Climate Dynamics

, Volume 42, Issue 11–12, pp 3151–3169 | Cite as

Impact of initialization procedures on the predictive skill of a coupled ocean–atmosphere model

Article

Abstract

The sensitivity of the predictive skill of a decadal climate prediction system is investigated with respect to details of the initialization procedure. For this purpose, the coupled ocean–atmosphere UCLA/MITgcm climate model is initialized using the following three different initialization approaches: full state initialization (FSI), anomaly initialization (AI) and FSI employing heat flux and freshwater flux corrections (FC). The ocean initial conditions are provided by the German contribution to Estimating the Circulation and Climate of the Ocean state estimate (GECCO project), from which ensembles of decadal hindcasts are initialized every 5 years from 1961 to 2001. The predictive skill for sea surface temperature (SST), sea surface height (SSH) and the Atlantic meridional overturning circulation (AMOC) is assessed against the GECCO synthesis. In regions with a deep mixed layer the predictive skill for SST anomalies remains significant for up to a decade in the FC experiment. By contrast, FSI shows less persistent skill in the North Atlantic and AI does not show high skill in the extratropical Southern Hemisphere, but appears to be more skillful in the tropics. In the extratropics, the improved skill is related to the ability of the FC initialization method to better represent the mixed layer depth, and the highest skill occurs during wintertime. The correlation skill for the spatially averaged North Atlantic SSH hindcasts remains significant up to a decade only for FC. The North Atlantic MOC initialized hindcasts show high correlation values in the first pentad while correlation remains significant in the following pentad too for FSI and FC. Overall, for the current setup, the FC approach appears to lead to the best results, followed by the FSI and AI procedures.

Keywords

Decadal predictions Full state initialization Anomaly initialization Flux correction 

Supplementary material

382_2013_1969_MOESM1_ESM.pdf (89 kb)
Supplementary material 1 (PDF 89 kb)

