Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model. (29th March 2021)

• Record Type: Journal Article
• Authors: Jansson, Fredrik; Edeling, Wouter; Attema, Jisk; Crommelin, Daan
• Is Part Of: Philosophical transactions. Volume 379:Number 2197(2021)
• Publication Date: 2021-03-29
• Subjects: atmospheric modelling -- large eddy simulation -- uncertainty quantification; Physical sciences -- Periodicals; Engineering -- Periodicals; Mathematics -- Periodicals; 500
• DOI: 10.1098/rsta.2020.0073
• Languages: English
• ISSNs: 1364-503X

Abstract

Abstract : In this study, we investigate uncertainties in a large eddy simulation of the atmosphere, employing modern uncertainty quantification methods that have hardly been used yet in this context. When analysing the uncertainty of model results, one can distinguish between uncertainty related to physical parameters whose values are not exactly known, and uncertainty related to modelling choices such as the selection of numerical discretization methods, of the spatial domain size and resolution, and the use of different model formulations. While the former kind is commonly studied e.g. with forward uncertainty propagation, we explore the use of such techniques to also assess the latter kind. From a climate modelling perspective, uncertainties in the convective response and cloud formation are of particular interest, since these affect the cloud-climate feedback, one of the dominant sources of uncertainty in current climate models. Therefore we analyse the DALES model in the RICO case, a well-studied convection benchmark. We use the VECMA toolkit for uncertainty propagation, assessing uncertainties stemming from physical parameters as well as from modelling choices. We find substantial uncertainties due to small random initial state perturbations, and that the choice of advection scheme is the most influential of the modelling choices we assessed. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico '.