• Ahmed, F., and C. Schumacher, 2017: Geographical differences in the tropical precipitation‐moisture relationship and rain intensity onset. Geophys. Res. Lett., 44, 11141122, https://doi.org/10.1002/2016GL071980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., E. D. Maloney, A. H. Sobel, D. M. W. Frierson, and L. J. Donner, 2013: Tropical intraseasonal variability in version 3 of the GFDL atmosphere model. J. Climate, 26, 426449, https://doi.org/10.1175/JCLI-D-12-00103.1.

    • 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
  • Bergemann, M., and C. Jakob, 2016: How important is tropospheric humidity for coastal rainfall in the tropics? Geophys. Res. Lett., 43, 58605868, https://doi.org/10.1002/2016GL069255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bernstein, D. N., and J. D. Neelin, 2016: Identifying sensitive ranges in global warming precipitation change dependence on convective parameters. Geophys. Res. Lett., 43, 58415850, https://doi.org/10.1002/2016GL069022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Booth, J. F., Y.-O. Kwon, S. Ko, R. J. Small, and R. Msadek, 2017: Spatial patterns and intensity of the surface storm tracks in CMIP5 models. J. Climate, 30, 49654981, https://doi.org/10.1175/JCLI-D-16-0228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boyle, J. S., S. A. Klein, D. D. Lucas, H.-Y. Ma, J. Tannahill, and S. Xie, 2015: The parametric sensitivity of CAM5’s MJO. J. Geophys. Res. Atmos., 120, 14241444, https://doi.org/10.1002/2014JD022507.

    • 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
  • Brown, R. G., and C. Zhang, 1997: Variability of midtropospheric moisture and its effect on cloud-top height distribution during TOGA COARE. J. Atmos. Sci., 54, 27602774, https://doi.org/10.1175/1520-0469(1997)054<2760:VOMMAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., M. K. Tippett, A. H. Sobel, G. A. Vecchi, and M. Zhao, 2014: Testing the performance of tropical cyclone genesis indices in future climates using the HiRAM model. J. Climate, 27, 91719196, https://doi.org/10.1175/JCLI-D-13-00505.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, D., and A. Dai, 2018: Dependence of estimated precipitation frequency and intensity on data resolution. Climate Dyn., 50, 36253647, https://doi.org/10.1007/s00382-017-3830-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., E. E. Ebert, K. J. E. Walsh, and N. E. Davidson, 2013: Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN data. J. Geophys. Res. Atmos., 118, 21842196, https://doi.org/10.1002/jgrd.50250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Covey, C., P. J. Gleckler, C. Doutriaux, D. N. Williams, A. Dai, J. Fasullo, K. Trenberth, and A. Berg, 2016: Metrics for the diurnal cycle of precipitation: Toward routine benchmarks for climate models. J. Climate, 29, 44614471, https://doi.org/10.1175/JCLI-D-15-0664.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derbyshire, S. H., I. Beau, P. Bechtold, J. Y. Grandpeix, J. M. Piriou, J. L. Redelsperger, and P. M. M. Soares, 2004: Sensitivity of moist convection to environmental humidity. Quart. J. Roy. Meteor. Soc., 130, 30553079, https://doi.org/10.1256/qj.03.130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaustad, K., and L. Riihimaki, 1996: MWR Retrievals (MWRRET1LILJCLOU), 1998-01-01 to 2010-12-31, Tropical Western Pacific (TWP) Central Facility, Manus I., PNG (C1) (updated hourly). ARM Climate Research Facility Data Archive, accessed 2016, https://doi.org/10.5439/1027369.

    • Crossref
    • Export Citation
  • Gaustad, K., and L. Riihimaki, 1998: MWR Retrievals (MWRRET1LILJCLOU), 1999-01-01 to 2008-12-31, Tropical Western Pacific (TWP) Central Facility, Nauru Island (C2) (updated hourly). ARM Climate Research Facility Data Archive, accessed 2016, https://doi.org/10.5439/1027369.

    • Crossref
    • Export Citation
  • Gaustad, K., and L. Riihimaki, 2015: MWR Retrievals (MWRRET1LILJCLOU), 2014-01-10 to 2015-10-20, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1) (updated hourly). ARM Climate Research Facility Data Archive, accessed 2016, https://doi.org/10.5439/1027369.

