ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-7109-2018Global modelling of the total OH reactivity: investigations on the
“missing” OH sink and its atmospheric implicationsGlobal modelling of the total OH reactivityFerracciValeriov.ferracci@cranfield.ac.ukhttps://orcid.org/0000-0001-6647-993XHeimannInesAbrahamN. Lukehttps://orcid.org/0000-0003-3750-3544PyleJohn A.https://orcid.org/0000-0003-3629-9916ArchibaldAlexander T.https://orcid.org/0000-0001-9302-4180Centre for Atmospheric Science, Department of Chemistry, University of
Cambridge, Lensfield Road, CB2 1EW, UKNational Centre for Atmospheric Science, University of Cambridge,
Cambridge, UKnow at: Centre for Environmental and Agricultural Informatics,
Cranfield University, College Road, MK43 0AL, UKValerio Ferracci (v.ferracci@cranfield.ac.uk)24May20181810710971293January20189January201816April2018Acceptedon26This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/7109/2018/acp-18-7109-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/7109/2018/acp-18-7109-2018.pdf
The hydroxyl radical (OH) plays a crucial role in the chemistry of the
atmosphere as it initiates the removal of most trace gases. A number of field
campaigns have observed the presence of a “missing” OH sink in a variety of
regions across the planet. A comparison of direct measurements of the OH loss
frequency, also known as total OH reactivity (kOH), with the sum of
individual known OH sinks (obtained via the simultaneous detection of species
such as volatile organic compounds and nitrogen oxides) indicates that, in
some cases, up to 80 % of kOH is unaccounted for. In this work,
the UM-UKCA chemistry-climate model was used to investigate the wider
implications of the missing reactivity on the oxidising capacity of the
atmosphere. Simulations of the present-day atmosphere were performed and the
model was evaluated against an array of field measurements to verify that the
known OH sinks were reproduced well, with a resulting good agreement found
for most species. Following this, an additional sink was introduced to
simulate the missing OH reactivity as an emission of a hypothetical molecule,
X, which undergoes rapid reaction with OH. The magnitude and spatial
distribution of this sink were underpinned by observations of the missing
reactivity. Model runs showed that the missing reactivity accounted for on
average 6 % of the total OH loss flux at the surface and up to 50 %
in regions where emissions of the additional sink were high. The lifetime of
the hydroxyl radical was reduced by 3 % in the boundary layer, whilst
tropospheric methane lifetime increased by 2 % when the additional OH
sink was included. As no OH recycling was introduced following the initial
oxidation of X, these results can be interpreted as an upper limit of the
effects of the missing reactivity on the oxidising capacity of the
troposphere. The UM-UKCA simulations also allowed us to establish the
atmospheric implications of the newly characterised reactions of peroxy
radicals (RO2) with OH. Whilst the effects of this chemistry on
kOH were minor, the reaction of the simplest peroxy radical,
CH3O2, with OH was found to be a major sink for
CH3O2 and source of HO2 over remote regions at the
surface and in the free troposphere. Inclusion of this reaction in the model
increased tropospheric methane lifetime by up to 3 %, depending on its
product branching. Simulations based on the latest kinetic and product
information showed that this reaction cannot reconcile models with
observations of atmospheric methanol, in contrast to recent suggestions.
Introduction
The removal of the vast majority of trace gases emitted into the atmosphere
is initiated by reaction with the hydroxyl radical, OH. OH is primarily
formed following the reaction of excited oxygen atoms, O(1D),
originating from the photolysis of ozone at short wavelengths, with water:
O3+hν(λ<340nm)→O(1D)+O2O(1D)+H2O→2OH.
OH generated via any route other than Reaction (R1) and Reaction (R2) is
referred to as secondary OH; examples of processes yielding secondary OH
include the photolysis of H2O2 and the reaction of HO2
with NO. Crucially, OH abundance and availability (and consequently the
oxidising capacity of the atmosphere) are governed by the balance between OH
sources (primary and secondary) and sinks, consisting of the totality of the
species that react with OH: these include volatile organic compounds (VOCs),
nitrogen oxides (NOx) and many others species, both biogenic and
anthropogenic.
In this respect, the total OH loss frequency, also known as the total OH
reactivity (kOH), is a useful measure of the total
amount of OH sinks present in a
particular environment. kOH is defined as the
pseudo first-order rate constant
for OH loss and is equivalent to the inverse of the OH lifetime,
τOH, as shown in Eq. (1):
kOH=∑i=1nkOH+Xi[Xi]=1/τOH,
where [Xi] designates the concentration (usually in molecules cm-3)
of OH sink Xi, and kOH+Xi is the rate constant
for the reaction of OH with Xi (usually expressed in
cm3 molecule-1 s-1). It follows from Eq. (1) that, if the
atmospheric abundance of all OH sinks is measured and provided that the rate
constants for their reaction with OH are known, kOH can be
determined as the sum of the individual sink reactivities.
Over the last two decades, techniques capable of measuring the total OH
reactivity directly, without the need to quantify individual sinks, have
become available: these rely either on direct measurements of the OH decay
rate (Di Carlo et al., 2004; Ingham et al., 2009; Kovacs and Brune, 2001) or
on the comparative reactivity method (Sinha et al., 2008), in which
kOH is determined from the reactivity of a reference species
(typically pyrrole). A review (Yang et al., 2016) recently described these
techniques in detail, whilst the various instruments developed for direct
measurements of kOH have been the subject of an extensive
intercomparison (Fuchs et al., 2017). These techniques, when deployed in the
field along with instruments for the detection of trace species, have enabled
the comparison of direct measurements of the total kOH with the sum
of reactivities of the individual OH sinks. In this respect, measurements of
the total OH reactivity can be used to test our understanding of tropospheric
oxidation; provided that the totality of OH sinks are accounted for and
measured, the sum of individual reactivities and the total kOH
should agree.
Field campaigns across the globe have, however, highlighted discrepancies
between these two approaches, with most measurements reporting values of the
total kOH higher than the sum of the individual reactivities
(Yang et al., 2016). These results
indicate that a fraction of the total OH reactivity cannot be accounted for:
this is often referred to as “missing” reactivity and attributed to a
“missing” OH sink. The magnitude of the observed missing reactivity in the
literature varies depending on the geographic location of the measurement and
the season, but could amount to as much as 80 % of the total kOH
as measured by Nölscher et al. (2016) in the Amazon rainforest.
Many studies have attempted to identify the missing sink: whilst some authors
have attributed the missing reactivity to the presence of primary emissions
that escaped detection (Holzinger et al., 2005; Kaiser et al., 2016; Sinha et
al., 2010), others have pointed to the reactions of OH with short-lived
oxidation intermediates (Hansen et al., 2014; Nakashima et al., 2014), which
are notoriously challenging to measure in the field. Other studies still, including
that by Nölscher et al. (2016) in the Amazon rainforest,
attributed the missing
reactivity to both unidentified biogenic emissions and photooxidation
products.
An exponential temperature dependence of the missing reactivity was observed
during two campaigns carried out in the same North American forest,
consistent with either primary biogenic emissions (Di Carlo
et al., 2004) or with their oxidation products (Hansen et al.,
2014). Some of the campaigns carried out in other forested environments also
observed a similar trend
(Kaiser
et al., 2016; Mao et al., 2012; Zannoni et al., 2017), whereas others found
no evidence for this correlation
(Ren
et al., 2006b; Sinha et al., 2010).
