Influence Of Madden-Julian Oscillation Research Articles

Madden Julian Oscillation Influence on Diurnal Variation of Rainfall in Mentawai Islands Indonesia from IMERG Observations

Abstract Mentawai is a small island located in the Indian Ocean, so its rainfall pattern tends to be different from the larger islands in Indonesian region. Various scales of factors influence this, one of which is Madden Julian Oscillation (MJO). The aim of this study is to determine effect of MJO in Mentawai Islands on the diurnal variation of rainfall. Rainfall data was collected from 2000-2022 from Integrated Multi-SatellitE Retrievals for GPM (IMERG). Diurnal variations were observed in the accumulation (PA), frequency (PF), and intensity (PI) of rainfall and MJO was classified into active phase (2, 3, 4, and 5) and inactive phase (1, 6, 7, and 8) during the seasonal period (DJF-MAM-JJA-SON). Diurnal peak times in PA, PF, and PI occur later when MJO is active than when MJO is inactive, especially in JJA and SON periods in Mentawai Islands and Mentawai Strait. When MJO is active, average PA and PF are higher than when MJO is inactive. The average of PI is higher around the ocean during active and inactive phases of MJO. The number of short-duration rainfall events occurred over the land, while long-duration rainfall occurred over the ocean.

MJO Influence on Subseasonal-to-Seasonal Prediction in the Northern Hemisphere Extratropics

Abstract The impacts of the Madden–Julian oscillation (MJO) on the subseasonal-to-seasonal (S2S) prediction in the Northern Hemisphere extratropics are examined using the reforecasts from the S2S Project and Subseasonal Experiment Project (SubX). When forecasts are initialized during an active MJO, extratropical prediction skill becomes significantly higher at 3–4-week windows compared to inactive MJO. Such prediction skill improvement is evident in the 500-hPa geopotential height over the Pacific–North America region and the North Atlantic and in surface temperature over North America, especially when the model is initialized during the MJO phases 6–7 and 8–1. However, the extratropical prediction skill is not modulated by the MJO phases 2–3 and 4–5. This phase dependency is likely determined by the arrival time of the MJO at the Maritime Continent (MC) barrier that substantially enhances the MJO amplitude error. This result suggests that only MJO phases whose convection lies east of the MC are a source of wintertime S2S predictability in the extratropics.

Seasonal Modulation of the Madden–Julian Oscillation’s Impact on Rainfall in Sri Lanka

Abstract For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method. Significance Statement Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.

Rainfall Variation on the West Coast of Sumatra during Each Phase of the Madden-Julian Oscillation from GPM Satellite Observation

The West Coast of Sumatra has a significantly higher annual rainfall than its surrounding areas, making this region crucial in controlling precipitation in Sumatra. The Madden-Julian Oscillation (MJO) heavily influences the West Coast of Sumatra. MJO is an intraseasonal activity that occurs in tropical regions and can be recognized by the eastward movement of convective activity from the Indian Ocean towards the Pacific Ocean for 30-60 days. Using Global Precipitation Measurement (GPM) satellite data from 2014 to 2020, this study investigates the influence of MJO on rainfall on the West Coast of Sumatra. The data used in this study is the attenuation-corrected reflectivity (dBZ) of Ku PR (Z-Ku) and Ka PR (Z-Ka) from GPM DPR Level 2 data. For convective rainfall, the Z-Ku and Z-Ka reflectivity values are higher during the inactive MJO phase than the active MJO phase, consistent with previous research in Sumatra. Thus, during the inactive MJO phase, strong convection occurs, leading to updrafts and increased formation of larger raindrops through coalescence processes. During the active MJO phase, there is a significant decrease in raindrop size, indicating dominant break-up processes.

