• Andersen, J. A., , and Z. Kuang, 2012: Moist static energy budget of MJO-like disturbances in the atmosphere of a zonally symmetric aquaplanet. J. Climate, 25, 27822804, doi:10.1175/JCLI-D-11-00168.1.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., , and D. A. Randall, 2007: Observed characteristics of the MJO relative to maximum rainfall. J. Climate, 64, 23322354, doi: 10.1175/JAS3968.1.

    • Search Google Scholar
    • Export Citation
  • Ching, L., , C.-H. Sui, , and M.-J. Yang, 2010: An analysis of multi-scale nature of tropical cyclone activities in June 2004: Climate background. J. Geophys. Res., 115, D24108, doi:10.1029/2010JD013803.

    • Search Google Scholar
    • Export Citation
  • Ciesielski, P. E., and et al. , 2014: Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and corrections. J. Atmos. Oceanic Technol., 31, 741764, doi:10.1175/JTECH-D-13-00165.1.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., , Y. Chen, , D. Kim, , and M.-S. Yao, 2012: The MJO transition from shallow to deep convection in CloudSat/CALIPSO data and GISS GCM simulations. J. Climate, 25, 37553770, doi:10.1175/JCLI-D-11-00384.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2003: MJO-like coherent structures: Sensitivity Simulations using the cloud-resolving convection parameterization (CRCP). J. Atmos. Sci., 60, 847864, doi:10.1175/1520-0469(2003)060<0847:MLCSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , and L. R. Leung, 2011: Moist thermodynamics of the Madden–Julian oscillation in a cloud-resolving simulation. J. Climate, 24, 55715583, doi:10.1175/2011JCLI4212.1.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , Z. Feng, , K. Landu, , and C. N. Long, 2014: Advection, moistening, and shallow-to-deep convection transitions during the initiation and propagation of Madden-Julian oscillation. J. Adv. Model. Earth. Syst., 6, 938949, doi:10.1002/2014MS000335.

    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., , and M. L. Salby, 1994: The life cycle of the Madden–Julian oscillation. J. Atmos. Sci., 51, 22252237, doi:10.1175/1520-0469(1994)051<2225:TLCOTM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., , and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 49144931, doi:10.1175/JCLI-D-11-00310.1.

    • Search Google Scholar
    • Export Citation
  • Hu, Q., , and D. A. Randall, 1994: Low-frequency oscillations in radiative–convective systems. J. Atmos. Sci., 51, 10891099, doi:10.1175/1520-0469(1994)051<1089:LFOIRC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., , R. F. Adler, , D. T. Bolvin, , G. Gu, , E. J. Nelkin, , K. P. Bowman, , E. F. Stocker, , and D. B. Wolff, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., , and P. E. Ciesielski, 2013: Structure and properties of Madden–Julian oscillations deduced from DYNAMO sounding arrays. J. Atmos. Sci., 70, 31573179, doi:10.1175/JAS-D-13-065.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., , T. M. Rickenbacg, , S. A. Rutledge, , R. E. Ciesielski, , and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kerns, B. W., , and S. Chen, 2014: Equatorial dry air intrusion and related synoptic variability in MJO initiation during DYNAMO. Mon. Wea. Rev., 142, 13261343, doi:10.1175/MWR-D-13-00159.1.

    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., , and Y. N. Takayabu, 2004: The development of organized convection associated with the MJO during TOGA COARE IOP: Trimodal characteristics. Geophys. Res. Lett., 31, L10101, doi:10.1029/2004GL019601.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., , K. H. Straub, , and P. T. Haertel, 2005: Zonal and vertical structure of the Madden–Julian oscillation. J. Atmos. Sci., 62, 27902809, doi:10.1175/JAS3520.1.

    • Search Google Scholar
    • Export Citation
  • Kim, D., , J.-S. Kug, , and A. H. Sobel, 2014: Propagating versus nonpropagating Madden–Julian oscillation events. J. Climate, 27, 111125, doi:10.1175/JCLI-D-13-00084.1.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., , C.-H. Ho, , H.-S. Kim, , C.-H. Sui, , and S. K. Park, 2008: Systematic variation of summertime tropical cyclone activity in the western North Pacific in relation to the Madden–Julian oscillation. J. Climate, 21, 11711191, doi:10.1175/2007JCLI1493.1.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and P. H. Chan, 1986: Aspects of the 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, doi:10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and L. Peng, 1987: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part I: Basic theory. J. Atmos. Sci., 44, 950972, doi:10.1175/1520-0469(1987)044<0950:OOLFOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and C.-H. Sui, 1997: Mechanisms of short-term sea surface temperature regulation: Observations during TOGA COARE. J. Climate, 10, 465472, doi:10.1175/1520-0442(1997)010<0465:MOSTSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and D. E. Waliser, 2005: Intraseasonal Variability in the Atmospheric-Ocean Climate System. Praxis, 436 pp.

  • Lau, K.-M., , and H.-T. Wu, 2010: Characteristics of precipitation, cloud, and latent heating associated with the Madden–Julian oscillation. J. Climate, 23, 504518, doi:10.1175/2009JCLI2920.1.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D., , and P. J. Webster, 2002: The boreal summer intraseasonal oscillation and the South Asian monsoon. J. Atmos. Sci., 59, 15931606, doi:10.1175/1520-0469(2002)059<1593:TBSIOR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , H. Hendon, , and J. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation. J. Meteor. Soc. Japan, 72, 401411.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., , and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Climate, 29, 26652690, doi:10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., 2009: The moist static energy budget of a composite tropical intraseasonal oscillation in a climate model. J. Climate, 22, 711729, doi:10.1175/2008JCLI2542.1.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and D. L. Hartmann, 2000: Modulation of eastern North Pacific hurricanes by the Madden–Julian oscillation. J. Climate, 13, 14511460, doi:10.1175/1520-0442(2000)013<1451:MOENPH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., , S. Tulich, , J.-L. Lin, , and P. Zuidema, 2006: The mesoscale convection life cycle: Building block or prototype for large-scale tropical waves. Dyn. Atmos. Oceans, 42, 329, doi:10.1016/j.dynatmoce.2006.03.003.

    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., 2008: Primary and successive events in the Madden-Julian oscillation. Quart. J. Roy. Meteor. Soc., 134, 439453, doi:10.1002/qj.224.

    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., , and J.-Y. Yu, 1994: Modes of tropical variability under convective adjustment and the Madden–Julian oscillation. Part I: Analytical results. J. Atmos. Sci., 51, 18761894, doi:10.1175/1520-0469(1994)051<1876:MOTVUC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pritchard, M. S., , and C. S. Bretherton, 2014: Causal evidence that rotational moisture advection is critical to the superparameterized Madden–Julian oscillation. J. Atmos. Sci., 71, 800815, doi:10.1175/JAS-D-13-0119.1.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , and E. Maloney, 2012: An idealized semi-empirical framework for modeling the Madden–Julian oscillation. J. Atmos. Sci., 69, 16911705, doi:10.1175/JAS-D-11-0118.1.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , and E. Maloney, 2013: Moisture modes and the eastward propagation of the MJO. J. Atmos. Sci., 70, 187192, doi:10.1175/JAS-D-12-0189.1.

    • Search Google Scholar
    • Export Citation
  • Sperber, K. R., 2003: Propagation and the vertical structure of the Madden–Julian oscillation. Mon. Wea. Rev., 131, 30183037, doi:10.1175/1520-0493(2003)131<3018:PATVSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., , and K.-M. Lau, 1992: Multiscale phenomena in the tropical atmosphere over the western Pacific. Mon. Wea. Rev., 120, 407430, doi:10.1175/1520-0493(1992)120<0407:MPITTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., , K.-M. Lau, , Y. N. Takayabu, , and D. A. Short, 1997: Diurnal variations in tropical oceanic cumulus convection during TOGA COARE. J. Atmos. Sci., 54, 639655, doi:10.1175/1520-0469(1997)054<0639:DVITOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., , K.-M. Lau, , and C.-H. Sui, 1996: Observation of a quasi-2-day wave during TOGA COARE. Mon. Wea. Rev., 124, 18921913, doi:10.1175/1520-0493(1996)124<1892:OOAQDW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., 1988: Dynamics of tropical low-frequency waves: An analysis of moist Kelvin waves. J. Atmos. Sci., 45, 20512065, doi:10.1175/1520-0469(1988)045<2051:DOTLFW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , and T. Li, 1994: Convective interaction with boundary dynamics in the development of a tropical intraseasonal system. J. Atmos. Sci., 51, 13861400, doi:10.1175/1520-0469(1994)051<1386:CIWBLD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, S., , A. Sobel, , F. Zhang, , Y. Sun, , Y. Yue, , and L. Zhou, 2015: Regional simulation of the October and November MJO events observed during the CINDY/DYNAMO field campaign at gray zone resolution. J. Climate, 28, 20972119, doi:10.1175/JCLI-D-14-00294.1.

