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’.

Highlights

  • Atmospheric moist convection and cloud dynamics are physical processes that have small spatial scales yet they are important for the behaviour of atmosphere and climate

  • We focus our attention on the Dutch atmospheric large eddy simulation (LES) (DALES) model [7], a model used in many previous studies of atmospheric convection [8,9,10,11,12], and apply the model to the RICO benchmark case [11]

  • We quantify uncertainties in the DALES model by measuring how it responds to variation of input parameters

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Summary

Introduction

Atmospheric moist convection and cloud dynamics are physical processes that have small spatial scales yet they are important for the behaviour of atmosphere and climate. With limited spatial domain but sufficiently high resolution to resolve convection explicitly, uncertainties arise because of the complexities of modelling and simulation mentioned above Assessing these uncertainties is important, for example because simulation results from local models are used intensively for designing, studying and validating parameterizations. The model has physical input parameters whose values are not precisely known Examples of these in the DALES model are cloud droplet concentration, temperature at the lower boundary of the simulation domain (earth or sea surface) and surface roughness length. Uncertainties in such physical input parameters are the most common in UQ studies. We show that some model results, especially the rain amount, vary between runs started with slightly different initial states, and recommend using ensembles rather than single model runs in order to measure the sensitivity of the results to such fluctuations

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