Abstract
The all-season real-time multivariate MJO (RMM) index was designed for an equal contribution of variance from the raw anomalous OLR and zonal winds at 850 (U850) and 200 hPa (U200), whereas it represents a notably larger proportion of the MJO variance in U850 and U200 than in OLR such that the index appears more dynamical and overestimates the MJO predictability and prediction skills. The revised RMM (RMM-r) substantially enhanced the fraction in OLR and reduced that in U850, whilst the distribution remains far from a balance. A new variant (RMM-b) is derived with the constraint of representing the same percentage (about 60.5%) of the globally total MJO variance in each field. The constraint determines 9 W m− 2, 2.73 m s− 1 and 4.1 m s− 1 to scale the OLR, U850 and U200, respectively, after the interannual variability is removed from the anomalies by a regression approach. The resultant RMM-b represents 30–40% more MJO power in OLR than the RMM, particularly at zonal wavenumbers 2–3 in eastward propagation and in the Western Pacific, and closer to the RMM-r. It carries 10–15% more MJO variance in U850 at zonal wavenumber 1 than the RMM-r, closer to the RMM. It detects the real-time MJO evolution closer to the RMM-r. And it discloses the MJO predictability and prediction skills in the recent Global Ensemble Forecasting System more reasonably than the RMM-r and closer to the RMM. The RMM-b is concluded to more suitably constitute the convectively coupled nature of the MJO.
Similar content being viewed by others
Notes
An ACC on each day is the spatial correlation between the observations and reforecasts in the equatorial area averaged in 15°S–15°N.
References
Ahn MS, Kim D, Sperber KR, Kang IS, Maloney E, Waliser D, Hendon H (2017) MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnostics. Clim Dyn. https://doi.org/10.1007/s00382-017-3558-4
Chen SS, Houze RA, Mapes BE (1996) Multiscale variability of deep convection in relation to large-scale circulation in TOGA COARE. J Atmos Sci 53:1380–1409
Drosdowsky W, Chambers LE (2001) Near-global sea surface temperature anomalies as predictors of Australian seasonal rainfall. J Clim 14:1677–1687
Gottschalck J et al (2010) A framework for assessing operational Madden–Julian Oscillation forecasts: a CLIVAR MJO Working Group Project. Bull Am Meteorol Soc 91:1247–1258
Hamill TM, Kiladis GN (2014) Skill of the MJO and Northern Hemisphere blocking in GEFS medium-range reforecasts. Mon Weather Rev 142:868–885
Hamill TM, Bates GT, Whitaker JS, Murray DR, Fiorino M, Galarneau TJ Jr, Zhu Y, Lapenta W (2013) NOAA’s second-generation global medium-range ensemble reforecast dataset. Bull Am Meteorol Soc 94:1553–1566
Hendon HH, Salby ML (1994) The life cycle of the Madden–Julian oscillation. J Atmos Sci 51:2225–2237
Hou D, Toth Z, Zhu Y, Yang W (2008) Evaluation of the impact of the stochastic perturbation schemes on global ensemble forecast. In: Proceedings of 19th conference on probability and statistics, New Orleans, LA, American Meteorological Society. https://ams.confex.com/ams/88Annual/webprogram/Paper134165.html
Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471
Kessler WS, McPhaden MJ, Weickmann KM (1995) Forcing of intraseasonal Kelvin waves in the equatorial Pacific. J Geophys Res 100:10613–10631
Kikuchi K, Wang B, Kajikawa Y (2012) Bimodal representation of the tropical intraseasonal oscillation. Clim Dyn 38:1989–2000
Kiladis GN et al (2014) A comparison of OLR and circulation-based indices for tracking the MJO. Mon Weather Rev 142:1697–1715
Kilidas GN, Straub KH, Haertel PT (2005) Zonal and vertical structure of the Madden–Julian oscillation. J Atmos Sci 62:2790–2809
Kim HM, Webster PJ, Toma VE, Kim D (2014) Predictability and prediction skill of the MJO in two operational forecasting systems. J Clim 27:5364–5378
Liebmann B, Smith CA (1996) Description of a complete (interpolated) outgoing longwave radiation dataset. Bull Am Meteorol Soc 77:1275–1277
Lin H, Brunet G, Derome J (2008) Forecast skill of the Madden–Julian oscillation in two Canadian atmospheric models. Mon Weather Rev 136:4130–4149