    • Search Google Scholar
    • Export Citation
  • Wang, W., , and M. E. Schlesinger, 1999: The dependence on convection parameterization of the tropical intraseasonal oscillation simulated by the UIUC 11-layer atmospheric GCM. J. Climate, 12, 1423145, doi:10.1175/1520-0442(1999)012<1423:TDOCPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of cloud and temperature in the wavenumber-frequency domain. J. Atmos. Sci., 56, 374399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, doi:10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yan, H.-M., , M. Zhong, , and Y.-Z. Zhu, 2004: Determination of the degree of freedom of digital filtered time series with an application to the correlation analysis between the length of day and the Southern Oscillation index. Chin. Astron. Astrophys., 28, 120126, doi:10.1016/S0275-1062(04)90014-8.

    • Search Google Scholar
    • Export Citation
  • Yanai, M., , S. Esbensen, , and J.-H. Chu, 1973: Determination of bulk properties of tropical cluster from large-scale heat and moisture budget. J. Atmos. Sci., 30, 611627, doi:10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden–Julian oscillation. Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.

  • View in gallery

    The 2-month (October and November) averaged SST (shading; °C) and 1000-hPa wind (vector; m s−1) in the DYNAMO/CINDY sounding array. The open and closed circles are intensive observation sounding sites that release four and eight radiosondes per day, respectively.

  • View in gallery

    The averaged power spectra of mean daily OLR (W m−2) in the 12 grid boxes of 5° × 5° over the central Indian Ocean (5°S–5°N, 60°–90°E, separated by every 5° × 5°) shown by vertical bars as a function of period. The red solid line is the red noise curve.

  • View in gallery

    The Hovmöller diagram of 15–60-day bandpass-filtered OLR (shading; W m−2), space–time-filtered MJO from OLR (black contours; interval of 10 W m−2), Kelvin waves from zonal wind at 850 hPa (purple contours; interval of 0.5 m s−1) averaged over 7.5°S–7.5°N, and equatorial Rossby waves from vorticity at 850 hPa (green contours; interval of 3 × 10−6 s−1) averaged over 5°–15°N and 5°–15°S.

  • View in gallery

    Hovmöller diagrams of (a) sea surface temperature (°C) and (b) precipitation (mm h−1) averaged within 7.5°S–7.5°N. Contours superimposed in (a) as in Fig. 3.

  • View in gallery

    (a) Time–height structure of [q]′ (mm) and (b) time series of OLR′ (blue line) and 1000–700-hPa integrated specific humidity (green line). All variables are bandpass filtered at 15–60 days. The numbers shown above the x axis in (b) are the corresponding real-time multivariate MJO (RMM) index.

  • View in gallery

    Convective activities within the DYNAMO/CINDY north sounding array: (a) time–height cross section of (shading; K day−1) and vertical velocity (contours: thin solid lines for upward motion at −0.01, −0.03, −0.15, and −0.25 hPa s−1; thin dashed lines for downward motion at 0.04 and 0.03 hPa s−1; and thick lines for 0 hPa s−1); (b) normalized frequency counts of storm height in different height bins (shadings in top half of panel), integrated frequency of storm height (vertical bars in bottom half of panel denoting fractional cloud coverage), and vertical velocity distribution in height [contours; as in (a)]; (c) temporal evolution of rain rate from TRMMv7 3B42 (mm h−1); and (d) time–height cross section of equivalent potential temperature from sounding data (K).

  • View in gallery

    The projections of (a) [∂q/∂t]′, , , and [−Q2L1]′; (b) scale-separated vertical advection budget terms; (c) scale-separated horizontal advection budget terms; (d) scale-separated zonal advection; and (e) scale-separated horizontal advection terms on normalized [∂q/∂t]′ (shown as percentages; all variables are nondimensional).

  • View in gallery

    Time series of 1000–700-hPa integrated [q]′ (blue dashed line; mm), [∂q/∂t]′ (green dashed line; mm h−1), and respective moisture budget terms (red solid line; mm h−1): (a) , (b) , (c) (scaled by 0.1), (d) [−Q2L1]′ (scaled by 0.1), and (e) (scaled by 0.5). All are vertically integrated 1000–700 hPa and areal averaged in the north sounding array.

  • View in gallery

    The projections of (a) , , and [−Q2L1]′; (b) scale-separated vertical advection budget terms; and (c) scale-separated horizontal advection budget terms on normalized [q]′ (shown in growth rate; day−1).

  • View in gallery

    The horizontal distributions of (a) (contours; interval of 0.3 mm day−1), [V]′ (vector; m s−1), and (shading; mm); (b) (contours; interval of 0.2 mm day−1), (vector; m s−1), and [q]′ (shading; mm); (c) 1000–700-hPa integrated (shading; mm day−1) and 700–100-hPa integrated (contours; interval of 0.5 mm day−1); (d) as in (c), but for [−Q2L1]′; and (e) (contours; interval of 0.8 mm day−1) and [q]′ (shading; mm) for (left) 1–9 and (right) 10–19 Oct.

  • View in gallery

    (a) Time series of 1000–700-hPa integrated (blue dashed line; Pa s−1), (green dashed line; mm day−1) and (red solid line; K). Vertical and temporal distributions of (b) , (c) , (d) , and (e) (K day−1). Contours in (d) and (e) are zero contours of Q2.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 37 37 3
PDF Downloads 22 22 2

Moistening Processes for Madden–Julian Oscillations during DYNAMO/CINDY

View More View Less
  • 1 Department of Atmospheric Science, National Taiwan University, Taipei, Taiwan
  • | 2 International Pacific Research Center, and Department of Meteorology, University of Hawai‘i at Mānoa, Honolulu, Hawaii
© Get Permissions
Full access

Abstract

Lower-tropospheric (1000–700 hPa) moistening processes of the two Madden–Julian oscillations (MJOs) over the Indian Ocean during Dynamics of the MJO (DYNAMO)/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (CINDY) are investigated by using soundings, operational assimilation, and satellite data. A scale-separated moisture budget is calculated at the sounding site by using time-decomposed wind and moisture fields. Each budget term is projected onto the intraseasonal moisture anomaly and its time tendency change. The projections and the corresponding temporal correlations are analyzed together with the temporal evolution of the budget terms to identify the dominant moistening process responsible for the MJO evolution. Results indicate that broad-scale advection by low-frequency and MJO flow and moisture fields are dominant moisture sources, while the residual of the moisture budget (−Q2) is a dominant sink contributing to the tendency term (propagation) and intraseasonal moisture anomaly (growth and decay). Dividing their life cycles into four phases (suppressed, cloud developing, convective, and decaying phases), the two MJOs exhibit different budget balances in the premoistening stage from the suppressed phase to the cloud-developing phase when low-frequency vertical motion is downward in MJO1 but upward in MJO2. The corresponding drying and moistening are balanced by negative Q2 (reevaporation in nonraining cloud) in MJO1 and positive Q2 in MJO2. The result implies that low-frequency flow (>60 days) can affect the initiation of MJOs. The premoistening in the lower troposphere by boundary layer moisture convergence leading the deep convection is observed but only in the cloud-developing phase to convective phase of the MJOs. Nonlinear moisture advection by synoptic disturbances always acts as a diffusive term. It is the dominant moisture source in the suppress phase of the two MJOs.

Corresponding author address: Chung-Hsiung Sui, Department of Atmospheric Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan. E-mail: sui@as.ntu.edu.tw

This article is included in the DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation special collection.

