Climate Dynamics

, Volume 49, Issue 7–8, pp 2365–2383 | Cite as

Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

  • Marisol OsmanEmail author
  • C. S. Vera


This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to those associated with the signal, especially at the extratropics.


South America Seasonal predictability El Niño Southern Oscillation Precipitation Temperature 



We acknowledge the WCRP/CLIVAR Working Group on Seasonal to Interannual Prediction (WGSIP) for establishing the Climate-system Historical Forecast Project (CHFP, see Kirtman and Pirani 2009) and the Centro de Investigaciones del Mar y la Atmósfera (CIMA) for providing the model output We also thank the data providers for making the model output available through CHFP. This research was supported by UBACyT 20020100100434, CONICET/PIP 112-20120100626CO, PIDDEF 2014/2017 No 15, ANR-15-JCL/-0002-01 “CLIMAX”. M.O. is supported by a Ph.D grant from CONICET, Argentina.


  1. Arora V, Scinocca J, Boer G, Christian J, Denman KL, Flato G, Kharin V, Lee W, Merryfield W (2011) Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett 38:L05805. doi: 10.1029/2010GL046270 CrossRefGoogle Scholar
  2. Barreiro M (2010) Influence of ENSO and the South Atlantic Ocean on climate predictability over Southeastern South America. Clim Dyn 35:1493–1508CrossRefGoogle Scholar
  3. Barreiro M, Chang P, Saravanan R (2002) Variability of the South Atlantic convergence zone simulated by an atmospheric general circulation model. J Clim. doi: 10.1175/1520-0442(2002)015<0745:VOTSAC>2.0.CO;2
  4. Barreiro M, Chang P, Saravanan R (2005) Simulated precipitation response to SST forcing and potential predictability in the region of the South Atlantic convergence zone. Clim Dyn. doi: 10.1007/s00382-004-0487-9 Google Scholar
  5. Becker E, van den Dool H, Zhang Q (2014) Predictability and forecast skill in NMME. J Clim 27:5891–5906. doi: 10.1175/JCLI-D-13-00597.1 CrossRefGoogle Scholar
  6. Chaves RR, Nobre P (2004) Interactions between the South Atlantic Ocean and the atmospheric circulation over South America. Geophys Res Lett 31:L03204. doi: 10.1029/2003GL018647 CrossRefGoogle Scholar
  7. Colman R, Deschamps L, Naughton M, Rikus L, Sulaiman A, Puri K, Roff G, Sun Z, Embury G (2005) BMRC atmospheric model (BAM) version 3.0: comparison with mean climatology. BMRC research report no. 108, Bur Met, Melbourne, AustraliaGoogle Scholar
  8. DelSole T, Kumar A, Jha B (2013) Potential seasonal predictability: comparison between empirical and dynamical model estimates. Geophys Res Lett 40:3200–3206. doi: 10.1002/grl.50581 CrossRefGoogle Scholar
  9. Feng X, DelSole T, Houser P (2011) Bootstrap estimated seasonal potential predictability of global temperature and precipitation. Geophys Res Lett 38:L07702CrossRefGoogle Scholar
  10. Feng X, DelSole T, Houser P (2012) A method for estimating potential seasonal predictability: analysis of covariance. J Clim 25:5292–5308. doi: 10.1175/JCLI-D-11-00342.1 CrossRefGoogle Scholar
  11. Frumkin A, Misra V (2013) Predictability of dry season reforecasts over the tropical and the sub-tropical South American region. Int J Climatol 33:137–1247CrossRefGoogle Scholar
  12. Gueremy JF, Deque M, Brau A, Piedelievre JP (2005) Actual and potential skill of seasonal predictions using the CNRM contribution to DEMETER: coupled versus uncoupled model. Tellus 57A:308–319CrossRefGoogle Scholar
  13. Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus A 57:219–233. doi: 10.1111/j.1600-0870.2005.00103.x Google Scholar