    • Crossref
    • Export Citation
  • Gonzalez, A., and X. Jiang, 2017: Winter mean lower-tropospheric moisture over the Maritime Continent as a climate model diagnostic metric for the propagation of the Madden-Julian oscillation. Geophys. Res. Lett., 44, 25882596, https://doi.org/10.1002/2016GL072430.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirota, H., Y. N. Takayabu, M. Watanabe, M. Kimoto, and M. Chikira, 2014: Role of convective entrainment in spatial distributions of and temporal variations in precipitation over tropical oceans. J. Climate, 27, 87078723, https://doi.org/10.1175/JCLI-D-13-00701.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holdridge, D., and J. Kyrouac, 1997: Surface Meteorological Instrumentation (MET), 1998-01-01 to 2010-12-31, Tropical Western Pacific (TWP) Central Facility, Manus I., PNG (C1) (updated hourly). ARM Climate Research Facility Data Archive, accessed 2016, https://doi.org/10.5439/1025220.

    • Crossref
    • Export Citation
  • Holdridge, D., and J. Kyrouac, 1998: Surface Meteorological Instrumentation (MET), 1999-01-01 to 2008-12-31, Tropical Western Pacific (TWP) Central Facility, Nauru Island (C2) (updated hourly). ARM Climate Research Facility Data Archive, accessed 2016, https://doi.org/10.5439/1025220.

    • Crossref
    • Export Citation
  • Holloway, C. E., and J. D. Neelin, 2007: The convective cold top and quasi equilibrium. J. Atmos. Sci., 64, 14671487, https://doi.org/10.1175/JAS3907.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., and J. D. Neelin, 2009: Moisture vertical structure, column water vapor, and tropical deep convection. J. Atmos. Sci., 66, 16651683, https://doi.org/10.1175/2008JAS2806.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., 2017: Key processes for the eastward propagation of the Madden-Julian oscillation based on multi-model simulations. J. Geophys. Res. Atmos., 122, 755770, https://doi.org/10.1002/2016JD025955.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., M. Zhao, E. Maloney, and D. E. Waliser, 2016: Convective moisture adjustment time-scale as a key factor in regulating model amplitude of the Madden-Julian oscillation. Geophys. Res. Lett., 43, 10 41210 419, https://doi.org/10.1002/2016GL070898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. Yang, J.J. Hnilo, M. Fiorino, and G.L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, https://doi.org/10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., and Coauthors, 2014: Process-oriented MJO simulation diagnostic: Moisture sensitivity of simulated convection. J. Climate, 27, 53795395, https://doi.org/10.1175/JCLI-D-13-00497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klingaman, N. P., G. M. Martin, and A. Moise, 2017: ASoP (v1.0): A set of methods for analyzing scales of precipitation in general circulation models. Geosci. Model Dev., 10, 5783, https://doi.org/10.5194/gmd-10-57-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koontz, A., J. Kyrouac, and S. Springston, 2015: Aerosol Observing System meteorological data (AOSMET), 2014-01-10 to 2015-10-20, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, MAOS (S1) (updated hourly). ARM Climate Research Facility Data Archive, accessed 2016, https://doi.org/10.5439/1025153.

    • Crossref
    • Export Citation
  • Kuo, Y.-H., and Coauthors, 2016: Convective transition statistics for climate model diagnostics. 2016 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract A13L-03.

  • Kuo, Y.-H., J. D. Neelin, and C. R. Mechoso, 2017: Tropical convective transition statistics and causality in the water vapor–precipitation relation. J. Atmos. Sci., 74, 915931, https://doi.org/10.1175/JAS-D-16-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langenbrunner, B., and J. D. Neelin, 2017: Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1. J. Adv. Model. Earth Syst., 9, 20082026, https://doi.org/10.1002/2017MS000942.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J. W.-B., and J. D. Neelin, 2003: Toward stochastic deep convective parameterization in general circulation models. Geophys. Res. Lett., 30, 1162, https://doi.org/10.1029/2002GL016203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B., and R. Neale, 2011: Parameterizing convective organization to escape the entrainment dilemma. J. Adv. Model. Earth Syst., 3, M06004, https://doi.org/10.1029/2011MS000042.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moncrieff, M. W., C. Liu, and P. Bogenschutz, 2017: Simulation, modeling, and dynamically based parameterization of organized tropical convection for global climate models. J. Atmos. Sci., 74, 13631380, https://doi.org/10.1175/JAS-D-16-0166.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muller, C. J., L. E. Back, P. A. O’Gorman, and K. A. Emanuel, 2009: A model for the relationship between tropical precipitation and column water vapor. Geophys. Res. Lett., 36, L16804, https://doi.org/10.1029/2009GL039667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., O. Peters, J. W.-B. Lin, K. Hales, and C. E. Holloway, 2008: Rethinking convective quasi-equilibrium: Observational constraints for stochastic convective schemes in climate models. Philos. Trans. Roy. Soc. London, 366A, 25812604, https://doi.org/10.1098/rsta.2008.0056.

