El Nino-Southern Oscillation Prediction Research Articles

Do asymmetries in ENSO predictability arise from different recharged states?

Prospects for El Nino–Southern Oscillation (ENSO) predictability at long lead-times lie in the subsurface oceanic memory along the equatorial Pacific. Long considered a reliable precursor to ENSO, the oceanic heat content in springtime, often referred to as the recharge-discharge, is considered the most promising indicator of an ENSO event to come. In this study, we utilize January initialized hindcasts from the North American Multi-model Ensemble (NMME) over 1982–2010 to confront the hypothesis that the springtime recharge is a skillful predictor of ENSO the following winter. We find that the NMME ensemble mean predictions for the springtime recharge are highly skilled, even at a 10-months lead. Overall, as an independent predictor of ENSO, the springtime recharge-discharge tips the scale towards like-sign ENSO, but the spread of ENSO outcomes remains large. In both observations and the NMME predictions, recharged (discharged) states rarely evolve into La Nina (El Nino) events, yet an ENSO-neutral state is as likely to occur after a preconditioned state as is a like-sign ENSO event, particularly in observations. However, more often than in observations, the initialized predictions follow springtime recharged, neutral, and discharged states with El Nino, ENSO-neutral, and La Nina events, respectively, indicating that the NMME underestimates the uncertainty in nature. Predictions from initially recharged and discharged states also produce comparable signal-to-noise ratios in December ENSO predictions over the hindcast period. Therefore, in the realistic forecast setting considered, neither a recharged nor a discharged state produces a more predictable ENSO outcome, which is at odds with conclusions from recent predictability studies.

Seasonal Cycle of Background in the Tropical Pacific as a Cause of ENSO Spring Persistence Barrier

AbstractStatistical model results suggest that the declining growth rate from autumn to spring is the key to cause El Niño‐Southern Oscillation (ENSO) spring persistence barrier (SPB). Using a dynamical approach, we develop the physical mechanisms responsible for ENSO SPB in the framework of recharge oscillator by adding a seasonally varying Bjerknes (BJ) stability index and linking it with ENSO growth rate. By decomposing BJ index, it is indicated that seasonal thermodynamic damping and thermocline positive feedback play an important role in determining the ENSO SPB. We further show that the increasing/decreasing upper‐level cloud/low‐level cloud and the deepening thermocline from autumn to spring are the main factors to control the SPB of ENSO. Our proposed mechanisms also have useful implications for the understanding of ENSO prediction.

The Optimal Precursors for ENSO Events Depicted Using the Gradient-definition-based Method in an Intermediate Coupled Model

The predictability of El Nino-Southern Oscillation (ENSO) has been an important area of study for years. Searching for the optimal precursor (OPR) of ENSO occurrence is an effective way to understand its predictability. The CNOP (conditional nonlinear optimal perturbation), one of the most effective ways to depict the predictability of ENSO, is adopted to study the optimal sea surface temperature (SST) precursors (SST-OPRs) of ENSO in the IOCAS ICM (intermediate coupled model developed at the Institute of Oceanology, Chinese Academy of Sciences). To seek the SST-OPRs of ENSO in the ICM, non-ENSO events simulated by the ICM are chosen as the basic state. Then, the gradient-definition-based method (GD method) is employed to solve the CNOP for different initial months of the basic years to obtain the SST-OPRs. The experimental results show that the obtained SST-OPRs present a positive anomaly signal in the western-central equatorial Pacific, and obvious differences exist in the patterns between the different seasonal SST-OPRs along the equatorial western-central Pacific, showing seasonal dependence to some extent. Furthermore, the non-El Nino events can eventually evolve into El Nino events when the SST-OPRs are superimposed on the corresponding seasons; the peaks of the Nino3.4 index occur at the ends of the years, which is consistent with the evolution of the real El Nino. These results show that the GD method is an effective way to obtain SST-OPRs for ENSO events in the ICM. Moreover, the OPRs for ENSO depicted using the GD method provide useful information for finding the early signal of ENSO in the ICM.

