Evaluation of MJO Predictive Skill in Multiphysics and Multimodel Global Ensembles

Details

• Journal Title:
  Monthly Weather Review
• Personal Author:
  Green, Benjamin W. ; Sun, Shan ; Bleck, Rainer ; Benjamin, Stanley G. ; Grell, Georg A.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; GSL (Global Systems Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)
• Description:
  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.
  
  
• Keywords:
  Mathematical Models Ocean-atmosphere Interaction Mathematical Models Ocean-atmosphere Interaction
• Source:
  Mon. Wea. Rev. (2017) 145 (7): 2555–2574.
• DOI:
  https://doi.org/10.1175/MWR-D-16-0419.1
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:d648d9a28446d467ca5def4f4ed0c972ebdf7b2ecc4a388d915d78e5ef1cd5934061767789eee26ac17990e0900fccca38fb63604d284f187e391fc761bb30fd
• Download URL:
  https://repository.library.noaa.gov/view/noaa/27154/noaa_27154_DS1.pdf
• File Type:
  [PDF - 4.34 MB ]