Abstract
Decadal prediction exploits sources of predictability from both the internal variability through the initialisation of the climate model from observational estimates, and the external radiative forcings. When a model is initialised with the observed state at the initial time step (Full Field Initialisation—FFI), the forecast run drifts towards the biased model climate. Distinguishing between the climate signal to be predicted and the model drift is a challenging task, because the application of a-posteriori bias correction has the risk of removing part of the variability signal. The anomaly initialisation (AI) technique aims at addressing the drift issue by answering the following question: if the model is allowed to start close to its own attractor (i.e. its biased world), but the phase of the simulated variability is constrained toward the contemporaneous observed one at the initialisation time, does the prediction skill improve? The relative merits of the FFI and AI techniques applied respectively to the ocean component and the ocean and sea ice components simultaneously in the EC-Earth global coupled model are assessed. For both strategies the initialised hindcasts show better skill than historical simulations for the ocean heat content and AMOC along the first two forecast years, for sea ice and PDO along the first forecast year, while for AMO the improvements are statistically significant for the first two forecast years. The AI in the ocean and sea ice components significantly improves the skill of the Arctic sea surface temperature over the FFI.
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Notes
We compute the climatology as the average of the predictions over the start dates and it evolves with the forecast time.
Coupled Model Intercomparison Project Phase 5 sponsored by WCRP: http://cmip-pcmdi.llnl.gov/cmip5/index.html.
With “reference” state we mean what is known as the truth state, which includes either an observational dataset, or a reanalysis.
PREDICATE: http://www.ugamp.nerc.ac.uk/predicate/.
References
Anderson DLT, Doblas-Reyes FJ, Balmaseda M, Weisheimer A (2009) Decadal variability: processes, predictability and prediction. ECMWF Technical Memorandum 591, http://www.ecmwf.int/publications/library/do/references/show?id=89132
Balmaseda MA, Mogensen KS, Weaver AT (2012) Evaluation of the ecmwf ocean reanalysis oras4. Quart J Roy Meteor Soc. doi:10.1002/qj.2063
Bellucci A, Haarsma R, Gualdi S, Athanasiadis PJ, Caian M, Cassou C, Fernandez E, Germe A, Jungclaus J, Kroger J, Matei D, Muller W, Pohlmann H, Salas-Mélia D, Sanchez E, Smith D, Terray L, Wyser K, Yang S (2014) An assessment of a multi-model ensemble of decadal climate predictions. Clim Dyn 44:2787–2806. doi:10.1007/s00382-014-2164-y
Blanchard-Wrigglesworth E, Bitz CM, Holland MM (2011) Influence of initial conditions and climate forcing on predicting arctic sea ice. Geophys Res Lett 38(L18503): doi:10.1029/2011GL048807
Booth J, Wang S, Polvani L (2013) Midlatitude storms in a moister world: lessons from idealized baroclinic life cycle experiments. Clim Dyn 41:787–802. doi:10.1007/s00382-012-1472-3
Brodeau L, Barnier B, Treguier A, Penduff T, Gulev S (2009) An era40-based atmospheric forcing for global ocean circulation models. Ocean Model 31:88–104. doi:10.1016/j.ocemod.2009.10.005
Chevallier M, Salas-Mélia D (2012) The role of sea ice thickness distribution in the arctic sea ice potential predictability: a diagnostic approach with a coupled gcm. J Clim 25:3025–3038
Collins M, Booth BBB, Harris GR, Murphy JM, Sexton DMH, Webb M (2006) Towards quantifying uncertainty in transient climate change. Clim Dyn 365:1957–1970
