2.1 Chemical mechanisms

The three chemistry schemes implemented in the IFS are described in more detail in the following subsections. A brief analysis of elemental differences is given in Sect. 2.1.4

2.1.4 Key differences in chemistry modules

An overview of the most important differences in the three chemistry modules described above is given in Table 1. First, there are large differences in the choices made to compile the tropospheric chemistry mechanism. IFS(MOZART) describes the degradation of organic carbon types C1, C2, C3, C4, C5, C7 and C10, together with lumped aromatics, while IFS(CB05BASCOE) only describes explicit degradation up to C3, with the same reactions as present in IFS(MOZART). Instead, emissions and degradation of higher volatile organic compounds (VOCs) in IFS(CB05BASCOE) are lumped to a few tracers. Furthermore, the parameterisation of isoprene and terpene degradation is simpler in IFS(CB05BASCOE) than in IFS(MOZART). Aromatics are currently not described in IFS(CB05BASCOE), while they are accounted for with simple approaches in IFS(MOZART).

IFS(MOCAGE) describes many more lumped organic species than IFS(CB05BASCOE) and IFS(MOZART), also accounting for the more complex organics beyond C3. Furthermore, IFS(MOCAGE) uses a rather different lumping approach and contains more complexity for different terpene components, also including aromatics. Such differences are bound to impact the effective degradation of VOCs and thus ozone production efficiency and oxidation capacity (e.g. Sander et al., 2019).

With respect to the inorganic chemistry, the schemes are mostly similar. Still, IFS(MOCAGE) includes nitrous acid (HONO) chemistry, which is missing in both IFS(CB05BASCOE) and IFS(MOZART) implementations. Gas-phase sulfur chemistry is mostly similar between IFS(CB05BASCOE) and IFS(MOZART), while IFS(MOCAGE) has some more complexity by considering reactions involving dimethyl sulfoxide (DMSO) and H₂S. Instead, IFS(CB05BASCOE) and IFS(MOZART) contain a treatment of gas–aerosol partitioning for nitrate and ammonium, which is missing in IFS(MOCAGE).

Significant uncertainty remains in the magnitude of heterogeneous reaction probabilities. Heterogeneous reactions of HO₂ and N₂O₅ on aerosol are included in IFS(CB05BASCOE) and IFS(MOZART), although with different efficiencies, but not in the IFS(MOCAGE) version considered here. This has only become available in a more recent model version. Also, for instance, a more recent version of IFS(MOZ) with updated values following Emmons et al. (2010) leads to a significantly reduced NO_x lifetime. So far, two-way coupling of secondary aerosol formation has not been available in any of the current model versions.

Regarding the treatment of photolysis in the troposphere, IFS(CB05BASCOE) applies a modified band approach, whereby for seven wavelengths the photolysis rates are computed online, taking into account the scattering and absorption properties of gases (overhead ozone and oxygen), clouds and aerosol. IFS(MOCAGE) adopts a lookup table approach, accounting for overhead ozone column, solar zenith angle, surface albedo and altitude, providing photolysis rates for clear-sky conditions. The impact of cloudiness on photolysis rates is applied online in IFS during the simulation using the parameterisation proposed by Brasseur et al. (1998). IFS(MOZART) applies the lookup table approach from MOZART-3 (Kinnison et al., 2007), considering overhead ozone column and cloud scattering effects on photolysis rates. Despite such larger differences, an intercomparison of an instantaneous field of photolysis rates showed similar average profiles, with a spread in magnitude in the range of 5 % in the tropical free troposphere for important photolysis rates like jO₃, jNO₂ and jHNO₃. Locally, differences are larger and associated, amongst other factors, with different cloud treatment (Hall et al., 2018).

As for the stratospheric chemistry, IFS(CB05BASCOE) contains the largest complexity of the three model versions, with more species and reactions compared to the other mechanisms.

Different methods are used to solve the reaction mechanism. IFS(CB05BASCOE) applies the Rosenbrock solver, IFS(MOCAGE) here applies a first-order semi-implicit solver with fixed time steps, and IFS(MOZART) applies the explicit Euler method for species with long lifetimes (e.g. N₂O) and an implicit backward Euler solver for other trace gases with short lifetimes. Experiments using different solvers for both IFS(CB05BASCOE) and IFS(MOCAGE) have revealed significant differences, with decreases in tropospheric ozone of the order of up to 20 % regionally when replacing a semi-implicit solver with the Rosenbrock solver. These differences are mostly traced to an increase in N₂O₅ chemical production (Cariolle et al., 2017), in turn reducing the NO_x lifetime because of a larger net N₂O₅ loss on aerosol. This in turn leads to reduced chemical ozone production efficiency.

