Novel approaches to observationally constrain aerosol effects in climate models

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The aim of this project is to develop and apply novel methods to reduce the large uncertainty in aerosol radiative forcing in the Met Office climate models. The uncertainty stems from both structural model deficiencies (essentially incorrect physical processes, known as structural error) and uncertain process-related parameters in the model (parametric uncertainty). Comparison of model spread across multiple climate models and in perturbed parameter ensembles shows that these two sources of uncertainty are of comparable magnitude. Recent results from Leeds show that structural deficiencies severely limit the extent to which the overall model uncertainty can be reduced through model calibration against observations. Therefore it is vital to be able to distinguish these two sources of uncertainty. The aims of this project will be to:1) Develop methods that enable structural model errors and parametric uncertainty to be distinguished against extensive observations of aerosols, clouds and radiation2) Develop and apply methods to identify the potential causes of structural error and define approaches to improve them in the model3) Define a ranked list of model structural improvements that affect aerosol forcing to feed into the Met Office model development priorities 4) Demonstrate that separation of these two sources of uncertainty enables a narrower observationally constrained estimate of aerosol forcing through model calibration.The student will use large sets of climate model simulations (ensembles) combined with extensive observations of aerosols, clouds and radiation from surface sites, aircraft and satellite data. The ensembles, combined with model emulators, will enable essentially millions of ""variants"" of the climate model to be created and analysed against the observations.