Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study

Abstract. Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source–receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40 % variation range for the UK emissions of SO₂, NO_x, and NH₃, from which regression coefficients for each input–output combination and each model grid ( >  10 000 across the UK) were calculated. Surface concentrations of SO₂, NO_x, and NH₃ (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO₃ and particulate SO₄²⁻, NO₃⁻, and NH₄⁺ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO₂, NO_x, and NH₃ (±4, ±10, and ±20 % respectively). The uncertainties in the surface concentrations of NH₃ and NO_x and the depositions of NH_x and NO_y were dominated by the uncertainties in emissions of NH₃, and NO_x respectively, whilst concentrations of SO₂ and deposition of SO_y were affected by the uncertainties in both SO₂ and NH₃ emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH₄⁺, NO₃⁻, SO₄²⁻, and HNO₃) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.