The hydroxyl radical (OH) is the main oxidant responsible for removing trace gases from the atmosphere, and its concentration defines the tropospheric oxidation capacity. OH is primarily produced by the photolysis of ozone in the presence of water vapor. The lifetimes of key atmospheric trace gases are governed by how quickly they are removed by reaction with OH. Oxidation of volatile organic compounds (VOCs) by OH produces tropospheric ozone and fine particulate matter which are detrimental to human health and vegetation and impact climate. The oxidation of VOCs, carbon monoxide (CO), and methane provides the main sink of OH in the troposphere. Oxidation of methane and VOCs accounts for over half of the global CO production (Duncan et al., 2007; Safieddine et al., 2017), resulting in a tight coupling of these compounds.

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). However, constraining models with observations of ozone and water vapor cannot resolve biases in model OH (Strode et al., 2015), which is impacted by additional complex factors such as the chemical mechanism and the ozone photolysis frequency (Nicely et al., 2017). Constraining the performance of model chemical mechanisms has largely focused on regions of strong biogenic and anthropogenic activity (Emmerson and Evans, 2009; Yu et al., 2010; Marvin et al., 2017), but at least half of the oxidation of methane occurs over the ocean, where models have received little evaluation due to a lack of observational constraints.

The introduction of airborne measurements of OH reactivity (OHR) provides a method to evaluate the sink of OH across a range of altitudes and a variety of locations and chemical environments (Mao et al., 2009; Thames et al., 2020). Previous work compared surface observations of OHR at a single site to the sum of individually calculated OHR components from measurements (Di Carlo, 2004; Yoshino et al., 2006; Sinha et al., 2008, 2010; Mao et al., 2010; Dolgorouky et al., 2012; Hansen et al., 2014; Nakashima et al., 2014; Nölscher et al., 2012, 2016; Ramasamy et al., 2016; Zannoni et al., 2016, 2017) or from simple models (Ren et al., 2006; Lee et al., 2009; Lou et al., 2010; Mogensen et al., 2011; Mao et al., 2012; Edwards et al., 2013; Kaiser et al., 2016; Whalley et al., 2016). Thames et al. (2020) found evidence of missing OHR between measurements and an observationally constrained box model during the first three ATom deployments. Chen et al. (2019) compared calculated OHR from a global model to OHR determined from a suite of VOCs but did not have measurements of OHR itself. Ferracci et al. (2018) found that missing OHR estimated from surface observations could result in a small increase in the methane lifetime in a global model. Safieddine et al. (2017) and Lelieveld et al. (2016) presented the first global model simulations of OHR but with only qualitative comparison to observations. No study has quantitatively compared simulated and observed OHR in a global model in an effort to constrain the OH sink.

The ATom campaign (Wofsy et al., 2018) provides an unprecedented opportunity to test models in the remote atmosphere with a detailed suite of chemical observations. We simulate the first two deployments (ATom-1: July–Agust 2016, ATom-2: January–February 2017) using the GEOS-Chem chemical transport model (CTM) as our tool to explore potential sources of systematic errors that could explain the community-wide model overestimate in global mean OH and underestimate of the methane lifetime. We include model evaluation with measurements of OHR, a relatively new constraint available for assessing atmospheric oxidation capacity. To our knowledge, this is the first quantitative use of this measurement to evaluate a CTM.

We compare observed and simulated OH concentrations to evaluate whether differences are consistent with the bias in τCH4 discussed in Sect. 2.1. Figure 3 shows modeled OH sampled along the flight tracks and compared to observed OH (Table 2) for ATom-1 (boreal summer 2016) and ATom-2 (boreal winter 2017) in each hemisphere from the lowest sampled altitude (∼200 m) to 10 km. There is no evidence of a systematic overestimate in modeled OH throughout the troposphere. Figure S2 shows similarly good agreement across the observed frequency distributions of OH concentration. A model OH overestimate is apparent in the lowest 2 km in the Northern Hemisphere summer that could indicate excessive OH production or an underestimated sink from emissions of ocean VOCs. Global models tend to overestimate OH against constraints from methyl chloroform observations (Shindell et al., 2006; Naik et al., 2013; Nicely et al., 2017), but we find here that tropospheric OH is successfully simulated within observational uncertainty (74 % to 135 %, 2σ confidence level). This result from a global CTM is consistent with good agreement between OH measurements and a box model during NASA's Pacific Exploratory Mission – Tropics (PEM-Tropic B) campaign in the clean remote Pacific (Tan et al., 2001) and a similar analysis by Brune et al. (2020) for ATom 1 through 4.

