Climate Dynamics

, Volume 41, Issue 11–12, pp 3325–3338 | Cite as

A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction

  • Doug M. Smith
  • Rosie Eade
  • Holger Pohlmann


There are two main approaches for dealing with model biases in forecasts made with initialized climate models. In full-field initialization, model biases are removed during the assimilation process by constraining the model to be close to observations. Forecasts drift back towards the model’s preferred state, thereby re-establishing biases which are then removed with an a posterior lead-time dependent correction diagnosed from a set of historical tests (hindcasts). In anomaly initialization, the model is constrained by observed anomalies and deviates from its preferred climatology only by the observed variability. In theory, the forecasts do not drift, and biases may be removed based on the difference between observations and independent model simulations of a given period. Both approaches are currently in use, but their relative merits are unclear. Here we compare the skill of each approach in comprehensive decadal hindcasts starting each year from 1960 to 2009, made using the Met Office decadal prediction system. Both approaches are more skilful than climatology in most regions for temperature and some regions for precipitation. On seasonal timescales, full-field initialized hindcasts of regional temperature and precipitation are significantly more skilful on average than anomaly initialized hindcasts. Teleconnections associated with the El Niño Southern Oscillation are stronger with the full-field approach, providing a physical basis for the improved precipitation skill. Differences in skill on multi-year timescales are generally not significant. However, anomaly initialization provides a better estimate of forecast skill from a limited hindcast set.


Seasonal to decadal climate prediction Full field initialization Anomaly initialization 



This work was supported by the joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), and the EU FP7 THOR and COMBINE projects. The authors thank Leon Hermanson and Nick Dunstone for comments and discussions, and two anonymous referees for constructive reviews.


