Madden–Julian Oscillation Index Research Articles

Modulation of the Madden–Julian Oscillation Center Stagnation on Typhoon Genesis over the Western North Pacific

Madden–Julian Oscillation (MJO) modulates the generation of typhoons (TYs) in the western North Pacific (WNP). Using IBTrACS v04 tropical cyclone best path data, ERA5 reanalysis data, and the MJO index from the Climate Prediction Center (CPC), this paper defines an index to describe the persistent anomalies of the MJO and to examine the statistical characteristics of TYs over 44 years (1978–2021), focusing on the analysis of major differences in environmental conditions after the removal of the ENSO signal over the WNP. The results indicate that the persistent anomalous state of the MJO influences the change in large-scale environmental factors, which, in turn, affects the generation of TYs, as follows: (1) For the I high-value years, the center of the MJO stagnates in the Indian Ocean–South China Sea (SCS), the monsoon trough retreats westward, the warm pool becomes warmer, and the Walker circulation is enhanced. There is stronger upper-level divergence and low-level convergence, larger low-level relative vorticity, higher mid-level relative humidity, and smaller vertical wind shear in the SCS and the seas near the Philippines. Consequently, these conditions foster a conducive environment for TY genesis in the SCS and the seas near the Philippines. (2) For the I low-value years, the center of the MJO stagnates in the WNP–North America region, the monsoon trough extends eastward, the warm pool becomes colder, and the Walker circulation is weakened. Consequently, these conditions are more likely to facilitate TY genesis in the central–eastern WNP. The results show that persistent anomalies in MJO active centers can effectively improve the predictive ability of TY frequency.

Intensity duration and frequency of Heat wave in different phases of MJO over India

The Madden-Julian Oscillation (MJO) is an important tropical atmospheric phenomenon that plays a significant role in modulating weather patterns across India. Heat Waves (HW) are extreme weather events that have severe impacts on human health, agriculture, and various socio-economic sectors. This research paper aims to investigate the dynamical relationship between MJO phases, as measured by the Real-time Multivariate MJO (RMM) index, and the occurrence of HW in India, as defined by the Indian Meteorological Department. This paper demonstrated the relationship among three aspects, i.e., intensity, duration, frequency of HW during the different MJO phases. The differential dynamics associated for about two-fold rise in the HW occurrence in the South East, Eastern and North West India in the MJO active phase. Inactive phase witnessed enhancement (suppression) of HW during wet (dry) over the regions. The novel finding of this paper is that, the weaker the MJO, the higher probability of HW occurrence in respective regions depending on the position of the MJO convection center. The maximum duration is observed to be significantly decreased by about 2 days in the active MJO phase over NW and north central parts of the country. It is also inferred from this study that there is a significantly decrease of 0.5 °C in HW intensity during the active wet phase of MJO over the NW India. To explain the linkage of HW with MJO, geopotential height (GPH), wind circulation, velocity potential (VP), sea surface temperature (SST) and outgoing longwave radiation (OLR) composite anomalies have been computed and used in the analysis. It is observed that all these parameters have a direct impact on the evolution, strength and spread of the HW in India. This dynamical study mechanism can be prescribed in the numerical weather prediction models for the improved prediction of such extreme weather events during summer for proactive disaster management.

Analisis Korelasi Statistikal Fenomena Kejadian ENSO dan MJO terhadap Kondisi Klimatologis di Provinsi Riau

El Nino-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) events are significant factors in the global climate system. Riau Province, located on the island of Sumatra, is highly influenced by these phenomena. This study collects climate data from weather stations in Riau Province and reliable sources for ENSO and MJO indices. Statistical analyses, including correlation and regression, are employed to determine the relationship between climate variables in Riau Province and ENSO and MJO. Based on data from 2001-2019, the results reveal a medium category correlation value (r = 0.452) with coefficients a and b of 1.288 and 0.089, respectively. The increased MJO value contrasts with the rainfall during the 2001-2019 timeframe. This information serves as a reference for understanding the ENSO and MJO phenomena in the Riau region.

