El Niño-Southern Oscillation (ENSO) Variations and Climate Changes Worldwide

This investigation aims to study the El-Niño-Southern Oscillation (ENSO) events in these three phases: El Niño, La Niña, and neutral. Warm and cold events relate to the Spring/Summer seasons. This paper will search for connections between the ENSO events and climate anomalies worldwide. There is some speculation that those events would be necessary for the climate anomalies observed worldwide. After analyzing the data from the reports to the ENSO, it shows almost periodicity from 1950-2023. We emphasized the occurrence of El Niño two years, when it was most prominent, and the climate anomalies (following NOAA maps), 2015 and 2023. The results indicated that the observed climate anomalies couldn’t be linked to the abnormal events observed. The worldwide temperatures in those years enhanced mostly in 2023. It shows an abnormal behavior compared with all the years scrutinized and analyzed since the records began. Therefore, there must be unknown factors beyond ENSO that rule the worldwide temperatures and the climate anomalies observed.

Wavelet Multiview-Based Hybrid Deep Learning Model for Forecasting El Niño-Southern Oscillation Cycles

The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.

Salinity effect-induced ENSO amplitude modulation in association with the interdecadal Pacific Oscillation

A 110-year ensemble simulation of an ocean general circulation model(OGCM)was analyzed to identify the modulation of salinity interdecadal variability on El Niño-Southern Oscillation(ENSO)amplitude in the tropical Pacific during 1901-2010.The simulating results show that sea surface salinity(SSS)variation in the region exhibits notable and coherent interdecadal variability signal,which is closely associated with the Interdecadal Pacific Oscillation(IPO).As salinity increases or reduces,the SSS modulations on ENSO amplitude during its warm/cold events vary asymmetrically with positive/negative IPO phases.Physically,salinity interdecadal variability can enhance or reduce ENSO-related conditions in upper-ocean stratification,contributing noticeably to ENSO variability.Salinity anomalies associated with the mixed layer depth and barrier layer thickness can modulate ENSO amplitude during positive and negative IPO phases,resulting in the asymmetry of sea surface temperature(SST)anomaly in the tropical Pacific.During positive IPO phases,SSS interdecadal variability contributes positively to El Niño amplitude but negatively to La Niña amplitude by enhancing or reducing SSS interannual variability,and vice versa during negative IPO phases.Quantitatively,the results indicate that the modulation of the ENSO amplitude by the SSS interdecadal variability is 15%-28%during negative IPO phases and 30%-20%during positive IPO phases,respectively.Evidently,the SSS interdecadal variability associated with IPO and its modulation on ENSO amplitude in the tropical Pacific are among factors essentially contributing ENSO diversity.

CMIP 6 models simulation of the connection between North/South Pacific Meridional Mode and ENSO

The subtropical North and South Pacific Meridional Modes(NPMM and SPMM)are well known precursors of El Niño-Southern Oscillation(ENSO).However,relationship between them is not constant.In the early 1980,the relationship experienced an interdecadal transition.Changes in this connection can be attributed mainly to the phase change of the Pacific decadal oscillation(PDO).During the positive phase of PDO,a shallower thermocline in the central Pacific is responsible for the stronger trade wind charging(TWC)mechanism,which leads to a stronger equatorial subsurface temperature evolution.This dynamic process strengthens the connection between NPMM and ENSO.Associated with the negative phase of PDO,a shallower thermocline over southeastern Pacific allows an enhanced wind-evaporation-SST(WES)feedback,strengthening the connection between SPMM and ENSO.Using 35 Coupled Model Intercomparison Project Phase 6(CMIP6)models,we examined the NPMM/SPMM performance and its connection with ENSO in the historical runs.The great majority of CMIP6 models can reproduce the pattern of NPMM and SPMM well,but they reveal discrepant ENSO and NPMM/SPMM relationship.The intermodal uncertainty for the connection of NPMM-ENSO is due to different TWC mechanism.A stronger TWC mechanism will enhance NPMM forcing.For SPMM,few models can simulate a good relationship with ENSO.The intermodel spread in the relationship of SPMM and ENSO owing to SST bias in the southeastern Pacific,as WES feedback is stronger when the southeastern Pacific is warmer.

