Methodologies for probabilistic uncertainty assessment

Our best estimates of future climate are based on the use of complex computer models that do not explicitly resolve the wide variety of spatio-temporal scales making up Earth's climate system. The non-linearity of the governing physical processes allows energy transfer between different scales, and many aspects of this complex behaviour can be represented by stochastic models. However, the theoretical basis for so doing is far from complete. Many uncertainties remain in predictions derived from climate models, yet governments are increasingly reliant on model predictions to inform mitigation and adaptation strategies. An overarching aim of climate scientists is to reduce the uncertainty in climate predictions and produce credible assessments of model accuracy. This programme focuses on two key themes that both require the close collaboration of mathematicians, statisticians and climate scientists in order to improve climate models and the interpretation of their output.

Read more at http://www.newton.ac.uk/programmes/CLP/index.html

Our best estimates of future climate are based on the use of complex computer models that do not explicitly resolve the wide variety of spatio-temporal scales making up Earth's climate system. The non-linearity of the governing physical processes allows energy transfer between different scales, and many aspects of this complex behaviour can be represented by stochastic models. However, the theoretical basis for so doing is far from complete. Many uncertainties remain in predictions derived from climate models, yet governments are increasingly reliant on model predictions to inform mitigation and adaptation strategies. An overarching aim of climate scientists is to reduce the uncertainty in climate predictions and produce credible assessments of model accuracy. This programme focuses on two key themes that both require the close collaboration of mathematicians, statisticians and climate scientists in order to improve climate models and the interpretation of their output. Read more at http://www.newton.ac.uk/programmes/CLP/index.html

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Methodologies for probabilistic uncertainty assessment

Methodologies for probabilistic uncertainty assessment

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