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

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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.

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References

  • Alexander M, Deser C (1994) A mechanism for the recurrence of wintertime midlatitude SST anomalies. J Phys Oceanogr 25:122–137

    Article  Google Scholar 

  • Bloom S, Takacs L, Da Silva A, Ledvina D (1996) Data assimilation using incremental analysis updates. Mon Weather Rev 124(6):1256–1271

    Article  Google Scholar 

  • Branstator G, Teng H (2010) Two limits of initial-value decadal predictability in a CGCM. J Clim 23(23):6292–6311

    Article  Google Scholar 

  • Carton J, Santorelli A (2008) Global decadal upper-ocean heat content as viewed in nine analyses. J Clim 21(22):6015–6035

    Article  Google Scholar 

  • 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):1929–1947

    Article  Google Scholar 

  • Ciasto L, Thompson D (2009) Observational evidence of reemergence in the extratropical Southern Hemisphere. J Clim 22(6):1446–1453

    Article  Google Scholar 

  • Collins M (2002) Climate predictability on interannual to decadal time scales: the initial value problem. Clim Dyn 19(8):671–692

    Article  Google Scholar 

  • 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):1195–1203

    Article  Google Scholar 

  • 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):D19111

    Article  Google Scholar 

  • 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 8

  • Frankignoul C, Hasselmann K (1977) Stochastic climate models, part II application to sea-surface temperature anomalies and thermocline variability. Tellus 29(4):289–305

    Article  Google Scholar 

  • 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 40:245–272

  • 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):621–629

    Article  Google Scholar 

  • 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):L12703

    Article  Google Scholar 

  • 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

  • Keenlyside N, Latif M, Jungclaus J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453(7191):84–88

    Article  Google Scholar 

  • Köhl A, Stammer D (2008a) Decadal sea level changes in the 50-year GECCO ocean synthesis. J Clim 21(9):1876–1890

    Article  Google Scholar 

  • 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):1913–1930

    Article  Google Scholar 

  • Kröger J, Müller W, von Storch J (2012) Impact of different ocean reanalyses on decadal climate prediction. Clim Dyn 39:795–810

    Google Scholar 

  • 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):201–223

    Article  Google Scholar 

  • Leeuwenburgh O, Stammer D (2001) The effect of ocean currents on sea surface temperature anomalies. J Phys Oceanogr 31(8):2340–2358

    Article  Google Scholar 

  • 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

  • Marshall J, Nurser A, Williams R (1993) Inferring the subduction rate and period over the North Atlantic. J Phys Oceanogr 23:1315

    Article  Google Scholar 

  • 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):1863–1898

    Article  Google Scholar 

  • 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 25:8502–8523

    Article  Google Scholar 

  • 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):1467–1485

    Article  Google Scholar 

  • 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

  • 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 118:2087–2096

    Google Scholar 

  • 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):1833–1837

    Article  Google Scholar 

  • Munoz E, Kirtman B, Weijer W (2011) Varied representation of the Atlantic Meridional Overturning across multidecadal ocean reanalyses. Deep Sea Res II 58:1848–1857

    Article  Google Scholar 

  • 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):3926–3938

    Article  Google Scholar 

  • Qiu B, Huang R (1995) Ventilation of the North Atlantic and North Pacific: subduction versus obduction. J Phys Oceanogr 25:2374–2390

    Article  Google Scholar 

  • 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):4407

    Article  Google Scholar 

  • 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):14783–14803

    Article  Google Scholar 

  • Sausen R, Barthel K, Hasselmann K (1988) Coupled ocean–atmosphere models with flux correction. Clim Dyn 2(3):145–163

    Article  Google Scholar 

  • Shackley S, Risbey J, Stone P, Wynne B (1999) Adjusting to policy expectations in climate change modeling. Clim Chang 43(2):413–454

    Article  Google Scholar 

  • 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):796–799

    Article  Google Scholar 

  • 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

  • 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 Society

  • Stammer D, Agarwal N, Herrmann P, Köhl A, Mechoso C (2011) Response of a coupled ocean–atmosphere model to Greenland ice melting. Surv Geophysics 32:621–642

    Google Scholar 

  • Stockdale T (1997) Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon Weather Rev 125(5):809–818

    Article  Google Scholar 

  • Tans P (2011) Mauna Loa CO2 annual mean data. ESRL report http://www.esrl.noaa.gov/gmd/ccgg/trends

  • Taylor K, Stouffer R, Meehl G (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485

    Article  Google Scholar 

  • Troccoli A, Palmer T (2007) Ensemble decadal predictions from analysed initial conditions. Philos Transa R Soc A Math Phys Eng Sci 365(1857):2179–2191

    Article  Google Scholar 

  • van Oldenborgh G, Doblas-Reyes F, Wouters B, Hazeleger W (2012) Decadal prediction skill in a multi-model ensemble. Clim Dyn 38:1263–1280

    Google Scholar 

  • 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):L03608

    Article  Google Scholar 

Download references

Acknowledgments

We thank Dr. Neeraj Agarwal for his help with setting up the model runs and for discussion of the results. We also thank Drs. Holger Pohlmann and Daniela Matei, Prof. Dr. Johanna Baehr and Prof. Dr. Jochem Marotzke for the discussion of our results and two anonymous reviewers whose comments helped us to improve this manuscript. This work was supported in part by the Max Planck Fellow Program through the project “Coupled Climate System Data Assimilation”. The authors acknowledge funding from the European Community’s 7th framework program (FP7/2007–2013) under Grant Agreement No. GA212643 (THOR: “Thermohaline Overturning—at Risk”, 2008–2012). All model simulations were performed at the German Climate Computing Centre (DKRZ). The altimeter products were produced by Ssalto/Duacs and distributed by Aviso, with support from Cnes (http://www.aviso.oceanobs.com/duacs/).

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Authors and Affiliations

  1. Max Planck Institute for Meteorology, Bundesstr. 53, 20146, Hamburg, Germany

    Iuliia Polkova & Detlef Stammer

  2. Institute of Oceanography, Center for Earth System Research and Sustainability, University of Hamburg, Bundesstr. 53, 20146, Hamburg, Germany

    Armin Köhl & Detlef Stammer

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  1. Iuliia Polkova
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Correspondence to Iuliia Polkova.

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Polkova, I., Köhl, A. & Stammer, D. Impact of initialization procedures on the predictive skill of a coupled ocean–atmosphere model. Clim Dyn 42, 3151–3169 (2014). https://doi.org/10.1007/s00382-013-1969-4

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