Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

North Atlantic climate far more predictable than models imply

Abstract

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Decadal prediction skill for boreal winter (December to March) mean sea-level pressure.
Fig. 2: Underestimated signals.
Fig. 3: Decadal predictions of the extreme-NAO period (1986–1997).

Similar content being viewed by others

Data availability

The datasets analysed in this study are available from the CMIP data archives: https://esgf-node.llnl.gov/projects/cmip5/ and https://esgf-node.llnl.gov/projects/cmip6/. NCAR data are available from http://www.cesm.ucar.edu/projects/community-projects/DPLE/.

Code availability

The code used in this study is available from the corresponding author on reasonable request.

References

  1. Bindoff, N. L. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. 10 (Cambridge Univ. Press, 2013).

  2. Kirtman, B. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. 11 (Cambridge Univ. Press, 2013).

  3. Collins, M. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Ch. 12 (Cambridge Univ. Press, 2013).

  4. Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).

    Article  ADS  CAS  Google Scholar 

  5. Hawkins, E. & Sutton, R. The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dyn. 37, 407–418 (2011).

    Article  Google Scholar 

  6. Knutti, R. & Sedláček, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Chang. 3, 369–373 (2013).

    Article  ADS  Google Scholar 

  7. Hawkins, E., Smith, R. S., Gregory, J. M. & Stainforth, D. A. Irreducible uncertainty in near-term climate projections. Clim. Dyn. 46, 3807–3819 (2016).

    Article  Google Scholar 

  8. Deser, C., Hurrell, J. W. & Phillips, A. S. The role of the North Atlantic Oscillation in European climate projections. Clim. Dyn. 49, 3141–3157 (2017).

    Article  Google Scholar 

  9. Marotzke, J. Quantifying the irreducible uncertainty in near term climate projections. Wiley Interdiscip. Rev. Clim. Change 10, e563 (2019).

    Article  Google Scholar 

  10. Scaife, A. A. & Smith, D. A signal-to-noise paradox in climate science. npj Clim. Atmos. Sci. 1, 28 (2018).

    Article  Google Scholar 

  11. Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).

    Article  ADS  Google Scholar 

  12. Fereday, D., Chadwick, R., Knight, J. & Scaife, A. A. atmospheric dynamics is the largest source of uncertainty in future winter European rainfall. J. Clim. 31, 963–977 (2018).

    Article  ADS  Google Scholar 

  13. Woollings, T. Dynamical influences on European climate: an uncertain future. Philos. Trans. R. Soc. Lond. 368, 3733–3756 (2010).

    ADS  MATH  Google Scholar 

  14. Zappa, G. & Shepherd, T. G. Storylines of atmospheric circulation change for European regional climate impact assessment. J. Clim. 30, 6561–6577 (2017).

    Article  ADS  Google Scholar 

  15. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  ADS  Google Scholar 

  16. Boer, G. J. et al. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geosci. Model Dev. 9, 3751–3777 (2016).

    Article  ADS  Google Scholar 

  17. Hurrell, J. W., Kushnir, Y., Ottersen, G. & Visbeck, M. (eds) The North Atlantic Oscillation: Climatic Significance and Environmental Impact (American Geophysical Union, 2003).

  18. Eade, R. et al. Do seasonal-to-decadal climate predictions underestimate the predictability of the real world? Geophys. Res. Lett. 41, 5620–5628 (2014).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  19. Smith, D. M. et al. Robust skill of decadal climate predictions. npj Clim. Atmos. Sci. 2, 13 (2019).

    Article  Google Scholar 

  20. Siegert, S. et al. A Bayesian framework for verification and recalibration of ensemble forecasts: how uncertain is NAO predictability? J. Clim. 29, 995–1012 (2016).

    Article  ADS  Google Scholar 

  21. Hurrell, J. W. Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science 269, 676–679 (1995).

    Article  ADS  CAS  PubMed  Google Scholar 

  22. Scaife, A. A. et al. The CLIVAR C20C project: selected twentieth century climate events. Clim. Dyn. 33, 603–614 (2009).

