Quantifying the uncertainty in forecasts of anthropogenic climate change View Full Text


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Article Info

DATE

2000-10

AUTHORS

Myles R. Allen, Peter A. Stott, John F. B. Mitchell, Reiner Schnur, Thomas L. Delworth

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. More... »

PAGES

617-620

References to SciGraph publications

  • 1996-07. A search for human influences on the thermal structure of the atmosphere in NATURE
  • 1998-03. Periodically synchronously coupled integrations with the atmosphere-ocean general circulation model ECHAM3/LSG in CLIMATE DYNAMICS
  • 1996. Global Sea-level Rise: Past and Future in SEA-LEVEL RISE AND COASTAL SUBSIDENCE
  • 1995-08. Climate response to increasing levels of greenhouse gases and sulphate aerosols in NATURE
  • 1997-09. Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change in CLIMATE DYNAMICS
  • 1997-09. Multi-pattern fingerprint method for detection and attribution of climate change in CLIMATE DYNAMICS
  • 1999-10. Do-it-yourself climate prediction in NATURE
  • 1997-02. The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation in CLIMATE DYNAMICS
  • 1998-03. A flexible climate model for use in integrated assessments in CLIMATE DYNAMICS
  • 1999-06. Causes of twentieth-century temperature change near the Earth's surface in NATURE
  • 1999-06. Changing spatial structure of the thermohaline circulation in response to atmospheric CO2 forcing in a climate model in NATURE
  • 1999-04. Signature of recent climate change in frequencies of natural atmospheric circulation regimes in NATURE
  • 1999-06. Checking for model consistency in optimal fingerprinting in CLIMATE DYNAMICS
  • 2000-11. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model in NATURE
  • 1997-11. Simulation of the influence of solar radiation variations on the global climate with an ocean-atmosphere general circulation model in CLIMATE DYNAMICS
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    http://scigraph.springernature.com/pub.10.1038/35036559

    DOI

    http://dx.doi.org/10.1038/35036559

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016303772

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/11034207


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