Evaluation of MJO Predictive Skill in Multi-Physics and Multi-Model Global Ensembles

Evaluation of MJO Predictive Skill in Multi-Physics and Multi-Model Global Ensembles AbstractMonth-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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Evaluation of MJO Predictive Skill in Multi-Physics and Multi-Model Global Ensembles

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
eISSN
1520-0493
D.O.I.
10.1175/MWR-D-16-0419.1
Publisher site
See Article on Publisher Site

Abstract

AbstractMonth-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.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Mar 17, 2017

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