    Article  Google Scholar 

  23. Bracegirdle, T. J., Lu, H., Eade, R. & Woollings, T. Do CMIP5 models reproduce observed low-frequency North Atlantic jet variability? Geophys. Res. Lett. 45, 7204–7212 (2018).

    Article  ADS  Google Scholar 

  24. Dobrynin, M. et al. Improved teleconnection-based dynamical seasonal predictions of boreal winter. Geophys. Res. Lett. 45, 3605–3614 (2018).

    Article  ADS  Google Scholar 

  25. Simpson, I. R., Yeager, S. G., McKinnon, K. A. & Deser, C. Decadal predictability of late winter precipitation in western Europe through an ocean–jet stream connection. Nat. Geosci. 12, 613–619 (2019).

    Article  ADS  CAS  Google Scholar 

  26. Yeager, S. G. & Robson, J. I. Recent progress in understanding and predicting Atlantic decadal climate variability. Curr. Clim. Change Rep. 3, 112–127 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Eden, C. & Willebrand, J. Mechanism of interannual to decadal variability of the North Atlantic circulation. J. Clim. 14, 2266–2280 (2001).

    Article  ADS  Google Scholar 

  28. McCarthy, G. D., Haigh, I. D., Hirschi, J. J.-M., Grist, J. P. & Smeed, D. A. Ocean impact on decadal Atlantic climate variability revealed by sea-level observations. Nature 521, 508–510 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  29. Clement, A. et al. The Atlantic Multidecadal Oscillation without a role for ocean circulation. Science 350, 320–324 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  30. Zanardo, S., Nicotina, L., Hilberts, A. G. J. & Jewson, S. P. Modulation of economic losses from European floods by the North Atlantic Oscillation. Geophys. Res. Lett. 46, 2563–2572 (2019).

    Article  ADS  Google Scholar 

  31. Eden, C., Greatbatch, R. J. & Lu, J. Prospects for decadal prediction of the North Atlantic Oscillation (NAO). Geophys. Res. Lett. 29, https://doi.org/10.1029/2001GL014069 (2002).

  32. Hoerling, M. P., Hurrell, J. W. & Xu, T. Tropical origins for recent North Atlantic climate change. Science 292, 90–92 (2001).

    Article  ADS  CAS  PubMed  Google Scholar 

  33. Greatbatch, R. J., Lin, H., Lu, J., Peterson, K. A. & Derome, J. Tropical/extratropical forcing of the AO/NAO: a corrigendum. Geophys. Res. Lett. 30, https://doi.org/10.1029/2003GL017406 (2003).

  34. Shin, S.-I. & Sardeshmukh, P. D. Critical influence of the pattern of tropical ocean warming on remote climate trends. Clim. Dyn. 36, 1577–1591 (2011).

    Article  Google Scholar 

  35. Scaife, A. A. et al. Skillful long-range prediction of European and North American winters. Geophys. Res. Lett. 41, 2514–2519 (2014).

    Article  ADS  Google Scholar 

  36. Dunstone, N. et al. Skilful seasonal predictions of summer European rainfall. Geophys. Res. Lett. 45, 3246–3254 (2018).

    Article  ADS  Google Scholar 

  37. Baker, L. H., Shaffrey, L. C., Sutton, R. T., Weisheimer, A. & Scaife, A. A. An intercomparison of skill and overconfidence/underconfidence of the wintertime North Atlantic Oscillation in multi-model seasonal forecasts. Geophys. Res. Lett. 45, 7808–7817 (2018).

    Article  ADS  Google Scholar 

  38. Dunstone, N. et al. Skilful predictions of the winter North Atlantic Oscillation one year ahead. Nat. Geosci. 9, 809–814 (2016).

