Atmospheric chemistry and transport of mercury play a key role in the global mercury cycle. However, there are still considerable knowledge gaps concerning the fate of mercury in the atmosphere. This is the second part of a model intercomparison study investigating the impact of atmospheric chemistry and emissions on mercury in the atmosphere. While the first study focused on ground-based observations of mercury concentration and deposition, here we investigate the vertical and interhemispheric distribution and speciation of mercury from the planetary boundary layer to the lower stratosphere. So far, there have been few model studies investigating the vertical distribution of mercury, mostly focusing on single aircraft campaigns. Here, we present a first comprehensive analysis based on various aircraft observations in Europe, North America, and on intercontinental flights.
The investigated models proved to be able to reproduce the distribution of total and elemental mercury concentrations in the troposphere including interhemispheric trends. One key aspect of the study is the investigation of mercury oxidation in the troposphere. We found that different chemistry schemes were better at reproducing observed oxidized mercury patterns depending on altitude. High concentrations of oxidized mercury in the upper troposphere could be reproduced with oxidation by bromine while elevated concentrations in the lower troposphere were better reproduced by OH and ozone chemistry. However, the results were not always conclusive as the physical and chemical parameterizations in the chemistry transport models also proved to have a substantial impact on model results.
At the time of publication the Minamata Convention has 128 signatories and has been ratified by 55 countries. After reaching the threshold of 50 ratifications the convention enters into force on 16 August 2017 (UNEP, 2013).
Once ratified by at least 50 parties, this international legally binding
treaty will oblige all participating parties to
assess the state of mercury pollution take actions to reduce mercury emissions and concentrations in the
environment and evaluate the success of the measures taken on a regular basis.
The state of mercury contamination is typically determined by measurement of
the relevant mercury species (e.g., total mercury (TM) in the atmosphere,
methylmercury in fish). However, in order to understand the sources of
mercury pollution and to predict the impact of various possible measures for
mercury emission reduction it is necessary to apply complex chemistry
transport models.
In the last decades, general chemistry transport models (CTMs) have been extended to model the global mercury cycle by including mercury chemistry and partitioning (Bergan et al., 1999; Xu et al., 2000; Lee et al., 2001; Petersen et al., 2001; Seigneur et al., 2001; Dastoor and Larocque, 2004; Selin et al., 2007; Hedgecock and Pirrone, 2004). Since then, extensive model intercomparison studies have been performed to evaluate and improve the original models (Bullock et al., 2008; Ryaboshapko et al., 2002, 2007a, b). However, until today, we have not fully understood all parts of the global mercury cycle. In the atmosphere, the main question is how elemental mercury emitted from anthropogenic, natural, and legacy sources is oxidized. This includes the relative importance of oxidizing reaction partners and the relevance of reduction pathways of oxidized mercury under environmental conditions. Once we understand the redox processes of atmospheric mercury, is it possible to determine the range of mercury transport and the fate of mercury emitted in the past and the future.
Consequently, mercury oxidation processes have been the focus of the international mercury community in recent years (Horowitz et al., 2017; Cohen et al., 2016; Amos et al., 2015; Dastoor et al., 2015; Song et al., 2015; Bieser et al., 2014a; De Simone et al., 2014; Qureshi et al., 2011; Travnikov et al., 2010).
In this study, we investigate the vertical distribution of mercury species
in the atmosphere. While gaseous elemental mercury (GEM) makes up the vast
majority of total atmospheric mercury near the surface (Sprovieri et al.,
2016 this issue), recent aircraft-based observations have indicated that
there is significant oxidation of mercury occurring in the free troposphere
(FT)
(Brooks et al., 2014; Lyman and Jaffe, 2012; Jaffe et al., 2014; Gratz et
al., 2015; Shah et al., 2016). However, apart from GEM no individual mercury
compound has been identified so far and the atmospheric oxidized mercury is
an unknown mixture of mercury bound to Br, Cl, OH, O, and NO
As oxidized mercury is much more rapidly removed from the atmosphere than elemental mercury, the free troposphere – the region between the planetary boundary layer (PBL) and the tropopause – is of great importance for the global mercury budget.
To investigate this issue further, the Mercury Modeling Task Force (MMTF) was founded during the course of the EU FP7 project GMOS (Global Mercury Observation System). The MMTF is a global collaboration not limited to GMOS project partners and, thus, incorporates most mercury CTMs currently in use in the scientific community. With a total of seven model combinations (including four global, one hemispheric, and two regional models), the partners in the MMTF carried out a set of sensitivity model runs and compared the results to airborne observations in Europe, North America, and on intercontinental flights.