Abstract

Lower-tropospheric (1000–700 hPa) moistening processes of the two Madden–Julian oscillations (MJOs) over the Indian Ocean during Dynamics of the MJO (DYNAMO)/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (CINDY) are investigated by using soundings, operational assimilation, and satellite data. A scale-separated moisture budget is calculated at the sounding site by using time-decomposed wind and moisture fields. Each budget term is projected onto the intraseasonal moisture anomaly and its time tendency change. The projections and the corresponding temporal correlations are analyzed together with the temporal evolution of the budget terms to identify the dominant moistening process responsible for the MJO evolution. Results indicate that broad-scale advection by low-frequency and MJO flow and moisture fields are dominant moisture sources, while the residual of the moisture budget (−Q2) is a dominant sink contributing to the tendency term (propagation) and intraseasonal moisture anomaly (growth and decay). Dividing their life cycles into four phases (suppressed, cloud developing, convective, and decaying phases), the two MJOs exhibit different budget balances in the premoistening stage from the suppressed phase to the cloud-developing phase when low-frequency vertical motion is downward in MJO1 but upward in MJO2. The corresponding drying and moistening are balanced by negative Q2 (reevaporation in nonraining cloud) in MJO1 and positive Q2 in MJO2. The result implies that low-frequency flow (>60 days) can affect the initiation of MJOs. The premoistening in the lower troposphere by boundary layer moisture convergence leading the deep convection is observed but only in the cloud-developing phase to convective phase of the MJOs. Nonlinear moisture advection by synoptic disturbances always acts as a diffusive term. It is the dominant moisture source in the suppress phase of the two MJOs.

Corresponding author address: Chung-Hsiung Sui, Department of Atmospheric Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan. E-mail: sui@as.ntu.edu.tw

This article is included in the DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation special collection.

1. Introduction

Since Madden and Julian (1972) found the eastward-propagating oscillations over the tropical Indo-Pacific region, many observational analyses have revealed a slowly eastward-propagating convective envelope characterized by planetary-scale circulation with a broad life span of 30–60 days (e.g., Lau and Chan 1986; Hendon and Salby 1994; Zhang 2005; Lau and Waliser 2005). Despite numerous studies about the Madden–Julian oscillation (MJO), some fundamental questions still remain to be answered, like what initiates an MJO and what determines the propagation and growth of MJOs. Our further progress hinges on a better understanding about multiscale interaction processes in MJO (Wheeler and Kiladis 1999; Mapes et al. 2006).

Understanding the MJO phenomenon is important for diagnosis and prediction of tropical weather and climate. For example, MJO is observed to virtually influence intraseasonal precipitation of the Asian and Australian monsoons (e.g., Sui and Lau 1992; Lawrence and Webster 2002). The cyclone geneses in the western Pacific, Indian Ocean, and Caribbean Sea basins are also modulated by the intraseasonal oscillation (e.g., Liebmann et al. 1994; Maloney and Hartmann 2000; Kim et al. 2008; Ching et al. 2010).

In a typical life cycle of the MJO, like primary and successive events classified by Matthews (2008), the convective envelop initiates from the Indian Ocean and propagates eastward to the Maritime Continent, where the MJO’s circulation weakens but reintensifies upon reaching the Pacific warm pool. Actually, many MJOs evolve differently from this typical life cycle: some behave more like a stationary dipole oscillation over the Indian Ocean and the warm pool.

The previous theories explaining the propagating mechanism can be separated into two sets of theories: the tropical wave dynamics and the moisture mode. In the set of wave dynamics, the eastward propagation of MJOs is first explained by Kelvin waves based on the forced wave dynamics with a wave–conditional instability of the second kind (wave-CISK)-type parameterization of convective heating (e.g., Lau and Peng 1987). The most unstable wave in such a simplified system is normally at a small wavelength, which is different from the observed planetary-scale circulation associated with MJOs. To remedy the scale selection problem, Wang (1988) and Wang and Li (1994) added friction-induced boundary layer convergence in the wave-CISK framework. In the wave-CISK framework, convection-reduced vertical stratification can slow down Kelvin wave speed (~15 m s−1), which is still faster than observed MJO (~5 m s−1). Since moisture is treated as a diagnostic variable in the wave-related theory, horizontal moisture advection is normally neglected and vertical moisture advection is crudely coupled with convective heating. As we will show in this study, these moistening processes are crucial for the MJO propagation.

In the second set of moisture-mode theories, the horizontal moisture advection and surface moisture flux that alter the moisture [therefore moist static energy (MSE)] tendency are regarded as the key processes of the MJO propagation (e.g., Neelin and Yu 1994; Hu and Randall 1994; Sobel and Maloney 2012, 2013). Some studies use column-integrated MSE or moist entropy because of the property of conservation, while most of the intraseasonal MSE variations are dominated by moisture variation with a relatively weak temperature variation. MJO simulation in some general circulation models can be improved by making deep convection sensitive to free-tropospheric moisture (e.g., Wang and Schlesinger 1999; Grabowski 2003). Recent studies performing moisture budgets and moist static energy budgets of the MJO show that cloud radiative feedback and horizontal moisture advection are important for the MJO’s growth and propagation (Maloney 2009; Hagos and Leung 2011; Andersen and Kuang 2012; Kim et al. 2014; Pritchard and Bretherton 2014; Hagos et al. 2014; Wang et al. 2015).

An important process recognized in both sets of theories is the premoistening ahead of the MJO convective phase as supported by contemporary observations revealing the prevalence of low and middle clouds during the suppressed phase of the MJO. Kikuchi and Takayabu (2004) used composite geostationary meteorology satellite data during TOGA COARE together with the upper-air soundings from the intensive observation period (IOP) of the experiment to obtain the vertical thermal structures and moisture structures. They showed lower-tropospheric moistening in the cloud-developing phase associated with the MJO, which is strongly modulated by shallow clouds and congestus cloud. Lau and Wu (2010) used 4 yr of Tropical Rainfall Measuring Mission data for MJO composite analyzing the top echo height and heating profile in different phases. They also showed the existence of shallow clouds and lower-tropospheric adiabatic heating during suppressed phase. An increase of moisture in the lower troposphere can destabilize the atmosphere for the following development of convection. Del Genio et al. (2012) analyzed CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Their composite of 10 MJO events revealed shallow and congestus clouds in advance of the peak deep clouds. Hsu and Li (2012) utilized 20-yr reanalysis data to show that the vertical advection, which results from boundary layer convergence, is the major moisture source of MJO evolution. Despite the studies cited above, the premoistening processes associated with the initiation and the propagation of the MJO are still unclear. In this study, we analyze scale-separated budget to quantify the dominant moistening process in different stages of the MJO life cycles. We also calculate apparent moisture sink (Q2) based on large-scale budgets (Yanai et al. 1973). The Q2 is an estimate of the collective effect of cloud moistening in a selected domain, which is large in the presence of deep convective system but weaker in the less convective environment, where shallow convection may moisten or dry the lower troposphere. The budget estimate requires data of higher quality and spatiotemporal resolution. The intensive sounding observations from the Dynamics of the MJO (DYNAMO)/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (CINDY) field campaign provide an opportunity for us to perform a moisture budget analysis in this study.

In this study, we calculate a diagnostic moisture budget in the DYNAMO/CINDY sounding array in the Indian Ocean (IO). Instead of using reanalysis data, we use operational data from the European Centre for Medium-Range Weather Forecasts, which are assimilated with field observations and satellite data during the DYNAMO/CINDY IOP. In section 2, we describe the data and method utilized in this study. In section 3, the MJO evolutions from October to December 2011 are discussed. Section 4 presents the diagnostic moisture budget of the MJO. Section 5 presents concluding remarks.

2. Data and method

In this study, we use the following three datasets for the DYNAMO/CINDY period from 1 October to 31 December 2011.

a. Sounding observations

The DYNAMO/CINDY upper-air sounding network comprises two quadrilateral arrays, one north and one south of the equator (Fig. 1), with a total of six sounding sites, including three atolls, one island, and two scientific ships. Intensive observations were made at the six sounding sites from 1 October to 15 December 2011, except for a port call of the research vessel (R/V) Mirai in the south sounding array after 30 November and two port calls of the R/V Revelle from 31 October to 7 November and from 8 to 16 December [see Johnson and Ciesielski (2013) for complete description]. Thus, in this study, we only use the sounding data from 1 October to 30 November. In Fig. 1, the open and closed circles are intensive observation sites, which released from four to eight radiosondes per day. All radiosonde observations are mass weighted for every 50 hPa and time averaged for each day.

Fig. 1.
Fig. 1.