We calculate the median air-mass-weighted column average OH (OHcol) from the median OH concentrations in Fig. 3 and the total tropospheric air mass over the ocean. During ATom-1, the modeled OHcol in the Northern (Southern) Hemisphere is 4.5(1.4)×106 molecules cm-3 compared against the observations of 4.4(1.1)×106 molecules cm-3 during ATom-1. Similarly, during ATom-2, OHcol is 0.8(2.8)×106 molecules cm-3 in the model and 0.9(2.6)×106 molecules cm-3 in the observations. Median model OHcol is within 30 % of observations during both deployments, with the smallest bias in the total column during Northern Hemisphere summer when OH is at a maximum. As discussed above, model OH is overestimated in the lowest 2 km during this period, but this bias is minimized in the column average. The observed air-mass-weighted ratio of Northern to Southern Hemisphere OH, calculated in the same manner as described in Sect. 2, is 2.8 during ATom-1 and 0.2 during ATom-2, indicating a strong seasonality that the model largely reproduces (ratios of 2.3 and 0.2). This ratio is less than the ratio of OHcol because there is approximately 30 % less air mass over the ocean in the Northern Hemisphere ocean than over the Southern Hemisphere. This seasonality is masked by calculations performed on an annual mean basis. The seasonality in this ratio reported by Wolfe et al. (2019) for satellite-derived OH during ATom-1 and ATom-2 is more modest because they calculate a daily average OH that extends to the tropopause, while here, we use largely daytime aircraft observations below 10 km.

The model is in good agreement with OH measurements during ATom, but the uncertainty in the observations is similar to a recent estimate of the GEOS-Chem model uncertainty for OH of 25 % to 40 % (Christian et al., 2018). In addition, the lifetime of OH is short (seconds) and atmospheric concentrations are highly variable; thus, direct model comparison to measured OH is insufficient to demonstrate model skill in capturing the broader remote oxidation capacity. Agreement between the model and observations could also result from compensating errors in the OH source and sink. We support the model comparison in Fig. 3 with an evaluation of the key factors governing OH production and loss measured by ATom and investigate potential missing sources of VOCs from the ocean during summertime.

In the remote troposphere, OH is primarily produced from the photolysis of ozone in the presence of water vapor (Monks, 2005) and is enhanced by nitrogen oxides (NOx) from lightning and transport from continental sources. Methane, CO, and VOCs provide the main OH sinks (Murray et al., 2014). We compare the model to ATom-1 and ATom-2 observations of the drivers of the tropospheric OH source (water vapor, ozone, ozone photolysis frequency, NOx) to determine possible broader sources of model bias.

Figure 4 compares observations of water vapor mixing ratios to the NASA MERRA-2 reanalysis product used by the model. MERRA-2 is generally successful at reproducing observed tropospheric water vapor (Gelaro et al., 2017), and we also find good agreement compared with ATom-1 and ATom-2 observations throughout the troposphere. We evaluate the model treatment of the incoming actinic flux and the resulting ozone photolysis frequency (j(O1D)) in Fig. 5. Hall et al. (2018) showed that GEOS-Chem actinic fluxes in both cloudy and clear skies were well simulated during the ATom-1 deployment. Figure 5 confirms the minimal model bias in j(O1D) and successful representation of the observed seasonality with median summertime values below 3 km (∼4×10-5 s-1) approximately 4 times higher than in winter (∼1×10-5 s-1).

The GEOS-Chem ozone simulation has been extensively tested against ozonesondes, aircraft, and satellite observations and shows no systematic overestimates (Hu et al., 2017), with the exception of continental surface concentrations (Fiore et al., 2009; Travis et al., 2016). Figure 6 shows that the highest (54–63 ppb) and lowest (14 ppb) tropospheric ozone observed during ATom-1 and ATom-2 occurs during summer in the mid to upper troposphere and marine boundary layer, respectively. Ozone is less variable in wintertime, with values between 30 and 50 ppb. The model generally reproduces the magnitude and shape of the tropospheric ozone profiles as well as the seasonality observed during both deployments. There is no evidence of the systematic Northern Hemisphere ozone bias previously seen in global model evaluations (Young et al., 2013) that was suggested as a cause of excessive OH (Naik et al., 2013). This may be reflected in the improved model interhemispheric OH ratio (Sect. 2.1) seen here over previous studies. Upper tropospheric ozone is overestimated in all cases but Northern Hemisphere summer, but this would not have a large influence on primary OH production (or the methane lifetime) at these altitudes (Brune et al., 2020).