Aircraft-based observations are expensive and thus rarely performed on a regular basis. They are made in a certain area at a limited time interval and as such are hardly representative enough to be used to evaluate model performance. However, in the year 2013 an unprecedented number of aircraft-based observations has been performed.
Within the European Tropospheric Mercury Experiment (ETMEP) five vertical profiles were flown in the PBL and the lower free troposphere (LFT) at an altitude of 500–3500 m over central Europe during August 2013 (Weigelt et al., 2016a). Mercury was measured using two collocated Tekran instruments (2537X and 2537B). Both Tekran instruments were run with upstream particle filters and one additionally with a quartz wool trap, which presumably removes GOM (Lyman and Jaffe, 2012; Ambrose et al., 2013). Neglecting PBM, the concentration of which is usually negligible, the measurement by Tekran without the quartz wool trap approximates TM and that with quartz wool trap GEM (Weigelt et al., 2016b). GEM was also measured by a modified Lumex instrument (Weigelt et al., 2016b). Additionally, GOM was collected on denuders and analyzed on return to the laboratory.
In the US Brooks et al. (2014) measured GEM, GOM, and PBM profiles on 28 flights between August 2012 and July 2013 at altitudes from 1000 to 6000 m. GEM was measured on board with a modified Tekran 2537B instrument with a temporal resolution of 2.5 min. GOM was collected on denuders and PBM on a filter tube downstream of the denuder. Both were later analyzed in the laboratory. In addition, 19 flights were flown in June and July 2013 mostly over the southeastern USA at altitudes between 500 and 7000 m during the NOMADSS (Nitrogen, Oxidants, Mercury and Aerosol Distributions, Sources and Sinks) campaign (Gratz et al., 2015; Shah et al., 2016). Here, oxidized mercury was calculated based on a differential method using two Tekran 2537B instruments, one of which was equipped with GOM trap (quartz wool or ion-exchange membrane) using the University of Washington Detector for Oxidized Hg Species (DOHGS) (Lyman and Jaffe, 2012; Ambrose et al., 2015).
Finally, there were 19 intercontinental flights between Germany and North and South America made within the CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrumented Container) project during which TM and GEM was measured in the upper troposphere and the lower stratosphere in altitudes between 6000 and 12 000 m using a modified Tekran 2537A instrument (Slemr et al., 2014, 2016).
The aircraft observations were complemented with ground-based observations from the GMOS measurement network (Sprovieri et al., 2016; GMOS, 2016). In particular, we used data from the ground-based stations in Mace Head, Ireland, and Waldhof, Germany, to augment the ETMEP profiles (Weigelt et al., 2013, 2015). At Mace Head and Waldhof GEM is measured with a Tekran 2537A. At Waldhof, additionally, GOM and PBM are measured with a Tekran 1130/1135 speciation unit.
Idealized observed TM and GEM mercury profiles for winter, spring, and summer in northern midlatitudes. The depicted profiles are based on aircraft observations from CARIBIC, ETMEP, NOMADSS, and Tullahoma flights. Data gaps in altitude where no observations are available were estimated.
These flights cover a large horizontal area in the midlatitudes above
Europe (45–55
Model description.
This study is based on an annual ensemble of seven different CTMs for the
year 2013 including global (GLEMOS, GEOS-Chem, GEM-MACH-Hg, ECHMERIT),
hemispheric (CMAQ-Hem), and regional (WRF-Chem, CCLM-CMAQ) models (Table 1).
The models differ considerably in the implemented physical and chemical
parameterizations, spatial and temporal resolution, and meteorological
drivers. The ensemble includes models that use external fields for chemical
reaction partners (GLEMOS, GEOS-Chem), models with a complete photochemical
reaction scheme (CCLM-CMAQ, CMAQ-Hem), and online coupled meteorological
models (GEM-MACH-Hg, ECHMERIT, WRF-Chem). The only model harmonization in
this study is the utilization of a common global 1 ozone and OH chemistry (GLEMOS, ECHMERIT, CMAQ-Hem, CCLM-CMAQ,
WRF-Chem); OH and bromine chemistry (GEM-MACH-Hg); bromine chemistry (GEOS-Chem).