Table 1Specification of elemental aspects describing the three chemistry versions.

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2.2 Emission, deposition and surface boundary conditions

The actual emission totals used in the simulation for 2011 from anthropogenic, biogenic and natural sources, biomass burning, and lightning NO are given in Table 2. MACCity emissions are used to prescribe the anthropogenic emissions (Granier et al., 2011), wherein wintertime CO traffic emissions have been scaled up according to Stein et al. (2014). Aircraft NO emissions are 1.8 Tg NO yr⁻¹ , following Lamarque et al. (2010). Lightning NO emissions are parameterised as described in Flemming et al. (2015).

Monthly specific biogenic emissions originating from the MEGAN-MACC inventory (Sindelarova et al., 2014) are adopted, complemented with POET-based oceanic emissions (Granier et al., 2005).

Daily biomass burning emissions are taken from the Global Fire Assimilation System (GFAS) version 1.2, which is based on satellite retrievals of fire radiative power (Kaiser et al., 2012).

As described above, the chemistry mechanisms vary, particularly in their description of VOC degradation, with the most explicit treatment described in IFS(MOZ), while IFS(MOCAGE) and IFS(CB05BASCOE) rely on a more extended lumping approach. This has consequences for the partitioning of the various emissions. Still, we have ensured that the total of VOC and aromatic emissions in terms of tetragrams of carbon are essentially the same for the three chemistry schemes.

For CB05BASCOE, the emissions of “paraffins” (toluene and higher alkane emissions), “olefins” (butenes and higher alkenes) and “aldehydes” (acetaldehyde and other aldehydes) have been prescribed. Likewise, MOZART applies emissions of BIGALK (butanes and higher alkanes) and BIGENE (butenes and higher alkenes). MOCAGE adopts tracers HC3, HC5 and HC8, over which emissions of ethyne, propane, butanes and higher alkanes, esters, methanol, and other alcohols are distributed, whereas DIEN (butadiene) contains butenes and higher alkene emissions.

As for the aromatics, IFS(CB05BASCOE) disregards those, but includes toluene carbon emissions as part of the paraffins. IFS(MOZART) additionally treats a toluene tracer, while IFS(MOCAGE) contains two types of aromatics, designated TOL and XYL. These aromatic emissions are composed from toluene, trimethylbenzene, xylene and other aromatics.

Dry deposition velocities in the current configuration were provided as monthly mean values from a simulation using the approach discussed in Michou et al. (2004). To account for the diurnal variation in deposition velocities, a cosine function of the solar zenith angle is adopted with ±50 % variation. Wet scavenging, including in-cloud and below-cloud scavenging as well as re-evaporation, is treated following Jacob et al. (2000). The reader is referred to Flemming et al. (2015) for further details on dry and wet deposition parameterisation.

Methane (CH₄), N₂O and a selection of chlorofluorocarbons (CFCs) are prescribed at the surface as boundary conditions. While for N₂O and CFC annually and zonally fixed values are currently assumed (Huijnen et al., 2016b), for CH₄ zonally and seasonally varying surface concentrations are adopted based on a climatology derived from NOAA flask observations ranging from 2003 to 2014.

2.3 Model configuration and meteorology

The IFS model versions evaluated here were implemented in IFS cycle 43R1 and are run on a T255 horizontal resolution (∼0.7^∘) with 60 model levels in the vertical up to 0.1 hPa, all excluding chemical data assimilation. The naming conventions and experiment IDs for the three model runs are specified in Table 3. For brevity we refer to the model runs as “CBA”, “MOC” and “MOZ”, respectively. A 30 min time stepping for the dynamics is applied, while meteorology is nudged towards ERA-Interim. To allow for sufficient model spin-up, the model versions are initialised for 1 July 2010 and run through until 1 January 2012. The initial condition (IC) fields have been generated for this date using fields that are as realistic and consistent as possible. For this purpose, tropospheric CO and O₃ from the CAMS interim reanalysis (Flemming et al., 2017) have been combined with VOCs from its control run. CFCs, halogens and other tracers relevant for stratospheric composition originate from the BASCOE reanalysis v05.06 (Skachko et al., 2016) and have been merged for altitudes below the tropopause with model fields from Huijnen et al. (2016b), all specified for 1 July 2010. For MOZ and MOC, these IC fields have been completed for a few missing VOCs and CFCs using separate MOZART and MOCAGE climatologies, respectively. The first 6 months of the simulation are considered as spin-up and therefore not evaluated.

For the evaluation, the model was sampled in the troposphere and lower stratosphere (i.e. the lowest 40 model levels) every 3 h to have full coverage of the daily cycle. These are used to compute monthly to yearly averages. Standard deviations are computed to represent the model variability for a specified range in time and space.

