• Adames, Á. F., and E. D. Maloney, 2021: Moisture mode theory’s contribution to advances in our understanding of the Madden-Julian oscillation and other tropical disturbances. Curr. Climate Change Rep., 7, 7285, https://doi.org/10.1007/s40641-021-00172-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arkin, P. A., and B. N. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon. Wea. Rev., 115, 5174, https://doi.org/10.1175/1520-0493(1987)115<0051:TRBLSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arnold, N. P., and W. M. Putman, 2018: Nonrotating convective self-aggregation in a limited area AGCM. J. Adv. Model. Earth Syst., 10, 10291046, https://doi.org/10.1002/2017MS001218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Back, L. E., and C. S. Bretherton, 2006: Geographic variability in the export of moist static energy and vertical motion profiles in the tropical Pacific. Geophys. Res. Lett., 33, L17810, https://doi.org/10.1029/2006GL026672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Back, L. E., Z. Hansen, and Z. Handlos, 2017: Estimating vertical motion profile top-heaviness: Reanalysis compared to satellite-based observations and stratiform rain fraction. J. Atmos. Sci., 74, 855864, https://doi.org/10.1175/JAS-D-16-0062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becker, T., C. S. Bretherton, C. Hohenegger, and B. Stevens, 2018: Estimating bulk entrainment with unaggregated and aggregated convection. Geophys. Res. Lett., 45, 455462, https://doi.org/10.1002/2017GL076640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., E. D. Maloney, A. H. Sobel, and D. M. W. Frierson, 2014: Gross moist stability and MJO simulation skill in three full-physics GCMs. J. Atmos. Sci., 71, 33273349, https://doi.org/10.1175/JAS-D-13-0240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bony, S., A. Semie, R. J. Kramer, B. Soden, A. M. Tompkins, and K. A. Emanuel, 2020: Observed modulation of the tropical radiation budget by deep convective organization and lower-tropospheric stability. AGU Adv., 1, e2019AV000155, https://doi.org/10.1029/2019AV000155.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., M. E. Peters, and L. E. Back, 2004: Relationships between water vapor path and precipitation over the tropical oceans. J. Climate, 17, 15171528, https://doi.org/10.1175/1520-0442(2004)017<1517:RBWVPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., P. N. Blossey, and M. Khairoutdinov, 2005: An energy-balance analysis of deep convective self-aggregation above uniform SST. J. Atmos. Sci., 62, 42734292, https://doi.org/10.1175/JAS3614.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, C.-C., J. H. Richter, C. Liu, M. W. Moncrieff, Q. Tang, W. Lin, S. Xie, and P. J. Rasch, 2021: Effects of organized convection parameterization on the MJO and precipitation in E3SMv1. Part I: Mesoscale heating. J. Adv. Model. Earth Syst., 13, e2020MS002401, https://doi.org/10.1029/2020MS002401.

    • 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
  • Hannah, W. M., and E. D. Maloney, 2011: The role of moisture–convection feedbacks in simulating the Madden–Julian oscillation. J. Climate, 24, 27542770, https://doi.org/10.1175/2011JCLI3803.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hannah, W. M., and E. D. Maloney, 2014: The moist static energy budget in NCAR CAM5 hindcasts during DYNAMO. J. Adv. Model. Earth Syst., 6, 420440, https://doi.org/10.1002/2013MS000272.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., 2017: Convective aggregation in realistic convective-scale simulations. J. Adv. Model. Earth Syst., 9, 14501472, https://doi.org/10.1002/2017MS000980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., A. A. Wing, S. Bony, C. Muller, H. Masunaga, T. S. L’Ecuyer, D. D. Turner, and P. Zuidema, 2017: Observing convective aggregation. Surv. Geophys., 38, 11991236, https://doi.org/10.1007/s10712-017-9419-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 1997: Stratiform precipitation in regions of convection: A meteorological paradox? Bull. Amer. Meteor. Soc., 78, 21792196, https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr, 2004: Mesoscale convective systems. Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004RG000150.