Data driven models of the Madden-Julian Oscillation: understanding its evolution and ENSO modulation

Understanding the physical mechanisms of the Madden-Julian Oscillation (MJO) and its evolution is a major concern within the climate community. Its main importance relies on its ability to act as a source of predictability within the intra-seasonal time-scale in tropical and extratropical regions, therefore filling the gap between weather and climate forecasts. However, most atmospheric general circulation models fail to correctly represent MJO’s evolution, and their prediction skills are still far from MJO’s theoretical predictability. In this work we infer low dimensional models of the MJO from data by applying a recently developed machine learning technique, the Sparse Identification of Non-linear Dynamics (SINDy). We use the daily-mean outgoing longwave radiation MJO index (OMI) as input data to infer bi-dimensional climatological models of the MJO, and analyse the inferred models during El Niño and La Niña years. This approach allows us to diagnose the MJO’s behaviour in OMI’s phase space. Our results show that MJO can be most frequently represented by a harmonic oscillator, which represents the MJO’s eastward propagation and characteristic period. Upon this basic oscillatory behaviour, we find that small non-linear corrections play a fundamental role in representing MJO’s non-uniform speed of propagation, explaining its acceleration over the Pacific Ocean region. Particularly, we find that MJO’s evolution is most frequently non-linear [linear] during El Niño [La Niña] years. Overall, our work shows that SINDy can robustly model MJO’s evolution as a linear oscillator with small non-linear corrections, contributing to understand the MJO’s dynamics and dependency on El Niño-Southern Oscillation.

The Role of Multiscale Interaction in the Maintenance and Propagation of MJO in Boreal Winter

Abstract The multiscale interaction and its role in the maintenance and propagation of the Madden–Julian oscillation (MJO) has been investigated using the newly developed multiscale window transform (MWT), the theory of canonical transfer, and the MWT-based multiscale energetics analysis (here particularly for this study, dry energetics analysis). The field variables are reconstructed/filtered with MWT onto three scale windows, namely a high-frequency window, intraseasonal window, and low-frequency window. Compositing the intraseasonal fields with respect to the real-time multivariate MJO (RMM) index unambiguously shows that the zonal extents of the easterlies and westerlies of MJO vary with the RMM phases, among which phases 4 and 2 are representative. In the former phase, the MJO has easterlies and westerlies within the same extent, while in the latter their extents are quite different. In phase 4, besides the previously discovered mechanisms such as pressure work and buoyancy conversion, the MJO is also energized by the canonical kinetic energy (KE) transfer from the low-frequency window to the intraseasonal window (signifying barotropic instability) on the west of its convection. But on the eastern side, MJO loses KE to the low-frequency window. The KE transport also functions like an energy sink. In phase 2, the MJO variabilities can be divided into an eastern part and a western part. The former is essentially the same as that in phase 4; for the latter, barotropic instability dominates. On the available potential energy (APE) budget, baroclinic instability and intraseasonal APE transport help produce and maintain the temperature anomalies. In contrast to previous energetics studies, our findings highlight the essential role played by multiscale interactions.

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.

The effect of climate oscillations on skipjack tuna (Katsuwonus pelamis) in the Indian Ocean

