Climate Dynamics

, Volume 47, Issue 3–4, pp 1225–1246 | Cite as

Decadal prediction skill in the ocean with surface nudging in the IPSL-CM5A-LR climate model

  • Juliette Mignot
  • Javier García-Serrano
  • Didier Swingedouw
  • Agathe Germe
  • Sébastien Nguyen
  • Pablo Ortega
  • Eric Guilyardi
  • Sulagna Ray
Article

Abstract

Two decadal prediction ensembles, based on the same climate model (IPSL-CM5A-LR) and the same surface nudging initialization strategy are analyzed and compared with a focus on upper-ocean variables in different regions of the globe. One ensemble consists of 3-member hindcasts launched every year since 1961 while the other ensemble benefits from 9 members but with start dates only every 5 years. Analysis includes anomaly correlation coefficients and root mean square errors computed against several reanalysis and gridded observational fields, as well as against the nudged simulation used to produce the hindcasts initial conditions. The last skill measure gives an upper limit of the predictability horizon one can expect in the forecast system, while the comparison with different datasets highlights uncertainty when assessing the actual skill. Results provide a potential prediction skill (verification against the nudged simulation) beyond the linear trend of the order of 10 years ahead at the global scale, but essentially associated with non-linear radiative forcings, in particular from volcanoes. At regional scale, we obtain 1 year in the tropical band, 10 years at midlatitudes in the North Atlantic and North Pacific, and 5 years at tropical latitudes in the North Atlantic, for both sea surface temperature (SST) and upper-ocean heat content. Actual prediction skill (verified against observational or reanalysis data) is overall more limited and less robust. Even so, large actual skill is found in the extratropical North Atlantic for SST and in the tropical to subtropical North Pacific for upper-ocean heat content. Results are analyzed with respect to the specific dynamics of the model and the way it is influenced by the nudging. The interplay between initialization and internal modes of variability is also analyzed for sea surface salinity. The study illustrates the importance of two key ingredients both necessary for the success of future coordinated decadal prediction exercises, a high frequency of start dates is needed to achieve robust statistical significance, and a large ensemble size is required to increase the signal to noise ratio.

Keywords

Decadal variability Oceanic predictability Surface nudging 

References

  1. Aumont O, Bopp L (2006) Globalizing results from ocean in situ iron fertilization studies. Glob Biogeochem Cycles. doi:10.1029/2005GB002591
  2. Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 139(674):1132–1161. doi:10.1002/qj.2063 CrossRefGoogle Scholar
  3. Batté L, Déqué M (2012) A stochastic method for improving seasonal predictions. Geophys Res Lett. doi:10.1029/2012GL051406
  4. Bellucci A, Gualdi S, Masina S, Storto A, Scoccimarro E, Cagnazzo C, Fogli P, Manzini E, Navarra A (2013) Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses. Clim Dyn 40(5–6):1483–1497. doi:10.1007/s00382-012-1468-z CrossRefGoogle Scholar
  5. Bellucci A, Haarsma R, Gualdi S, Athanasiadis PJ, Caian M, Cassou C, Fernandez E, Germe A, Jungclaus J, Kröger J, Matei D, Müller W, Pohlmann H,Salas y Melia 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. doi:10.1007/s00382-014-2164-y