Enhanced Seasonal Predictability of Spring Soil Moisture over the Indo-China Peninsula for Eastern China Summer Precipitation under Non-ENSO Conditions

Seasonal prediction of summer precipitation over eastern China is closely linked to the East Asian monsoon circulation,which is largely affected by the El Niño-Southern Oscillation(ENSO).In this study,results show that spring soil moisture(SM)over the Indo-China peninsula(ICP)could be a reliable seasonal predictor for eastern China summer precipitation under non-ENSO conditions.When springtime SM anomalies are present over the ICP,they trigger a structured response in summertime precipitation over most of eastern China.The resultant south-to-north,tri-polar configuration of precipitation anomalies has a tendency to yield increased(decreased)precipitation in the Yangtze River basin and decreased(increased)in South and North China with a drier(wetter)spring soil condition in the ICP.The analyses show that ENSO exerts a powerful control on the East Asian circulation system in the ENSO-decaying summer.In the case of ENSO forcing,the seasonal predictability of the ICP spring SM for eastern China summer precipitation is suppressed.However,in the absence of the influence of ENSO sea surface temperature anomalies from the preceding winter,the SM anomalies over the ICP induce abnormal local heating and a consequent geopotential height response owing to its sustained control on local temperature,which could,in turn,lead to abnormal eastern China summer precipitation by affecting the East Asian summer monsoon circulation.The present findings provide a better understanding of the complexity of summer climate predictability over eastern China,which is of potential significance for improving the livelihood of the people.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

The Subsurface and Surface Indian Ocean Dipoles and Their Association with ENSO in CMIP6 models

This study assesses the reproducibility of 31 historical simulations from 1850 to 2014 in the Coupled Model Intercomparison Project phase 6(CMIP6) for the subsurface(Sub-IOD) and surface Indian Ocean Dipole(IOD) and their association with El Ni?o-Southern Oscillation(ENSO). Most CMIP6 models can reproduce the leading east-west dipole oscillation mode of heat content anomalies in the tropical Indian Ocean(TIO) but largely overestimate the amplitude and the dominant period of the Sub-IOD. Associated with the much steeper west-to-east thermocline tilt of the TIO, the vertical coupling between the Sub-IOD and IOD is overly strong in most CMIP6 models compared to that in the Ocean Reanalysis System 4(ORAS4). Related to this, most models also show a much tighter association of Sub-IOD and IOD events with the canonical ENSO than observations. This explains the more(less) regular Sub-IOD and IOD events in autumn in those models with stronger(weaker) surface-subsurface coupling in TIO. Though all model simulations feature a consistently low bias regarding the percentage of the winter–spring Sub-IOD events co-occurring with a Central Pacific(CP) ENSO, the linkage between a westward-centered CP-ENSO and the Sub-IOD that occurs in winter–spring, independent of the IOD, is well reproduced.

ENSO事件下赤道中东太平洋海温场非对称性特征

利用1950—2020年冬季HadISST逐月海面温度(sea surface temperature, SST)资料、SODAv2.2.4逐月SST和三维海洋流速同化资料以及NCEP/NCAR 2 m高度上的逐月气温(surface air temperature, SAT)资料,使用非对称合成差分析方法、海洋混合层热量收支诊断方法等,探究El Ni1o事件和La Ni1a事件下造成赤道东太平洋(E区:110°W~80°W,10°S~10°N)、赤道中太平洋(C区:160°E~170°W,10°S~10°N)SST异常场显著不同非对称性特征的可能海洋动力过程,分析ENSO事件非对称强迫下2 m高度上SAT异常场的非对称空间响应。结果表明:E区El Ni1o事件的强度显著强于La Ni1a事件,C区则相反。非线性动力学加热作用对E区和C区El Ni1o年和La Ni1a年SST异常场的非对称分量都起到了正反馈作用,是造成这两个区域SST异常场产生正、负非对称分量的主导动力因子。埃克曼输送作用不利于E区SST异常场正非对称分量的形成,但有利于C区SST异常场负非对称分量的形成。平均流、纬向平流和温跃层的非对称正反馈作用阻碍了C区SST异常场负非对称分量的形成。2 m高度上SAT异常场的非对称分布与SST异常场的非对称分布较为一致,但SAT异常场正、负非对称分量的显著范围明显减小,部分区域的非对称结果不显著。

