Climate Dynamics

, Volume 42, Issue 5–6, pp 1425–1448 | Cite as

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

  • Vasubandhu Misra
  • H. Li
  • Z. Wu
  • S. DiNapoli


This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.


Root Mean Square Error Ensemble Member Empirical Mode Decomposition Atmospheric General Circulation Model Seasonal Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This paper is dedicated to the memory of Dr. Masao Kanamitsu, without whose pioneering development of the FISH50 AGCM this work would not have been possible. This work was supported by Grants from NOAA (NA12OAR4310078, NA10OAR4310215, NA11OAR4310110), USGS (06HQGR0125), and USDA (027865). All computations for this paper were done on the computational resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE) under TG-ATM120017 and TG-ATM120010.


  1. Bacmeister J, Pegion PJ, Schubert SD, Suarez MJ (2000) An atlas of seasonal means simulated by the NSIPP 1 atmospheric GCM, vol 17. NASA Tech. Memo. 104606, Goddard Space Flight Center, Greenbelt, p 194Google Scholar
  2. Barnett TP, Bengtsson L, Arpe K, Flugel M, Graham N, Latif M, Ritchie J, Roeckner E, Schlese U, Schulzweida U, Tyree M (1993) Forecasting global ENSO-related climate anomalies. Tellus 46A:381–397Google Scholar
  3. Bengtsson L, Schlese U, Roeckner E, Latif M, Barnett T, Graham N (1993) A two-tiered approach to long-range climate forecasting. Science 261:1026–1029CrossRefGoogle Scholar
  4. Berger AL (1978) Long-term variations of daily insolation and quaternary climate changes. J Atmos Sci 25:2362–2367CrossRefGoogle Scholar
  5. Bohn TJ, Sonessa MY, Lettenmaier DP (2010) Do multi-model ensemble average always yield improvements in forecast skill? J Hydromet. doi: 10.1175/2010JHM1267.1 Google Scholar
  6. Cantelaube P, Terres J-M (2005) Seasonal weather forecast for crop yield modeling in Europe. Tellus A 57:476–487CrossRefGoogle Scholar
  7. Challinor AJ, Slingo JM, Wheeler TJ, Doblas-Reyes FJ (2005) Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles. Tellus A 57:498–512CrossRefGoogle Scholar
  8. Chou M-D, Lee K-T (1996) Parameterizations for the absorption of solar radiation by water vapor and ozone. J Atmos Sci 53:1203–1208CrossRefGoogle Scholar
  9. Chou M-D, Suarez MJ (1994) An efficient thermal infrared radiation parameterization for use in general circulation models. Technical report series on global modeling and data assimilation, NASA/TM-1994-104606, 3, p 85Google Scholar
  10. Chun H, Baik J (1998) Momentum flux by thermally induced interval gravity wave and its approximation for large-scale models. J Atmos Sci 55:3299–3310CrossRefGoogle Scholar
  11. Clark MP, Hay LE (2004) Use of medium-range numerical weather prediction model output to produce forecasts of streamflow. J Hydrometeorol 5:15–32CrossRefGoogle Scholar
  12. Clough SA, Shephard MW, Mlawer EJ, Delamere JS, Iacono MJ, Cady-Pereira K, Boaukabara S, Brown PD (2005) Atmospheric radiative transfer modeling: a summary of the AER codes. J Quant Spectrosc Radiat Transf 91:233–244CrossRefGoogle Scholar
  13. DelSole T, Shukla J (2012) Climate models produce skillful predictions of Indian summer monsoon rainfall. Geophys Res Lett 39:L09703. doi: 10.1029/2012GL051279 CrossRefGoogle Scholar
  14. DeWitt DG (2005) Retrospective forecasts of interannual sea surface temperature anomalies from 1982 to present using a directly coupled atmosphere–ocean general circulation model. Mon Weather Rev 133:2972–2995CrossRefGoogle Scholar
  15. Doblas-Reyes J et al (2005) A forecast quality assessment and end-to-end probabilistic multi-model seasonal forecast system using a malaria model. Tellus A 57:464–475CrossRefGoogle Scholar
  16. Drijfhout SS, Walsteijn FH (1998) Eddy induced heat transport in a coupled ocean-atmosphere anomaly model. J Phys Oceanogr 28:250–265CrossRefGoogle Scholar
  17. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale 437 Eta model. J Geophys Res 108:8851. doi: 10.1029/2002JD003296 CrossRefGoogle Scholar
  18. Hack JJ (1994) Parameterization of moist convection in the National Center for Atmospheric Research Community Climate Model (CCM2). J Geophys Res 99:5551–5568CrossRefGoogle Scholar