  • Houze, R. A., Jr, 2018: 100 years of research on mesoscale convective systems. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0001.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, https://doi.org/10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inoue, K., and L. E. Back, 2015: Gross moist stability assessment during TOGA COARE: Various interpretations of gross moist stability. J. Atmos. Sci., 72, 41484166, https://doi.org/10.1175/JAS-D-15-0092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inoue, T., M. Satoh, H. Miura, and B. Mapes, 2008: Characteristics of cloud size of deep convection simulated by a global cloud resolving model over the western tropical Pacific. J. Meteor. Soc. Japan, 86A, 115, https://doi.org/10.2151/jmsj.86A.1.

    • Crossref
    • 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, https://doi.org/10.1175/JAS-D-13-065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kadoya, T., and H. Masunaga, 2018: New observational metrics of convective self-aggregation: Methodology and a case study. J. Meteor. Soc. Japan, 96, 535548, https://doi.org/10.2151/jmsj.2018-054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., and Coauthors, 2011: Globally gridded satellite observations for climate studies. Bull. Amer. Meteor. Soc., 92, 893907, https://doi.org/10.1175/2011BAMS3039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., and Coauthors, 2018: Evolution of precipitation structure during the November DYNAMO MJO event: Cloud-resolving model intercomparison and cross validation using radar observations. J. Geophys. Res. Atmos., 123, 35303555, https://doi.org/10.1002/2017JD027775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., 2021: Toward form-function relationships for mesoscale structure in convection. J. Meteor. Soc. Japan, 99, 847878, https://doi.org/10.2151/jmsj.2021-041.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., and R. A. Houze Jr., 1993: Cloud clusters and superclusters over the oceanic warm pool. Mon. Wea. Rev., 121, 13981416, https://doi.org/10.1175/1520-0493(1993)121<1398:CCASOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., and R. A. Houze Jr, 1995: Diabatic divergence profiles in western Pacific mesoscale convective systems. J. Atmos. Sci., 52, 18071828, https://doi.org/10.1175/1520-0469(1995)052<1807:DDPIWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masunaga, H., C. E. Holloway, H. Kanamori, S. Bony, and T. H. M. Stein, 2021: Transient aggregation of convection: Observed behavior and underlying processes. J. Climate, 34, 16851700, https://doi.org/10.1175/JCLI-D-19-0933.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muller, C. J., and I. M. Held, 2012: Detailed investigation of the self-aggregation of convection in cloud-resolving simulations. J. Atmos. Sci., 69, 25512565, https://doi.org/10.1175/JAS-D-11-0257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muller, C. J., and S. Bony, 2015: What favors convective aggregation and why? Geophys. Res. Lett., 42, 56265634, https://doi.org/10.1002/2015GL064260.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., and I. M. Held, 1987: Modeling tropical convergence based on the moist static energy budget. Mon. Wea. Rev., 115, 312, https://doi.org/10.1175/1520-0493(1987)115<0003:MTCBOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., O. Peters, and K. Hales, 2009: The transition to strong convection. J. Atmos. Sci., 66, 23672384, https://doi.org/10.1175/2009JAS2962.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pritchard, M. S., C. S. Bretherton, and C. A. DeMott, 2014: Restricting 32–128 km horizontal scales hardly affects the MJO in the Superparameterized Community Atmosphere Model v.3.0 but the number of cloud-resolving grid columns constrains vertical mixing. J. Adv. Model. Earth Syst., 6, 723739, https://doi.org/10.1002/2014MS000340.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raymond, D. J., and Ž. Fuchs, 2007: Convectively coupled gravity and moisture modes in a simple atmospheric model. Tellus, 59A, 627640, https://doi.org/10.1111/j.1600-0870.2007.00268.x.