Intraseasonal variability of the equatorial Pacific Ocean and its relationship with ENSO based on Self-Organizing Maps analysis

We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation(ENSO) using Self-Organizing Maps(SOM) analysis.Variation in intraseasonal subsurface temperature is mainly found along the thermocline.The SOM patterns concentrate in basin-wide seesaw or sandwich structures along an east-west axis.Both the seesaw and sandwich SOM patterns oscillate with periods of 55 to 90 days,with the sequence of them showing features of equatorial intraseasonal Kelvin wave,and have marked interannual variations in their occurrence frequencies.Further examination shows that the interannual variability of the SOM patterns is closely related to ENSO;and maxima in composite interannual variability of the SOM patterns are located in the central Pacific during CP El Nino and in the eastern Pacific during EP El Nino.The se results imply that some of the ENSO forcing is manife sted through changes in the occurrence frequency of intraseasonal patterns,in which the change of the intraseasonal Kelvin wave plays an important role.

A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance.Meanwhile,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO modeling.We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made recently.As ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and improvements.Now,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and applications.Here,model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models,pinpointing to the areas where they need to be further improved for ENSO studies.These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.

Will the Historic Southeasterly Wind over the Equatorial Pacific in March 2022 Trigger a Third-year La Niña Event?

Based on the updates of the Climate Prediction Center and International Research Institute for Climate and Society(CPC/IRI)and the China Multi-Model Ensemble(CMME)El Niño-Southern Oscillation(ENSO)Outlook issued in April 2022,La Niña is favored to continue through the boreal summer and fall,indicating a high possibility of a three-year La Niña(2020-23).It would be the first three-year La Niña since the 1998-2001 event,which is the only observed three-year La Niña event since 1980.By examining the status of air-sea fields over the tropical Pacific in March 2022,it can be seen that while the thermocline depths were near average,the southeasterly wind stress was at its strongest since 1980.Here,based on a quaternary linear regression model that includes various relevant air-sea variables over the equatorial Pacific in March,we argue that the historic southeasterly winds over the equatorial Pacific are favorable for the emergence of the third-year La Niña,and both the anomalous easterly and southerly wind stress components are important and contribute~50%of the third-year La Niña growth,respectively.Additionally,the possible global climate impacts of this event are discussed.

Seasonal Prediction Skill and Biases in GloSea5 Relating to the East Asia Winter Monsoon

The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.

The role of oceanic feedbacks in the 2014–2016 El Niño events as derived from ocean reanalysis data

Why did the predicted“super El Niño”fade out in the summer 2014 and the following year develop into one of the three strongest El Niño on record?Although some hypotheses have been proposed in previous studies,the quantitative contribution of oceanic processes to these events remains unclear.We investigated the role of various oceanic feedbacks,especially in response to intra-seasonal westerly wind busts,in the evolution of the 2014–2016 El Niño events,through a detailed heat budget analysis using high temporal resolution Estimating the Circulation and Climate of the Ocean—Phase II(ECCO2)simulation outputs and satellite-based observations.Results show that the Ekman feedback and zonal advective feedback were the two dominant oceanic processes in the developing phase of the warm event in the spring of 2014 and its decay in June.In the 2015–2016 super El Niño event,the zonal advective feedback and thermocline feedback played a signifi cant role in the eastern Pacifi c warming.Moreover,the thermocline feedback tended to weaken in the central Pacifi c where the zonal advection feedback became the dominant positive feedback.

