• Andersson, E., and Coauthors, 2005: Assimilation and modeling of the atmospheric hydrological cycle in the ECMWF forecasting system. Bull. Amer. Meteor. Soc., 86, 387402.

    • Search Google Scholar
    • Export Citation
  • Bacmeister, J. T., , M. J. Suarez, , and F. R. Robertson, 2006: Rain reevaporation, boundary layer–convection interactions, and Pacific rainfall patterns in an AGCM. J. Atmos. Sci., 63, 33833403.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., , and D. A. Randall, 2007: Observed characteristics of the MJO relative to maximum rainfall. J. Atmos. Sci., 64, 23322354.

    • Search Google Scholar
    • Export Citation
  • Biasutti, M., , A. H. Sobel, , and Y. Kushnir, 2006: AGCM precipitation biases in the tropical Atlantic. J. Climate, 19, 935958.

  • Bloom, S., , L. Takacs, , A. da Silva, , and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 12561271.

    • Search Google Scholar
    • Export Citation
  • Boyle, J., , S. Klein, , G. Zhang, , S. Xie, , and X. Wei, 2008: Climate model forecast experiments for TOGA COARE. Mon. Wea. Rev., 136, 808832.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., and Coauthors, 2010: Estimates of tropical diabatic heating profiles: Commonalities and uncertainties. J. Climate, 23, 542558.

    • Search Google Scholar
    • Export Citation
  • Jeuken, A. B. M., , P. C. Siegmung, , L. C. Heijboer, , J. Feichter, , and L. Bengtsson, 1996: On the potential of assimilating meteorological analysis in a global climate model for the purpose of model validation. J. Geophys. Res., 101 (D12), 16 93916 950.

    • Search Google Scholar
    • Export Citation
  • Judd, K., , C. A. Reynolds, , T. E. Rosmond, , and L. A. Smith, 2008: The geometry of model error. J. Atmos. Sci., 65, 17491772.

  • Jung, T., 2011: Diagnosing remote origins of forecast error: Relaxation versus 4D-Var data-assimilation experiments. Quart. J. Roy. Meteor. Soc., 137, 598606, doi:10.1002/qj.781.

    • Search Google Scholar
    • Export Citation
  • Katsumata, M., , R. H. Johnson, , and P. E. Ciesielski, 2009: Observed synoptic-scale variability during the developing phase of an ISO over the Indian Ocean during MISMO. J. Atmos. Sci., 66, 34343448.

    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S. R., , and B. C. Weare, 2001: The onset of convection in the Madden–Julian Oscillation. J. Climate, 14, 780793.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Kim, D., and Coauthors, 2009: Application of MJO simulation diagnostics to climate models. J. Climate, 22, 64136436.

  • Klinker, E., , and P. D. Sardeshmukh, 1992: The diagnosis of mechanical dissipation in the atmosphere from large-scale balance requirements. J. Atmos. Sci., 49, 608627.

    • Search Google Scholar
    • Export Citation
  • Lee, M. I., , M. J. Suarez, , I. S. Kang, , I. M. Held, , and D. Kim, 2008: A moist benchmark calculation for atmospheric general circulation models. J. Climate, 21, 49344954.

    • Search Google Scholar
    • Export Citation
  • Ling, J., , and C. Zhang, 2011: Structural evolution in heating profiles of the MJO in global reanalyses and TRMM retrievals. J. Climate, 24, 825842.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., , and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814837.

  • Mapes, B. E., , and R. A. Houze, 1995: Diabatic divergence profiles in western Pacific mesoscale convective systems. J. Atmos. Sci., 52, 18071828.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., , J. Bacmeister, , M. Khairoutdinov, , C. Hannay, , and M. Zhao, 2009: Virtual field campaigns on deep tropical convection in climate models. J. Climate, 22, 244257.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., , S. F. Milton, , C. A. Senior, , M. E. Brooks, , S. Ineson, , T. Reichler, , and J. Kim, 2010: Analysis and reduction of systematic errors through a seamless approach to modeling weather and climate. J. Climate, 23, 59335957.

    • Search Google Scholar
    • Export Citation
  • Masunaga, H., 2009: A 9-season observation TRMM observation of the austral summer MJO and low-frequency equatorial waves. J. Meteor. Soc. Japan, 87A, 295315.

    • Search Google Scholar
    • Export Citation
  • Masunaga, H., , T. S. L’Ecuyer, , and C. D. Kummerow, 2006: The Madden–Julian Oscillation recorded in early observations from the Tropical Rainfall Measuring Mission (TRMM). J. Atmos. Sci., 63, 27772794.

    • Search Google Scholar
    • Export Citation
  • Misra, V., , S. Chan, , R. Wu, , and E. P. Chassignet, 2009: Air-sea interaction over the Atlantic warm pool in the NCEP CFS. Geophys. Res. Lett., 36, L15702, doi:10.1029/2009GL038737.

    • Search Google Scholar
    • Export Citation
  • Moorthi, S., , and M. J. Suarez, 1992: Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 9781002.

    • Search Google Scholar
    • Export Citation
  • Morita, J., , Y. N. Takayabu, , S. Shige, , and Y. Kodama, 2006: Analysis of rainfall characteristics of the Madden-Julian oscillation using TRMM satellite data. Dyn. Atmos. Oceans, 42, 107126, doi:10.1016/j.dynatmoce.2006.02.002.

    • Search Google Scholar
    • Export Citation
  • Phillips, T. J., and Coauthors, 2004: Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction. Bull. Amer. Meteor. Soc., 85, 19031915.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2008: The GEOS-5 data assimilation system—Documentation of versions 5.0.1, 5.1.0, and 5.2.0. NASA GSFC Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-2008-104606, Vol. 27, 118 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Rienecker369.pdf.]