Liu P (2014) MJO structure associated with the higher-order CEOF modes. Clim Dyn 43:1939–1950
Liu P et al (2009a) An MJO simulated by the NICAM at 14- and 7-km resolutions. Mon Weather Rev 137:3254–3268
Liu P et al (2009b) Tropical intraseasonal variability in the MRI-20 km60L AGCM. J Clim 22:2006–2022
Liu P, Zhang Q, Zhang C, Zhu Y, Khairoutdinov M, Kim HM, Schumacher C, Zhang M (2016) A revised real-time multivariate MJO index. Mon Weather Rev 144:627–642
Madden RA, Julian PR (1971) Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J Atmos Sci 28:702–708
Madden RA, Julian PR (1972) Description of global-scale circulation cells in the tropics with a 40–50 day period. J Atmos Sci 29:1109–1123
Maloney ED, Hartmann DL (1998) Frictional moisture convergence in a composite life cycle of the Madden–Julian oscillation. J Clim 11:2387–2403
North GR et al (1982) Sampling errors in the estimation of Empirical Orthogonal Functions. Mon Weather Rev 110:699–706
Rayner NA et al (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407. https://doi.org/10.1029/2002JD002670
Roundy PE (2015) On the interpretation of EOF analysis of ENSO, atmospheric Kelvin waves, and the MJO. J Clim 28:1148–1165
Rui H, Wang B (1990) Development characteristics and dynamic structure of tropical intraseasonal convection anomalies. J Atmos Sci 47:357–379
Slingo JM, Powell DP, Sperber KR, Nortley E (1999) On the predictability of the interannual behavior of the Madden–Julian oscillation and its relationship with El Nino. Q J R Meteorol Soc 125:583–609
Sperber KR (2003) Propagation and the vertical structure of the Madden–Julian oscillation. Mon Weather Rev 131:3018–3037
Straub KH (2013) MJO initiation in the real-time multivariate MJO index. J Clim 26:1130–1151
Ventrice MJ et al (2013) A modified multivariate Madden–Julian oscillation index using velocity potential. Mon Weather Rev 141:4197–4210
Waliser D, Lau KM, Stern W, Jones C (2003) Potential predictability of the Madden–Julian oscillation. Bull Am Meteorol Soc 84:33–50
Waliser D et al (2009) MJO simulation diagnostics. J Clim 22:3006–3030
Wang B (1988) Dynamics of tropical low-frequency waves: an analysis of the moist Kelvin wave. J Atmos Sci 45:2051–2065
Webster PJ, Lukas R (1992) TOGA COARE: the coupled ocean–atmosphere response experiment. Bull Am Meteorol Soc 73:1377–1416
Wei M, Toth Z, Wobus R, Zhu Y (2008) Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus 60A:62–79. https://doi.org/10.1111/j.1600-0870.2007.00273.x
Weickmann KM, Lussky GR, Kutzbach JE (1985) Intraseasonal (30–60 day) fluctuations of outgoing longwave radiation and 250 mb streamfunction during northern winter. Mon Weather Rev 113:941–961
Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon Weather Rev 132:1917–1932
Wheeler M, Kiladis GN (1999) Convectively coupled equatorial waves: analysis of clouds and temperature in the wavenumber-frequency domain. J Atmos Sci 56:374–399
Wolding BO, Maloney ED (2015) Objective diagnosis and the Madden–Julian oscillation. Part I: methodology. J Clim 28:4127–4140
Yomeyama K, Zhang C, Long C (2013) Tracking pulses of the Madden–Julian oscillation. Bull Am Meteorol Soc 94:1871–1891
Zhang C (2005) Madden–Julian oscillation. Rev Geophys. https://doi.org/10.1029/2004RG000158
Zhang C (2013) Madden–Julian oscillation: bridging weather and climate. Bull Am Meteorol Soc 94:1849–1870
Zhu Y, Toth Z (2008) Ensemble based probabilistic verification. Preprints, 19th conference on predictability and statistics, New Orleans, LA, American Meteorology Society 2.2. https://ams.confex.com/ams/pdfpapers/131645.pdf
Acknowledgements
This study is supported by the National Oceanic and Atmospheric Administration under the Grants NA15NWS4680015 and NA15OAR4320064. The GEFS Reforecasts and NCEP-NCAR Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, P. MJO evolution and predictability disclosed by the RMM variant with balanced MJO variance in convection and zonal winds. Clim Dyn 52, 2529–2543 (2019). https://doi.org/10.1007/s00382-018-4274-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-018-4274-4