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

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....

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Bibliographic Details
Published in:arXiv.org 2023-05
Main Authors: Carzon, James, de Abreu, Bruno R, Leighton Regayre, Carslaw, Kenneth, Deaconu, Lucia, Stier, Philip, Gordon, Hamish, Kuusela, Mikael
Format: Article
Language:English
Subjects:
Aerosols
Atmospheric aerosols
Atmospheric models
Climate models
Cloud computing
Constraining
Constraint modelling
Optical thickness
Parameters
Satellite observation
Statistical inference
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Summary: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.
ISSN:2331-8422