Simulating organic aerosol in Delhi with WRF-Chem using the VBS approach: Exploring model uncertainty with a Gaussian Process emulator

Abstract

The nature and origin of organic aerosol in the atmosphere remain unclear. The gas-particle partitioning of semi- volatile organic compounds (SVOC) that constitute primary organic aerosols (POA) and the multigenerational chemical aging of SVOCs are particularly poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem, can be a useful tool to describe emissions of POA and its chemical evolution. However, the evaluation of model uncertainty and the optimal model parameterisation maybe expensive to probe using only WRF-Chem simulations. Gaussian process emulators, trained on simulations from relatively few WRF-Chem simulations, are capable of reproducing model results and estimating the sources of model uncertainty within a defined range of model parameters. In this study, a WRF-Chem VBS parameterisation is proposed; we then generate a perturbed parameter ensemble of 111 model runs, perturbing ten parameters of the WRF-Chem model relating to organic aerosol emissions and the VBS oxidation reactions. This allowed us to cover the model’s uncertainty space and compare output from each run to aerosol mass spectrometer observations of organic aerosol concentrations and O:C ratios measured in New Delhi, India. The simulations spanned the organic aerosol concentrations measured with the AMS. However, they also highlighted potential structural errors in the model that may be related to unsuitable diurnal cycles in the emissions and/or failure to adequately represent the dynamics of the planetary boundary layer. While the structural errors prevented us from clearly identifying an optimised VBS approach in WRF-Chem, we were able to apply the emulator in two periods: the full period (1st -29th May) and a subperiod period 14:00-16:00 hrs local time, 1st-29th May. The combination of emulator analysis and model evaluation metrics allowed us to identify plausible parameter combinations for the analysed periods. We demonstrate that the methodology presented in this study can be used to determine the model uncertainty and identify the appropriate parameter combination for the VBS approach, and hence provide valuable information to improve our understanding on OA production.