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648.

    • Search Google Scholar
    • Export Citation
  • Riley, E. M., , B. E. Mapes, , and S. N. Tulich, 2011: Clouds associated with the Madden–Julian oscillation: A new perspective from CloudSat. J. Atmos. Sci., 68, 30323051.

    • Search Google Scholar
    • Export Citation
  • Robertson, F. R., , and J. B. Roberts, 2012: Intraseasonal variability in MERRA energy fluxes over the tropical oceans. J. Climate, 25, 56295647.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., , and T. N. Palmer, 2007: Using numerical weather prediction to assess climate models. Quart. J. Roy. Meteor. Soc., 133, 129146, doi:10.1002/qj.23.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., , and T. Jung, 2008: Understanding the local and global impacts of model physics changes: An aerosol example. Quart. J. Roy. Meteor. Soc., 134, 14791497.

    • Search Google Scholar
    • Export Citation
  • Schubert, S., , and Y. Chang, 1996: An objective method for inferring sources of model error. Mon. Wea. Rev., 124, 325340.

  • Song, S., and B. E. Mapes, 2012: Interpretations of systematic errors in the NOAA Climate Forecast System at lead times of 2, 4, 8, … , 256 days. J. Adv. Model. Earth Syst., in press.

  • Wheeler, M. C., , and H. H. Hendon, 2004: An all-season multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932.

    • Search Google Scholar
    • Export Citation
  • Yasunaga, K., , and B. E. Mapes, 2012: Differences between more divergent and more rotational types of convectively coupled equatorial waves. Part II: Composite analysis based on space–time filtering. J. Atmos. Sci., 69, 1734.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., , and X. Song, 2009: Interaction of deep and shallow convection is key to Madden-Julian Oscillation simulation. Geophys. Res. Lett., 36, L09708, doi:10.1029/2009GL037340.

    • Search Google Scholar
    • Export Citation
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Diagnosis of Tropical Biases and the MJO from Patterns in the MERRA Analysis Tendency Fields

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  • 1 Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida
  • | 2 National Center for Atmospheric Research,* Boulder, Colorado
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Abstract

The Modern-Era Reanalysis for Research and Applications (MERRA) is realistic, including its Madden–Julian oscillation (MJO), which the underlying model [Goddard Earth Observing System, version 5 (GEOS-5)] lacks. In the MERRA budgets, analysis tendencies (ATs) make evolution realistic despite model shortcomings. The ATs are the negative of physical process errors, if dynamical tendencies are accurate. Pattern resemblances between ATs and physical tendencies suggest which processes are erroneous. The authors examined patterns of tropical ATs in four dimensions and found several noteworthy features. Temperature AT profiles show that moist physics has erroneous sharp cooling at 700 hPa, a signature of misplaced melting and perhaps excessive precipitation evaporation. This excites a distinctive (fingerprint) erroneous short vertical wavelength temperature structure, perhaps a cause of the GEOS-5 too-slow convectively coupled waves. The globe’s largest AT of 200-hPa wind stems from overactive heating over the intra-Americas seas region in summer, with the same moist physics fingerprint. The erroneous heating produces a baroclinic vortex that is countered by ATs opposing its temperature and momentum fields in a thermal wind balanced sense. Lack of restraint in the deep convection scheme is also indicated in MJO composites, where the water vapor AT is anomalously positive on the leading edge, indicating a premature vapor sink. Since GEOS-5 lacks an MJO, this diagnosis suggests that the transition from shallow to deep convection (moistening to drying) is crucial in the real-world MJO. This is not news, but its diagnosis by ATs provides an objective, repeatable way to measure the effect that could be a useful guide in model development.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Brian E. Mapes, Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL 33149. E-mail: mapes@miami.edu

This article is included in the Modern Era Retrospective-Analysis for Research and Applications (MERRA) special collection.

Abstract

The Modern-Era Reanalysis for Research and Applications (MERRA) is realistic, including its Madden–Julian oscillation (MJO), which the underlying model [Goddard Earth Observing System, version 5 (GEOS-5)] lacks. In the MERRA budgets, analysis tendencies (ATs) make evolution realistic despite model shortcomings. The ATs are the negative of physical process errors, if dynamical tendencies are accurate. Pattern resemblances between ATs and physical tendencies suggest which processes are erroneous. The authors examined patterns of tropical ATs in four dimensions and found several noteworthy features. Temperature AT profiles show that moist physics has erroneous sharp cooling at 700 hPa, a signature of misplaced melting and perhaps excessive precipitation evaporation. This excites a distinctive (fingerprint) erroneous short vertical wavelength temperature structure, perhaps a cause of the GEOS-5 too-slow convectively coupled waves. The globe’s largest AT of 200-hPa wind stems from overactive heating over the intra-Americas seas region in summer, with the same moist physics fingerprint. The erroneous heating produces a baroclinic vortex that is countered by ATs opposing its temperature and momentum fields in a thermal wind balanced sense. Lack of restraint in the deep convection scheme is also indicated in MJO composites, where the water vapor AT is anomalously positive on the leading edge, indicating a premature vapor sink. Since GEOS-5 lacks an MJO, this diagnosis suggests that the transition from shallow to deep convection (moistening to drying) is crucial in the real-world MJO. This is not news, but its diagnosis by ATs provides an objective, repeatable way to measure the effect that could be a useful guide in model development.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Brian E. Mapes, Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL 33149. E-mail: mapes@miami.edu

This article is included in the Modern Era Retrospective-Analysis for Research and Applications (MERRA) special collection.

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