  14. Hagedorn R, Doblas-Reyes FJ, Palmer TN (2006) DEMETER and the application of seasonal forecasts. In: Palmer T, Hagendom R (eds) Predictability of weather and climate. Cambridge University Press, Cambridge, pp 674–692CrossRefGoogle Scholar
  15. Jha B, Kumar A (2009) A comparative analysis of change in the first and second moment of the PDF of seasonal mean 200-mb heights with ENSO SSTs. J Clim 22:1412–1423. doi: 10.1175/2008JCLI2495.1 CrossRefGoogle Scholar
  16. Kalnay et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–470CrossRefGoogle Scholar
  17. Kirtman B, Pirani A (2009) The state of the art of seasonal prediction: outcomes and recommendations from the first world climate research program workshop on seasonal prediction. Bull Am Meteorol Soc 90:455–458CrossRefGoogle Scholar
  18. Kumar A, Hoerling MP (1998) Annual cycle of Pacific-North American seasonal predictability associated with different phases of ENSO. J Clim 11:3295–3308. doi: 10.1175/1520-0442(1998)011<3295:ACOPNA>2.0.CO;2 CrossRefGoogle Scholar
  19. Kumar A, Jha B, Zhang Q, Bounoua L (2007) A new methodology for estimating the unpredictable component of seasonal atmospheric variability. J Clim 20:3888–3901. doi: 10.1175/JCLI4216.1 Google Scholar
  20. Landman WA, Goddard L (2002) Statistical recalibration of GCM forecasts over Southern Africa using model output statistics. J Clim 15:2038–2055CrossRefGoogle Scholar
  21. Li H, Misra V (2014) Global seasonal climate predictability in a two tiered forecast system. Part II: boreal winter and spring seasons. Clim Dyn 42:1449. doi: 10.1007/s00382-013-1813-x CrossRefGoogle Scholar
  22. Marsland S, Haak H, Jungclaus JH, Latif M, Röske F (2003) The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5(2):91–127CrossRefGoogle Scholar
  23. Misra V (2004) An evaluation of the predictability of austral summer season precipitation over South America. J Clim 17:1161–1175CrossRefGoogle Scholar
  24. Misra V, Li H, Wu Z, Di Napoli S (2014) Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons. Clim Dyn 42:1425. doi: 10.1007/s00382-013-1812-y
  25. Molteni F, Stockdale T, Balmaseda M, Balsamo G, Buizza R, FerrantiL, Magnusson L, Mogensen K, Palmer T, Vitart F (2011) The new ECMWF seasonal forecast system (System 4). ECMWF Technical Memorandum 656Google Scholar
  26. National Research Council (2010) Assessment of intraseasonal to interannual climate prediction and predictability. The National Academies Press, WashingtonGoogle Scholar
  27. Nobre P et al (2004) Seasonal-to-decadal predictability and prediction of South American climate. White Paper prepared for the CLIVAR Workshop on Atlantic Predictability Reading, UK, 19–23 April 2004Google Scholar
  28. Osman M, Vera CS, Doblas-Reyes FJ (2016) Predictability of the tropospheric circulation in the Southern Hemisphere from CHFP models. Clim Dyn 46(7):2423–2434. doi: 10.1007/s00382-015-2710-2 CrossRefGoogle Scholar
  29. Peng P, Kumar A, Wang W (2011) An analysis of seasonal predictability in coupled model forecasts. Clim Dyn 36:637–648CrossRefGoogle Scholar
  30. Quan XW, Webster PJ, Moore AM, Chang HR (2004) Seasonality in SST-forced atmospheric short-term climate predictability. J Clim 17:3090–3108CrossRefGoogle Scholar
  31. Rowell DP (1998) Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J Clim 11:109–120CrossRefGoogle Scholar
  32. Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Van den Dool HM, Pan HL, Moorthi S, Behringer D, Stokes D, Peña M, Lord S, White G, Ebisuzaki W, Peng P, Xie P (2006) The NCEP climate forecast system. J Clim 19:3483–3517. doi: 10.1175/JCLI3812.1 CrossRefGoogle Scholar