The 2-month (October and November) averaged SST (shading; °C) and 1000-hPa wind (vector; m s−1) in the DYNAMO/CINDY sounding array. The open and closed circles are intensive observation sounding sites that release four and eight radiosondes per day, respectively.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

b. Satellite data

The dataset of Tropical Rainfall Measuring Mission, version 7 (TRMMv7), including storm height (2A23) and rain rate (3B42), is used to describe cloud and rainfall evolutions in different phases of the MJO in DYNAMO. The horizontal resolution of TRMM level 2A data is 4 × 4 km2, with a temporal resolution of 12 h. The 3B42 data are on a 0.25° × 0.25° grid resolution and 3-h temporal resolution (Huffman et al. 2007). The sea surface temperature data are from the TRMM Microwave Imager (TMI) with spatial and temporal resolutions of 0.25° × 0.25° and 1 day, respectively. The interpolated daily outgoing longwave radiation (OLR) obtained from the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites (Liebmann and Smith 1996) is gridded at 2.5° × 2.5°.

c. ECMWF operational data

The budget analysis is computed using operational data from the European Centre for Medium-Range Weather Forecasts (ECMWF). About 95% of DYNAMO/CINDY sounding observations are transmitted to operational centers in real time, so the region of the sounding array is strongly influenced by in situ observations. Before doing budget analysis, we compare the ECMWF operational data and sounding data. The result (not shown) reveals that operational data capture synoptic-scale and intraseasonal-scale signal well. Ciesielski et al. (2014) carried out an assessment of moisture fields from the ECMWF operational products using quality-controlled upper-air sounding data. They found an overall good agreement, with the exception at upper levels, where the assimilated temperature values have a positive bias. The bias is not expected to affect our diagnostic result of lower-tropospheric budget. The 6-hourly operational data used in this study include zonal winds, meridional winds, vertical velocity, and specific humidity at 15 vertical layers from 1000 to 100 hPa and 0.25° × 0.25° latitude–longitude horizontal spatial resolutions.

d. Diagnostic moisture budget

Total moisture tendency is determined by horizontal advection, vertical advection, and −Q2L1 by
e1
where q is specific humidity, u and υ are zonal and meridional winds, ω is vertical pressure (p) velocity, and L is the latent heat of condensation. Following Yanai et al. (1973), Q2 is calculated as a residual of the moisture budget [Eq. (1)] using 6-hourly u, υ, ω, and q from ECMWF operational data. The variable Q2 represents the physical processes
e2
including subgrid-scale contributions consisting of condensation c, evaporation e, and eddy moisture flux convergence. The vertically integrated Q2 equals the difference between surface evaporation E and precipitation P,
e3

e. Scale-separated moisture budget

The MJO is a multiscale phenomenon: a power spectral analysis of the daily OLR over the equatorial Indian Ocean (5°S–5°N, 60°–90°E) during the DYNAMO/CINDY period shows significant peaks at synoptic (<10 days), biweekly (~15 days), and 20–60-day time scales (Fig. 2). A comparison of the power spectrum of space–time-filtered OLR fields of the MJO and equatorial Kelvin and Rossby waves (see the end of this section) indicates that the biweekly spectral peaks are contributed by Kelvin and Rossby waves and the intraseasonal 20–60-day spectral band is contributed by the MJO and long Kelvin and Rossby waves. To examine the contribution to intraseasonal-scale variability by scale interactions, we apply fast Fourier transform (FFT) to moisture and winds to separate them into three time scales: 1) synoptic scale with period less than 15 days, 2) 15–60-day intraseasonal scale, and 3) low-frequency background state (LFBS) with period longer than 60 days. Then, by substituting (where a is u, υ, ω, or q, and , , and denote the scale-separated a at the synoptic, intraseasonal, and low-frequency bands) into the moisture equation [Eq. (1)], we can express the nonlinear advection terms as a combination of all three scales. We then apply an intraseasonal (15–60 day) filter (denoted with [ ]′) to the equation to obtain
e4
Fig. 2.
Fig. 2.

The averaged power spectra of mean daily OLR (W m−2) in the 12 grid boxes of 5° × 5° over the central Indian Ocean (5°S–5°N, 60°–90°E, separated by every 5° × 5°) shown by vertical bars as a function of period. The red solid line is the red noise curve.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

To compare the time-filtered result with the theoretical waves structures, we also perform the time–space spectral analysis described by Wheeler and Kiladis (1999) to extract the MJO, equatorial Rossby waves, and equatorial Kelvin waves. The MJO is extracted by eastward-propagating components of wavenumbers 0–5 and periods of 30–90 days. The equatorial Kelvin waves are extracted by wavenumbers 0–5 and periods of 10–30 days, and the equatorial Rossby (ER) waves correspond to wavenumbers 1–10 and periods of 10–40 days.

3. The MJO evolution in the DYNAMO/CINDY period

a. Large-scale environment and rainfall evolution

Time averaged winds at 1000 hPa and sea surface temperature (SST) are shown in Fig. 1 for the IOP of the DYNAMO/CINDY (October and November). The SST shows a meridionally and zonally asymmetric pattern in the western and the southern Indian Ocean relative to the DYNAMO/CINDY sounding array. While most of the precipitation occurs in the region of SST higher than 28°C, the strongest rainfall is located in the region of large SST gradient, which is characteristic of substantial ITCZ rainfall (see Fig. 4 in Johnson and Ciesielski 2013). The near-surface winds are characterized by a pair of cyclonic flows to the north and south of the DYNAMO/CINDY sounding array. On the equator, the strong westerly is related to the two MJO events in October and November.

The overall evolution of the MJOs in the DYNAMO/CINDY is shown by the Hovmöller diagram of the 15–60-day bandpass-filtered OLR in Fig. 3 and unfiltered SST and precipitation in Fig. 4, all averaged within the 7.5°S–7.5°N latitude band. Also shown in Figs. 3 and 4 are space–time-filtered MJO from OLR and Kelvin waves from zonal wind at 850 hPa averaged over 7.5°S–7.5°N and ER waves from vorticity at 850 hPa averaged over 5°–15°N and 5°–15°S. From early October 2011 to January 2012, there are two strong MJO events (MJO1 and MJO2) and one weak event (MJO3). Both MJO1 and MJO2 appear to initiate at around 50°–60°E and propagate eastward. MJO1 starts over the Indian Ocean from a suppressed phase (anomalous high OLR) in early October with no prior propagating MJO event. The OLR anomaly then propagates eastward to initiate the following development of deep convective phase of MJO1 over the Indian Ocean in middle-to-late October. The MJO1 further leads to the initiation and propagation of MJO2 in November. The relevant mechanism for initiation and development is the focus of this study, which is discussed in section 4. The intraseasonal oscillation in December, on the other hand, is a less organized event, with an eastward-moving suppressed phase and a westward-moving convective phase (Fig. 3). The event is not discussed in this study. We find the convection center of both MJO1 and MJO2 located in the zonal wind convergence of Kelvin wave, but the major rainband shows more detailed features. In the MJO1 convective phase (15–30 October), precipitation exhibits 1–2-day oscillations emerging from the Maritime Continent near 120°E and propagating westward to central IO. The disturbance is related to the strong diurnal cycle in the suppressed phase of the MJO, especially over the Maritime Continent, as identified before in previous studies (e.g., Takayabu et al. 1996; Sui et al. 1997). This phenomenon is relatively weak and confined east of 90°E in MJO2. The MJO2 precipitation consists of two Kelvin wave–like rainbands (Fig. 4b) resulting from subtropical dry air intrusion (Kerns and Chen 2014). The SST in Fig. 4a exhibits multiscale variability that results from air–sea flux exchanges associated with the MJO evolution as well as ocean transports. The SST variability in October is not correlated with MJO1, likely more as a result of dominant ocean transports than the air–sea fluxes. However, the warming in the Indian Ocean in the first half of November is in phase with the suppressed phase of MJO2 and the cooling in the second half of November is in phase with the westerly Kelvin wave. These can be attributed to the enhanced solar heating in the suppressed phase and evaporation cooling associated with westerly wind burst and cloud albedo effect in the convective phase of MJO2, similar to the intraseasonal variability observed in the western Pacific (e.g., Sui and Lau 1992).

Fig. 3.
Fig. 3.

The Hovmöller diagram of 15–60-day bandpass-filtered OLR (shading; W m−2), space–time-filtered MJO from OLR (black contours; interval of 10 W m−2), Kelvin waves from zonal wind at 850 hPa (purple contours; interval of 0.5 m s−1) averaged over 7.5°S–7.5°N, and equatorial Rossby waves from vorticity at 850 hPa (green contours; interval of 3 × 10−6 s−1) averaged over 5°–15°N and 5°–15°S.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

Fig. 4.
Fig. 4.

