MJO Simulation Diagnostics

• Published in: Journal of climate Vol. 22; no. 11; pp. 3006 - 3030
• Main Authors: Waliser, D., Sperber, K., Hendon, H., Kim, D., Maloney, E., Wheeler, M., Weickmann, K., Zhang, C., Donner, L., Gottschalck, J., Higgins, W., Kang, I.-S., Legler, D., Moncrieff, M., Schubert, S., Stern, W., Vitart, F., Wang, B., Wang, W., Woolnough, S.
• Format: Journal Article
• Language: English
• Published: Boston, MA American Meteorological Society 01.06.2009
• Subjects: Administrative support; Climate change; Climate models; Climatology. Bioclimatology. Climate change; Convection; Earth, ocean, space; Exact sciences and technology; External geophysics; Geophysics. Techniques, methods, instrumentation and models; Meteorology; Meteors; Modeling; Oceans; Precipitation; Simulation; Simulations; Statistical variance; Studies; Summer; Winter; Performance evaluation; General circulation models; Objective analysis; spectral analysis; Empirical orthogonal function; climate variability; variance analysis; Climate models; digital simulation; Interannual variation; Dynamical climatology; Life cycle; Correlation analysis; algorithm performance; Madden Julian oscillation; Composite analysis; Comparative study
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/2008jcli2731.1

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.