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Quantifying the uncertainty in forecasts of anthropogenic climate change

Author

Listed:
  • Myles R. Allen

    (Space Science and Technology Department Rutherford Appleton Laboratory)

  • Peter A. Stott

    (Hadley Centre for Climate Prediction and Research, The Meteorological Office)

  • John F. B. Mitchell

    (Hadley Centre for Climate Prediction and Research, The Meteorological Office)

  • Reiner Schnur

    (Max-Planck-Institut für Meteorologie)

  • Thomas L. Delworth

    (Geophysical Fluid Dynamics Laboratory/NOAA)

Abstract

Forecasts of climate change are inevitably uncertain. It is therefore essential to quantify the risk of significant departures from the predicted response to a given emission scenario. Previous analyses of this risk have been based either on expert opinion1, perturbation analysis of simplified climate models2,3,4,5 or the comparison of predictions from general circulation models6. Recent observed changes that appear to be attributable to human influence7,8,9,10,11,12 provide a powerful constraint on the uncertainties in multi-decadal forecasts. Here we assess the range of warming rates over the coming 50 years that are consistent with the observed near-surface temperature record as well as with the overall patterns of response predicted by several general circulation models. We expect global mean temperatures in the decade 2036–46 to be 1–2.5 K warmer than in pre-industrial times under a ‘business as usual’ emission scenario. This range is relatively robust to errors in the models' climate sensitivity, rate of oceanic heat uptake or global response to sulphate aerosols as long as these errors are persistent over time. Substantial changes in the current balance of greenhouse warming and sulphate aerosol cooling would, however, increase the uncertainty. Unlike 50-year warming rates, the final equilibrium warming after the atmospheric composition stabilizes remains very uncertain, despite the evidence provided by the emerging signal.

Suggested Citation

  • Myles R. Allen & Peter A. Stott & John F. B. Mitchell & Reiner Schnur & Thomas L. Delworth, 2000. "Quantifying the uncertainty in forecasts of anthropogenic climate change," Nature, Nature, vol. 407(6804), pages 617-620, October.
    • Handle: RePEc:nat:nature:v:407:y:2000:i:6804:d:10.1038_35036559
    DOI: 10.1038/35036559
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    Citations

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    Cited by:

    1. Maxwell T Boykoff & Adam Bumpus & Diana Liverman & Samual Randalls, 2009. "Theorizing the Carbon Economy: Introduction to the Special Issue," Environment and Planning A, , vol. 41(10), pages 2299-2304, October.
    2. Laia Domingo & Mar Grande & Florentino Borondo & Javier Borondo, 2024. "Quantifying the Uncertainty of Reservoir Computing: Confidence Intervals for Time-Series Forecasting," Mathematics, MDPI, vol. 12(19), pages 1-11, October.
    3. McDermott, Shana M. & Finnoff, David C. & Shogren, Jason F. & Kennedy, Chris J., 2021. "When does natural science uncertainty translate into economic uncertainty?," Ecological Economics, Elsevier, vol. 184(C).
    4. Michael C. Runge & Clark S. Rushing & James E. Lyons & Madeleine A. Rubenstein, 2023. "A Simplified Method for Value of Information Using Constructed Scales," Decision Analysis, INFORMS, vol. 20(3), pages 220-230, September.
    5. Alexandra M. Schmidt & Marco A. Rodríguez, 2022. "Discussion on “A combined estimate of global temperature”," Environmetrics, John Wiley & Sons, Ltd., vol. 33(3), May.
    6. Yeon-Hee Kim & Seung-Ki Min & Nathan P. Gillett & Dirk Notz & Elizaveta Malinina, 2023. "Observationally-constrained projections of an ice-free Arctic even under a low emission scenario," Nature Communications, Nature, vol. 14(1), pages 1-8, December.

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