  19. Holtsalag AAM, Boville BA (1993) Local versus nonlocal boundary-layer diffusion in a global climate model. J Clim 6:1825–1842CrossRefGoogle Scholar
  20. Hong S-Y, Pan H-L (1996) Nonlinear boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124:2322–2339CrossRefGoogle Scholar
  21. Hong S-Y, Pan H-L (1998) Convective trigger function for a mass-flux cumulus parameterization scheme. Mon Weather Rev 126:2599–2620CrossRefGoogle Scholar
  22. Huang NE, Shen Z, Long SR, Wu MC, Shih EH, Zheng Q, Tung CC, Liu HH (1998) The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis. Proc R Soc Lond 454A:903–995CrossRefGoogle Scholar
  23. Hurrell JW, Bader D, Delworth T, Kirtman B, Meehl J, Pan HL, Wielicki B (2007) White paper on seamless prediction. Available at
  24. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritsch scheme. The representation of cumulus convection in numerical models, Meteorol. Monogr., No. 46, Am Meteorol Soc 165–170Google Scholar
  25. Kanamitsu M, Ebusuzaki W, Woollen J, Yang S-K, Hnilo J, Fiorino M, Potter GL (2002a) NCEP-DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1643CrossRefGoogle Scholar
  26. Kanamitsu M et al (2002b) NCEP dynamical seasonal forecast system 2000. Bull Am Meteorol Soc 83:1019–1037CrossRefGoogle Scholar
  27. Kanamitsu M, Yoshimura K, Yhang Y-B, Hong S-Y (2010) Errors of interannual variability and multi-decadal trend in dynamical regional climate downscaling and its corrections. J Geophys Res 115:D17115. doi: 10.1029/2009JD013511 CrossRefGoogle Scholar
  28. Kirtman BP (2003) The COLA anomaly coupled model: ensemble ENSO prediction. Mon Weather Rev 10:2324–2341CrossRefGoogle Scholar
  29. Kirtman BP, Min D (2009) Multimodel ensemble ENSO prediction with CCSM and CFS. Mon Weather Rev 137:2908–2930CrossRefGoogle Scholar
  30. Kirtman BP, Fan Y, Schneider EK (2002) The COLA coupled and anomaly coupled ocean-atmosphere GCM. J Clim 15:2301–2320CrossRefGoogle Scholar
  31. Kirtman BP et al. (2013) The North American Multi-Model Ensemble (NMME) for intra-seasonal to interannual prediction. Bull Am Met Soc (Submitted)Google Scholar
  32. LaRow TE (2013) The impact of SST bias correction in north Atlantic hurricane retrospective forecasts. Mon Weather Rev 141:490–498CrossRefGoogle Scholar
  33. Mason SJ, Graham NE (1999) Conditional probabilities, relative operating characteristics, and relative operating levels. Weather Forecast 14:713–725CrossRefGoogle Scholar
  34. Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: significance and interpretation. Quart J R Meteorol Soc 128:2145–2166CrossRefGoogle Scholar
  35. McFarlane NA (1987) The effects of orographically excited gravity wave drag on the general circulation of the lower stratosphere and troposphere. J Atmos Sci 44:1775–1800CrossRefGoogle Scholar
  36. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high resolution grids. Int J Climatol 25:693–712CrossRefGoogle Scholar
  37. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(14):16663–16682CrossRefGoogle Scholar
  38. Moorthi S, Suarez MJ (1992) Relaxed Arakawa-Schubert. A parameterization of moist convection for general circulation models. Mon Weather Rev 120:978–1002CrossRefGoogle Scholar
  39. Morse AP, Doblas-Reyes FJ, Hoshen MB, Hagedorn R, Palmer TN (2005) A forecast quality assessment of an end-to-end probabilistic multi-model seasonal forecast system using a malaria model. Tellus A 57:464–475CrossRefGoogle Scholar
  40. Oleson KW et al. (2004) Technical description of the community land model (CLM). Technical report. NCAR/TN-461+STR, NCAR, Boulder, p 174Google Scholar
  41. Palmer TN, Brankovic C, Richardson DS (2000) A probability and decision model analysis of PROVOST seasonal multimodel ensemble integrations. Quart J R Meteor Soc 126:2013–2034CrossRefGoogle Scholar
  42. Palmer TN et al (2004) Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER). Bull Am Meteorol Soc 85:853–872CrossRefGoogle Scholar
  43. Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ (2008) Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull Am Meteorol Soc 89:459–470CrossRefGoogle Scholar
