• Argüeso, D., R. Romero, and V. Homar, 2020: Precipitation features of the Maritime Continent in parameterized and explicit convection models. J. Climate, 33, 24492466, https://doi.org/10.1175/JCLI-D-19-0416.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baranowski, D. B., M. K. Flatau, P. J. Flatau, and A. J. Matthews, 2016: Impact of atmospheric convectively coupled equatorial Kelvin waves on upper ocean variability. J. Geophys. Res. Atmos., 121, 20452059, https://doi.org/10.1002/2015JD024150.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Birch, C. E., M. J. Roberts, L. Garcia-Carreras, D. Ackerley, M. J. Reeder, A. P. Lock, and R. Schiemann, 2015: Sea-breeze dynamics and convection initiation: The influence of convective parameterization in weather and climate model biases. J. Climate, 28, 80938108, https://doi.org/10.1175/JCLI-D-14-00850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Birch, C. E., S. Webster, S. C. Peatman, D. J. Parker, A. J. Matthews, Y. Li, and M. E. E. Hassim, 2016: Scale interactions between the MJO and the western Maritime Continent. J. Climate, 29, 24712492, https://doi.org/10.1175/JCLI-D-15-0557.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Climate Prediction Center, 2020: Oceanic Niño Index. CPC, accessed 10 November 2020, https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferrett, S., G.-Y. Yang, S. J. Woolnough, J. Methven, K. Hodges, and C. E. Holloway, 2020: Linking extreme precipitation in Southeast Asia to equatorial waves. Quart. J. Roy. Meteor. Soc., 146, 665684, https://doi.org/10.1002/qj.3699.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hassim, M. E. E., T. P. Lane, and W. W. Grabowski, 2016: The diurnal cycle of rainfall over New Guinea in convection-permitting WRF simulations. Atmos. Chem. Phys., 16, 161175, https://doi.org/10.5194/acp-16-161-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heale, C. J., J. B. Snively, A. N. Bhatt, L. Hoffmann, C. C. Stephan, and E. A. Kendall, 2019: Multilayer observations and modeling of thunderstorm-generated gravity waves over the Midwestern United States. Geophys. Res. Lett., 46, 14 16414 174, https://doi.org/10.1029/2019GL085934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Houze, R. A., S. G. Geotis, F. D. Marks, and A. K. West, 1981: Winter monsoon convection in the vicinity of north Borneo. Part I: Structure and time variation of the clouds and precipitation. Mon. Wea. Rev., 109, 15951614, https://doi.org/10.1175/1520-0493(1981)109<1595:WMCITV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-M., P. J. Webster, V. E. Toma, and D. Kim, 2014: Predictability and prediction skill of the MJO in two operational forecasting systems. J. Climate, 27, 53645378, https://doi.org/10.1175/JCLI-D-13-00480.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klingaman, N. P., and S. J. Woolnough, 2014: Using a case-study approach to improve the Madden-Julian oscillation in the Hadley Centre model. Quart. J. Roy. Meteor. Soc., 140, 24912505, https://doi.org/10.1002/qj.2314.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91, 363376, https://doi.org/10.1175/2009BAMS2755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Love, B. S., A. J. Matthews, and G. M. S. Lister, 2011: The diurnal cycle of precipitation over the Maritime Continent in a high-resolution atmospheric model. Quart. J. Roy. Meteor. Soc., 137, 934947, https://doi.org/10.1002/qj.809.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lubis, S. W., and M. R. Respati, 2021: Impacts of convectively coupled equatorial waves on rainfall extremes in Java, Indonesia. Int. J. Climatol., 41, 24182440, https://doi.org/10.1002/joc.6967.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations. Theory of Statistics, Vol. I, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, 281–297, http://projecteuclid.org/euclid.bsmsp/1200512992.

  • Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Crossref
    • 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. Atmos. Sci., 29, 11091123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., T. T. Warner, and M. Xu, 2003: Diurnal patterns of rainfall in northwestern South America. Part III: Diurnal gravity waves and nocturnal convection offshore. Mon. Wea. Rev., 131, 830844, https://doi.org/10.1175/1520-0493(2003)131<0830:DPORIN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., 2000: Propagation mechanisms for the Madden–Julian Oscillation. Quart. J. Roy. Meteor. Soc., 126, 26372651, https://doi.org/10.1002/qj.49712656902.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Met Office, 2020: Iris: A Python library for analysing and visualising meteorological and oceanographic data sets. http://scitools.org.uk.