3.1 Aircraft measurements

Aircraft measurements of trace gas composition from a database produced by Emmons et al. (2000) were used for the evaluation of distributions of collocated monthly mean modelled fields. Although these measurements cover only limited time periods, they provide valuable information about the vertical distribution of the analysed trace gases. The database is formed with data from a number of aircraft campaigns that took place during 1990–2001 which are gridded onto global maps, forming data composites of chemical species important for tropospheric ozone photochemistry. These are used to create observation-based climatologies (Emmons et al., 2000). Here we use measurements of ozone, CO, CH₂O, C₂H₆, C₂H₄, methyl hydroperoxide (CH₃OOH), NO₂, nitric acid (HNO₃) and sulfur dioxide (SO₂). Note that the field campaigns used in this evaluation have been extended, also including data observed after the year 2000, such as the TOPSE and TRACE-P campaigns. The geographical distribution of the aircraft campaigns and their coverage areas are shown in Fig. 1.

Although the specific field campaign data are in theory representative for the specific year, the averaging of a large number of measurements over space and time partly solves the problem of interannual variability, and therefore these data can be considered as a climatology. Pozzer et al. (2009) showed that the correlation between model results and these observations would vary less than 5 % if model results 5 years apart were used. For the total anthropogenic VOC emissions the changes between the year 1990 and 2011 are of the order of 14 %, following the Emissions Database for Global Atmospheric Research (EDGARv4.3.2 database). Nevertheless, the evaluations presented here are all sampling background locations or outflow regions and are hence only partly affected by such changes in anthropogenic emissions. Also, the variability as well as measurement uncertainties present in the observations are larger than 14 %, implying that we can still consider these observations representative. Finally, these data summaries are useful for providing a picture of the global distributions of NMHCs and nitrogen-containing trace gases.

Table 2Specification of annual emission totals from anthropogenic, biogenic and natural sources, and biomass burning for 2011, in tetragrams of species mass, for three chemistry versions.

^* Anthropogenic surface NO emissions (Tg NO) are split according to 90 % NO and 10 % NO₂ emissions. Additionally, they contain a contribution of 1.8 Tg NO aircraft emissions and 9.2 Tg NO lightning emissions (LiNO).

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Table 3Specifications of the experiments evaluated.

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Figure 1Geographical distribution of the aircraft campaigns presented by Emmons et al. (2000). Each field campaign is represented by a different colour. Further information on the campaigns is found in Emmons et al. (2000).

3.2 Near-surface CO and ozone-sondes

In situ observations for monthly mean CO for the year 2011 are used to evaluate monthly mean modelled surface CO fields. Observational data are taken from the World Data Centre for Greenhouse Gases (WDCGG), the data repository and archive for greenhouse and related gases of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) programme. The uncertainty of the CO observations is estimated to be of the order of 1–3 ppm (Novelli et al., 2003).

Tropospheric ozone was evaluated using sonde measurement data available from the World Ozone and Ultraviolet Radiation Data Center (WOUDC; http://woudc.org, last access: 25 April 2019), further expanded with observations from the Network for the Detection of Atmospheric Composition Change (NDACC) network. About 50 individual stations covering various worldwide regions are taken into account for the evaluation over the Arctic, Northern Hemisphere mid-latitudes, tropics, Southern Hemisphere mid-latitudes and the Antarctic. The 3-hourly output of the three model versions has been collocated to match the location and launch time of the individual sonde observations during 2011. The precision of ozone-sonde observations in the troposphere is of the order of −7 % to 17 % (Komhyr et al., 1995; Steinbrecht et al., 1998), while larger errors are found in the presence of steep gradients and where the ozone amount is low.

Figure 2Geographical distribution of the ozone-sondes during 2011 used for evaluation, coloured for the various seasons. The size of the triangles provides information on the relative number of observations available for each of the seasons and locations compared to the other locations. The geographical aggregation for the five latitude bands presented in Figs. 5 and 7, as well as the western Europe and eastern US regions, is also given.

