  Aerosol-cloud interactions constitute the largest source of uncertainty in
assessments of the anthropogenic climate change. This uncertainty arises in
part from the difficulty in measuring the vertical distributions of aerosols,
and only sporadic vertically resolved observations are available. We often have
to settle for less informative vertically aggregated proxies such as aerosol
optical depth (AOD). In this work, we develop a framework for the vertical
disaggregation of AOD into extinction profiles, i.e. the measure of light
extinction throughout an atmospheric column, using readily available vertically
resolved meteorological predictors such as temperature, pressure or relative
humidity. Using Bayesian nonparametric modelling, we devise a simple Gaussian
process prior over aerosol vertical profiles and update it with AOD
observations to infer a distribution over vertical extinction profiles. To
validate our approach, we use ECHAM-HAM aerosol-climate model data which offers
self-consistent simulations of meteorological covariates, AOD and extinction
profiles. Our results show that, while very simple, our model is able to
reconstruct realistic extinction profiles with well-calibrated uncertainty,
outperforming by an order of magnitude the idealized baseline which is
typically used in satellite AOD retrieval algorithms. In particular, the model
demonstrates a faithful reconstruction of extinction patterns arising from
aerosol water uptake in the boundary layer. Observations however suggest that
other extinction patterns, due to aerosol mass concentration, particle size and
radiative properties, might be more challenging to capture and require
additional vertically resolved predictors.