Analysis of Madden-Julian Oscillation (MJO) on extreme rainfall event in the west coastal south Sulawesi for mitigation disaster

Madden Julian Oscillation (MJO) is one of the global phenomena that affects weather and climate conditions in Indonesia. MJO increases the rainfall rate and causes a plethora of extreme rainfall occurrences in areas along its trajectory. Those extreme rainfall events could trigger hydrometeorological hazards that endanger the surrounding environment. As the first step to analyse this extreme weather event, this research tries to determine the threshold of the extreme rainfall rate. The method used for determining the threshold is the statistical method 98th percentile. The next step is to identify the frequency trend of the extreme rainfall in the period of 1991 to 2020, by measuring the rainfall rate and comparing it with the normal value. If the rainfall rate is above the normal condition in a certain threshold, then it is considered an extreme rainfall event. After that, these extreme rainfall occurrences are compared to the active MJO phase to find out the influence of MJO to the rainfall in the west coast of South Sulawesi. Then, the dynamical atmospheric conditions are to be analysed during those extreme rainfall events. The result shows that the frequency trend of extreme rainfall events are generally negative in 5 (five) regions, which means an insignificant correlation between MJO and rainfall rate. In contrast, 3 (three) other regions show a positive trend. The influence of an active MJO on the extreme rainfall rate is about 34,1%. Meanwhile, the rest for about 65,9% is influenced by other factors. The use of MJO indices for generating early warning hydrometeorological disasters is by utilising the MJO monitoring data, supported with the analysis of dynamical atmospheric condition in the west coast of South Sulawesi.

Identification of Rainfall events on Climate Phenomena in Medan based on Machine Learning

Indonesia has diverse topographical conditions that result in Indonesia having a unique climate. One of the unique climate elements to be studied is rainfall, because rainfall has a different pattern in each region, this different rainfall pattern is caused by several climate phenomena factors that affect the rainfall pattern, including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO). Medan City is the capital of North Sumatra province which is one of the areas in the flood-prone category in North Sumatra, where the factor of flooding is due to rainfall events in a long period of time, so the author wants to know which climatic phenomena factors can affect rainfall events in Medan city by using Machine Learning technology through the Matlab application, where in this study has a method by forming four combination models, namely the combination of the influence of IOD, SOI and MJO; second combination of IOD and SOI; third combination of SOI and MJO; and fourth combination of MJO and IOD, these four combinations will be the rainfall value of the four models. Furthermore, the rainfall value of the model is compared with the observed rainfall value and verification test using Mean Absolute Error (MAE) and correlation. Then the calculation of the comparison between the four rainfall models with the observed rainfall obtained the lowest MAE value during the SOI and MJO phenomenon of 15.0 mm and the highest correlation value during the IOD and SOI and SOI and MJO phenomena. So it is concluded that the combination of SOI and MJO has the best verification value. This shows that based on Machine Learning modeling, the model shown as the best predictor in Medan city is when the model combination consists of SOI and MJO.

Projected Changes in the Seasonal Cycle of Madden‐Julian Oscillation Precipitation and Wind Amplitude

AbstractAlthough recent studies have examined the responses of annual‐ and seasonal‐mean Madden‐Julian oscillation (MJO) activity to global warming, little is known about the seasonal cycle changes that determine the timing of the peak MJO influence on local weather and climate. By analyzing the Community Earth System Model version 2 large ensemble at the end of the 21st century, we report changes in both the phase and amplitude of the seasonal cycle of MJO precipitation over the tropical Indian Ocean and southwestern Pacific. While the seasonal delay in phase of the MJO is related to the seasonal delay of total precipitation, with contributions from surface latent heat flux and circulation changes, the amplification in seasonal amplitude is consistent with the increase in MJO amplitude due to mean temperature increases. The shifts in phase of the seasonal cycle of the MJO under global warming may influence the predictability of extreme events.

