Accession Number : AD1002798

Title :   Evaluating MJO Event Initiation and Decay in the Skeleton Model using an RMM-like Index

Descriptive Note : Journal Article

Corporate Author : University of California Los Angeles Los Angeles United States

Personal Author(s) : Stachnik,Justin P ; Waliser,Duane E ; Majda,Andrew J ; Stechmann,Samuel N ; Thual,Sulian

Full Text :

Report Date : 25 Nov 2015

Pagination or Media Count : 67

Abstract : The Madden-Julian oscillation (MJO) skeleton model is a low-order dynamic model that is capable of simulating many of the observed features of the MJO. This study develops a model-based MJO index that is similar to the well-known real-time multivariate MJO (RMM) index to better facilitate comparison between the skeleton model and observational data. Multivariate and univariate empirical orthogonal function (EOF) analyses were performed on the convective heating and zonal wind data taken from the skeleton model for simulations forced with an idealized warm pool and observed sea surface temperatures (SSTs). The leading EOF modes indicated a wave number 1 convectively coupled circulation anomaly with zonal asymmetries that closely resembled the observed RMM EOFs, especially when the model was forced with observed SSTs. The RMM-like index was used to compute an MJO climatology and document the occurrence of primary, continuing, and terminating MJO events in the skeleton model. The overall amount of MJO activity and event lengths compared reasonably well to observations for such a simple model. Attempts at reconciling the observed geographic distribution of individual MJO initiation and termination events were not successful for the stochastic simulations, though stochasticity is necessary in order to produce composite MJOs that initiate and decay with time scales similar to observations. Finally, analysis indicates that the existence of slow-moving, eastward traveling waves with higher wave numbers (k12) embedded within the large-scale flow often precedes MJO termination in the skeleton model.

Descriptors :   models , Meteorology , STOCHASTIC PROCESSES , ATMOSPHERIC SCIENCES , Indian Ocean , Geophysics , Simulation , Climatology

Distribution Statement : APPROVED FOR PUBLIC RELEASE