Skipjack tuna (Katsuwonus pelamis) (SKJ) is one of the most commercially important marine fish species distributed throughout the world's tropical and subtropical oceans. The Indian Ocean is a main fishing ground for SKJ, whose exploitation rate is just below the maximum sustainable yield. Therefore, SKJ stocks may potentially be significantly affected by climate change. In this study, climatic oscillation indices related to the Indian Ocean, including the Indian Ocean Dipole (IOD), the mean water temperature anomaly in the Eastern Indian Ocean (EIO) and Western Indian Ocean (WIO), the Madden-Julian Oscillation Index at 80°E (MJO80) and the Arctic Oscillation Index (AOI), were associated with SKJ catch per unit effort (CPUE). This study aimed to understand the effect of these five climatic oscillations on SKJ, with the goal of optimizing the utilization of skipjack tuna in the Indian Ocean. We combined gradient forest analysis (GFA) and generalized additive models (GAMs) to evaluate the importance of each climatic index with a 0–5 year lag on the impact of SKJ CPUE and to establish an optimal prediction model. The GFA results show that MJO80 is the most important climatic index influencing SKJ CPUE, followed by MJO80_1, AOI_2, IOD_4, WIO_2, etc. The best GAM model includes MJO80, AOI_2, and WIO_2, which could be related to the recruitment and larval survival of SKJ by influencing water temperature. Meanwhile, there is a significant negative correlation between SST and SKJ CPUE in the tropical regions of the western and central Indian Ocean. Our results suggest that climate oscillations have a 0–2 year lag effect on the SKJ fishery in the Indian Ocean, which can be used to predict resource changes in SKJ over the next 2 years.

Information-Based Probabilistic Verification Scores for Two-Dimensional Ensemble Forecast Data: A Madden–Julian Oscillation Index Example

Abstract Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts—probabilistic forecast verification—is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose; however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar, and two-dimensional vector data. The proposed method uses a fixed-radius near-neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the continuous ranked probability score (CRPS) and the proposed score with a relatively small ensemble size (<25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden–Julian oscillation index, which is a two-dimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various fields. Significance Statement In the Earth sciences, stochastic future states are estimated by solving a large number of forecasts (called ensemble forecasts) based on physical equations with slightly different initial conditions and stochastic parameters. The verification of probabilistic forecasts is an essential part of forecasting and modeling activity in the Earth sciences. However, there is no information-based probabilistic verification score applicable for vector variables of ensemble forecasts. The purpose of this study is to introduce a novel method for verifying scalar and two-dimensional vector variables of ensemble forecasts. The proposed method offers a new approach to probabilistic verification and is expected to advance probabilistic ensemble forecasts in various fields.

Influence of the Madden–Julian Oscillation (MJO) on Tropical Cyclones Affecting Tonga in the Southwest Pacific

The modulating influence of the Madden–Julian oscillation (MJO) on tropical cyclones (TCs) has been examined globally, regionally, and subregionally, but its impact on the island scale remains unclear. This study investigates how TC activity affecting the Tonga region is being modulated by the MJO, using the Southwest Pacific Enhanced Archive of Tropical Cyclones (SPEArTC) and the MJO index. In particular, this study investigates how the MJO modulates the frequency and intensity of TCs affecting the Tonga region relative to the entire study period (1970–2019; hereafter referred to as all years), as well as to different phases of the El Niño southern oscillation (ENSO) phenomenon. Results suggest that the MJO strongly modulates TC activity affecting the Tonga region. The frequency and intensity of TCs is enhanced during the active phases (phases six to eight) in all years, including El Niño and ENSO-neutral years. The MJO also strongly influences the climatological pattern of genesis of TCs affecting the Tonga region, where more (fewer) cyclones form in the active (inactive) phases of the MJO and more genesis points are clustered (scattered) near (away from) the Tonga region. There were three regression curves that best described the movement of TCs in the region matching the dominant steering mechanisms in the Southwest Pacific region. The findings of this study can provide climatological information for the Tonga Meteorological Service (TMS) and disaster managers to better understand the TC risk associated with the impact of the MJO on TCs affecting the Tonga region and support its TC early warning system.

How Important Are Horizontal Eddy Moisture Transports for the MJO's Eastward Propagation?

AbstractThe eastward propagation of the Madden‐Julian oscillation (MJO) is known to hinge crucially on the effects of horizontal moisture advection, which involve two main types of circulation anomalies. The first are those of the MJO itself, while the second are those of embedded Rossby‐type “eddies,” which tend to be most active to the west of the MJO's convective center. To quantify the relative importance of the eddies, a novel approach is taken in which their formal definition is given by the residual of a least‐squares fit to an observed bivariate MJO index. Results show that the eddies, when defined in this way, are generally of leading importance for fostering the MJO's eastward propagation in terms of column‐integrated moisture. The picture is seen to be reversed, however, when using a traditional filter‐based method to define the eddies, which are then strictly “high‐frequency” in nature.