  6. Boer GJ, Kharin VV, Merryfield WJ (2013) Decadal predictability and forecast skill. Clim Dyn 41(7–8):1817–1833. doi:10.1007/s00382-013-1705-0 CrossRefGoogle Scholar
  7. Bombardi RJ, Zhu J, Marx L, Huang B, Chen H, Lu J, Krishnamurthy L, Krishnamurthy V, Colfescu I, Kinter JL, Kumar A, Hu ZZ, Moorthi S, Tripp P, Wu X, Schneider EK (2014) Evaluation of the CFSv2 CMIP5 decadal predictions. Clim Dyn. doi:10.1007/s00382-014-2360-9
  8. Branstator G, Teng H (2010) Two limits of initial-value decadal predictability in a CGCM. J Clim 23:6292–6311. doi:10.1175/2010JCLI3678.1 CrossRefGoogle Scholar
  9. Branstator G, Teng H (2012) Potential impact of initialization on decadal predictions as assessed for CMIP5 models. Geophys Res Lett. doi:10.1029/2012GL051974
  10. Bretherton CS, Widmann M, Dymnikov VP, Wallace JM, Bladé I (1999) The effective number of spatial degrees of freedom of a time-varying field. J Clim 12(7):1990–2009. doi:10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2
  11. Chikamoto Y, Kimoto M, Ishii M, Mochizuki T, Sakamoto TT, Tatebe H, Komuro Y, Watanabe M, Nozawa T, Shiogama H, Mori M, Yasunaka S, Imada Y (2013) An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC. Clim Dyn 40(5–6):1201–1222. doi:10.1007/s00382-012-1351-y CrossRefGoogle Scholar
  12. Collins M, Knutti R, Dufresne JL, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2014) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. In: Stocker T, Qin D, Plattner GK, Tignor M, Allen S, Boschung J, Nauels A, Xia Y, Bex Y, Midgley VP (eds) Long-term climate change: projections, commitments and irreversibility. Cambridge University Press, Cambridge, New YorkGoogle Scholar
  13. Corti S, Weisheimer A, Palmer TN, Doblas-Reyes FJ, Magnusson L (2012) Reliability of decadal predictions. Geophys Res Lett. doi:10.1029/2012GL053354
  14. Deser C, Phillips AAS, Hurrell JWJ (2004) Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900. J Clim 17(16):3109–3124. doi:10.1175/1520-0442(2004)017<3109:PICVLB>2.0.CO;2
  15. Di Lorenzo E, Schneider N, Cobb KM, Franks PJS, Chhak K, Miller AJ, McWilliams JC, Bograd SJ, Arango H, Curchitser E, Powell TM, Rivière P (2008) North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophys Res Lett 35(8):L08,607. doi:10.1029/2007GL032838
  16. Ding H, Greatbatch RJ, Latif M, Park W, Gerdes R (2013) Hindcast of the 1976/77 and 1998/99 Climate Shifts in the Pacific. J Clim 26(19):7650–7661. doi:10.1175/JCLI-D-12-00626.1 CrossRefGoogle Scholar
  17. Doblas-Reyes FJ, Andreu-Burillo I, Chikamoto Y, García-Serrano J, Guemas V, Kimoto M, Mochizuki T, Rodrigues LRL, van Oldenborgh GJ (2013) Initialized near-term regional climate change prediction. Nat Commun 4:1715. doi:10.1038/ncomms2704
  18. Du H, Doblas-Reyes FJ, García-Serrano J, Guemas V, Soufflet Y, Wouters B (2012) Sensitivity of decadal predictions to the initial atmospheric and oceanic perturbations. Clim Dyn 39(7–8):2013–2023. doi:10.1007/s00382-011-1285-9 CrossRefGoogle Scholar