In an attempt to account for the additional OH reactivity potentially arising
from unmeasured oxidation intermediates, a number of studies invoked box
modelling to determine the abundance of these species and their contribution
to kOH. These efforts have been met with mixed results: whilst some
managed to reconcile the total kOH with the sum of reactivities
once the oxidation intermediates were taken into account
(Whalley et al.,
2016), others obtained different degrees of improvement on the agreement
between the two, leaving different fractions of kOH still
unaccounted for
(Edwards
et al., 2013; Elshorbany et al., 2012; Kaiser et al., 2016; Kovacs et al.,
2003; Lee et al., 2009; Lou et al., 2010; Mao et al., 2012; Mogensen et al.,
2011; Yang et al., 2017).
Regardless of its identity, the very presence of an additional OH sink would
lead to shorter τOH in the real atmosphere than in current
models; this would, in turn, lead to longer lifetimes for species that are
primarily removed by reaction with OH, such as the vast majority of biogenic
and anthropogenic VOCs as well as high-impact greenhouse gases such as
methane. Given the complex interactions of the OH radical in the
photochemistry of the troposphere, global atmospheric modelling provides a
powerful tool with which to investigate potential candidates for the missing sink as
well as to establish its impacts on the oxidising capacity of the lower
atmosphere.
So far only two studies have attempted global modelling of kOH: the
focus of these works was either modelling the global OH budget (Lelieveld et al., 2016) or the
total reactive organic carbon budget (Safieddine et al., 2017).
Detailed comparisons of the modelled kOH with observations or the
missing reactivity were not addressed.
Summary of the model runs described in this work.
Run nameChemistry schemeDescriptionBase runCheSTThis run provides a means to assess how well the model captures known OH sinks, i.e. the individual reactivities in the sum term of Eq. (1). It also provides a point of comparison for the runs that follow. The base run is discussed in Sect. 3.X + OH runCheST with Reaction (R3)An additional OH sink, species X, is introduced in the model to account for the missing kOH. This run is described in Sect. 4.CH3O2+ OH run 1CheST with Reaction (R4)The multi-channel reaction of methyl peroxy radicals (CH3O2) with OH was included in the chemistry scheme with branching ratios α=1, γ=0*. This run is described in Sect. 5.2.CH3O2+ OH run 2CheST with Reaction (R4)Same as CH3O2+ OH run 1 but with branching ratios α=0.8, γ=0.2*. This run is described in Sect. 5.2.CH3O2+ OH run 3CheST with Reaction (R4)Same as CH3O2+ OH run 1 but with branching ratios α=0.6, γ=0.4*. This run is described in Sect. 5.2.
* Branching ratios α and γ are defined in
Sect. 5.2.
This work will make an extensive comparison between modelled kOH
and observations, with particular attention to the contribution of individual
sinks to the total OH reactivity. Section 3 describes our base integration
and discusses a comparison with observations. Sections 4 and 5 tackle the
challenge of modelling the missing reactivity using two approaches. Firstly
(Sect. 4), we introduce an additional OH sink, the geographical distribution
and abundance of which are underpinned by the observations of missing
reactivity available. Secondly (Sect. 5), we include in the model the
reactions of peroxy radicals (RO2) with OH. As this novel
RO2+ OH chemistry has been characterised in the laboratory
only in recent years, the role of RO2 as an OH sink may have been
overlooked (Fittschen et al., 2014). The implications of both approaches for
the oxidising capacity of the atmosphere are then discussed.
Method
Annual mean of the total OH reactivity (in s-1) calculated in
the base run at the surface. Crosses indicate sites of field campaigns with
which the model is compared.
State-of-the-art chemistry-climate models have become an extremely important
tool in the study of atmospheric science, allowing the exploration of a
number of global scenarios with an unprecedented level of detail. However,
recent studies have shown that the way the chemistry is implemented in the
model (e.g. different oxidation schemes for complex emitted species) can
have a major impact on crucial properties of the atmosphere such as the
formation of tropospheric ozone (Squire et al., 2015). It is therefore
important to validate these models against observations of relevant chemical
species whenever possible. In this work, a global chemistry-climate model,
the Met Office's Unified Model with the United Kingdom Chemistry and Aerosols
scheme, UM-UKCA version 8.4, (Abraham et al., 2012) was used to investigate
the total OH reactivity, kOH. The model was run in the N96-L85
configuration, providing a horizontal resolution of 1.875∘ in
longitude × 1.25∘ in latitude on 85 vertical levels from the
surface up to a height of 85 km.
UM-UKCA was run with the Chemistry of the Stratosphere and Troposphere
(CheST) scheme, combining previous tropospheric (O'Connor et al., 2014) and
stratospheric (Morgenstern et al., 2009) chemical schemes as used by Banerjee
et al. (2014). The scheme includes 83 advected chemical tracers and 310
photochemical reactions, describing the Ox, HOx and NOx
chemical cycles and the oxidation of CO, methane, ethane, propane and
isoprene (Archibald et al., 2010, 2011).
Reaction rate coefficients were based on recommended values from the
International Union of Pure and Applied Chemistry (IUPAC) Subcommittee for
Gas Kinetic Data Evaluation (http://iupac.pole-ether.fr, last access:
18 May 2018), the JPL-NASA Evaluation of Chemical Kinetics and Photochemical
Data for Use in Atmospheric Studies (Burkholder et al., 2015) and the Master
Chemical Mechanism, MCM v3.2 (Jenkin et al., 2015), via the website:
http://mcm.leeds.ac.uk/MCM (last access: 18 May 2018).
Surface emissions for the years 2000–2005 of nine trace gas species
(NOx, methane, CO, formaldehyde, ethane, propane, acetone, acetaldehyde
and isoprene) were included based on Banerjee et al. (2014) as well as
multilevel aircraft emissions for NOx. Isoprene emissions were based on
the MEGAN emission model (Guenther et al., 2006).
The aerosol scheme also included emissions of organic carbon (OC, from both
fossil fuels and biofuels), black carbon (BC, also from both fossil fuels and
biofuels), monoterpenes, SO2, dimethyl sulfide and biogenic
methanol.
The model runs used in this work are described in Table 1. They comprise a
base run, discussed in detail in Sect. 3, a run with an imposed sink to
account for the missing kOH (X + OH run), the results of which
are described in Sect. 4, and three additional experiments to explore the
possible role of reactions of peroxy radicals, described in Sect. 5.2. In
each run, the model was run for 5 years, with 1-year spin-up time.
A number of diagnostics widely used in models to evaluate the oxidising
capacity of the troposphere, such as methane lifetime with respect to
tropospheric loss via reaction with OH (τCH4), OH lifetime
(τOH) and tropospheric ozone burden, were calculated for each
model scenario. τCH4 was calculated in accordance with
Lawrence et al. (2001), with the troposphere defined as the domain below
250 hPa. This is also consistent with the convention used in the Atmospheric
Chemistry and Climate Model Intercomparison Project (ACCMIP) (Naik et al.,
2013; Voulgarakis et al., 2013). The same definition of the troposphere was
used here in the calculation of tropospheric reaction fluxes and
τOH. For the calculation of the tropospheric ozone burden, the
troposphere was defined as the domain in which the ozone mixing ratio was
below 150 ppbv (or nmol mol-1), in accordance with previous studies
and model intercomparisons (Ehhalt et al., 2001; Stevenson et al., 2006;
Young et al., 2013). Species lifetimes were calculated by dividing the
species burden by their removal rate.
Comparison of modelled kOH and known OH sinks with
observations
The modelled kOH at the surface, obtained from the base run using
the standard CheST scheme, is shown in Fig. 1. The total OH reactivity is
lowest over oceans and remote deserts (< 1 s-1), highest over
tropical forests (> 10 s-1) and somewhere
between these values in urban-influenced areas. This global distribution and
magnitude of kOH are in reasonably good agreement with those
calculated in previous modelling studies (Lelieveld et al., 2016; Safieddine
et al., 2017). For reference, the modelled abundances of OH, HO2
and RO2 radicals in the base run are shown in Figs. S1–S3 in the
Supplement.