    Article  ADS  CAS  Google Scholar 

  39. Yeager, S. G. et al. Predicting near-term changes in the earth system: a large ensemble of initialized decadal prediction simulations using the Community Earth System Model. Bull. Am. Meteorol. Soc. 99, 1867–1886 (2018).

    Article  ADS  Google Scholar 

  40. Athanasiadis, P. J. et al. Decadal predictability of North Atlantic blocking and the NAO. npj Clim. Atmos. Sci. 3, 20 (2020).

    Article  Google Scholar 

  41. O’Reilly, C. H., Weisheimer, A., Woollings, T., Gray, L. J. & MacLeod, D. The importance of stratospheric initial conditions for winter North Atlantic Oscillation predictability and implications for the signal-to-noise paradox. Q. J. R. Meteorol. Soc. 145, 131–146 (2019).

    Article  ADS  Google Scholar 

  42. Zhang, W. & Kirtman, B. Understanding the signal-to-noise paradox with a simple Markov model. Geophys. Res. Lett. 46, 13308–13317 (2019).

    Article  ADS  Google Scholar 

  43. Jin, Y., Rong, X. & Liu, Z. Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule. Clim. Dyn. 51, 2725–2742 (2018).

    Article  Google Scholar 

  44. Strommen, K. & Palmer, T. N. Signal and noise in regime systems: a hypothesis on the predictability of the North Atlantic Oscillation. Q. J. R. Meteorol. Soc. 145, 147–163 (2019).

    Article  ADS  Google Scholar 

  45. Czaja, A., Frankignoul, C., Minobe, S. & Vannière, B. Simulating the midlatitude atmospheric circulation: what might we gain from high-resolution modeling of air–sea interactions? Curr. Clim. Change Rep. 5, 390–406 (2019).

    Article  Google Scholar 

  46. Scaife, A. A. et al. Does increased atmospheric resolution improve seasonal climate predictions? Atmos. Sci. Lett. 20, e922 (2019).

    Article  Google Scholar 

  47. Mori, M., Kosaka, Y., Watanabe, M., Nakamura, H. & Kimoto, M. A reconciled estimate of the influence of Arctic sea-ice loss on recent Eurasian cooling. Nat. Clim. Chang. 9, 123–129 (2019).

    Article  ADS  Google Scholar 

  48. Cheung, A. H. et al. Comparison of low-frequency internal climate variability in CMIP5 models and observations. J. Clim. 30, 4763–4776 (2017).

    Article  ADS  Google Scholar 

  49. Kravtsov, S. Pronounced differences between observed and CMIP5-simulated multidecadal climate variability in the twentieth century. Geophys. Res. Lett. 44, 5749–5757 (2017).

    Article  ADS  Google Scholar 

  50. Wang, X., Li, J., Sun, C. & Liu, T. NAO and its relationship with the Northern Hemisphere mean surface temperature in CMIP5 simulations. J. Geophys. Res. Atmos. 122, 4202–4227 (2017).

    Article  ADS  Google Scholar 

  51. Kim, W. M., Yeager, S. G. & Danabasoglu, G. Key role of internal ocean dynamics in Atlantic multidecadal variability during the last half century. Geophys. Res. Lett. 45, 13449–13457 (2018).

    ADS  Google Scholar 

  52. Baker, A. J. et al. Enhanced climate change response of wintertime North Atlantic circulation, cyclonic activity, and precipitation in a 25-km-resolution global atmospheric model. J. Clim. 32, 7763–7781 (2019).

    Article  ADS  Google Scholar 

  53. Morice, C. P., Kennedy, J. J., Rayner, N. A. & Jones, P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J. Geophys. Res. 117, D08101 (2012).

    ADS  Google Scholar 

  54. Hansen, J., Ruedy, R., Sato, M. & Lo, K. Global surface temperature change. Rev. Geophys. 48, RG4004 (2010).

    Article  ADS  Google Scholar 

  55. Karl, T. R. et al. Possible artifacts of data biases in the recent global surface warming hiatus. Science 348, 1469–1472 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  56. Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2014).