The Relationship Between South Pacific Atmospheric Internal Variability and ENSO in the North American Multimodel Ensemble Phase‐II Models

AbstractDespite substantial progress made in the theoretical understanding and practical prediction of the El Niño‐Southern Oscillation (ENSO), accurate predictions of particular ENSO characteristics (e.g., evolution, intensity, and spatial pattern) remain challenging. Using two models from the North American Multimodel Ensemble (NMME) Phase‐II hindcasts, we find that the austral winter atmospheric internal variability is a key determinant of how the South Pacific atmospheric circulation responds to concurrent tropical Pacific sea surface temperature anomalies. While this internal variability may not trigger ENSO onsets, it regulates the southeasterly trades and contributes to thermodynamic feedbacks that grow into an ENSO‐like structure during the following austral summer. The difference in the simulation of South Pacific atmospheric variability amongst ensemble members appears to be a significant source of the inter‐member spread in ENSO predictions. Monitoring South Pacific atmospheric variability provides an opportunity to improve the prediction of ENSO intensity and flavor with about a two‐season lead time.

The effects of model climate bias on ENSO variability and ensemble prediction

New methods are presented for determining the role of coupled ocean-atmosphere model climate bias on the strength and variability of the El Nino-Southern Oscillation (ENSO) and on the seasonal ensemble prediction of El Nino and La Nina events. An intermediate complexity model with a global atmosphere coupled to a Pacific basin ocean is executed with parallelised algorithms to produce computationally efficient year-long forecasts of large ensembles of coupled flow fields, beginning every month between 1980 and 1999. Firstly, the model is provided with forcing functions that reproduce the average annual cycle of climatology of the atmosphere and ocean based on reanalysed observations. We also configure the model to generate realistic ENSO fluctuations. Next, an ensemble prediction scheme is employed which produces perturbations that amplify rapidly over a month. These perturbations are added to the analyses and give the initial conditions for the ensemble forecasts. The skill of the forecasts is presented and the dependency on the annual and ENSO cycles determined. Secondly, we replace the forcing functions in our model with functions that reproduce the averaged annual cycles of climatology of two state of the art, comprehensive Coupled General Circulation Models. The changes in skill of subsequent ensemble forecasts elucidate the roles of model bias in error growth and potential predictability.
 
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Prediction of the ENSO and EQUINOO indices during June–September using a deep learning method

AbstractThe Equatorial Indian Ocean Oscillation (EQUINOO) and El Niño Southern Oscillation (ENSO) are important climatic oscillations over the Indian and Pacific oceans influencing the inter‐annual variation of the Indian monsoon. The study of these indices, including their relationship and influence over various climatic phenomena, is the main focus in the literature. However, an attempt is made here to predict the indices for different temporal periods. Though ENSO prediction is established by many statistical and numerical models, the prediction of the EQUINOO index is not much studied. A deep‐learning method using an autoencoder is proposed for the prediction of the EQUINOO and ENSO. An autoencoder assists in feature learning. The learned features are ranked using linear and nonlinear correlation studies. This assists in identifying a set of potential predictors, used for indices prediction, with an ensemble of regression trees and decision forest models. Predictors identified by nonlinear correlation are observed to predict with better accuracy as compared with linear correlation. The predicted indices show high correlation against the observed values. The EQUINOO prediction is provided with a high lead of 7 months with a 0.88 correlation co‐efficient (p < 0.001) and the ENSO with a lead of 1 month with a 0.87 correlation co‐efficient (p < 0.001) between the observed and predicted indices. Moreover, the proposed method proves efficient in predicting the positive or negative index values with an appropriate sign. The ENSO prediction by the proposed approach is observed to be comparable with the existing models.

An Improved ENSO Ensemble Forecasting Strategy Based on Multiple Coupled Model Initialization Parameters

AbstractAccurate prediction of El Niño–Southern Oscillation (ENSO) at relatively long timescales is propitious to forecasting other climate variables and meteorological disasters. Here, we use a coupled general circulation model, ICMv2, to investigate the influence of an initialization parameter, sea surface temperature (SST)‐nudging strength, on ENSO prediction skill in the model's 1981–2010 hindcast experiments and suggest a multiple initialization parameter ensemble (MIPE) forecast as a new ensemble forecasting strategy to improve ENSO prediction skill. Different SST‐nudging strengths produce different ENSO prediction skill via the generated various initial values. Selecting initial values closest to the observed SST (represented by reanalysis data) and increasing the ensemble size is inefficient in improving the skill of single initialization parameter ensemble (SIPE) forecasts. With ensemble members from different SST‐nudging strength groups, the MIPE forecasts are significantly more skillful than the SIPE forecasts at 1‐ to 10‐month lead time. More than 96% of 20,000 MIPE experiments generated by a Monte Carlo approach have larger anomaly correlation coefficient than SIPE at 1‐ to 9‐month lead time. Our findings suggest that MIPE forecasting is another efficient strategy that can improve ENSO prediction skill besides multimodel and multimember ensembles using different initial values with random disturbances.

Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts

AbstractDespite recent progress in seasonal forecast systems, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic‐dynamical model (SDM) to predict the IOD using seasonally modulated El Niño–Southern Oscillation (ENSO) forcing together with a seasonally modulated Indian Ocean coupled ocean‐atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System version 2 and the Scale Interaction Experiment–Frontier system. The improvements mainly originate from better prediction of ENSO‐dependent IOD events and from reducing false alarms. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal‐to‐noise ratio of the system. Therefore, potential future ENSO improvements in models should translate to more skillful IOD predictions.

Covariations between the Indian Ocean dipole and ENSO: a modeling study

The coevolution of the Indian Ocean dipole (IOD) and El Nino-Southern Oscillation (ENSO) is examined using both observational data and coupled global climate model simulations. The covariability of IOD and ENSO is analyzed by applying the extended empirical orthogonal function (EEOF) method to the surface and subsurface ocean temperatures in the tropical Indian Ocean and western Pacific. The first EEOF mode shows the evolution of IOD that lags ENSO, whereas the second mode exhibits the transition from a dipole mode to a basin-wide mode in the tropical Indian Ocean that leads ENSO. The lead-lag relationships between IOD and ENSO are consistent with two-way interactions between them. A comparison between two 500-year model simulations with and without ENSO shows that ENSO can enhance the variability of IOD at interannual time scale. The influence of ENSO on the IOD intensity is larger for the eastern pole than for the western pole, and further, is stronger in the negative IOD phase than in the positive phase. The influence of IOD on ENSO is demonstrated by the improvement of ENSO prediction using sea surface temperature (SST) in the tropical Indian Ocean as an ENSO precursor. The improvement of the ENSO forecast skill is found at both a short lead time (0 month) and long leads (10–15 months). The SST in the western pole has more predictive value than in the eastern pole. The eastward propagation of surface and subsurface temperature signals from the western Indian Ocean that precedes the development of heat content anomaly in the tropical western Pacific is the key for extending the lead time for ENSO prediction. Our results are consistent with previously reported findings but highlight the spatial–temporal evolution of the ENSO-IOD system. It is also illustrated that IOD would have been more helpful in predicting the 1997/98 El Nino than the 2015/16 El Nino.

ENSO Regime Changes Responsible for Decadal Phase Relationship Variations Between ENSO Sea Surface Temperature and Warm Water Volume

AbstractThe relationship between the equatorial Pacific warm water volume (WWV) and El Niño–Southern Oscillation (ENSO) sea surface temperature (SST) has varied considerably on decadal timescales. These changes are strongly related to the occurrence frequency of central Pacific (CP) ENSO events. While both eastern Pacific (EP) and CP ENSO events show clear signatures of WWV recharge/discharge, their phase‐lag relationships between WWV and Niño3.4 SST are different. The WWV usually leads the Niño3.4 SST by two to three seasons during EP ENSO, while the lead time is reduced to one season during CP ENSO. The different phase‐lag relationships can be explained by distinct periodicities of the two ENSO types. Hence, ENSO regime changes associated with decadal predominance of either EP or CP ENSO events can give rise to decadal variations in the statistical WWV‐ENSO SST relationship. We emphasize the importance of identifying these different ENSO types and potentially different ENSO regimes to assess ENSO predictability.

Use of El Niño–Southern Oscillation related seasonal precipitation predictability in developing regions for potential societal benefit

AbstractSome of the biggest emerging market economies include countries in South America, Asia and Africa. Broad‐scale political and developmental similarities (e.g., societally impactful developmental challenges related to climate variability) offer opportunities for comparative research resulting in potentially improved understanding of the complexities of various climate adaptation interventions including disaster risk reduction. Countries or geographical regions of the world significantly affected by climate extremes may consider collaboration on issues such as understanding and modelling of the climate system, especially when there is a common, dominant and somewhat plausible climate mode such as the El Niño–Southern Oscillation (ENSO) affecting the regions' climate variability. Better ENSO and subsequent climate predictions alone, however, are not enough to reduce the risks associated with such events. The socio‐economic and political context in which climate finds expression and in which climate forecasts have potential value also need to be understood. Here we present seasonal precipitation forecast skill over 20 geographical regions including emerging or developing regions, but also a few developed regions, in order to rank their ENSO‐related seasonal rainfall predictability in an attempt to cluster regions of similar ENSO climate predictability. We then also provide some of the broad contours to investigate the level of human “development” within these clusters in order to begin to understand some of the socio‐economic factors that configure vulnerabilities. Such profiles begin to show some areas of macro‐level vulnerability that may then provide further possible inter‐area collaborations, albeit at very gross level scales.