Evaluation and Process-Oriented Diagnosis of the GEFSv12 Reforecasts

Abstract Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10. Significance Statement The latest U.S. operational weather prediction model—Global Ensemble Forecast System version 12—is evaluated using a suite of physics-based diagnostic metrics from a climatic perspective. The foci of our study consist of three levels: 1) systematic biases in physical processes, 2) tropical and extratropical extended-range predictability sources, and 3) high-impact weather systems like hurricanes and blockings. Such process-oriented diagnostics help us link the model performance to the deficiencies of physics parameterization and thus provide useful information on future model improvement.

Identification of the Madden–Julian Oscillation With Data‐Driven Koopman Spectral Analysis

AbstractThe Madden‐Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, is commonly identified using the realtime multivariate MJO (RMM) index based on joint empirical orthogonal function (EOF) analysis of near‐equatorial upper and lower level zonal winds and outgoing longwave radiation. Here, in place of conventional EOFs, we apply an operator‐theoretic formalism based on dynamic systems theory (the Koopman operator) to extract an analog to RMM that exhibits certain features that refine the characterization and predictability of the MJO. In particular, the spectrum of Koopman operator eigenfunctions, with eigenvalues corresponding to mode periods, contains a leading intraseasonal mode with period of ∼50 days. Moreover, the amplitude of this leading intraseasonal eigenfunction exhibits a seasonal modulation clearly peaked in boreal winter. Finally, the phase space formed by the complex Koopman MJO eigenfunction exhibits a smoother temporal evolution and higher degree of autocorrelation than RMM, which may contribute to enhanced predictive skill.

Potential Predictability of Seasonal Global Precipitation Associated with ENSO and MJO

A covariance decomposition method is applied to a monthly global precipitation dataset to decompose the interannual variability in the seasonal mean time series into an unpredictable component related to “weather noise” and to a potentially predictable component related to slowly varying boundary forcing and low-frequency internal dynamics. The “potential predictability” is then defined as the fraction of the total interannual variance accounted for by the latter component. In tropical oceans (30° E–0° W, 30° S–30° N), the consensus is that the El Nino-Southern Oscillation (ENSO, with 4–8 year cycles) is a dominant driver of the potentially predictable component, while the Madden-Julian Oscillation (MJO, with 30–90 days cycles) is a dominant driver of the unpredictable component. In this study, the consensus is verified by using the Nino3-4 SST index and a popular MJO index. It is confirmed that Nino3-4 SST does indeed explain a significant part of the potential predictable component, but only limited variability of the unpredictable component is explained by the MJO index. This raises the question of whether the MJO is dominant in the variability of the unpredictable component of the precipitation, or the current MJO indexes do not represent MJO variability well.

Ensemble forecast of an index of the Madden–Julian Oscillation using a stochastic weather generator based on circulation analogs

Abstract. The Madden–Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the tropical region. The MJO affects precipitation over highly populated areas, especially around southern India. Therefore, predicting its phase and intensity is important as it has a high societal impact. Indices of the MJO can be derived from the first principal components of zonal wind and outgoing longwave radiation (OLR) in the tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our goal is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG). We generate an ensemble of 100 members for the MJO amplitude for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using probabilistic scores and deterministic skill scores. According to score-based criteria, we find that a reasonable forecast of the MJO index could be achieved within 40 d lead times for the different seasons. We compare our SWG forecast with other forecasts of the MJO. The comparison shows that the SWG forecast has skill compared to ECMWF forecasts for lead times above 20 d and better skill compared to machine learning forecasts for small lead times.

