Evaluation of Mjo Predictive Skill In Multi-physics and Multi-model Global Ensembles.

Abstract

Month-long hindcasts of the Madden-Julian Oscillation (MJO) from the atmospheric Flow-following Icosahedral Model coupled with an icosahedral-grid version of the Hybrid Coordinate Ocean Model (“FIM-iHYCOM”), and from the coupled Climate Forecast System version 2 (CFSv2), are evaluated over the 12-year period 1999-2010. Two sets of FIM-iHYCOM hindcasts are run to test the impact of using Grell-Freitas (FIM-CGF) versus Simplified Arakawa-Schubert (FIM-SAS) deep convection parameterizations. Each hindcast set consists of 4 time-lagged ensemble members initialized weekly every 6 hours from 1200 UTC Tuesday through 0600 UTC Wednesday. The ensemble means of FIM-CGF, FIM-SAS, and CFSv2 produce skillful forecasts of a variant of the Real-time Multivariate MJO index (RMM) out to 19, 17, and 17 days, respectively; this is consistent with FIM-CGF having the lowest root-mean-square errors (RMSEs) for zonal winds at both 850 and 200 hPa. FIM-CGF and CFSv2 exhibit similar RMSEs in RMM, and their multi-model ensemble mean extends skillful RMM prediction out to 21 days. Conversely, adding FIM-SAS – with much higher RMSEs – to CFSv2 (as a multi-model ensemble) or FIM-CGF (as a multi-physics ensemble) yields either little benefit, or even a degradation, compared to the better single-model ensemble mean. This suggests that multi-physics/multi-model ensemble mean forecasts may only add value when the individual models possess similar skill and error. An atmosphere-only version of FIM-CGF loses skill after 11 days, highlighting the importance of ocean coupling. Further examination reveals some sensitivity in skill and error metrics to the choice of MJO index.