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Atmospheric aerosols influence the Earth's climate, primarily by affectingcloud formation and scattering visible radiation. However, aerosol-relatedphysical processes in climate simulations are highly uncertain. Constrainingthese processes could help improve model-based climate predictions. We proposea scalable statistical framework for constraining parameters in expensiveclimate models by comparing model outputs with observations. Using the C3.aiSuite, a cloud computing platform, we use a perturbed parameter ensemble of theUKESM1 climate model to efficiently train a surrogate model. A method forestimating a data-driven model discrepancy term is described. The strict boundsmethod is applied to quantify parametric uncertainty in a principled way. Wedemonstrate the scalability of this framework with two weeks' worth ofsimulated aerosol optical depth data over the South Atlantic and CentralAfrican region, written from the model every three hours and matched in time totwice-daily MODIS satellite observations. When constraining the model usingreal satellite observations, we establish constraints on combinations of twomodel parameters using much higher time-resolution outputs from the climatemodel than previous studies. This result suggests that, within the limitsimposed by an imperfect climate model, potentially very powerful constraintsmay be achieved when our framework is scaled to the analysis of moreobservations and for longer time periods.

Statistical constraints on climate model parameters using a scalable cloud-based inference framework