Moreover, some models also consider reduction of Hg
GLEMOS is a multi-scale chemistry
transport model developed for the simulation of environmental dispersion and
cycling of different chemicals, including mercury, based on the older
hemispheric model MSCE-HM-Hem (Travnikov, 2005; Travnikov and Ilyin, 2009;
Travnikov et al., 2009). The model simulates atmospheric transport, chemical
transformations, and deposition of three Hg species (GEM, GOM, and PBM). The
atmospheric transport of the tracers is driven by meteorological fields
generated with the Weather Research and Forecast modeling system (WRF
3.7.2) (Skamarock et al., 2007), which is fed by operational analysis data
from the European Centre for Medium-Range Weather Forecast (ECMWF) (ECMWF,
2016). In the default setup configuration the model grid has a horizontal resolution
of 1
The GEOS-Chem global chemistry transport model (v9-02;
GEM-MACH-Hg is a new chemical transport model for mercury that is based on
the GRAHM model developed by Environment and Climate Change Canada (Dastoor
and Larocque, 2014; Dastoor et al., 2008, 2015; Durnford et al., 2010, 2012;
Kos et al., 2013) GEM-MACH-Hg uses a newer version of the Environment and
Climate Change Canada's operational meteorological model. The horizontal
resolution of the model is 1
OH fields are from MOZART (Emmons et al., 2010) while BrO is derived from 2007–2009 satellite observations of BrO vertical columns. The associated Br concentration is then calculated from photochemical steady-state conditions (Platt and Janssen, 1995). Dry deposition in GEM-MACH-Hg is based on the resistance approach (Zhang, 2001; Zhang et al., 2003). In the wet deposition scheme, GEM and GOM are partitioned between cloud droplets and air using a temperature-dependent Henry's law constant. Total global emissions from natural sources and re-emissions of previously deposited Hg (from land and oceans) in GEM-MACH-Hg are based on the global Hg budgets by Gbor et al. (2007), Shetty et al. (2008), and Mason (2009). Land-based natural emissions are spatially distributed according to the natural enrichment of Hg. Terrestrial re-emissions are spatially distributed according to the historic deposition of Hg and land-use type and depend on solar radiation and the leaf area index. Oceanic emissions depend on the distributions of primary production and atmospheric deposition.
ECHMERIT is a global online meteorological chemistry transport model, based
on the ECHAM5 global circulation model, with a highly flexible chemistry
mechanism designed to facilitate the investigation of atmospheric mercury
chemistry (Jung et al., 2009; De Simone et al., 2014, 2015, 2017). The model
uses the same spectral grid as ECHAM. The standard horizontal resolution of
the model is T42 (approximately 2.8
This is a hemispheric setup of the Community Multi-Scale Air Quality System
(CMAQ) version 4.6 (Byun and Schere, 2006; Byun and Ching, 1999). The model
is based on a three-dimensional Eulerian atmospheric chemistry and transport
modeling system that simulates Hg, ozone, particulate matter, acid
deposition, and visibility simultaneously. The model components and
scientific backgrounds have been documented elsewhere (Bullock and Brehme,
2002; Bullock et al., 2008; Travnikov et al., 2010). A spin-up period of 10 days is used to eliminate the impact of initial conditions for atmospheric
oxidants (O
Hourly meteorological data were prepared using the WRF model version 3.7 (Skamarock et al., 2008). The selected physics options were Thompson (Microphysics Options) (Thompson et al., 2004), Betts–Miller–Janjic (Cumulus Parameterization Options) (Janjic, 1994, 2000), RRTMG (Radiation Physics Options), and BouLac (PBL Physics Options) based on the results of meteorological model performance evaluation (Wang et al., 2014). The ARW outputs were processed using MCIPv3.4.1 (Byun and Ching, 1999; Otte and Pleim, 2010) to generate model-ready meteorology for chemical transport simulations.
The WRF-Chem-Hg model (Gencarelli et al., 2014a, 2015, 2017) is a modified
version of WRF-Chem (version 3.4, Grell et al., 2005) model, developed to
reproduce the emission, transport, chemical transformation, and deposition of
Hg at local scales with elevated spatial and temporal resolutions. The gas-phase chemistry of Hg and a parameterized representation of atmospheric Hg
aqueous chemistry have been added to the RADM2 chemical mechanism using KPP
(Sandu and Sander, 2006) and the WKC coupler (Salzmann and Lawrence, 2006)
in order to represent four Hg species: GEM, GOM, PBM, and dissolved oxidized
mercury (Hg
This modeling system is based on the meteorological model CCLM and the
chemistry transport model CMAQ v5.0.1. All physical atmospheric parameters
were taken from regional atmospheric simulations with the COSMO-CLM v4.8
mesoscale meteorological model (Geyer, 2014) using NCEP reanalysis data as
forcing (Kalnay et al., 1996). COSMO-CLM is the climate version of the
regional-scale meteorological community model COSMO (Rockel et al., 2008),
originally developed by Deutscher Wetterdienst (DWD) (Steppeler et al.,
2003; Schaettler et al., 2008). It has been run on a 0.22
To evaluate the impact of emissions and atmospheric chemistry on the vertical distribution of mercury a set of sensitivity runs was made. While for the BASE case each model uses its default setup, for the sensitivity runs certain aspects of the models were harmonized. The list of all sensitivity runs is given in Table 2. Concerning emissions, we tested the impact of anthropogenic emissions by considering only natural and legacy emissions (NOANT) and by altering the speciation of anthropogenic emissions to 100 % GEM (ANTSPEC). In addition, we investigated different oxidation reactions by considering only one reaction at a time, namely ozone (O3CHEM), hydroxy radicals (OHCHEM), and bromine (BRCHEM). In these cases, the models used the same input fields for the investigated reactant. For bromine chemistry two alternative sets of bromine fields were used from GEOS-Chem (BRCHEM1) and from the p-TOMCAT model (BRCHEM2).
