Examining the Predictability of the Successive MJO Events of November 2011 Using Coupled 30-Day NAVGEM and COAMPS Simulations

William A. Komaromi Naval Research Laboratory, Monterey, California

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Xiaodong Hong Naval Research Laboratory, Monterey, California

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Matthew A. Janiga Naval Research Laboratory, Monterey, California

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Carolyn A. Reynolds Naval Research Laboratory, Monterey, California

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James A. Ridout Naval Research Laboratory, Monterey, California

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James D. Doyle Naval Research Laboratory, Monterey, California

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Abstract

Given the prohibitive expense of running a global coupled high-resolution model for multiweek forecasts, we explore the feasibility of running a limited-area model forced by a global model on monthly time scales. Specifically, we seek to understand the constraints of the accuracy of lateral boundary conditions (LBCs) produced by NAVGEM on the skill of limited-area COAMPS forecasts. In this study, we analyze simulations of the successive MJO events of November 2011. In the NAVGEM simulations, the effect of ocean boundary conditions are examined, including fixed sea surface temperature (SST), observed SST, and coupled SST with HYCOM. With fixed SST, the second MJO fails to develop, highlighting the importance of the ocean response in the ability to model successive MJO events. Next, we examine the dependence of the regional COAMPS skill on the global model forecast performance. It is found that even when using the inferior but computationally inexpensive uncoupled NAVGEM for LBCs, coupled COAMPS can accurately predict the successive MJO events. A well-resolved atmospheric Rossby wave that slowly propagates westward in the COAMPS domain contributes to increased predictive skill. Ocean coupling and the ability of the model to sufficiently warm the ocean during the convectively suppressed phase also appears to be critical. Last, while COAMPS exhibits a significant moist bias, the sign and magnitude of the vertical and horizontal moisture flux appear to be consistent with reanalysis, a necessary attribute of any model to be used in multiweek MJO prediction.

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

Corresponding author: William A. Komaromi, will.komaromi@nrlmry.navy.mil

Abstract

Given the prohibitive expense of running a global coupled high-resolution model for multiweek forecasts, we explore the feasibility of running a limited-area model forced by a global model on monthly time scales. Specifically, we seek to understand the constraints of the accuracy of lateral boundary conditions (LBCs) produced by NAVGEM on the skill of limited-area COAMPS forecasts. In this study, we analyze simulations of the successive MJO events of November 2011. In the NAVGEM simulations, the effect of ocean boundary conditions are examined, including fixed sea surface temperature (SST), observed SST, and coupled SST with HYCOM. With fixed SST, the second MJO fails to develop, highlighting the importance of the ocean response in the ability to model successive MJO events. Next, we examine the dependence of the regional COAMPS skill on the global model forecast performance. It is found that even when using the inferior but computationally inexpensive uncoupled NAVGEM for LBCs, coupled COAMPS can accurately predict the successive MJO events. A well-resolved atmospheric Rossby wave that slowly propagates westward in the COAMPS domain contributes to increased predictive skill. Ocean coupling and the ability of the model to sufficiently warm the ocean during the convectively suppressed phase also appears to be critical. Last, while COAMPS exhibits a significant moist bias, the sign and magnitude of the vertical and horizontal moisture flux appear to be consistent with reanalysis, a necessary attribute of any model to be used in multiweek MJO prediction.

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

Corresponding author: William A. Komaromi, will.komaromi@nrlmry.navy.mil
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  • Allard, R. A., and Coauthors, 2010: Validation test report for the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) version 5. Naval Research Laboratory Rep. NRL/MR/7320-10-9283, 172 pp.

  • Bellenger, H., and J. P. Duvel, 2009: An analysis of tropical ocean diurnal warm layers. J. Climate, 22, 36293646, https://doi.org/10.1175/2008JCLI2598.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bleck, R., 2002: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Modell., 4, 5588, https://doi.org/10.1016/S1463-5003(01)00012-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., and Coauthors, 2003: COAMPS version 3 model description: General theory and equations. Naval Research Laboratory Tech. Rep. NRL/PU7500-04-448, 141 pp.