    Article  ADS  Google Scholar 

  57. Allan, R. J. & Ansell, T. J. A new globally complete monthly historical gridded mean sea level pressure data set (HadSLP2): 1850–2003. J. Clim. 19, 5816–5842 (2006).

    Article  ADS  Google Scholar 

  58. Trenberth, K. E. & Shea, D. J. Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett. 33, L12704 (2006).

    Article  ADS  Google Scholar 

  59. Doblas-Reyes, F. J. et al. Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts. Q. J. R. Meteorol. Soc. 135, 1538–1559 (2009).

    Article  ADS  Google Scholar 

  60. Hodson, D. L. R. & Sutton, R. T. Exploring multi-model atmospheric GCM ensembles with ANOVA. Clim. Dyn. 31, 973–986 (2008).

    Article  Google Scholar 

  61. Weisheimer, A. et al. How confident are predictability estimates of the winter North Atlantic Oscillation? Q. J. R. Meteorol. Soc. 145, 140–159 (2019).

    Article  Google Scholar 

  62. Kumar, A. & Chen, M. Causes of skill in seasonal predictions of the Arctic Oscillation. Clim. Dyn. 51, 2397–2411 (2018).

    Article  Google Scholar 

  63. Borchert, L. F. et al. Decadal predictions of the probability of occurrence for warm summer temperature extremes. Geophys. Res. Lett. 46, 14042–14051 (2019).

    Article  ADS  Google Scholar 

  64. Krishnamurti, T. N. et al. Improved weather and seasonal climate forecasts from multimodel superensemble. Science 285, 1548–1550 (1999).

    Article  CAS  PubMed  Google Scholar 

  65. Yun, W. T., Stefanova, L. & Krishnamurti, T. N. Improvement of the multimodel superensemble technique for seasonal forecasts. J. Clim. 16, 3834–3840 (2003).

    Article  ADS  Google Scholar 

  66. Kug, J.-S., Lee, J.-Y., Kang, I.-S., Wang, B. & Park, C.-K. Optimal multi-model ensemble method in seasonal prediction. Asia-Pac. J. Atmos. Sci. 44, 259–267 (2008).

    Google Scholar 

  67. Gangstø, R., Weigel, A. P., Lineger, M. A. & Appenzeller, C. Methodological aspects of the validation of decadal predictions. Clim. Res. 55, 181–200 (2013).

    Article  Google Scholar 

  68. Smith, D., Eade, R. & Pohlmann, H. A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Clim. Dyn. 41, 3325–3338 (2013).

    Article  Google Scholar 

  69. Wilks, D. S. Statistical Methods in the Atmospheric Sciences 4th edn, Ch. 5 (Elsevier, Amsterdam, 2019).

  70. Goddard, L. et al. A verification framework for interannual-to-decadal predictions experiments. Clim. Dyn. 40, 245–272 (2013).

    Article  Google Scholar 

  71. Doblas-Reyes, F. J. et al. Using EC-Earth for climate prediction research. ECMWF Newsletter 154, 35–40 (2018).

    Google Scholar 

  72. Haarsma, R. et al. HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR. Description, model performance, data handling and validation. Geosci. Model Dev. https://doi.org/10.5194/gmd-2019-350 (2020).

  73. Counillon, F. et al. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model. Tellus A 68, 32437 (2016).

    Article  ADS  Google Scholar 

  74. Wang, Y. et al. Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Model. 114, 33–44 (2017).

    Article  ADS  Google Scholar 

  75. Kharin, V. V., Boer, G. J., Merryfield, W. J., Scinocca, J. F. & Lee, W.-S. Statistical adjustment of decadal predictions in a changing climate. Geophys. Res. Lett. 39, L19705 (2012).