  19. Dufresne JL, Ma Foujols, Denvil S, Caubel A, Marti O, Aumont O, Balkanski Y, Bekki S, Bellenger H, Benshila R, Bony S, Bopp L, Braconnot P, Brockmann P, Cadule P, Cheruy F, Codron F, Cozic A, Cugnet D, Noblet N, Duvel JP, Ethé C, Fairhead L, Fichefet T, Flavoni S, Friedlingstein P, Grandpeix JY, Guez L, Guilyardi E, Hauglustaine D, Hourdin F, Idelkadi A, Ghattas J, Joussaume S, Kageyama M, Krinner G, Labetoulle S, Lahellec A, Lefebvre MP, Lefevre F, Levy C, Li ZX, Lloyd J, Lott F, Madec G, Mancip M, Marchand M, Masson S, Meurdesoif Y, Mignot J, Musat I, Parouty S, Polcher J, Rio C, Schulz M, Swingedouw D, Szopa S, Talandier C, Terray P, Viovy N, Vuichard N (2013) Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim Dyn 40(9–10):2123–2165. doi:10.1007/s00382-012-1636-1 CrossRefGoogle Scholar
  20. Dunstone NJ, Smith DM (2010) Impact of atmosphere and sub-surface ocean data on decadal climate prediction. Geophys Res Lett. doi:10.1029/2009GL041609
  21. 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. doi:10.1029/2011GL047949
  22. Escudier R, Mignot J, Swingedouw D (2013) A 20-year coupled ocean–sea ice–atmosphere variability mode in the North Atlantic in an AOGCM. Clim Dyn. doi:10.1007/s00382-012-1402-4
  23. Fedorov AV, Harper SL, Philander SG, Winter B, Wittenberg A (2003) How predictable is El Niño? Bull Am Meteorol Soc 84(7):911–919. doi:10.1175/BAMS-84-7-911 CrossRefGoogle Scholar
  24. Ferro CAT (2014) Fair scores for ensemble forecasts. Q J R Meteorol Soc 140(683):1917–1923. doi:10.1002/qj.2270 CrossRefGoogle Scholar
  25. Fichefet T, Maqueda MAM (1997) Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J Geophys Res 102:12609–612646. doi:10.1029/97JC00480
  26. Frankignoul C, Kestenare E (2002) The surface heat flux feedback. Part I: estimates from observations in the Atlantic and the North Pacific. Clim Dyn 19(8):633–647. doi:10.1007/s00382-002-0252-x CrossRefGoogle Scholar
  27. García-Serrano J, Doblas-Reyes FJ (2012) On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ENSEMBLES decadal hindcast. Clim Dyn 39(7–8):2025–2040. doi:10.1007/s00382-012-1413-1 CrossRefGoogle Scholar
  28. García-Serrano J, Doblas-Reyes FJ, Coehlo CAS (2012) Understanding Atlantic multi-decadal variability prediction skill. Geophys Res Lett. doi:10.1029/2012GL053283
  29. García-Serrano J, Guemas V, Doblas-Reyes FJ (2014) Added-value from initialization in predictions of Atlantic multi-decadal variability. Clim Dyn. doi:10.1007/s00382-014-2370-7
  30. Germe A, Chevallier M, Salas y, Mélia D, Sanchez-Gomez E, Cassou C (2014) Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution. Clim Dyn 43(9–10):2519–2538. doi:10.1007/s00382-014-2071-2 CrossRefGoogle Scholar
  31. Giese BS, Ray S (2011) El Niño variability in simple ocean data assimilation (SODA), 1871 2008. J Geophys Res 116(C2):C02,024. doi:10.1029/2010JC006695
  32. 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
  33. Guemas V, Doblas-Reyes FJ, Lienert F, Soufflet Y, Du H (2012) Identifying the causes of the poor decadal climate prediction skill over the North Pacific. J Geophys Res: Atmos. doi:10.1029/2012JD018004
  34. Hare SR, Mantua NJ (2000) Empirical evidence for North Pacific regime shifts in 1977 and 1989. Prog Oceanogr 47(2–4):103–145. doi:10.1016/S0079-6611(00)00033-1 CrossRefGoogle Scholar
  35. Hazeleger W, Guemas V, Wouters B, Corti S, Andreu-Burillo I, Doblas-Reyes FJ, Wyser K, Caian M (2013a) Multiyear climate predictions using two initialization strategies. Geophys Res Lett 40(9):1794–1798. doi:10.1002/grl.50355 CrossRefGoogle Scholar
  36. Hazeleger W, Wouters B, van Oldenborgh GJ, Corti S, Palmer T, Smith D, Dunstone N, Kröger J, Pohlmann H, von Storch JS (2013b) Predicting multiyear North Atlantic Ocean variability. J Geophys Res: Oceans 118(3):1087–1098. doi:10.1002/jgrc.20117 CrossRefGoogle Scholar