As described in Sect. 1, many measurements of kOH have been taken
over the last two decades, with an exhaustive summary presented in a recent
review (Yang et al., 2016). Only measurements of kOH and its
speciation taken over reasonably long timescales (≥ 1 week) and
covering the full diurnal variation of kOH
and of the OH sinks were considered in
this work, in order to minimise biases due to day-to-day variability and to
obtain a meaningful comparison with the model. A small number of field
campaigns measured only the total kOH and not the abundance of the
individual sinks, therefore precluding the quantification of the missing
reactivity or any further analysis on the speciation of kOH
(Michoud et al., 2015; Ren et al., 2005; Sinha et al., 2008, 2012). For these
reasons, 28 field measurements of the total kOH and of the
individual OH sinks were used in the analysis described in this work; these
are summarised in Table 2, where the values of the total kOH and of
the missing reactivity are reported, averaged over the whole duration of each
campaign. These observations took place in a variety of environments, the
vast majority of which were situated in the Northern Hemisphere (as shown in
Fig. 1). Measurement sites can be grouped into suburban (7 measurements),
urban (9 measurements) and remote areas (12 measurements).
Total observed OH reactivity and missing reactivity from field
campaigns. These values represent averages over the whole duration of each
campaign. Measurement sites are grouped into three categories (suburban,
urban and remote environments respectively).
Location (campaign)Total observedMissingMissing kOHkOH fromReferencekOH s-1akOH s-1bafter the inclusion ofUKCA basemodel intermediates s-1crun s-1SuburbanWhiteface Mountain, USA (PMTACS-NY2002)5.400.027.1Ren et al. (2006b)Weybourne, UK (TORCH-2)4.62.01.37.4Lee et al. (2009)Yufa, China (CAREBeijing-2006)19.72.210.5Lu et al. (2010)Backgarden, China (PRIDE-PRD)31.415.76.316.2Lou et al. (2010)Jülich, Germany (HOx Comp)8.63.22.57.1Elshorbany et al. (2012)Heshan, China30.69.85.35.4Yang et al. (2017)Ersa, Corsica (CARBOSOR-ChArMeX)5.62.35.2Zannoni et al. (2017)UrbanNashville, USA (SOS)11.03.814.8Kovacs et al. (2003)New York, USA (PMTACS-NY2001)18.80.715.4Ren et al. (2003a, b)New York, USA (PMTACS-NY2004)25.14.09.1Ren et al. (2006a)Mexico City, Mexico (MCMA-2003)47.514.35.2Shirley et al. (2006)Houston, USA (TexAQS)9.40.48.7Mao et al. (2010)Houston, USA (TRAMP2006)12.240.038.9Mao et al. (2010)Paris, France (MEGAPOLI)40.322.84.6Dolgorouky et al. (2012)Lille, France7.404.8Hansen et al. (2015)London, UK (ClearfLo)18.15.92.7d6.0Whalley et al. (2016)RemoteMichigan, USA (Prophet2000)7.82.64.8Di Carlo et al. (2004)Hyytiälä, Finland (BFORM)8.63.96.3Sinha et al. (2010)Hyytiälä, Finland (HUMPPA-COPEC2010)11.58.96.6Nölscher et al. (2012)Rocky Mountains, USA (BEACHON-SRM08)6.72.13.5Nakashima et al. (2014)Michigan, USA (CABINEX)11.66.34.8Hansen et al. (2014)Amazon, Brazil (ATTO) dry season49.635.835.8Nölscher et al. (2016)Amazon, Brazil (ATTO) wet season8.33.943.4Nölscher et al. (2016)Haute Provence, France (CANOPEE)17.91.15.6Zannoni et al. (2016)Borneo, Malaysia (OP3)15.310.25.815.3Edwards et al. (2013)Alabama, USA (SOAS)19.44.93.621.1Kaiser et al. (2016)California, USA (BEARPEX09)17.37.13.56.9Mao et al. (2012)North Pacific (INTEX-B)4.02.21.1Mao et al. (2009)
a These values are the mean of
the total
kOH measured over the whole duration of each field campaign.b Missing reactivity calculated as the difference between the
total kOH and the sum of the individual reactivities arising
exclusively from measured OH sinks.c Missing reactivity calculated as the difference between the
total kOH and the sum of the individual reactivities arising from
both measured OH sinks
andintermediates modelled in the particular studies referenced.d The addition of unidentified compounds observed by
two-dimensional gas chromatography with flame ionization detection
(GC × GC-FID) reduced the missing reactivity further to only
∼ 1.1 s-1.
Observed kOH is compared to that simulated by UM-UKCA for the same
longitude, latitude and month in Fig. 2 (and in Fig. S4 in the Supplement as
a scatter plot). The total observed kOH in Fig. 2 is made up of
contributions from the measured OH sinks, modelled intermediates (only
available for some of the field campaigns presented here) and reactivity
that is unaccounted for by known OH sinks, i.e. the missing reactivity. The
total modelled reactivity is expected to be in good agreement with that
arising from measured sinks, but this was only the case (to within
∼ 20 %) in 12 out of 28 cases. Of the remaining 16 campaigns, the
model underestimated the reactivity from measured sinks in 9 cases and
overestimated it in 7. It is therefore important to ascertain that the
modelled OH reactivity is indeed the result of the same sinks observed in the
field. For this purpose, individual reactivities measured in each campaign
provide a large amount of information that can be used to establish how well
the model captures the speciation of kOH. This is shown in Fig. 3,
where the modelled reactivities from the known OH sinks are plotted against
those measured in the field, and in Fig. S5 in the Supplement.
Comparison of the average observed kOH with modelled
kOH. The total kOH measured in each field campaign is
represented by the sum of the blue, green and yellow bars. Blue bars
represent the OH reactivity accounted for by measured OH sinks, green bars
represent the OH reactivity accounted for by modelled reaction intermediates
(only available for some of the studies presented here) and yellow bars
represent the OH reactivity which is unaccounted for (i.e. the missing
reactivity). Red bars represent the total OH reactivity calculated from the
modelled data in the UM-UKCA base run. Error bars represent the uncertainties
(at the 1σ level) in the total kOH measurement. Asterisks
indicate campaigns in which only the total kOH was measured,
without detection of the individual OH sinks. In these cases the blue bar
represents the total observed kOH. A version of this plot detailing
the speciation of both observed and modelled reactivity is given in the
Supplement in Fig. S5.
Figures 3 and S5 provide a useful guide on the magnitude of the contributions
of different OH sinks to the total kOH. The main contributors to
kOH, with reactivities ranging between 1 and 10 s-1, are
isoprene in forested environments and NOx, CO, formaldehyde and
non-methane hydrocarbons (NMHCs, indicating primarily alkanes and alkenes) in
urban environments. At the other end of the spectrum, species such as
methane, ozone and hydrogen only give rise to small contributions
(< 1 s-1) to the total kOH.