Seasonality of MJO Impacts on Precipitation Extremes over the Western United States

Abstract The Madden–Julian oscillation (MJO) is a unique type of organized tropical convection varying primarily on subseasonal time scales and is recognized as an important source of subseasonal predictability for midlatitude weather phenomena. This study provides observational evidence of MJO impacts on precipitation extreme intensity, frequency, and duration over the western United States. The results suggest a robust increase in precipitation extremes, especially in frequency, relative to climatological conditions over most of the western United States when the MJO is in its western Pacific phases during the extended boreal winter (October–March). Opposite changes are observed when the MJO is located over the Indian Ocean and Maritime Continent. The above MJO influence is characterized by strong seasonality, with the increase in extreme frequency mainly found in late autumn/early winter (OND) over California and a weaker or opposite response found in late winter (JFM). Also, MJO impacts have stronger regional consistency and persist for a longer time in OND compared to JFM. The seasonality of MJO impacts largely originates from the different amplitudes and patterns of both the MJO and basic states that are weaker and located/retreated more northwestward in OND than in JFM. This leads to different responses in MJO teleconnections including moisture transport and AR activity that contribute to the different precipitation extreme changes. The strong seasonality of the relationship between the MJO and western U.S. extreme precipitation shown in this study has implications to the source of subseasonal-to-seasonal predictions, which has a potential value to stakeholders including water resource managers.

Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing

Abstract. The Madden–Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10 to 90 d) timescale. An improved forecast of the MJO may have important socioeconomic impacts due to the influence of MJO on both tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved their capability for forecasting the MJO exceeding the 5-week prediction skill, there is still room for improving the prediction. In this study we use multiple linear regression (MLR) and a machine learning (ML) algorithm as post-processing methods to improve the forecast of the model that currently holds the best MJO forecasting performance, the European Centre for Medium-Range Weather Forecasts (ECMWF) model. We find that both MLR and ML improve the MJO prediction and that ML outperforms MLR. The largest improvement is in the prediction of the MJO geographical location and intensity.

Simulating the influence of Madden Julian oscillation on the MAMJ intra-seasonal variations over West Africa

Simulating the influence of Madden Julian oscillation on the MAMJ intra-seasonal variations over West Africa

Impacts of the MJO on Rainfall at Different Seasons in Indonesia

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability of rainfall in Indonesia, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. This study aims to investigate the general impacts of MJO on rainfall at different seasons in Indonesia, particularly during boreal summer. Impacts of the MJO on daily rainfall anomaly during the four climatic seasons: DJF, MAM, JJA, and SON in Indonesia have been evaluated using in-situ data from 86 stations during 1981 - 2012 and remote sensing data using GPM IMERGV06 from 2001 - 2019. The greatest impact of the MJO on rainfall over Indonesia occurs during the DJF and MAM seasons (austral summer), with the magnitude varying across regions. Enhanced rainfall generally occurs over the western parts of Indonesia on phases 2 to 4, central parts of Indonesia on phase 4, and eastern parts of Indonesia on phases 4 to 5. Conversely, suppressed rainfall generally occurs over the western parts of Indonesia on phases 5 to 8, central parts of Indonesia on phases 6 to 8, and eastern parts of Indonesia on phases 1 to 2 and 6 to 8. In addition, the MJO influence during the JJA and SON seasons are slightly less, in terms of intensity, than during the DJF and MAM seasons, which is likely due to the northward shift of ITCZ and, hence, the intraseasonal oscillation convective envelope during boreal summer. Generally, enhanced rainfall occurs over the western and northern parts of Indonesia on phases 2 to 3, and suppressed rainfall on phases 6 to 7. The results indicate that convectively active MJO may increase the possibility of daily extreme rainfall in particular regions in Indonesia at different seasons.

