We report on an evaluation of tropospheric ozone and its
precursor gases in three atmospheric chemistry versions as implemented in the
European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated
Forecasting System (IFS), referred to as IFS(CB05BASCOE), IFS(MOZART) and
IFS(MOCAGE). While the model versions were forced with the same overall
meteorology, emissions, transport and deposition schemes, they vary largely
in their parameterisations describing atmospheric chemistry, including the
organics degradation, heterogeneous chemistry and photolysis, as well as
chemical solver. The model results from the three chemistry versions are
compared against a range of aircraft field campaigns, surface observations,
ozone-sondes and satellite observations, which provides quantification of the
overall model uncertainty driven by the chemistry parameterisations. We find
that they produce similar patterns and magnitudes for carbon monoxide (CO)
and ozone (
The analysis and forecasting capabilities of trace gases are key objectives of the European Copernicus Atmosphere Monitoring Service (CAMS) in order to provide operational information on the state of the atmosphere. This service relies on a combination of satellite observations with state-of-the-art atmospheric composition modelling (Flemming et al., 2017). For that purpose, the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction (NWP) system, the Integrated Forecasting System (IFS), contains modules for describing atmospheric composition, including aerosols (Morcrette et al., 2009; Benedetti et al., 2009), greenhouse gases (Agustí-Panareda et al., 2016; Engelen et al., 2009) and reactive gases (Flemming et al., 2015).
Having atmospheric chemistry available within the IFS allows for the use of detailed meteorological parameters to drive the fate of constituents and its capabilities to constrain trace gas concentrations through assimilation of satellite retrievals. Furthermore, having atmospheric chemistry as an integral element of the IFS enables the study of feedback processes between atmospheric chemistry and other parts of the earth system, such as the impact of ozone in the radiation scheme on temperature and the provision of trace gases as precursors for aerosol.
Other examples in which chemistry modules have been implemented in general circulation models (GCMs) for NWP applications have been, for instance, GEM-AQ (Kaminski et al., 2008; Struzewska et al., 2015), GEMS-BACH (de Grandpré et al., 2009; Robichaud et al., 2010), the Met Office's Unified Model (Morgenstern et al., 2009; O'Connor et al., 2014) and, on a regional scale, WRF-Chem (Powers et al., 2017).
The chemistry module that is currently used operationally in the CAMS
originates from the chemistry transport model TM5 (Huijnen et al., 2010). The
chemistry module is based on a modified version of CB05 tropospheric
chemistry (Williams et al., 2013), while stratospheric ozone is modelled
using a linear ozone scheme (Cariolle and Deque, 1986; Cariolle and
Teyssèdre, 2007). This version, referred to as IFS(CB05), is used in a
range of applications, such as for the CAMS operational analyses and
forecasts of atmospheric composition (
Other chemistry versions have also been implemented in the IFS, and each version has its choice regarding the gas-phase chemical mechanism, computation of photolysis rates, definition of cloud and heterogeneous reactions, and solver specifics. This enables flexibility in the choice of the atmospheric chemistry component in the global CAMS system. A model version which contains the extension of the CB05 scheme with a comprehensive stratospheric chemistry originating from the Belgian Assimilation System for Chemical ObsErvations (BASCOE; Skachko et al., 2016) has been developed (Huijnen et al., 2016b). Furthermore, in predecessors of the current system, the MOZART (Kinnison et al., 2007) and MOCAGE (Bousserez et al., 2007) chemistry transport models had also been coupled with IFS (Flemming et al., 2009). Afterwards, their chemistry modules were technically integrated into the IFS (Flemming et al., 2015). Recently, three fully functioning systems have been prepared, as are presented here, based on CB05BASCOE, MOZART and MOCAGE chemistry.