  1. Brohan P, Kennedy J, Harris I, Tett SFB, Jones PD (2006) Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850. J Geophys Res 111:D12106CrossRefGoogle Scholar
  2. Collins M, Sinha B (2003) Predictability of decadal variations in the thermohaline circulation and climate. Geophys Res Lett 30:1306. doi: 10.1029/2002GL016504 CrossRefGoogle Scholar
  3. Delworth TL, Zhang R, Mann ME (2007) Decadal to centennial variability of the Atlantic from observations and models. Ocean Circulation: Mechanisms and Impacts, Geophysical Monograph Series 173, Washington, DC, American Geophysical Union, pp 131–148Google Scholar
  4. Dunstone NJ, Smith DM (2010) Impact of atmosphere and sub-surface ocean data on decadal climate prediction. Geophys Res Lett 37:L02709. doi: 10.1029/2009GL041609 CrossRefGoogle Scholar
  5. Dunstone NJ, Smith DM, Eade R (2011) Multi-year predictability of the tropical Atlantic atmosphere driven by the high latitude north Atlantic ocean. Geophys Res Lett 38:L14701. doi: 10.1029/2011GL047949 CrossRefGoogle Scholar
  6. Eade R, Hamilton E, Smith DM, Graham RJ, Scaife AA (2012) Forecasting the number of extreme daily events out to a decade ahead. J Geophys Res 117:D21110. doi: 10.1029/2012JD018015 CrossRefGoogle Scholar
  7. Fyfe JC, Merryfield WJ, Kharin V, Boer GJ, Lee W-S, von Salzen K (2011) Skillful predictions of decadal trends in global mean surface temperature. Geophys Res Lett 38:L22801. doi: 10.1029/2011GL049508 CrossRefGoogle Scholar
  8. Goddard L, Kumar A, Solomon A, Smith D, Boer G, Gonzalez P, Kharin V, Merryfield W, Deser C, Mason S, Kirtman B, Msadek R, Sutton R, Hawkins E, Fricker T, Hegerl G, Ferro C, Stephenson D, Meehl GA, Stockdale T, Burgman R, Greene A, Kushnir Y, Newman M, Carton J, Fukumori I, Delworth T (2012) A verification framework for interannual-to-decadal predictions experiments. Clim Dyn 40:245–272. doi: 10.1007/s00382-012-1481-2
  9. Gordon C et al (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
  10. Griffies SM, Bryan K (1997) Predictability of North Atlantic multidecadal climate variability. Science 275:181. doi: 10.1126/science.275.5297.181 CrossRefGoogle Scholar
  11. ICPO (International CLIVAR Project Office) (2011) Data and bias correction for decadal climate predictions. International CLIVAR Project Office, CLIVAR Publication Series No. 150, p 6. Available from
  12. Jones PD, New M, Parker DE, Martin S, Rigor IG (1999) Surface air temperature and its changes over the past 150 years. Rev Geophys 37(2):173–199. doi: 10.1029/1999RG900002 CrossRefGoogle Scholar
  13. Keenlyside N, Latif M, Jungclaus J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:84–88CrossRefGoogle Scholar
  14. Kharin VV, Boer GJ, Merryfield WJ, Scinocca JF, Lee W-S (2012) Statistical adjustment of decadal predictions in a changing climate. Geophys Res Lett 39:L19705. doi: 10.1029/2012GL052647 Google Scholar
  15. Kim H-M, Webster PJ, Curry JA (2012) Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophys Res Lett 39:L10701. doi: 10.1029/2012GL051644 Google Scholar
  16. Knight JR, Allan RJ, Folland CK, Vellinga M, Mann ME (2005) A signature of persistent natural thermohaline circulation cycles in observed climate. Geophys Res Lett 32:L20708. doi: 10.1029/2005GL024233 CrossRefGoogle Scholar
  17. Knight JR, Folland CK, Scaife AA (2006) Climatic impacts of the Atlantic multidecadal oscillation. Geophys Res Lett 33:L17706. doi: 10.1029/2006GL026242 CrossRefGoogle Scholar
  18. Latif M, Collins M, Pohlmann H, Keenlyside N (2006) A review of predictability studies of Atlantic sector climate on decadal time scales. J Clim 19:5971–5987CrossRefGoogle Scholar
  19. Magnusson L, Balmaseda M, Molteni F (2012a) On the dependence of ENSO simulation on the coupled model mean state. Clim Dyn. doi: 10.1007/s00382-012-1574-y
  20. Magnusson L, Balmaseda M, Corti S, Molteni F, Stockdale T (2012b) Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors. Clim Dyn. doi: 10.1007/s00382-012-1599-2
  21. Meehl GA, Stocker TF, Collins W, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao ZC (2007) Global climate projections. In: Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  22. Meehl GA, Goddard L, Murphy J, Stouffer RJ, Boer G, Danabasoglu G, Dixon K, Giorgetta MA, Greene A, Hawkins E, Hegerl G, Karoly D, Keenlyside N, Kimoto M, Kirtman B, Navarra A, Pulwarty R, Smith D, Stammer D, Stockdale T (2009) Decadal prediction: can it be skillful? Bull Am Meteorol Soc 90:1467–1485CrossRefGoogle Scholar
  23. Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque JF, Matsumoto K, Montzka SA, Raper SCB, Riahi K, Thomson A, Velders GJM, van Vuuren DPP (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 109(1–2):213–241CrossRefGoogle Scholar