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3.3 Satellite observations

MOPITT (Measurements of Pollution in the Troposphere) v7 CO column observations (Deeter et al., 2017) are used to evaluate the CO total columns. The MOPITT instrument is a multi-channel thermal infrared (TIR) and near infrared (NIR) instrument operating onboard the Terra satellite. The total column CO product is based on the integral of the retrieved CO volume mixing ratio profile. A climatology based on CAM4-Chem (Lamarque et al., 2012) is used to provide the MOPITT a priori profiles. For our study we use the TIR-derived CO total column observations, which are provided over both the oceans and over land. The highest CO sensitivities of these MOPITT TIR measurements are in the middle troposphere at around 500 hPa. Sensitivity to the lower troposphere depends on the thermal contrast between the land and lower atmosphere, which is higher during the day than in the night. Therefore, in our study we only use daytime MOPITT TIR observations. The standard deviation of the error in individual pixels for the MOPITT v7 TIR product evaluated against NOAA flask measurements is reported as 0.13×10¹⁸ mol cm⁻² (Deeter et al., 2017), i.e. of the order of 10 % of the observation value. Daily mean model CO columns have been gridded to a $\mathrm{1}{}^{\circ }\times \mathrm{1}{}^{\circ }$ spatial resolution, and for our analysis we applied the MOPITT averaging kernels to the logarithm of the mixing ratio profiles, following Deeter et al. (2012).

OMI retrievals of tropospheric NO₂ were taken from the QA4ECV dataset (Boersma et al., 2017). For this evaluation the 3-hourly model output of NO₂ was interpolated in time to the local overpass of the satellite (13:30 h), while pixels with a satellite-observed radiance fraction originating from clouds greater than 50 % were filtered out. The averaging kernels of the retrievals are taken into account, hence making the evaluation independent of the a priori NO₂ profiles used in the retrieval algorithm. Note that by using the averaging kernels the model levels in the free troposphere are given relatively greater weight in the column calculation, which means that errors in the shape of the NO₂ profile can contribute to biases in the total column.

5.1 Ozone (O₃)

Figure 4 compares tropospheric O₃ profiles simulated by the three model versions with ozone-sonde observations for six different regions over the four seasons. Overall the three chemistry versions deliver a similar performance, reproducing the regionally averaged variability in O₃ observations, with various biases depending on the season, region and altitude range. Typically, the model versions tend to simulate lower O₃ mixing ratios in the SH middle and high latitudes compared to sonde observations and higher in the tropics. Over the Arctic, western Europe, the eastern US and the tropics, MOZ simulates too-high O₃ concentrations at all altitudes and for all seasons except June–July–August (JJA), with average positive biases ranging from 1 to 12 ppbv in the free troposphere. Here it is worth mentioning that recent updates to reaction probabilities and aerosol radius assumptions in the heterogeneous chemistry module in IFS(MOZART) significantly improved O₃ concentrations, particularly in the NH.

MOC shows positive biases over the NH mid-latitudes during winter and spring and negative biases during Arctic winter in the lower troposphere (< 700 hPa) as well as in the 700–300 hPa range in summer. CBA simulates O₃ mixing ratios that are generally in close agreement with observations over the Arctic and NH mid-latitudes, but negative biases up to 10 ppbv are obtained in the Arctic upper troposphere (500–300 hPa) during wintertime (Fig. 5, top panel). All three model versions are consistently too high close to the surface (> 800 hPa) over the tropics for all seasons, but particularly during December–January–February (DJF). Over the Antarctic and, to a lesser extent, the SH mid-latitudes all three model versions underestimate O₃, with negative biases up to 10 ppbv for a large part of the year. However, it should be noted that in the SH regions this evaluation is less representative because there are very few observations.

Figure 5Tropospheric ozone profiles from model versions CBA (red), MOC (blue) and MOZ (green) against sondes (black) in volume mixing ratios (ppbv) over six different regions (from top row to bottom row): NH polar (90–60^∘ N), western Europe (45–54^∘ N; 0–23^∘ E), eastern US (32–45^∘ N; 90–65^∘ W), the tropics (30^∘ N–30^∘ S), SH mid-latitudes (30–60^∘ S) and the Antarctic (60–90^∘ S), averaged over four seasons (from left to right: December–January–February, March–April–May, June–July–August, September–October–November).

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Figure 6Mean tropospheric ozone profiles from model versions CBA (red), MOC (blue) and MOZ (green) against sondes (black) in volume mixing ratios (ppbv) during DJF and JJA at selected individual stations. Error bars represent the 1σ spread in the seasonal mean observations.

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Figure 5 shows an evaluation of O₃ profiles against sondes at selected individual WOUDC sites representative of the Arctic (Ny-Ålesund), NH mid-latitudes (Lindenberg), the tropics (Hong Kong, Nairobi), SH mid-latitudes (Lauder) and the Antarctic (Neumayer) for DJF and JJA seasons in 2011. We note generally similar biases compared to those for the regional averages, even though local conditions play a larger role in explaining the different performance statistics for these stations. Overall, the evaluation at individual stations provides reasonable agreement between model simulations and sondes.

