Climate Dynamics

, Volume 53, Issue 12, pp 7397–7409 | Cite as

An update on the estimate of predictability of seasonal mean atmospheric variability using North American Multi-Model Ensemble

  • Bhaskar JhaEmail author
  • Arun Kumar
  • Zeng-Zhen Hu


In this analysis, an update in the estimate of predictable component in the wintertime seasonal variability of atmosphere documented by Kumar et al. (J Clim 20: 3888–3901, 2007) is provided. The updated estimate of seasonal predictability of 200-hPa height (Z200) was based on North American Multi-Model Ensemble (NMME) forecast system. The seasonal prediction systems participating in the NMME have gone through an evolution over a 10-year period compared to models that were used in the analysis by Kumar et al. (J Clim 20: 3888–3901, 2007). The general features in the estimates of predictable signal conform with previous results—estimates of predictability remain high in the tropical latitudes and decrease towards the extratropical latitudes; and predictability in the initialized coupled seasonal forecast systems is still primarily associated with ENSO variability. As the horizontal and vertical resolution of the models used in the current analysis is generally higher, it did not have a marked influence on the estimate of the relative amplitude of predictable component. Although the analysis indicates an increase in the estimate of predictable component, however, it maybe related to the increase in ENSO related SST variance over 1982–2000 relative to 1950–2000 (over which the analysis of Kumar et al. in J Clim 20: 3888–3901, 2007 was). The focus of the analysis is wintertime variability in Z200 and its comparison with results in Kumar et al. (J Clim 20: 3888–3901, 2007), some analyses for summertime variability in Z200, and further, for sea surface temperature, 2-m temperature and precipitation are also presented.


NMME Predictability Ensemble mean 200 hPa height 



The NMME project and data dissemination are supported by NOAA, NSF, NASA and DOE. The help of NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive is acknowledged. All the data used in this paper are available at NOAA Climate Prediction Center (CPC) ( The scientific results and conclusions, as well as any view or opinions expressed herein, are those of the authors and do not necessarily reflect the views of NWS, NOAA, or the Department of Commerce.


