Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model

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 ’.

Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large‐Domain Large‐Eddy Simulations of the Trades

“…The input files for the initial vertical profiles of the prognostic variables are produced with a Python script. The setup for using EasyVVUQ to run DALES experiments was presented in Jansson et al (2021).…”

VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations

“…model, DALES [40,41], which is used to simulate atmospheric processes including turbulence, convection, clouds, and rain. Here, we quantify the uncertainty in model output from different sources, including uncertain physical input parameters, model choices and numerical settings, and the stochastic nature of turbulence modelling.…”

“…EasyVVUQ allows us to assess the uncertainty in the output statistics due to the imperfectly known coefficients. In addition, we currently use EasyVVUQ for uncertainty quantification of an established local atmospheric model, DALES [ 52 , 53 ], which is used to simulate atmospheric processes including turbulence, convection, clouds and rain. Here, we quantify the uncertainty in model output from different sources, including uncertain physical input parameters, model choices and numerical settings, and the stochastic nature of turbulence modelling.…”

Reliability and reproducibility in computational science: implementing validation, verification and uncertainty quantification in silico

“…Jansson et al . [9] in ‘Assessing uncertainties from physical parameters and modelling choices in an atmospheric LES model' apply a range of modern UQ methods to investigate uncertainties in a large eddy simulation. To do this, they use the stochastic collocation scheme with the EasyVVUQ package to calculate the Sobol indices for a range of parameters.…”