AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Integrating Aerosol Size Distribution Measurements with a 3D Chemical Transport Model
DANA MCGUFFIN, Peter Adams, Erik B. Ydstie, Carnegie Mellon University
Abstract Number: 118 Working Group: Remote and Regional Atmospheric Aerosol
Abstract Accurately modeling aerosol dynamics is required to obtain an understanding of the particle number size distribution (PNSD), cloud condensation nuclei (CCN) activity, and therefore aerosol indirect effects. Any uncertain processes or uncertain model inputs will lead to uncertainty in the predicted concentration fields. Key uncertainties in predicting CCN concentration fields include formation of primary particles due to aerosol emissions and the nucleation of condensable vapors as well as formation of secondary particles due to the condensation of volatile organic compounds.
The goal of this work is to improve 3D Chemical Transport Model (CTM) predictions by constraining several uncertain dynamics with a network of ground-based PNSD measurements. The CTM utilized here is GEOS-Chem TOMAS using meteorological fields from the Goddard Earth Observing System (GEOS) Data Assimilation System. The model is run with a nested grid over Europe, where European Supersites for Atmospheric Aerosol Research (EUSAAR) has locations measuring the PNSD. We aim to constrain the primary organic aerosol (POA) emissions, nucleation rate, and secondary organic aerosol (SOA) production rates over this region to improve predicted concentration of CCN.
We use a stability-based inverse model previously developed for a box model that estimates POA emission, nucleation, and SOA production rates based on measured values calculated from an observed number size distribution. This method transforms the full PNSD into three summary metrics, each of which are sensitive to one of the uncertain process rates we aim to constrain. In this work, we distribute the inversion technique among each grid block in the 3D CTM that contains a EUSAAR measurement site.
We evaluate the limitations of applying the inverse model based on nonlinear control theory to a CTM. Additionally, we will analyze the error between the estimated and measured PNSDs as the error in the three summary metrics approach zero.