  37. Ho CK, Hawkins E, Shaffrey L, Underwood FM (2012) Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions. Clim Dyn 41(3–4):917–935. doi:10.1007/s00382-012-1531-9 Google Scholar
  38. Ho CK, Hawkins E, Shaffrey L, Bröcker J, Hermanson L, Murphy JM, Smith DM, Eade R (2013) Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion. Geophys Res Lett 40(21):5770–5775. doi:10.1002/2013GL057630 CrossRefGoogle Scholar
  39. Hourdin F, Foujols MA, Codron F (2013) Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Clim Dyn 40(9–10):2167–2192. doi:10.1007/s00382-012-1411-3 CrossRefGoogle Scholar
  40. Ingleby B, Huddleston M (2007) Quality control of ocean temperature and salinity profiles historical and real-time data. J Mar Syst 65(1–4):158–175. doi:10.1016/j.jmarsys.2005.11.019 CrossRefGoogle Scholar
  41. Ja Carton, Giese BS (2008) A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon Weather Rev 136(8):2999–3017. doi:10.1175/2007MWR1978.1 CrossRefGoogle Scholar
  42. Karspeck A, Yeager S, Danabasoglu G, Teng H (2014) An evaluation of experimental decadal predictions using CCSM4. Clim Dyn. doi:10.1007/s00382-014-2212-7
  43. Keenlyside N, Latif M, Jungclaus J (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:1–5. doi:10.1038/nature06921
  44. Kim HM, Webster PJ, Curry JA (2012) Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophys Res Lett. doi:10.1029/2012GL051644
  45. Kim HM, Ham YG, Scaife Aa (2014) Improvement of Initialized Decadal Predictions over the North Pacific Ocean by Systematic Anomaly Pattern Correction. Journal of Climate p 140416111812004, doi:10.1175/JCLI-D-13-00519.1
  46. Kirtman B, Power S, Adedoyin J, Boer G, Bojariu R, Camilloni I, Doblas-Reyes F, Fiore A, Kimoto M, Meehl G, Prather M, Sarr A, Schär C, Sutton R, van Oldenborgh G, Vecchi G, Wang H, Schär C, van Oldenborgh G (2013) Near-term Climate Change: Projections and Predictability. In: Stocker T, D Qin GK, Plattner M, Tignor S, Allen J, Boschung A, Nauels Y, Xia Y, Bex P, Midgley V (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, chap 11, pp 953–1028Google Scholar
  47. Kleeman R, Moore AM (1997) A theory for the limitation of ENSO predictability due to stochastic atmospheric transients. J Atmos Sci 54(6):753–767. doi:10.1175/1520-0469(1997)054<0753:ATFTLO>2.0.CO;2
  48. 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):L20,708. doi:10.1029/2005GL024233
  49. Kumar A, Wang H, Xue Y, Wang W (2014) How much of monthly subsurface temperature variability in the equatorial Pacific can be recovered by the specification of sea surface temperatures? J Clim 27(4):1559–1577. doi:10.1175/JCLI-D-13-00258.1 CrossRefGoogle Scholar
  50. Latif M, Böning C, Willebrand J (2006) Is the thermohaline circulation changing? J Clim 19:4631–4637. doi:10.1175/JCLI3876.1 CrossRefGoogle Scholar
  51. Lozier MS, Leadbetter S, Williams RG, Roussenov V, Reed MSC, Moore NJ (2008) The spatial pattern and mechanisms of heat-content change in the North Atlantic. Science 319(5864):800–3. doi:10.1126/science.1146436 CrossRefGoogle Scholar
  52. Luo J, Masson S, Behera S (2005) Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J Clim 18:4474–4497. doi:10.1175/JCLI3526.1 CrossRefGoogle Scholar
