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Constraints on radiative forcing and future climate change from observations and climate model ensembles


The assessment of uncertainties in global warming projections is often based on expert judgement, because a number of key variables in climate change are poorly quantified. In particular, the sensitivity of climate to changing greenhouse-gas concentrations in the atmosphere and the radiative forcing effects by aerosols are not well constrained, leading to large uncertainties in global warming simulations1. Here we present a Monte Carlo approach to produce probabilistic climate projections, using a climate model of reduced complexity. The uncertainties in the input parameters and in the model itself are taken into account, and past observations of oceanic and atmospheric warming are used to constrain the range of realistic model responses. We obtain a probability density function for the present-day total radiative forcing, giving 1.4 to 2.4 W m-2 for the 5–95 per cent confidence range, narrowing the global-mean indirect aerosol effect to the range of 0 to –1.2 W m-2. Ensemble simulations for two illustrative emission scenarios suggest a 40 per cent probability that global-mean surface temperature increase will exceed the range predicted by the Intergovernmental Panel on Climate Change (IPCC), but only a 5 per cent probability that warming will fall below that range.

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Figure 1: Relation between radiative forcing, climate sensitivity, and modelled atmospheric and oceanic warming.
Figure 2: Constraints on the radiative forcing from the observed atmospheric and oceanic warming.
Figure 3: Probabilistic surface warming projections for two IPCC scenarios, as constrained by the observed atmospheric and oceanic warming.


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We thank T. Crowley for providing the volcanic and solar radiative forcing data. This work was supported by the Swiss National Science Foundation.

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Correspondence to Thomas F. Stocker.

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Knutti, R., Stocker, T., Joos, F. et al. Constraints on radiative forcing and future climate change from observations and climate model ensembles. Nature 416, 719–723 (2002).

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