Specification of model experiments.
For the model evaluation we used hourly model results for the year 2013 for
all models with the exception of ECHMERIT, which provided a lower temporal
resolution that resulted in 3-hourly average concentrations. The grid cell
and time step matching each individual measurement were taken using a
four-dimensional bilinear interpolation to the nearest model space and time
coordinate. For the analysis we used three aggregated model species: TM, GEM,
and OM
Due to the small number of aircraft observations available, such a comparison faces the problem that the model bias will not average out as it tends to do for larger datasets (e.g., 8760 hourly observations for a single year of ground-based station data). Moreover, the vertical model performance is highly dependent on meteorological parameters (e.g., PBL height, vertical transport). Thus, for an individual profile the model bias can be quite large. We did not perform a detailed analysis of the meteorological fields because this would be beyond the scope of this paper. To increase sample sizes, we summed several vertical profiles into seasonal average profiles in order to increase the number of observations per altitude. On average, each of the resulting seasonal average profiles consists of 58 data points per 1000 m altitude slice.
Moreover, to completely remove the model bias from the analysis of the
vertical distribution of mercury we calculated a relative vertical profile
which we call the mean deviation profile (MDP) (Eqs. 6–8). The MDP indicates
the difference for each individual altitude from the average column
concentration and is calculated for models and observations independently.
Thus, it indicates whether each model is able to reproduce the observed
vertical distribution rather than the actual concentration of mercury
species (Eq. 8). This is especially valuable for the analysis of oxidized
mercury species, as there is an ongoing discussion about an underestimation
of concentrations due to limitations of the current measurement techniques
(Lyman et al., 2010, 2016; Ariya et al., 2015; Gustin et al., 2015; Huang and
Gustin, 2015; Jaffe et al., 2014; McClure et al., 2014; Ambrose et al.,
2013; Huang et al., 2013; Kos et al., 2013). Generally,
the model error can be separated into three parts: the bias, which
represents any systematic errors; the variance, which gives the variability
around the mean value; and the covariance, which represents the correlation
between model and observations (Solazzo and Galmarini, 2016). By using MDPs
we completely remove the bias and all systematic errors from our evaluation.
Combining MDP and correlation coefficient, we are able to investigate the
models capabilities to reproduce areas with high and low production of
oxidized mercury and the influence of different chemistry schemes. The idea
behind this is that even if the absolute measurements are not correct, we
can use them to identify regions with mercury oxidation in the vertical
column.
Observations indicate that there is a tripartite distribution of TM in the
atmosphere. The highest concentrations (1.4–1.8 ng m
Upper panel: GEM
Finally, in North America a peak of OM concentrations in the range of 100–300 pg m
Mean normalized bias (MNB) and mean normalized error (MNE) for each model as well as for the model ensemble for GEM in Europe and North America.
Here, we investigate capability of the models to reproduce the observed atmospheric distribution of TM, GEM, and OM. To increase the sample size for the model evaluation we created seasonal average profiles for Europe and North America. For this, we integrated the high-resolution 2.5 min Tekran data to hourly values, separated all observations into bins of 1000 m (0–1000, 1000–2000, etc.), and calculated the mean concentration as well as the 66 % quantile range for each bin. In addition to the absolute concentrations we investigate mean deviation profiles as described in Sect. 2.4.
Based on the combination of ground-based observations from the GMOS network (Sprovieri et al., 2016; GMOS, 2016; Weigelt et al., 2013, 2015) and ETMEP observations inside the PBL and the lower troposphere (Weigelt et al., 2016), as well as CARIBIC observations in the upper troposphere and the lower stratosphere (Slemr et al., 2016), we were able to obtain comprehensive vertical mercury profiles for Europe from the surface up to 12 000 m. Here, we present two individual profiles (Fig. 2).
Model ensemble vertical distribution of model mean normalized bias (MNB) and mean normalized error (MNE) for GEM in Europe and North America.