Simulated future changes in ENSO dynamics in the framework of the linear recharge oscillator model

This study analysed the changes in El Nino Southern Oscillation (ENSO) dynamics as they are simulated in 25 models of the CMIP5 simulations for the RCP8.5 scenario relative to the historical control simulation. The ENSO linear recharge oscillator (ReOsc) framework is used to focus on changes in the growth rate of eastern equatorial Pacific sea surface temperature (T) and mean equatorial Pacific thermocline depth (h) anomalies, the coupling between the two and the noise forcing driving the ENSO variability. We further focused on the feedbacks controlling the growth rate of T, namely the Bjerknes wind to sea surface temperature (SST) feedback, the atmospheric net heat flux and the residual oceanic feedback. We find significant changes in nearly all of these important elements of the ENSO dynamics, despite the fact that the ensemble shows very little changes in the overall ENSO variability. The growth rate of T weakens resulting from a combination of increased negative atmospheric net heat flux feedbacks, increased positive Bjerknes wind-SST feedback and increased residual oceanic feedbacks. Further notable changes are, an increase in the growth rate of h and a stronger coupling of T to h. Sensitivity analysis of the ReOsc model can explain why these strong dynamical changes lead to effectively no changes in the overall ENSO variability, but are likely to affect the predictability of ENSO.

Improving the CPC’s ENSO Forecasts using Bayesian model averaging

Statistical and dynamical model simulations have been commonly used separately in El Nino–Southern Oscillation (ENSO) prediction. Current models are imperfect representations of ENSO and each of them has strength and weakness for capturing different aspects in ENSO prediction. Thus, it is important to utilize the results from a variety of different models. The Bayesian model averaging (BMA) is an effective tool not only in describing uncertainties associated with each model simulation but also providing the forecast performance of different models. The BMA method was developed to combine the NCEP/CPC three statistical and one dynamical model forecasts of seasonal Ocean Nino Index (ONI) from 1982 to 2010. The BMA weights were derived directly from the predictive performance of the combined models. The highly efficient expectation–maximization (EM) algorithm was used to achieve numerical solutions. We show that the BMA method can be used to assess the performance of the individual models and assign greater weights to better performing models. The continuous ranked probability score is applied to evaluate the BMA probability forecasts. As an elaboration of the reliability diagram, the attributes diagram is used that includes the calibration function, refinement distribution, and reference lines. The combination of statistical and dynamical models is found to provide a more skillful prediction of ENSO than only using a suite of statistical models, a single bias-corrected dynamical model, or the equally weighted average forecasts from all four models. Probability forecasts of El Nino events based only on winter ONI values are reliable and exhibit sharpness. In contrast, an under-forecasting bias and less reliable forecasts are noted for La Nina.

Dynamics and Predictability of El Niño–Southern Oscillation: An Australian Perspective on Progress and Challenges

AbstractEl Niño and La Niña, the warm and cold phases of El Niño–Southern Oscillation (ENSO), cause significant year-to-year disruptions in global climate, including in the atmosphere, oceans, and cryosphere. Australia is one of the countries where its climate, including droughts and flooding rains, is highly sensitive to the temporal and spatial variations of ENSO. The dramatic impacts of ENSO on the environment, society, health, and economies worldwide make the application of reliable ENSO predictions a powerful way to manage risks and resources. An improved understanding of ENSO dynamics in a changing climate has the potential to lead to more accurate and reliable ENSO predictions by facilitating improved forecast systems. This motivated an Australian national workshop on ENSO dynamics and prediction that was held in Sydney, Australia, in November 2017. This workshop followed the aftermath of the 2015/16 extreme El Niño, which exhibited different characteristics to previous extreme El Niños and whose early evolution since 2014 was challenging to predict. This essay summarizes the collective workshop perspective on recent progress and challenges in understanding ENSO dynamics and predictability and improving forecast systems. While this essay discusses key issues from an Australian perspective, many of the same issues are important for other ENSO-affected countries and for the international ENSO research community.