The Indo-Pacific Maritime Continent Barrier Effect on MJO Prediction

Abstract The Madden–Julian oscillation (MJO) is often observed to weaken or sometimes completely decay as its convective anomaly moves from the Indian Ocean over to the Maritime Continent (MC), which is known as the MC barrier effect on the MJO. This barrier effect is often exaggerated in numerical models. Using 23 years of the retrospective intraseasonal forecast from two coupled model systems with useful MJO prediction skills, we show that the predictive skill of the real-time multivariate MJO (RMM) index for the continuously propagating MJO events across the MC region is higher than for the blocked MJO events. The greater prediction skill is not related to the higher initial RMM amplitude for the continuous MJO events. Rather the higher skill arises from the more persistent behavior of the propagating MJO events as the convective anomaly moves through the MC region into the western Pacific. The potential predictability is similar for both types of MJO events, suggesting the forecast models hardly differentiate the two types of MJO events in prediction; they only maintain higher RMM magnitudes of the continuously propagating events. The global reanalysis dataset indicates that the blocked events are often associated with persistent higher surface pressures over colder sea surface temperatures in the central Pacific, suggesting the large-scale environment plays a role in promoting or inhibiting the MJO propagation across the MC region. Caveats in the models to reproduce the observed MJO events are also discussed.

Assessing the Impact of Ocean In Situ Observations on MJO Propagation Across the Maritime Continent in ECMWF Subseasonal Forecasts

AbstractDespite the well‐recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in situ data assimilation on the propagation of Madden–Julian Oscillation (MJO) events across the Maritime Continent in the European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in situ ocean observations in the initial conditions. Besides using the Real‐time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist static energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast may hinder the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model.

The wet and dry spells within the MJO-phase 8 and the role of ENSO and IOD on the modulation of these spells: A regional to continental-scales analysis

To improve our understanding of the reasons behind the wet or dry spells within a phase of the Madden-Julian Oscillation (MJO), we separated the dates of the spells with pervasive precipitation (SWPP) from the spells with widespread dryness (SWWD) during phase 8. Although Iran's southwest precipitation data were used to extract these dates, we showed that the SWPP or SWWD is associated with large-scale climate variability. After proving that the MJO indices are insufficient to explain differences between SWPP and SWWD, we introduced several non-MJO indexes to explain these differences. Among the proposed indicators, two quasi-oscillations of the sea surface temperature and air pressure systems were more significant. These oscillations operate between the west and east of the Arabian Sea and between the Arabian sea and Mediterranean Sea.The study explained why compared to La Niña, El Niño enhances (suppresses) precipitation in western and northern (eastern and southern) Iran and the Arabian Peninsula. We showed that during all subsets of phase 8 including –IOD, a dipole mode of convective activities similar to that expected for +IOD dominates the Indian Ocean. Conversely, the climate systems over the tropical East Atlantic Ocean and West Africa are generally opposite between –IOD and + IOD. We justified how the interaction between climate systems in the Atlantic and Indian Oceans shortens (elongates) phase 8's lifetime during +IOD (-IOD). Ultimately, the study specified the areas of the Middle East, India, and Central Asian Countries where precipitation is significantly different between the +IOD and –IOD parts of phase 8.

Madden–Julian Oscillation Impacts on Australian Temperatures and Extremes

Abstract We assess seasonally varying impacts of the Madden–Julian oscillation (MJO) on Australian maximum and minimum temperature anomalies and extremes, and their modulation by El Niño–Southern Oscillation (ENSO), for the period June 1974–May 2022. Our composite-based approach uses observed temperatures from the Australian Gridded Climate Data, and 850-hPa wind data from the NCEP–NCAR reanalysis, to show how relationships to temperature and circulation evolve over the eight-phase life cycle of the MJO, which we derive from the real-time multivariate MJO index. The MJO has significant impacts on Australian temperatures and winds in all parts of the country at various times throughout the year, and to varying degrees. Two of the most pronounced impacts are 1) daytime warming across southeastern Australia in MJO phase 3 during spring associated with a strong anomalous anticyclone and 2) nighttime cooling over Queensland in MJO phase 7 during winter associated with anomalous advection of cool dry continental air. La Niña acts to significantly lessen both of these impacts, while El Niño enhances both the phase 3 warming over southern Australia in spring and the phase 7 overnight cooling over southern Queensland in winter. We show how the MJO can combine with El Niño and La Niña to have strong compounding influences, thus highlighting the importance of understanding interactions between multiple modes of climate variability and how they relate to Australian temperatures and extremes.