Development of Tropical Cyclone over the Bay of Bengal during South West Monsoon

South West Monsoon (SW Monsoon) and Tropical Cyclone (TC) are two important weather systems for Bangladesh. During SW Monsoon i.e. during rainy season Bangladesh gets 70% to 85% of her annual rainfall. TC accompanied with strong gale winds, tornadoes, torrential rains and storm surges is considered as a deadly natural disaster. TC’s are mostly formed during pre-monsoon and post-monsoon season, but not uncommon during SW monsoon. This study consults the best track data (cyclone e-atlas) of India Meteorological Department (IMD) containing tracks of cyclones and depression over North Indian Ocean (NIO) for the years 1891 to 2020 i.e. of 130 years. In these 130 years, among total 1219 storms, 608 had formed during SW monsoon. If only Cyclonic Storms (CS) and Severe Cyclonic Storms (SCS) are considered then, 150 storms formed during SW Monsoon. This paper studied two cyclogenesis factors; vertical wind shear and upper level anticyclone for eight cases of tropical storms formed during SW Monsoon. Besides cyclogenesis factors, influence of Madden Julian Oscillation (MJO) is also studied. Threat analysis associated with tropical storms during SW Monsoon has been done which might help in planning of National Disaster Management Program. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 10(1), 2021, P 57-65

The inverse influence of MJO on the cyclogenesis in the north Indian Ocean

We examine the influence of Madden Julian Oscillation (MJO) on tropical cyclones (TCs) in the north Indian Ocean (NIO). To analyse cyclogenesis, we have used the Genesis Potential Index (GPI) and Accumulated Cyclonic Energy (ACE) in the active years (AYs) and the non-active years (NAYs) of MJO during the period 1979–2019. The GPI anomaly shows enhanced cyclogenesis in NIO except for the northern part of the Bay of Bengal (BoB) in AYs, and an exact opposite anomaly of GPI is observed in NAYs in the Arabian Sea (AS) and BoB. Additionally, a strong negative correlation exists in the anomaly of relative humidity, potential intensity and absolute vorticity in AYs and NAYs. The reduction in MJO phase duration by 3–4 days in NIO is in accordance with the declining frequency of cyclones in AYs in BoB, but in contrast with the frequency of cyclones in AS. Similarly, an increasing trend of ACE is observed in AS with a clear indication of shifting the increased frequency of cyclones towards the western NIO (AS) in AYs. An inverse characteristic of cyclonic activity is observed in AS and BoB during AYs, particularly from 2014 onwards. Our analysis exposes apparent changes in MJO activity and its impact on cyclogenesis and cyclone activity in NIO, which may be attributed to the recent warming in NIO.

Impacts of the Madden-Julian Oscillation (MJO) on rainfall in Sri Lanka

The influence of the Madden Julian Oscillation (MJO) on rainfall in Sri Lanka (SL) is examined based on 30 years of daily station data from 1981-2010. Composites are constructed for each of the eight phases of the MJO defined with the Real-time Multivariate MJO (RMM) index, using daily rainfall data from 44 stations over SL for four climatic seasons and comparing to similar results from a satellite-based rainfall product. Composites of lower tropospheric wind and convective anomaly are also investigated in order to examine how the local rainfall anomalies are associated with large-scale circulations. The greatest impact of the MJO on rainfall over SL occurs in the Second Inter-Monsoon (SIM) and Southwest Monsoon (SWM) seasons. Enhanced rainfall generally occurs over SL during RMM phases 2 and 3 when the MJO convective envelop is located in the Indian Ocean and conversely suppressed rainfall in phases 6 and 7. This rainfall impact is due to the direct influence of the MJO’s tropical convective anomalies and associated low-level circulations in the vicinity of SL. In contrast, the MJO influence during the Northeast Monsoon (NEM) season is slightly less than during the SWM and SIM seasons as a result of the southward shift of the MJO convective envelop during boreal winter. Occurrence of extreme rainfall events is most frequent during phase 2 in First Inter-Monsoon (FIM) phases 2 and 3 in SWM, phases 1, 2 and 3 in SIM and phases 2 and 3 in NEM seasons. The analysis of this study provides a useful reference of when and where the MJO has significant impacts on rainfall as well as extreme rainfall events during four climatic seasons in SL. This information can be used along with accurately predicted MJO phase by dynamical or statistical models, to improve extended range forecasting in SL.