  24. Mochizuki T, Ishiia M, Kimoto M, Chikamoto Y, Watanabe M, Nozawa T, Sakamoto TT, Shiogama H, Awaji T, Sugiura N, Toyoda T, Yasunaka S, Tatebe H, Mori M (2009) Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proc Natl Acad Sci 107:1833–1837CrossRefGoogle Scholar
  25. Pierce DW, Barnett TP, Tokmakian R, Semtner A, Maltrud M, Lysne J, Craig A (2004) The ACPI project, element 1: initializing a coupled climate model from observed initial conditions. Clim Change 62:13–28CrossRefGoogle Scholar
  26. Pohlmann H, Jungclaus J, Köhl A, Stammer D, Marotzke J (2009) Initializing decadal climate predictions with the GECCO oceanic synthesis: effects on the North Atlantic. J Clim 22:3926–3938CrossRefGoogle Scholar
  27. Robson JI (2010) Understanding the performance of a decadal prediction system, Ph.D thesis, University of ReadingGoogle Scholar
  28. Schneider U, Becker A, Meyer-Christoffer A, Ziese M, Rudolf B (2011) Global precipitation analysis products of the GPCC. Global Precipitation Climatology Centre (GPCC), DWD, Internet Publikation, pp 1–13Google Scholar
  29. Smith DM, Murphy JM (2007) An objective ocean temperature and salinity analysis using covariances from a global climate model. J Geophys Res 112:C02022. doi: 10.1029/2005JC003172 CrossRefGoogle Scholar
  30. Smith DM, Cusack S, Colman AW, Folland CK, Harris GR, Murphy JM (2007) Improved surface temperature prediction for the coming decade from a global climate model. Science 317:796–799. doi: 10.1126/science.1139540 CrossRefGoogle Scholar
  31. Smith DM, Eade R, Dunstone NJ, Fereday D, Murphy JM, Pohlmann H, Scaife AA (2010) Skilful multi-year predictions of Atlantic hurricane frequency. Nat Geosci 3:846–849. doi: 10.1038/ngeo1004 Google Scholar
  32. Smith DM, Scaife AA, Kirtman B (2012a) What is the current state of scientific knowledge with regard to seasonal and decadal forecasting? Environ Res Lett 7:015602. doi: 10.1088/1748-9326/7/1/015602 CrossRefGoogle Scholar
  33. Smith DM, Scaife AA, Boer GJ, Caian M, Doblas-Reyes FJ, Guemas V, Hawkins E, Hazeleger W, Hermanson L, Ho CK, Ishii M, Kharin V, Kimoto M, Kirtman B, Lean J, Matei D, Merryfield WJ, Muller WA, Pohlmann H, Rosati A, Wouters B, Wyser K (2012b) Real-time multi-model decadal climate predictions. Clim Dyn. doi: 10.1007/s00382-012-1600-0
  34. Stockdale TN (1997) Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon Weather Rev 125:809–818CrossRefGoogle Scholar
  35. Sutton RT, Hodson DLR (2005) Atlantic Ocean forcing of North American and European summer climate. Science 309:115–118CrossRefGoogle Scholar
  36. Talley LD, Reid JL, Robbins PE (2003) Data-based meridional overturning streamfunctions for the Global Ocean. J Clim 16, 3213–3226. doi: 10.1175/1520-0442(2003)016<3213:DMOSFT>2.0.CO;2 CrossRefGoogle Scholar
  37. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 92:485–498. doi: 10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  38. Uppala SM et al (2005) The ERA-40 reanalysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  39. van Oldenborgh GJ, Doblas-Reyes FJ, Wouters B, Hazeleger W (2012) Skill in the trend and internal variability in a multi-model decadal prediction ensemble. Clim Dyn 38(7):1263–1280. doi: 10.1007/s00382-012-1313-4 CrossRefGoogle Scholar
  40. Vecchi GA, Zhao M, Wang H, Villarini G, Rosati A, Kumar A, Held IM, Gudgel R (2011) Statistical-dynamical predictions of seasonal North Atlantic hurricane activity. Mon Weather Rev 139:1070–1082. doi: 10.1175/2010MWR3499.1 Google Scholar
  41. Wang B, Lee J-Y, Kang I-S, Shukla J, Park C-K, Kumar A, Schemm J, Cocke S, Kug J-S, Luo J–J, Zhou T, Wang B, Fu X, Yun W-T, Alves O, Jin EK, Kinter J, Kirtman B, Krishnamurti T, Lau NC, Lau W, Liu P, Pegion P, Rosati T, Schubert S, Stern W, Suarez M, Yamagata T (2009) Advance and prospectus of seasonal prediction, 2008: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Clim Dyn 33:93–117. doi: 10.1007/s00382-008-0460-0 CrossRefGoogle Scholar
  42. Wilks DS (2011) Statistical methods in the atmosphere sciences, 3rd edn. International Geophysics Series, vol. 100, Academic Press, ElsevierGoogle Scholar
  43. Yeager S, Karspeck A, Danabasoglu G, Tribbia J, Teng H (2012) A decadal prediction case study: late 20th century north atlantic ocean heat content. J Clim 25:5173–5189. doi: 10.1175/JCLI-D-11-00595.1 Google Scholar
  44. Zhang R, Delworth TL (2006) Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys Res Lett 33:L17712. doi: 10.1029/2006GL02626 CrossRefGoogle Scholar

Copyright information

© Crown Copyright 2013

Authors and Affiliations

  1. 1.Met Office Hadley CentreExeterUK
  2. 2.Max-Planck-Institut für MeteorologieHamburgGermany

Personalised recommendations