The first profile measured on 21 August at 11–12:00 UTC at Leipzig, Germany,
which combines ETMEP and CARIBIC data, was published by Weigelt et
al. (2016). Based on the discussion above and ETMEP GOM measurements being in
the range of 20 to 40 pg m
Looking at the stratosphere, only the GLEMOS model is able to reproduce a
decrease of TM concentrations above the tropopause. Due to the low resolution
at this altitude, GLEMOS has only two layers between 10 000 and 15 000 m,
and
the modeled gradient is less steep than that observed. None of the other
models give significantly lower TM concentrations in the stratosphere.
However, GEOS-Chem and GEM-MACH-Hg have increased oxidation above the
tropopause. In GEM-MACH-Hg the GEM
The second profile is a combination of ground-based observations at the GMOS
station Mace Head, Ireland, with the CARIBIC flight of 19 September at
06–08:00 UTC (Fig. 2). In 2013,
the CARIBIC aircraft passed close to Mace Head six times within a range of
86–220 km (27, 28 April, 8, 7 June, 19, 20 September) but the other
profiles look similar. The CARIBIC data are separated into tropospheric and
stratospheric measurements based on the relative height above the tropopause
(Sprung and Zahn, 2010). Here, we depict the profile for the nearest CARIBIC
overflight. In this region, which is influenced by clean air from the
Atlantic Ocean, we did not observe a gradient between the surface and the
upper troposphere. Again, models tend to underestimate mercury
concentrations. At Mace Head all models are able to reproduce the constant TM
concentrations in the free troposphere. However, several models overestimate
the concentrations near the surface. It has to be noted, however, that Mace
Head is a coastal station with predominantly westerly winds from the open
Atlantic which might be difficult to reproduce for models with a coarse
resolution, and thus higher ground-based concentrations could be due to
anthropogenic emissions from Ireland. At the tropopause, the observations
show an almost instantaneous decrease of TM concentrations from 1.4 to
1.0 ng m
Comparison of modelled average mercury profile for Europe to
observations based on vertical profiles from ETMEP and CARIBIC campaigns
amended with ground-based observations at Waldhof and Mace Head (Weigelt et
al., 2013; Slemr et al., 2016). The error bars indicate the 66 % quantile
range of the observations in each altitude; the sample size for each altitude
is indicated on the
Comparison of modelled average mercury profile for North America to
observations based on vertical profiles at Tullahoma, TN, from January and
February 2013 (Brooks et al., 2014). The error bars indicate the 66 %
quantile range of the observations in each altitude; the sample size for each
altitude is indicated on the
As described above we calculated an average summer vertical profile for Europe using data from five ETMEP profiles in Germany and Slovenia performed between the 19 and 23 August, complemented by CARIBIC flights on 21 and 22 August and 18 and 19 September. Thus, we created an average profile with 290 hourly samples based on a sampling interval of the co-located Tekran instruments of 2.5 min (Fig. 4). We did not use measurements from the Lumex instrument for this evaluation as none of the other aircraft were equipped with such an instrument. The performance of the Lumex instrument on this flight is discussed in Weigelt et al. (2016, this issue). The resulting GEM and TM profiles are depicted in Fig. 3a and b, respectively. Again, it can be seen that the models generally underestimate mercury concentrations in central Europe during August 2013. However, when looking at the mean deviation profile (MDP), which depicts the relative vertical distribution compared to the total column average concentration, all the models are within the observed range. By investigating the experimental model runs, it can be seen that in the case with all anthropogenic emissions emitted as elemental mercury (ANTSPEC) the models have slightly higher mercury concentrations near the surface which leads to better agreement with observed gradients. While all models give similar vertical profiles for the BASE and ANTSPEC cases, in the cases without anthropogenic emissions (NOANT) and without atmospheric chemistry (NOCHEM) the models show different responses. In these cases the modeled vertical distributions of mercury start to diverge from the observations and each other. This shows the strong impact of atmospheric chemistry on the vertical GEM distribution and global mercury transport in general.
