Time dependency of the prediction skill for the North Atlantic subpolar gyre in initialized decadal hindcasts

Abstract

We analyze the time dependency of decadal hindcast skill in the North Atlantic subpolar gyre within the time period 1961–2013. We compare anomaly correlation coefficients and temporal interquartile ranges of total upper ocean heat content and sea surface temperature for three differently initialized sets of hindcast simulations with the global coupled model MPI-ESM. All initializations use weakly coupled assimilation with the same full value nudging in the atmospheric component and different assimilation techniques for oceanic temperature and salinity: (1) ensemble Kalman filter assimilating EN4 observations and HadISST data, (2) nudging of anomalies to ORAS4 reanalysis, (3) nudging of full values to ORAS4 reanalysis. We find that hindcast skill depends strongly on the evaluation time period, with higher hindcast skill during strong multiyear trends, especially during the warming in the 1990s and lower hindcast skill in the absence of such trends. Differences between the prediction systems are more pronounced when investigating any 20-year subperiod within the entire hindcast period. In the ensemble Kalman filter initialized hindcasts, we find significant correlation skill for up to 5–8 lead years, albeit along with an overestimation of the temporal interquartile range. In the hindcasts initialized by anomaly nudging, significant correlation skill for lead years greater than two is only found in the 1980s and 1990s. In the hindcasts initialized by full value nudging, correlation skill is consistently lower than in the hindcasts initialized by anomaly nudging in the first lead years with re-emerging skill thereafter. The Atlantic meridional overturning circulation reacts on the density changes introduced by oceanic nudging, this limits the predictability in the subpolar gyre in the first lead years. Overall, we find that a model-consistent assimilation technique can improve hindcast skill. Further, the evaluation of 20 year subperiods within the full hindcast period provides essential insights to judge the success of both the assimilation and the subsequent hindcast quality.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart J Roy Meteor Soc 139(674):1132–1161. https://doi.org/10.1002/qj.2063

    Article  Google Scholar 

  2. Branstator G, Teng H (2012) Potential impact of initialization on decadal predictions as assessed for CMIP5 models. Geophys Res Lett 39(12): https://doi.org/10.1029/2012GL051974

  3. Brune S, Nerger L, Baehr J (2015) Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter. Ocean Model 96 (Part 2):254–264. https://doi.org/10.1016/j.ocemod.2015.09.011

  4. Buckley MW, Marshall J (2016) Observations, inferences, and mechanisms of the atlantic meridional overturning circulation: a review. Rev Geophys 54(1):5–63. https://doi.org/10.1002/2015RG000493

    Article  Google Scholar 

  5. Chang YS, Zhang S, Rosati A, Delworth TL, Stern WF (2013) An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation. Climate Dyn 40(3–4):775–803. https://doi.org/10.1007/s00382-012-1412-2

    Article  Google Scholar 

  6. Counillon F, Bethke I, Keenlyside NS, Bentsen M, Bertino L, Zheng F (2014) Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment. Tellus A 66. https://doi.org/10.3402/tellusa.v66.21074

  7. Cox P, Stephenson D (2007) A changing climate for prediction. Science 317(5835):207–208. https://doi.org/10.1126/science.1145956

    Article  Google Scholar 

  8. DCPP-C (2016) Technical Note for DCPP-Component C—II. Recommendations for ocean restoring and ensemble generation. Tech. rep., World Climate Research Programme. https://www.wcrp-climate.org/wgsip/documents/Tech-Note-2.pdf

  9. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart J Roy Meteor Soc 137(656):553–597. https://doi.org/10.1002/qj.828

    Article  Google Scholar 

  10. Delworth TL, Manabe S, Stouffer RJ (1993) Interdecadal variations of the thermohaline circulation in a coupled ocean-atmosphere model. J Clim 6(11):1993–2011. https://doi.org/10.1175/1520-0442(1993)006<1993:IVOTTC>2.0.CO;2

    Article  Google Scholar 

  11. Delworth TL, Zeng F, Zhang L, Zhang R, Vecchi GA, Yang X (2017) The central role of ocean dynamics in connecting the North Atlantic oscillation to the extratropical component of the Atlantic multidecadal oscillation. J Clim 30(10):3789–3805. https://doi.org/10.1175/JCLI-D-16-0358.1

    Article  Google Scholar 

  12. Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99(C5):10,143–10,162. https://doi.org/10.1029/94JC00572

    Article  Google Scholar 

  13. Giorgetta MA et al (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the coupled model intercomparison project phase 5. J Adv Mod Earth Sys 5(3):572–597. https://doi.org/10.1002/jame.20038