References

  1. Alexander M, Deser C (1994) A mechanism for the recurrence of wintertime midlatitude SST anomalies. J Phys Oceanogr 25:122–137CrossRefGoogle Scholar
  2. Bloom S, Takacs L, Da Silva A, Ledvina D (1996) Data assimilation using incremental analysis updates. Mon Weather Rev 124(6):1256–1271CrossRefGoogle Scholar
  3. Branstator G, Teng H (2010) Two limits of initial-value decadal predictability in a CGCM. J Clim 23(23):6292–6311CrossRefGoogle Scholar
  4. Carton J, Santorelli A (2008) Global decadal upper-ocean heat content as viewed in nine analyses. J Clim 21(22):6015–6035CrossRefGoogle Scholar
  5. Cazes-Boezio G, Menemenlis D, Mechoso C (2008) Impact of ECCO ocean-state estimates on the initialization of seasonal climate forecasts. J Clim 21(9):1929–1947CrossRefGoogle Scholar
  6. Ciasto L, Thompson D (2009) Observational evidence of reemergence in the extratropical Southern Hemisphere. J Clim 22(6):1446–1453CrossRefGoogle Scholar
  7. Collins M (2002) Climate predictability on interannual to decadal time scales: the initial value problem. Clim Dyn 19(8):671–692CrossRefGoogle Scholar
  8. Collins M, Botzet M, Carril A, Drange H, Jouzeau A, Latif M, Masina S, Otteraa O, Pohlmann H, Sorteberg A et al (2006) Interannual to decadal climate predictability in the North Atlantic: a multimodel-ensemble study. J Clim 19(7):1195–1203CrossRefGoogle Scholar
  9. Doblas-Reyes F, Balmaseda M, Weisheimer A, Palmer T (2011a) Decadal climate prediction with the European Centre for Medium-Range Weather Forecasts coupled forecast system: impact of ocean observations. J Geophys Res 116(D19):D19111CrossRefGoogle Scholar
  10. Doblas-Reyes F, van Oldenborgh G, García-Serrano J, Pohlmann H, Scaife A, Smith D (2011b) CMIP5 near-term climate prediction. WCRP Coupled Model Intercomparison Project-Phase 5 (CMIP5), p 8Google Scholar
  11. Frankignoul C, Hasselmann K (1977) Stochastic climate models, part II application to sea-surface temperature anomalies and thermocline variability. Tellus 29(4):289–305CrossRefGoogle Scholar
  12. Goddard L, Kumar A, Solomon A, Smith D, Boer G, Gonzalez P, Kharin V, Merryfield W, Deser C, Mason S, et al (2012a) A verification framework for interannual-to-decadal predictions experiments. Clim Dyn 40:245–272Google Scholar
  13. Goddard L, Hurrell J, Kirtman BP, Murphy J, Stockdale T, Vera C (2012b) Two time scales for the price of one (almost). Bull Am Meteorol Soc 93(5):621–629CrossRefGoogle Scholar
  14. Gregory JM, Dixon KW, Stouffer RJ, Weaver AJ, Driesschaert E, Eby M, Fichefet T, Hasumi H, Hu A, Jungclaus JH et al (2005) A model intercomparison of changes in the Atlantic thermohaline circulation in response to increasing atmospheric CO2 concentration. Geophys Res Lett 32(12):L12703CrossRefGoogle Scholar
  15. Hazeleger W, Guemas V, Wouters B, Corti S, Andreu-Burillo I, Doblas-Reyes FJ, Wyser K, Caian M (2013) Multiyear climate predictions using two initialisation strategies CO2 concentration. Geophy Res Lett. doi:10.1002/grl.50355
  16. Keenlyside N, Latif M, Jungclaus J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453(7191):84–88CrossRefGoogle Scholar
  17. Köhl A, Stammer D (2008a) Decadal sea level changes in the 50-year GECCO ocean synthesis. J Clim 21(9):1876–1890CrossRefGoogle Scholar
  18. Köhl A, Stammer D (2008b) Variability of the meridional overturning in the North Atlantic from the 50-year GECCO state estimation. J Phys Oceanogr 38(9):1913–1930CrossRefGoogle Scholar
  19. Kröger J, Müller W, von Storch J (2012) Impact of different ocean reanalyses on decadal climate prediction. Clim Dyn 39:795–810Google Scholar
  20. Lee T, Awaji T, Balmaseda M, Ferry N, Fujii Y, Fukumori I, Giese B, Heimbach P, Köhl A, Masina S et al (2010) Consistency and fidelity of Indonesian-throughflow total volume transport estimated by 14 ocean data assimilation products. Dyn Atmos Ocean 50(2):201–223CrossRefGoogle Scholar
  21. Leeuwenburgh O, Stammer D (2001) The effect of ocean currents on sea surface temperature anomalies. J Phys Oceanogr 31(8):2340–2358CrossRefGoogle Scholar
  22. Magnusson L, Alonso-Balmaseda M, Corti S, Molteni F, Stockdale T (2012) Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors. Clim Dyn 1–17. doi:10.1007/s00,382-012-1599-2
  23. Marshall J, Nurser A, Williams R (1993) Inferring the subduction rate and period over the North Atlantic. J Phys Oceanogr 23:1315CrossRefGoogle Scholar
