Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model

Abstract

Abstract. Atmospheric chemistry transport models (ACTMs) are extensively used to provide scientific support for the development of policies to mitigate the detrimental effects of air pollution on human health and ecosystems. Therefore, it is essential to quantitatively assess the level of model uncertainty and to identify the model input parameters that contribute the most to the uncertainty. For complex process-based models, such as ACTMs, uncertainty and global sensitivity analyses are still challenging and are often limited by computational constraints due to the requirement of a large number of model runs. In this work, we demonstrate an emulator-based approach to uncertainty quantification and variance-based sensitivity analysis for the EMEP4UK model (regional application of the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre-West). A separate Gaussian process emulator was used to estimate model predictions at unsampled points in the space of the uncertain model inputs for every modelled grid cell. The training points for the emulator were chosen using an optimised Latin hypercube sampling design. The uncertainties in surface concentrations of O3, NO2, and PM2.5 were propagated from the uncertainties in the anthropogenic emissions of NOx, SO2, NH3, VOC, and primary PM2.5 reported by the UK National Atmospheric Emissions Inventory. The results of the EMEP4UK uncertainty analysis for the annually averaged model predictions indicate that modelled surface concentrations of O3, NO2, and PM2.5 have the highest level of uncertainty in the grid cells comprising urban areas (up to ±7 %, ±9 %, and ±9 %, respectively). The uncertainty in the surface concentrations of O3 and NO2 were dominated by uncertainties in NOx emissions combined from non-dominant sectors (i.e. all sectors excluding energy production and road transport) and shipping emissions. Additionally, uncertainty in O3 was driven by uncertainty in VOC emissions combined from sectors excluding solvent use. Uncertainties in the modelled PM2.5 concentrations were mainly driven by uncertainties in primary PM2.5 emissions and NH3 emissions from the agricultural sector. Uncertainty and sensitivity analyses were also performed for five selected grid cells for monthly averaged model predictions to illustrate the seasonal change in the magnitude of uncertainty and change in the contribution of different model inputs to the overall uncertainty. Our study demonstrates the viability of a Gaussian process emulator-based approach for uncertainty and global sensitivity analyses, which can be applied to other ACTMs. Conducting these analyses helps to increase the confidence in model predictions. Additionally, the emulators created for these analyses can be used to predict the ACTM response for any other combination of perturbed input emissions within the ranges set for the original Latin hypercube sampling design without the need to rerun the ACTM, thus allowing for fast exploratory assessments at significantly reduced computational costs.

Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport

“…In summary, a weak sensitivity of modeled concentrations to the resolution of the driving meteorological as well as CTM is seen, as concluded earlier by Markakis et al (2015), who performed similar climate-driven air quality simulations over Paris (France). Indeed, the largest uncertainty of modeled concentrations is associated with emissions, especially over urban areas (Aleksankina et al, 2019); however, recall that in our case, emissions were kept constant and only the uncertainty of the representation of urban boundary layer was analyzed.…”

“…Secondly, cities represent distinct surfaces compared to their rural counterparts due to a high percentage of artificial coverage with a specific geometric layout. These surfaces, comprising the urban canopy (UC), modify the thermal and radiative balance of the overlying air (Arnfield, 2003) which results in the well-known and documented urban heat island effect (UHI; Oke, 1982;Oke et al, 2017), when urban temperatures are higher than those over rural surroundings depending on the synoptic conditions (Žák et al, 2019). However, UC has an impact on other meteorological variables.…”

Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity

“…For each variable, we define a range that encompasses the maximum and minimum likely values and that is loosely based on published studies from the literature, and these are presented in Table 2. We assume uncertainty ranges of ±25 % for surface NO x , representing 30-50 Tg N yr −1 , ±60 % for lightning NO (Schumann and Huntrieser, 2007) and ±60 % for isoprene emissions (Ashworth et al, 2010). For dry and wet deposition, we assume an uncertainty in removal rates of ±60 % that is applied to all species considered.…”

“…Christian et al, 2017), and they have focussed on sensitivities in a single model framework. In this study we demonstrate the use of Gaussian process emulation for global sensitivity analysis, applied previously to models of aerosol processes (Lee et al, 2011(Lee et al, , 2013 and air quality (Beddows et al, 2017;Aleksankina et al, 2019), and we apply it to explore the sensitivity of global tropospheric O 3 and CH 4 lifetime to uncertainty in key model processes and inputs. We investigate how the sensitivities differ across three independent chemistry-transport models and demonstrate how this approach may be used to explore the diversity in model responses and to identify where model results differ.…”

A hybrid model approach for estimating health burden from NO2 in megacities in China: a case study in Guangzhou

“…emission inventories), meteorological parameters, and background concentrations, which are currently not fully available/accessible in most cities in China. Aleksankina et al (2018Aleksankina et al ( , 2019 have investigated model uncertainties in relation to emission input data in detail, highlighting that atmospheric chemistry transport models show relatively robust responses to changes in emission input data. LUR is an efficient modelling approach, but in areas with limited monitoring sites such as Guangzhou, with only 11 monitoring sites, the selected variables may overfit the model and hence cause bias in health-effect estimates (Basagaña et al 2012).…”