Dee DP, Uppala SM, Simmons AJ, Berrisford P, PP, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kȧllbergc P, Kähler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thpaut JN, Vitart F, (2011) The era-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteor Soc 137:553–597
Deser C, Alexander MA, Xie SP, Phillips AS (2010) Sea surface temperature variability: patterns and mechanisms. Ann Rev Mar Sci 2:115–143. doi:10.1146/annurev-marine-120408-151453
Doblas-Reyes F, Andreu-Burillo I, Chikamoto Y, García-Serrano J, Guèmas V, Kimoto M, Mochizuki T, Rodrigues L, van Oldenborgh G (2013a) Initialized near-term regional climate change prediction. Nat Commun 4(1715): doi:10.1038/ncomms2704
Doblas-Reyes FJ, Weisheimer A, Palmer TN, Murphy JM, Smith D (2010) Forecast quality assessment of the ensembles seasonal-to-decadal stream 2 hindcasts. ECMWF Technical Memorandum 621, http://www.ecmwf.int/publications/library/do/references/show?id=89771
Doblas-Reyes FJ, García-Serrano J, Lienert F, Biescas AP, Rodrigues LRL (2013b) Seasonal climate predictability and forecasting: status and prospects. WIREs Clim Change 4:245–268. doi:10.1002/wcc.217
Du H, Doblas-Reyes FJ, García-Serrano J, Guèmas V, Soufflet Y, Wouters B (2012) Sensitivity of decadal predictions to the initial atmospheric and ocean perturbations. Clim Dyn. doi:10.1007/s00382-011-1285-9
Ethe C, Aumont O, Foujols MA, Levy M (2006) Nemo reference manual, tracer component: Nemo-top. preliminary version. Note du Pole de modlisation, Institut Pierre-Simon Laplace (IPSL) France 28:1288–1619
Fan Y, van den Dool H (2008) A global monthly land surface air temperature analysis for 1948-present. Geophys Res Lett 113(D01103): doi:10.1029/2007JD008470
Fichefet T, Maqueda MAM (1997) Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J Geophys Res 102:12609–12646
García-Serrano J, Doblas-Reyes F (2012) On the assessment of near-surface global temperature and north atlantic multi-decadal variability in the ensembles decadal hindcast. Clim Dyn. doi:10.1007/s00382-012-1413-1
Gastineau G, Frankignoul C (2014) Influence of the north atlantic sst variability on the atmospheric circulation during the twentieth century. J Clim 28:1396–1416. doi:10.1175/JCLI-D-14-00424.1
Goddard L, Kumar A, Solomon A, Smith D, Boer G, Gonzalez P, Kharin V, Merryfield W, Deser C, Mason SJ, Kirtman BP, Msadek R, Sutton R, Hawkins E, Fricker T, Hegerl G, Ferro CAT, Stephenson DB, Meehl GA, Stockdale T, Burgman R, Greene AM, Kushnir Y, Newman M, Carton J, Fukumori I, Delworth T (2012) A verification framework for interannual-to-decadal predictions experiments. Clim Dyn. doi:10.1007/s00382-012-1481-2
Goosse H, Fichefet T (1999) Importance of ice-ocean interactions for the global ocean circulation: a model study. J Geophys Res 104:13337–23355
Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mithell JFB, Wood RA (2000) The simulation of sst, sea ice extents and ocean heat and ocean heat transports in a version of the hadley centre coupled model without flux adjustments. Clim Dyn 16:147–168
Guemas V, Doblas-Reyes F, Mogensen K, Keely S, Tang Y (2014) Ensemble of sea ice initial conditions for interannual climate predictions. Clim Dyn 43:2813–2829. doi:10.1007/s00382-014-2095-7
Hansen J, Ruedy R, Sato M, Lo K (2010) Global surface temperature change. Rev Geophys 48(RG4004): doi:10.1029/2010RG000345
Hazeleger W, Severijns C, Semmler T, Stefânescu S, Yang S, Wang X, Wyser K, Dutra E, Baldasano JM, Bintanja R, Bougeault P, Caballero R, Ekman AML, Christensen JH, van den Hurk B, Jimenez P, Jones C, Kȧllberg P, Koenigk T, McGrath R, Miranda P, van Noije T, Palmer T, Parodi JA, Schmith T, Selten F, Storelvmo T, Sterl A, Tapamo H, Vancoppenolle M, Viterbo P, Willân U (2010) Ec-earth: a seamless earth-system prediction approach in action. Bull Am Meteorol Soc 91(10):1357–1363. doi:10.1175/2010BAMS2877.1