  44. Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ (2009) Toward seamless prediction: calibration of climate change projections using seasonal forecasts reply. Bull Am Meteorol Soc 90:1551–1554CrossRefGoogle Scholar
  45. Ramanathan V, Downey P (1986) A nonisothermal emissivity and absorptivity formulation for water vapor. J Geophys Res 91:8649–8666CrossRefGoogle Scholar
  46. Saha S et al (2006) The climate forecast system at NCEP. J Clim 19:3483–3517. doi: 10.1175/JCLI3812.1 CrossRefGoogle Scholar
  47. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
  48. Saha S et al. (2013) The NCEP climate forecast system version 2. J. climate. Available from (Submitted)
  49. Shapiro M et al (2010) An earth-system prediction initiative for the twenty-first century. Bull Am Soc 91:1377–1388CrossRefGoogle Scholar
  50. Shimpo A, Kanamitsu M, Iacobellis SF, Hong S-Y (2008) Comparison of four cloud schemes in simulating the seasonal mean field forced by the observed sea surface temperature. Mon Weather Rev 136:2557–2575CrossRefGoogle Scholar
  51. Shukla J (1998) Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282:728–731CrossRefGoogle Scholar
  52. Shukla J, Anderson J, Baumhefner D, Brankovic C, Chang Y, Kalnay E, Marx L, Palmer T, Paolino DA, Ploshay J, Schubert S, Straus DM, Suarez M, Tribbia J (2000) Dynamical seasonal prediction. Bull Am Meteorol Soc 81:2593–2606CrossRefGoogle Scholar
  53. Shukla J et al (2009) Revolution in climate prediction is both necessary and possible. A declaration at the world modeling summit for climate prediction. Bull Am Soc 2:175–178CrossRefGoogle Scholar
  54. Shukla J, Palmer TN, Hagedorn R, Hoskins B, Kinter J, Marotzke J, Miller M, Slingo J (2010) Toward a new generation of world climate research and computing facilities. Bull Am Meteorol Soc 91:1407–1412CrossRefGoogle Scholar
  55. Shukla S, Voisin N, Lettenmaier DP (2012) Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill. Hydrol Earth Syst Sci Dis 9:1827–1857CrossRefGoogle Scholar
  56. Smith TM, Reynolds RW, Peterson TC, Lawrimore J (2008) Improvements to NOAA’s historical land-ocean surface temperature analysis (1880–2006). J Clim 21:2283–2296CrossRefGoogle Scholar
  57. Stockdale TN, Anderson DLT, Balmaseda MA, Doblas-Reyes FJ, Ferranti L, Mogensen K, Palmer TN, Molteni F, Vitart F (2011) ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Clim Dyn 37:455–471CrossRefGoogle Scholar
  58. Taylor KE, William D, Zwiers F (2000) The SST and seaice boundary conditions for AMIPII simulation. PCMDI report 60. Available from p 24
  59. Tiedtke M (1983) The sensitivity of the time-mean large-scale flow to cumulus convection in the ECWMF model. In: Proceedings of ECMWF workshop on convection in large-scale models. European Centre for Medium-Range Forecasts, Reading, United Kingdom, pp 297–316Google Scholar
  60. Wang B et al (2009) Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Clim Dyn 33:93–117CrossRefGoogle Scholar
  61. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1:1–41CrossRefGoogle Scholar
  62. Wu Z, Huang NE, Chen X (2009) The multi-dimensional ensemble empirical mode decomposition method. Adv Adapt Data Anal 1:339–372CrossRefGoogle Scholar
  63. Wu Z, Huang NE, Wallace JM, Smoliak B, Chen X (2011) On the time-varying trend in global-mean surface temperature. Clim Dyn 37:759–773. doi: 10.1007/s00382-011-1128-8 CrossRefGoogle Scholar
  64. Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558CrossRefGoogle Scholar
  65. Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos Ocean 33:407–446CrossRefGoogle Scholar
  66. Zhang S, Harrison MJ, Rosati A, Wittenberg AT (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 135(10). doi: 10.1175/MWR3466.1

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vasubandhu Misra
    • 1
    • 2
    • 3
  • H. Li
    • 1
    • 2
  • Z. Wu
    • 1
    • 2
  • S. DiNapoli
    • 1
  1. 1.Department of Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeUSA
  2. 2.Center for Ocean-Atmospheric Prediction StudiesFlorida State UniversityTallahasseeUSA
  3. 3.Florida Climate InstituteFlorida State UniversityTallahasseeUSA

Personalised recommendations