Comparison of modelled average mercury profiles for North America to
observations based on NOMADSS flights in June and July 2013 (Shah et al.,
2016; Gratz et al., 2016). The error bars indicate the 66 % quantile
range of the observations in each altitude; the sample size for each altitude
is indicated on the
Comparison of modelled average mercury profile for North America to
observations based on vertical profiles at Tullahoma, TN, from April to
June 2013 (Brooks et al., 2014). The error bars indicate the 66 %
quantile range of the observations in each altitude; the sample size for each
altitude is indicated on the
We created similar average vertical mercury profiles for North America based on 185 hourly samples from three profile flights at Tullahoma, TN, between 18 January and 14 April 2013 (Brooks et al., 2014) (Fig. 4) and 898 hourly samples from seven NOMADSS flights between 20 June and 12 July 2013 (Fig. 5). For the NOMADSS flights we selected vertical flight paths for this evaluation and discarded horizontal flight paths. Here, the observations exhibit a similar vertical distribution with higher concentrations inside the PBL and lower concentrations in the FT. The NOMADSS profile contains one flight with a stratospheric intrusion and thus shows a slightly decreasing trend in the upper troposphere. Observed profiles and model results for North America are comparable to Europe. For the summer profile (Fig. 5) there are elevated TM concentrations inside the PBL and no trend inside the FT. Models tend to underestimate TM and GEM concentrations but are in good agreement with the relative distribution. The average MNB and MNE as given in Table 3 are similar to those for Europe. For North America only the GEM-MACH-Hg model exhibits a positive bias and on average the models underestimate GEM concentrations by 13 %. As for Europe, the model error shows no significant vertical gradient and exhibits a minimum near the PBL (Table 4).
The higher concentrations near the surface in the ANTSPEC case lead to better agreement with observations. For the winter profile (Fig. 4) GEOS-Chem and GEM-MACH-Hg are in good agreement with the absolute GEM and TM observations. However, models do overestimate concentrations near the surface, which could be due to modelled PBL height and anthropogenic emission fluxes.
GOM profiles at Waldhof Germany (23 August 2013) (Weigelt et al., 2016). The observations are a combination of ground-based measurements and a total column measurement in altitudes from 500 to 3000 m. Model values are given for BASE (solid line), ANTSPEC (dashed line), and NOCHEM (dotted line).
Finally, we created a third profile for spring from three profile flights at
Tullahoma, TN, on 15 April, 10 May, and 4 June 2013 (Brooks et al., 2014)
(Fig. 6). This profile looks different than the others. Again, TM and GEM
concentrations are highest inside the PBL but there is a second decreasing
gradient between 4000 and 5000 m. Above 6000 m GEM and TM concentrations
fall below 1.0 ng m
As the different implementations of the mercury redox chemistry in the
models presented here is not directly compatible, we decided to sum all
oxidized model species for this comparison. Thus, in the following section
we compare modeled reactive mercury OM (OM
Measurements at Waldhof, Germany, indicate that there is a strong OM gradient inside the PBL with very low concentrations at the surface and 10–15 times higher concentrations above 500 m. This is to be expected because of the high stickiness and therefore fast dry deposition of OM on surfaces (Zhang et al., 2009). During the ETMEP campaign a total column OM measurement was performed inside the PBL above the ground-based measurement station Waldhof (Fig. 6). Five of the seven models (GLEMOS, GEOS-Chem, GEM-MACH-Hg, CMAQ-Hem, CCLM-CMAQ) are able to reproduce the OM concentrations above the surface with one over and one underestimating the concentration. It has to be noted that ECHMERIT, which strongly overestimates OM is able to reproduce the low concentrations at the surface and thus is in good agreement with the relative vertical distribution. An investigation of the experimental model runs indicated that the overestimation at the surface is due to anthropogenic emissions and was reduced significantly in the ANTSPEC run, while concentrations above the surface are mainly driven by atmospheric chemistry. This is in line with the findings of Bieser et al. (2014a) and Weigelt et al. (2016).
For North America, we use the same profiles as described in Sect. 3.1.2. On the flights at Tullahoma, GOM and PBM were measured and for the analysis we plotted the sum as total OM. Due to the long sampling times necessary for denuder measurements, the sample size is much smaller than for the GEM observations. The winter profiles are based on 32 samples (Fig. 7) and the spring profiles on 48 samples (Fig. 8).
GOM
During winter, OM concentrations varied around 30 pg m
Correlation of individual models for OM profiles depicted in Figs. 8, 9, and 10.
Comparison of average reactive mercury profiles (GOM
Comparison of modeled average oxidized mercury (OM) concentration to
observations based on NOMADSS flights in June and July 2013 (Shah et al.,
2016; Gratz et al., 2016). The error bars indicate the 66 % quantile range
of the observations in each altitude; the sample size for each altitude is
indicated on the
The spring profile for OM at Tullahoma is depicted in Fig. 9. Here, a
strong OM peak up to 150 pg m
Finally, we evaluate the model performance for OM for the summer profile
based on NOMADSS data from June and July 2013. Due to the differential
measurements approach of the DOHGS instrumental setup the sample size is
equal to that of the GEM profiles (Lymann and Jaffe, 2012; Ambrose et al.,
2013, 2015). The larger sampling size together with the fact
that NOMADSS observations cover a region larger than the vertical profiles
over Tullahoma leads to a higher variability in the measurements given by
the 66 % quantile range (Fig. 10). We created the average OM profile from
the same data as the GEM profile. For OM measurements below the detection
limit we used half the reported detection limit which varied between 74 and
138 pg m
The resulting profile exhibits a distinct vertical distribution with lower
concentrations inside the PBL (40–60 pg m
The finding that the ozone and OH reactions cannot reproduce the observed
increase in OM concentrations in the upper troposphere is in line with
findings from CARIBIC, where no correlation of ozone with the GEM
Similarly to the spring profile at Tullahoma, the lower OM peak lies directly above the PBL, which is an area of enhanced photolytic activity due to higher solar radiation and low particle density concentrations compared to the PBL. Also, due to the low water vapor content in this region little aqueous reduction of OM can take place. This OM peak cannot be reproduced by model runs with bromine chemistry. In fact, the resulting profiles are even inverse to the observations. Ozone and OH chemistry, in contrast, led to increased oxidation above the PBL with the OH chemistry run with the best agreement with the observed vertical distribution and ozone with the actual concentrations (Fig. 10; O3CHEM and OHCHEM).