Hovmöller diagrams of (a) sea surface temperature (°C) and (b) precipitation (mm h−1) averaged within 7.5°S–7.5°N. Contours superimposed in (a) as in Fig. 3.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

b. Evolution of moisture, vertical motion, and convective activity over the DYNAMO/CINDY sounding array

Figure 4 shows clearly the passage of MJOs through the DYNAMO/CINDY sounding array; we further examine the evolution of convective activities associated with MJOs within the sounding array. To help better define the evolution of MJOs at the sounding array, we first show the temporal evolution of 15–60-day-filtered OLR and specific humidity in Fig. 5. The filtered time–height distribution of specific humidity in Fig. 5a shows the dry–wet phases of the three MJO events with the maximum variability near 700 hPa. The moisture anomaly field below 700 hPa shows a distinct vertical tilt with time, showing a moistening boundary layer prior to the development of deep convective phase. This is an important feature that was noted previously by Sperber (2003) and Kiladis et al. (2005). In Fig. 5b we show the temporal evolution of 15–60-day-filtered OLR and vertically integrated [q]′ from the surface to 700 hPa to quantify the phase relation between clouds and lower-troposphere moisture in the MJO evolution. Based on the two variables, the evolution of MJO1 can be separated into the following four phases: suppressed phase (1–9 October, when OLR′ is positive and [q]′ is low); cloud-developing phase (10–19 October, when [q]′ grows to a maximum and OLR′ turns negative); convective phase (20–29 October, when OLR′ reaches minimum); and decaying phase (30 October–5 November, when OLR′ increases and [q]′ decreases to zero). In terms of the MJO index defined in Wheeler and Hendon (2004), the above four phases correspond to 567, 81, 2, and 34, respectively.

Fig. 5.
Fig. 5.

(a) Time–height structure of [q]′ (mm) and (b) time series of OLR′ (blue line) and 1000–700-hPa integrated specific humidity (green line). All variables are bandpass filtered at 15–60 days. The numbers shown above the x axis in (b) are the corresponding real-time multivariate MJO (RMM) index.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

Next, we examine in Fig. 6 the temporal evolution in quantities associated with convective activities including Q2, vertical velocity, frequency count of storm height, rain rate, and equivalent potential temperature observed in the DYNAMO/CINDY north sounding array. Here, Q2 and vertical velocity are derived from ECMWF operational analysis, equivalent potential temperature is derived from the DYNAMO/CINDY soundings, and the rest are from TRMM data, as discussed in section 2. Color shadings in Figs. 6a,b show time–height cross section of daily averaged Q2 (Fig. 6a) and normalized frequency time series of storm height (Fig. 6b). The vertical velocity is also shown in the two figures by contours. The vertically integrated frequency counts of storm heights in Fig. 6b give the cloud fraction (vertical bars in Fig. 6b). The most distinguished evolution from early October to late October is the dramatic increasing of cloud population. In the suppressed phase (1–9 October), large-scale downward motion confines the development of convection, with storm heights below 5 km and the domain cloud coverage less than 10%. The corresponding Q2 (Fig. 6a) in the MJO1 suppressed phase is significantly negative below 6 km, with the largest values exceeding −7 K day−1. Since negative Q2 can extend up to 600 hPa, where the boundary layer eddy activities cannot reach, the moistening implies reevaporation of shallow convection and congestus (Johnson et al. 1999). The shallow convection can efficiently moisten the low-level and midlevel troposphere and enhanced the convective instability that are required for the following development of deep convection.

Fig. 6.
Fig. 6.

Convective activities within the DYNAMO/CINDY north sounding array: (a) time–height cross section of (shading; K day−1) and vertical velocity (contours: thin solid lines for upward motion at −0.01, −0.03, −0.15, and −0.25 hPa s−1; thin dashed lines for downward motion at 0.04 and 0.03 hPa s−1; and thick lines for 0 hPa s−1); (b) normalized frequency counts of storm height in different height bins (shadings in top half of panel), integrated frequency of storm height (vertical bars in bottom half of panel denoting fractional cloud coverage), and vertical velocity distribution in height [contours; as in (a)]; (c) temporal evolution of rain rate from TRMMv7 3B42 (mm h−1); and (d) time–height cross section of equivalent potential temperature from sounding data (K).

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

In the cloud-developing phase (10–19 October), we observe a gradual increase of cloud coverage and storm height. Some storms can exceed the freezing level by about 5 km, even to 10 km. The development of organized convection is associated with increasing domain-mean precipitation (Fig. 6c), weakening domain-mean downward motion, and changing Q2 in the low troposphere from negative to positive values. The above features indicate a gradual deepening of convection from nonprecipitating to precipitating clouds. In the convection active phase (21–27 October), the height of maximum Q2 matches well with the storm height distribution from TRMM observations, which is around 6 km, whereas the stronger convection can reach 12 km. The large positive values of Q2 indicate dominant condensation heating (exceeding 7 K day−1), as revealed by the intermittent strong precipitation (~1.5 mm h−1). The distribution of equivalent potential temperature (θe) shows a more neutral troposphere in the convective phase than that in the suppressed phase (Fig. 6d). In the decaying phase (30 October–5 November), the maximum storm height decreases to 8 km but the maximum vertical velocity and positive Q2 remain in upper troposphere, which is characteristic of prevailing stratiform clouds.

For MJO2, the evolution in the DYNAMO/CINDY sounding array is also divided into the four phases based on the same criteria as for MJO1: suppressed phase (6–12 November), cloud-developing phase (13–20 November), convective phase (21–25 November), and decaying phase (25 November–4 December). While there are some overall similarities between the two MJOs, some distinctive differences are noted. First, the duration of the four phases in MJO2 is generally shorter than that in MJO1, except the decaying phase. Second, the low-level Q2 in the suppressed phase of MJO2 (6–12 November) exhibits more positive Q2 values in the lower troposphere and gradually deepens to 600 hPa during the period from early to middle November (Fig. 6a). The positive Q2 here implies a drying (heating) effect by precipitating clouds. Contrary to recharging moisture by dominant nonprecipitating shallow convection in suppressed phase of MJO1, emerging precipitating convection discharges moisture in the suppressed phase of MJO2. This is supported by the relatively unstable atmosphere in the suppressed phase of MJO1 and a more neutral atmosphere in the suppressed phase of MJO2 (Fig. 6d), and the storm height in the suppressed phase of the MJO2 is significantly higher compared to the MJO2.

The overall evolution through the MJO life cycle discussed above is consistent with previous findings (Lau and Sui 1997; Kikuchi and Takayabu 2004; Benedict and Randall 2007; Mapes et al. 2006). Thus, the key to understand the mechanism of MJO propagation (local phase change) is to reveal specific processes causing lower-tropospheric moistening and drying. In the next section, we introduce the scale-separated moisture budget to quantify the dominant processes responsible for the MJO phase change.

4. Moisture budget

In this section, we present an analysis of vertically integrated moisture budget [Eq. (4)]. All budget terms are temporally filtered to retain the intraseasonal (15–60 day) band, areal averaged in the DYNAMO/CINDY north sounding array (0°–5°N, 73°–80°E) and vertically integrated in the lower troposphere from 1000 to 700 hPa (referred to here as the lower troposphere unless otherwise stated).

a. Projection of budget terms onto moisture anomaly and its tendency change

To determine the relative contribution of each budget term to the local moisture tendency change in the sounding array, we project time series of each vertically integrated budget term to the time series of tendency change term [∂q/∂t]′ in the period of the DYNAMO/CINDY intensive observation period (1 October–30 November). The projections shown in Fig. 7 are normalized to show fractional contribution of each moisture budget terms to [∂q/∂t]′. Figure 7a shows that both horizontal and vertical advection terms project to positive tendency change, while Q2 project negatively to moisture tendency change. The projection by each scale-separated advection terms are shown in Figs. 7b,c. The three leading terms, , , and , all project positively to [∂q/∂t]′, contributing to MJO phase change. The horizontal advection is primarily contributed by and , as shown in Figs. 7d,e. The correlation coefficients between [∂q/∂t]′ and all moisture budget terms in Eq. (4) are shown in Table 1. The corresponding correlation coefficients for , , , and Q2 are 0.49, 0.81, 0.83, and −0.63, indicating large-scale horizontal advection as dominant mechanism of MJO propagation. The terms and Q2, on the other hand, are not well correlated with [∂q/∂t]′. However, the magnitudes of the two terms are one order larger than that of the other budget terms, which still makes them vitally important in moistening process. To further examine the amplitude and variability of the budget terms, the time series of the budget terms are shown in Fig. 8. Note that and Q2 are scaled by 0.1 in Figs. 8c,d because of their large magnitudes. The two terms, however, tend to cancel each other. The temporal evolution of the budget terms in Fig. 8 indicates a balance in the suppressed stage of MJO1 and MJO2 between drying by large-scale advection (Figs. 8a–c) and moistening by high-frequency advection and convective mixing (Figs. 8e and 8d, respectively). From the transition to mature stage, the budget balance is primarily dominated by vertical advection moistening (Fig. 8c) and convective drying (Fig. 8d). Figure 8c further reveals a significant moistening by the vertical advection in the lower troposphere in the cloud-developing phase ahead of major ascending motion. The spatial distribution of the moistening in the lower and upper troposphere is to be discussed in the next subsection. In addition, and [∂q/∂t]′ are completely uncorrelated, with a near-zero correlation coefficient.

