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North Atlantic climate far more predictable than models imply

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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.

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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).

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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.

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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.

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Author notes

  1. K. Modali

    Present address: Regional Computing Center, University of Hamburg, Hamburg, Germany

Authors and Affiliations

  1. Met Office Hadley Centre, Exeter, UK

    D. M. Smith, A. A. Scaife, R. Eade, N. J. Dunstone & L. Hermanson

  2. College of Engineering, Mathematics and Physical Sciences, Exeter University, Exeter, UK

    A. A. Scaife

  3. Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy

    P. Athanasiadis, A. Bellucci, D. Nicolí & P. Ruggieri

  4. Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway

    I. Bethke, F. Counillon & N. Keenlyside

  5. Barcelona Supercomputing Center, Barcelona, Spain

    R. Bilbao, L.-P. Caron, F. J. Doblas-Reyes, P. Ortega & S. Wild

  6. Sorbonne Universités, LOCEAN Laboratory, Institut Pierre Simon Laplace (IPSL), Paris, France

    L. F. Borchert, V. Estella-Perez, S. Flavoni & J. Mignot

  7. Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway

    F. Counillon, N. Keenlyside & Y. Wang

  8. National Center for Atmospheric Research, Boulder, CO, USA

    G. Danabasoglu & S. Yeager

  9. Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton, NJ, USA

    T. Delworth, X. Yang & L. Zhang

  10. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    F. J. Doblas-Reyes

  11. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

    V. Kharin, W. J. Merryfield & R. Sospedra-Alfonso

  12. Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan

    M. Kimoto

  13. Department of Earth and Planetary Sciences, Kyushu University, Fukuoka, Japan

    T. Mochizuki

  14. Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

    T. Mochizuki

  15. Max-Planck-Institut für Meteorologie, Hamburg, Germany

    K. Modali, W. A. Müller & H. Pohlmann

  16. National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK

    P.-A. Monerie & J. Robson

  17. Deutscher Wetterdienst, Hamburg, Germany

    K. Pankatz & H. Pohlmann

  18. CNRS-EPOC, Université de Bordeaux, Pessac, France

    D. Swingedouw

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  1. D. M. Smith
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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.

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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.

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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.

a–c, Observed linear trends over hindcast start dates of 1973–1989, inclusive, for temperature (a), precipitation (b) and mean sea-level pressure (c). d–f, As in a–c, but for raw lagged ensemble-mean forecasts. g–i, As in d–f, but standardized by the standard deviation of ensemble-mean 8-yr means. j–l, As in d–f, but for NAO-matched forecasts. Units are standard deviations of 8-yr means per decade. The raw lagged ensemble (d–f) 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.

a–l, 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.

a–f, 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
Full size table

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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

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