Real-time Multivariate Madden–Julian Oscillation Index Research Articles

Clouds Associated with the Madden–Julian Oscillation: A New Perspective from CloudSat

Abstract The evolution of total cloud cover and cloud types is composited across the Madden–Julian oscillation (MJO) using CloudSat data for June 2006–May 2010. Two approaches are used to define MJO phases: 1) the local phase is determined at each longitude and time from filtered outgoing longwave radiation, and 2) the global phase is defined using a popular real-time multivariate MJO (RMM) index, which assigns the tropics to an MJO phase each day. In terms of local phase, CloudSat results show a familiar evolution of cloud type predominance: in the growing stages shallow clouds coexist with deep, intense, but narrow convective systems. Widespread cloud coverage and rainfall appear during the active phases, becoming more anvil dominated with time, and finally suppressed conditions return. Results are compared to the convectively coupled Kelvin wave, which has a similar life cycle to the MJO. Convection in the MJO tends to be modulated more by moisture variations compared to the Kelvin wave. In terms of global phases, wide deep precipitating, anvil, cumulus congestus, and altocumulus types exhibit similar eastward propagation from the Indian Ocean to the central Pacific, while the narrow deep precipitating type only propagates to the Maritime Continent. These propagating types also show coherent Western Hemisphere signals. Generally, negative Western Hemisphere anomalies occur when anomalies are positive over the Indian Ocean. In both approaches, sampling leads to pictorial renderings of actual clouds across MJO phases. These mosaics provide an objective representation of the cloud field that was unavailable before CloudSat and serve as a reminder to the complex nature of the MJO’s multiscale features.

Impact of MJO on the diurnal cycle of rainfall over the western Maritime Continent in the austral summer

This paper investigates the impact of the Madden-Julian Oscillation (MJO) on the diurnal cycle of rainfall over the western Maritime Continent during the austral summer. For this purpose, cyclostationary empirical orthogonal function analysis is applied to the tropical rainfall measuring mission rain rate and the Japanese Reanalysis-25 data for the period 1998–2008. The real-time multivariate MJO index by Wheeler and Hendon (Mon Wea Rev 132:1917–1932, 2004) is adopted to define the intensity and the phase of MJO. It is demonstrated that the hourly maximum rain rate over the domain tends to increase when convectively active phase of MJO approaches the Maritime Continent. In contrast, the hourly maximum rain rate tends to decrease when convectively suppressed phase of MJO resides over the region. The changes in the rain rate due to MJO differ over the ocean and the land. This difference is the greatest when the MJO is in the mature stage. Throughout the day during this stage, terrestrial rain rates show minimum values while diurnally varying oceanic rain rates record maximum values. Thus, precipitation becomes more intense in the morning over the Java Sea and is weakened in the evening over Borneo and Sumatra during the mature stage of MJO. During the decaying stage of MJO over the Maritime Continent, the diurnal cycle of precipitation weakens significantly over the ocean but only weakly over land. Analyses suggest that the anomalous lower level winds accompanied by MJO interact with the monsoonal flow over the Maritime Continent. Westerlies induced by MJO convection in the mature stage are superimposed on the monsoonal westerlies over the equator and increase wind speed mainly over the Java Sea due to the blocking effect of orography. Mountainous islands induce flow bifurcation, causing near-surface winds to converge mainly over the oceanic channels between two islands. As a result, heat flux release from the ocean to the atmosphere is enhanced by the increased surface wind resulting in instability as described in the wind-induced surface heat exchange mechanism. This may contribute to heavy rainfall over the Java Sea in the morning during the mature stage. On the other hand, convergence and vertical velocity over the islands, which play important roles in inducing nighttime rainfall, tend to be weak in the evening during the mature stage of MJO. Strong westerlies arising from MJO and the seasonal flow during the mature stage tend to interrupt convergence over islands. This interruption of convergence by MJO gives rise to decreased rain rates over the land regions.

The Madden–Julian Oscillation’s Influence on African Easterly Waves and Downstream Tropical Cyclogenesis

The influence of the Madden–Julian oscillation (MJO) over tropical Africa and Atlantic is explored during the Northern Hemisphere summer months. The MJO is assessed by using real-time multivariate MJO (RMM) indices. These indices divide the active convective signal of the MJO into 8 phases. Convection associated with the MJO is enhanced over tropical Africa during RMM phases 8, 1, and 2. Convection becomes suppressed over tropical Africa during the subsequent RMM phases (phases 3–7). African convective signals are associated with westward-propagating equatorial Rossby waves. The MJO modulates African easterly wave (AEW) activity. AEW activity is locally enhanced during RMM phases 1–3 and suppressed during RMM phases 6–8. Enhanced AEW activity occurs during periods of enhanced convection over tropical Africa, consistent with stronger or more frequent triggering of AEWs as well as more growth associated with latent heat release. Enhanced AEW activity occurs during the low-level westerly wind phase of the MJO, which increases the cyclonic shear on the equatorward side of the AEJ, increasing its instability. Atlantic tropical cyclogenesis frequency varies coherently with the MJO. RMM phases 1–3 show the greatest frequency of tropical cyclogenesis events whereas phases 7 and 8 show the least. RMM phase 2 is also the most likely phase to be associated with a train of three or more tropical cyclones over the tropical Atlantic. This observed evolution of tropical cyclogenesis frequency varies coherently with variations in AEW activity and the large-scale environment.