Many studies such as HTAP and AQMEII (Galmarini et al., 2017) try to explore the uncertainties of global chemistry modelling through changing emissions. But in such multi-model assessments meteorological model parameterisations, such as advection, deposition or vertical diffusion, also vary (e.g. Emmons et al., 2015; Huang et al., 2017; Im et al., 2018). While such a multi-model approach is appropriate to define the overall uncertainty, it makes it hard to isolate the impact of the differences in the chemistry parameterisations. In this work we study the model spread caused by three chemistry modules that are fully independent in an otherwise identical configuration for describing meteorology, transport, emissions and deposition. This endeavour intends to provide insights into the uncertainty induced purely by the simulation of chemistry and as such complements the many model intercomparison studies that try to explore other sources of uncertainty in global atmospheric modelling.
The central application of tropospheric chemistry analyses and forecasts
in the IFS is to provide global coverage of the current state of
atmospheric composition, along with its long-term trends (Inness et al.,
2019). These are intensively used as boundary conditions for regional models
(Marécal et al., 2015). Uncertainty information is relevant to CAMS users
of global chemistry forecasts, in particular for the trace gases that are
not constrained or are poorly constrained by observations, such as the non-methane
hydrocarbons (NMHCs) and reactive nitrogen species. Therefore, we focus here
not only on the model ability to represent tropospheric ozone (
In this study, we rely on various sets of observations. Comparatively dense
in situ observation networks exist to measure surface and tropospheric CO and
The paper is structured as follows. Section 2 provides a description of the various chemistry schemes implemented in IFS. Section 3 provides an overview of the observational datasets used for model evaluation, while in Sect. 4 a basic assessment of model differences for tracers playing a key role in tropospheric ozone is provided. Section 5 contains the evaluation against observations of a full year simulation with the three atmospheric chemistry versions of IFS with a focus on tropospheric chemistry. The paper is concluded with a summary and an outlook in Sect. 6, where the recent model evolution in the various versions is also briefly described.
The three chemistry schemes implemented in the IFS are described in more detail in the following subsections. A brief analysis of elemental differences is given in Sect. 2.1.4
For IFS(CB05BASCOE), a merging approach has been developed whereby the
tropospheric and stratospheric chemistry schemes are used side by side
within IFS (Huijnen et al., 2016b). The tropospheric chemistry in the IFS is
based on a modified version of the CB05 mechanism (Yarwood et al., 2005). It
adopts a lumping approach for organic species by defining a separate tracer
species for specific types of functional groups. Modifications and extensions
to this include an explicit treatment of C1 to C3 species, as described in
Williams et al. (2013), and
The modified band approach (MBA) is adopted for the online computation of
photolysis rates in the troposphere (Williams et al., 2012) and uses
seven absorption bands across the spectral range 202–695 nm, accounting for
cloud and aerosol optical properties. At instances of large solar zenith
angles (71–85
For the modelling of atmospheric composition above the tropopause, the chemical scheme and the parameterisation for polar stratospheric clouds (PSCs) have been taken over from the BASCOE system (Huijnen et al., 2016b) version “sb14a”. Lookup tables of photolysis rates were computed offline by the TUV package (Madronich and Flocke, 1999) as a function of log-pressure altitude, ozone overhead column and solar zenith angle. Gas-phase and heterogeneous reaction rates are taken from JPL evaluation 17 (Sander et al., 2011) and JPL evaluation 13 (Sander et al., 2000), respectively.
For solving both the tropospheric and stratospheric reaction mechanism we use
KPP-based four stages and third-order Rosenbrock solvers (Sandu and Sander, 2006).
Photolysis rates for reactions occurring in both the troposphere and
stratosphere are merged at the interface in order to ensure a smooth
transition between the two schemes. To distinguish between the tropospheric
and stratospheric regime, we use a chemical definition of the tropopause
level, whereby tropospheric grid cells are defined at
The MOCAGE chemical scheme (Bousserez et al., 2007; Lacressonnière et al., 2012) is a merge of reactions of the tropospheric RACM (Regional Atmospheric Chemistry Mechanism) scheme (Stockwell et al., 1997) with the reactions relevant to the stratospheric chemistry of REPROBUS (REactive Processes Ruling the Ozone BUdget in the Stratosphere) (Lefèvre et al., 1994, 1998). It uses a lumping approach for organic trace gas species. The MOCAGE chemistry has been extended, in particular by the inclusion of the sulfur cycle in the troposphere (Ménégoz et al., 2009) and peroxyacetyl nitrate (PAN) photolysis.
