Algas

Figure 3.25 compares GMV4-nitrate simulated organic aerosol vertical concentration profiles against observational organic aerosol concentrations obtained and collated from aircraft field campaigns as compiled by Heald et al. (2011). In order to conduct this comparison an organic aerosol mass (OA) to organic carbon (OC) ratio of 1.4 is used (Russell, 2003). Russell (2003) found that the ratio of OA:OC is dependent on the number of functional groups to the number of carbons in the chain. This results in 90% of the measurements collected returning ratios between 1.2–1.6, with mean values just below 1.4. This is in agreement with the range of 1.3–

1.5 from Canagaratna et al. (2015), but lower than their ‘Improved-Ambient’ OM:OC ratio of total OA of 1.84; based on ambient Aitken mode measurements.

GMV4-nitrate underestimates OA over the majority of field campaigns, with the exceptions being comparisons against the VOCALS-UK and OP3 campaigns (Heald et al., 2011). Despite underestimating OA aircraft campaign profiles, 9 out of the 15 site’s GMV4-nitrate simulated profiles lie within the standard deviation of observations (ADRIEX, TexAQS, MILAGRO, ITCT-2K4, AMMA, DABEX/DODO, ARCTAS summer, ARCTAS spring, IMPEX and ITOP); but this does not necessitate good agreement between GMV4-nitrate and field campaign data. The model over the EUCAARI, ADIENT, TexAQS and ITCT-2K4 field campaigns shows agreement with the

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standard deviation of observations above ~2 km. Additionally 9 out of the 15 model follow the shape of observational profiles.

Figure 3.25: Annual mean model simulated organic aerosol profiles compared against mean aircraft field campaign data collated by Heald et al. (2011): GMV4-nitrate vertical profiles in dashed red; mean, median and standard deviation of aircraft field campaigns in black solid, dashed and solid horizontal lines respectively.

Over Europe OC emissions are found to increase by up to 15–20 % (ECCAD GEIA-ACCMIP emissions inventory data) in 2004, and by up to ~40% over 2008 (Lamarque et al., 2010a) relative to 2000; emissions increases which contribute to model underestimations of OA profiles over Europe. Model profiles over Europe tend to replicate the shape of observational

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profiles, but underestimate OA concentrations below ~4 km and in most cases simulate concentrations outside the standard deviation of observational data (Heald et al., 2011).

Over North and Central America increases and decreases in OC emissions are observed in 2006 and 2004 in relation to 2000. Relating to the ITCT-2K4 campaign (which underestimates OA profile) in 2004 the field campaign crosses regions which see increases of ~10% and reductions of up to ~35% (Lamarque et al., 2010a). While in relation to the MILAGRO field campaign (2006) increases in OC emissions of up to ~40% are seen. In relation to the TexAQS and IMPEX campaigns increases in OC emissions of ~10% are seen (Lamarque et al., 2010a). These increases help contribute to explaining why the model underestimates over these field campaign routes.

Over Asia small reductions in OC emissions are seen in 2001 (ACE-Asia) and 2008 (OP3).

Despite these reductions the model underestimates the OA profile over the ACE-Asia field campaign, but greatly overestimates the OA profile over the OP3 field campaign; with comparisons for both campaigns showing very little agreement with the standard deviation of observational data (Heald et al., 2011; Lamarque et al., 2010a).

In relation to ARCTAS spring small increases (Northern Canada) and small reductions (Alaska) are seen over the field campaign, while in relation to ARCTAS summer small increases in OC emissions are primarily seen over the field campaign path. Despite this the model underestimates OA in relation to the associated field campaigns (Lamarque et al., 2010a).

In 2000 over West Africa (relating to the AMMA and DABEX/DODO campaigns) increase in OC emissions of between ~10 to ~20% are observed (Lamarque et al., 2010a). Despite these increases the model demonstrates varying levels of skill in this region; skilfully replicating the DABEX/DODO field campaign while underestimating the AMMA profile above ~1 km (Heald et al., 2011).

Differences between model-observation comparisons over the ITCT-2K4 (Warneke et al., 2006), TexAQS, MILAGRO (Karl et al., 2009), ARCTAS summer, ARCTAS spring (de Gouw et al., 2006), IMPEX, AMMA (Murphy et al., 2010; Capes et al., 2009) can be attributed to local or transported biomass burning emissions which through the release of VOCs (volatile organic compounds) result in the formation of organic aerosols (Murphy et al., 2010).

