1. MJO Task Force: Summary of activities and accomplishments Co-chairs: Matthew Wheeler (CAWCR/Bureau of Meteorology/Australia) and Eric Maloney (Colorado State University/USA) Members Duane WaliserJet Propulsion Laboratory/Caltech Ken SperberPCDMI/Lawrence Livermore National Laboratory Xiouhua FuUniversity of Hawaii Jon GottschalckNational Centers for Environmental Prediction Richard NealeNational Center for Atmospheric Research Chidong ZhangUniversity of Miami Daehyun KimColumbia University Augustin VintzileosNational Centers for Environmental Prediction Masaki SatohFrontier Research Center for Global Change Hai LinEnvironment Canada Prince XavierUK Met Office June-Yi LeeUniversity of Hawaii Steve WoolnoughUniversity of Reading Important others X. Jiang, N. Klingaman, J. Petch, F. Vitart, J. Benedict, H. Hendon, D. Raymond Established in early 2010 for an initial term of 3 years Sponsor: WCRP-WWRP/THORPEX under YOTC Follow on from the US-CLIVAR MJO Working Group Overall goal: Facilitate improvements in the representation of the MJO in weather and climate models in order increase the predictive skill of the MJO and related weather and climate phenomena. 1. Web site - www.ucar.edu/yotc/mjo.html The MJO-TF web site includes teleconference minutes, summary of past and present activities, related papers and presentations, and links to our related activities. 2. Workshop at APCC, South Korea BAMS Summary: Hendon, Sperber, Waliser, and Wheeler (2011) 5. Process-oriented diagnostics/metrics Development and continuing refinement of a set of process-oriented diagnostics for the evaluation of the MJO and related processes in dynamical models. One aspect of the treatment of convection that appears important is the relationship between precipitation rate and column saturation fraction. Having precipitation rate increase exponentially with saturation fraction appears to be a necessary, but not sufficient, condition for a good MJO simulation (Zhu et al. 2009). The vertical distribution of moisture as a function of precipitation rate also appears important (Kim et al. 2009). Models that have a better match with the observed RH vertical structure (and as a function of precipitation rate) tend to have a stronger MJO (as measure by the east/west power ratio metric). MJO-TF and related activities at the Pan-GASS conference  Thursday afternoon Breakout Session, and presentations by Steve Woolnough, Xianan Jiang, Prince Xavier, and Nick Klingaman, on the MJO Vertical Structure and Diabatic Heating Project.  Poster by Eric Maloney et al. and talk by Jim Benedict et al. on process-oriented diagnostics of the MJO.  Poster by June-Yi Lee et al. on real-time indices for the boreal summer intraseasonal oscillation (BSISO).  Poster by Daehyun Kim and Ken Sperber on simplified metrics for the MJO in models.  Wednesday afternoon Breakout Session on tropical convection observed during CINDY/DYNAMO.  Poster by Mitch Moncrieff and Duane Waliser on the Year of Tropical Convection (YOTC). For further information, e-mail: m.wheeler@bom.gov.au and emaloney@atmos.colostate.edu 6. MJO-TF/GASS project on Vertical Structure and Diabatic Heating Observational products and reanalysis are starting to give estimates of vertical diabatic heating, but what do the models look like? Are the observations good enough? These are some of the questions we hope to answer with this joint project (http://www.ucar.edu/yotc/mjodiab.html).http://www.ucar.edu/yotc/mjodiab.html Vertical-temporal evolution of anomalous heating Q 1 or Q 1 -Q R for TRMM SLH (colour shading) and TRMM 3B42 rainfall (black lines). There are 3 modelling components, allowing for a focus on different aspects of the science. Cases selected from YOTC (see figure below) and CINDY2011/DYNAMO. Jiang et al. (2011) 4. Simplified metrics for Climate Metrics Panel Provision of MJO metrics for assessing climate models, especially those in CMIP. Metric #1 Project model data onto observed OLR EOF pair and determine the maximum correlation between the projection coefficients, and the lag at which it occurs (Sperber and Kim 2012). Metric #2 East/west power ratio from wavenumber-frequency spectral analysis of convection. 3. Forecast model diagnostics/metrics and verification Continuing work on model MJO/BSISO forecasts and their verification. Operational Dynamical Model Forecasts of the Real-time Multivariate MJO (RMM) index (CPC web-site; Gottschalck et al. 2010) Development of new indices of the Boreal Summer Intraseasonal Oscillation (BSISO; Lee et al. 2012). ● Co-development of the ISV Hincast Experiment hosted at the IPRC – designed for MJO and other ISV prediction and predictability studies. ● Involvement in MJO forecasting/modelling support for DYNAMO/CINDY