The RACMOBUS (RACM-REPROBUS) chemistry scheme implemented in IFS uses 115 species in total, including long-lived and short-lived species, family groups, and a PSC tracer. A total of 326 thermal reactions and 53 photolysis reactions are considered to model both tropospheric and stratospheric gaseous chemistry. Nine heterogeneous reactions are taken into account for the stratosphere and two for the aqueous oxidation reaction of sulfur dioxide into sulfuric acid in the troposphere (Lacressonnière et al., 2012). For photolysis rates, a lookup table of photolysis rates was computed offline by the TUV package (Madronich and Flocke, 1997, version 5.3.1) as a function of solar zenith angle, ozone column above each cell, altitude and surface albedo.
The atmospheric chemistry in IFS(MOZART) is based on the MOZART-3 mechanism (Kinnison et al., 2007) and includes additional species and reactions from MOZART-4 (Emmons et al., 2010) with further updates from the Community Atmosphere Model with interactive chemistry, referred to as CAM4-Chem (Lamarque et al., 2012; Tilmes et al., 2016). As for IFS(CB05BASCOE), the heterogeneous reactions in the troposphere are parameterised based on aerosol surface area density (SAD), which is derived using the CAMS aerosol fields. IFS(MOZART) contains a parameterisation for the gas–aerosol partitioning of nitrate and ammonium (Emmons et al., 2010). The heterogeneous chemistry in the stratosphere accounts for heterogeneous processes on liquid sulfate aerosols and polar stratospheric clouds following the approach of Considine et al. (2000).
The photolysis frequencies in wavelengths from 200 to 750 nm are calculated
from a lookup table based on the four-stream version of the Stratosphere,
Troposphere, Ultraviolet (STUV) radiative transfer model (Madronich et al.,
1989). For wavelengths from 120 to 200 nm, the wavelength-dependent cross
sections and quantum yields are specified, and the transmission function is
calculated explicitly for each wavelength interval. In the case of
An overview of the most important differences in the three chemistry modules described above is given in Table 1. First, there are large differences in the choices made to compile the tropospheric chemistry mechanism. IFS(MOZART) describes the degradation of organic carbon types C1, C2, C3, C4, C5, C7 and C10, together with lumped aromatics, while IFS(CB05BASCOE) only describes explicit degradation up to C3, with the same reactions as present in IFS(MOZART). Instead, emissions and degradation of higher volatile organic compounds (VOCs) in IFS(CB05BASCOE) are lumped to a few tracers. Furthermore, the parameterisation of isoprene and terpene degradation is simpler in IFS(CB05BASCOE) than in IFS(MOZART). Aromatics are currently not described in IFS(CB05BASCOE), while they are accounted for with simple approaches in IFS(MOZART).
IFS(MOCAGE) describes many more lumped organic species than IFS(CB05BASCOE) and IFS(MOZART), also accounting for the more complex organics beyond C3. Furthermore, IFS(MOCAGE) uses a rather different lumping approach and contains more complexity for different terpene components, also including aromatics. Such differences are bound to impact the effective degradation of VOCs and thus ozone production efficiency and oxidation capacity (e.g. Sander et al., 2019).
With respect to the inorganic chemistry, the schemes are mostly similar.
Still, IFS(MOCAGE) includes nitrous acid (HONO) chemistry, which is missing in both
IFS(CB05BASCOE) and IFS(MOZART) implementations. Gas-phase sulfur chemistry
is mostly similar between IFS(CB05BASCOE) and IFS(MOZART), while IFS(MOCAGE)
has some more complexity by considering reactions involving dimethyl sulfoxide (DMSO) and
Significant uncertainty remains in the magnitude of heterogeneous reaction
probabilities. Heterogeneous reactions of
Regarding the treatment of photolysis in the troposphere, IFS(CB05BASCOE)
applies a modified band approach, whereby for seven wavelengths the photolysis
rates are computed online, taking into account the scattering and absorption
properties of gases (overhead ozone and oxygen), clouds and aerosol.
