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Understanding uncertainty via statistical analysis of a global aerosol model

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Huge investment in observations and more complex models of atmospheric aerosol have improved understanding of aerosol-cloud processes but the uncertainty in aerosol radiative forcing has not been reduced over successive IPCC reports. We have used a statistical analysis of a single global aerosol GLOMAP to better understand its sources of uncertainty. We can use this uncertainty information to target research in the right places and to quantify the value of observations with respect to reducing model uncertainty. This talk will show how statistical methods applied to this problem, including expert elicitation, Gaussian Process emulation and sensitivity analysis have helped to understand why uncertainty in aerosol radiative is not being reduced using current techniques. I will also show how the work has fed into further projects aiming to reduce the uncertainty in aerosol forcing and into the first multi-model perturbed parameter ensemble of global aerosol models.

This talk is part of the AI4ER Seminar Series series.

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