Fig. 7.
Fig. 7.

The projections of (a) [∂q/∂t]′, , , and [−Q2L1]′; (b) scale-separated vertical advection budget terms; (c) scale-separated horizontal advection budget terms; (d) scale-separated zonal advection; and (e) scale-separated horizontal advection terms on normalized [∂q/∂t]′ (shown as percentages; all variables are nondimensional).

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

Table 1.

The correlation coefficients between moisture budget terms and [∂q/∂t]′ in the DYNAMO/CINDY period. The correlations are calculated for two vertically integrated budgets: surface–700 hPa and 700–100 hPa. The correlation coefficients passing 95% significance of t tests are shown in boldface. The effective degree of freedom of bandpass-filtered data is 15 (Yan et al. 2004).

Table 1.
Fig. 8.
Fig. 8.

Time series of 1000–700-hPa integrated [q]′ (blue dashed line; mm), [∂q/∂t]′ (green dashed line; mm h−1), and respective moisture budget terms (red solid line; mm h−1): (a) , (b) , (c) (scaled by 0.1), (d) [−Q2L1]′ (scaled by 0.1), and (e) (scaled by 0.5). All are vertically integrated 1000–700 hPa and areal averaged in the north sounding array.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

We also check the projection of each budget term onto [q]′, to evaluate their contribution to local moisture growing or decaying (Fig. 9). The figure illustrates that , , , and [−Q2L1]′] are the four dominant terms. All of these terms are highly correlated with [q]′ (correlation coefficients >0.73; Table 2). The significant correlations of [−Q2L1]′ and with [q]′ (−0.92 and 0.96, respectively) and the corresponding projections along with the temporal evolution of the budget terms in Fig. 8 indicate that the strong condensation by precipitation ([−Q2L1]′) can remove moisture from free troposphere, which largely counteracts the effect of (Figs. 8 and 9). This suggests that the boundary layer moistening process is an important component of the ensemble convection (Q2) interacting with intraseasonal dynamics (), which is vital not only for eastward propagation but also for growth of the MJO. Figure 9 further indicates an important role of the horizontal advection by high-frequency eddies, which tend to diffuse the intraseasonal moisture anomaly, while the vertical advection by high-frequency eddies is less important. We will discuss the high-frequency advection terms in the next subsection.

Fig. 9.
Fig. 9.

The projections of (a) , , and [−Q2L1]′; (b) scale-separated vertical advection budget terms; and (c) scale-separated horizontal advection budget terms on normalized [q]′ (shown in growth rate; day−1).

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

Table 2.

The correlation coefficients between moisture budget terms and [q]′ in the DYNAMO/CINDY period. The correlations are calculated for two vertically integrated budgets: surface–700 hPa and 700–100 hPa. The correlation coefficients passing 95% significance of t tests are shown in boldface.

Table 2.

b. Horizontal distribution of moisture budgets

To better understand the processes causing moisture changes, we examine horizontal distributions of dominant budget terms and associated variables in the four phases of MJO1 and MJO2 as identified in section 3b. Important features in the two MJOs are similar, so we only show results from selected phases in MJO1 here. Figure 10 shows the budget distribution averaged in a suppressed phase (1–9 October; Fig. 10, left) and a cloud-developing phase (10–19 October; Fig. 10, right). First, we show horizontal distribution of along with intraseasonal flow [V]′ and mean moisture in Fig. 10a and horizontal distribution of with intraseasonal moisture anomaly [q]′ and mean flow [V]′ in Fig. 10b, all integrated vertically from 1000 to 700 hPa. Figure 10a shows that in the suppressed phase (left panel) intraseasonal westerlies east of the major MJO subsidence and the meridional flow over the DYNAMO/CINDY array advect mean dry air eastward and equatorward from the equatorial Indian Ocean and extratropics, respectively, to the central Indian Ocean, while in the cloud-developing phase (right panel) easterlies advect moist air to the DYNAMO/CINDY sounding array. Furthermore, by decomposing into and (figures not shown but consistent with Figs. 7d,e), we find the meridional component a dominant term and this is consistent with previous numerical model studies (Hagos and Leung 2011; Pritchard and Bretherton 2014; Hagos et al. 2014). On the other hand, Fig. 10b shows that mean westerlies over equatorial Indian Ocean advect intraseasonal dry (moist) air associated with the intraseasonal descending (ascending) region toward the DYNAMO/CINDY sounding array in the suppressed (cloud developing) phase. The decomposed budget term of into and (see Figs. 7d,e) shows that zonal flow overweighs the meridional components. The transition from drying to moistening by horizontal moisture advection of mean intraseasonal interaction efficiently induces the MJO propagation. The importance of horizontal moisture advection in MJO propagation has been emphasized in recent studies (Benedict and Randall 2007; Maloney 2009; Andersen and Kuang 2012; Hsu and Li 2012; Hagos et al. 2014; Kim et al. 2014; Wang et al. 2015). Although some of these numerical studies pay more attentions on import/export of column-integrated moist static energy, which causes the quantitative differences on dominant terms because of the further consideration of radiative feedback process, the contribution of horizontal advection terms are still mutually consistent.

Fig. 10.
Fig. 10.

The horizontal distributions of (a) (contours; interval of 0.3 mm day−1), [V]′ (vector; m s−1), and (shading; mm); (b) (contours; interval of 0.2 mm day−1), (vector; m s−1), and [q]′ (shading; mm); (c) 1000–700-hPa integrated (shading; mm day−1) and 700–100-hPa integrated (contours; interval of 0.5 mm day−1); (d) as in (c), but for [−Q2L1]′; and (e) (contours; interval of 0.8 mm day−1) and [q]′ (shading; mm) for (left) 1–9 and (right) 10–19 Oct.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

We then show the term in Fig. 10c, the vertical advection of mean moisture by intraseasonal vertical velocity, vertically integrated in the lower troposphere (1000–700 hPa) and middle-to-upper troposphere (700–100 hPa), shown by shadings and contours, respectively. The figure shows strong downward motion dries the lower troposphere in the suppressed phase (left panel) while equatorial moistening in lower troposphere extends eastward of the deep moistening region by major ascending motion over 65°–80°E in the cloud-developing phase (right panel). As a result, the lower-tropospheric vertical moisture advection contributes to the moistening tendency change ahead of the peak phase of the MJO moisture anomaly at the sounding array over the equatorial Indian Ocean near 22 October when [q]′ is near maximum. A similar feature is evident in the convective phase (20–29 October), but results are not shown.

Similar to in Fig. 10c, the vertically integrated fields of [−Q2L1]′ in the lower troposphere and middle-to-upper troposphere are shown in Fig. 10d. In the suppressed phase, negative Q2 indicates less convective condensation and vertical moisture mixing by nonprecipitating cloud in the lower troposphere. Positive Q2, on the other hand, indicates stronger convective condensation by precipitating cloud in the cloud-developing phase. The spatial distributions of the two dominant budget terms (Figs. 10c,d) resemble each other but with opposite sign, indicating again the tight coupling of convection to large-scale dynamics.