    • Search Google Scholar
    • Export Citation
  • Raymond, D. J., S. L. Sessions, A. H. Sobel, and Ž. Fuchs, 2009: The mechanics of gross moist stability. J. Adv. Model. Earth Syst., 1 (3), https://doi.org/10.3894/JAMES.2009.1.9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, P., D. Kim, M.-S. Ahn, D. Kang, and H.-L. Ren, 2021: Intercomparison of MJO column moist static energy and water vapor budget among six modern reanalysis products. J. Climate, 34, 29773001, https://doi.org/10.1175/JCLI-D-20-0653.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roca, R., and V. Ramanathan, 2000: Scale dependence of monsoonal convective systems over the Indian Ocean. J. Climate, 13, 12861298, https://doi.org/10.1175/1520-0442(2000)013<1286:SDOMCS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612288, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., A. Mekonnen, C. Pearl, and W. Goncalves, 2013: Tropical precipitation extremes. J. Climate, 26, 14571466, https://doi.org/10.1175/JCLI-D-11-00725.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, C., R. A. Houze Jr., and I. Kraucunas, 2004: The tropical dynamical response to latent heating estimates derived from the TRMM Precipitation Radar. J. Atmos. Sci., 61, 13411358, https://doi.org/10.1175/1520-0469(2004)061<1341:TTDRTL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., 2000: On moist instability. Mon. Wea. Rev., 128, 41394142, https://doi.org/10.1175/1520-0493(2000)129<4139:OMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stein, T. H. M., C. E. Holloway, I. Tobin, and S. Bony, 2017: Observed relationships between cloud vertical structure and convective aggregation over tropical ocean. J. Climate, 30, 21872207, https://doi.org/10.1175/JCLI-D-16-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straub, K. H., and G. N. Kiladis, 2002: Observations of a convectively coupled Kelvin wave in the eastern Pacific ITCZ. J. Atmos. Sci., 59, 3053, https://doi.org/10.1175/1520-0469(2002)059<0030:OOACCK>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sumi, Y., and H. Masunaga, 2016: A moist static energy budget analysis of quasi-2-day waves using satellite and reanalysis data. J. Atmos. Sci., 73, 743759, https://doi.org/10.1175/JAS-D-15-0098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tobin, I., S. Bony, and R. Roca, 2012: Observational evidence for relationships between the degree of aggregation of deep convection, water vapor, surface fluxes, and radiation. J. Climate, 25, 68856904, https://doi.org/10.1175/JCLI-D-11-00258.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tobin, I., S. Bony, C. E. Holloway, J.-Y. Grandpeix, G. Sèze, D. Coppin, S. J. Woolnough, and R. Roca, 2013: Does convective aggregation need to be represented in cumulus parameterizations? J. Adv. Model. Earth Syst., 5, 692703, https://doi.org/10.1002/jame.20047.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tulich, S. N., D. A. Randall, and B. E. Mapes, 2007: Vertical-mode and cloud decomposition of large-scale convectively coupled gravity waves in a two-dimensional cloud-resolving model. J. Atmos. Sci., 64, 12101229, https://doi.org/10.1175/JAS3884.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., A. H. Sobel, F. Zhang, Y. Q. 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, https://doi.org/10.1175/JCLI-D-14-00294.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., A. H. Sobel, and J. Nie, 2016: Modeling the MJO in a cloud-resolving model with parameterized large-scale dynamics: Vertical structure, radiation, and horizontal advection of dry air. J. Adv. Model. Earth Syst., 8, 121139, https://doi.org/10.1002/2015MS000529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, B. A., A. M. Buchanan, C. E. Birch, P. Stier, and K. J. Pearson, 2018: Quantifying the effects of horizontal grid length and parameterized convection on the degree of convective organization using a metric of the potential for convective interaction. J. Atmos. Sci., 75, 425450, https://doi.org/10.1175/JAS-D-16-0307.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilcox, E. M., and L. J. Donner, 2007: The frequency of extreme rain events in satellite rain-rate estimates and an atmospheric general circulation model. J. Climate, 20, 5369, https://doi.org/10.1175/JCLI3987.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wing, A. A., 2019: Self-aggregation of deep convection and its implications for climate. Curr. Climate Change Rep., 5, 111, https://doi.org/10.1007/s40641-019-00120-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wing, A. A., K. Emanuel, C. E. Holloway, and C. Muller, 2018: Convective self-aggregation in numerical simulations: A review. Shallow Clouds, Water Vapor, Circulation, and Climate Sensitivity, R. Pincus et al., Eds., Springer International, 125.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., 2019: Convective heating leads to self-aggregation by generating available potential energy. Geophys. Res. Lett., 46, 10 68710 696, https://doi.org/10.1029/2019GL083805.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evidence of Aggregation Dependence of 5°-Scale Tropical Convective Evolution Using a Gross Moist Stability Framework