  • Mohd Nor, M. F. F., C. E. Holloway, and P. M. Inness, 2020: The role of local orography on the development of a severe rainfall event over western peninsular Malaysia: A case study. Mon. Wea. Rev., 148, 21912209, https://doi.org/10.1175/MWR-D-18-0413.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mori, S., H. Jun-Ichi, Y. I. Tauhid, and M. D. Yamanaka, 2004: Diurnal land–sea rainfall peak migration over Sumatera Island, Indonesian Maritime Continent, observed by TRMM satellite and intensive rawinsonde soundings. Mon. Wea. Rev., 132, 20212039, https://doi.org/10.1175/1520-0493(2004)132<2021:DLRPMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muhammad, F. R., S. W. Lubis, and S. Setiawan, 2021: Impacts of the Madden–Julian Oscillation on precipitation extremes in Indonesia. Int. J. Climatol., 41, 19701984, https://doi.org/10.1002/joc.6941.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neal, R., D. Fereday, R. Crocker, and R. E. Comer, 2016: A flexible approach to defining weather patterns and their application in weather forecasting over Europe. Meteor. Appl., 23, 389400, https://doi.org/10.1002/met.1563.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neal, R., J. Robbins, R. Dankers, A. Mitra, A. Jayakumar, E. N. Rajagopal, and G. Adamson, 2020: Deriving optimal weather pattern definitions for the representation of precipitation variability over India. Int. J. Climatol., 40, 342360, https://doi.org/10.1002/joc.6215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oh, J.-H., K.-Y. Kim, and G.-H. Lim, 2012: Impact of MJO on the diurnal cycle of rainfall over the western Maritime Continent in the austral summer. Climate Dyn., 38, 11671180, https://doi.org/10.1007/s00382-011-1237-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peatman, S. C., A. J. Matthews, and D. P. Stevens, 2014: Propagation of the Madden–Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation. Quart. J. Roy. Meteor. Soc., 140, 814825, https://doi.org/10.1002/qj.2161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pedregosa, F., and Coauthors, 2011: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 28252830, https://www.jmlr.org/papers/v12/pedregosa11a.html.

    • Search Google Scholar
    • Export Citation
  • Qian, J.-H., 2008: Why precipitation is mostly concentrated over islands in the Maritime Continent. J. Atmos. Sci., 65, 14281441, https://doi.org/10.1175/2007JAS2422.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qian, J.-H., 2020: Mechanisms for the dipolar patterns of rainfall variability over large islands in the Maritime Continent associated with the Madden–Julian oscillation. J. Atmos. Sci., 77, 22572278, https://doi.org/10.1175/JAS-D-19-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramage, C. S., 1968: Role of a tropical “Maritime Continent” in the atmospheric circulation. Mon. Wea. Rev., 96, 365370, https://doi.org/10.1175/1520-0493(1968)096<0365:ROATMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rauniyar, S. P., and K. J. E. Walsh, 2011: Scale interaction of the diurnal cycle of rainfall over the Maritime Continent and Australia: Influence of the MJO. J. Climate, 24, 325348, https://doi.org/10.1175/2010JCLI3673.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rauniyar, S. P., and K. J. E. Walsh, 2013: Influence of ENSO on the diurnal cycle of rainfall over the Maritime Continent and Australia. J. Climate, 26, 13041321, https://doi.org/10.1175/JCLI-D-12-00124.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakaeda, N., G. Kiladis, and J. Dias, 2020: The diurnal cycle of rainfall and the convectively coupled equatorial waves over the Maritime Continent. J. Climate, 33, 33073331, https://doi.org/10.1175/JCLI-D-19-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vincent, C. L., and T. P. Lane, 2016: Evolution of the diurnal precipitation cycle with the passage of a Madden–Julian oscillation event through the Maritime Continent. Mon. Wea. Rev., 144, 19832005, https://doi.org/10.1175/MWR-D-15-0326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warner, T. T., B. E. Mapes, and M. Xu, 2003: Diurnal patterns of rainfall in northwestern South America. Part II: Model simulations. Mon. Wea. Rev., 131, 813829, https://doi.org/10.1175/1520-0493(2003)131<0813:DPORIN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., 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, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, P., M. Hara, J.-I. Hamada, M. D. Yamanaka, and F. Kimura, 2009: Why a large amount of rain falls over the sea in the vicinity of western Sumatra Island during nighttime. J. Appl. Meteor. Climatol., 48, 13451361, https://doi.org/10.1175/2009JAMC2052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xavier, P., R. Rahmat, W. K. Cheong, and E. Wallace, 2014: Influence of Madden–Julian Oscillation on Southeast Asia rainfall extremes: Observations and predictability. Geophys. Res. Lett., 41, 44064412, https://doi.org/10.1002/2014GL060241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, G.-Y., B. J. Hoskins, and J. M. Slingo, 2003: Convectively coupled equatorial waves: A new methodology for identifying wave structures in observational data. J. Atmos. Sci., 60, 16371654, https://doi.org/10.1175/1520-0469(2003)060<1637:CCEWAN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, G.-Y., S. Ferrett, S. Woolnough, J. Methven, and C. Holloway, 2021: Real-time identification of equatorial waves and evaluation of waves in global forecasts. Wea. Forecasting, 36, 171193, https://doi.org/10.1175/WAF-D-20-0144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yokoi, S., S. Mori, F. Syamsudin, U. Haryoko, and B. Geng, 2019: Environmental conditions for nighttime offshore migration of precipitation area as revealed by in situ observation off Sumatra Island. Mon. Wea. Rev., 147, 33913407, https://doi.org/10.1175/MWR-D-18-0412.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoneyama, K., and C. Zhang, 2020: Years of the Maritime Continent. Geophys. Res. Lett., 47, e2020GL087182, https://doi.org/10.1029/2020GL087182.