    • 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
  • Oueslati, B., and G. Bellon, 2013: Convective entrainment and large-scale organization of tropical precipitation: Sensitivity of the CNRM-CM5 hierarchy of models. J. Climate, 26, 29312946, https://doi.org/10.1175/JCLI-D-12-00314.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parsons, D. B., J.-L. Redelsperger, and K. Yoneyama, 2000: The evolution of the tropical western Pacific atmosphere-ocean system following the arrival of a dry intrusion. Quart. J. Roy. Meteor. Soc., 126, 517548, https://doi.org/10.1002/qj.49712656307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, O., and J. D. Neelin, 2006: Critical phenomena in atmospheric precipitation. Nat. Phys., 2, 393396, https://doi.org/10.1038/nphys314.

  • Redelsperger, J. L., D. B. Parsons, and F. Guichard, 2002: Recovery processes and factors limiting cloud-top height following the arrival of a dry intrusion observed during TOGA COARE. J. Atmos. Sci., 59, 24382457, https://doi.org/10.1175/1520-0469(2002)059<2438:RPAFLC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ridout, J. A., 2002: Sensitivity of tropical Pacific convection to dry layers at mid- to upper levels: Simulation and parameterization tests. J. Atmos. Sci., 59, 33623381, https://doi.org/10.1175/1520-0469(2002)059<3362:SOTPCT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., 2012: Sources of uncertainty in future changes in local precipitation. Climate Dyn., 39, 19291950, https://doi.org/10.1007/s00382-011-1210-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rushley, S. S., D. Kim, C. S. Bretherton, and M.-S. Ahn, 2018: Reexamining the nonlinear moisture-precipitation relationship over the tropical oceans. Geophys. Res. Lett., 45, 11331140, https://doi.org/10.1002/2017GL076296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahany, S., J. D. Neelin, K. Hales, and R. B. Neale, 2012: Temperature–moisture dependence of the deep convective transition as a constraint on entrainment in climate models. J. Atmos. Sci., 69, 13401358, https://doi.org/10.1175/JAS-D-11-0164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahany, S., J. D. Neelin, K. Hales, and R. B. Neale, 2014: Deep convective transition characteristics in the Community Climate System Model and changes under global warming. J. Climate, 27, 92149232, https://doi.org/10.1175/JCLI-D-13-00747.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanderson, B. M., 2011: A multimodel study of parametric uncertainty in predictions of climate response to rising greenhouse gas concentrations. J. Climate, 24, 13621377, https://doi.org/10.1175/2010JCLI3498.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schiro, K. A., J. D. Neelin, D. K. Adams, and B. R. Lintner, 2016: Deep convection and column water vapor over tropical land versus tropical ocean: A comparison between the Amazon and the tropical western Pacific. J. Atmos. Sci., 73, 40434063, https://doi.org/10.1175/JAS-D-16-0119.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schiro, K. A., F. Ahmed, S. E. Giangrande, and J. D. Neelin, 2018: GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales. Proc. Natl. Acad. Sci. USA, https://doi.org/10.1073/pnas.1719842115, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., and R. Wahrlich, 1999: Observed evolution of tropical deep convective events and their environment. Mon. Wea. Rev., 127, 17771795, https://doi.org/10.1175/1520-0493(1999)127<1777:OEOTDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., S. Bony, and J.-L. Dufresne, 2014: Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505, 3742, https://doi.org/10.1038/nature12829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stechmann, S. N., and J. D. Neelin, 2011: A stochastic model for the transition to strong convection. J. Atmos. Sci., 68, 29552970, https://doi.org/10.1175/JAS-D-11-028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stechmann, S. N., and J. D. Neelin, 2014: First-passage-time prototypes for precipitation statistics. J. Atmos. Sci., 71, 32693291, https://doi.org/10.1175/JAS-D-13-0268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., and M. W. Moncrieff, 2009: Multiscale cloud system modeling. Rev. Geophys., 47, RG4002, https://doi.org/10.1029/2008RG000276.

  • Tompkins, A. M., 2001: Organization of tropical convection in low vertical wind shears: The role of water vapor. J. Atmos. Sci., 58, 529545, https://doi.org/10.1175/1520-0469(2001)058<0529:OOTCIL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torri, G., Z. Kuang, and Y. Tian, 2015: Mechanisms for convection triggering by cold pools. Geophys. Res. Lett., 42, 19431950, https://doi.org/10.1002/2015GL063227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • TRMM, 2011a: TRMM Precipitation Radar rainfall rate and profile L2 1.5 hours V7. Goddard Earth Sciences Data and Information Services Center, accessed 19 August 2016, https://disc.gsfc.nasa.gov/datacollection/TRMM_2A25_7.html.