    Article  Google Scholar 

  14. Good SA, Martin MJ, Rayner NA (2013) EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J Geophys Res 118(12):6704–6716. https://doi.org/10.1002/2013JC009067

    Article  Google Scholar 

  15. Hermanson L, Eade R, Robinson NH, Dunstone NJ, Andrews MB, Knight JR, Scaife AA, Smith DM (2014) Forecast cooling of the Atlantic subpolar gyre and associated impacts. Geophys Res Lett 41(14):5167–5174. https://doi.org/10.1002/2014GL060420, 2014GL060420

    Article  Google Scholar 

  16. ICPO (2011) Decadal and bias correction for decadal climate predictions. Tech. Rep. 150, International CLIVAR Project Office, http://www.clivar.org/sites/default/files/documents/ICPO150_Bias.pdf

  17. Jungclaus JH, Fischer N, Haak H, Lohmann K, Marotzke J, Matei D, Mikolajewicz U, Notz D, von Storch JS (2013) Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model. J Adv Mod Earth Sys 5(2):422–446. https://doi.org/10.1002/jame.20023

    Article  Google Scholar 

  18. Karspeck AR, Yeager SG, Danabasoglu G, Hoar T, Collins N, Raeder K, Anderson JL, Tribbia J (2013) An ensemble adjustment kalman filter for the CCSM4 ocean component. J Clim 26(19):7392–7413. https://doi.org/10.1175/JCLI-D-12-00402.1

    Article  Google Scholar 

  19. Karspeck AR, Stammer D, Köhl A, Danabasoglu G, Balmaseda M, Smith DM, Fujii Y, Zhang S, Giese B, Tsujino H, Rosati A (2015a) Comparison of the atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products. Clim Dyn :1–26, https://doi.org/10.1007/s00382-015-2787-7

  20. Karspeck AR, Yeager SG, Danabasoglu G, Teng H (2015b) An evaluation of experimental decadal predictions using CCSM4. Clim Dyn 44:907–923. https://doi.org/10.1007/s00382-014-2212-7

    Article  Google Scholar 

  21. Keenlyside NS, 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 

  22. Kröger J, Müller WA, von Storch JS (2012) Impact of different ocean reanalyses on decadal climate prediction. Clim Dyn 39(3–4):795–810. https://doi.org/10.1007/s00382-012-1310-7

    Article  Google Scholar 

  23. Levitus S, Antonov JI, Boyer TP, Baranova OK, Garcia HE, Locarnini RA, Mishonov AV, Reagan JR, Seidov D, Yarosh ES, Zweng MM (2012) World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys Res Lett 39(10). https://doi.org/10.1029/2012GL051106

  24. Marini C, Polkova I, Köhl A, Stammer D (2016) A comparison of two ensemble generation methods using oceanic singular vectors and atmospheric lagged initialization for decadal climate prediction. Mon Wea Rev 144(7):2719–2738. https://doi.org/10.1175/MWR-D-15-0350.1

    Article  Google Scholar 

  25. Marotzke J (2016) MiKlip: a national research project on decadal climate prediction. Bull Amer Meteor Soc 97(12):2379–2394. https://doi.org/10.1175/BAMS-D-15-00184.1

    Article  Google Scholar 

  26. Matei D, Pohlmann H, Jungclaus JH, Müller WA, 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. https://doi.org/10.1175/JCLI-D-11-00633.1

    Article  Google Scholar 

  27. Menary MB, Hermanson L, Dunstone NJ (2016) The impact of Labrador Sea temperature and salinity variability on density and the subpolar AMOC in a decadal prediction system. Geophys Res Lett 43(23):12,217–12,227. https://doi.org/10.1002/2016GL070906,2016GL070906

    Article  Google Scholar 

  28. Mignot J, García-Serrano J, Swingedouw D, Germe A, Nguyen S, Ortega P, Guilyardi E, Ray S (2016) Decadal prediction skill in the ocean with surface nudging in the IPSL-CM5A-LR climate model. Climate Dyn 47(3):1225–1246. https://doi.org/10.1007/s00382-015-2898-1

    Article  Google Scholar 

  29. Msadek R, Delworth TL, Rosati A, Anderson W, Vecchi G, Chang YS, Dixon K, Gudgel RG, Stern WF, Wittenberg A, Yang X, Zeng F, Zhang R, Zhang S (2014) Predicting a decadal shift in North Atlantic climate variability using the GFDL forecast system. J Clim 27(17):6472–6496. https://doi.org/10.1175/JCLI-D-13-00476.1

