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  • Review Article
  • Published:

Initialized Earth System prediction from subseasonal to decadal timescales

Nature Reviews Earth & Environment volume 2, pages 340–357 (2021)Cite this article

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Abstract

Initialized Earth System predictions are made by starting a numerical prediction model in a state as consistent as possible to observations and running it forward in time for up to 10 years. Skilful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, ranging from agriculture to water resource management to human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skilful predictions can be made: on S2S timescales, there has been some skill in predicting the Madden–Julian Oscillation and North Atlantic Oscillation; on S2I, in predicting the El Niño–Southern Oscillation; and on S2D, in predicting ocean and atmosphere variability in the North Atlantic region. However, challenges remain, and future work must prioritize reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could enhance predictive skill and, in turn, confidence. As numerical models progress towards Earth System models, initialized predictions are expanding to include prediction of sea ice, air pollution, and terrestrial and ocean biochemistry that can bring clear benefit to society and various stakeholders.

Key points

  • Initialization methods vary greatly across different prediction timescales, creating difficulties for seamless prediction.

  • Model error and drift limit predictability across all timescales. Although higher resolution models show promise in reducing these errors, improvements in physical parameterizations are needed to improve predictability.

  • The effects of land processes, interactions across various ocean basins and the role of stratospheric processes in predictability are not well understood.

  • Predictability on seasonal to decadal timescales is largely associated with predictability of the major modes of variability in the atmosphere and the ocean.

  • Evolution of Earth System models will lead to predictability of more societal-relevant variables spanning multiple parts of the Earth System.

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Fig. 1: Timescales and processes involved with initialized predictions.
Fig. 2: Influence of ensemble size and lead year ranges on predictive skill.
Fig. 3: Extending proxy observations of S2D variability back in time derived from corals.
Fig. 4: Impact of model drift on initialized predictions.
Fig. 5: Initialized S2S predictions of the MJO.
Fig. 6: S2D predictions and aspects of time-evolving globally averaged temperature.

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Acknowledgements

The foundations of this Review emerged from a workshop held by the National Academies of Sciences, Engineering and Medicine in 2015 at Woods Hole, MA, USA, and the authors gratefully acknowledge support from A. Purcell and N. Huddleston. G.A.M., J.H.R. and N.R. were supported in part by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological & Environmental Research (BER) via National Science Foundation IA 1844590, and by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement No. 1852977. M.H.E. acknowledges support from the Australian Research Council (Grant CE170100023). H.T. was partly supported by DOE/BER RGMA HiLAT-RASM. M.E.M. was supported by a grant from the NSF Paleoclimate Program #1748097. F.D.-R. and M.G.D. were supported by the H2020 EUCP project under Grant agreement no. 776613, and M.G.D also by the Ramón y Cajal 2017 grant reference RYC-2017-22964. A.C. acknowledges support from the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office’s Modeling Analysis, Prediction and Projections (MAPP) Program and from the NOAA Climate Program Office’s Climate Variability and Predictability (CVP) Program. A.C.S. acknowledges support from the NOAA Climate Variability and Predictability Program (Award NA18OAR4310405) and NOAA-MAPP (NA17OAR4310106). N.S.L. is grateful for support from the NSF (OCE-1752724). D.T. acknowledges support from the NCAR Advanced Study Program and NSF (OCE-1931242). S.C.S was supported by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) Postdoctoral Fellowship. A.A.S. and D.S were supported by the Met Office Hadley Centre Climate Programme funded by the Department for Business, Energy & Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (Defra) and by the European Commission Horizon 2020 EUCP project (GA 776613).

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Authors and Affiliations

  1. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Gerald A. Meehl, Jadwiga H. Richter & Nan Rosenbloom

  2. Pacific Northwest National Laboratory, Richland, WA, USA

    Haiyan Teng

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

    Antonietta Capotondi

  4. NOAA Physical Sciences Laboratory, Boulder, CO, USA

    Antonietta Capotondi

  5. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Kim Cobb

  6. Department of Earth Sciences, Barcelona Supercomputing Center-Centro Nacional de Supercomputación, Barcelona, Spain

    Francisco Doblas-Reyes & Markus G. Donat

  7. Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain

    Francisco Doblas-Reyes

  8. Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

    Matthew H. England

  9. Canadian Centre for Climate Modeling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

    John C. Fyfe & William J. Merryfield

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

    Weiqing Han, Nicole S. Lovenduski & Aneesh C. Subramanian

  11. School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA

    Hyemi Kim

  12. Rosenstiel School for Marine and Atmospheric Science, University of Miami, Miami, FL, USA

    Ben P. Kirtman

  13. Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA

    Yochanan Kushnir

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

    Nicole S. Lovenduski

  15. Department of Meteorology & Atmospheric Science, Pennsylvania State University, State College, PA, USA

    Michael E. Mann

  16. Earth & Environmental Systems Institute, Pennsylvania State University, State College, PA, USA

    Michael E. Mann

  17. Image Processing Laboratory, University of Valencia, Valencia, Spain

    Veronica Nieves

  18. Department of Atmospheric Oceanic and Earth Sciences, George Mason University, Fairfax, VA, USA

    Kathy Pegion

  19. Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, WA, USA

    Sara C. Sanchez

  20. Hadley Centre, Exeter, UK

    Adam A. Scaife & Doug Smith

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

    Adam A. Scaife

  22. Department of Atmospheric Science, Colorado State University, Ft. Collins, CO, USA

    Lantao Sun

  23. Department of Geosciences, University of Arizona, Tucson, AZ, USA

    Diane Thompson

  24. Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Caroline C. Ummenhofer

  25. ARC Centre of Excellence for Climate Extremes, Sydney, New South Wales, Australia

    Caroline C. Ummenhofer

  26. Scripps Institution of Oceanography, La Jolla, CA, USA

    Shang-Ping Xie

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Contributions

H.T. suggested the original concept. G.A.M. led the overall conceptual design and coordinated the writing. J.H.R. and H.T. made major contributions to the conceptual design and organization. J.H.R. generated Fig. 1a. H.T. generated Fig. 4. All authors discussed the concepts presented and contributed to the writing.

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Correspondence to Gerald A. Meehl.

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Nature Reviews Earth & Environment thanks Debra Hudson, Constantin Ardilouze, Terrence O’Kane and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

APCC climate predictions: https://www.apcc21.org/

Copernicus Climate Change Service: https://www.copernicus.eu/en

North American Multi-model Ensemble: https://www.cpc.ncep.noaa.gov/products/NMME/

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Meehl, G.A., Richter, J.H., Teng, H. et al. Initialized Earth System prediction from subseasonal to decadal timescales. Nat Rev Earth Environ 2, 340–357 (2021). https://doi.org/10.1038/s43017-021-00155-x

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