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Insights from Earth system model initial-condition large ensembles and future prospects

Nature Climate Change volume 10, pages 277–286 (2020)Cite this article

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A Publisher Correction to this article was published on 26 June 2020

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Abstract

Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.

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Fig. 1: Internal variability and model differences in continental temperature trends.
Fig. 2: Decision-making under uncertainty: changes in mean and variability.
Fig. 3: Decision-making under uncertainty: changes in extremes.
Fig. 4: Schematic showing the how model LEs can be used to test methods suitable for application to the single observational record; for example, those aimed at separating forced climate change from internal variability.
Fig. 5: Interplay between a Model LE and an Observational LE.

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Data availability

All data used in this study are publicly available. The CMIP5 simulations are available through PCMDI, the large ensembles are available at the MMLE Archive and the observational data are available through the respective institutions.

Code availability

Code to produce Figs. 1–3 can be obtained from F.L.

Change history

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Acknowledgements

We thank the US National Science Foundation, National Oceanic and Atmospheric Administration, National Aeronautics and Space Administration, and Department of Energy for sponsoring the activities of the US CLIVAR Working Group on Large Ensembles. We also gratefully acknowledge all of the modelling groups listed in Table 1 for making their Large Ensemble simulations available in the Multi-Model Large Ensemble data repository. We thank the three anonymous reviewers for their constructive comments and suggestions, and J. Mankin for inspirational discussions on Large Ensemble use. This Perspective also benefited from discussions that took place at the US CLIVAR Workshop on Large Ensembles held July 2019 in Boulder, CO, USA. Some of this material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (cooperative agreement no. 1852977).

Author information

Authors and Affiliations

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

    C. Deser, F. Lehner & I. R. Simpson

  2. US CLIVAR Working Group on Large Ensembles, Washington, DC, USA

    C. Deser, F. Lehner, K. B. Rodgers, T. Ault, T. L. Delworth, P. N. DiNezio, A. Fiore, C. Frankignoul, J. C. Fyfe, D. E. Horton, J. E. Kay, R. Knutti, N. S. Lovenduski, J. Marotzke, K. A. McKinnon, S. Minobe, J. Randerson, J. A. Screen, I. R. Simpson & M. Ting

  3. Center for Climate Physics, Institute for Basic Science, Busan, South Korea

    K. B. Rodgers

  4. Pusan National University, Pusan, South Korea

    K. B. Rodgers

  5. Cornell University, Ithaca, NY, USA

    T. Ault

  6. Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA

    T. L. Delworth

  7. Institute for Geophysics, University of Texas, Austin, Austin, TX, USA

    P. N. DiNezio

  8. Lamont Doherty Earth Observatory of Columbia University, Palisades, NY, USA

    A. Fiore & M. Ting

  9. Sorbonne University, Paris, France

    C. Frankignoul

  10. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, University of Victoria, Victoria, British Columbia, Canada

    J. C. Fyfe

  11. Department of Earth and Planetary Sciences, Northwestern University, Evanston, IL, USA

    D. E. Horton

  12. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA

    J. E. Kay & N. S. Lovenduski

  13. Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA

    J. E. Kay

  14. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

    R. Knutti

  15. Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA

    N. S. Lovenduski

  16. Max Planck Institute for Meteorology, Hamburg, Germany

    J. Marotzke

  17. Institute of the Environment and Sustainability and Department of Statistics, University of California, Los Angeles, Los Angeles, CA, USA

    K. A. McKinnon

  18. Hokkaido University, Sapporo, Hokkaido, Japan

    S. Minobe

  19. Department of Earth System Science, University of California Irvine, Irvine, CA, USA

    J. Randerson

  20. University of Exeter, Exeter, UK

    J. A. Screen

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C.D., F.L. and K.R. conceived the study. C.D., F.L. and K.A.M. performed the analysis and created the figures. C.D. and F.L. led the writing of the manuscript, with contributions from all authors. C.D. and F.L. contributed equally to the work.

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Correspondence to C. Deser.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Ryan Abernathey, Jens Christensen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Deser, C., Lehner, F., Rodgers, K.B. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Chang. 10, 277–286 (2020). https://doi.org/10.1038/s41558-020-0731-2

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