  53. Luo JJ, Masson S, Behera SK, Yamagata T (2008) Extended ENSO predictions using a fully coupled ocean–atmosphere model. J Clim 21(1):84–93. doi:10.1175/2007JCLI1412.1 CrossRefGoogle Scholar
  54. Madec G (2008) NEMO ocean engine. Tech. Rep. 27, Institut Pierre Simon LaplaceGoogle Scholar
  55. 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 41(9–10):2393–2409. doi:10.1007/s00382-012-1599-2 Google Scholar
  56. 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(6):1069–1079. doi:10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2
  57. Marini C, Frankignoul C (2013) An attempt to deconstruct the Atlantic Multidecadal Oscillation. Clim Dyn 3–4:607–625. doi:10.1007/s00382-013-1852-3 Google Scholar
  58. 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(24):8502–8523. doi:10.1175/JCLI-D-11-00633.1 CrossRefGoogle Scholar
  59. Meehl G, Teng H (2012) Case studies for initialized decadal hindcasts and predictions for the Pacific region. Geophys Res Lett. doi:10.1029/2012GL053423
  60. Meehl G, Hu A, Tebaldi C (2010) Decadal prediction in the Pacific region. J Clim 23:2259–2973. doi:10.1175/2010JCLI3296.1 CrossRefGoogle Scholar
  61. Meehl GA, Goddard L, Boer G, Burgman R, Branstator G, Cassou C, Corti S, Danabasoglu G, Doblas-Reyes F, Hawkins E, Karspeck A, Kimoto M, Kumar A, Matei D, Mignot J, Msadek R, Navarra A, Pohlmann H, Rienecker M, Rosati T, Schneider E, Smith D, Sutton R, Teng H, van Oldenborgh GJ, Vecchi G, Yeager S (2014) Decadal climate prediction: an update from the Trenches. Bull Am Meteorol Soc 95(2):243–267. doi:10.1175/BAMS-D-12-00241.1 CrossRefGoogle Scholar
  62. Mehta VM, Wang H, Mendoza K (2013) Decadal predictability of tropical basin average and global average sea surface temperatures in CMIP5 experiments with the HadCM3, GFDL-CM2.1, NCAR-CCSM4, and MIROC5 global Earth System Models. Geophys Res Lett 40(11):2807–2812. doi:10.1002/grl.50236 CrossRefGoogle Scholar
  63. Merryfield WJ, Lee W, Boer GJ, Kharin VV, Pal B, Scinocca JF, Flato GM (2010) The first coupled historical forecasting project (CHFP1). Atmos Ocean 48(4):263–283. doi:10.3137/AO1008.2010 CrossRefGoogle Scholar
  64. Mignot J, Frankignoul C (2003) On the interannual variability of surface salinity in the Atlantic. Clim Dyn 20:555–565. doi:10.1007/s00382-002-0294-0 Google Scholar
  65. Mignot J, Swingedouw D, Deshayes J, Marti O, Talandier C, Séférian R, Lengaigne M, Madec G (2013) On the evolution of the oceanic component of the IPSL climate models from CMIP3 to CMIP5: A mean state comparison. Ocean Model 72:167–184. doi:10.1016/j.ocemod.2013.09.001 CrossRefGoogle Scholar
  66. Minobe S (2000) Spatio-temporal structure of the pentadecadal variability over the North Pacific. Prog Oceanogr 47(2–4):381–408. doi:10.1016/S0079-6611(00)00042-2 CrossRefGoogle Scholar
  67. Mochizuki T, Ishii 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 (2010) Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proc Nat Acad Sci USA 107(5):1833–1837. doi:10.1073/pnas.0906531107 CrossRefGoogle Scholar
  68. Neelin JD, Battisti DS, Hirst AC, Jin FF, Wakata Y, Yamagata T, Zebiak SE (1998) ENSO theory. J Geophys Res 103(C7):14261. doi:10.1029/97JC03424
  69. 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 CrossRefGoogle Scholar
  70. Newman M (2013) An empirical benchmark for decadal forecasts of global surface temperature anomalies. J Clim 26(14):5260–5269. doi:10.1175/JCLI-D-12-00590.1 CrossRefGoogle Scholar