  1. Barnett TP (1995) Monte Carlo climate forecasting. J Clim 8:1005–1022CrossRefGoogle Scholar
  2. Barnston AG, Kumar A, Goddard L, Hoerling MP (2005) Improving seasonal predictions practices through attribution of climate variability. Bull Am Meteorol Soc 85:59–72CrossRefGoogle Scholar
  3. Barnston AG, Tippett MK, L’Heureux ML, Li S, DeWitt DG (2012) Skill of real-time seasonal ENSO model predictions during 2002–2011—is our capability increasing? Bull Am Meteorol Soc 93(5):631–651CrossRefGoogle Scholar
  4. Becker E, van Dool Huug, Zhang Q (2014) Predictability and forecast skill in NMME. J Clim 27:5891–5906. doi: 10.1175/JCLI-D13-00597.1 CrossRefGoogle Scholar
  5. Chen M, Kumar A, Wang W (2015) A Study of the predictability of sea surface temperature over the tropics. Clim Dyn 44:1767–1776CrossRefGoogle Scholar
  6. Chervin RM (1986) Interannual variability and seasonal climate variability. J Atmos Sci 43:233–251CrossRefGoogle Scholar
  7. Delsole T, Kumar A, Jha B (2013) Potential seasonal predictability: comparison between empirical and dynamical model estimates. Geophys Res Lett 40:1–7CrossRefGoogle Scholar
  8. Guan Y, Zhu J, Huang B, Hu Z-Z, Kinter JL III (2014) South Pacific Ocean dipole: a predictable mode on multi-seasonal time scales. J Clim 27:1648–1658CrossRefGoogle Scholar
  9. Hoerling MP, Kumar A (2002) Atmospheric response patterns associated with tropical forcing. J Clim 8:474–495Google Scholar
  10. Hoerling MP, Kumar A, Zhong M (1997) El Nino, La Nina, and the nonlinearity of their teleconnections. J Clim 10:1769–1786CrossRefGoogle Scholar
  11. Horel JD, Wallace JM (1981) Planetary-scale atmospheric phenomenon associated with the Southern Oscillation. Mon Weather Rev 109:2080–2092CrossRefGoogle Scholar
  12. Jha B, Kumar A (2009) A comparative analysis of change in the first and second moment of the PDF of seasonal means with ENSO SSTs. J Clim 22:1412–1423CrossRefGoogle Scholar
  13. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  14. Kirtman BP et al (2014) The North American multimodel ensemble: phase 1 seasonal-to-interannual prediction; phase-2 toward developing intraseasoanl prediction. Bull Am Meteorol Soc 95:585–601CrossRefGoogle Scholar
  15. Kumar A (2009) Finite samples and uncertainty estimates for skill measures for seasonal predictions. Mon Weather Rev 137:385–392CrossRefGoogle Scholar
  16. Kumar A, Hoerling MP (1995) Prospects and limitation of seasonal atmospheric GCM predictions. Bull Am Meteorol Soc 76:335–345CrossRefGoogle Scholar
  17. Kumar A, Hoerling MP (2000) Analysis of a conceptual model of seasonal climate variability and implications for seasonal prediction. Bull Am Meteorol Soc 81:255–264CrossRefGoogle Scholar
  18. Kumar A, Hu Z-Z (2014) How variable is the uncertainty in ENSO sea surface prediction? J Clim 27(7):2779–2788. doi: 10.1175/JCLI-D-13-00576.1 CrossRefGoogle Scholar
  19. Kumar A, Murtugudde R (2013) Predictability and uncertainty: a unified perspective to build a bridge from weather to climate. COSUST 5:327–333Google Scholar
  20. Kumar A, Barnston AG, Hoerling MP (2001) Seasonal prediction, probabilistic verifications, and ensemble size. J Clim 14:1671–1676CrossRefGoogle Scholar
  21. Kumar A, Schubert SD, Suarez MS (2003) Variability and predictability of 200-mb seasonal mean heights during summer and winter. J Geophys Res 108:4169. doi: 10.1029/2002JD002728 CrossRefGoogle Scholar
  22. Kumar A, Jha B, Zhang Q, Bounoua L (2007) A new methodology for estimating the uppredictable component of seasonal atmospheric variability. J Clim 20:3888–3901CrossRefGoogle Scholar
  23. Kumar A, Perlwitz J, Eischeid J, Quan X, Xue T, Zhang T, Hoerling M, Jha B, Wang W (2010) Contribution of sea ice loss to Artic amplification. Geophys Res Lett 37:1–6Google Scholar
  24. Kumar A, Chen M, Wang W (2011) An analysis of prediction skill of monthly mean climate variability. Clim Dyn 37:1119–1131CrossRefGoogle Scholar
  25. Kumar A, Hu Z-Z, Jha B, Peng P (2016) Estimating ENSO predictability: based on multi-model hindcasts. Clim Dyn. doi: 10.1007/s00382-016-3060-4 CrossRefGoogle Scholar
  26. Madden RA (1976) Estimates of the natural variability of time-averaged sea-level pressure. Mon Weather Rev 104:942–952CrossRefGoogle Scholar
  27. National Research Council (2010) Assessment of intraseasonal to interannual climate prediction and predictability. the National Academies Press, Washington, 192 pp., ISBN-10: 0-309-15183-XGoogle Scholar
  28. Peng P, Kumar A, Barnston AG, Goddard L (2000) Simulation skills of the SST-forced global climate variability of the NCEP-MRF9 and Scripps-MPI ECHAM3 models. J Clim 13:3657–3679CrossRefGoogle Scholar
  29. Quan X, Hoerling M, Whitaker J, Bates G, Xu T (2006) Diagnosing sources of US seasonal forecast skill. J Clim 19:3279–3293. doi: 10.1175/JCLI3789.1 CrossRefGoogle Scholar
  30. Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625CrossRefGoogle Scholar
  31. Rowell DP (1998) Assessing potential predictability with an ensemble of multi-decadal GCM simulations. J Clim 11:109–120CrossRefGoogle Scholar
  32. Sardeshmukh PD, Compo GL, Penland C (2000) Changes of probability associated with El Niño. J Clim 13:4268–4286CrossRefGoogle Scholar
  33. Screen JA, Deser C, Simmonds I, Tomas R (2014) Atmospheric impacts of Arctic sea-ice loss, 1979–2009: separating forced change from atmospheric internal variability. Clim Dyn 43:333–344CrossRefGoogle Scholar
  34. Shukla J et al (2000) Dynamical seasonal prediction. Bull Am Meteorol Soc 81:2593–2606CrossRefGoogle Scholar
  35. Stern W, Miyakoda K (1995) Feasibility of seasonal forecasts inferred from multiple GCM simulations. J Clim 8:1071–1085CrossRefGoogle Scholar
  36. Straus D, Shukla J, Paolino D, Schubert S, Suarez M, Pegion P, Kumar A (2003) Predictability of the seasonal mean atmospheric circulation during autumn, winter, and spring. J Clim 16:3629–3649CrossRefGoogle Scholar
  37. Treenberth KE, Branstator GW, Karoly D, Kumar A, Lau N-C, Ropelewski C (1998) Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperature. J Geophys Res 103(7):14291–14324CrossRefGoogle Scholar
  38. Zwiers FW, Wang XL, Sheng J (2000) Effects of specifying bottom boundary conditions in an ensemble of atmospheric GGCM simulations. J Geophys Res 105:7295–7316CrossRefGoogle Scholar

Copyright information

© Springer 2016

Authors and Affiliations

  1. 1.Climate Prediction CenterNCEP/NWS/NOAACollege ParkUSA
  2. 2.Innovim LLCGreenbeltUSA

Personalised recommendations