ITCT-2K4 campaign observations conducted in 2004 are influenced by wide-spread fires over Northern Canada and Alaska (Heald et al., 2006; Heald et al., 2011). ARCTAS summer and ARCTAS spring field campaign observations (conducted in 2008) were influenced by boreal

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fires, including Siberia and North America with most of the pollution plumes being transported in from Europe and Asia (Singh et al., 2010; Wang et al., 2011). In 2006 the AMMA campaign coincided with a peak in SH agriculture burning (Reeves et al., 2010), resulting in contributions from aged and elevated fire plumes (Murphy et al., 2010), which could explain the peaks in high altitude OA concentrations in the AMMA and ARCTAS (spring) field campaign concentration profile. In 2006 the DABEX/DODO field campaign observations were dominated by Western Africa fire activity (Heald et al., 2011).

Figure 3.26: Comparison of annual mean model simulated organic aerosol profile concentrations against mean aircraft field campaign profile concentrations for the aircraft field campaigns collated by Heald et al. (2011). Pearson regression (R) and normalised mean biases (bias) are presented to highlight disparity between model and observational data.

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In addition to the influences from fire on field campaign OA concentration profiles, anthropogenic pollution outflow has been shown to affect OA concentrations; as seen with ACE-Asia (2001), MILAGRO (2006), ADIENT (2008), ADRIEX (2004), EUCAARI (2008) and TexAQS (2006) aircraft fields campaign observations (Heald et al., 2011).

The degree to which the model underestimates OA concentration profiles collated by Heald et al., (2011) is highlighted in Figure 3.26. Over all but two sites (VOCALS-UK and OP3) the model underestimates OA concentration profiles (indicated by the negative biases returned): with campaign biases ranging between –94.42% to +215.09%, and a global model bias of –71.91%.

Figure 3.27: Comparison of regionally partitioned annual mean model simulated organic aerosol profile concentrations against aircraft field campaign profile concentrations for the aircraft field campaigns collated by Heald et al. (2011): for Europe, North America, South America, West Africa and Asia. Pearson regression (R) and normalised mean biases (bias) are presented to highlight disparity between model and observational data.

When resolving individual model-observation site comparisons in to continental regions (Europe, North America, South America, West Africa and Asia) the South American region is the only one to present a positive bias, i.e. indicating that the model overestimates in this region, but also returns a negative correlation. Figure 3.27 shows that negative biases are returned over Europe [bias = –78.53%; R = 0.697], North America [bias = –81.49%; R = 0.474],

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West Africa [bias = –42.38%; R = 0.785] and Asia [bias = –74.06%; R = 0.205], while a positive bias is returned over South America [bias = +180.80%; R = –0.500].

Mann et al. (2010) evaluated the annual mean organic carbon concentrations for the lowest model level within TOMCAT-GLOMAP-mode version 6 (uncoupled model) over North America using the IMPROVE network. Mann et al. (2010) evaluated a bias of –0.72% [R = 0.40] over December 2000 and a bias of –0.42% [R = 0.83] over June 2000. In comparison to Mann et al.

(2010) and using data from Heald et al. (2011) over North America (corresponding with the Tex-AQS, MILAGRO, ITCT-2K4, ARCTAS summer, ARCTAS spring and IMPEX campaigns) and for the lowest observations (at ~250 m) an annual mean bias of –77.35% [R = 0.950] was calculated. Even though GMV4-nitrate returns a far greater negative bias (i.e.

underestimations in organic aerosols), the values derived here are in relative agreement with the analysis of the full profiles over the North America region (in Figure 3.27).

Figure 3.28: Difference between annual mean model simulated organic aerosol profiles and aircraft field campaign observations collated by Heald et al. (2011) over field campaigns.

Figure 3.28 shows the accumulated site mean differences between the model and field campaign observations. This indicates that GMV4-nitrate underestimates organic aerosol profiles globally; with the greatest underestimations observed at ground level, and an additional peak at ~3.5 km.

Even though there have been increases in global OC emissions, model underestimations of OA are primarily due to underestimations in global OA sources (Heald et al., 2011; Spracklen et al., 2011b), and the lack of anthropogenic SOA within GMV4-nitrate. Spracklen et al. (2011b) suggest that the use of a 100 Tg yr^-1 source of anthropogenically-controlled SOA (secondary

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organic aerosol) would result in better agreement with IMPROVE observations. The result of increasing anthropogenic SOA emissions to ~100 Tg yr^-1 on model-observation comparisons has been investigated by Heald et al. (2011), showing improved model simulation correlation with aircraft field campaigns.

Additionally, due to the model underestimations of OA being primarily due to underestimations in global OA sources and the lack of anthropogenic SOA within GMV4-nitrate is difficult to identify the extent to which comparison annual mean OA concentration profiles against campaign data acquired over varying periods of time (as illustrated in Figure 3.16) can have. Though based on sulfate, nitrate and ammonium model-observations it would be likely to result in an impact model evaluation, with campaigns conducted over greater time frames returning mean concentrations more representative of the annual mean concentrations simulated by GMV4-nitrate.