OH is enhanced in the presence of NOx (≡NO+NO2). We use NOy here (Fig. 7a) as our constraint as observed NO2 was generally near the detection limit in both deployments. We also show NO (Fig. 7b) given its role in secondary OH production. The model reproduces the maximum in NOy that occurs in the Northern Hemisphere upper troposphere in summertime due to lightning (Marais et al., 2018). Observations show little variability between summer and winter NOy in the lower troposphere. Southern Hemisphere NOy is underestimated in the lowest few kilometers in both seasons, which could be due to missing ocean production of methyl nitrate (Fisher et al., 2018). The largest model discrepancy is an overestimate of approximately 70 % in the Northern Hemisphere wintertime. Observations of NO reflect the structure of NOy, with the exception of Northern Hemisphere winter.

The primary sinks of tropospheric OH are CO, methane, and VOCs; OHR measurements represent the sum effect of these species. Previous aircraft measurements of OHR provided evidence of missing reactivity in the remote atmosphere linked to unknown highly reactive VOCs (Mao et al., 2009). During ATom, Thames et al. (2020) measured OHR over the Atlantic and Pacific oceans and determined that missing OHR also correlated with oxygenated VOCs, suggesting the presence of unknown ocean emissions. We compare directly measured OHR during the ATom-1 and ATom-2 deployments to calculated OHR (cOHRobs) according to Eq. (1) from the full ATom measurement suite and from the model (cOHRmod) sampled along the flight path. Table 2 describes the observations used to calculate cOHR.

Figure 9 shows the comparison of OHR and cOHR from the model and observations. The observed cOHR is typically less than observed OHR. Along the flight tracks, cOHRobs and cOHRmod show good agreement and strong correlation (r2=0.95 for ATom-1 and ATom-2). The model underestimates cOHRobs by 10 % to 12 % in the lowest 3 km; we discuss this difference below. The measured relationship between OHR and cOHRobs is weaker (r2=0.53 for ATom-1, r2=0.56 for ATom-2) and cOHRobs is less than OHR below 3 km by 0.2 to 0.4 s-1. Thames et al. (2020) showed that median missing reactivity (between OHR and an observationally constrained box model) below 4 km during the ATom-1, ATom-2, and ATom-3 deployments was between 0.2 and 0.8 s-1. They provided statistical evidence that while near the level of the instrument accuracy, missing OHR in the marine boundary layer was statistically significant. We find that missing OHR is not associated with acetonitrile or CO (r2 < 0.06), indicating that biomass burning is not the cause. Acetaldehyde in Northern Hemisphere summer has the strongest relationship with missing OHR (r2=0.19, p-value ≪0.01, Fig. S3), which suggests a potential role for unmeasured reactive VOCs or their oxidation products from the ocean, as also suggested by Read et al. (2012) and Thames et al. (2020).

Ocean emissions of VOCs have been suggested as a source of remote secondary organic aerosols (Gantt et al., 2010; Kim et al., 2017; Mungall et al., 2017), but their impact on remote reactivity has not been quantified. Our base simulation, described in Sect. 2.1, only includes air–sea exchange of acetone, acetaldehyde, methanol, and dimethyl sulfide. We determine whether additional compounds emitted from the ocean, but not generally included in models, could increase cOHRmod and reconcile the observed discrepancy described above. We follow the standard methodology for air–sea exchange described in Millet et al. (2008) to include emission of the species listed in Table 3 using available measured seawater concentrations, with the addition of isoprene implemented as a direct emission according to Arnold et al. (2009). As shown in Table 3, air–sea exchange represents a net sink of VOCs on an annual basis (-75 Tg C yr-1), but this is mainly due to ocean uptake of acetone, which is a negligible component of cOHR.

Interfacial photochemistry may provide an additional abiotic source of VOCs from the ocean. We model abiotic ocean emissions of VOCs according to Brüggemann et al. (2018) by applying species-specific emission factors to the monthly ocean photochemical potential derived in their study. We use the emission factor appropriate for the upper bound of this source according to Brüggemann et al. (2017) (Table S4). Table 4 provides a breakdown of these additional VOCs with a total annual emission of 28 Tg C yr-1.

