Abstract
Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.
Highlights
There is strong observational evidence for a significant warming of the Earth’s climate system, from both instrumental records of atmospheric temperature over the last 150 years (Jones et al 1999), and a recent reanalysis of ocean temperature data of the last 50 years (Levitus et al 2000)
For climate projections covering the 100 years, the most important are the uncertainties in climate sensitivity, in the radiative forcing, in the carbon cycle, and in oceanic heat uptake
Considering all of these main uncertainties, we find a probability of about 40% that the surface warming at year 2100 exceeds the uncertainty range proposed by IPCC (2001), but only one of about 10% for the warming to be lower than the range of IPCC (2001)
Summary
There is strong observational evidence for a significant warming of the Earth’s climate system, from both instrumental records of atmospheric temperature over the last 150 years (Jones et al 1999), and a recent reanalysis of ocean temperature data of the last 50 years (Levitus et al 2000). Most of the global-mean temperature increase of the last 50 years can be attributed to anthropogenic influence through the emissions of greenhouse gases and other radiatively active gases (IPCC 2001). Optimal fingerprint methods used for detection and attribution of climate change have shown that the observed atmospheric warming can neither be explained by natural variability, nor by an anthropogenically forced increase in greenhouse gases alone, but is best matched when taking into account different anthropogenic forcings (greenhouse gases and sulfate aerosols) together with natural forcings (variations in the solar irradiance and stratospheric volcanic aerosols) (Santer et al 1996; Tett et al 1999; Stott et al 2000, 2001; Hegerl et al 2000). A prime concern about such future projections are their rather large uncertainties, which arise from uncertainties in future emissions of radiatively active trace gases and Knutti et al.: Probabilistic climate change projections using neural networks aerosols, in converting emissions to concentration changes, in calculating the radiative forcings from the increased atmospheric concentrations, and in estimating climate changes in response to forcing changes
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