  24. Marshall J, Kushnir Y, Battisti D, Chang P, Czaja A, Dickson R, Hurrell J, McCARTNEY M, Saravanan R, Visbeck M (2001) North Atlantic climate variability: phenomena, impacts and mechanisms. Int J Climatol 21(15):1863–1898CrossRefGoogle Scholar
  25. Matei D, Pohlmann H, Jungclaus J, Müller W, Haak H, Marotzke J (2012) Two tales of initializing decadal climate prediction experiments with the ECHAM5/MPI-OM model. J Clim 25:8502–8523CrossRefGoogle Scholar
  26. Meehl G, Goddard L, Murphy J, Stouffer R, Boer G, Danabasoglu G, Dixon K, Giorgetta M, Greene A, Hawkins E et al (2009) Decadal prediction. Bull Am Meteorol Soc 90(10):1467–1485CrossRefGoogle Scholar
  27. Meehl G, Goddard L, Boer G, Burgman R, Branstator G, Cassou C, Corti S, Danabasoglu G, Doblas-Reyes F, Hawkins E, et al (2013) Decadal climate prediction: an update from the Trenches. Bull Am Meteorol Soc. doi:10.1175/BAMS-D-12-00241.1
  28. Menary M, Roberts C, Palmer M, Halloran P, Jackson L, Wood R, Müller W, Matei D Lee S (2013) Mechanisms of aerosol-forced AMOC variability in a state of the art climate model. J Geophys Res Oceans 118:2087–2096Google Scholar
  29. Mochizuki T, Ishii M, Kimoto M, Chikamoto Y, Watanabe M, Nozawa T, Sakamoto T, Shiogama H, Awaji T, Sugiura N et al (2010) Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proc Nat Acad Sci 107(5):1833–1837CrossRefGoogle Scholar
  30. Munoz E, Kirtman B, Weijer W (2011) Varied representation of the Atlantic Meridional Overturning across multidecadal ocean reanalyses. Deep Sea Res II 58:1848–1857CrossRefGoogle Scholar
  31. 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(14):3926–3938CrossRefGoogle Scholar
  32. Qiu B, Huang R (1995) Ventilation of the North Atlantic and North Pacific: subduction versus obduction. J Phys Oceanogr 25:2374–2390CrossRefGoogle Scholar
  33. Rayner N, Parker D, Horton E, Folland C, Alexander L, Rowell D, Kent E, 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(D14):4407CrossRefGoogle Scholar
  34. Robertson A, Overpeck J, Rind D, Mosley-Thompson E, Zielinski G, Lean J, Koch D, Penner J, Tegen I, Healy R (2001) Hypothesized climate forcing time series for the last 500 years. J Geophys Res 106(D14):14783–14803CrossRefGoogle Scholar
  35. Sausen R, Barthel K, Hasselmann K (1988) Coupled ocean–atmosphere models with flux correction. Clim Dyn 2(3):145–163CrossRefGoogle Scholar
  36. Shackley S, Risbey J, Stone P, Wynne B (1999) Adjusting to policy expectations in climate change modeling. Clim Chang 43(2):413–454CrossRefGoogle Scholar
  37. Smith D, Cusack S, Colman A, Folland C, Harris G, Murphy J (2007) Improved surface temperature prediction for the coming decade from a global climate model. Science 317(5839):796–799CrossRefGoogle Scholar
  38. Smith D, Eade R, Pohlmann H (2013) A comparison of full-field and anomaly initialization for seasonal and decadal climate prediction. Clim Dyn 1–14. doi:10.1007/s00382-013-1683-2
  39. Stammer D, Kohl A, Awaji T, Balmaseda M, ECMWF S, Reading R, Behringer D, Center C, Carton J, Ferry N, et al (2009) Ocean information provided through ensemble ocean syntheses. Proceedings of OceanObs’ 09: Sustained Ocean Observations for SocietyGoogle Scholar
  40. Stammer D, Agarwal N, Herrmann P, Köhl A, Mechoso C (2011) Response of a coupled ocean–atmosphere model to Greenland ice melting. Surv Geophysics 32:621–642Google Scholar
  41. Stockdale T (1997) Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon Weather Rev 125(5):809–818CrossRefGoogle Scholar
  42. Tans P (2011) Mauna Loa CO2 annual mean data. ESRL report http://www.esrl.noaa.gov/gmd/ccgg/trends
  43. Taylor K, Stouffer R, Meehl G (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485CrossRefGoogle Scholar
  44. Troccoli A, Palmer T (2007) Ensemble decadal predictions from analysed initial conditions. Philos Transa R Soc A Math Phys Eng Sci 365(1857):2179–2191CrossRefGoogle Scholar
  45. van Oldenborgh G, Doblas-Reyes F, Wouters B, Hazeleger W (2012) Decadal prediction skill in a multi-model ensemble. Clim Dyn 38:1263–1280Google Scholar
  46. Vinogradova N, Ponte R, Stammer D (2007) Relation between sea level and bottom pressure and the vertical dependence of oceanic variability. Geophys Res Lett 34(3):L03608CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Max Planck Institute for MeteorologyHamburgGermany
  2. 2.Institute of Oceanography, Center for Earth System Research and SustainabilityUniversity of HamburgHamburgGermany

Personalised recommendations