  • Chen, S., and Coauthors, 2015: A study of CINDY/DYNAMO MJO suppressed phase. J. Atmos. Sci., 72, 37553779, https://doi.org/10.1175/JAS-D-13-0348.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • DeMott, C. A., N. P. Klingaman, and S. J. Woolnough, 2015: Atmosphere–ocean coupled processes in the Madden–Julian oscillation. Rev. Geophys., 53, 10991154, https://doi.org/10.1002/2014RG000478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, Q., and K. N. Liou, 1993: Parameterization of the radiative properties of cirrus clouds. J. Atmos. Sci., 50, 20082025, https://doi.org/10.1175/1520-0469(1993)050<2008:POTRPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., W. Wang, J.-Y. Lee, B. Wang, K. Kikuchi, J. Xu, J. Li, and S. Weaver, 2015: Distinctive roles of air–sea coupling on different MJO events: A new perspective revealed from the DYNAMO/CINDY field campaign. Mon. Wea. Rev., 143, 794812, https://doi.org/10.1175/MWR-D-14-00221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Y., D. E. Waliser, and X. Jiang, 2015: A systematic relationship between the representations of convectively coupled equatorial wave activity and the Madden–Julian oscillation in climate model simulations. J. Climate, 28, 18811904, https://doi.org/10.1175/JCLI-D-14-00485.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haltiner, G. J., and R. T. Williams, 1980: Numerical Prediction and Dynamic Meteorology. John Wiley and Sons, 477 pp.

  • Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125, 14141430, https://doi.org/10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hogan, T. F., and Coauthors, 2014: The navy global environmental model. Oceanography, 27, 116125, https://doi.org/10.5670/oceanog.2014.73.

  • Hong, X., S. Wang, T. R. Holt, P. J. Martin, and L. O’Neill, 2013: Modulation of the sea-surface temperature in the Southeast Pacific by the atmospheric low-level coastal jet. J. Geophys. Res. Oceans, 118, 39793998, https://doi.org/10.1002/jgrc.20289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, X., C. Reynolds, J. Doyle, P. May, and L. O’Neill, 2017: Assessment of upper-ocean variability and the Madden-Julian Oscillation in extended-range air-ocean coupled mesoscale simulations. Dyn. Atmos. Oceans, 78, 89105, https://doi.org/10.1016/j.dynatmoce.2017.03.002.

    • Crossref
    • 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, https://doi.org/10.1175/JCLI-D-11-00310.1.

    • Crossref
    • 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
  • Hung, C., and C. Sui, 2018: A diagnostic study of the evolution of the MJO from Indian Ocean to Maritime Continent: Wave dynamics versus advective moistening processes. J. Climate, 31, 40954115, https://doi.org/10.1175/JCLI-D-17-0139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The Los Alamos Sea Ice Model documentation and software user’s manual version 4.1. Doc. LA-CC-06-012, 76 pp., http://csdms.colorado.edu/w/images/CICE_documentation_and_software_user's_manual.pdf.

  • Janiga, M. A., and C. Zhang, 2016: MJO moisture budget during DYNAMO in a cloud-resolving model. J. Atmos. Sci., 73, 22572278, https://doi.org/10.1175/JAS-D-14-0379.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janiga, M. A., C. J. Schreck, J. A. Ridout, M. Flatau, N. P. Barton, E. J. Metzger, and C. A. Reynolds, 2018: Subseasonal forecasts of convectively coupled equatorial waves and the MJO: Activity and predictive skill. Mon. Wea. Rev., 146, 23372360, https://doi.org/10.1175/MWR-D-17-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., M. C. Wheeler, P. T. Haertel, K. H. Straub, and P. E. Roundy, 2009: Convectively coupled equatorial waves. Rev. Geophys., 47, RG2003, https://doi.org/10.1029/2008RG000266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klemp, J., and R. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 10701096, https://doi.org/10.1175/1520-0469(1978)035<1070:TSOTDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2010: On the Madden–Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282293, https://doi.org/10.1175/2009JCLI2978.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2014: The Madden–Julian oscillation’s impacts on worldwide tropical cyclone activity. J. Climate, 27, 23172330, https://doi.org/10.1175/JCLI-D-13-00483.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, H.-T., 2014: Climate Algorithm Theoretical Basis Document (C-ATBD): Outgoing Longwave Radiation (OLR)—Daily. NOAA’s Climate Data Record (CDR) Program. CDRP-ATBD-0526, 46 pp., http://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Outgoing%20Longwave%20Radiation%20-%20Daily/AlgorithmDescription.pdf.

  • Li, T., C. Zhao, P. C. Hsu, and T. Nasuno, 2015: MJO initiation processes over the tropical Indian Ocean during DYNAMO/CINDY2011. J. Climate, 28, 21212135, https://doi.org/10.1175/JCLI-D-14-00328.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 10651092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, M., J. E. Nachamkin, and D. L. Westphal, 2009: On the improvement of COAMPS weather forecasts using an advanced radiative transfer model. Wea. Forecasting, 24, 286306, https://doi.org/10.1175/2008WAF2222137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Louis, J. F., M. Tiedtke, and J. F. Geleyn, 1982: A short history of the operational PBL parameterization at ECMWF. Proc. ECMWF Workshop on Planetary Boundary Layer Parameterizations, Reading, United Kingdom, ECMWF, 59–79.