Hazeleger W, Guemas V, Wouters B, Corti S, Andreu-Burillo I, Doblas-Reyes FJ, Wyser K, Caian M (2013) Multiyear climate predictions using two initialization strategies. Geophys Res Lett 40(9):1794–1798. doi:10.1002/grl.50355
Kerr RA (2000) A north atlantic climate pacemaker for the centuries. Science 288(5473):1984–1985. doi:10.1126/science.288.5473.1984
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(20): doi:10.1029/2005GL024233
Kushnir Y (1994) Interdecadal variations in the north atlantic sea surface temperature and associated atmospheric conditions. J Clim 7:141–157
Lienert F, Doblas-Reyes F (2013) Decadal prediction of interannual tropical and north pacific sea surface temperature. J Geophys Res 118:5913–5922. doi:10.1002/jgrd.50469
Madec G (2008) Nemo ocean engine. Note du Pole de modlisation, Institut Pierre-Simon Laplace (IPSL) France 27:12881619
Magnusson L, Leutbecher M, Kallen E (2008) Comparison between singular vectors and breeding vectors as initial perturbations for the ecmwf ensemble prediction system. Mon Wea Rev 134:4092–4104
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. ECMWF Technical Memorandum 676. http://www.ecmwf.int/publications/library/do/references/show?id=90506
Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteorol Soc 78:1069–1079
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. doi:10.1175/JCLI-D-11-00633.1
Mignot J, Frankignoul C (2004) Interannual to interdecadal variability of sea surface salinity in the atlantic and its link to the atmosphere in a coupled model. J Geophys Res 109(C04005): doi:10.1029/2003JC002005
Mogensen KS, Balmaseda MA, Weaver A (2012) The nemovar ocean data assimilation as implemented in the ecmwf ocean analysis for system4. ECMWF Technical Memorandum 657 (in preparation)
Msadek R, Frankignoul C, Li LZX (2011) Mechanisms of the atmospheric response to north atlantic multidecadal variability: a model study. Clim Dyn 36:1255–1276. doi:10.1007/s00382-010-0958-0
Msadek R, Johns W, Yeager S, Danabasoglu G, Delworth T, Rosati A (2013) The atlantic meridional heat transport at 26.5 n and its relationship with the moc in the rapid array and the gfdl and ncar coupled models. J Clim 26:4335–4356
Newman M (2007) Interannual to decadal predictability of tropical and north pacific sea surface temperatures. J Clim 20:2333–2356. doi:10.1175/JCLI4165.1
Old C, Haines K (2006) North atlantic subtropical mode waters and ocean memory in hadcm3. J Clim 19:1126–1148
Otterȧ OH, Bentsen M, Drange H, Suo L (2010) External forcing as a metronome for atlantic multidecadal variability. Nat Geosci 3:688–694
Persechino A, Mignot J, Swingedouw D, Labetoulle S, Guilyardi E (2012) Decadal predictability of the atlantic meridional overturning circulation and climate in the ipsl-cm5a-lr model. Clim Dyn 40(9):2359–2380. doi:10.1007/s00382-012-1466-1
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 22:3926–3938
Robson J, Sutton R, Lohmann K, Smith D, Palmer MD (2012) Causes of the rapid warming of the north atlantic ocean in the mid-1990s. J Clim 25: doi:10.1175/JCLI-D-11-00443.1
Rudolf B, Becker A, Schneider U, Meyer-Christoffer A, Ziese M (2010) The new gpcc full data reanalysis version 5 providing high-quality gridded monthly precipitation data for the global land-surface is public available since december 2010. GPCC Status Report. http://www.dwd.de/bvbw/generator/DWDWWW/Content/Oeffentlichkeit/KU/KU4/KU42/en/Reports__Publications/GPCC__status__report__2010,templateId=raw,property=publicationFile.pdf/GPCC_status_report_2010.pdf