Impacts of the MJO on winter rainfall and circulation in China

Impacts of the MJO on winter rainfall and circulation in China are investigated using a real-time multivariate MJO index. Composite results using the daily rainfall anomalies and “rainy day” anomalies according to eight different MJO phases show that the MJO has considerable influence on winter rainfall in China. Rainfall anomalies show systematic and substantial changes (enhanced/suppressed) in the Yangtze River Basin and South China with the eastward propagation of the MJO convective center from the Indian Ocean to the western Pacific. When the MJO is in phase 2 and 3 (MJO convective center is located over the Indian Ocean), rainfall probability is significantly enhanced. While in phase 6 and 7 (MJO convective center is over the western Pacific), rainfall probability is significantly reduced.

Scale Interaction of the Diurnal Cycle of Rainfall over the Maritime Continent and Australia: Influence of the MJO

Abstract The influence of the MJO on the phase and amplitude of the diurnal cycle of rainfall during Australian summer [December–February (DJF)] over the Maritime Continent (MC) and northern Australia is investigated using the Tropical Rainfall Measuring Mission (TRMM) 3B42 and 3G68 datasets. The gridded rainfall was partitioned into MJO categories (active, suppressed, and weak) based on their longitudinal position and by utilizing the real-time multivariate MJO (RMM) index of Wheeler and Hendon. The diurnal cycles were composited and an empirical orthogonal function (EOF) analysis was applied to extract the spatial and temporal variability. Distinct variations in the rainfall distribution pattern among categories of the MJO over land and ocean are seen. The result of the composite-mean rainfall distribution shows that the average daily rainfall rate over islands is higher during suppressed MJO days, while for surrounding oceans and northern regions of Australia, more rainfall occurs during MJO active days. The normalized relative amplitude (NRA) of the diurnal cycle of rainfall shows that morning rainfall near coastal areas during active days of the MJO is 1.5 times greater than the climatological-mean rainfall but is less than or equal to the climatological mean during other phases of the MJO. Similarly, during the suppressed phase of the MJO evening rainfall is greater over the islands than in other MJO phases. The first two modes of the EOF alone explain more than 88% (65%) of the variance for the 3B42 (3G68) rainfall, and the corresponding principal component time series show a marked diurnal cycle. The results show that both the amplitude and phase of the diurnal cycle of rainfall are modulated by the categories of the MJO. In general, the peak in the diurnal cycle for active (suppressed/weak) days of the MJO lags (leads) the peak in the diurnal cycle for total rainfall by 2 h. Over Darwin and its adjacent regions, the active phase of the MJO is responsible for the occurrence of maximum rainfall after midnight, which is unusual in this region.

Predictability of the Madden–Julian Oscillation Estimated Using Observational Data

Abstract Existing numerical models produce large error in simulating the Madden–Julian oscillation (MJO), thereby underestimating its predictability. In this paper, the predictability limit of the MJO is determined by the nonlinear local Lyapunov exponent approach, which provides an estimate of atmospheric predictability based on the observational data. The results show that the predictability limit of the MJO obtained from the bandpass-filtered (30–80 days) outgoing longwave radiation and wind fields, which serves as an empirical estimate of the theoretical potential predictability of the MJO, can exceed 5 weeks, which is well above the 1-week predictability of background noise caused by bandpass filtering. In contrast, a real-time analysis of MJO predictability using the real-time multivariate MJO (RMM) index, as introduced by Wheeler and Hendon, reveals a predictability limit of about 3 weeks. The findings reported here raise the possibility of obtaining a higher predictability limit in real-time prediction by improving the RMM index or by introducing a better method of extracting intraseasonal signals.

Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system

Predictions of the Madden–Julian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean–atmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980–2006. The MJO is diagnosed using the Wheeler-Hendon Real-time Multivariate MJO (RMM) index, which involves projection of daily data onto the leading pair of eigenmodes from an analysis of zonal winds at 200 and 850 hPa and outgoing longwave radiation (OLR) averaged about the equator. Forecasts of the two component (RMM1 and RMM2) index are quantitatively compared with observed behaviour derived from NCEP reanalyses and satellite OLR using the bivariate correlation skill, root-mean-square error (RMSE), and measures of the MJO amplitude and phase error. Comparison is also made with a simple vector autoregressive (VAR) prediction model of RMM as a benchmark. Using the full hindcast set, we find that the MJO can be predicted with the POAMA ensemble out to about 21 days as measured by the bivariate correlation exceeding 0.5 and the bivariate RMSE remaining below ~1.4 (which is the value for a climatological forecast). The VAR model, by comparison, drops to a correlation of 0.5 by about 12 days. The prediction limit from POAMA increases by less than 2 days for times when the MJO has large initial amplitude, and has little sensitivity to the initial phase of the MJO. The VAR model, on the other hand, shows a somewhat larger increase in skill for times of strong MJO variability and has greater sensitivity to initial phase, with lower skill for times when MJO convection is developing in the Indian Ocean. The sensitivity to season is, however, greater for POAMA, with maximum skill occurring in the December–January–February season and minimum skill in June–July–August. Examination of the MJO amplitudes shows that individual POAMA members have slightly above observed amplitude after a spin-up of about 10 days, whereas examination of the MJO phase error reveals that the model has a consistent tendency to propagate the MJO slightly slower than observed. Finally, an estimate of potential predictability of the MJO in POAMA hindcasts suggests that actual MJO prediction skill may be further improved through continued development of the dynamical prediction system.

An MJO Simulated by the NICAM at 14- and 7-km Resolutions

Abstract This study discloses detailed Madden–Julian oscillation (MJO) characteristics in the two 30-day integrations of the global cloud-system-resolving Nonhydrostatic Icosahedral Atmospheric Model (NICAM) using the all-season real-time multivariate MJO index of Wheeler and Hendon. The model anomaly is derived by excluding the observed climatology because the simulation is sufficiently realistic. Results show that the MJO has a realistic evolution in amplitude pattern, geographical locations, eastward propagation, and baroclinic- and westward-tilted structures. In the central Indian Ocean, convection develops with the low-level easterly wind anomaly then matures where the low-level easterly and westerly anomalies meet. Anomalous moisture tilts slightly with height. In contrast, over the western Pacific, the convection grows with a low-level westerly anomaly. Moisture fluctuations, leading convection in eastward propagation, tilt clearly westward with height. The frictional moisture convergence mechanism operates to maintain the MJO. Such success can be attributed to the explicit representation of the interactions between convection and large-scale circulations. The simulated event, however, grows faster in phases 2 and 3, and peaks with 30% higher amplitude than that observed, although the 7-km version shows slight improvement. The fast-growth phases are induced by the fast-growing low-level convergence in the Indian Ocean and the strongly biased ITCZ in the west Pacific when the model undergoes a spinup. The simulated OLR has a substantial bias in the tropics. Possible solutions to the deficiencies are discussed.

Ocean–atmosphere coupling and the boreal winter MJO

The influence of ocean-atmosphere coupling on the simulation and prediction of the boreal winter Madden-Julian Oscillation (MJO) is examined using the Seoul National University coupled general circulation model (CGCM) and atmospheric—only model (AGCM). The AGCM is forced with daily SSTs interpolated from pentad mean CGCM SSTs. Forecast skill is examined using serial extended simulations spanning 26 different winter seasons with 30-day forecasts commencing every 5 days providing a total of 598 30-day simulations. By comparing both sets of experiments, which share the same atmospheric components, the influence of coupled ocean- atmosphere processes on the simulation and prediction of MJO can be studied. The mean MJO intensity possesses more realistic amplitude in the CGCM than in AGCM. In general, the ocean-atmosphere coupling acts to improve the simulation of the spatio-temporal evolution of the eastward propagating MJO and the phase relationship between convection (OLR) and SST over the equatorial Indian Ocean and the western Pacific. Both the CGCM and observations exhibit a near-quadrature relationship between OLR and SST, with the former lagging by about two pentads. However, the AGCM shows a less realistic phase relationship. As the initial conditions are the same in both models, the additional forcing by SST anomalies in the CGCM extends the prediction skill beyond that of the AGCM. To test the applicability of the CGCM to real-time prediction, we compute the Real-time Multivariate MJO (RMM) index and compared it with the index computed from observations. RMM1 (RMM2) falls away rapidly to 0.5 after 17-18 (15-16) days in the AGCM and 18-19 (16- 17) days in the CGCM. The prediction skill is phase dependent in both the CGCM and AGCM.