Overall more than half (53 %) of the reactivities calculated from
modelled sinks agree with observations within a factor of 2 and the vast
majority (88 %) within a factor of 10. The main source of discrepancy
between modelled and observed individual reactivities are differences in the
number densities of the OH sinks ([Xi] in Eq. 1) and not the
temperature-dependent rate constants, as temperature differences between the
model and the measurements only have a minor effect on the rate constants
used to calculate the reactivities. The abundance of each individual OH sink
species is determined by the balance between its sources (e.g. emissions) and
sinks (largely, reaction with OH). It is, however, difficult to establish
whether the differences between observed and modelled OH sinks arise from
misrepresenting emissions or abundances of the hydroxyl radical itself
without comparing modelled and observed [OH], and measurements of the OH
concentrations are only available for a small subset of the campaigns
considered here. A correlation plot of modelled against observed [OH] is
given in the Supplement (Fig. S6) for those campaigns that measured OH as
well as the total reactivity and the individual OH sinks. At a number of
sites in remote environments (BEARPEX09, CABINEX, OP3 and SOAS) the model
overestimated OH by up to a factor of 6. This was attributed to the model
overrepresenting NOx in all these cases, leading to enhanced ozone
abundances compared to the observations. This led to additional primary OH
(from Reaction R1) in the model as well as secondary OH from
HO2+ NO. Overestimated OH in the model accounted for modelled
isoprene concentrations lower than the measurements in a couple of cases
(BEARPEX09 and CABINEX). However, in the case of the OP3 campaign, some of
the modelled OH sinks were actually higher than the observations, in spite of
the overestimated OH. This unexpected behaviour was attributed to the
difference between the emission rates in the field and those used in the
model. Emission rates of many biogenic VOCs are known to increase with
temperature following an exponential curve (Di Carlo et al., 2004; Hansen et
al., 2014). Upon closer inspection, the temperature at the measurement site
in Borneo during the OP3 campaign was on average 5 K lower than in the model
(295 K vs. 300 K). Extrapolating the isoprene emission rates from the model
to the temperature observed at the measurement site resulted in a value that
was half the emission rate at the temperature in the model, indicating that in
this case overestimated concentrations of modelled biogenic VOCs are likely the
result of the emissions rates used in the model.
Scatter plot of modelled OH reactivity arising from known OH sinks
against measurements. Also shown is the 1 : 1 line (solid black line) as
well as the factor-of-2 and factor-of-10 deviations from it (dark-grey and
light-grey areas respectively). Specific data points that are discussed in
the text are labelled with the name of the field campaign in which those
particular measurements were taken.
The speciation of the total OH reactivity shown in Fig. 3 allows us to
investigate the reasons for the discrepancy between some of the modelled
kOH and observations highlighted in Fig. 2. For instance, the
disagreement between modelled and observed kOH in some urban
environments (notably Mexico City; wintertime New York; Houston; and
Yufa, Beijing) is largely attributable to the underrepresentation of NMHCs in
the model. This can be accounted for in terms of species lumping. As with
many state-of-the-art models, instead of adding numerous hydrocarbons to the
emission and chemistry schemes, the heavier alkanes and alkenes were lumped
into the emission fields of ethane and propane, and weighted by carbon number. We
can see that lumping serves as a reasonable approximation for the
representation of the abundance of some carbon-containing species (such as CO
and formaldehyde, the ultimate products of hydrocarbon oxidation, which are
in reasonable agreement with observations as shown in Fig. 3 and also in
Fig. S7 in the Supplement for CO). However, lumping introduces an additional
complication when the OH reactivity of NMHCs is calculated. As the reactivity
is defined as the product of the rate constant for the reaction of the NMHCs
with OH and the number density of the NMHCs, and as the rate constants for
the reaction of OH with ethane and propane are 1–2 orders of magnitude
smaller than those of OH with higher alkanes (C ≥ 4) and alkenes
(C ≥ 2), lumping leads to an underestimate of the same magnitude in
the reactivity of the NMHCs.
Figure 3 also offers an explanation for the instances in which the model
significantly overpredicted kOH. For example, the abundance of
isoprene measured during the wet season of the ATTO campaign in the Amazon
(∼ 1 ± 0.1 ppbv, or nmol mol-1, in March 2013) was more
than an order of magnitude lower than that predicted by the model for the
same time of the year (∼ 14.6 ppbv). As discussed above, this might
arise from either overestimated isoprene emissions or from underestimated OH
abundances in the model. As OH concentrations were not measured during the
ATTO campaign, a direct comparison of modelled and observed [OH] is not
possible. However, [OH] measurements from campaigns carried out in
neighbouring parts of the Amazon (Liu et al., 2016) and in the Suriname
rainforest (Martinez et al., 2010) might help address this point. Indeed the
model underestimates [OH] by almost a factor of 4 on average in both cases,
although it is worth noting that [OH] measurements from Liu et al. (2016)
only cover ∼ 7 h on a single day, whilst the GABRIEL campaign in
Suriname consisted of airborne measurements, and only the OH data for the
boundary layer were considered for comparison with the model. It may also be
indicative of underrepresented [OH] in model that the abundance of other
short-lived OH sinks in the ATTO campaign is also overestimated by the model;
notably, the observed concentration of monoterpenes (reported to be below the
detection limit of the proton transfer reaction mass spectrometer used by Nölscher et
al. (2016), and here approximated to 0.01 ppbv) was much lower than in the
model (2.2 ppbv). Underrepresented OH in the model might arise from
underestimating the secondary OH originating from the oxidation of large
organics (e.g. isoprene and monoterpenes, as described in Archibald et al.,
2010). In this specific instance the model also underestimated the
concentration of NO (34 pptv, or pmol mol-1, vs. the observed
∼ 1 ± 0.05 ppbv), which might have limited the production of
secondary OH via the reaction of HO2 with NO relative to
observations, in contrast with other remote environments in which OH was
overestimated.
The methane lifetime with respect to tropospheric loss via reaction with OH,
τCH4, for the base run was 8.75 years, which is within
1σ of the ACCMIP multimodel mean for the year 2000 (9.7 ± 1.5
years). τCH4 from the base run is also in good agreement
with the value of 8.5 years reported by Lelieveld et al. (2016). Notably the
model used by these authors exhibited some differences from the one used in
the current work: Lelieveld et al. (2016) used emissions for the year 2010,
defined the tropopause via their own diagnostic and employed an extensive
chemistry scheme consisting of 1630 reactions. Notwithstanding these
differences, the values for τCH4 from the two studies are
in very good agreement. In the base run, the average lifetime of the OH
radical, τOH, was 1.18 s for the whole troposphere, 0.57 s
within the boundary layer and 0.45 s at the surface, as summarised in
Table 4.
Modelling the missing reactivity: addition of sink X
Figure 3 highlights that whilst the model represents many of the individual
components of OH reactivity within at least an order of magnitude (and often
within a factor of 2) of observations, the model underrepresents total
kOH in the majority of the cases (Fig. 2). There are a number of
ways to account for this missing reactivity in the model. For example,
additional species (such as more reactive NMHCs or more reactive reaction
intermediates) and their chemistry could be included in the model. However,
observations indicate that this approach would still leave outstanding
missing reactivity (Edwards et al., 2013; Elshorbany et al., 2012; Kaiser et
al., 2016; Kovacs et al., 2003; Lee et al., 2009; Lou et al., 2010; Mao et
al., 2012; Mogensen et al., 2011; Yang et al., 2017). In this work, we have
taken a different, simpler approach. A new species representing a direct sink
of OH was added to the model and its atmospheric implications were assessed.
Emissions of the unspecified OH sink, species X, were introduced in the model
simulations along with the reaction:
X+OH→products,
with a temperature-independent bimolecular rate constant, k3=1×10-10 cm3 molecules-1 s-1, set to represent reactions
with a very reactive compound (i.e. OH + reactive VOCs). Crucially, the
global and seasonal abundance of X was underpinned by field observations of
missing kOH. This section discusses the implementation of this
scheme in the model and its effects.