Figure 10 shows the annual mean impact of all ocean emissions described in Tables 3 and 4 (including an adjustment to the acetaldehyde seawater concentration described below in Sect. 5.1) on cOHRmod by turning off those ocean sources in a 1-year simulation. Average annual surface cOHRmod over the ocean increases by 6 % over the base simulation and 12 % over the simulation with no ocean emissions. The largest increases occur in regions of higher biogenic activity along coastlines and in the Southern Ocean due to the adjustment to acetaldehyde emissions discussed in Sect. 5.1. The incremental impact of the additional ocean emissions over the base simulation is shown in Fig. S4. Without any ocean emissions, global mean OH would be 2 % greater than in the case with comprehensive treatment of ocean VOCs. Figure 9 shows that along the flight tracks, cOHRmod increases below 3 km by 3 % to 9 %, which reduces the model bias against cOHRobs. However, the majority of the added species (Tables 3 and 4) were measured during ATom, would therefore contribute to cOHRobs, and cannot explain the gap in OHR.

We evaluate the impact of further expanding the oceanic source of reactive VOCs to reconcile the discrepancy between cOHRobs and OHR in a similar manner to Mao et al. (2009). Here, we test a source of alkanes as previously suggested by Read et al. (2012), using the model species ALK4 (> C4 alkanes) that has a calculated lifetime of less than 2 d in the Northern Hemisphere summer (kOH=2.3×10-12 cm3 molecules-1 s-1 at 298 K). Known alkanes have been measured in seawater (Plass-Dülmer et al., 1993), but the implied source is small. Consequently, we use ALK4 for testing only. Generating the missing OHR in this way requires an implausibly large oceanic ALK4 source of approximately 340 Tg C yr-1 compared against all other sources of VOCs in the model (Tables 3 and 4). A sensitivity test with this source, shown in Fig. 9, largely closes the gap between cOHRmod and OHR but would result in a 20 % to 50 % reduction in OH below 3 km, biasing the model OH simulation (Fig. 3) and degrading model NOy (Fig. 7) due to increased PAN formation.

Thames et al. (2020) found that a partial recycling of OH would be required to maintain consistency with observed OH and HO2 during ATom when adding an unknown source of reactivity. If the unknown VOC we suggest includes some OH recycling in its oxidation mechanism and does not produce PAN, the model bias in OH could be mitigated. We use isoprene as our test of a more reactive VOC that includes OH recycling by scaling the ALK4 emission source by the reaction rate of isoprene with OH to obtain a more reasonable emission source of approximately 9 Tg C yr-1. Figure 9 shows that this source actually has a minimal impact on cOHRmod of no more than 0.1 s-1. Only one-third in summer and two-thirds in winter of the additional cOHRmod from the ocean source of ALK4 are attributable to ALK4; the rest is due to CO, acetaldehyde, and other aldehydes from both increased chemical production and longer lifetimes from suppressed OH. Therefore, a larger source of even a reactive VOC like isoprene is required to close the gap in missing OHR. Reconciling cOHRmod and OHR is therefore difficult using the existing suite of ATom measurement constraints and possible known precursors; further investigation of the accuracy of the OHR measurements in challenging remote conditions may be needed.

We also assess whether the model accurately represents the components of cOHRobs and explore potential additional sources of missing cOHRmod. Figures 11 and 12 show the components of median cOHR in the base simulation below 3 km for each deployment. The composition of cOHRmod is generally consistent with cOHRobs. CO and methane make up half or greater of both cOHRobs and cOHRmod. There is no systematic underestimate in CO reactivity as might be expected from the general model bias described by Shindell et al. (2006), with the exception of a 9 % underestimate during Northern Hemisphere winter when the lifetime of CO is longer and biases in continental sources could have a larger impact. During the ATom-1 deployment, cOHRobs is 50 % higher in the Northern Hemisphere (summer) than in the Southern Hemisphere (winter) primarily due to the increase in methyl hydroperoxide (MHP) concentrations. During the ATom-2 deployment, cOHRobs is 60 % higher in the Northern Hemisphere (winter) than in the Southern Hemisphere (summer) due to the large contribution of CO in Northern Hemisphere wintertime. The model successfully represents the observed seasonality during both deployments but underestimates cOHRobs by 12 % in the Northern Hemisphere and 9 % in the Southern Hemisphere.

The difference between measured and simulated cOHR is mainly due to differences between measured and simulated OVOCs. These compounds contribute on average 25 % to cOHRobs but 17 % to cOHRmod. The largest difference in reactivity is from acetaldehyde. Differences between simulated and measured MHP (Fig. S5) are also important and could reflect an error in the MHP lifetime (Müller et al., 2016). However, interferences in the MHP measurement in the boundary layer (Supplement, Sect. S6) have yet to be resolved, and therefore we do not further evaluate causes of underestimated MHP here. We do consider potential missing sources of model acetaldehyde constrained by the ATom measurements over the ocean and assess their impact on simulated OH and CO in Sect. 5.1.

