  Atmospheric aerosols influence the Earth's climate, primarily by affecting
cloud formation and scattering visible radiation. However, aerosol-related
physical processes in climate simulations are highly uncertain. Constraining
these processes could help improve model-based climate predictions. We propose
a scalable statistical framework for constraining parameters in expensive
climate models by comparing model outputs with observations. Using the C3.ai
Suite, a cloud computing platform, we use a perturbed parameter ensemble of the
UKESM1 climate model to efficiently train a surrogate model. A method for
estimating a data-driven model discrepancy term is described. The strict bounds
method is applied to quantify parametric uncertainty in a principled way. We
demonstrate the scalability of this framework with two weeks' worth of
simulated aerosol optical depth data over the South Atlantic and Central
African region, written from the model every three hours and matched in time to
twice-daily MODIS satellite observations. When constraining the model using
real satellite observations, we establish constraints on combinations of two
model parameters using much higher time-resolution outputs from the climate
model than previous studies. This result suggests that, within the limits
imposed by an imperfect climate model, potentially very powerful constraints
may be achieved when our framework is scaled to the analysis of more
observations and for longer time periods.
