Climate Dynamics

, Volume 51, Issue 1–2, pp 493–510 | Cite as

Potential predictability and actual skill of Boreal Summer Tropical SST and Indian summer monsoon rainfall in CFSv2-T382: Role of initial SST and teleconnections

  • Prasanth A. Pillai
  • Suryachandra A. Rao
  • Renu S. Das
  • Kiran Salunke
  • Ashish Dhakate


The present study assess the potential predictability of boreal summer (June through September, JJAS) tropical sea surface temperature (SST) and Indian summer monsoon rainfall (ISMR) using high resolution climate forecast system (CFSv2-T382) hindcasts. Potential predictability is computed using relative entropy (RE), which is the combined effect of signal strength and model spread, while the correlation between ensemble mean and observations represents the actual skill. Both actual and potential skills increase as lead time decreases for Niño3 index and equatorial East Indian Ocean (EEIO) SST anomaly and both the skills are close to each other for May IC hindcasts at zero lead. At the same time the actual skill of ISMR and El Niño Modoki index (EMI) are close to potential skill for Feb IC hindcasts (3 month lead). It is interesting to note that, both actual and potential skills are nearly equal, when RE has maximum contribution to individual year’s prediction skill and its relationship with absolute error is insignificant or out of phase. The major contribution to potential predictability is from ensemble mean and the role of ensemble spread is limited for Pacific SST and ISMR hindcasts. RE values are able to capture the predictability contribution from both initial SST and simultaneous boundary forcing better than ensemble mean, resulting in higher potential skill compared to actual skill for all ICs. For Feb IC hindcasts at 3 month lead time, initial month SST (Feb SST) has important predictive component for El Niño Modoki and ISMR leading to higher value of actual skill which is close to potential skill. This study points out that even though the simultaneous relationship between ensemble mean ISMR and global SST is similar for all ICs, the predictive component from initial SST anomalies are captured well by Feb IC (3 month lead) hindcasts only. This resulted in better skill of ISMR for Feb IC (3 month lead) hindcasts compared to May IC (0 month lead) hindcasts. Lack of proper contribution from initial SST and teleconnections induces large absolute error for ISMR in May IC hindcasts resulting in very low actual skill. Thus the use of potential predictability skill and actual skill collectively help to understand the fidelity of the model for further improvement by differentiating the role of initial SST and simultaneous boundary forcing to some extent.


Indian summer monsoon Seasonal prediction E Nino El Nino Modoki Indian Ocean Dipole Actual skill and potential skill Relative entropy 



IITM is fully supported by ministry of earth Sciences (MoES), Govt. of India. Original version of CFSv2 model is obtained from NCEP through MoU with MoES as part of monsoon mission and is run at IITM HPC “aadithya”. Authors acknowledge, the director IITM for the support provided. The model hindcast runs are performed in house and hindcast data used in this paper can be obtained from IITM, Pune (Email: Observation rainfall data is from India Meteorological Department (for IMD data contact Email: and GPCP (GPCP data is obtained via HadISST data is obtained from UK Met. Office website (