    Article  Google Scholar 

  30. Müller W, Matei D, Bersch M, Jungclaus J, Haak H, Lohmann K, Compo G, Sardeshmukh P, Marotzke J (2015) A twentieth-century reanalysis forced ocean model to reconstruct the north atlantic climate variation during the 1920s. Clim Dyn 44(7–8):1935–1955. https://doi.org/10.1007/s00382-014-2267-5

    Article  Google Scholar 

  31. Müller WA, Baehr J, Haak H, Jungclaus JH, Kröger J, Matei D, Notz D, Pohlmann H, von Storch JS, Marotzke J (2012) Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophys Res Lett 39(22). https://doi.org/10.1029/2012GL053326

  32. Müller WA, Pohlmann H, Sienz F, Smith DM (2014) Decadal climate predictions for the period 1901–2010 with a coupled climate model. Geophys Res Lett 41:2100–2107. https://doi.org/10.1002/2014GL059259

    Article  Google Scholar 

  33. Nerger L, Hiller W (2013) Software for ensemble-based data assimilation systems—implementation strategies and scalability. Comput Geosci 55:110–118. https://doi.org/10.1016/j.cageo.2012.03.026

    Article  Google Scholar 

  34. Pham DT (2001) Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon Wea Rev 129(5):1194–1207. https://doi.org/10.1175/1520-0493(2001)129<1194:SMFSDA$>2.0.CO;2

    Article  Google Scholar 

  35. Pham DT, Verron J, Gourdeau L (1998) Singular evolutive Kalman filters for data assimilation in oceanography. C R Acad Sci, Ser II 326(4):255–260. https://doi.org/10.1016/S1251-8050(97)86815-2

    Google Scholar 

  36. Pohlmann H, Sienz F, Latif M (2006) Influence of the multidecadal atlantic meridional overturning circulation variability on european climate. J Clim 19(23):6062–6067. https://doi.org/10.1175/JCLI3941.1

    Article  Google Scholar 

  37. Pohlmann H, Jungclaus JH, Köhl A, Stammer D, Marotzke J (2009) Initializing decadal climate predictions with the GECCO oceanic synthesis: effects on the North Atlantic. J Climate 22(14):3926–3938. https://doi.org/10.1175/2009JCLI2535.1

    Article  Google Scholar 

  38. Pohlmann H, Müller WA, Kulkarni K, Kameswarrao M, Matei D, Vamborg FSE, Kadow C, Illing S, Marotzke J (2013a) Improved forecast skill in the tropics in the new MiKlip decadal climate predictions. Geophys Res Lett 40(21):5798–5802. https://doi.org/10.1002/2013GL058051

    Article  Google Scholar 

  39. Pohlmann H, Smith DM, Balmaseda MA, Keenlyside NS, Masina S, Matei D, Müller WA, Rogel P (2013b) Predictability of the mid-latitude atlantic meridional overturning circulation in a multi-model system. Climate Dyn 41(3):775–785. https://doi.org/10.1007/s00382-013-1663-6

    Article  Google Scholar 

  40. Polkova I, Köhl A, Stammer D (2015) Predictive skill for regional interannual steric sea level and mechanisms for predictability. J Clim 28(18):7407–7419. https://doi.org/10.1175/JCLI-D-14-00811.1

    Article  Google Scholar 

  41. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, 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). https://doi.org/10.1029/2002JD002670

  42. Robson JI, Sutton RT, Lohmann K, Smith DM, Palmer MD (2012a) Causes of the rapid warming of the North Atlantic Ocean in the mid-1990s. J Clim 25(12):4116–4134. https://doi.org/10.1175/JCLI-D-11-00443.1

    Article  Google Scholar 

  43. Robson JI, Sutton RT, Smith DM (2012b) Initialized decadal predictions of the rapid warming of the north Atlantic ocean in the mid 1990s. Geophys Res Lett 39(19): https://doi.org/10.1029/2012GL053370

  44. Robson JI, Sutton RT, Smith DM (2014) Decadal predictions of the cooling and freshening of the North Atlantic in the 1960s and the role of ocean circulation. Climate Dyn 42(9):2353–2365. https://doi.org/10.1007/s00382-014-2115-7

    Article  Google Scholar 

  45. Romanova V, Hense A (2015) Anomaly transform methods based on total energy and ocean heat content norms for generating ocean dynamic disturbances for ensemble climate forecasts. Clim Dyn :1–21. https://doi.org/10.1007/s00382-015-2567-4

  46. Servonnat J, Mignot J, Guilyardi E, Swingedouw D, Séférian R, Labetoulle S (2015) Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework. Clim Dyn 44(1):315–338. https://doi.org/10.1007/s00382-014-2184-7

