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2014 Madden Julian Oscillation Progress

 
Published: 23 June 2014

Early research results from AMIE, the ARM Madden Julian Oscillation Investigation Experiment

Every 30–90 days during the northern hemisphere winter, the equatorial tropical atmosphere experiences pulses of extraordinarily strong deep convection and rainfall. This phenomenon is referred to as the Madden–Julian Oscillation, or MJO, named after the scientists who identified this cycle. The MJO significantly affects weather and rainfall patterns around the world.1
An unprecedented number of soundings were collected from the Indian Ocean and western Pacific region from October 2011 through March 2012 through international scientific efforts.To improve predictions of the MJO—especially about how it forms and evolves throughout its life cycle—an international group of scientists collected an unprecedented set of observations from the Indian Ocean and western Pacific region from October 2011 through March 2012 through the following coordinated efforts:

The rich set of observations from these efforts will be used for many years to study the physics of the MJO. Here, early research results are highlighted using data from AMIE, sponsored by the U.S. Department of Energy.

A Close Look at the MJO

The coordinated field campaigns captured six distinct MJO cycles in the Indian Ocean.2,3 Three of the MJO cycles were observed during the 3.5 month intensive operational period (IOP) at the start of the campaign, which combined measurements from research ships in the Indian Ocean, a sounding array, a radar supersite on Addu Atoll, and the Manus fixed site observations.
These MJO events spanned a range of larger-scale atmospheric and oceanic conditions, allowing investigation of several scientific issues pertaining to understanding of the MJO, or lack thereof. These issues include the roles of planetary waves in MJO convective initiation, decoupling of convection-circulation on the MJO scale, applications of different methods for identifying and filtering the MJO signal in observations and model simulations, and ocean-atmosphere coupling.3

Cloud Characteristics and Properties

One hypothesis is that heating and moistening provided by the distribution of cloud types (such as cirrus, stratocumulus, etc.)—or cloud populations—at different stages of the MJO is essential to its initiation and evolution. Therefore, one major thrust of the campaign was to document the cloud populations and their properties as a function of MJO onset, as well as the pre- and post-onset periods of the MJO in the Indian Ocean. To date, several teams of researchers have gleaned results from the various radar observations during the campaign.As shown in this illustration, the MJO life cycle includes (A) pre-onset, (B) onset, and (C) post-onset phases, with warming sea surface temperature during the onset period, followed by cooling in the post-onset phase. Radar data have been used for documenting the cloud populations4 and the evolution of precipitating systems5 by MJO state—pre-onset, onset, and post-onset. Rowe and Houze6 used data from the scanning precipitation radars to determine microphysical characteristics of precipitating convection. Feng et al.7 have produced combined radar products, leading to profiles of macrophysics and radiative heating rate profiles submitted as principal investigator (PI) products to the ARM Data Archive. Hagos et al.8 are using the combined radar product with sonde data and a cloud permitting model to test hypotheses related to the processes of mid-level moistening on MJO scales for initiation.

Additional researchers have developed novel new retrievals using the ARM zenith radar data to classify convective or stratiform rain and estimate rain rate.9,10 Others are using the zenith radar data to investigate shallow convection at Manus11 and adding cloud-resolving models to study the relationship of clouds to heat and moisture budgets.12

Large-scale Atmospheric State

One potent observational data set of which AMIE data are a key element is the atmospheric profiles from the many sonde sites.13,14 These sounding array data have been used to illustrate the large-scale atmospheric conditions during the observed MJO cycles and to study specific processes related to MJO initiation. Johnson and Ciesielski15 find the rainfall maximum is characterized by east-west bands north and south of the equator during the inactive phase of the MJO, switching to a single rainfall maximum on the equator during the active phases.

Judt and Chen16 investigated an unusually large, explosive mesoscale convective cloud system observed over the equatorial Indian Ocean during the initiation of a strong MJO event; Kerns and Chen17 found that a synoptic scale dry air intrusion played a key role in the evolution of this MJO occurrence. Powell and Houze18 are studying the relationship between upper tropospheric wind and temperature anomalies and MJO convective onset, again using the sounding data.

Future Research Efforts to Focus on Model Evaluation and Improvement

Most results to date, only about a year after the official release of the international campaign data, naturally tend to be based on analyses of observations gathered during the campaign. But like the Hagos et et al.8 efforts noted above, the testing and improvement of model representation of the MJO is gaining momentum. Already Ling et al.19 are using the data to test the forecast skill of the European Centre for Medium-Range Weather Forecasts model. The observational data now available are a unique and powerful record that will increase our understanding of this phenomenon and improve the ability of computer models to realistically simulate the observed MJO initiation and propagation characteristics.