Strengthened Causal Connections Between the MJO and the North Atlantic With Climate Warming

AbstractWhile the Madden‐Julian oscillation (MJO) is known to influence the midlatitude circulation and its predictability on subseasonal‐to‐seasonal timescales, little is known how this connection may change with anthropogenic warming. This study investigates changes in the causal pathways between the MJO and the North Atlantic oscillation (NAO) within historical and SSP585 simulations of the Community Earth System Model 2‐Whole Atmosphere Community Climate Model (CESM2‐WACCM) coupled climate model. Two data‐driven approaches are employed, namely, the STRIPES index and graphical causal models. These approaches collectively indicate that the MJO's influence on the North Atlantic strengthens in the future, consistent with an extended jet‐stream. In addition, the graphical causal models allow us to distinguish the causal pathways associated with the teleconnections. While both a stratospheric and tropospheric pathway connect the MJO to the North Atlantic in CESM2‐WACCM, the strengthening of the MJO‐NAO causal connection over the 21st century is shown to be due exclusively to teleconnections via the tropospheric pathway.

The Influence of ENSO and MJO on Drought in Different Ecological Geographic Regions in China

Mastering the spatial and temporal differences of ENSO (EI Niño-Southern Oscillation) and MJO (Madden–Julian Oscillation) and their influence on drought is very important for accurately monitoring and forecasting drought. In this study, spatiotemporal characteristics and variability of the impact of ENSO and MJO on drought were analyzed from the perspectives of meteorological drought and agricultural drought through temporal and spatial correlation analyses of China’s 48 eco-geographical regions. The results show a strong correlation between drought and ENSO and MJO in general. The spatial correlation coefficients are different, and the response of extreme events varies in different regions. The influence of ENSO and MJO on agricultural drought is higher than that on meteorological drought. ENSO and MJO have a considerable influence on agricultural drought in regions such as the Qinghai-Tibet Plateau and Xinjiang, with the highest correlation coefficient of 0.72. A significant influence of ENSO and MJO on meteorological drought was found in the Jiangnan region with the highest correlation coefficient of 0.40. In addition, agricultural drought shows a significant time lag in response to ENSO events. When the lag time is six months, the time series presents the highest correlation coefficient with the mean value of the correlation coefficient reaching 0.38 and the maximum value reaching 0.75. This research is of great significance for understanding the spatiotemporal correlation between climate patterns and drought on a large regional scale and it provides further insights into the teleconnection mechanisms of drought.

Evaluation of subseasonal impacts of the MJO/BSISO in the East Asian extended summer

The Madden–Julian Oscillation (MJO)/Boreal Summer Intraseasonal Oscillation (BSISO) has been considered an important climate mode of variability on subseasonal timescales for East Asian summer. However, it is unclear how well the MJO/BSISO indices would serve as guidance for subseasonal forecasts. Using a probabilistic forecast model determined through multiple linear regression (MLR) with MJO, ENSO, and long-term trend as predictors, we examine lagged impacts of each predictor on East Asia extended summer (May–October) climate from 1982 to 2015. The forecast skills of surface air temperature (T2m) contributed by each predictor is evaluated for lead times out to five weeks. We also provide a systematic evaluation of three commonly used, real-time MJO/BSISO indices in the context of lagged temperature impacts over East Asia. It is found that the influence of the trend provides substantial summertime skill over broad regions of East Asia on subseasonal timescales. In contrast, the MJO influence shows regional as well as phase dependence outside the tropical band of the main action centers of the MJO convective anomalies. All three MJO/BSISO indices generate forecasts that yield high skill scores for week 1 forecasts. For some initial phases of the MJO/BSISO, skill reemerges over some regions for lead times of 3–5 weeks. This emergence indicates the existence of windows of opportunity for skillful subseasonal forecasts over East Asia in summer. We also explore the dynamics that contribute to the elevated skills at long lead times over Tibet and Taiwan–Philippine regions following the initial state of phases 7 and 5, respectively. The elevated skill is rooted in a wave train forced by the MJO convective heating over the Arabian Sea and feedbacks between MJO convection and SSTs in Taiwan–Philippine region. Two out of the three commonly used MJO/BSISO indices tend to identify MJO events that evolve consistently in time, allowing them to serve as reliable predictors for subseasonal forecasts for up to 5 weeks.