Outcome of the multiple linear regression (MLR). The predictors are
sorted by increasing p value.
PredictorCoefficient/1012 kg-1 m2OC biofuel emissions4.41BC biofuel emissions-12.97Acetone emissions0.78CO emissions-0.02OC fossil fuel emissions-0.67BC fossil fuel emissions1.25Monoterpene emissions0.04NOx emissions-0.02Isoprene emissions-0.02Methane emissions0.01Acetaldehyde emissions-1.18Formaldehyde emissions1.20Biogenic methanol emissions-0.02Ethane emissions-0.22Propane emissions0.76Generating a global field of missing reactivity
To generate a surface field for the missing reactivity, multiple linear
regression was applied. This method consisted of fitting the missing
reactivity from observations to the corresponding model grid box emissions of
VOCs, NOx and aerosol precursors at the individual observation sites
where the OH reactivity was measured (described in Sect. 2). This resulted in
a time-varying spatial field for the missing reactivity based on the
predictors (emission fields) listed in Table 3. Correlation of the missing
kOH with some of the emitted species would be expected, both if the
missing sink was an oxidation intermediate (in which case it would correlate
with its precursors) and if it was a primary emission (in which case it could
be expected to correlate to other primary emitted VOCs such as anthropogenic
or biogenic VOCs). This is also supported by observations. For instance,
measurements taken during the INTEX-B campaign (Mao et al., 2009) found that
the missing reactivity correlated with formaldehyde concentrations; the
authors concluded that this indicated that the missing reactivity potentially
arose from VOCs that formed formaldehyde upon oxidation. Similarly,
measurements taken in a forest in Michigan during the CABINEX campaign
showed good correlation between the missing reactivity and both isoprene and
its oxidation products (methyl vinyl ketone, MVK, and methacrolein, MACR)
when the missing reactivity was highest (Hansen et al., 2014).
Values of the OH and methane lifetimes in the model runs described
in this work. Numbers in brackets indicate the percentage change with respect
to the base run.
The results of the multiple linear regression using all 15 predictors in
Table 3 are shown in Fig. 4; the R2 value for the fit was 0.75. The
strongest predictors of the missing reactivity were the emissions of organic
carbon from biofuels (OC biofuel), black carbon from biofuels (BC biofuel),
acetone and CO. Whilst these might indicate both an anthropogenic and a
biogenic component of the missing reactivity, none of the predictors had a
p value ≤ 0.05 (with OC biofuel coming close with p=0.067),
perhaps as a result of the small sample size available. The coefficients from
the multiple linear regression are shown in Table 3. The fact that the
strongest predictors were the emissions for aerosol tracers not included in
the model gaseous chemistry might also indicate potential contributions to
the total kOH from condensed-phase particles. However, the role of
particulate matter in OH loss is very poorly characterised, as highlighted in
the recent review by Yang et al. (2016).
Scatter plot of missing OH reactivity modelled by multiple linear
regression against observed missing OH reactivity (as reported in Table 2).
Each point represents one observation site. The error bars mirror the
uncertainties (at the 1σ level) from each individual measurement of
the total kOH. Also shown is the 1 : 1 line (solid line), as
well as the 20 and 50 % deviations from it (dark-grey and light-grey areas
respectively). The R2 value from the multiple linear regression was
0.75.
To establish the robustness of the outcome of the multiple linear regression
routine, the analysis was repeated using different subsets of predictors.
Unsurprisingly, the iterations using larger numbers of predictors returned
better error statistics (R2 values, normalised mean biases, etc.). The
inclusion of the bottom three predictors in Table 3 (biogenic methanol,
ethane and propane emissions) led to only marginal improvements in the
quality of the fit (e.g. increases in R2 < 0.1 %) in all
cases. On the other hand, the top four entries in Table 3 were the strongest
predictors in all iterations that included them, and their exclusion from the
fitting routine affected the quality of the fit significantly (e.g. decreases
in R2 > 10 %). For the purpose of this work, the
outcome of the multiple linear regression with all 15
predictors was used.
The multiple linear regression resulted in an expression of missing
reactivity at the surface, varying with longitude, latitude and time. Figure 5
shows global seasonal averages of the modelled missing reactivity at the
surface in boreal winter (DJF) and summer (JJA). The multiple linear
regression captured some of the seasonality of the missing reactivity over
forested regions at midlatitudes: this behaviour is consistent with the
temperature dependence of the missing reactivity observed in a number of
field campaigns in forested areas (as described in Sect. 1). In addition, a
number of urban areas are also distinguishable as regions of high missing
reactivity.
Overall, the modelled missing reactivity obtained from the multiple linear
regression had values other than zero in 23 % of the surface grid cells
in DJF and in 32 % of the grid cells in JJA. Of all the non-zero values
plotted in Fig. 5, 57 % are below 1 s-1, 77 % are below 5 s-1,
87 % are below 10 s-1 and 99.8 % are below 50 s-1. Only a
very small number of grid cells have modelled missing reactivities in the
range 50–100 s-1 (12 grid cells in DJF and 15 in JJA). These regions
correspond to areas of high anthropogenic emissions that resulted in large
contributions of the strongest predictors (OC and BC biofuels) to the
calculated missing reactivity.
Global distribution of the simulated missing OH reactivity (in
s-1) in boreal winter (DJF, a) and boreal summer (JJA,
b).
In order to establish the effects of the additional sink on tropospheric
oxidation chemistry it is necessary to emit species X in the model and let it
interact with OH via Reaction (R3). However, the conversion of the global
missing reactivity derived from the multiple linear regression into an
emission field is not straightforward. Initially, the emission rate of X is
calculated as equal to the rate of removal of X within the turbulent boundary
layer for each surface grid cell, according to Eq. (2):
EmissionrateofX/kgm-2s-1=k3[X][OH]×hMrNA×103,
where k3 is the rate constant for Reaction (R3) (in
cm3 molecules-1 s-1), [X] and [OH] are the number densities
of X and OH respectively (in units of molecules cm-3), h is the height
of the turbulent boundary layer (in metres), Mr is the molar mass of
species X (arbitrarily assigned a value of 30 g mol-1) and
NA is Avogadro's number (in molecules mol-1). The term
k3[X] corresponds to the missing reactivity from the multiple linear
regression, whilst the values of [OH] are taken from the base run. The model
was run for 1 year with these emissions of X. Then the OH reactivity arising
from the newly emitted species X was calculated for the model run (as
k3[X]) and compared with the missing reactivity obtained from the
multiple linear regression. It was found that the OH reactivity arising from
species X in this initial model run was significantly higher than the missing
reactivity determined via multiple linear regression (as shown in Fig. 6b).
This overestimate was attributed to the fact that the OH field from the base
run used in Eq. (2) is itself an overestimate of the OH field from a scenario
where an additional sink (species X) is introduced. A routine was therefore
developed, in which the initial emission field of species X was iteratively
optimised in a series of model runs until the OH reactivity from species X in
the model matched the missing reactivity determined via multiple linear
regression within the tolerances specified in Fig. 6a. The procedure used is
summarised in Fig. 6. As the missing reactivity from the multiple linear
regression was underpinned by observations, this routine ensured that the
additional sink emitted in the model would still account for the observed
missing reactivity.
Implementation of the routine used to convert the global missing
reactivity field obtained from multiple linear regression (MLR) into
emissions of species X. Panel (a) illustrates the procedure as a
flow chart, whilst panel (b) shows the correlation plots between
modelled k3[X] and the MLR missing reactivity from non-consecutive
iterations. As the R2 values and the gradients of the correlation plots
converge to 1, the emissions of X in the model lead to X-reactivities
identical to the missing reactivity derived by MLR.