    Article  Google Scholar 

  47. Smeed DA, McCarthy GD, Cunningham SA, Frajka-Williams E, Rayner D, Johns WE, Meinen CS, Baringer MO, Moat BI, Duchez A, Bryden HL (2014) Observed decline of the Atlantic meridional overturning circulation 2004–2012. Ocean Sci 10(1):29–38. https://doi.org/10.5194/os-10-29-2014

    Article  Google Scholar 

  48. 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(5839):796–799. https://doi.org/10.1126/science.1139540

    Article  Google Scholar 

  49. Smith DM, Eade R, Pohlmann H (2013a) A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Clim Dyn 41(11–12):3325–3338. https://doi.org/10.1007/s00382-013-1683-2

    Article  Google Scholar 

  50. Smith DM et al (2013b) Real-time multi-model decadal climate predictions. Clim Dyn 41(11–12):2875–2888. https://doi.org/10.1007/s00382-012-1600-0

    Article  Google Scholar 

  51. Stevens B et al (2013) Atmospheric component of the MPI-M earth system model: ECHAM6. J Adv Mod Earth Sys 5(2):146–172. https://doi.org/10.1002/jame.20015

    Article  Google Scholar 

  52. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Amer Meteor Soc 93(4):485–498. https://doi.org/10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  53. Timmreck C, Pohlmann H, Illing S, Kadow C (2016) The impact of stratospheric volcanic aerosol on decadal-scale climate predictions. Geophys Res Lett 43(2):834–842. https://doi.org/10.1002/2015GL067431,2015GL067431

    Article  Google Scholar 

  54. Trenberth KE, Shea DJ (2006) Atlantic hurricanes and natural variability in 2005. Geophys Res Lett 33(12). https://doi.org/10.1029/2006GL026894,l12704

  55. Uppala SM et al (2005) The ERA-40 re-analysis. Quart J Roy Meteor Soc 131(612):2961–3012. https://doi.org/10.1256/qj.04.176

    Article  Google Scholar 

  56. Valcke S (2013) The OASIS3 coupler: a European climate modelling community software. Geosci Model Dev 6(2):373–388. https://doi.org/10.5194/gmd-6-373-2013

    Article  Google Scholar 

  57. Volpi D, Guemas V, Doblas-Reyes FJ (2016) 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 :1–15. https://doi.org/10.1007/s00382-016-3373-3

  58. Wilks D (2011) Statistical methods in the atmospheric sciences, international geophysics series, vol 100. Academic Press, New York

    Google Scholar 

  59. Yeager SG, Robson JI (2017) Recent progress in understanding and predicting atlantic decadal climate variability. Curr Clim Chang Reports 3(2):112–127. https://doi.org/10.1007/s40641-017-0064-z

    Article  Google Scholar 

  60. Yeager SG, Karspeck AR, 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. https://doi.org/10.1175/JCLI-D-11-00595.1

    Article  Google Scholar 

  61. Zhang J, Zhang R (2015) On the evolution of atlantic meridional overturning circulation fingerprint and implications for decadal predictability in the north atlantic. Geophys Res Lett 42(13):5419–5426. https://doi.org/10.1002/2015GL064596,2015GL064596

    Article  Google Scholar 

Download references

Acknowledgements

We thank two anonymous reviewers for their constructive and helpful comments for improving the manuscript. We thank Kameswarrao Modali and Helmuth Haak for technical help with the model, and Lars Nerger, AWI Bremerhaven, for providing PDAF and support in its implementation. Sea surface temperature data from HadISST and EN4 oceanic profile data have been retrieved through www.metoffice.gov.uk/hadobs, and NOCL heat content data through http://www.nodc.noaa.gov. This research was supported by the German Ministry of Education and Research (BMBF) under the MiKlip projectcs AODA-PENG (Grants 01LP1157C, 01LP1516A; SB, JB) and FlexForDec (Grant 01LP1519A; HP, WM) and through the Cluster of Excellence CliSAP (EXC177), Universität Hamburg, funded through the German Science Foundation (DFG) (AD, JB). The model simulations were performed at the German Climate Computing Centre (DKRZ).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sebastian Brune.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brune, S., Düsterhus, A., Pohlmann, H. et al. Time dependency of the prediction skill for the North Atlantic subpolar gyre in initialized decadal hindcasts. Clim Dyn 51, 1947–1970 (2018). https://doi.org/10.1007/s00382-017-3991-4

Download citation

Keywords

  • Decadal Hindcasts
  • Ensemble Kalman Filter (EnKF)
  • 1-year Lead
  • Correlation Skill
  • Hindcast Skill