Stratospheric observations from intercontinental CARIBIC flights indicate
that the GEM
GLEMOS shows the best agreement with observations. It is able to reproduce
the slow GEM
Seasonal vertical profiles of modeled GEM
Average interhemispheric transects for 19 flights from Munich to Sao Paulo (left) and 8 flights from Munich to Cape Town (right). Error bars indicate the 66 % quantile range of all observations for a given latitude. Average OM concentrations are calculated as TM–GEM where TM was measured on the outward and GEM on return flights; thus negative values for OM are possible (Slemr et al., 2014).
Finally, observations on 8 flights from Munich, Germany, to Cape Town, South
Africa, and 19 flights from Munich to Sao Paulo, Brazil, are used to
investigate the models' capability to reproduce interhemispheric gradients.
The interhemispheric CARIBIC flights were performed between 2013 and 2017.
The CARIBIC Tekran instrument, which is usually set up to measure TM, was
equipped with a quartz wool filter on each return flight to measure GEM only
(Slemr et al., 2016). The Tekran raw data were manually reintegrated (Slemr
et al., 2016). This allows us to look at interhemispheric gradients of
elemental and total mercury. However, as the two quantities were not measured
on the same flights only a range of possible oxidized mercury concentrations
can be deduced. Long-range transport and a variable tropopause height can
easily lead to differences larger than the expected OM concentrations on the
return flight on the same flight track. Because of this, the calculated
average difference of TM and GEM can sometimes be lower than zero. Most of
the TM and GEM measurements were within each other's 66 % quantile range
(Fig. 12a, b). The difference between the average TM and GEM concentrations
was 70 pg m
To create average interhemispheric transects we grouped all observations
which were at least 1 km below the tropopause into bins of 5
Relative interhemispheric transects for 19 flights from Munich to Sao Paulo. TM (left side) was measured on the outward and GEM (right side) on return flights (Slemr et al., 2014). Error bars indicate the 66 % quantile range of all observations for a given latitude. Plot in the left column are for TGM and in the right column for GEM.
Average interhemispheric transects for 19 flights from Munich to Sao Paulo. TM (left side) was measured on the outward and GEM (right side) on return flights (Slemr et al., 2014). Error bars indicate the 66 % quantile range of all observations for a given latitude.
For the model evaluation we use monthly average GEM and TM concentrations
for the month during which each flight was performed from the grid cell
closest to the aircraft and aggregate the model data into bins similar to
the observational data. It has to be kept in mind that for models with a low
vertical resolution the relevant grid cell might extend above the
tropopause. Here, we focus on the relative interhemispheric gradient to
evaluate the models. The relative TM and GEM trends on flights to Sao Paulo
are depicted in Fig. 13 and absolute values are given in Fig. 14.
Similar plots for the flights to Cape Town are given in the Supplement (Figs. S2 and S3). The models are generally in better agreement
with absolute and relative observations for total mercury (Figs. 13, 14).