Outlook for El Niño and the Indian Ocean Dipole in autumn-winter 2018–2019

El Nino-Southern Oscillation (ENSO) in the equatorial Pacific Ocean and the Indian Ocean Dipole (IOD) in the equatorial Indian Ocean are two major natural variabilities on seasonal and inter-annual timescales. In this study, the Flexible Global Ocean-Atmosphere-Land System Model, finite volume version 2 (FGOALS-f2), sub-seasonal to seasonal (S2S) climate prediction system, was used to make a seasonal prediction for autumn and winter 2018–2019. The FGOALS-f2 S2S prediction system was developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), and is run on Chinas Tianhe-2 supercomputer located at the National Supercomputer Center in Guangzhou, China. The model used in the prediction system is CAS FGOALS-f2, which is a next-generation climate system model of LASG-IAP, representing the interaction between the atmosphere, oceans, land, and sea ice. The seasonal prediction products from this system have been submitted to and used operationally by the National Climate Center of the China Meteorological Administration, as well as the National Marine Environmental Forecasting Center of China, since June 2017. The FGOALS-f2 S2S prediction system has achieved 37 a retrospective forecasts (reforecasts) covering the period 1981–2017. The reforecast experiments include 24 ensemble members, while the real-time prediction uses 35 ensemble members. The latest prediction results, in July 2018, reveal that: (1) A positive IOD will persist through autumn and winter 2018–2019, and the peak phase will be in October with an amplitude of approximately 0.4°C. Based on the 37 a reforecasts predicted from each July 20th, the one-month-lead prediction skill of the IOD is 0.82 in the IOD prediction of July, and the five-month-lead prediction skill is 0.56. (2) In the equatorial Pacific Ocean, the prediction results reveal a Moderate El Nino is under development, and Nino3.4 index values may reach approximately 1.3°C. Based on the 37 a reforecasts predicted from each July 20th, the one-month lead prediction skill of the Nino3.4 index is 0.97 in the ENSO prediction of July, and the six-month lead prediction skill is 0.83. (3) The Moderate El Nino and positive IOD may induce a weak China winter monsoon, characterized by a warm winter. For North China, the meteorological conditions are expected to have adverse effects on atmospheric diffusion; while for South China, warm and wet conditions are likely to prevail, since the lower-level jet of the Indian Ocean is predicted to strengthen. To better predict the equatorial sea surface temperature anomalies in the eastern Pacific and Indian oceans, the FGOALS-f2 S2S climate prediction team will continue to update the prediction results on the 20th of every month, releasing them (with respect to ENSO, the IOD, climate, and average monthly weather in China) on the website, and report the latest prediction results via the WeChat public platform.

The South Pacific Meridional Mode and Its Role in Tropical Pacific Climate Variability

The Pacific meridional mode, a thermodynamically coupled mode of variability, links extratropical Pacific oceanic and atmospheric anomalies to the tropical Pacific and may give rise to El Niño–Southern Oscillation (ENSO) events. While mechanistic studies on the impacts of the North Pacific meridional mode (NPMM) are prevalent in the literature, the South Pacific meridional mode (SPMM) has only recently been examined. Reanalysis and models from the archive of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used in this study to investigate further the SPMM and its role in ENSO predictability. Akin to the NPMM, the SPMM features anomalous subtropical South Pacific sea surface temperature (SST) warming in response to a weakened South Pacific subtropical high and relaxed trade winds. However, as opposed to the NPMM, the seasonality of the SPMM SST and wind components vary out of phase, in part because of the seasonal cycle of local mixed layer depth. Thus, the SPMM and the associated wind–evaporation–SST feedback are most energetic during austral summer, facilitating interaction with the concurrent NPMM conditions to regulate the occurrence, evolution, amplitude, and diversity of ENSO events. Parallel analyses with the CMIP5 models indicate that the models reasonably capture the temporal statistics and physical processes of the observed SPMM. Nonetheless, ENSO predictability within the models relies more on the slowly evolving equatorial ocean heat content than on the meridional modes. These findings reveal how the SPMM energizes components of ENSO variability, providing an additional benchmark for model evaluation of Pacific climate variability across multiple time scales.

Identification Standard for ENSO Events and Its Application to Climate Monitoring and Prediction in China