Impacts of the Madden–Julian Oscillation on Australian Rainfall and Circulation

Abstract Impacts of the Madden–Julian oscillation (MJO) on Australian rainfall and circulation are examined during all four seasons. The authors examine circulation anomalies and a number of different rainfall metrics, each composited contemporaneously for eight MJO phases derived from the real-time multivariate MJO index. Multiple rainfall metrics are examined to allow for greater relevance of the information for applications. The greatest rainfall impact of the MJO occurs in northern Australia in (austral) summer, although in every season rainfall impacts of various magnitude are found in most locations, associated with corresponding circulation anomalies. In northern Australia in all seasons except winter, the rainfall impact is explained by the direct influence of the MJO’s tropical convective anomalies, while in winter a weaker and more localized signal in northern Australia appears to result from the modulation of the trade winds as they impinge upon the eastern coasts, especially in the northeast. In extratropical Australia, on the other hand, the occurrence of enhanced (suppressed) rainfall appears to result from induced upward (downward) motion within remotely forced extratropical lows (highs), and from anomalous low-level northerly (southerly) winds that transport moisture from the tropics. Induction of extratropical rainfall anomalies by remotely forced lows and highs appears to operate mostly in winter, whereas anomalous meridional moisture transport appears to operate mainly in the summer, autumn, and to some extent in the spring.

Impact of the Madden–Julian Oscillation on Summer Rainfall in Southeast China

Abstract The impact of the Madden–Julian oscillation (MJO) on summer rainfall in Southeast China is investigated using the Real-time Multivariate MJO (RMM) index and the observational rainfall data. A marked transition of rainfall patterns from being enhanced to being suppressed is found in Southeast China (east of 105°E and south of 35°N) on intraseasonal time scales as the MJO convective center moves from the Indian Ocean to the western Pacific Ocean. The maximum positive and negative anomalies of regional mean rainfall are in excess of 10% relative to the climatological regional mean. Such different rainfall regimes are associated with the corresponding changes in physical fields such as the western Pacific subtropical high (WPSH), moisture, and vertical motions. When the MJO is mainly over the Indian Ocean, the WPSH shifts farther westward, and the moisture and upward motions in Southeast China are increased. In contrast, when the MJO enters the western Pacific, the WPSH retreats eastward, and the moisture and upward motions in Southeast China are decreased. It is suggested that the MJO may influence summer rainfall in Southeast China through remote and local dynamical mechanisms, which correspond to the rainfall enhancement and suppression, respectively. The remote role is the energy propagation of the Rossby wave forced by the MJO-related heating over the Indian Ocean through the low-level westerly waveguide from the tropical Indian Ocean to Southeast China. The local role is the northward shift of the upward branch of the anomalous meridional circulation when the MJO is over the western Pacific, which causes eastward retreat of the WPSH and suppressed moisture transport toward Southeast China.

Contributions of Convectively Coupled Equatorial Rossby Waves and Kelvin Waves to the Real-Time Multivariate MJO Indices

Abstract The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices have been widely applied to diagnose and track the progression of the MJO. Although it has been well demonstrated that the MJO contributes to the leading signals in these indices, the RMM indices vary erratically from day to day. These variations are associated with noise in the outgoing longwave radiation (OLR) and wind data used to generate the indices. This note demonstrates that some of this “noise” evolves systematically and is associated with other types of propagating modes that project onto the RMM eigenmodes. OLR and zonal wind data are filtered in the wavenumber–frequency domain for the MJO, convectively coupled equatorial Rossby (ER) waves, and convectively coupled Kelvin waves. The filtered data are then projected onto the RMM modes. An example phase space associated with these projections is presented. Linear regression is then applied to isolate the wave signals from random variations in the same bands of the wavenumber–frequency domain, and the regressed data are projected onto the RMM EOFs. Results demonstrate the magnitudes of the contributions of the systematically evolving signals associated with these waves to variations in the RMM principal components, and how these contributions vary with the longitude of the active moist deep convection coupled to the waves.

An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction

Abstract A seasonally independent index for monitoring the Madden–Julian oscillation (MJO) is described. It is based on a pair of empirical orthogonal functions (EOFs) of the combined fields of near-equatorially averaged 850-hPa zonal wind, 200-hPa zonal wind, and satellite-observed outgoing longwave radiation (OLR) data. Projection of the daily observed data onto the multiple-variable EOFs, with the annual cycle and components of interannual variability removed, yields principal component (PC) time series that vary mostly on the intraseasonal time scale of the MJO only. This projection thus serves as an effective filter for the MJO without the need for conventional time filtering, making the PC time series an effective index for real-time use. The pair of PC time series that form the index are called the Real-time Multivariate MJO series 1 (RMM1) and 2 (RMM2). The properties of the RMM series and the spatial patterns of atmospheric variability they capture are explored. Despite the fact that RMM1 and RMM...