controls the variability of ENSO predictability, while the magnitude of ENSO predictability is determined by the DC. The seasonal and interdecadal variations of ENSO predictability in the CGCM are generally consistent with results based on intermediate complexity and hybrid coupled models. However, the DC has a greater contribution in the CGCM than that in the intermediate complexity and hybrid coupled models.

Role of Atlantic SST in the Recent ENSO Predictability Change in NMME Model Hindcasts

The present study analyses the possible change in the seasonal prediction skill of El Nino Southern Oscillation (ENSO) in association with the reported climate modification in the tropical Pacific during the early 21st century. The hindcasts of nine models that participated in the National Multimodel Ensemble Project (NMME) are used for the analysis. Both the boreal summer (JJAS) and winter (DJF) seasons ENSO indices from 4 months and 1-month lead for the period 1981-2018/19 are studied. The analysis shows that all the models have reduced interannual variability as observations for both seasons. There is not much skill (both actual and potential) difference for DJF season for all the models for both the lead times. Summer skill loss for Feb IC is more for models such as CanSIPv2, CCSM3 and NEMO, while it is minimum for CCSM4. There is an increase of skill for Feb IC hindcasts for three GFDL models for JJAS season. Most of the models failed to simulate the ENSO events during the second period. The summer season ENSO pattern in the recent period are influenced by spring time north Atlantic SST anomalies. The models with maximum decrease of skill after 2000 fail to simulate the tropical Atlantic SST anomalies during the initialization months and also the summer season SST anomalies induced by these SST anomalies. The models with better or close to observed patterns with Atlantic SST induced ENSO patterns are only able to maintain the same skill as previous decades.

ENSO-ASC 1.0.0: ENSO Deep Learning Forecast Model with a Multivariate Air–Sea Coupler

ENSO is an extremely sophisticated air-sea coupling phenomenon, the development and decay of which are usually modulated by the energy interactions between multiple physical variables. In this paper, we design a multivariate air-sea coupler (ASC) based on graph using features of multiple physical variables. On the basis of the coupler, an ENSO deep learning forecast model (named ENSO-ASC) is proposed, whose structure is adapted to the characteristics of the ENSO dynamics, including the encoder/decoder for capturing/restoring the multi-scale spatial-temporal correlations, and two attention components for grasping the different air-sea coupling strength on different start calendar month and varied contributions of physical variables in ENSO amplitudes. In addition, two datasets at different resolutions are used to train the model. We firstly tune the model performance to optimal and compare it with the other state-of-the-art ENSO deep learning forecast models. Then, we evaluate the ENSO forecast skill from the contributions of different predictors, the effective lead time with the different start calendar months, and the forecast spatial uncertainties, further analyze the underlying ENSO mechanisms. Finally, we make ENSO predictions over the validation period from 2014 to 2020. Experiment results demonstrate that ENSO-ASC outperforms the other models. Sea surface temperature (SST) and zonal wind are two crucial predictors. The correlation skill of Niño3.4 index is over 0.78/0.65/0.5 within the lead time of 6/12/18 months. From two heat map analyses, we also discover the common challenges in ENSO predictability, such as the forecasting skills declining faster when making forecasts through June-July-August and the forecast errors more likely showing up in the western-central equatorial Pacific with a longer lead time. ENSO-ASC can simulate El Niño and La Niña events with different strengths. The forecasted SST and wind patterns reflect obvious Bjerknes positive feedback mechanism. These results indicate the effectiveness and superiority of our model with the multivariate air-sea coupler in predicting sophisticated ENSO and analyzing the underlying dynamic mechanisms.

The asymmetric influence of ocean heat content on ENSO predictability in the CNRM-CM5 coupled general circulation model

Unusually high western Pacific oceanic heat content often leads to El Niño about 1 year later, while unusually low heat content leads to La Niña. Here, we investigate if El Niño Southern Oscillation (ENSO) predictability also depends on the initial state recharge, and discuss the underlying mechanisms. To that end, we use the CNRM-CM5 model, which has a reasonable representation of the main observed ENSO characteristics, asymmetries and feedbacks. Observations and a 1007-years long CNRM-CM5 simulation indicate that discharged states evolve more systematically into La Niña events than recharged states into neutral states or El Niño events. We ran 70-members ensemble experiments in a perfect-model setting, initialized in boreal fall from either recharged or discharged western Pacific heat content, sampling the full range of corresponding ENSO phases. Predictability measures based both on spread and signal-to-noise ratio confirm that discharged states yield a more predictable ENSO outcome one year later than recharged states. As expected from recharge oscillator theory, recharged states evolve into positive central Pacific sea surface temperature anomalies in boreal spring, inducing stronger and more variable Westerly Wind Event activity and a fast growth of the ensemble spread during summer and fall. This also enhances the positive wind stress feedback in fall, but the effect is offset by changes in thermocline and heat flux feedbacks. The state-dependent component of westerly wind events is thus the most likely cause for the predictability asymmetry in CNRM-CM5, although changes in the low-frequency wind stress feedback may also contribute.

A theory of Spring Persistence Barrier on ENSO. Part I: The role of ENSO period

In this paper, we investigate the role of El Niño-Southern Oscillation (ENSO) period 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 a 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 the maximum persistence decline lag months. In particular, the phase of a 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 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. Similar phase shift is also identified for the SPB in the damped ENSO regime, unstable ENSO regime and observation. Our theory provides a null hypothesis for the role of ENSO period in SPB.

Contributions of tropical-extratropical oceans to the prediction skill of ENSO after 2000

<p>The skills of most ENSO prediction models have declined significantly since 2000. This decline may be due to a weakening of the correlation between tropical predictors and ENSO. Moreover, the effects of extratropical ocean variability on ENSO have increased during this period. To improve ENSO predictability, we investigate the influence of the tropical-extratropical Atlantic and Pacific sea surface temperature(SST) on ENSO during the periods of pre-2000 and post-2000. We find that the influence of the northern tropical Atlantic(NTA) SST on ENSO has significantly increase since 2000. Meanwhile, there is a much earlier and stronger SST responses between NTA SST and ENSO over the central-eastern Pacific during June–July–August in the post-2000 period compared with the pre-2000 period. Furthermore, the extratropical Pacific SST predictors for ENSO still retain a ~10-month lead time after 2000. We use SST signals in the extratropical Atlantic and Pacific to predict ENSO using a statistical prediction model. These results reveal a significant improvement in ENSO prediction skills. These results indicate that the Atlantic and Pacific SSTAs can make substantial contributions to ENSO prediction, and can be further used to enhance ENSO predictability after 2000.</p>