  71. van Oldenborgh GGJ, Doblas-Reyes FJF, Wouters B, Hazeleger W (2012) Decadal prediction skill in a multi-model ensemble. Clim Dyn 38(7–8):1263–1280. doi:10.1007/s00382-012-1313-4 CrossRefGoogle Scholar
  72. Ortega P, Lehner F, Swingedouw D, Masson-Delmotte, Valerie Raible C, Casado M, Yiou P (2015a) A model-tested North Atlantic Oscillation reconstruction for the past millennium. Nature (in press)Google Scholar
  73. Ortega P, Mignot J, Swingedouw D, Sévellec F, Guilyardi E (2015) Reconciling two alternative mechanisms behindbidecadal AMOC variability. Prog Oceanogr 137(A):237–249. doi:10.1016/j.pocean.2015.06.009 CrossRefGoogle Scholar
  74. Perigaud CM, Cassou C (2000) Importance of oceanic decadal trends and westerly wind bursts for forecasting El Niño. Geophys Res Lett 27(3):389–392. doi:10.1029/1999GL010781 CrossRefGoogle Scholar
  75. Persechino A, Mignot J, Swingedouw D (2013) Decadal predictability of the Atlantic meridional overturning circulation and climate in the IPSL-CM5A-LR model. Clim Dyn 40(9–10):2359–2380. doi:10.1007/s00382-012-1466-1 CrossRefGoogle Scholar
  76. Pohlmann H, Smith DM, Ma Balmaseda, Keenlyside NS, Masina S, Matei D, Wa Müller, Rogel P (2013) Predictability of the mid-latitude Atlantic meridional overturning circulation in a multi-model system. Clim Dyn 41(3–4):775–785. doi:10.1007/s00382-013-1663-6 CrossRefGoogle Scholar
  77. Ray S, Giese BS (2012) Historical changes in El Niño and La Niña characteristics in an ocean reanalysis. J Geophys Res 117(C11):C11,007. doi:10.1029/2012JC008031
  78. Ray S, Swingedouw D, Mignot J, Guilyardi E (2015) Effect of surface restoring on subsurface variability in a climate model during 1949–2005. Clim Dyn 44(9–10):2333–2349. doi:10.1007/s00382-014-2358-3 CrossRefGoogle Scholar
  79. Rayner NA (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. doi:10.1029/2002JD002670 CrossRefGoogle Scholar
  80. Reichler T, Kim J, Manzini E, Kröger J (2012) A stratospheric connection to Atlantic climate variability. Nat Geosci 5(September):783–787. doi:10.1038/NGEO1586 CrossRefGoogle Scholar
  81. Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20(22):5473–5496. doi:10.1175/2007JCLI1824.1 CrossRefGoogle Scholar
  82. Robock A (2000) Volcanic eruptions and climate. Rev Geophys 38(1998):191–219. doi:10.1029/1998RG000054 CrossRefGoogle Scholar
  83. Séférian R, Bopp L, Gehlen M, Swingedouw D, Mignot J, Guilyardi E, Servonnat J (2014) Multi-year prediction of Tropical Pacific Marine Productivity. PNAS 111(32):11646–11651. doi:10.1073/pnas.1315855111
  84. Servonnat J, Mignot J, Guilyardi E, Swingedouw D, Séférian R, Labetoulle S (2014) Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework. Clim Dyn. doi:10.1007/s00382-014-2184-7
  85. Smith DM, Aa Scaife, 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, Wa Müller, Pohlmann H, Rosati A, Wouters B, Wyser K (2012) Real-time multi-model decadal climate predictions. Clim Dyn 41(11–12):2875–2888. doi:10.1007/s00382-012-1600-0 Google Scholar
  86. Smith DM, Eade R, Pohlmann H (2013) A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Clim Dyn 41(11–12):3325–3338. doi:10.1007/s00382-013-1683-2 CrossRefGoogle Scholar
  87. Sugiura N, Awaji T, Masuda S, Toyoda T, Igarashi H, Ishikawa Y, Ishii M, Kimoto M (2009) Potential for decadal predictability in the North Pacific region. Geophys Res Lett 36(20):L20,701. doi:10.1029/2009GL039787
