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Uncertainties

Uncertainties are inherent in all projections of the future and not peculiar to climate modelling.

With respect to climate change and associated impacts the uncertainties are related to the future path of emissions, that is total effect of global development of technology, energy consumption, world population, and many other socio-economic factors, as well as limitation in climate models that is due to our understanding of the climate system and due to the simplifications necessary in the climate models.

While the uncertainty associated with climate scenarios is often regarded as problematic, it also enables an assessment of robustness by using an ensemble of scenarios.

 

The combined effect of emission scenario uncertainty and climate modelling uncertainty on global mean temperature. The coloured lines represent the multi-model ensemble best estimate of selected SRES emission scenarios, and the multi-model ensemble spread (i.e. climate model uncertainty) is indicated by the faint coloured area surrounding each line, and the grey bars in the right part. Image source: IPCC AR4 WG1 SPM Fig. 5

While research can decrease some of these uncertainties, others are more fundamental because of the complexity and/or randomness of the processes and systems involved. Climate science has made substantial progress during the last decades now allowing robust conclusions concerning many aspects of the climate and its future trends. However, many uncertainties are still large, in some regions and for some variables more than others.

Climate science and model development efforts are striving to improve the scientific understanding of processes and interactions in the global climate system and how these should best be represented in climate models. This results in successively updated models that include new processes and more sophisticated descriptions. A consequence of this is that previosuly unknown – or unaccounted for – uncertainties are taken into account. Thus, improved climate models do not necessarily lead to less uncertainty, but rather to a better description of real uncertainties.

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