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Standard assessments of climate forecast skill can be misleading

Author

Listed:
  • James S. Risbey

    (CSIRO Oceans & Atmosphere)

  • Dougal T. Squire

    (CSIRO Oceans & Atmosphere)

  • Amanda S. Black

    (CSIRO Oceans & Atmosphere)

  • Timothy DelSole

    (George Mason University)

  • Chiara Lepore

    (Columbia University)

  • Richard J. Matear

    (CSIRO Oceans & Atmosphere)

  • Didier P. Monselesan

    (CSIRO Oceans & Atmosphere)

  • Thomas S. Moore

    (CSIRO Oceans & Atmosphere)

  • Doug Richardson

    (CSIRO Oceans & Atmosphere)

  • Andrew Schepen

    (CSIRO Land & Water)

  • Michael K. Tippett

    (Columbia University)

  • Carly R. Tozer

    (CSIRO Oceans & Atmosphere)

Abstract

Assessments of climate forecast skill depend on choices made by the assessor. In this perspective, we use forecasts of the El Niño-Southern-Oscillation to outline the impact of bias-correction on skill. Many assessments of skill from hindcasts (past forecasts) are probably overestimates of attainable forecast skill because the hindcasts are informed by observations over the period assessed that would not be available to real forecasts. Differences between hindcast and forecast skill result from changes in model biases from the period used to form forecast anomalies to the period over which the forecast is made. The relative skill rankings of models can change between hindcast and forecast systems because different models have different changes in bias across periods.

Suggested Citation

  • James S. Risbey & Dougal T. Squire & Amanda S. Black & Timothy DelSole & Chiara Lepore & Richard J. Matear & Didier P. Monselesan & Thomas S. Moore & Doug Richardson & Andrew Schepen & Michael K. Tipp, 2021. "Standard assessments of climate forecast skill can be misleading," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23771-z
    DOI: 10.1038/s41467-021-23771-z
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    Cited by:

    1. Dragana Bojovic & Andria Nicodemou & Asun Lera St.Clair & Isadora Christel & Francisco J. Doblas-Reyes, 2022. "Exploring the landscape of seasonal forecast provision by Global Producing Centres," Climatic Change, Springer, vol. 172(1), pages 1-23, May.
    2. Joel Katzav & Erica L. Thompson & James Risbey & David A. Stainforth & Seamus Bradley & Mathias Frisch, 2021. "On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives," Climatic Change, Springer, vol. 169(1), pages 1-20, November.
    3. Soukayna Mouatadid & Paulo Orenstein & Genevieve Flaspohler & Judah Cohen & Miruna Oprescu & Ernest Fraenkel & Lester Mackey, 2023. "Adaptive bias correction for improved subseasonal forecasting," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Weston Anderson & Shraddhanand Shukla & Jim Verdin & Andrew Hoell & Christina Justice & Brian Barker & Kimberly Slinski & Nathan Lenssen & Jiale Lou & Benjamin I. Cook & Amy McNally, 2024. "Preseason maize and wheat yield forecasts for early warning of crop failure," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    5. Ryan O’Loughlin, 2024. "Why we need lower-performance climate models," Climatic Change, Springer, vol. 177(2), pages 1-20, February.

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