In Fig. 10e, we show the synoptic-scale advection term and the intraseasonal moisture anomaly [q]′ (shadings) integrated from 1000 to 700 hPa. The two fields are highly anticorrelated, indicating the diffusion of intraseasonal moisture anomaly by synoptic-scale disturbances through advection. The result here leads us to further interpret results shown in Fig. 8e that nonlinear advection by synoptic-scale eddies can diffuse the intraseasonal moisture anomaly. The results here are consistent with Andersen and Kuang (2012), who discussed the diffusion effect by horizontal eddy mixing through diagnosing eddy kinetic energy. It is interesting to further note the two nonlinear advection terms by high-frequency eddies ( and ) that are highly correlated with [q]′ but of opposite sign (see Table 2).

c. Moisture budget in the premoistening stage

Although projections of moisture budgets on the intraseasonal moisture anomaly and its derivative through the life cycles of MJO1 and MJO2 support the findings in previous studies that moisture advection is important for MJO propagation and growing/decaying, we note some differences in the premoistening processes from suppressed phase to cloud-developing phase between MJO1 and MJO2. In section 3b, we find large-scale downward motion is stronger in the suppressed phase of MJO1 than that in MJO2. The difference is primarily due to the low-frequency variability in background flow, as revealed in the time series of low-frequency vertical motion () in the lower troposphere shown in Fig. 11a. To contrast the difference, we mark the premoistening stage in each of the two MJOs in Fig. 11a by shadings, which is defined based on [∂q/∂t]′ and OLR′ when both are positive. The so-defined stage covers a period of moistening process in the suppressed to cloud-developing phase: 7–17 October for MJO1 and 9–19 November for MJO2. Figure 11a shows that low-frequency motion is downward in the premoistening stage of MJO1 but upward in the premoistening stage of MJO2. To examine the contrast in vertical advection, we show the time–height distribution of decomposed vertical advections (Fig. 11b), (Fig. 11c), and the sum of the two terms (Fig. 11d). The moisture budget terms in Figs. 11b–d are multiplied by −L/Cp so the unit is converted to heating rate in kelvin per day. The figures show that produces a persistent drying (moistening) in MJO1 (MJO2) in the premoistening stage, while the vertical advection by MJO motion remains as a dominant term. The combined moisture advection by the two terms (Fig. 11d) provides background drying and moistening in the lower troposphere for MJO1 and MJO2, respectively. The background drying and moistening for MJO1 and MJO2, respectively, from appears to affect convective mixing quite significantly in the lower troposphere, as shown by −Q2L1 in Fig. 11e, showing convective moistening and convective drying correspondingly. The convection in the initiation phase of MJO1 west of the DYNAMO/CINDY sounding array (60°–70°E) is composed mostly of nonraining shallow clouds ahead of deep convection. In the corresponding phase of MJO2, on the other hand, more raining clouds are developed in the cumulus ensemble before entering the DYNAMO/CINDY sounding array.

Fig. 11.
Fig. 11.

(a) Time series of 1000–700-hPa integrated (blue dashed line; Pa s−1), (green dashed line; mm day−1) and (red solid line; K). Vertical and temporal distributions of (b) , (c) , (d) , and (e) (K day−1). Contours in (d) and (e) are zero contours of Q2.

Citation: Journal of Climate 28, 8; 10.1175/JCLI-D-14-00416.1

5. Conclusions and discussion

In this study, soundings, ECMWF operational data, and TRMM data are used to investigate the moistening processes in the lower troposphere (1000–700 hPa) for the October and November 2011 MJOs during the DYNAMO/CINDY IOP period. MJO1 starts over the Indian Ocean from a suppressed phase (high OLR anomaly) in early October with no prior propagating MJO event. On the other hand, MJO2 is a successive event following MJO1. The two MJOs are divided into four phases: suppressed, cloud developing, convective, and decaying, based on 15–60-day-filtered OLR and lower-tropospheric moisture.

By synthesizing vertical velocity, equivalent potential temperature, and convective activities, the two MJOs exhibit similar evolution except in the premoistening stage. In MJO1, the suppressed phase is characterized by large-scale downward motion, cloud population composed mostly of nonraining clouds with storm heights below 5 km and cloud coverage less than 10%, and negative Q2 indicating a collective effect of moistening by reevaporation in shallow convection and congestus. Different from that in MJO1, raining clouds and positive Q2 appear in the lower troposphere during the suppressed phase in MJO2, indicating a net drying and heating effect by convection. Overall, the MJOs evolve from a buildup of moisture in the lower troposphere that leads to enhanced potential instability and the consequent cloud-developing phase. A full development of deep convection neutralizes the troposphere and eventually terminates deep convection, leading to the decaying phase.

The dominant moistening processes responsible for the MJO evolution is investigated by a scale-separated moisture budget in the lower troposphere using decomposed wind and moisture fields in three frequency bands: low frequency (>60 days), MJO (15–60 days), and synoptic scale (<15 days). By projecting the budget terms onto the moisture tendency change term and checking their correlation over the whole DYNAMO/CINDY IOP, three broad-scale advection terms by the low-frequency and intraseasonal flow and moisture fields: namely, , , and are identified as dominant moisture sources, while [−Q2L1]′ is identified as dominant sink, contributing significantly to the tendency term (propagation) and the intraseasonal moisture anomaly (growth and decay). The results are consistent with the conclusions made in other studies that horizontal moisture advection is important for eastward propagation. Comparing with previous numerical model studies (e.g., Hagos and Leung 2011; Andersen and Kuang 2012; Pritchard and Bretherton 2014; Hagos et al. 2014; Wang et al. 2015), most of them showed the moisture advection induced by meridional flow or the rotational flow is the dominant moistening process that contributes to MJO propagation. Our result is in line with these studies that outweighs . In addition, our study shows that the advection by zonal-mean flow has comparable amplitude to the moisture advection by intraseasonal flow (). The significant moisture advection by the slowly varying zonal flow (>60 days) can cause variability of the MJO evolution in different regions and seasons: for example, the mean westerly over the Indian Ocean induces moisture advection differently from the mean easterly over the western Pacific does. The current result further shows that moisture advection by low-frequency motion is particularly important in the initial phase of the MJO development, as revealed in the premoistening stage of MJO1 and MJO2: that is, downward (upward) motion in MJO1 (MJO2) produces large-scale drying (moistening) that is accompanied by different convective responses, as summarized above. The result implies that slowly changing flow (>60 days) can affect the initiation of MJOs significantly.

In addition to horizontal advection, the moistening of vertical advection by large-scale divergent flow and the counter drying by convection precipitation are also dominant processes for the MJO propagation. This is consistent with Hsu and Li (2012), who performed a diagnostic study to show that lower-tropospheric moisture convergence as the dominant moisture source for the MJO propagation. The two dominant terms, however, do not always show a clear phase relation with moisture tendency changes through the life cycle of the two MJOs. In fact, the premoistening in the lower troposphere ahead of the deep convection is observed only at the cloud-developing to convective phases of the two MJOs.

This study further reveals a diffusive effect of moisture advection by high-frequency disturbances . This is one of the dominant moisture sources in the suppressed phase of MJO1 and MJO2 when the intraseasonal moisture anomaly is relatively dry.

Through the analysis of scale-separated moisture budget, we identify dominant moistening processes through life cycles or different phases of MJOs. Such an analysis can provide a better insight about the cause of MJO evolutions. The study also provides useful references for evaluating and understanding model simulations. However, we need to confirm the findings by analyzing more MJOs of different types in different seasons and regions. We also need to include a heat budget in addition to a moisture budget to understand the physical processes that determine the time and space scales of intraseasonal oscillations.

Acknowledgments

We thank Steve Williams of the National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) for providing the ECMWF operational data. We also acknowledge Richard Johnson and Paul Ciesielski for sounding data support and Hung-Jui Yu for discussion of sounding data quality. We thank the NASA TRMM project for cloud and rainfall data. We acknowledge all participants of the DYNAMO/CINDY project who make the observation and analysis data available, especially Chi-Dong Zhang for his leadership in planning and carrying out the experiment. We also thank Po-Hsuing Lin, Wei-Ting Chen, and Chien-Ming Wu of the National Taiwan University for discussion of scientific issues. This research was fund by National Sciences Council in Taiwan. KCT and CHS were supported by the NSC under Grants NSC 102-2745-M-002-003-ASP.

REFERENCES

  • Andersen, J. A., , and Z. Kuang, 2012: Moist static energy budget of MJO-like disturbances in the atmosphere of a zonally symmetric aquaplanet. J. Climate, 25, 27822804, doi:10.1175/JCLI-D-11-00168.1.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., , and D. A. Randall, 2007: Observed characteristics of the MJO relative to maximum rainfall. J. Climate, 64, 23322354, doi: 10.1175/JAS3968.1.

    • Search Google Scholar
    • Export Citation
  • Ching, L., , C.-H. Sui, , and M.-J. Yang, 2010: An analysis of multi-scale nature of tropical cyclone activities in June 2004: Climate background. J. Geophys. Res., 115, D24108, doi:10.1029/2010JD013803.

    • Search Google Scholar
    • Export Citation
  • Ciesielski, P. E., and et al. , 2014: Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and corrections. J. Atmos. Oceanic Technol., 31, 741764, doi:10.1175/JTECH-D-13-00165.1.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., , Y. Chen, , D. Kim, , and M.-S. Yao, 2012: The MJO transition from shallow to deep convection in CloudSat/CALIPSO data and GISS GCM simulations. J. Climate, 25, 37553770, doi:10.1175/JCLI-D-11-00384.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2003: MJO-like coherent structures: Sensitivity Simulations using the cloud-resolving convection parameterization (CRCP). J. Atmos. Sci., 60, 847864, doi:10.1175/1520-0469(2003)060<0847:MLCSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , and L. R. Leung, 2011: Moist thermodynamics of the Madden–Julian oscillation in a cloud-resolving simulation. J. Climate, 24, 55715583, doi:10.1175/2011JCLI4212.1.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , Z. Feng, , K. Landu, , and C. N. Long, 2014: Advection, moistening, and shallow-to-deep convection transitions during the initiation and propagation of Madden-Julian oscillation. J. Adv. Model. Earth. Syst., 6, 938949, doi:10.1002/2014MS000335.