Influence of Madden–Julian Oscillation on extreme rainfall events in Spring in southern Uruguay

AbstractThe influence of Madden–Julian Oscillation (MJO) on the dynamics of extreme rainfall events during austral Spring in Uruguay is investigated. The research is focused on the southern region of the country, which includes 15 weather stations. Extreme events are defined as days in which accumulated rainfall exceeds the 90 percentile of rainy days, and MJO is classified according to the Real‐time Multivariate MJO series 1 (RMM1) and 2 (RMM2) indices. Given that the extratropical teleconnections associated with MJO take at least 1 week to set up, we explore the influence of MJO up to 11 days prior to extreme events. A nonlinear time series analysis is performed (using symbolic patterns known as ordinal patterns) in order to consider the effects of the persistence of particular phases of MJO on the dynamic of extreme rainfall events. We find that MJO has the highest influence on extreme rainfall events in the region when it shows a persistence in phases 4 and 5 for more than 5 days, which intensifies the polar jet, influencing the trajectories of the transient waves that propagate in high latitudes, favouring geopotential disturbances over Uruguay. In other cases, the atmospheric pattern that leads to extreme rainfall events is characterized by a blocking episode that prevents transient activity from high latitudes to reach Uruguay, and the disturbances associated with the extreme events propagate along the subtropics.

Active and break phases of the South American summer monsoon: MJO influence and subseasonal prediction

The role of the Madden–Julian Oscillation (MJO) in producing active and break periods of the South American (SA) monsoon and the performance of the ECMWF and NCEP models in predicting these periods at multiweek lead times are assessed. Two monsoon indices, based on precipitation and wind, are proposed to characterize these periods. The models represent well the observed association of active and break monsoon days with large scale convection and circulation anomalies. Although reproducing approximately the distribution of active and break days proportions in each phase of the MJO cycle, models produce a phase shift between observed and simulated distributions because they establish the teleconnection between Central Pacific and South America, as well as its impacts, sooner than in observations. The predictive skill of both rainfall and wind anomalies is limited to about 2 weeks, with the monsoon wind index displaying higher correlation score till week 3. The forecast performance is apparently not affected by initialization on active or break monsoon days. However, it is higher for prediction of lower precipitation in break days than heavier rainfall in active days. Wind is much better predicted than rainfall for active days, which could be used for extreme rainfall events forecast. Although relatively small at shorter lead times, the MJO contribution is the major source of rainfall predictability after week 3. To improve the multiweek prediction of SA monsoon, models need not only to predict correctly the MJO phase, but also to reproduce in the right phase the MJO-related SA rainfall anomalies.

MJO Wind Energy and Prediction of El Niño

AbstractThis study applies the energetic framework developed to quantify the interaction between the Madden‐Julian Oscillation (MJO) and El Niño–Southern Oscillation (ENSO) to El Niño forecasting. The MJO wind power, measured by the covariability of MJO‐related wind stress and oceanic Kelvin wave activity, is combined with the sea surface temperature (SST) anomalies, and two new indices are proposed. The MJO‐Kelvin wave‐ENSO (MaKE) index is proposed as an ENSO predictor and is shown to slightly outperform Niño 3.4 when applied to observational data sets and to greatly outperform Niño 3.4 when applied to CFSv2 reforecasts of the years 1980–2014. The MJO‐Kelvin wave Influence (MaKI) index is proposed to predict MJO influence on developing El Niño events. This index performs reasonably well when applied to observations. The forecast skill of MaKI in the CFSv2 reforecasts suggests that this model does not predict the observed MJO‐ENSO relationship as measured by this index.