Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model
Authors: Aleksankina Ksenia , Reis Stefan , Vieno Massimo , Heal Mathew R. 
Published: March 2019

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.