  88. Sutton RT, Hodson DLR (2005) Atlantic Ocean Forcing of North American and European Summer Climate. Science 309(5):115–118. doi:10.1126/science.110949616 CrossRefGoogle Scholar
  89. Swingedouw D, Mignot J, Labetoulle S, Guilyardi E, Madec G (2013) Initialisation and predictability of the AMOC over the last 50 years in a climate model. Clim Dyn 40(9–10):2381–2399. doi:10.1007/s00382-012-1516-8 CrossRefGoogle Scholar
  90. 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. Nat Commun 6:6545. doi:10.1038/ncomms7545 CrossRefGoogle Scholar
  91. Taylor KE, Stouffer RJ, Meehl GA (2012) An Overview of CMIP5 and the Experiment Design. Bull Am Meteorol Soc 93(4):485–498. doi:10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  92. Trenberth KE, Hurrell JW (1994) Decadal atmosphere-ocean variations in the Pacific. Clim Dyn 9(6):303–319. doi:10.1007/BF00204745 CrossRefGoogle Scholar
  93. Vial J, Dufresne JL, Bony S (2013) On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Clim Dyn 41(11–12):3339–3362. doi:10.1007/s00382-013-1725-9 CrossRefGoogle Scholar
  94. Voldoire A, Claudon M, Caniaux G, Giordani H, Roehrig R (2014) Are atmospheric biases responsible for the tropical Atlantic SST biases in the CNRM-CM5 coupled model? Clim Dyn. doi:10.1007/s00382-013-2036-x
  95. Volpi D, Doblas-Reyes FJ, García-Serrano J, Guemas V (2013) Dependence of the climate prediction skill on spatiotemporal scales: internal versus radiatively-forced contribution. Geophys Res Lett 40(12):3213–3219. doi:10.1002/grl.50557 CrossRefGoogle Scholar
  96. Weisheimer A, Palmer TN, Doblas-Reyes FJ (2011) Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles. Geophys Res Lett. doi:10.1029/2011GL048123
  97. 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(15):5173–5189. doi:10.1175/JCLI-D-11-00595.1 CrossRefGoogle Scholar
  98. Yeh SW, Kang YJ, Noh Y, Miller AJ (2011) The North Pacific climate transitions of the winters of 1976/1977 and 1988/1989. J Clim 24(4):1170–1183. doi:10.1175/2010JCLI3325.1 CrossRefGoogle Scholar
  99. Zhang R (2007) Anticorrelated multidecadal variations between surface and subsurface tropical North Atlantic. Geophys Res Lett 34(12):L12,713. doi:10.1029/2007GL030225 CrossRefGoogle Scholar
  100. Zhang S, Rosati A, Delworth T (2010) The adequacy of observing systems in monitoring the Atlantic Meridional overturning circulation and North Atlantic climate. J Clim 23(19):5311–5324. doi:10.1175/2010JCLI3677.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Juliette Mignot
    • 1
    • 2
    • 3
  • Javier García-Serrano
    • 3
  • Didier Swingedouw
    • 4
  • Agathe Germe
    • 3
  • Sébastien Nguyen
    • 3
  • Pablo Ortega
    • 3
    • 5
  • Eric Guilyardi
    • 3
    • 6
  • Sulagna Ray
    • 3
    • 7
  1. 1.Climate and Environmental Physics, Physics InstituteUniversity of BernBernSwitzerland
  2. 2.Oeschger Center for Climate Change ResearchUniversity of BernBernSwitzerland
  3. 3.LOCEAN/IPSL (Sorbonne Universités UPMC-CNRS-IRD-MNHN)ParisFrance
  4. 4.Environnements et Paléoenvironnements Océaniques et Continentaux (EPOC)UMR CNRS 5805 EPOC - OASU - Université de BordeauxPessacFrance
  5. 5.NCAS-Climate, Department of MeteorologyUniversity of ReadingReadingUK
  6. 6.NCAS-Climate, Department of MeteorologyUniversity of ReadingReadingUK
  7. 7.Atmospheric and Oceanic Sciences ProgramPrinceton UniversityPrincetonUSA

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