  33. Scaife AA et al (2014) Skillful long-range prediction of European and North American winters. Geophys Res Lett 41:2514–2519. doi: 10.1002/2014GL059637 CrossRefGoogle Scholar
  34. Schiller A, Godfrey JS, McIntosh PC, Meyers G, Smith NR, Alves O, Wang G, Fiedler R (2002) A new version of the Australian community ocean model for seasonal climate prediction. CSIRO marine research report no. 240Google Scholar
  35. Schubert SD, Suarez MJ, Pegion PJ, Kistler MA, Kumar A (2002) Predictability of zonal means during boreal summer. J Clim 15:420–434CrossRefGoogle Scholar
  36. Scinocca JF, McFarlane NA, Lazare M, Li J (2008) The CCCma third generation AGCM and its extension into the middle atmosphere. Atmos Chem Phys 8:7055–7074CrossRefGoogle Scholar
  37. Smith D, Scaife AA, Kirtman BP (2012) What is the current state of scientific knowledge with regard to seasonal and decadal forecasting? Environ Res Lett 7:015602CrossRefGoogle Scholar
  38. Stefanova L, Misra V, O’Brien JJ et al (2012) Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States. Clim Dyn 38:161. doi: 10.1007/s00382-010-0988-7 CrossRefGoogle Scholar
  39. Stevens et al (2013) The atmospheric component of the MPI earth system model: ECHAM6. J Adv Model Earth Syst. doi: 10.1002/jame.20015 Google Scholar
  40. 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. doi: 10.1007/s00382-010-0947-3 Google Scholar
  41. Taschetto AS, Wainer I (2008) Reproducibility of South American Precipitation due to Subtropical South Atlantic SSTs. J Clim. doi: 10.1175/2007JCLI1865.1 Google Scholar
  42. Van den Dool H (2007) Empirical methods in short-term climate prediction. Oxford University Press, OxfordGoogle Scholar
  43. Vera C, Baez J, Douglas M, Emmanuel CB, Marengo J, Meitin J, Nicolini M, Nogues-Paegle J, Paegle J, Penalba O, Salio P, Saulo C, Silva Dias MA, Silva Dias P, Zipser E (2006a) The South American low-level jet experiment. Bull Am Meteorol Soc 87:63–77. doi: 10.1175/BAMS-87-1-63 CrossRefGoogle Scholar
  44. Vera C, Higgins W, Amador J, Ambrizzi T, Garreaud R, Gochis D, Gutzler D, Lettenmaier D, Marengo J, Mechoso CR, Nogues-Paegle J, Silva Dias PL, Zhang C (2006b) Toward a unified view of the American monsoon systems. J Clim 19:4977–5000. doi: 10.1175/JCLI3896.1 CrossRefGoogle Scholar
  45. Watanabe M et al (2010) Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J Clim 23:6312–6335. doi: 10.1175/2010JCLI3679.1 CrossRefGoogle Scholar
  46. Wu R, Kirtman BP (2006) Changes in spread and predictability associated with ENSO in an ensemble coupled GCM. J Clim 19:4378–4396CrossRefGoogle Scholar
  47. Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M, Tanaka YT, Shindo E, Tsujino H, Deushi M, Mizuta R, Yabu S, Obata A, Nakano H, Koshiro T, Ose T, Kitoh A (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3—model description and basic performance. J Meterol Soc Jpn 90A:23–64. doi: 10.2151/jmsj.2012-A02 CrossRefGoogle Scholar
  48. Zipser EJ, Cecil DJ, Liu C, Nesbitt SW, Yorty DP (2006) Where are the most intense thunderstorms on earth? Bull Am Meteorol Soc 87:1057–1071CrossRefGoogle Scholar
  49. Zwiers FW, Wang XL, Sheng J (2000) Effects of specifying bottom boundary conditions in an ensemble of atmospheric GCM simulations. J Geophys Res Atmos 105:7295–7315CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Ciudad UniversitariaBuenos AiresArgentina
  2. 2.Centro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET-UBA), UMI IFAECI/CNRSBuenos AiresArgentina
  3. 3.Facultad de Ciencias Exactas y Naturales, Departamento de Ciencias de la Atmósfera y los OcéanosUniversidad de Buenos AiresBuenos AiresArgentina

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