Evaluation against the aircraft climatology as provided in Table 4 shows on average a positive bias in the range of 10 (CBA and MOC) to 16 ppbv (MOZ), while the correlation statistics show generally acceptable values (R²>0.57), giving overall confidence in the model ability to describe ozone variability. Figure 6 shows annually averaged model biases and root mean square errors (RMSEs) for various latitude bands and for altitude ranges 900–700, 700–500 and 500–300 hPa against WOUDC sondes. In this evaluation we also present data from the CAMS interim reanalysis (CAMSiRA) for the year 2011 to put the current model evaluation into perspective. This summary analysis shows averaged biases within ±10 ppbv, which is also in line with the O₃ bias statistics against the aircraft climatology. At lower altitudes the model biases are mostly equal to or better than those from CAMSiRA, while above 500 hPa CAMSiRA delivers mostly smaller biases thanks to the assimilation of satellite ozone observations. The RMSE shows a larger spread in the lower troposphere of the NH, while at higher altitudes above 500 hPa the overall magnitude of the RMSE for the three chemistry versions converges to values ranging from 10 to 16 ppbv, depending on the latitude. Here CAMSiRA shows overall better performance, mainly for the tropics and SH, while over the NH its performance is similar to IFS(CBA). This evaluation summarises common discrepancies between model versions and observations, such as the negative bias over the Antarctic and positive bias below 700 hPa for tropical stations (see also Fig. 4), suggesting biases in common parameterisations such as transport, emissions and deposition. The largest discrepancies between model versions have been detected at northern middle and high latitudes below 500 hPa, with significantly higher values for RMSE for MOC and MOZ compared to CBA. A comparatively large positive bias for MOZ was detected, which has been linked to an underestimate of the N₂O₅ heterogeneous loss efficiency. The differences between MOC and CBA can likely be explained by similar aspects that are likely as important to explain differences with respect to the performance of IFS(MOCAGE).

Figure 7Mean of all model biases (a) and RMSE (b) values against ozone-sondes as a function of latitude for various pressure ranges (top row: 300–500; middle row: 500–700; bottom row: 700–900 hPa), averaged over the full year. Same colour codes as in the previous figure. The numbers in each latitude range indicate the number of stations that contribute to these statistics. For reference, the corresponding results from the CAMS interim reanalysis (CAMSiRA) are also given in orange.

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5.2 Carbon monoxide (CO)

Carbon monoxide is a key tracer for tropospheric chemistry, as a marker of biomass burning and anthropogenic pollution, and provides the most important sink for OH. Approximately half of the CO burden is directly emitted, and the rest is formed through degradation of CH₄ and other VOCs (Hooghiemstra et al., 2011). Hence, a correct simulation of this tracer is very important for studies of atmospheric oxidants. Considering the use of the same emissions and CH₄ surface conditions, differences in CO concentrations are essentially caused by differences in chemistry.

Figures 7 and 8 show the monthly mean evaluation against MOPITT total CO columns for April and August 2011. Whereas generally the model versions show good agreement with the observations in terms of their spatial patterns, persistent seasonal biases remain, such as the negative bias over the NH during April (further analysed in, e.g. Shindell et al., 2006; Stein et al., 2014) and a negative bias over Eurasia during August. For all three chemistry versions the patterns of enhanced CO in the tropics, associated with biomass burning, are generally well captured, as is the magnitude of CO columns over the SH. Looking at differences between model versions, CBA shows the overall highest magnitudes, implying a smaller negative bias over the NH, particularly during April, while this simultaneously results in an emerging positive bias in the tropics.

Figure 8MOPITT CO total column retrieval for April 2011 (a) and simulated by IFS(CBA) (b), IFS(MOZ) (c) and IFS(MOC) (d).

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Figure 9MOPITT CO total column retrieval for August 2011 (a) and simulated by IFS(CBA) (b), IFS(MOZ) (c) and IFS(MOC) (d).

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In Fig. 9 the annual cycle at selected GAW stations is shown, while Fig. 10 additionally shows the corresponding temporal correlation between the simulated monthly mean CO for all stations. Even though the phase and amplitude of the annual cycle are well reproduced by the model versions at several locations (e.g. Mauna Loa, Hawaii), the concentrations tend to be overestimated in the Southern Hemisphere, particularly by CBA and to a lesser extent by the other chemistry versions, and underestimated over the remote Northern Hemisphere. This points to sensitivities due to the applied chemistry scheme mainly associated with differences in OH, which is lowest in CBA and highest in MOC (see also Sect. 4). A possible overestimation of CO over the tropics and Southern Hemisphere could relate to uncertainties in the biogenic emissions (Sindelarova et al., 2014).