IFS(MOCAGE) adopts a lookup table approach, accounting for overhead ozone
column, solar zenith angle, surface albedo and altitude, providing photolysis
rates for clear-sky conditions. The impact of cloudiness on photolysis rates
is applied online in IFS during the simulation using the parameterisation
proposed by Brasseur et al. (1998). IFS(MOZART) applies the lookup table
approach from MOZART-3 (Kinnison et al., 2007), considering overhead ozone
column and cloud scattering effects on photolysis rates. Despite such larger
differences, an intercomparison of an instantaneous field of photolysis rates
showed similar average profiles, with a spread in magnitude in the range of
5 % in the tropical free troposphere for important photolysis rates like
As for the stratospheric chemistry, IFS(CB05BASCOE) contains the largest complexity of the three model versions, with more species and reactions compared to the other mechanisms.
Different methods are used to solve the reaction mechanism. IFS(CB05BASCOE)
applies the Rosenbrock solver, IFS(MOCAGE) here applies a first-order
semi-implicit solver with fixed time steps, and IFS(MOZART) applies the
explicit Euler method for species with long lifetimes (e.g.
Specification of elemental aspects describing the three chemistry versions.
The actual emission totals used in the simulation for 2011 from
anthropogenic, biogenic and natural sources, biomass burning, and
lightning NO are given in Table 2. MACCity emissions are used to prescribe
the anthropogenic emissions (Granier et al., 2011), wherein wintertime CO
traffic emissions have been scaled up according to Stein et al. (2014).
Aircraft NO emissions are 1.8 Tg NO yr
Monthly specific biogenic emissions originating from the MEGAN-MACC inventory (Sindelarova et al., 2014) are adopted, complemented with POET-based oceanic emissions (Granier et al., 2005).
Daily biomass burning emissions are taken from the Global Fire Assimilation System (GFAS) version 1.2, which is based on satellite retrievals of fire radiative power (Kaiser et al., 2012).
As described above, the chemistry mechanisms vary, particularly in their description of VOC degradation, with the most explicit treatment described in IFS(MOZ), while IFS(MOCAGE) and IFS(CB05BASCOE) rely on a more extended lumping approach. This has consequences for the partitioning of the various emissions. Still, we have ensured that the total of VOC and aromatic emissions in terms of tetragrams of carbon are essentially the same for the three chemistry schemes.
For CB05BASCOE, the emissions of “paraffins” (toluene and higher alkane emissions), “olefins” (butenes and higher alkenes) and “aldehydes” (acetaldehyde and other aldehydes) have been prescribed. Likewise, MOZART applies emissions of BIGALK (butanes and higher alkanes) and BIGENE (butenes and higher alkenes). MOCAGE adopts tracers HC3, HC5 and HC8, over which emissions of ethyne, propane, butanes and higher alkanes, esters, methanol, and other alcohols are distributed, whereas DIEN (butadiene) contains butenes and higher alkene emissions.
As for the aromatics, IFS(CB05BASCOE) disregards those, but includes toluene carbon emissions as part of the paraffins. IFS(MOZART) additionally treats a toluene tracer, while IFS(MOCAGE) contains two types of aromatics, designated TOL and XYL. These aromatic emissions are composed from toluene, trimethylbenzene, xylene and other aromatics.
Dry deposition velocities in the current configuration were provided as
monthly mean values from a simulation using the approach discussed in Michou
et al. (2004). To account for the diurnal variation in deposition velocities,
a cosine function of the solar zenith angle is adopted with
Methane (
The IFS model versions evaluated here were implemented in IFS cycle 43R1 and
are run on a T255 horizontal resolution (
For the evaluation, the model was sampled in the troposphere and lower stratosphere (i.e. the lowest 40 model levels) every 3 h to have full coverage of the daily cycle. These are used to compute monthly to yearly averages. Standard deviations are computed to represent the model variability for a specified range in time and space.