The El Nino–Southern Oscillation (ENSO) reflects anomalous variations in the sea surface temperature (SST) and atmospheric circulation over the tropical central–eastern Pacific. It remarkably impacts on weather and climate worldwide, so monitoring and prediction of ENSO draw intensive research. However, there is not yet a unique standard internationally for identifying the timing, intensity, and type of ENSO events. The National Climate Center of China Meteorological Administration (NCC/CMA) has led the effort to establish a national identification standard of ENSO events, which was officially endorsed by the National Standardization Administration of China and implemented operationally in NCC/CMA in 2017. In this paper, two key aspects of this standard are introduced. First, the Nino3.4 SST anomaly index, which is well-recognized in the international ENSO research community and used operationally in the US, has replaced the previous Nino Z index and been used to identify the start, end, and peak times, and intensity of ENSO events. Second, two new indices—the eastern Pacific ENSO (EP) index and the central Pacific ENSO (CP) index, based on the SST conditions in Nino3 and Nino4 region respectively, are calculated to first determine the ENSO type before monitoring and assessing the impacts of ENSO on China’s climate. With this standard, all historical ENSO events since 1950 are consistently re-identified; their distinct properties are diagnosed and presented; and the impacts of ENSO events under different types on China’s climate are re-assessed. This standard is also employed to validate the intensity, grade, and type of the ENSO events predicted by the NCC/CMA operational ENSO prediction system. The new standard and the thus derived unified set of re-analyzed historical ENSO events and associated information provide a good reference for better monitoring and prediction of future ENSO events.

An interview with Mark A. Cane

The El Nino-Southern Oscillation (ENSO) is the largest interannual fluctuation in the Earths climate system, and it has strong climatic, environmental and socioeconomic impacts on global scales. Thus ENSO has been a hot research topic in oceanic and atmospheric sciences for more than three decades. As one of the pioneers and leading experts in this research field, Prof. Mark A. Cane of the Lamont-Doherty Earth Observatory of Columbia University, a member of the National Academy of Sciences of the USA, has made seminal contributions to our understanding and prediction of ENSO. In this interview with NSR, Prof. Cane shares his insights on the history and future of ENSO research with a distinct personal touch.

The Role of Stochastic Forcing in Generating ENSO Diversity

AbstractNumerous oceanic and atmospheric phenomena influence El Niño–Southern Oscillation (ENSO) variability, complicating both prediction and analysis of the mechanisms responsible for generating ENSO diversity. Predictability of ENSO events depends on the characteristics of both the forecast initial conditions and the stochastic forcing that occurs subsequent to forecast initialization. Within a linear inverse model framework, stochastic forcing reduces ENSO predictability when it excites unpredictable growth or interference after the forecast is initialized, but also enhances ENSO predictability when it excites optimal initial conditions that maximize deterministic ENSO growth. Linear inverse modeling (LIM) allows for straightforward separation between predictable signal and unpredictable noise and so can diagnose its own skill. While previous LIM studies of ENSO focused on deterministic dynamics, here we explore how noise forcing influences ENSO diversity and predictability. This study identifies stochastic forcing details potentially contributing to the development of central Pacific (CP) or eastern Pacific (EP) ENSO characteristics. The technique is then used to diagnose the relative roles of initial conditions and noise forcing throughout the evolution of several ENSO events. LIM results show varying roles of noise forcing for any given event, highlighting its utility in separating deterministic from noise-forced contributions to the evolution of individual ENSO events. For example, the strong 1982 event was considerably more influenced by noise forcing late in its evolution than the strong 1997 event, which was more predictable with long lead times due to its deterministic growth. Furthermore, the 2014 deterministic trajectory suggests that a strong event in 2014 was unlikely.

An ENSO-induced aerosol dipole in the west-central Pacific and its potential feedback to ENSO evolution

We examine the impact of El Nino–Southern Oscillation (ENSO) on satellite observed aerosol optical depths (AODs) over the equatorial Pacific. AODs are found to decrease close to the International Dateline and increase in the vicinity of the Maritime Continent during the El Nino developing phase (and vice versa during La Nina) to form an AOD dipole. The positive AOD anomaly over the Maritime Continent is caused by increases in fire-induced fine smoke aerosols in Indonesia and coarse dust particles transported from Australia via the strengthening winds when El Nino develops and affects the Pacific walker circulation. The negative AOD anomaly around the International Dateline is caused by decreases in the emission of marine aerosols, as the developing El Nino is associated with weakening surface wind speeds in the region. This aerosol dipole is found particularly strong during the Central-Pacific type of ENSO, which has most of its sea surface temperature and wind variations around the International Dateline. The Eastern-Pacific type of ENSO produces positive AOD anomalies over the Maritime Continent but weaker negative AOD anomalies near the International Dateline. As a result, the AOD dipole is less obvious during the EP ENSO. The shortwave radiative forcing associated with the aerosol dipole is 29% (21%) as large as the ENSO-induced outgoing longwave radiation in the Maritime Continent (International Dateline) region and represents an important positive feedback to help sustain the ENSO development that may be potentially important in Central Pacific ENSO studies and predictions.