The correlations (in terms of R²) of monthly mean time series against GAW stations are mostly above 0.8. Particularly over Antarctica, the correlation is very high with R²≈0.9, indicating that the main processes controlling the CO abundance are indeed well represented by the model. Nevertheless, at locations between 40 and 60^∘ N the correlation is lower. These regions are strongly influenced by local chemistry and emissions, including industry and biomass burning. Clearly, the seasonal cycle is not optimally reproduced in northern America (Canada regions) by any of the three chemistry versions, indicating that uncertainties in regional emissions, such as boreal biomass burning, could be responsible for these disagreements.

Figure 10Comparison of CO mixing ratios (ppbv) at the surface as simulated (red, blue and green are model results from CBA, MOC and MOZ, respectively) and observed (black) at 12 stations sorted by decreasing latitudes. The bars represent 1 standard deviation of the monthly average for the location of the station.

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Figure 11Temporal correlation (R²) between monthly mean surface CO as derived from observations (GAW network) and model simulations (a: CBA, b: MOC, c: MOZ).

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Compared to aircraft observations (see Fig. 11), the three model versions produce similar CO mixing ratio vertical profiles, with differences among them typically within the range of 10 %–20 %, depending on the location. The biomass burning plumes are reproduced consistently (see Fig. 11, TRACE-A, West Africa coast), and all three models compare well with observations for both background conditions in the Northern Hemisphere (SONEX, Ireland) and highly polluted conditions (PEM-West-B, China coast).

Figure 12Comparison of simulated CO vertical profiles by using the CBA (red solid line), MOC (blue solid line) and MOZ (green solid line) chemistry versions against aircraft data (black dots). Also shown are the modelled (dashed lines) and measured (black rectangular) standard deviations. The numbers on the right vertical axis indicate the number of available measurements.

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5.3 Formaldehyde (CH₂O) and methyl hydroperoxide (CH₃OOH)

Formaldehyde is important as one of the most ubiquitous carbonyl compounds in the atmosphere (Fortems-Cheiney et al., 2012). It is mainly formed through the oxidation of methane, isoprene and other VOCs such as methanol (Jacob et al., 2005), while its oxidation and photolysis are responsible for about half of the CO in the atmosphere. A good agreement of the simulations with the observations can be seen from Fig. 12, where the vertical profile from selected aircraft observations and model simulations is shown. Also from Table 4 it is clear that all three model versions reproduce formaldehyde accurately. The weighted bias is always well below 1 standard deviation unit (i.e. −0.11, 0.31 and 0.26 for CBA, MOC and MOZ, respectively), indicating that the simulations are well within the statistical uncertainties.

Figure 13Comparison of simulated CH₂O vertical profiles by using the CBA (red), MOC (blue) and MOZ (green) chemistry versions against aircraft data (black). Line styles and symbols have the same meaning as in Fig. 12.

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Figure 14Comparison of simulated CH₃OOH vertical profiles by using the CBA (red), MOC (blue) and MOZ (green) chemistry versions against aircraft data (black). Line styles and symbols have the same meaning as in Fig. 11.

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CH₃OOH is a main organic peroxide acting as a temporary reservoir of oxidising radicals (Zhang et al., 2012). It is mainly formed through reaction of CH₃O₂+HO₂, which are both produced in the oxidation process of many hydrocarbons. The CH₃OOH lifetime of about 1 d globally is mainly governed by its reaction with OH and photolysis. Figure 13 presents an evaluation for CH₃OOH for the same sites presented for CH₂O in Fig. 12. Mixing ratios are generally reasonably within the range of the observations, for example over the tropical Pacific over Fiji. A larger spread between model versions, with a strong overestimate for CBA, is found in the Amazon region over Brazil. As a global average, a comparatively large underestimate for MOZ and, to a lesser extent, also for CBA was found; see also Table 4. Nevertheless, correlations, especially those weighted with the uncertainties, are overall good, giving general confidence in the modelling.

Considering the short lifetimes for CH₂O (a few hours in daytime) and also CH₃OOH, as well as the large dependence of their abundances on details of the VOC degradation scheme, which vary across the chemistry versions presented here, it is beyond the scope of this paper to explain these differences. This would require a detailed assessment of the respective production and loss budgets, which are currently not available.

5.4 Ethene (C₂H₄)

Ethene is the smallest alkene which is primarily emitted from biogenic sources. In our configuration, biogenic C₂H₄ emissions are 30 Tg yr⁻¹, which appears at the upper end of such emission estimates as reported by Toon et al. (2018). The rest of the emissions are attributed to incomplete combustion from biomass burning or anthropogenic sources.