The detailed set of chemical information available from the ATom field campaign provides the most comprehensive dataset ever collected to evaluate models in the remote atmosphere. The sampling strategy of collecting observations throughout the troposphere in multiple seasons is ideally suited for improving our understanding of tropospheric chemistry in a poorly observed region of the atmosphere. We use the first two deployments of the ATom field campaign during July–August 2016 and January–February 2017 to investigate sources of bias in model simulations of OH. Global models such as the GEOS-Chem CTM used here tend to overestimate the loss of methane by OH and underestimate CO, which provides the main tropospheric sink of OH. Comparisons of the model with observations from the first two ATom deployments do not show systematic bias in the simulation of OH or the drivers of remote OH production (water vapor, photolysis of ozone, ozone, and NOy), with the exception of wintertime NOy, which is overestimated by 70 %.

The model overestimate of wintertime NOy is largely attributable to nitric acid. This bias is not due to an anthropogenic inventory overestimate but may reflect insufficient wet scavenging as well as loss to sea-salt aerosols by nitric acid, although the former mechanism may be counteracted by photolysis of the resulting nitrate aerosols. The impact of resolving this wintertime NOy bias is uncertain but could marginally reduce the model overestimate of OH. Future work should improve constraints on these mechanisms, which have all received only preliminary validation, and carefully examine their impact in the context of broader atmospheric chemistry, particularly NOy partitioning throughout the troposphere.

We present the first comparison of measured OH reactivity (OHR) from aircraft with a global model to evaluate the tropospheric sink of OH. We calculate OH reactivity (cOHRobs) from relevant species observed during ATom and compare this to cOHR from the model (cOHRmod). Measured OHR is higher than cOHRobs by approximately 0.2 to 0.4 s-1 below 3 km. This missing OHR correlates with acetaldehyde during summer, indicating a potential source of missing reactive VOCs, similar to the findings of Mao et al. (2009) and S. Wang et al. (2020). The addition of a comprehensive set of ocean emissions of VOCs increases global mean cOHR by 6 % but cannot reproduce the observed OHR enhancement during ATom-1. Adding sufficient alkanes to the model to resolve this bias requires an improbably large ocean source (340 Tg C yr-1) and would degrade the model simulation of OH and NOy. Only one-third of the increase in cOHR in summer in this test is due to the alkanes; the rest is from oxidation products and changes in OH. Therefore, a more reactive VOC would still need to be emitted in large amounts.

The model successfully simulates the seasonality and hemispheric gradient in cOHR but has a persistent underestimate of up to 12 % in the lowest 3 km, primarily due to missing model acetaldehyde. The model does not underestimate CO, with the exception of Northern Hemisphere winter, which has been previously recognized by Kopacz et al. (2010) and attributed to underestimated fossil fuel emissions. The inability to reproduce observations of remote acetaldehyde was first observed during the PEM-Tropics campaign (Singh et al., 2001, 2003; Millet et al., 2010), but the measurement was uncertain. Improvements in measurement precision and the accompanying measurement of PAA during ATom (S. Wang et al., 2019) strengthen the conclusion that there is a large amount of acetaldehyde present in the atmosphere that cannot be explained by current models. We investigate possible underestimates in known sources of acetaldehyde, including emissions of VOCs from anthropogenic, biomass, or oceanic sources or production from the photolysis of organic aerosols. No known source can fully resolve the bias in acetaldehyde throughout the troposphere, and particularly in the Northern Hemisphere summer. We consider the possibility that there is a large, diffuse source of unknown VOCs by implementing 100 Tg yr-1 of such a compound from ocean, biomass burning, or anthropogenic sources. This hypothetical source modestly reduces the model acetaldehyde bias and is compatible with the simulation of OH and cOHR; however, an additional source is required to resolve the largest bias in the Northern Hemisphere summer. Errors or omissions in the oxidation mechanism of known VOCs could be another source of bias. For example, significant uncertainties exist in peroxy radical (RO2) chemistry for large RO2 molecules (Praske et al., 2017), although the flux of carbon through a minor pathway would have to be large, restricting the possible known sources. Further laboratory and field observations are needed to understand which precursors and sources could lead to the sustained production of acetaldehyde observed during ATom and prior campaigns.

This study demonstrates that long-standing model biases in global mean OH are unlikely to be due to errors in simulating tropospheric chemistry over the ocean. This implies that a large bias must be present in OH production or loss over land and future work should focus on evaluating continental OH sources and sinks. Errors in modeled OH were recently investigated by Strode et al. (2015), and when overestimates related to production terms were corrected, model OH remained too high in the Northern Hemisphere, suggesting that future studies should focus on errors in OH loss.