Climate Dynamics

, Volume 41, Issue 3–4, pp 1009–1023 | Cite as

MJO change with A1B global warming estimated by the 40-km ECHAM5

  • Ping LiuEmail author
  • Tim Li
  • Bin Wang
  • Minghua Zhang
  • Jing-jia Luo
  • Yukio Masumoto
  • Xiaocong Wang
  • Erich Roeckner


This study estimates MJO change under the A1B greenhouse gas emission scenario using the ECHAM5 AGCM whose coupled version (ECHAM5/MPI-OM) has simulated best MJO variance among fourteen CGCMs. The model has a horizontal resolution at T319 (about 40 km) and is forced by the monthly evolving SST derived from the ECHAM5/MPI-OM at a lower resolution of T63 (about 200 km). Two runs are carried out covering the last 21 years of the twentieth and twenty-first centuries. The NCEP/NCAR Reanalysis products and observed precipitation are used to validate the simulated MJO during the twentieth century, based on which the twenty-first century MJO change is compared and predicted. The validation indicates that the previously reported MJO variances in the T63 coupled version are reproduced by the 40-km ECHAM5. More aspects of MJO, such as the eastward propagation, structure, and dominant frequency and zonal wavenumber in power spectrum, are simulated reasonably well. The magnitude in power, however, is still low so that the signal is marginally detectable and embedded in the over-reddened background. Under the A1B scenario, the T63 ECHAM5/MPI-OM projected an over 3 K warmer tropical sea surface that forces the 40-km ECHAM to produce wetter tropics. The enhanced precipitation variance shows more spectral enhancement in background than in most wavebands. The zonal winds associated with MJO, however, are strengthened in the lower troposphere but weakened in the upper. On the one hand, the 850-hPa zonal wind has power nearly doubled in 30–60-days bands, demonstrating relatively clearer enhancement than the precipitation in MJO with the warming. A 1-tailed Student’s t test suggests that most of the MJO changes in variance and power spectra are statically significant. Subject to a 20–100-days band-pass filtering of that wind, an EOF analysis indicates that the two leading components in the twentieth-century run have a close structure to but smaller percentage of explained-to-total variance than those in observations; the A1B warming slightly increases the explained percentage and alters the structure. An MJO index formed by the two leading principal components discloses nearly doubling in the number of prominent MJO events with a peak phase occurring in February and March. A composite MJO life cycle of these events favors the frictional moisture convergence mechanism in maintaining the MJO and the nonlinear wind-induced surface heat exchange (WISHE) mechanism also appears in the A1B warming case. On the other hand, the Slingo index based on the 300-hPa zonal wind discloses that the upper-level MJO tends to be suppressed by the A1B warming, although the loose relationship with ENSO remains unchanged. Possible cause for the different change of MJO in the lower and upper troposphere is discussed.


MJO A1B scenario Global warming 



We thank Dr. Pang-Chi Hsu for helping with the ECHAM5 data. The background power spectrum is derived using the package of Matthew Wheeler and George N. Kiladis. Dr. Andrew Marshall also gave some useful comments. T. Li acknowledged the support by NSF grant AGS-1106536. This study contributes to SOEST (#8742) and IPRC (#911). IPRC is sponsored by JAMSTEC, NASA and NOAA.


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Ping Liu
    • 1
    • 3
    Email author
  • Tim Li
    • 1
  • Bin Wang
    • 1
  • Minghua Zhang
    • 3
  • Jing-jia Luo
    • 2
    • 6
  • Yukio Masumoto
    • 2
  • Xiaocong Wang
    • 4
  • Erich Roeckner
    • 5
  1. 1.International Pacific Research Center, SOESTUniversity of Hawaii at ManoaHonoluluUSA
  2. 2.Research Institution for Global ChangeJapan Agency for Marine-Earth Science and TechnologyKanagawaJapan
  3. 3.School of Marine and Atmospheric SciencesStony Brook UniversityStony BrookUSA
  4. 4.The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG)BeijingChina
  5. 5.Max Planck Institute for MeteorologyHamburgGermany
  6. 6.Centre for Australian Weather and Climate Research, Bureau of MeteorologyMelbourneAustralia

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