The three chemical mechanisms produce mostly very similar mixing ratios of C₂H₄. Nevertheless, as indicated by the bias (Table 4), which ranges between −2 and −14 in standard deviation units, as well as the weighted correlations, the model versions have difficulties in simulating C₂H₄. Even though this evaluation should only be considered in a climatological sense, the vertical profiles (see Fig. 13) are strongly biased (e.g. SONEX, Newfoundland and PEM-Tropics-A, Tahiti), with positive biases occurring at the surface and negative biases in the free troposphere. In remote regions and at higher altitudes, where the direct influence of emissions is lower, the model is at the lower end of the range of observations, with frequent underestimates (see Fig. 13, PEM-Tropics-A, Christmas Island). This was already observed in other studies (e.g. Pozzer et al., 2007), implying that the chemistry of this tracer is not well understood. As the underestimation appears to be ubiquitously distributed, this suggests that C₂H₄ decomposition is too strong or that the model versions miss some chemical production terms (e.g. Sander et al., 2019).

Furthermore, it is interesting to note the comparatively large difference present between the simulations at high latitudes (e.g. SONEX, Newfoundland), where the largest relative differences in modelled OH have been found (see also Sect. 4), illustrating the importance of OH for explaining inter-model differences. CBA indeed shows the largest values for C₂H₄, which is explained by the comparatively low abundance of OH in this model version.

Figure 15Comparison of simulated C₂H₄ vertical profiles by using the CBA (red), MOC (blue) and MOZ (green) chemistry versions against aircraft data (black). Line styles and symbols have the same meaning as in Fig. 12.

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5.5 Ethane (C₂H₆)

Ethane (C₂H₆) is the lightest trace gas of the family of alkanes and has an atmospheric lifetime of about 2 months. Ethane emissions are primarily of anthropogenic nature and have seen a relatively strong decrease since the 1980s (Aydin et al., 2011). Nevertheless, since 2009 an increase in C₂H₆ concentrations has been observed, believed to be associated with recent increases in CH₄ fossil fuel extraction activities (Hausmann et al., 2016; Monks et al., 2018).

Compared to aircraft observations, all three model versions significantly underestimate the C₂H₆ observed mixing ratios at all locations and ubiquitously (see Fig. 14). A particularly strong underestimation is found in the Northern Hemisphere, where most of the observations are located (e.g. the SONEX campaign over Ireland). A strong negative bias was also reported in the overall statistics (Table 4), even though, contrarily to C₂H₄, the weighted correlation showed acceptable values for all versions (R²>0.7). These findings can be explained well by an underestimation of the MACCity-based C₂H₆ emissions, which are at least a factor of 2 lower than the corresponding estimates of 12–17 Tg yr⁻¹ reported in the literature (Monks et al., 2018; Aydin et al., 2011; Emmons et al., 2015; Folberth et al., 2006). On the other hand, the comparison with the TRACE-A field campaign, which covered long-range transport of biomass burning plumes, shows a reasonable agreement in the lower troposphere (1–4 km), i.e. at the location of the biomass plume, suggesting appropriate biomass burning emissions. Still, a considerable underestimation is present in the upper troposphere, probably due to the missing background concentration.

Figure 16Comparison of simulated C₂H₆ vertical profiles by using the CBA (red), MOC (blue) and MOZ (green) chemistry versions against aircraft data (black). Line styles and symbols have the same meaning as in Fig. 12.

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5.6 Nitrogen dioxide (NO₂)

Nitrogen dioxide is a trace gas difficult to compare with in situ observations due to its photochemical balance with nitric oxide. Nitrogen dioxide shows a strong diurnal cycle, mainly due to the fast photolysis rate. Here only daytime values have been used to construct the model averages because the observations from the various field campaigns were equally conducted in daylight conditions. Figure 15 shows the strong variability in daytime NO₂ values in both the measurements and the simulations. In general the MOC simulation shows the highest concentration of NO₂ in different locations, particularly over source regions (see Fig. 15; TRACE-P, Japan, and TOPSE-Feb, Boulder), with MOZ and CBA being more similar. This is in line with the analysis given in Sect. 4. Outside the source regions the secondary processes (such as its equilibrium with HNO₃; see also next section) have larger influences, and hence the model and observation profiles of NO₂ show even stronger variability and larger differences (see Fig. 15; TOPSE-May, Thule). Still, in general all the chemical mechanisms are able to reproduce NO₂ within 1 standard deviation (see Table 4), even though the unweighted mean bias for MOC is significantly higher than for CBA and MOZ.

Figure 17Comparison of daytime NO₂ vertical profiles simulated by CBA (red), MOC (blue) and MOZ (green) chemistry versions against aircraft data (black). Line styles and symbols have the same meaning as in Fig. 12.