Sanchez-Gomez E, Cassou C, Ruprich-Robert Y, Fernandez E, Terray L (2015) Drift dynamics in a coupled model initialized for decadal forecasts. Clim Dyn 46(5):1819–1840. doi:10.1007/s00382-015-2678-y
Schlesinger ME, Ramankutty N (1994) An oscillation in the gloabl climate system of period 65–70 years. Nature 367:723–726. doi:10.1038/367723a0
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
Smith DM, Eade R, Pohlmann H (2013) A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Clim Dyn. doi:10.1007/s00382-013-1683-2
Smith T, Reynolds R, Peterson T, Lawrimore J (2008) Improvements to noaa’s historical merged land-ocean surface temperature analysis (1880–2006). J Clim 21:2283–2296
Sutton RT, Hodson DLR (2003) Influence of the ocean on north atlantic climate variability 1871–1999. J Clim 16:3296–3313
Swingedouw D, Ortega P, Mignot J, Guilyardi E, Masson-Delmotte V, Butler PG, Khodri M, Séférian R (2015) Bidecadal north atlantic ocean circulation variability controlled by timing of volcanic eruptions. Nature Communications 6(6545): doi:10.1038/ncomms7545
Trenberth KE (1984) Some effects of finite sample size and persistence on meteorological statistics. part i: Autocorrelations. Mon Wea Rev 112:2359–2368. doi:10.1175/1520-0493(1984)112,2359:SEOFSS.2.0.CO;2
Trenberth KE (2008) Observational needs for climate prediction and adaptation. WMO Bull 57:17–21
Trenberth KE, Shea DJ (2006) Atlantic hurricanes and natural variability in 2005. Geophys Res Lett 33(L12704): doi:10.1029/2006GL026894
Uppala SM, Kȧllberg PW, Simmons AJ, Andrae U, Bechtold VDC, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Berg LVD, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hlm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, Mcnally AP, Mahfouf J, Morcrette J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The era-40 reanalysis. Q J R Meteor Soc 131:2961–3012
Valcke S (2006) Oasis3 user guide. PRISM Support Initiative Report 3:64
Van Oldenborgh GJ, Doblas-Reyes FJ, Wouters B, Hazeleger W (2011) Decadal prediction skill in a multi-model ensemble. Clim Dyn 38:1263–1280. doi:10.1007/s00382-012-1313-4
Vitart F (2014) Evolution of ecmwf sub-seasonal forecast skill scores. Q J R Meteorol Soc 140(683):1889–1899. doi:10.1002/qj.2256
Volpi D, Guemas V, Doblas-Reyes FJ, Hawkins E, Nichols N (2016) Decadal climate prediction with a refined anomaly initialisation approach. Clim Dyn. doi:10.1007/s00382-016-3176-6
Von Storch H, Zwiers F (2001) Statistical analysis in climate research. Cambridge University Press, Cambridge
Yeager S, Karspeck A, Danabasoglu G, Tribbia J, Teng H (2012) A decadal prediction case study: Late twentieth-century north atlantic ocean heat content. J Clim 25: doi:10.1175/JCLI-D-11-00595.1
Zhang J, Rothrock DA (2003) Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon Weather Rev 131:845–861
Acknowledgments
The authors acknowledge funding support for this study from the SPECS (ENV-2012-308378) project funded by the Seventh Framework Programme (FP7) of the European Commission and the PICA-ICE (CGL2012-31987) project funded by the Ministry of Economy and Competitiveness of Spain. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center.
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Volpi, D., Guemas, V. & Doblas-Reyes, F.J. Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state. Clim Dyn 49, 1181–1195 (2017). https://doi.org/10.1007/s00382-016-3373-3
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DOI: https://doi.org/10.1007/s00382-016-3373-3