Effects of introducing sink X
The chemistry used for sink X does not allow any secondary OH formation
following the initial oxidation of X in Reaction (R3). This is unrealistic,
as the oxidation of the vast majority of trace species leads to some degree
of secondary OH production; however, with so little information available on
the identity of species X, the system was too poorly constrained to even
attempt an educated guess on the OH recycling probability of the products of
Reaction (R3). Therefore the effects of introducing sink X in the model,
discussed at length in this section, can be seen as a “worst case”
scenario, one in which no OH is regenerated following Reaction (R3) and in
which OH is removed from regions of high emissions of X. This may be the case
for reactions of OH on aerosol surfaces.
Annual mean change in OH concentration following inclusion of
Reaction (R3): absolute change in 106 molecules cm-3(a)
at the surface and (b) as a zonal mean, and relative percentage
change (c) at the surface and (d) as a zonal mean.
OH abundances are reduced following inclusion of Reaction (R3) in the
chemistry scheme, as a result of both direct removal of OH via Reaction (R3)
and less efficient production of secondary OH (as highlighted in the fluxes
reported in the Supplement). As shown in Fig. 7, the most OH-depleted areas
at the surface include Scandinavia, eastern Europe and the coastlines of the Persian Gulf, Venezuela and
Java. Peak OH reductions in these regions are of the order of
5–6 × 106 molecules cm-3. In these regions as much as
90 % of the mean annual [OH] is removed, but it must be stressed that
secondary OH production from the products of Reaction (R3) would mitigate
these effects. Interestingly some of the regions affected by the highest
emissions of X (i.e. the Amazon region and central Africa) only exhibit relatively small decreases in OH
concentrations of ∼ 20 %; as these areas are rich in OH sinks, the
introduction of an additional sink does not affect the OH budget
significantly. OH depletion is most pronounced at the surface and in the
boundary layer in the Northern Hemisphere (also in Fig. 7), which is
consistent with the vertical distribution of the additional OH reactivity
brought about by species X shown in Fig. 8. Mean tropospheric OH decreased by
1.6 %, whilst mean OH abundances in the boundary layer and at the surface
were reduced by 5.6 and 8.1 % respectively. Seasonal changes in OH
abundances, both in absolute and relative terms, are shown in Figs. S8 and S9
in the Supplement. These show significant relative reductions in the mean
boreal winter OH in the Northern Hemisphere (> 60 %), which
are, however, very small in absolute terms (< 5 ×104 molecules cm-3). Changes in HO2 mainly mirror those
in OH, albeit with a somewhat smaller relative magnitude (see Fig. S10 in the
Supplement).
Annual zonal mean of the OH reactivity (in s-1) arising from
sink X.
τCH4 for the model run including Reaction (R3) was 8.95
years (∼ 2.3 % higher than the base run). τOH was
reduced by approximately 2 % at the surface, by 3 % in the boundary
layer and by 1.5 % in the whole troposphere (Table 4). Another metric of
interest when discussing tropospheric oxidation chemistry is the OH recycling
probability, r, which describes the sensitivity of the OH chemistry to
perturbations (Lelieveld et al., 2002). r was calculated in accordance with
Lelieveld et al. (2002, 2016), using Eq. (3):
r=1-P/G,
where P is the rate of formation of primary OH (i.e. via Reaction R1 and
Reaction R2) and G is the gross OH formation rate, consisting of the sum of
P and the formation rate of secondary OH, S (i.e. via any route other
than Reaction R1 and Reaction R2). Using the OH formation fluxes tabulated in
the Supplement (Tables S1–S3), it was found that r remained larger than
60 % in both runs, indicating that tropospheric oxidation was effectively
“buffered”.
Annual mean percentage change in O3 concentration at the
surface (a) and as a zonal mean (b).
The ozone burden at the surface decreased slightly by 0.9 %, but the
overall tropospheric ozone burden increased by 0.3 %. This somewhat
contradictory behaviour in ozone, also shown in Fig. 9, can be accounted for
in terms of the changes brought about by decreases in OH in regions with
different NOx abundances. In general, OH decreases are accompanied by
lower concentrations of HO2 and peroxy radicals (RO2); in
OH-depleted, NOx-rich regions (e.g. Europe in Fig. 9) less HO2
and RO2 are available to react with NO, leading to a reduction in
ozone produced from NO2 photolysis. On the other hand, in remote
regions with low NOx the lower abundance of HO2 and
RO2 following sequestration of OH by X leads to less efficient
ozone removal via the reaction of O3 with HO2 (and, to a
lesser extent, via the reaction of HO2 with RO2,
producing soluble alkyl hydroperoxides), which appears as a slight positive
change in Fig. 9. Decreases in surface ozone in the Northern Hemisphere are
also exacerbated by the absence of further chemistry following Reaction (R3).
This rules out the production of X-peroxy radicals from the initial oxidation
of X, which in turn would react with NO to produce NO2, the
photolysis of which would then lead to O3 formation. However, to
what degree the ozone changes shown in Fig. 9 would be mitigated by
subsequent chemistry would be highly dependent on the actual nature of the
missing sink. Comparison of ozone seasonal observations from a number of
remote sites with the model output (illustrated in Fig. S11 in the
Supplement) shows that, whilst generally the model and the observation are in
good agreement in a number of the locations shown, the inclusion of the
additional OH sink X has a minimal impact on the modelled ozone at these
locations.
On average, Reaction (R3) accounts for approximately 6 % of the total OH
loss flux at the surface (see Table S3 in the Supplement.); in some
particular regions, such as the Amazon rainforest, this contribution
increases to up to 20 %, whilst
in areas of eastern Europe it increases up to 50 % (see Fig. S12 in the
Supplement). As a result, reactions of OH with other sinks (e.g. methane)
become less efficient and the lifetime of such species increases: the
lifetime of isoprene, a major tropospheric OH sink, increases by 17 %
from 2.36 to 2.76 h. This is reflected in the reduced flux through its
reaction with OH in the regions of high X reactivity (Tables S1–S3). As a
result of its longer lifetime, isoprene is transported to higher altitudes
than in the base run, as the flux through its reaction with OH increases
above the boundary layer.
Whilst the work described in this section can be considered a worst-case
scenario due to the absence of OH recycling following Reaction (R3), it is
important to observe that the impacts on the wider atmosphere are relatively
minor. Reconciling the observations of missing OH reactivity in our model in
a worst-case scenario (for OH) has little impact on the global oxidising
capacity. We report a small increase in methane lifetime, and an overall
minor decrease in surface ozone; however, the actual extent of these changes
in the real atmosphere is likely to be mitigated to some degree by OH
recycling following the initial oxidation of the missing sink.
Effects of RO2+ OH chemistry
The previous section focused on simulating the observed missing reactivity by
introducing an additional sink in the form of species X. Based on
structural-reactivity arguments, Archibald et al. (2009) postulated that
there could be a reaction between peroxy radicals (RO2) and OH
which could act as a sink for OH and, depending on the mechanism, a potential
source of oxygenated VOCs. A lack of
any experimental data hampered estimations of the rate constant for the
reaction. However, recent laboratory studies have confirmed that peroxy
radicals do indeed react with OH and, subsequently, the kinetics of the
simplest RO2+ OH reactions have been characterised (Fittschen
et al., 2014). The potential impact of RO2 species as OH sinks and
their contribution to the total OH reactivity have not been investigated to
date.
Changes in total kOH from the inclusion of
RO2+ OH reactions: (a) global surface change in
s-1 and (b) zonal mean in s-1; (c) percentage
change at the surface and (d) percentage change zonal mean.