Distinct Evolution of the SST Anomalies in the Far Eastern Pacific between the 1997/98 and 2015/16 Extreme El Niños

The 2015/16 El Niño displayed a distinct feature in the SST anomalies over the far eastern Pacific(FEP)compared to the 1997/98 extreme case.In contrast to the strong warm SST anomalies in the FEP in the 1997/98 event,the FEP warm SST anomalies in the 2015/16 El Niño were modest and accompanied by strong southeasterly wind anomalies in the southeastern Pacific.Exploring possible underlying causes of this distinct difference in the FEP may improve understanding of the diversity of extreme El Niños.Here,we employ observational analyses and numerical model experiments to tackle this issue.Mixed-layer heat budget analysis suggests that compared to the 1997/98 event,the modest FEP SST warming in the 2015/16 event was closely related to strong vertical upwelling,strong westward current,and enhanced surface evaporation,which were caused by the strong southeasterly wind anomalies in the southeastern Pacific.The strong southeasterly wind anomalies were initially triggered by the combined effects of warm SST anomalies in the equatorial central and eastern Pacific(CEP)and cold SST anomalies in the southeastern subtropical Pacific in the antecedent winter,and then sustained by the warm SST anomalies over the northeastern subtropical Pacific and CEP.In contrast,southeasterly wind anomalies in the 1997/98 El Niño were partly restrained by strong anomalously negative sea level pressure and northwesterlies in the northeast flank of the related anomalous cyclone in the subtropical South Pacific.In addition,the strong southeasterly wind and modest SST anomalies in the 2015/16 El Niño may also have been partly related to decadal climate variability.

Variation of the coastal upwelling off South Java and their impact on local fishery resources

There is a vast upwelling area induced by the southeast monsoon in the waters off South Java,making the region an important fishing ground.Climate events can affect the variation of upwelling,but oceanographers have different understandings on the extent to which climate events control upwelling in this area,which leads to a lack of basis for studies on the evaluation and mechanisms of the variability of fishery resources in the region.The correlation between environmental parameters,including surface temperature(SST),chlorophyll-a(Chl-a)concentration,and climate event indices in South Java from 2003 to 2020 was analyzed.Results show that the Indian Ocean Dipole(IOD)has a greater influence on the interannual variability of upwelling intensity than ENSO.During the IOD,variations in equatorial latitudinal winds excite different types of Kelvin waves that anomalously deepen or shallow the thermocline,which is the main cause of anomalous variations in upwelling,independent of variations in the local wind field.A correlation between the interannual variability in upwelling and the annual catches was revealed,showing that climatic events indirectly affect fishery resources through upwelling effects.During positive IOD/El Niño periods,strong upwelling delivers more nutrients to the surface layer,which favors fish growth and reproduction,resulting in higher annual catches.A negative IOD/La Niña,on the other hand,leads to weaker upwelling and fewer nutrients into the surface waters.Fish tend to move in deeper waters,making traditional fishing methods less efficient and consequently lower annual catches.

Mathematical Analysis of the Jin-Neelin Model of El Niño-Southern-Oscillation

The Jin-Neelin model for the El Nio–Southern Oscillation(ENSO for short) is considered for which the authors establish existence and uniqueness of global solutions in time over an unbounded channel domain. The result is proved for initial data and forcing that are sufficiently small. The smallness conditions involve in particular key physical parameters of the model such as those that control the travel time of the equatorial waves and the strength of feedback due to vertical-shear currents and upwelling; central mechanisms in ENSO dynamics.From the mathematical view point, the system appears as the coupling of a linear shallow water system and a nonlinear heat equation. Because of the very different nature of the two components of the system, the authors find it convenient to prove the existence of solution by semi-discretization in time and utilization of a fractional step scheme. The main idea consists of handling the coupling between the oceanic and temperature components by dividing the time interval into small sub-intervals of length k and on each sub-interval to solve successively the oceanic component, using the temperature T calculated on the previous sub-interval, to then solve the sea-surface temperature(SST for short) equation on the current sub-interval. The passage to the limit as k tends to zero is ensured via a priori estimates derived under the aforementioned smallness conditions.