    Article  ADS  Google Scholar 

  76. Swart, N. C. et al. The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci. Model Dev. 12, 4823–4873 (2019).

    Article  ADS  CAS  Google Scholar 

  77. Sospedra-Alfonso, R. & Boer, G. J. Assessing the impact of initialization on decadal prediction skill. Geophys. Res. Lett. 47, e2019GL086361 (2020).

    Article  ADS  Google Scholar 

  78. Yang, X. et al. A predictable AMO-like pattern in GFDL fully-coupled ensemble initialization and decadal forecasting system. J. Clim. 26, 650–661 (2013).

    Article  ADS  Google Scholar 

  79. Williams, K. D. et al. The Met Office Global Coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. J. Adv. Model. Earth Syst. 10, 357–380 (2018).

    Article  ADS  Google Scholar 

  80. Müller, W. A. et al. Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophys. Res. Lett. 39, L22707 (2012).

    ADS  Google Scholar 

  81. Pohlmann, H. et al. Realistic quasi-biennial oscillation variability in historical and decadal hindcast simulations using CMIP6 forcing. Geophys. Res. Lett. 46, 14118–14125 (2019).

    Article  ADS  Google Scholar 

  82. Chikamoto, Y. et al. An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC. Clim. Dyn. 40, 1201–1222 (2012).

    Article  Google Scholar 

  83. Mochizuki, T. et al. Decadal prediction using a recent series of MIROC global climate models. J. Meteorol. Soc. Jpn Ser. II 90A, 373–383 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

D.M.S, A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). F.J.D.-R., L.-P.C., S.W. and R.B. also acknowledge support from the EUCP project (GA 776613) and from the Ministerio de Economía y Competitividad (MINECO) as part of the CLINSA project (grant no. CGL2017-85791-R). S.W. received funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and P.O. from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE. W.A.M., H.P., K.M. and K.P. were supported by the German Federal Ministry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). N.K., I.B., F.C. and Y.W. received support from EU H2020 Blue-Action (727852), the Trond Mohn Foundation (BFS2018TMT01), the Norwegian Research Council projects INES (270061) and SFE (270733) and UNINETT Sigma2 (nn9039k, ns9039k). J.R. acknowledges support from NERC via NCAS and the ACSIS program (NE/N018001/1). J.M., V.E.-P. and D.S. are supported by Blue-Action (European Union Horizon 2020 research and innovation programme, grant no. 727852). J.M., L.F.B., V.E.-P. and D.S. were supported by EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no. 776613). The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under cooperative agreement no. 1852977. The NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 grant OCE-1243015. MIROC simulations were supported by MEXT through the Integrated Research Program for Advancing Climate Models (JPMXD0717935457). A.B., D.N. and P.R. were supported by the H2020 EUCP project (GA 776613). T.D., X.Y. and L.Z. were supported by base funding from the Oceanic and Atmospheric Research Office of NOAA to the Geophysical Fluid Dynamics Laboratory.

Author information

Authors and Affiliations

Authors

Contributions

D.M.S. led the analysis and writing, with comments from all authors. R.E. processed the CMIP5 data. A.A.S. suggested NAO matching. All authors except A.A.S., P.A., A.B., P.-A.M., D.N., J.R. and P.R. contributed to creating the decadal prediction data.

Corresponding author

Correspondence to D. M. Smith.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks June-Yi Lee, Ángel G. Muñoz, Tianjun Zhou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Improvement of NAO matching over variance adjustment.

a, Time series of observed (black curve) and variance-adjusted model forecast (years 2–9; red curve, mean of the 676-member lagged ensemble; red shading, 5%–95% confidence interval diagnosed from the forecast ensemble-mean error variance) 8-yr running-mean December-to-March AMV index. b, As in a, but for the NAO-matched forecast (see Methods). c, d, As in a, b, but for northern European rainfall. ACC for the forecast ensemble mean, ACC for a forecast made by persisting the latest observed 8-yr mean available before each start date and RPC are indicated. Indices are defined in Methods. Time series are anomalies relative to the average of all year-2–9 hindcasts. Variance adjustment does not affect the correlation skill, but the uncertainties (red shading) capture the observations better, especially for northern European precipitation (compare c with Fig. 2e). However, NAO matching clearly improves predictions of the timing of the AMV minimum in the late 1980s and the subsequent rapid warming. It also captures the observed increase in northern European precipitation from the 1960s to late 1980s and decrease thereafter.