Overall contribution of modelled RO2 to kOH
The UM-UKCA base model described in Sect. 2 includes the formation and
reactions of a number of peroxy radicals. These include not only those
originating from the oxidation of the simplest alkanes (methane, ethane and
propane) and carbonyl compounds (acetaldehyde, propanal, acetone), but also
those formed from isoprene and its oxidation products (methacrolein and
methyl vinyl ketone). This allowed the offline calculation of an additional
term for kOH arising from the contributions of all RO2
radicals in the model, kOH′, as shown in Eq. (4):
kOH′=∑i=1nkOH+RO2,iRO2,i,
where [RO2,i] is the concentration of peroxy radical i
and kOH+RO2,i is the rate constant of its
reaction with OH. The total concentration of all RO2 species in the
base run is shown in Fig. S3 in the Supplement. Assumptions had to be made as
to the individual values of
kOH+RO2,i, as only the rate constants of the
reactions of OH with methyl (Assaf et al., 2016, 2017b; Bossolasco et al.,
2014; Yan et al., 2016), ethyl (Assaf et al., 2017a; Faragó et al.,
2015), propyl (Assaf et al., 2017a) and i- and n-butyl (Assaf et al.,
2017a) peroxy radicals have been measured in the laboratory to date. Reported
experimental values for the rate constant of the reaction of the simplest
peroxy radical, CH3O2, with OH disagree by more than a factor of
3. The highest value to date, reported by Bossolasco et al. (2014) (2.8×10-10 cm3 molecule-1 s-1 at T=294 K), has
since been revised to a lower value (1.6×10-10 cm3 molecule-1 s-1 at T=295 K) by the same
group (Assaf et al., 2016): the difference is thought to arise from
complicating secondary chemistry due to the presence of electronically
excited iodine atoms following the photolysis of the gaseous mixture used by
Bossolasco et al. (2014). However, an even lower value has been reported by
Yan et al. (2016) (8.4×10-11 cm3 molecule-1 s-1
at T=298 K), and the reason for the discrepancy between this study and that
by Assaf et al. (2016) is still unclear.
In general, the rate constants of these reactions exhibit no significant
dependence on the size of the alkyl group on the RO2 radical. For
the purpose of this study all kOH+RO2,i were
assumed to be equal to the rate constant of the reaction of the methyl peroxy
radical with OH at T=295 K as measured by Assaf et al. (2016) and
were independent of
temperature. Equation (4) therefore can be rewritten as
kOH′=kOH+RO2∑i=1nRO2,i,
where kOH+RO2=1.6×10-10 cm3 molecule-1 s-1.
The additional annual mean OH reactivity at the surface resulting from
RO2 chemistry is shown in Fig. 10: whilst the largest contributions
in absolute terms to the total kOH are found in forested tropical
regions (Fig. 10a, where RO2 radicals from isoprene dominate),
these are indeed very small when compared to the total reactivity present in
the same regions. In relative terms, RO2 radicals are most
significant as OH sinks over remote tropical oceans (Fig. 10c), where the
majority (> 90 %) of the RO2 contribution to
kOH arises from the methyl peroxy radical. This contribution
extends beyond the boundary layer and into the free troposphere over tropical
latitudes, as shown in Fig. 10b and d.
Annual mean percentage change in HO2 concentrations from
run 1 (relative to the base run) at the surface (a) and as a zonal
mean (b).
Annual mean percentage change in CH3O2 concentrations
from run 1 (relative to the base run) at the surface (a) and as a
zonal mean (b).
The CH3O2+ OH reaction and product branching
simulations
Recent studies on the products of the reaction of the simplest peroxy
radical, CH3O2, with OH allow extension
of the work described in the
previous section by investigating the impact on the total kOH not
only of the peroxy radical, but also of some of the reaction products. Three
product channels can be envisaged for this reaction (Archibald et al., 2009):
CH3O2+OH→HO2+CH3O,→H2O+CH2O2,→O2+CH3OH,
with branching ratios defined as
α=k4a/k4a+k4b+k4c,β=k4b/k4a+k4b+k4cγ=k4c/k4a+k4b+k4c.
Annual mean changes in methanol abundances from run 1 (γ=0,
a, b), run 2 (γ=0.2, c, d), and run 3 (γ=0.4, e, f), relative to the base run. Changes are shown in absolute
(in pptv, a, c, e) and relative terms (percentage change, b, d, f).
Recent laboratory studies identified Reaction (R4a) as the major product
channel, with α=0.80±0.20 (Assaf et al., 2017b).
Reaction channel (R4b), producing the
Criegee intermediate CH2O2, was found to be a minor contributor
to the overall reaction (β < 0.05). As the set-up used in
the study was not suitable for the detection of methanol (CH3OH),
the magnitude of γ could not be established. A theoretical study
identified Reaction (R4c) as a potentially significant source of methanol in
the remote boundary layer and modelled its impacts (Müller et al., 2016).
However, this study predated the first (and, so far, only) experimental
determination of the products of Reaction (R4), and also used the very high
value of k4 reported by Bossolasco et al. (2014), which has since been
revised to a value almost a factor of 2 lower than the original (as discussed
in Sect. 5.1).
In the current work, three simulations were run with different sets of values
for α and γ (run 1: α=1, γ=0; run 2:
α=0.8, γ=0.2; run 3: α=0.6, γ=0.4) in
order to establish the atmospheric implications of different product
branching for Reaction (R4) over the uncertainty range of α reported
by Assaf et al. (2017b).
As shown in Table 4, introduction of Reaction (R4) to the model led to
shorter τOH (by approximately 3 %), regardless of the
product branching. Tropospheric methane lifetime increased by as much as
3 % in run 3. HO2 abundances increased in all runs and in
particular in run 1, which exhibited HO2 concentrations higher than
the base run by as much as 12 % over remote oceans, as shown in Fig. 11.
Mean tropospheric HO2 abundances increased by 3.9 % in run 1,
2.8 % in run 2 and 1.7 % in run 3.
Regardless of the product branching of Reaction (R4), the concentration of
methyl peroxy radicals at the surface decreased significantly (by as much as
30 %) over remote oceans and more moderately (by 5–10 %) over land
at midlatitudes (Fig. 12). Mean tropospheric CH3O2 abundances
decreased by 14 % in all runs, whilst mean tropospheric OH was
reduced by 1.5 % in run 1,
2.1 % in run 2 and 2.7 % in run 3.
The inclusion of Reaction (R4) in the model led to a small reduction
(∼ 1 %) in the tropospheric ozone burden. This is mainly driven by
the increase in HO2 abundances, leading to enhanced ozone removal
via O3+HO2 over remote oceans. Ozone abundances
over NOx-rich areas were largely unchanged, as the reaction of
CH3O2 with NO dominates over Reaction (R4) and therefore
HO2 concentrations did not deviate significantly from the base run.
The largest difference in the impact of different branching ratios for
Reaction (R4) was observed for methanol concentrations, as shown in Fig. 13.