  1. Adler RF et al (2003) The version 2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4:1147–1167CrossRefGoogle Scholar
  2. Annamalai H, Liu P (2005) Response of the Asian Summer Monsoon to changes in El Niño properties. Quart J Roy Meteor Soc 131:805–831CrossRefGoogle Scholar
  3. Ashok K, Guan Z, Yamagata T (2001) IMapct of the Indian Ocean Dipole on the relationship between Indian monsoon rainfall and ENSO. Geophy Res Lett 28:4499–4502CrossRefGoogle Scholar
  4. Ashok K, Behera S, Rao SA, Weng H, Yamagata T (2007) El Niño Modoki and its teleconnection. J Geophys Res 112:C11007. doi: 10.1029/2006JC003798 CrossRefGoogle Scholar
  5. Becker EJ, van den Dool HM, Pena M (2013) short-term climate extremes: prediction skill and predictability. J Climate 26:512–531CrossRefGoogle Scholar
  6. Boer GJ, Kharin VV, Merryfield WJ (2013) Decadal predictability and forecast skill. Clim Dyn 41:1817–1833CrossRefGoogle Scholar
  7. Buizza R, Palmer TN (1998) Impact of ensemble size on ensemble prediction. Mon Weather Rev 126:2503–2518CrossRefGoogle Scholar
  8. Charney JG, Shukla J (1981) Predictability of monsoons, in Monsoon dynamics, Lighthill J, Pearce RP (eds), p. 99–110, Cambridge University, CambridgeCrossRefGoogle Scholar
  9. Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, NJGoogle Scholar
  10. Delsole T (2005) Predictability and information theory. Part II: imperfect forecasts. J Atmos Sci 62:3368–3381CrossRefGoogle Scholar
  11. Delsole T, Tippett MK (2007) Predictability: Recent insight from information theory. Rev Geophys 45:RG4002. doi: 10.1029/2006RG000202 CrossRefGoogle Scholar
  12. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarplay JD (2003) Implementation of Noah land surface model advances in the National Centers for environmental prediction operational mesoscale Eta mode. J Geophys Res 1089(D22):8851. doi: 10.1029/2002JD003296 CrossRefGoogle Scholar
  13. Goswami BN (1998) Interannual variations of Indian summer monsoon in a GCM: external conditions versus internal feedbacks. J Clim 11:501–522CrossRefGoogle Scholar
  14. Griffies SM, Harrison MJ, Pacanowski RC, Rosati A (2004) A technical guide to MOM4. GFDL Ocean Group technical report 5, p 337Google Scholar
  15. Hendon HH, Lim E, Wang G, Alves O, Hudson Hendon HH, Lim E, Wang G, Alves O, Hudson D (2009) Prospects for predicting two flavours of El Nino. Geophys Res Lett 36:L19713CrossRefGoogle Scholar
  16. Jeong HI, Lee DY, Ashok K et al (2012) Asssessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavours of ENSO during boreal winter. Clim Dyn 39:475–493CrossRefGoogle Scholar
  17. Ju J, Slingo JM (1995) The Asian summer monsoon and ENSO. Q J R Mcteorol Soc 121:1133–1108CrossRefGoogle Scholar
  18. Kang IS, Shukla J (2006) Dynamic seasonal prediction and predictability of the monsoon. In: Wang B (ed.) The Asian Monsoon. Springer, Berlin, pp 585–612CrossRefGoogle Scholar
  19. Kleeman R (2002) Measuring dynamical prediction utility using relative entropy. J Atmos Sci 59:2057–2072CrossRefGoogle Scholar
  20. Kumar KK, Hoerling MP (1995) Prospects and limitations of seasonal atmospheric GCM predictions. Bull Am Meteorol Soc 76:335–345CrossRefGoogle Scholar
  21. Kumar KK, Hoerling MP (2000) Analysis of a conceptual model of seasonal climate varaiability and implications for seasonal predictions. Bull Am Meteorol Soc 81:255–264CrossRefGoogle Scholar
  22. Kumar A, Barnston AB, Peng P, Hoerling MP, Goddard L (2000) Changes in spread of the varaiability of the seasonal mean atmospheric sattes associated with ENSO. J Clim 13:3139–3151CrossRefGoogle Scholar
  23. Lau NC, Nath MJ (2000) Impact of ENSO on the variability of the Asian Australian monsoons as simulated in GCM experiments. J Climate 13:4287–4309CrossRefGoogle Scholar
  24. Leung LY, North GR (1990) Information theory and climate prediction. J Clim 3:5–14CrossRefGoogle Scholar
  25. Mehta VM, Suarez MJ, Manganello JV, Delworth TL (2000) Oceanic influence on North Atlantic Oscillation and associated Northern Hemisphere climate varaiations: 1953–1993. Gophy Res Lett. doi: 10.1029/1999GL002381 Google Scholar
  26. Palmer TN, Anderson LT (1994) The prospects for seasonal forecasting—a review paper. Q J R Meteorol Soc 120:755–793Google Scholar
  27. Palmer TN et al (2004) Development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER). Bull Am Meteorol Soc 85:853–872. doi: 10.1175/BAMS-85-6-853 CrossRefGoogle Scholar
  28. Pillai PA, Rao SA, George G, Rao DN, Mahapatra S, Rajeevan M, Dhakate A, Salunke K (2017) How distinct are the two flavors of El Niño in retrospective forecasts of climate forecast system version 2 (CFSv2)?. Clim Dyn 48:3829–3854. doi: 10.1007/s003821-26-016-3305-2 CrossRefGoogle Scholar