Climatological diagnostics and subseasonal‐to‐seasonal predictions of Madden–Julian Oscillation events

AbstractThe Madden–Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability, which serves as a primary source of subseasonal‐to‐seasonal (S2S) predictability. Noticeably, MJO is not always a regularly recurring cycle but is characterized by discrete episodes. In this study, considering the quasi‐consecutive actives and eastward propagating features, a standard metric is proposed to identify MJO events based on the real‐time multivariate MJO (RMM) index. The re‐identification of historical MJO events reveals that there were 5.4 MJO events each year since 1981, and the average duration of each event is about 31.5 days. More MJO events tend to occur in the boreal winter and spring, with stronger intensity, longer duration and faster propagating speed than those in the boreal summer. Furthermore, MJO events are more likely to initiate in Phases 2 and 5, with a longer lifetime than those initiating in other phases. The amplitude and propagation characteristics of MJO events are strongly modulated by the dominant modes of sea surface temperature interannual variability with a strong regional dependence. Based on hindcast datasets of six S2S models, the prediction skill for the MJO is evaluated in the perspective of individual events. The longest leading time of the skilful prediction for individual MJO events ranges from 11 to 17 days, far below the traditional recognition. This result could be attributed to the apparent prediction barrier of MJO initiation, that is, a rapid decrease in prediction skill when predictions are carried out before the initiation of MJO events. In addition, the prediction skills of MJO events depends on interannual variabilities, with relatively higher skills under the conditions of the El Niño and Indian Ocean basin warming. These findings may shed light on the complexity and challenges of profoundly understanding and skilfully predicting MJO events.

Tropical‐wave activity and Madden–Julian oscillation termination

AbstractThe Madden–Julian oscillation (MJO) is the dominant mode of intraseasonal variability in the tropics and generates large‐scale precipitation, cloud, and circulation anomalies. Interactions between the MJO and dry and convectively coupled equatorial waves (CCEWs) have been identified in previous works; however, these studies have not focused on MJO events that maintain themselves versus those that decay. Knowledge on how pre‐existing MJO events interact with CCEWs is important for producing more realistic and reliable representations of MJO events in weather forecast and global climate models. This study presents an analysis of intraseasonally varying outgoing longwave radiation (OLR) and zonal wind anomalies at 200 and 850 hPa from National Oceanic and Atmospheric Administration (NOAA) observations and ERA‐Interim reanalysis data respectively, throughout the lifetime of observed MJO events. A climatology of continuing and terminating MJO events is created from an event identification algorithm using common tracking indices including the OLR‐based MJO Index (OMI), filtered OMI (FMO), real‐time multivariate MJO (RMM), and velocity potential MJO (VPM) index. Wheeler–Kiladis power spectra are calculated for each event type to identify predominant wave modes. Symmetric and anti‐symmetric wave spectra are composited and results include the sensitivity of the variables used to define the waves and index choice for identifying the MJO. Our results show fractional differences between continuing and terminating MJO events that are statistically significant at the 95% level. Statistical significance is most prominent within the MJO band where continuing events have higher power, as well as global mixed Rossby–gravity (MRG) waves and tropical depression (TD) waves where terminating events have higher power. These results are mostly robust to meteorological variable and index although the importance of Kelvin waves is amplified in the VPM. A regional analysis including phase separation contains results that are sensitive to variable and domain although westward traveling waves are commonly associated with MJO termination regardless of location.