In the scenario in which α=1 (and γ=0), methanol
concentrations decrease by as much as 40–50 % over remote oceans as
Reaction (R4a) efficiently inhibits methanol production via the
CH3O2 self-reaction. However, increasing γ increases
methanol mixing ratios. When γ=0.4 methanol abundances are enhanced
by up to 300 % (relative to the base case) over remote regions. Mean
tropospheric methanol decreased by 8.4 % in run 1 and increased by 35.9
and 80.2 % in runs 2 and 3 respectively. There remains uncertainty in the
tropospheric methanol budget (Khan et al., 2014; Millet et al., 2008), with
models currently underestimating atmospheric methanol concentrations
significantly. Müller et al. (2016) suggested that a scenario with high
methanol yield from Reaction (R4) could reconcile models with observations,
based on a model run with effectively γ=0.4, which produced an
additional 117 Tg year-1 of methanol. However, these simulations used
the high value of k4 reported by Bossolasco et al. (2014). If the
preferred lower value reported more recently by Assaf et al. (2016) is used
with γ=0.4, we calculate methanol production via Reaction (R4c) to
be 60 Tg year-1 and we estimate that a value of γ greater than
0.8 is needed to produce the amount of methanol necessary to reconcile models
with observations. This is well beyond the uncertainty in the laboratory
measurements of the branching of Reaction (R4) and highlights that missing
sources of methanol must come from direct emissions (or re-emissions) or as
yet-undiscovered photochemical sources, rather than from the reaction between
OH and CH3O2. However, it has to be noted that so far the product
branching of Reaction (R4) has only been measured at low pressure (50 Torr)
by Assaf et al. (2017b). Calculations by Müller et al. (2016) suggest the
presence of an association channel leading to the formation of a trioxide
(CH3OOOH) species, which might potentially decompose to methanol
and molecular oxygen. As the stabilisation of association products is
generally a pressure-dependent process, it is very important that future
studies address the branching of Reaction (R4) at ambient pressure.
As the species affected by Reaction (R4) (HO2, CH3O2
and CH3OH) are all OH sinks, changes in their concentrations are
accompanied by changes in kOH. However, these are modest
(< 0.25 s-1) in all the scenarios considered in this work, as
shown in Figs. S13 and S14 in the Supplement. Whilst the reactions of OH with
both HO2 and CH3O2 have large rate constants
(> 1 ×10-10 cm3 molecules-1 s-1),
the general low abundance of these species (of the order of
∼ 108 molecules cm-3, as shown in Figs. S2 and S3) results
in small changes to the total kOH. On the other hand, whilst some of
the changes in methanol concentrations arising from Reaction (R4) are
significant (with increases up to 400 pptv in run 3, corresponding to
∼ 1 ×1010 molecules cm-3), the very small rate
constant of its reaction with OH (< 1 ×10-12 cm3 molecules-1 s-1) leads to small
contributions to kOH. These results are consistent with the
magnitude of the changes in kOH calculated offline for all
RO2 radicals in Sect. 5.1. It remains to be seen whether any of the
reaction products, or combination of products, of more complex RO2
radicals with OH might have a significant impact on kOH.
Results from this work agree with the box model calculations by
Assaf et al. (2017b), indicating that the largest impacts of Reaction (R4)
are on HO2 and CH3O2 abundances. In addition, we show
that these effects are not limited to the surface or the boundary layer but
also extend well into the free troposphere.
Conclusions
The hydroxyl radical plays a pivotal role in the chemistry of the atmosphere.
Its abundance determines the lifetime of most emitted compounds so that OH is
often known as the “atmospheric detergent”. However, our understanding of
the chemistry and distribution of OH is far from complete. This study has
examined the total tropospheric OH reactivity, kOH, using the
UM-UKCA chemistry-climate model. In the first instance, the model was
evaluated against available measurements of known OH sinks. This comparison
indicated that, whilst the model captured the abundances of a number of known
OH sinks reasonably well in a variety of regions across the planet, the total
modelled OH reactivity was generally much lower than observed, and there are
significant biases in the model's ability to accurately simulate reactivity
from NMHCs. This error was partly linked to the limited NMHC chemistry
included in chemistry-climate models like UM-UKCA.
Existing observations of the missing OH reactivity were used to develop a
method to account for the missing OH sink in the model by introducing an
additional reaction and OH sink species, X, in the model chemistry scheme.
Observations of missing reactivity were correlated with underlying inputs
into the model (emissions of VOCs, NOx and aerosol precursors) through
multiple linear regression analysis. The multiple linear regression fit
highlighted correlation with both biogenic and anthropogenic emissions,
consistent with observations of missing reactivity in remote and urban
environments. The fitting routine also indicated a strong correlation of the
missing reactivity with the emissions of particulate matter, perhaps pointing
at OH loss processes involving condensed-phase particles that have been
overlooked to date. The multiple linear regression indicated that the areas
most affected by the missing reactivity would be tropical remote regions,
where biogenic emissions dominate,
as well as urban regions all over the globe, where anthropogenic emissions
are significant. This result agrees with the type of environments in which
missing reactivity has been observed. Our simulations showed that the largest
impacts of the global missing OH reactivity were at the surface and in the
boundary layer, where sink X accounted on average for 6 % of the total OH
loss flux. Inclusion of X in the model led to decreases in mean OH abundances
of 8.1% at the surface, 5.6 % in the boundary layer and 1.6 % in
the whole troposphere. Inclusion of missing reactivity, in the form of X,
reduces and increases the lifetimes of OH and methane, respectively, by
approximately 2 %. The inclusion of X modifies the global ozone burden
only slightly (< 1 %) but has larger impacts on simulated
surface ozone, particularly in the Northern Hemisphere. It has to be noted
that, as no OH recycling was introduced following the initial oxidation of X,
these results should be interpreted as an upper limit of the effects of the
missing reactivity on the oxidising capacity of the troposphere.
Finally, we performed a series of model simulations including novel reactions
of peroxy radicals with OH. These reactions have been recently confirmed
(Bossolasco et al., 2014; Assaf et al., 2017) after being postulated to be
potentially important in the marine boundary layer (Archibald et al., 2009).
Using the UM-UKCA model, we have calculated that whilst these processes cannot
account for the missing OH reactivity, they have important implications for
the troposphere. Model runs including the reaction of the simplest peroxy
radical, CH3O2, with OH indicated that this process is a major
sink of peroxy radicals (with [CH3O2] reduced by a third) and an
important source of HO2 radicals, the abundance of which increased
by up to 12 % over remote oceans. These runs also show that, with the
current understanding of the kinetics and product branching of this process,
the reaction of CH3O2 with OH cannot be a major source of
atmospheric methanol. As information on the kinetics and products of these
reactions become available from laboratory studies, and given the impact they
have on tropospheric radical species, we recommend their inclusion in
atmospheric models.
There remain a number of challenges in understanding the chemistry of
hydroxyl radicals in the atmosphere. We have shown, using the UM-UKCA model,
that accounting for potential new sinks of OH and including a representation
of the observed missing OH reactivity in the model has a relatively negligible
impact on important long-lived atmospheric trace gases. However, we conclude
that further studies are needed to identify the source and nature of the
observed missing OH reactivity, in particular to understand whether it acts as a
net OH sink (as we have included) or if it couples with other radical
propagation cycles and so feeds back on OH itself. Lastly, as observations
of the missing reactivity so far are largely limited to ground-level
measurements in the Northern Hemisphere, further observations in the
Southern Hemisphere as well as aircraft measurements both in the boundary
layer and the free troposphere would provide additional constraints to the
modelled oxidising capacity of the atmosphere.
Data available upon request.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-7109-2018-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the European Research Council for funding through the Atmospheric
Chemistry-Climate Interactions (ACCI) project, no. 267760. We would also like
to acknowledge the UKCA team at the UK Met Office for their help and support.
This work used the ARCHER UK National Supercomputing Service
(http://www.archer.ac.uk, last access: 18 May 2018). Alexander T.
Archibald thanks the Kundert Walters Trust and NERC through NE/M00273X/1. The
authors would also like to thank the three anonymous referees who reviewed
this paper. Edited by: Dwayne
Heard
Reviewed by: three anonymous referees
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