This is mainly due to an overestimation of oxidized mercury in the Northern
Hemisphere (45 to 35
For TM, no chemistry setup could be found that most accurately reproduced
the observed concentrations and trends. As was shown before in the
evaluation of the vertical profiles, differences in the CTM formulation can
have a larger impact than the choice of oxidant. Looking at GEM, it can be
see that different oxidants lead to different interhemispheric
distributions. Here, the use of bromine fields leads to an overestimation of
oxidation in the Northern Hemisphere (50–25
We investigated the total atmospheric mercury burden as predicted by the
four global models. Looking at the vertical distribution the models predict
30 % inside the PBL, 60 % in the free troposphere, and 10 % in the
stratosphere (Fig. 15a). On average the models have a total atmospheric mercury burden of 4800 Mg
(ECHMERIT 4650 Mg, GEOS-Chem 5100 Mg, GLEMOS 4200 Mg, GEM-MACH-Hg
5300 Mg),
which is comparable to the 5300 Mg estimated by Amos et al. (2013). The average vertical distribution in the model
ensemble is 1500 Mg in the PBL, 2800 Mg in the FT, and 500 Mg in the stratosphere. For the
oxidized mercury species model results exhibit larger differences compared
to TGM, leading to a smaller spread of the predicted atmospheric total GEM
burden. We found that all models have a similar inter hemispheric mercury
distribution with 54 to 58 % of the total mercury in the Northern
Hemisphere (Fig. 15b). Finally, we investigated the latitudinal distribution
of GEM
In this model intercomparison study we investigated the vertical distribution of mercury in the atmosphere and evaluated the impact of mercury chemistry and emissions. The key finding is that models are generally able to reproduce the vertical profile of TM and elemental gaseous mercury (GEM) from the surface up to the tropopause. This means largely uniform concentrations inside the PBL and FT. Increased GEM concentrations observed inside the PBL could be attributed to anthropogenic emissions. However, the models tend to overestimate GEM concentrations in the lower stratosphere and those models which feature declining GEM concentrations above the tropopause do so by oxidation to reactive mercury (OM) species, thus overestimating TM. Moreover, it was found that a high vertical resolution near the tropopause is very important for a better reproduction of the observed declining mercury gradient.
For OM, the
observations indicate low concentrations inside the PBL, often below
50 pg m
Our interpretation of the observations is that stratospheric intrusions and
tropopause folds, which mainly occur during spring time, play an important
role for elevated OM concentrations in the upper FT at altitudes above
6000 m. The frequency of stratosphere-to-troposphere transport is regionally
variable and has shown to be most common in latitudes where the
measurements were performed. However, also long-range transport of marine
bromine species as observed by Gratz et al. (2015) during the NOMADSS
flights can be an important source of stratospheric Br. Thus, we emphasize
the importance of further research regarding the atmospheric bromine cycle
to better understand the oxidation pathways of mercury. Besides bromine
species, stratosphere-to-troposphere transport could also be a source for OM
already formed in the lower stratosphere. This could also explain the
missing correlation of ozone concentrations and GEM
Uniformly low OM concentrations were observed during winter and could be
reproduced by the models. In spring and summer, increased OM concentrations
were observed above the PBL in the LFT. This could only
be reproduced by models using O
Finally, we have investigated TM and GEM concentrations and gradients in the upper troposphere between the Northern and Southern Hemisphere based on intercontinental CARIBIC flights. The models were more adept in reproducing TM concentrations and trends compared to GEM. Model runs using bromine reactions showed a better agreement to observed intercontinental TM gradients. However, the current bromine fields led to a strong overestimation of mercury oxidation in midlatitudes. Ozone and OH chemistry, however, led to overestimated oxidation in the tropics. Interestingly, reducing the OM fraction in the anthropogenic emission inventories led to a better agreement with observed concentrations. This could be due high OM fractions for coal-fired power plants in current emission inventories, which have high stacks, and thus effective emission heights can even be above the PBL at times.
Mercury
modeling and measurement data discussed in this paper are reported within the
GMOS central database and are available upon request at
The following authors were responsible for model results: OT (GLEMOS), NS, SS (GEOS-CHEM), AD, AR (GEM-MACH-Hg), FDS, IMH (ECHMERIT), C-JL, YZ (CMAQ-Hem), CG, IMH (WRF-CHEM), JB, VM, BG (CCLM-CMAQ), and XY (p-TOMCAT). The following authors were responsible for measurements: AW, JB, RE, NP (ETMEP), DJ, JA, LG, LJ (NOMADSS), SB, XR, WL, PK (Tullahoma), CB, AZ, FS, AW, RE (CARIBIC), AW, RE (Waldhof), AW, RE, and NP (Mace Head).
The authors declare that they have no conflict of interest.
This study was financially supported in part by the EU FP7-ENV-2010 project, “Global Mercury Observation System” (GMOS, grant agreement no. 265113). Noelle E. Selin and Shaojie Song also acknowledge the US National Science Foundation Atmospheric Chemistry Program (grant no. 1053648) for their financial support. Steve Brooks, Xinrong Ren, Winston T. Luke, and Paul Kelley acknowledge the National Oceanic and Atmospheric Administration (NOAA), which funded the Tullahoma aircraft campaign (project no. NA09OAR4600198 and NA10OAR4600209). Finally we want to acknowledge the US National Science Foundation, which provided funding for the NOMADSS project (award no. 1217010).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. Edited by: Francesca Sprovieri Reviewed by: three anonymous referees