    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., , and M. L. Salby, 1994: The life cycle of the Madden–Julian oscillation. J. Atmos. Sci., 51, 22252237, doi:10.1175/1520-0469(1994)051<2225:TLCOTM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., , and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 49144931, doi:10.1175/JCLI-D-11-00310.1.

    • Search Google Scholar
    • Export Citation
  • Hu, Q., , and D. A. Randall, 1994: Low-frequency oscillations in radiative–convective systems. J. Atmos. Sci., 51, 10891099, doi:10.1175/1520-0469(1994)051<1089:LFOIRC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., , R. F. Adler, , D. T. Bolvin, , G. Gu, , E. J. Nelkin, , K. P. Bowman, , E. F. Stocker, , and D. B. Wolff, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., , and P. E. Ciesielski, 2013: Structure and properties of Madden–Julian oscillations deduced from DYNAMO sounding arrays. J. Atmos. Sci., 70, 31573179, doi:10.1175/JAS-D-13-065.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., , T. M. Rickenbacg, , S. A. Rutledge, , R. E. Ciesielski, , and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kerns, B. W., , and S. Chen, 2014: Equatorial dry air intrusion and related synoptic variability in MJO initiation during DYNAMO. Mon. Wea. Rev., 142, 13261343, doi:10.1175/MWR-D-13-00159.1.

    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., , and Y. N. Takayabu, 2004: The development of organized convection associated with the MJO during TOGA COARE IOP: Trimodal characteristics. Geophys. Res. Lett., 31, L10101, doi:10.1029/2004GL019601.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., , K. H. Straub, , and P. T. Haertel, 2005: Zonal and vertical structure of the Madden–Julian oscillation. J. Atmos. Sci., 62, 27902809, doi:10.1175/JAS3520.1.

    • Search Google Scholar
    • Export Citation
  • Kim, D., , J.-S. Kug, , and A. H. Sobel, 2014: Propagating versus nonpropagating Madden–Julian oscillation events. J. Climate, 27, 111125, doi:10.1175/JCLI-D-13-00084.1.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., , C.-H. Ho, , H.-S. Kim, , C.-H. Sui, , and S. K. Park, 2008: Systematic variation of summertime tropical cyclone activity in the western North Pacific in relation to the Madden–Julian oscillation. J. Climate, 21, 11711191, doi:10.1175/2007JCLI1493.1.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and P. H. Chan, 1986: Aspects of the 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, doi:10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and L. Peng, 1987: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part I: Basic theory. J. Atmos. Sci., 44, 950972, doi:10.1175/1520-0469(1987)044<0950:OOLFOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and C.-H. Sui, 1997: Mechanisms of short-term sea surface temperature regulation: Observations during TOGA COARE. J. Climate, 10, 465472, doi:10.1175/1520-0442(1997)010<0465:MOSTSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., , and D. E. Waliser, 2005: Intraseasonal Variability in the Atmospheric-Ocean Climate System. Praxis, 436 pp.

  • Lau, K.-M., , and H.-T. Wu, 2010: Characteristics of precipitation, cloud, and latent heating associated with the Madden–Julian oscillation. J. Climate, 23, 504518, doi:10.1175/2009JCLI2920.1.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D., , and P. J. Webster, 2002: The boreal summer intraseasonal oscillation and the South Asian monsoon. J. Atmos. Sci., 59, 15931606, doi:10.1175/1520-0469(2002)059<1593:TBSIOR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , H. Hendon, , and J. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation. J. Meteor. Soc. Japan, 72, 401411.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., , and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Climate, 29, 26652690, doi:10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., 2009: The moist static energy budget of a composite tropical intraseasonal oscillation in a climate model. J. Climate, 22, 711729, doi:10.1175/2008JCLI2542.1.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and D. L. Hartmann, 2000: Modulation of eastern North Pacific hurricanes by the Madden–Julian oscillation. J. Climate, 13, 14511460, doi:10.1175/1520-0442(2000)013<1451:MOENPH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., , S. Tulich, , J.-L. Lin, , and P. Zuidema, 2006: The mesoscale convection life cycle: Building block or prototype for large-scale tropical waves. Dyn. Atmos. Oceans, 42, 329, doi:10.1016/j.dynatmoce.2006.03.003.

    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., 2008: Primary and successive events in the Madden-Julian oscillation. Quart. J. Roy. Meteor. Soc., 134, 439453, doi:10.1002/qj.224.

    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., , and J.-Y. Yu, 1994: Modes of tropical variability under convective adjustment and the Madden–Julian oscillation. Part I: Analytical results. J. Atmos. Sci., 51, 18761894, doi:10.1175/1520-0469(1994)051<1876:MOTVUC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pritchard, M. S., , and C. S. Bretherton, 2014: Causal evidence that rotational moisture advection is critical to the superparameterized Madden–Julian oscillation. J. Atmos. Sci., 71, 800815, doi:10.1175/JAS-D-13-0119.1.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , and E. Maloney, 2012: An idealized semi-empirical framework for modeling the Madden–Julian oscillation. J. Atmos. Sci., 69, 16911705, doi:10.1175/JAS-D-11-0118.1.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , and E. Maloney, 2013: Moisture modes and the eastward propagation of the MJO. J. Atmos. Sci., 70, 187192, doi:10.1175/JAS-D-12-0189.1.

    • Search Google Scholar
    • Export Citation
  • Sperber, K. R., 2003: Propagation and the vertical structure of the Madden–Julian oscillation. Mon. Wea. Rev., 131, 30183037, doi:10.1175/1520-0493(2003)131<3018:PATVSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., , and K.-M. Lau, 1992: Multiscale phenomena in the tropical atmosphere over the western Pacific. Mon. Wea. Rev., 120, 407430, doi:10.1175/1520-0493(1992)120<0407:MPITTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., , K.-M. Lau, , Y. N. Takayabu, , and D. A. Short, 1997: Diurnal variations in tropical oceanic cumulus convection during TOGA COARE. J. Atmos. Sci., 54, 639655, doi:10.1175/1520-0469(1997)054<0639:DVITOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., , K.-M. Lau, , and C.-H. Sui, 1996: Observation of a quasi-2-day wave during TOGA COARE. Mon. Wea. Rev., 124, 18921913, doi:10.1175/1520-0493(1996)124<1892:OOAQDW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., 1988: Dynamics of tropical low-frequency waves: An analysis of moist Kelvin waves. J. Atmos. Sci., 45, 20512065, doi:10.1175/1520-0469(1988)045<2051:DOTLFW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , and T. Li, 1994: Convective interaction with boundary dynamics in the development of a tropical intraseasonal system. J. Atmos. Sci., 51, 13861400, doi:10.1175/1520-0469(1994)051<1386:CIWBLD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, S., , A. Sobel, , F. Zhang, , Y. Sun, , Y. Yue, , and L. Zhou, 2015: Regional simulation of the October and November MJO events observed during the CINDY/DYNAMO field campaign at gray zone resolution. J. Climate, 28, 20972119, doi:10.1175/JCLI-D-14-00294.1.

    • Search Google Scholar
    • Export Citation
  • Wang, W., , and M. E. Schlesinger, 1999: The dependence on convection parameterization of the tropical intraseasonal oscillation simulated by the UIUC 11-layer atmospheric GCM. J. Climate, 12, 1423145, doi:10.1175/1520-0442(1999)012<1423:TDOCPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of cloud and temperature in the wavenumber-frequency domain. J. Atmos. Sci., 56, 374399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, doi:10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yan, H.-M., , M. Zhong, , and Y.-Z. Zhu, 2004: Determination of the degree of freedom of digital filtered time series with an application to the correlation analysis between the length of day and the Southern Oscillation index. Chin. Astron. Astrophys., 28, 120126, doi:10.1016/S0275-1062(04)90014-8.

    • Search Google Scholar
    • Export Citation
  • Yanai, M., , S. Esbensen, , and J.-H. Chu, 1973: Determination of bulk properties of tropical cluster from large-scale heat and moisture budget. J. Atmos. Sci., 30, 611627, doi:10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden–Julian oscillation. Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.

Save