An evaluation of global organic aerosol schemes using airborne observations

Abstract

Abstract. Chemical transport models have historically struggled to accurately simulate the magnitude and variability of observed organic aerosol (OA), with previous studies demonstrating that models significantly underestimate observed concentrations in the troposphere. In this study, we explore two different model OA schemes within the standard GEOS-Chem chemical transport model and evaluate the simulations against a suite of 15 globally distributed airborne campaigns from 2008 to 2017, primarily in the spring and summer seasons. These include the ATom, KORUS-AQ, GoAmazon, FRAPPE, SEAC4RS, SENEX, DC3, CalNex, OP3, EUCAARI, ARCTAS and ARCPAC campaigns and provide broad coverage over a diverse set of atmospheric composition regimes – anthropogenic, biogenic, pyrogenic and remote. The schemes include significant differences in their treatment of the primary and secondary components of OA – a “simple scheme” that models primary OA (POA) as non-volatile and takes a fixed-yield approach to secondary OA (SOA) formation and a “complex scheme” that simulates POA as semi-volatile and uses a more sophisticated volatility basis set approach for non-isoprene SOA, with an explicit aqueous uptake mechanism to model isoprene SOA. Despite these substantial differences, both the simple and complex schemes perform comparably across the aggregate dataset in their ability to capture the observed variability (with an R2 of 0.41 and 0.44, respectively). The simple scheme displays greater skill in minimizing the overall model bias (with a normalized mean bias of 0.04 compared to 0.30 for the complex scheme). Across both schemes, the model skill in reproducing observed OA is superior to previous model evaluations and approaches the fidelity of the sulfate simulation within the GEOS-Chem model. However, there are significant differences in model performance across different chemical source regimes, classified here into seven categories. Higher-resolution nested regional simulations indicate that model resolution is an important factor in capturing variability in highly localized campaigns, while also demonstrating the importance of well-constrained emissions inventories and local meteorology, particularly over Asia. Our analysis suggests that a semi-volatile treatment of POA is superior to a non-volatile treatment. It is also likely that the complex scheme parameterization overestimates biogenic SOA at the global scale. While this study identifies factors within the SOA schemes that likely contribute to OA model bias (such as a strong dependency of the bias in the complex scheme on relative humidity and sulfate concentrations), comparisons with the skill of the sulfate aerosol scheme in GEOS-Chem indicate the importance of other drivers of bias, such as emissions, transport and deposition, that are exogenous to the OA chemical scheme.

Constraining remote oxidation capacity with ATom observations

“…We calculate the ratio of total 2016 air-mass-weighted OH in the Northern (> 0 • N) to Southern Hemisphere (< 0 • S) to be 1.14. This exceeds observationally derived ratios of 0.85 to 0.97 (Montzka et al, 2000;Patra et al, 2014) but is at the low end of previous model estimates ranging from 1.13 to 1.42 (Naik et al, 2013).…”

“…Models generally overestimate global mean tropospheric OH and its ratio in the Northern Hemisphere to Southern Hemisphere (Naik et al, 2013;Patra et al, 2014). These biases may be linked to the persistent CO underestimate in models (Shindell et al, 2006), as prescribing OH from observations improves simulated CO (Müller et al, 2018).…”

Characterization of organic aerosol across the global remote troposphere: a comparison of ATom measurements and global chemistry models

“…edu/geos-chem/index.php/GEOS-Chem_12#12.0.1, last access: January 2020) representation of SOA formation based on Marais et al (2016) for isoprene-derived SOA and on the volatility basis set (VBS) of Pye et al (2010) for all other precursors. Note that this GEOS-Chem REF simulation is similar to the version 12 default "complex option", which includes non-volatile POA and semi-volatile SOA (semivolatile POA is an optional switch within this version used in Pai et al, 2020). The second configuration (referred to as GC12-DYN) includes a more dynamic representation of the SOA life cycle based on Hodzic et al (2016), with the exception of the treatment of isoprene SOA that is formed in aqueous aerosols as in Marais et al (2016).…”

“…3.2), with f (BB) = 1 taken as BC and OA being of pure BB air mass origin and f (BB) = 0 exclusively from a non-biomass burning source. By using the POA / BC ratio at the source regions after most evaporation but before POA chemical degradation evaporation has taken place, we implicitly assume POA to be chemically inert, while in reality it can slowly be lost to the gas phase by heterogeneous chemistry (e.g., George and Abbatt, 2010;Palm et al, 2018). Thus, the observationbased method provides an upper limit to the fraction of POA.…”

“…Finally, we have examined whether the non-volatile treatment of POA in models could lead to these unrealistically high POA fractions in remote regions. Figure S16 shows a comparison of POA vertical profiles as predicted by the GC12-REF simulations that use non-volatile POA and a sensitivity simulation, GC12-REF-SVPOA, that uses semivolatile POA similar to the standard treatment in GEOS-Chem as described in Pai et al (2020). Note, however, that Pai et al (2020) included marine POA emissions, used different reanalysis meteorology, and used a different model version (12.1.1 rather than 12.0.1 here), so their resulting comparisons to ATom measurements are somewhat different than found here for GC12-REF-SVPOA.…”