  29. Rajeevan M, Bhate J, Kale JD, Lal B (2006) High resolution daily gridded rainfall data for Indian region: Analysis of break and active monsoon spells. Curr Sci 9(3):296–306Google Scholar
  30. Rasmusson EM, Carpenter TH (1983) The relationship between eastern equatorial Pacific sea surface temperatures and rainfall over India and Sri Lanka. Mon Weather Rev 1:517–528CrossRefGoogle Scholar
  31. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407CrossRefGoogle Scholar
  32. Rowell DP (1998) Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J Clim 11:109–120. doi: 10.1175/1520-044 CrossRefGoogle Scholar
  33. Saha S et al (2006) The NCEP climate forecast system. J Clim 19:3483–3517CrossRefGoogle Scholar
  34. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
  35. Saha S et al (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208CrossRefGoogle Scholar
  36. Saha SK, Pokhrel S, Salunke K, Dhakate A, Chaudhari HS, Rahman H, Sujith K, Hazra A, Sikka DR (2016) Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2. J Adv Model Earth Syst 8:1–25CrossRefGoogle Scholar
  37. Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363. doi: 10.1038/43854 Google Scholar
  38. Sardeshmukh PD, Compo GP, Penland C (2000) Changes of probability associated with El Niño. J Clim 13:4268–4286. doi: 10.1175/1520-044 CrossRefGoogle Scholar
  39. Scherrer S, Appenzeller C, Eckert P, Cattani D (2004) Analysis of the spread-skill relation using the ECMWF ensemble prediction system over Europe. Weather Forecast 19:1403–1420CrossRefGoogle Scholar
  40. Schneider T, Griffies S (1999) A conceptual framework for predictability studies. J Clim 12:3133–3155CrossRefGoogle Scholar
  41. Shukla J (1981) Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282:728–731CrossRefGoogle Scholar
  42. Shukla RP, Huang B, Marx L, Kinter JL, Shin CS (2017) Predictability and prediction of Indian summer monsoon by CFSv2: implications of initial shock effect. Clim Dyn. doi: 10.1007/s00382-017-3594-0 (online)Google Scholar
  43. Singh SV, Kripalani RH (1986) Potential predictability of lower tropspheric monsoon circulation and rainfall over India. Mon Weather Rev 114:758–763CrossRefGoogle Scholar
  44. Soman MK, Slingo J (1997) Sensitivity of the Asian monsoon to aspects of sea-surface-temperature anomalies in the tropical Pacific Ocean. Quart J Roy Meteor Soc 123:309–336CrossRefGoogle Scholar
  45. Tang Y, Kleeman R, Moore AM (2005) On the reliability of ENSO dynamical predictions. J Atmos Sci 62:1770–1791. doi: 10.1175/JAS3445.1 CrossRefGoogle Scholar
  46. Tang Y, Lin H, Derome J, Tippett MK(2007) A predictability measure applied to seasonal predictions of the Arctic Oscillation. J Clim, 20:4733–4750. doi: 10.1175/JCLI4276.1 CrossRefGoogle Scholar
  47. Tippett MK, Kleeman R, Yang Y (2005) Measuring the potential utility of seasonal climate predictions. Geophy Res Lett 31:L22201Google Scholar
  48. Waliser DE, Lau KM, Stern W, Jones C (2003) Potential predictability of the Madden–Julian Oscillation. Bull Amer Meteor Soc 84:33–50. doi: 10.1175/BAMS-84-1-33 CrossRefGoogle Scholar
  49. Wang W, Chen M, Kumar A (2013) Seasonal prediction of arctic sea ice external from a coupled dynamical forecast system. Mon Wea Rev 141:1375–1394CrossRefGoogle Scholar
  50. Wang B, Xiang B, Li J, Webster PJ, Rajeevan M, Liu J, Ha KJ (2015) Rethinking Indian monsoon rainfall prediction in the context of recent global warming. Nat Commun 6:7154. doi: 10.1038/ncomms8154 CrossRefGoogle Scholar
  51. Webster PJ, Moore AM, Loschnigg JP, Leben RR (1999) Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401:356–360CrossRefGoogle Scholar
  52. Whitaker JS, Loughe AF (1998) The relationship between ensemble spread and ensemble mean skill. Mon Weather Rev 126:3292–3302CrossRefGoogle Scholar
  53. Winton M (2000) A reformulated three-layer sea ice model. J Atmos Oceanic Technol 17:525–531CrossRefGoogle Scholar
  54. Yang XQ, Anderson JL, Stern WF (1998) Reproducible forced modes in AGCM ensemble integrations and potential predictability of atmospheric seasonal variations in the extratropics. J Clim 11:2942–2959CrossRefGoogle Scholar
  55. Yang D, Tang Y, Zhang Y, Yang X (2012) Information-based potential predictability of the Asian summer monsoon in a coupled model. J Geophys Res 117:D03119. doi: 10.1029/2011JD016775 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Prasanth A. Pillai
    • 1
  • Suryachandra A. Rao
    • 1
  • Renu S. Das
    • 1
  • Kiran Salunke
    • 1
  • Ashish Dhakate
    • 1
  1. 1.Indian Institute of Tropical MeteorologyPuneIndia

Personalised recommendations