  • TRMM, 2011b: TRMM (TMPA) rainfall estimate L3 3 hour 0.25 degree × 0.25 degree V7. Goddard Earth Sciences Data and Information Services Center, accessed 8 July 2016, https://disc.gsfc.nasa.gov/datacollection/TRMM_3B42_7.html.

  • University of Wyoming, 2017: Atmospheric soundings. Dept. of Atmospheric Sciences, University of Wyoming, accessed 21 August 2017, http://weather.uwyo.edu/upperair/sounding.html.

  • Wentz, F. J., C. Gentemann, and K. A. Hilburn, 2015: Remote Sensing Systems TRMM TMI Daily Environmental Suite on 0.25 deg grid, version 7.1. Remote Sensing Systems, accessed 8 July 2016, www.remss.com/missions/tmi.

  • Yano, J.-I., C. Liu, and M. W. Moncrieff, 2012: Self-organized criticality and homeostasis in atmospheric convective organization. J. Atmos. Sci., 69, 34493462, https://doi.org/10.1175/JAS-D-12-069.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yokohata, T., J. D. Annan, M. Collins, C. S. Jackson, M. Tobis, M. Webb, and J. C. Hargreaves, 2012: Reliability of multi-model and structurally different single-model ensembles. Climate Dyn., 39, 599616, https://doi.org/10.1007/s00382-011-1203-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 22 22 22
PDF Downloads 8 8 8

Convective Transition Statistics over Tropical Oceans for Climate Model Diagnostics: Observational Baseline

View More View Less
  • 1 Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
Restricted access

Abstract

Convective transition statistics, which describe the relation between column-integrated water vapor (CWV) and precipitation, are compiled over tropical oceans using satellite and ARM site measurements to quantify the temperature and resolution dependence of the precipitation–CWV relation at fast time scales relevant to convection. At these time scales, and for precipitation especially, uncertainties associated with observational systems must be addressed by examining features with a variety of instrumentation and identifying robust behaviors versus instrument sensitivity at high rain rates. Here the sharp pickup in precipitation as CWV exceeds a certain critical threshold is found to be insensitive to spatial resolution, with convective onset occurring at higher CWV but at lower column relative humidity as bulk tropospheric temperature increases. Mean tropospheric temperature profiles conditioned on precipitation show vertically coherent structure across a wide range of temperature, reaffirming the use of a bulk temperature measure in defining the convective transition statistics. The joint probability distribution of CWV and precipitation develops a peak probability at low precipitation for CWV above critical, with rapidly decreasing probability of high precipitation below and near critical, and exhibits systematic changes under spatial averaging. The precipitation pickup with CWV is reasonably insensitive to time averaging up to several hours but is smoothed at daily time scales. This work demonstrates that CWV relative to critical serves as an effective predictor of precipitation with only minor geographic variations in the tropics, quantifies precipitation-related statistics subject to different spatial–temporal resolution, and provides a baseline for model comparison to apply these statistics as observational constraints on precipitation processes.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAS-D-17-0287.s1.

© 2018 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: Yi-Hung Kuo, yhkuo@atmos.ucla.edu

This article is included in the Process-Oriented Model Diagnostics Special Collection.

Abstract

Convective transition statistics, which describe the relation between column-integrated water vapor (CWV) and precipitation, are compiled over tropical oceans using satellite and ARM site measurements to quantify the temperature and resolution dependence of the precipitation–CWV relation at fast time scales relevant to convection. At these time scales, and for precipitation especially, uncertainties associated with observational systems must be addressed by examining features with a variety of instrumentation and identifying robust behaviors versus instrument sensitivity at high rain rates. Here the sharp pickup in precipitation as CWV exceeds a certain critical threshold is found to be insensitive to spatial resolution, with convective onset occurring at higher CWV but at lower column relative humidity as bulk tropospheric temperature increases. Mean tropospheric temperature profiles conditioned on precipitation show vertically coherent structure across a wide range of temperature, reaffirming the use of a bulk temperature measure in defining the convective transition statistics. The joint probability distribution of CWV and precipitation develops a peak probability at low precipitation for CWV above critical, with rapidly decreasing probability of high precipitation below and near critical, and exhibits systematic changes under spatial averaging. The precipitation pickup with CWV is reasonably insensitive to time averaging up to several hours but is smoothed at daily time scales. This work demonstrates that CWV relative to critical serves as an effective predictor of precipitation with only minor geographic variations in the tropics, quantifies precipitation-related statistics subject to different spatial–temporal resolution, and provides a baseline for model comparison to apply these statistics as observational constraints on precipitation processes.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAS-D-17-0287.s1.

© 2018 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: Yi-Hung Kuo, yhkuo@atmos.ucla.edu

This article is included in the Process-Oriented Model Diagnostics Special Collection.

Supplementary Materials

    • Supplemental Materials (PDF 6.23 MB)
Save