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A Local-to-Large Scale View of Maritime Continent Rainfall: Control by ENSO, MJO, and Equatorial Waves

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  • 1 a Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, United Kingdom
  • | 2 b Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences and School of Mathematics, University of East Anglia, Norwich, United Kingdom
  • | 3 c National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, United Kingdom
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Abstract

The canonical view of the Maritime Continent (MC) diurnal cycle is deep convection occurring over land during the afternoon and evening, tending to propagate offshore overnight. However, there is considerable day-to-day variability in the convection, and the mechanism of the offshore propagation is not well understood. We test the hypothesis that large-scale drivers such as ENSO, the MJO, and equatorial waves, through their modification of the local circulation, can modify the direction or strength of the propagation, or prevent the deep convection from triggering in the first place. Taking a local-to-large scale approach, we use in situ observations, satellite data, and reanalyses for five MC coastal regions, and show that the occurrence of the diurnal convection and its offshore propagation is closely tied to coastal wind regimes that we define using the k-means cluster algorithm. Strong prevailing onshore winds are associated with a suppressed diurnal cycle of precipitation, while prevailing offshore winds are associated with an active diurnal cycle, offshore propagation of convection, and a greater risk of extreme rainfall. ENSO, the MJO, equatorial Rossby waves, and westward mixed Rossby–gravity waves have varying levels of control over which coastal wind regime occurs, and therefore on precipitation, depending on the MC coastline in question. The large-scale drivers associated with dry and wet regimes are summarized for each location as a reference for forecasters.

Significance Statement

Extreme precipitation can be life-threatening in the Maritime Continent region, for example, due to flash floods and landslides. The main form of variability of convective storms is the diurnal cycle, but this can be modulated by large-scale weather drivers. By quantifying the effect of these drivers on local-scale weather regimes for a range of Maritime Continent locations, we identify which drivers are most important (and in which phase) to consider when understanding the local risk of extreme rainfall. Given that these large-scale drivers may be forecast with greater skill than is possible for quantitative precipitation forecasts, this study provides crucial extra information for forecasters to aid prediction of life-threatening weather conditions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Simon C. Peatman, earspe@leeds.ac.uk

Abstract

The canonical view of the Maritime Continent (MC) diurnal cycle is deep convection occurring over land during the afternoon and evening, tending to propagate offshore overnight. However, there is considerable day-to-day variability in the convection, and the mechanism of the offshore propagation is not well understood. We test the hypothesis that large-scale drivers such as ENSO, the MJO, and equatorial waves, through their modification of the local circulation, can modify the direction or strength of the propagation, or prevent the deep convection from triggering in the first place. Taking a local-to-large scale approach, we use in situ observations, satellite data, and reanalyses for five MC coastal regions, and show that the occurrence of the diurnal convection and its offshore propagation is closely tied to coastal wind regimes that we define using the k-means cluster algorithm. Strong prevailing onshore winds are associated with a suppressed diurnal cycle of precipitation, while prevailing offshore winds are associated with an active diurnal cycle, offshore propagation of convection, and a greater risk of extreme rainfall. ENSO, the MJO, equatorial Rossby waves, and westward mixed Rossby–gravity waves have varying levels of control over which coastal wind regime occurs, and therefore on precipitation, depending on the MC coastline in question. The large-scale drivers associated with dry and wet regimes are summarized for each location as a reference for forecasters.

Significance Statement

Extreme precipitation can be life-threatening in the Maritime Continent region, for example, due to flash floods and landslides. The main form of variability of convective storms is the diurnal cycle, but this can be modulated by large-scale weather drivers. By quantifying the effect of these drivers on local-scale weather regimes for a range of Maritime Continent locations, we identify which drivers are most important (and in which phase) to consider when understanding the local risk of extreme rainfall. Given that these large-scale drivers may be forecast with greater skill than is possible for quantitative precipitation forecasts, this study provides crucial extra information for forecasters to aid prediction of life-threatening weather conditions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Simon C. Peatman, earspe@leeds.ac.uk

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