88th Annual Meeting (20-24 January 2008)

Thursday, 24 January 2008: 8:30 AM
US CLIVAR MJO Working Group: MJO Climate Simulation Diagnostics
217-218 (Ernest N. Morial Convention Center)
Duane Edward Waliser, JPL, Pasadena, CA; and K. R. Sperber, L. Donner, J. Gottschalck, H. H. Hendon, W. Higgins, I. S. Kang, D. Kim, E. D. Maloney, M. W. Moncrieff, S. Schubert, W. Stern, F. Vitart, B. Wang, W. Wang, K. M. Weickmann, M. C. Wheeler, S. Woolnough, and C. Zhang
In spring 2006, US CLIVAR established the Madden-Julian Oscillation (MJO) Working Group (MJOWG). The formation of this 2-year limited lifetime WG was motivated by: 1) the wide range of weather and climate phenomena that the MJO interacts with and influences, 2) the fact that the MJO represents an important, and as yet unexploited, source of predictability at the subseasonal time scale, 3) the considerable shortcomings in our global climate and forecast models in representing the MJO, and 4) the need for coordinating the multiple threads of programmatic and investigator level research on the MJO. Near-term tasks involve the development of diagnostics for assessing model performance in both climate simulation and extended-range/subseasonal forecast settings, as well as designing and coordinating multi-model experimentation and analysis to diagnose and improve model shortcomings and assess MJO predictability characteristics and present-day prediction skill. In addition, the WG will help to coordinate MJO-related activities across other programmatic bodies (e.g., GEWEX, International CLIVAR, Thorpex) and will explore the applications and potential user base for subseasonal predictions based on the MJO. The purpose of this presentation is to make the community aware of these activities and more specifically to present the diagnostics that have been developed for assessing model performance in simulating the MJO and show results of their application to a number of present-day GCMs. The metrics include: 1) relatively simple diagnostics, such as both map and time series variance measures, EOFs, time series and correlation diagnostics, 2) more advanced measures such as combined EOFs, frequency-wave spectra and life-cycle composites, as well as 3) diagnostics associated with the mean state and interannual variability. For additional details, see www.usclivar.org/Organization/MJO_WG.html.

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