  • MacRitchie, K., and P. E. Roundy, 2012: Potential vorticity accumulation following atmospheric Kelvin waves in the active convective region of the MJO. J. Atmos. Sci., 69, 908914, https://doi.org/10.1175/JAS-D-11-0231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • Martin, P. J., 2000: Description of the Navy Coastal Ocean Model version 1.0. NRL Rep. NRL/FR/7322/00/9962, 45 pp. [Available from NRL, Code 7322, Bldg. 1009, Stennis Space Center, MS 39529-5004.]

  • Matthews, A. J., 2008: Primary and successive events in the Madden–Julian oscillation. Quart. J. Roy. Meteor. Soc., 134, 439453, https://doi.org/10.1002/qj.224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851875, https://doi.org/10.1029/RG020i004p00851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molinari, J., K. Lombardo, and D. Vollaro, 2007: Tropical cyclogenesis within an equatorial Rossby wave packet. J. Atmos. Sci., 64, 13011317, https://doi.org/10.1175/JAS3902.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, H.-L., and W.-S. Wu, 1995: Implementing a mass flux convective parameterization package for the NMC medium-range forecast model. NMC Office Note 409, NOAA, 40 pp., https://repository.library.noaa.gov/view/noaa/11429.

  • Reynolds, C. A., J. D. Doyle, and X. Hong, 2016: Examining tropical cyclone–Kelvin wave interactions using adjoint diagnostics. Mon. Wea. Rev., 144, 44214439, https://doi.org/10.1175/MWR-D-16-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ridout, J. A., Y. Jin, and C.-S. Liou, 2005: A cloud-base quasi-balance constraint for parameterized convection: Application to the Kain–Fritsch cumulus scheme. Mon. Wea. Rev., 133, 33153334, https://doi.org/10.1175/MWR3034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40, 11851206, https://doi.org/10.1175/1520-0469(1983)040<1185:TMAMSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shinoda, T., and H. H. Hendon, 1998: Mixed layer modeling of intraseasonal variability in the tropical western Pacific and Indian Oceans. J. Climate, 11, 26682685, https://doi.org/10.1175/1520-0442(1998)011<2668:MLMOIV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A., S. G. Wang, and D. Kim, 2014: Moist static energy budget of the MJO during DYNAMO. J. Atmos. Sci., 71, 42764291, https://doi.org/10.1175/JAS-D-14-0052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., P. J. Webster, R. H. Johnson, R. Engelen, and T. L’Ecuyer, 2004: Observational evidence for the mutual regulation of the tropical hydrological cycle and tropical sea surface temperatures. J. Climate, 17, 22132224, https://doi.org/10.1175/1520-0442(2004)017<2213:OEFTMR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., J. Teixeira, and G. Matheou, 2012: Eddy diffusivity/mass flux and shallow cumulus boundary layer: An updraft PDF mulitple mass flux scheme. J. Atmos. Sci., 69, 15131533, https://doi.org/10.1175/JAS-D-11-090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tseng, K. C., C. H. Sui, and T. Li, 2015: Moistening processes for Madden–Julian oscillations during DYNAMO/CINDY. J. Climate, 28, 30413057, https://doi.org/10.1175/JCLI-D-14-00416.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulate, M., C. Zhang, and J. Dudhia, 2015: Role of water vapor and convection-circulation decoupling in MJO simulations by a tropical channel model. J. Adv. Model. Earth Syst., 7, 692711, https://doi.org/10.1002/2014MS000393.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., M. C. Wheeler, H. H. Hendon, C. J. Schreck III, C. D. Thorncroft, and G. N. Kiladis, 2013: A modified multivariate Madden–Julian oscillation index using velocity potential. Mon. Wea. Rev., 141, 41974210, https://doi.org/10.1175/MWR-D-12-00327.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weller, R. A., and S. P. Anderson, 1996: Surface meteorology and air–sea fluxes in the western equatorial Pacific warm pool during the TOGA Coupled Ocean–Atmosphere Response Experiment. J. Climate, 9, 19591990, https://doi.org/10.1175/1520-0442(1996)009<1959:SMAASF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B., M. Zhao, X. Jiang, S.-J. Lin, T. Li, X. Fu, and G. Vecchi, 2015: The 3–4 week MJO prediction skill in a GFDL coupled model. J. Climate, 28, 53515364, https://doi.org/10.1175/JCLI-D-15-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden-Julian Oscillation. Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158.

  • Zhang, C., 2013: Madden–Julian oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 18491870, https://doi.org/10.1175/BAMS-D-12-00026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and J. Ling, 2012: Potential vorticity of the Madden–Julian oscillation. J. Atmos. Sci., 69, 6578, https://doi.org/10.1175/JAS-D-11-081.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, Q., and F. H. Carr, 1997: A prognostic cloud scheme for operational NWP models. Mon. Wea. Rev., 125, 19311953, https://doi.org/10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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