Extended Data Fig. 2 Effect of NAO matching on trends during the increasing-NAO period.

ac, Observed linear trends over hindcast start dates of 1973–1989, inclusive, for temperature (a), precipitation (b) and mean sea-level pressure (c). df, As in ac, but for raw lagged ensemble-mean forecasts. gi, As in df, but standardized by the standard deviation of ensemble-mean 8-yr means. jl, As in df, but for NAO-matched forecasts. Units are standard deviations of 8-yr means per decade. The raw lagged ensemble (df) is divided by the observed standard deviation of 8-yr means to show the signal relative to the observed variability. NAO matching clearly improves the cooling trend over the Labrador Sea and the warming trend over Eurasia, as well as the drying (wetting) trends over southern (northern) Europe.

Extended Data Fig. 3 Effect of NAO matching on trends during the decreasing-NAO period.

al, As in Extended Data Fig. 2, but over hindcast start dates of 1989–2005, inclusive. NAO matching improves the cooling trend over northern Eurasia, drying (wetting) over northern (southern) Europe and the increasing pressure trend across most of the Arctic.

Extended Data Fig. 4 Effect of NAO matching on skill.

af, ACC skill (a, c, e) for the 20-member NAO-matched ensemble mean, and the effect of NAO matching (b, d, f), for year-2–9 boreal winter (December to March) forecasts of near-surface temperature (a, b), precipitation (c, d) and mean sea-level pressure (e, f). The effect of NAO matching on skill is computed as the partial correlation between the observed and forecast residuals after regressing out the lagged ensemble-mean forecast19, thereby focusing on the variability not already captured by the lagged ensemble mean. Stippling shows where correlations with observations (a, c, e) and of residuals (b, d, f) are significant (95% confidence; see Methods). Improvements from NAO matching are consistent with the NAO-related quadrupole pattern affecting eastern North America, Greenland, western Europe, northern Africa, Eurasia, China and the Arctic. Despite the use of fewer members (20 in the NAO-matched ensemble compared to 676 in the lagged ensemble), skill is not significantly degraded in most other regions. Negative mean sea-level pressure skill in the Indian Ocean could be related to inconsistencies in initialization of surface temperature and atmospheric circulation, as discussed previously19.

Extended Data Fig. 5 NAO not solely driven by AMV.

a, Time series of observed (black curve) and variance-adjusted lagged ensemble forecasts (years 2–9; red curve, ensemble-mean; red shading, 5%–95% confidence interval diagnosed from the error variance) 8-yr running-mean December-to-March NAO. b, As in a, but for the AMV-matched forecasts. AMV matching uses the same procedure as NAO matching (see Methods), except that the 20 ensemble members are selected on the basis of AMV instead of NAO. If the NAO signal were solely driven by AMV, then selecting the most skilful AMV ensemble members via AMV matching should increase the NAO skill. However, AMV matching clearly reduces the NAO skill (ACC reduces from 0.79, P < 0.01, to 0.37, P = 0.1). By contrast, NAO matching clearly improves the forecasts of AMV (Fig. 2c, d). We therefore conclude that the NAO signal is not solely driven by AMV.

Extended Data Table 1 Forecast systems and ensemble sizes

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smith, D.M., Scaife, A.A., Eade, R. et al. North Atlantic climate far more predictable than models imply. Nature 583, 796–800 (2020). https://doi.org/10.1038/s41586-020-2525-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2525-0

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing