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A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows. EPSRC under grant agreement EP/W007711/1, as well as by the VECMA and HiDALGO projects, which have received funding from the European Union Horizon 2020 research and innovation programme under grant agreement nos 800925 and 824115. In addition, FabFlee was supported by the ITFLOWS project and FabCovid19 by the STAMINA project, both of which have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 882986 and No 883441 respectively

Lockdowns and associated measures imposed in response to the COVID-19 crisis inflict severe damage to society. Across the globe, scientists and policymakers study ways to lift measures while maintaining control of virus spread in circumstances that continuously change due to the evolution of new variants and increasing vaccination coverage. In this process, it has become clear that finding and analysing exit strategies, which are a key aspect of pandemic mitigation in all consecutive waves of infection, is not solely a matter of epidemiological modeling but has many different dimensions that need to be balanced and therefore requires input from many different disciplines. Here, we document an attempt to investigate exit strategies from a multidisciplinary perspective through the Science versus Corona project in the Netherlands. In this project, scientists and laypeople were challenged to submit (components of) exit strategies. A selection of these were implemented in a formal model, and we have evaluated the scenarios from a multidisciplinary perspective, utilizing expertise in epidemiology, economics, psychology, law, mathematics, and history. We argue for the integration of multidisciplinary perspectives on COVID-19 and more generally in pandemic mitigation, highlight open challenges, and present an agenda for further research into exit strategies and their assessment.

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 ’.

It is well known that the wide range of spatial and temporal scales present in geophysical flow problems represents a (currently) insurmountable computational bottleneck, which must be circumvented by a coarse-graining procedure. The effect of the unresolved fluid motions enters the coarse-grained equations as an unclosed forcing term, denoted as the ’eddy forcing’. Traditionally, the system is closed by approximate deterministic closure models, i.e. so-called parameterizations. Instead of creating a deterministic parameterization, some recent efforts have focused on creating a stochastic, data-driven surrogate model for the eddy forcing from a (limited) set of reference data, with the goal of accurately capturing the long-term flow statistics. Since the eddy forcing is a dynamically evolving field, a surrogate should be able to mimic the complex spatial patterns displayed by the eddy forcing. Rather than creating such a (fully data-driven) surrogate, we propose to precede the surrogate construction step by a procedure that replaces the eddy forcing with a new source term which: i) is tailor-made to capture spatially-integrated quantities of interest, ii) strikes a balance between physical insight and data-driven modelling , and iii) significantly reduces the amount of training data that is needed. Instead of creating a surrogate model for an evolving field, we now only require a surrogate model for one scalar time series per quantity-of-interest. We derive the new source terms for a simplified an ocean model of two-dimensional turbulence in a doubly periodic square domain, and show that the time-series training data produces the same statistics for our quantities of interest as the full-field eddy-forcing.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus has rapidly spread worldwide since December 2019, and early modelling work of this pandemic has assisted in identifying effective government interventions. The UK government relied in part on the CovidSim model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London, to model various non-pharmaceutical intervention strategies, and guide its government policy in seeking to deal with the rapid spread of the COVID-19 pandemic during March and April 2020. CovidSim is subject to different sources of uncertainty, namely parametric uncertainty in the inputs, model structure uncertainty (i.e., missing epidemiological processes) and scenario uncertainty, which relates to uncertainty in the set of conditions under which the model is applied. We have undertaken an extensive parametric sensitivity analysis and uncertainty quantification of the current CovidSim code. From the over 900 parameters that are provided as input to CovidSim, we identified a key subset of 19 parameters to which the code output is most sensitive. We find that the uncertainty in the code is substantial, in the sense that imperfect knowledge in these inputs will be magnified to the outputs, up to the extent of ca. 300%. Most of this uncertainty can be traced back to the sensitivity of three parameters. Compounding this, the model can display significant bias with respect to observed data, such that the output variance does not capture this validation data with high probability. We conclude that quantifying the parametric input uncertainty is not sufficient, and that the effect of model structure and scenario uncertainty cannot be ignored when validating the model in a probabilistic sense.

It is well known that the wide range of spatial and temporal scales present in geophysical flow problems represents a (currently) insurmountable computational bottleneck, which must be circumvented by a coarse-graining procedure. The effect of the unresolved fluid motions enters the coarse-grained equations as an unclosed forcing term, denoted as the ’eddy forcing’. Traditionally, the system is closed by approximate deterministic closure models, i.e. so-called parameterizations. Instead of creating a deterministic parameterization, some recent efforts have focused on creating a stochastic, data-driven surrogate model for the eddy forcing from a (limited) set of reference data, with the goal of accurately capturing the long-term flow statistics. Since the eddy forcing is a dynamically evolving field, a surrogate should be able to mimic the complex spatial patterns displayed by the eddy forcing. Rather than creating such a (fully data-driven) surrogate, we propose to precede the surrogate construction step by a proce- dure that replaces the eddy forcing with a new model-error source term which: i) is tailor-made to capture spatially-integrated statistics of interest, ii) strikes a balance between physical in- sight and data-driven modelling , and iii) significantly reduces the amount of training data that is needed. Instead of creating a surrogate for an evolving field, we now only require a surrogate model for one scalar time series per statistical quantity-of-interest. Our current surrogate mod- elling approach builds on a resampling strategy, where we create a probability density function of the reduced training data that is conditional on (time-lagged) resolved-scale variables. We derive the model-error source terms, and construct the reduced surrogate using an ocean model of two-dimensional turbulence in a doubly periodic square domain.

EasyVVUQ is an open source Python library (https://github.com/UCL-CCS/EasyVVUQ) designed to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations. The goal of EasyVVUQ is to make it as easy as possible to implement advanced VVUQ techniques for existing application codes or workflows. Our aim is to expose these features in an accessible way for users of scientific software, in particular for simulation codes running on high performance computers. Funding statement: We acknowledge funding support from the European Union’s Horizon 2020 research and innovation programme under grant agreement 800925 (VECMA project, www.vecma.eu) and the UK Consortium on Mesoscale Engineering Sciences (UK-COMES, http://www.ukcomes.org), EPSRC reference EP/L00030X/1.