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Figure 18Monthly mean tropospheric NO₂ columns from OMI satellite retrievals from the QA4ECV product for April 2011, along with the corresponding collocated model biases.

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Figure 19Monthly mean tropospheric NO₂ columns from OMI satellite retrievals from the QA4ECV product for August 2011, along with the corresponding collocated model biases.

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Figures 16 and 17 evaluate tropospheric NO₂ using the OMI satellite observations. The simulations deliver generally appropriate distributions with a correct extent of the regions with high pollution, as largely dictated by the emission patterns. Nevertheless, a general underestimation of NO₂ over West Africa in April and Central Africa and South America in August is found, suggesting uncertainties associated with the modelling of biomass burning emissions.

Another interesting finding is a relatively strong negative bias over the Eurasian and North American continents in April for CBA, which is stronger than modelled in MOZ and MOC. In contrast, MOC in particular (but also MOZ) overestimates NO₂ over the comparatively clean North Atlantic and North Pacific oceans in April. This all suggests a relatively short NO_x lifetime in CBA compared to MOZ and MOC, which in turn helps to explain the lower O₃ over the NH mid-latitude regions as modelled with CBA (see Fig. 5). The causes of these differences in modelled NO₂ are mainly the use of a different numerical solver and differences in the efficiency assumed for N₂O₅ heterogeneous reactions (see Sect. 2.1.4). In August the differences in tropospheric NO₂ between the three model versions are smaller than in April.

5.7 Nitric acid (HNO₃)

Compared to several of the trace gases previously analysed, nitric acid is not primary emitted but is purely photochemically formed in the atmosphere. It has a very high solubility and therefore tends to be scavenged by precipitation very efficiently, providing an effective sink for the NO_x family. Furthermore, it can act as a precursor for nitrate aerosols (Bian et al., 2017). HNO₃ concentrations are therefore expected to show the largest variation between the simulations, as the production and sink terms can largely differ due to uncertainties in the parameterisations. In Fig. 18, the model results are compared with selected aircraft measurements. Although all three models tend to reproduce HNO₃ in a statistically similar way, over the lower troposphere and up to 2 km of height MOC tends to result in higher HNO₃ concentrations compared to the other two chemical mechanisms and measurements. This is also reflected by the overall lowest negative biases in Table 4. While MOC performs better at higher altitudes, in a biomass burning plume (e.g. TRACE-A; Fig. 18), it also overestimates the production of HNO₃ or underestimates its sinks. Over polluted regions (Fig. 18; TRACE-P, Japan), all models tend to perform well, but in remote areas (Fig. 18; TOPSE, Churchill) the discrepancies between the models increase, with MOC delivering twice as much HNO₃ as the other two model versions. Nevertheless, as the variability of the observations is very large, all the model versions still fall within the range of uncertainties of the observations. The discrepancies between the model versions can be mainly attributed to differences in NO_x lifetimes, associated with differences in heterogeneous chemistry, and parameterisations for nitrate aerosol formation, as discussed in Sect. 2.1.4.

Figure 20Comparison of simulated HNO₃ vertical profiles by using the CBA (red), MOC (blue) and MOZ (green) chemistry versions against aircraft data (black). Line styles and symbols have the same meaning as in Fig. 12.

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Figure 21Comparison of simulated SO₂ vertical profiles by using the CBA (red), MOC (blue) and MOZ (green) chemistry versions against aircraft data (black). Line styles and symbols have the same meaning as in Fig. 12.

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5.8 Sulfur dioxide (SO₂)

Similar to HNO₃, SO₂ is also strongly influenced by wet deposition due to its high solubility. Furthermore, SO₂ is primarily emitted and converted to sulfuric acid (H₂SO₄) both by gas-phase and aqueous-phase oxidation, an essential process for the production of new sulfate aerosol particles. Considering the complexity of the processes that control the SO₂ fate in the atmosphere, large variability is expected for this tracer. The evaluation of SO₂ shows that among the three chemistry versions, CBA always produces the highest SO₂ mixing ratios, whereas MOC produces the lowest, and MOZ always lies in between. Nevertheless, all three mechanisms tend to underpredict SO₂ mixing ratios (see Table 4) compared to the aircraft observations (see Fig. 19). Notwithstanding significant uncertainties regarding SO₂ emissions, the simulated mixing ratios over polluted regions seem to reproduce the observed values (Fig. 19; Trace-P, China and Japan). CBA presents the best comparison with aircraft observations, as can be seen in Fig. 19 for the TOPSE aircraft measurements. Also from Table 4, only CBA delivers a normalised weighted bias within [$-\mathrm{1},\mathrm{1}$] for SO₂, while for the other model versions these are below −1 (−2.25 and −1.20 for MOC and MOZ, respectively).