Publication Abstracts

Green et al. 2017

Green, B.W., S. Sun, R. Bleck, S.G. Benjamin, and G.A. Grell, 2017: Evaluation of MJO predictive skill in multiphysics and multimodel global ensembles. Mon. Weather Rev., 145, no. 7, 2555-2574, doi:10.1175/MWR-D-16-0419.1.

Monthlong 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-yr 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 four time-lagged ensemble members initialized weekly every 6 h from 1200 UTC Tuesday to 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 (RMM) index 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 multimodel ensemble mean extends skillful RMM prediction out to 21 days. Conversely, adding FIM-SAS — with much higher RMSEs — to CFSv2 (as a multimodel ensemble) or FIM-CGF (as a multiphysics ensemble) yields either little benefit, or even a degradation, compared to the better single-model ensemble mean. This suggests that multiphysics/multimodel 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.

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BibTeX Citation

  author={Green, B. W. and Sun, S. and Bleck, R. and Benjamin, S. G. and Grell, G. A.},
  title={Evaluation of MJO predictive skill in multiphysics and multimodel global ensembles},
  journal={Mon. Weather Rev.},

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RIS Citation

ID  - gr01110h
AU  - Green, B. W.
AU  - Sun, S.
AU  - Bleck, R.
AU  - Benjamin, S. G.
AU  - Grell, G. A.
PY  - 2017
TI  - Evaluation of MJO predictive skill in multiphysics and multimodel global ensembles
JA  - Mon. Weather Rev.
VL  - 145
IS  - 7
SP  - 2555
EP  - 2574
DO  - 10.1175/MWR-D-16-0419.1
ER  -

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