References

  1. Zhang, C, 2013: Madden-Julian Oscillation: Bridging Weather and Climate. Bulletin of the American Meteorological Society 94: 1849-1870, doi: 10.1175/BAMS-D-12-00026.1.
  2. Yoneyama, K, C Zhang, and CN Long. 2013. Tracking Pulses of the Madden-Julian Oscillation. Bulletin of the American Meteorological Society 94: 1871-1891, doi: 10.1175/BAMS-D-12-00157.1.
  3. Gottschalck, J, PE Roundy, CJ Schreck III, A Vintzileos, and C Zhang. 2013. Large-scale atmospheric and oceanic conditions during the 2011-2012 DYNAMO field campaign. Monthly Weather Review 141: 4173-4196, doi: 10.1175/MWR-D-13-00022.1.
  4. Powell, SW, and RA Houze Jr. 2013. The cloud population and onset of the Madden-Julian Oscillation over the Indian Ocean during DYNAMO-AMIE. Journal of Geophysical Research: Atmospheres 118: 11979-11995, doi: 10.1002/2013JD020421.
  5. Zuluaga, MD, and RA Houze Jr. 2013. Evolution of the population of precipitating convective systems over the equatorial Indian Ocean in active phases of the Madden-Julian Oscillation. Journal of the Atmospheric Sciences 70: 2713-2725, doi: 10.1175/JAS-D-12-0311.1.
  6. Rowe, AK, and RA Houze Jr. 2014. Microphysical characteristics of MJO convection over the Indian Ocean during DYNAMO. Journal of Geophysical Research: Atmospheres 119: 2543-2554, doi: 10.1002/2013JD020799.
  7. Feng, Z, SA McFarlane, C Schumacher, S Ellis, J Comstock, and N Bharadwaj, 2014. Constructing a merged cloud-precipitation radar dataset for tropical convective clouds during the DYNAMO/AMIE experiment at Addu Atoll. Journal of Atmospheric and Oceanic Technology 31: 1021-1042, doi: 10.1175/JTECH-D-13-00132.1.
  8. Hagos S, Z Feng, K Landu, and CN Long. 2014. Advection, moistening, and shallow-to-deep convection transitions during the initiation and propagation of Madden-Julian Oscillation. Journal of Advances in Modeling Earth Systems 6: 938-949, doi: 10.1002/2014MS000335.
  9. Deng, M, P Kollias, Z Feng, C Zhang, CN Long, H Kalesse, A Chandra, VV Kumar, and A Protat. 2014. Stratiform and convective precipitation observed by multiple radars during the DYNAMO/AMIE experiment. Journal of Applied Meteorology and Climatology 53: 2503-2523, doi: 10.1175/JAMC-D-13-0311.1.
  10. Chandra, A, C Zhang, P Kollias, S Matrosov, and W Szyrmer. 2014. Automated rain rate estimates using the Ka-band ARM Zenith Radar (KAZR). Atmospheric Measurement Techniques 7: 1807-1833, doi: 10.5194/amtd-7-1807-2014.
  11. Zermeno-Diaz, DM, C Zhang, P Kollia, and H Kalesse. 2015. The role of shallow cloud moistening in MJO and non-MJO convective events over the ARM Manus site. Journal of the Atmospheric Sciences, 72: 4797-4820, doi: 10.1175/JAS-D-14-0322.1.
  12. Janiga, MA, X Li, W-K Tao, AK Rowe, and C Zhang. 2014. Cloud-Resolving Simulations of Convection during AMIE/DYNAMO: Heat and Moisture Budgets. Journal of Advances in Modeling Earth Systems, in preparation.
  13. Ciesielski, PE, H Yu, RH Johnson, K Yoneyama, M Katsumata, CN Long, J Wang, SM Loehrer, K Young, SF Williams, W Brown, J Braun, and T Van Hove. 2014. Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and corrections. Journal of Atmospheric and Oceanic Technology 31: 741-764, doi: 10.1175/JTECH-D-13-00165.1.
  14. Yu, H, PE Ciesielski, J Wang, H-C Kuo, H Vomel, and R Dirksen. 2015. Evaluation of humidity correction methods for Vaisala RS92 tropical sounding data. Journal of Atmospheric and Oceanic Technology 32: 397-411, doi: 10.1175/JTECH-D-14-00166.1.
  15. Johnson, RH, and PE Ciesielski. 2013. Structure and properties of Madden-Julian Oscillations deduced from DYNAMO sounding arrays. Journal of the Atmospheric Sciences 70: 3157-3179, doi: 10.1175/JAS-D-13-065.1.
  16. Judt, F, and SS Chen. 2014. An explosive convective cloud system and its environmental conditions in MJO initiation observed during DYNAMO. Journal of Geophysical Research: Atmospheres 119: 2781-2795, doi: 10.1002/2013JD021048.
  17. Kerns, BW, and SS Chen. 2014. Equatorial dry air intrusion and related synoptic variability in MJO initiation during DYNAMO. Monthly Weather Review 142: 1326-1343, doi: 10.1175/MWR-D-13-00159.1.
  18. Powell, SW, and RA Houze Jr. 2014. Upper-tropospheric dynamic and thermodynamic structures moving over the equatorial Indian Ocean and their relations to MJO convective onset. Journal of Geophysical Research: Atmospheres, in review.
  19. Ling, J, P Bauer, P Bechtold, A Beljaars, R Forbes, F Vitart, M Ulate, and C Zhang. 2014. Global vs. local MJO forecast skill of the ECMWF model during DYNAMO. Monthly Weather Review 142: 2228-2247, doi: 10.1175/MWR-D-13-00292.1.
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This work was supported by the U.S. Department of Energy’s Office of Science, through the Biological and Environmental Research program as part of the Atmospheric System Research program.