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  • 1 aRosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida
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Abstract

Spatial aggregation of deep convection and its possible role in larger-scale atmospheric behavior have received growing attention. Here we seek aggregation-correlated statistical properties of convective events in 5° × 5° boxes over the tropical Indian Ocean. Events are identified by box-averaged rainfall exceeding 5 mm day−1 at the center of a 4-day time window, and aggregation is estimated by an index [simple convective aggregation index (SCAI)] based on contiguous cold cloud areas and their geometrical distances in infrared imagery. A physical framework using gross moist stability (GMS) helps to interpret relationships between aggregation, box-scale ascent profiles, moist static energy budgets, and time evolution both within composite events and on longer time scales. For a given precipitation rate, more-aggregated events (with fewer and larger cloud objects on average) exhibit a drier area mean, greater horizontal gradient of moisture, more bottom-heavy ascent profile, and a greater prevalence of low-altitude cloud tops, especially for lower rain rates. In the GMS budget, this bottom-heavy ascent implies net energy import into the atmospheric column during the 4-day event composite. Consistently, net energy variations filtered to reveal longer time scales do indeed exhibit more-aggregated rain events in their growth phase than in their flat and decaying phases. More-aggregated scenes also have more drying by analysis than less-aggregated scenes in MERRA-2’s assimilation budgets. This suggests that parameterized convection (lacking any organization effect) is raining out less water than nature’s real, aggregated convection in such scenes.

© 2022 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: Wei-Ming Tsai, wxt108@rsmas.miami.edu

Abstract

Spatial aggregation of deep convection and its possible role in larger-scale atmospheric behavior have received growing attention. Here we seek aggregation-correlated statistical properties of convective events in 5° × 5° boxes over the tropical Indian Ocean. Events are identified by box-averaged rainfall exceeding 5 mm day−1 at the center of a 4-day time window, and aggregation is estimated by an index [simple convective aggregation index (SCAI)] based on contiguous cold cloud areas and their geometrical distances in infrared imagery. A physical framework using gross moist stability (GMS) helps to interpret relationships between aggregation, box-scale ascent profiles, moist static energy budgets, and time evolution both within composite events and on longer time scales. For a given precipitation rate, more-aggregated events (with fewer and larger cloud objects on average) exhibit a drier area mean, greater horizontal gradient of moisture, more bottom-heavy ascent profile, and a greater prevalence of low-altitude cloud tops, especially for lower rain rates. In the GMS budget, this bottom-heavy ascent implies net energy import into the atmospheric column during the 4-day event composite. Consistently, net energy variations filtered to reveal longer time scales do indeed exhibit more-aggregated rain events in their growth phase than in their flat and decaying phases. More-aggregated scenes also have more drying by analysis than less-aggregated scenes in MERRA-2’s assimilation budgets. This suggests that parameterized convection (lacking any organization effect) is raining out less water than nature’s real, aggregated convection in such scenes.

© 2022 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: Wei-Ming Tsai, wxt108@rsmas.miami.edu
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