Simulated Indonesian Throughflow in Makassar Strait across the SODA3 products

The Indonesian Throughflow(ITF), which connects the tropical Pacific and Indian oceans, plays important roles in the inter-ocean water exchange and regional or even global climate variability. The Makassar Strait is the main inflow passage of the ITF, carrying about 77% of the total ITF volume transport. In this study, we analyze the simulated ITF in the Makassar Strait in the Simple Ocean Data Assimilation version 3(SODA3) datasets. A total of nine ensemble members of the SODA3 datasets, of which are driven by different surface forcings and bulk formulas, and with or without data assimilation, are used in this study. The annual mean water transports(i.e.,volume, heat and freshwater) are related to the combination of surface forcing and bulk formula, as well as whether data assimilation is employed. The phases of the seasonal and interannual variability in water transports cross the Makassar Strait, are basically consistent with each other among the SODA3 ensemble members. The interannual variability in Makassar Strait volume and heat transports are significantly correlated with El Ni?oSouthern Oscillation(ENSO) at time lags of-6 to 7 months. There is no statistically significant correlation between the freshwater transport and the ENSO. The Makassar Strait water transports are not significantly correlated with the Indian Ocean Dipole(IOD), which may attribute to model deficiency in simulating the propagation of semiannual Kelvin waves from the Indian Ocean to the Makassar Strait.

厄尔尼诺-南方涛动(ENSO)研究的战略部署与研究热点

厄尔尼诺—南方涛动(ENSO)影响着全球气候,被认为与众多全球灾害性天气事件有关。为了更好地了解ENSO研究的发展趋势及其对全球海洋事务的影响,本文对WCRP、GOOS和TPOS 2020等与ENSO研究相关的国际研究计划和各国家地区的ENSO研究战略情况进行梳理,并结合VOSviewer文献计量学方法进行分析,探讨ENSO研究的国际发展趋势及研究热点,以便为我国ENSO相关研究与决策提供参考。

Variations in Column Concentration of Greenhouse Gases in China and Their Response to the 2015-2016 El Niño Event

Since the industrial revolution,enhancement of atmospheric greenhouse gas concentrations as a result of human activities has been the primary cause of global warming.The monitoring and evaluation of greenhouse gases are significant prerequisites for carbon emission control.Using monthly data of global atmospheric carbon dioxide(CO_(2))and methane(CH4)column concentrations(hereinafter XCO_(2) and XCH_(4),respectively)retrieved by the Greenhouse Gas Observation Satellite(GOSAT),we analyzed the variations in XCO_(2)and XCH_(4)in China during 2010-2022 after confirming the reliability of the data.Then,the influence of a strong El Niño event in 2015-2016 on XCO_(2) and XCH_(4) variations in China was further studied.The results show that the retrieved XCO_(2) and XCH_(4) from GOSAT have similar temporal variation trends and significant correlations with the ground observation and emission inventory data of an atmospheric background station,which could be used to assess the variations in XCO_(2) and XCH_(4) in China.XCO_(2) is high in spring and winter while XCH_(4) is high in autumn.Both XCO_(2) and XCH_(4) gradually declined from Southeast China to Northwest and Northeast China,with variation ranges of 401-406 and 1.81-1.88 ppmv,respectively;and the high value areas are located in the middle-lower Yangtze River basin.XCO_(2) and XCH_(4) in China increased as a whole during 2010-2022,with rapid enhancement and high levels of XCO_(2) and XCH_(4) in several areas.The significant increases in XCO_(2) and XCH_(4) over China in 2016 might be closely related to the strong El Niño-Southern Oscillation(ENSO)event during 2015-2016.Under a global warming background in 2015,XCO_(2) and XCH_(4) increased by 0.768%and 0.657%in 2016 in China.Data analysis reveals that both the XCO_(2) and XCH_(4) variations might reflect the significant impact of the ENSO event on glacier melting in the Tibetan Plateau.

Effect of Stochastic MJO Forcing on ENSO Predictability

Within the frame of the Zebiak-Cane model,the impact of the uncertainties of the Madden-Julian Oscillation（MJO） on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal forcing.The results show that the uncertainties of MJO have little effect on the maximum prediction error for ENSO events caused by conditional nonlinear optimal perturbation（CNOP）;compared to CNOP-type initial error,the model error caused by the uncertainties of MJO led to a smaller prediction uncertainty of ENSO,and its influence over the ENSO predictability was not significant.This result suggests that the initial error might be the main error source that produces uncertainty in ENSO prediction,which could provide a theoretical foundation for the data assimilation of the ENSO forecast.