Sources and characteristics of summertime organic aerosol in the Colorado Front Range: perspective from measurements and WRF-Chem modeling

Bahreini, R.; Ahmadov, Ravan; McKeen, S. A.; Vu, Kennedy; Dingle, Justin H.; Apel, E. C.; Blake, D. R.; Blake, N. J.; Campos, T.; Cantrell, C. A.; Flocke, Frank; Fried, Alan; Gilman, J. B.; Hills, A. J.; Hornbrook, R. S.; Huey, G.; Kaser, L.; Lerner, B. M.; Mauldin, R. L.; Meinardi, Simone; Montzka, D. D.; Richter, Dirk; Schroeder, J.; Stell, Meghan; Tanner, David J.; Walega, J.; Weibring, P.; Weinheimer, A. J.

doi:10.5194/acp-18-8293-2018

Cited by 13 publications

(11 citation statements)

References 95 publications

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“…The bias is likely driven by a variety of factors that need to be explored on a regional basis. For instance, a previous model 445 analysis of FRAPPE observations over Colorado suggested that an underestimate of anthropogenic emissions from the oil and gas sector contributed to an underestimate of ASOA in the region (Bahreini et al, 2018). Both schemes over-estimate OA loadings in the northern latitudes (over parts of Alaska and Canada), likely due to an overestimate in POA from fires ( Fig.…”

Section: Evaluation Of Model Simulations Against Airborne Measurementsmentioning

confidence: 90%

An evaluation of global organic aerosol schemes using airborne observations

Pai¹,

Heald²,

Pierce³

et al. 2019

Preprint

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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&#8211;2017. 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 &#8211; anthropogenic, biogenic, pyrogenic and remote. The schemes include significant differences in their treatment of the primary and secondary components of OA &#8211; a <q>simple scheme</q> that models primary OA (POA) as non-volatile and takes a fixed-yield approach to secondary OA (SOA) formation, and a <q>complex scheme</q> 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 NMB of 0.04, compared to 0.29 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 GEOS-Chem. However, there are significant differences in model performance across different chemical source regimes, classified here into 7 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. A comparison of the POA loadings from the complex scheme with SOA loadings from the simple scheme (and vice versa) also suggests that a semi-volatile treatment of POA is superior to a non-volatile treatment. 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.

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Section: Evaluation Of Model Simulations Against Airborne Measurementsmentioning

confidence: 90%

An evaluation of global organic aerosol schemes using airborne observations

Pai¹,

Heald²,

Pierce³

et al. 2019

Preprint

Self Cite

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“…The campaigns are also influenced by different OA sources depending on their sampling region. The EUCAARI campaign over western Europe (Morgan et al, 2010), KORUS-AQ over the Korean Peninsula (Nault et al, 2018), CalNex over California (Ryerson et al, 2013), and DC3 (Barth et al, 2014) and FRAPPE over the central US (Dingle et al, 2016) sample over regions that are heavily influenced by anthropogenic emissions. In contrast, the GoAmazon campaigns during the wet and dry seasons (Martin et al, 2016;Shilling et al, 2018) over the Manaus region in the Amazon and the OP3 campaign (Hewitt et al, 2010) over equatorial forests in southeast Asia are heavily influenced by biogenic emissions, although the GoAmazon campaign in the dry season is also strongly influenced by biomass burning.…”

Section: Coefficient Of Determinationmentioning

confidence: 99%

An evaluation of global organic aerosol schemes using airborne observations

et al. 2020

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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.

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“…Many of those studies suggest that the model-measurement discrepancies and differences among emission inventories approach a factor of two or more. To represent anthropogenic emissions during weekdays and weekend, the simulation uses the National Emission Inventory (NEI-2011), version 2 which correct VOC speciation profiles for oil and gas sources (Bahreini et al, 2018;McDonald et al, 2018). A Sunday emission inventory was used to represent the Monday of 2 September, which was the U.S. Labor Day holiday.…”

Section: Model Configurationmentioning

confidence: 99%

Vertical Transport, Entrainment, and Scavenging Processes Affecting Trace Gases in a Modeled and Observed SEAC⁴RS Case Study

et al. 2020

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The convectively driven transport of soluble trace gases from the lower to the upper troposphere can occur on timescales of less than an hour, and recent studies suggest that microphysical scavenging is the dominant removal process of tropospheric ozone precursors. We examine the processes responsible for vertical transport, entrainment, and scavenging of soluble ozone precursors (formaldehyde and peroxides) for midlatitude convective storms sampled on 2 September 2013 during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) study. Cloud‐resolving simulations using the Weather Research and Forecasting with Chemistry model combined with aircraft measurements were performed to understand the effect of entrainment, scavenging efficiency (SE), and ice physics processes on these trace gases. Analysis of the observations revealed that the SEs of formaldehyde (43–53%) and hydrogen peroxide (~80–90%) were consistent between SEAC4RS storms and the severe convection observed during the Deep Convective Clouds and Chemistry Experiment (DC3) campaign. However, methyl hydrogen peroxide SE was generally smaller in the SEAC4RS storms (4%–27%) compared to DC3 convection. Predicted ice retention factors exhibit different values for some species compared to DC3, and we attribute these differences to variations in net precipitation production. The analyses show that much larger production of precipitation between condensation and freezing levels for DC3 severe convection compared to smaller SEAC4RS storms is largely responsible for the lower amount of soluble gases transported to colder temperatures, reducing the amount of soluble gases which eventually interact with cloud ice particles.

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Sources and characteristics of summertime organic aerosol in the Colorado Front Range: perspective from measurements and WRF-Chem modeling

Cited by 13 publications

References 95 publications

An evaluation of global organic aerosol schemes using airborne observations

An evaluation of global organic aerosol schemes using airborne observations

An evaluation of global organic aerosol schemes using airborne observations

Vertical Transport, Entrainment, and Scavenging Processes Affecting Trace Gases in a Modeled and Observed SEAC⁴RS Case Study

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Sources and characteristics of summertime organic aerosol in the Colorado Front Range: perspective from measurements and WRF-Chem modeling

Cited by 13 publications

References 95 publications

An evaluation of global organic aerosol schemes using airborne observations

An evaluation of global organic aerosol schemes using airborne observations

An evaluation of global organic aerosol schemes using airborne observations

Vertical Transport, Entrainment, and Scavenging Processes Affecting Trace Gases in a Modeled and Observed SEAC4RS Case Study

Contact Info

Product

Resources

About

Vertical Transport, Entrainment, and Scavenging Processes Affecting Trace Gases in a Modeled and Observed SEAC⁴RS Case Study