Aircraft measurements of trace gas composition from a database produced by
Emmons et al. (2000) were used for the evaluation of distributions of collocated
monthly mean modelled fields. Although these measurements cover only limited
time periods, they provide valuable information about the vertical
distribution of the analysed trace gases. The database is formed with data from
a number of aircraft campaigns that took place during 1990–2001 which are gridded onto
global maps, forming data composites of chemical species important for
tropospheric ozone photochemistry. These are used to create observation-based
climatologies (Emmons et al., 2000). Here we use measurements of ozone, CO,
Although the specific field campaign data are in theory representative for the specific year, the averaging of a large number of measurements over space and time partly solves the problem of interannual variability, and therefore these data can be considered as a climatology. Pozzer et al. (2009) showed that the correlation between model results and these observations would vary less than 5 % if model results 5 years apart were used. For the total anthropogenic VOC emissions the changes between the year 1990 and 2011 are of the order of 14 %, following the Emissions Database for Global Atmospheric Research (EDGARv4.3.2 database). Nevertheless, the evaluations presented here are all sampling background locations or outflow regions and are hence only partly affected by such changes in anthropogenic emissions. Also, the variability as well as measurement uncertainties present in the observations are larger than 14 %, implying that we can still consider these observations representative. Finally, these data summaries are useful for providing a picture of the global distributions of NMHCs and nitrogen-containing trace gases.
Specification of annual emission totals from anthropogenic, biogenic and natural sources, and biomass burning for 2011, in tetragrams of species mass, for three chemistry versions.
Specifications of the experiments evaluated.
Geographical distribution of the aircraft campaigns presented by Emmons et al. (2000). Each field campaign is represented by a different colour. Further information on the campaigns is found in Emmons et al. (2000).
In situ observations for monthly mean CO for the year 2011 are used to evaluate monthly mean modelled surface CO fields. Observational data are taken from the World Data Centre for Greenhouse Gases (WDCGG), the data repository and archive for greenhouse and related gases of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) programme. The uncertainty of the CO observations is estimated to be of the order of 1–3 ppm (Novelli et al., 2003).
Tropospheric ozone was evaluated using sonde measurement data available from
the World Ozone and Ultraviolet Radiation Data Center (WOUDC;
Geographical distribution of the ozone-sondes during 2011 used for evaluation, coloured for the various seasons. The size of the triangles provides information on the relative number of observations available for each of the seasons and locations compared to the other locations. The geographical aggregation for the five latitude bands presented in Figs. 5 and 7, as well as the western Europe and eastern US regions, is also given.
MOPITT (Measurements of Pollution in the Troposphere) v7 CO column
observations (Deeter et al., 2017) are used to evaluate the CO total columns.
The MOPITT instrument is a multi-channel thermal infrared (TIR) and near
infrared (NIR) instrument operating onboard the Terra satellite. The total
column CO product is based on the integral of the retrieved CO volume mixing
ratio profile. A climatology based on CAM4-Chem (Lamarque et al., 2012) is used
to provide the MOPITT a priori profiles. For our study we use the
TIR-derived CO total column observations, which are provided over both the
oceans and over land. The highest CO sensitivities of these MOPITT TIR
measurements are in the middle troposphere at around 500 hPa. Sensitivity to
the lower troposphere depends on the thermal contrast between the land and
lower atmosphere, which is higher during the day than in the night.
Therefore, in our study we only use daytime MOPITT TIR observations. The standard
deviation of the error in individual pixels for the MOPITT v7 TIR product
evaluated against NOAA flask measurements is reported as
OMI retrievals of tropospheric
In this section we provide a basic assessment of the magnitude and differences in
annual and zonal mean concentration fields between the three chemistry
versions for a few essential tracers:
The annual zonal mean
Likewise, annual zonal mean CO mixing ratios show the highest values associated with pollution regions in the tropics and over the NH. The highest values are obtained with CBA and the lowest with MOC, with differences ranging between 10 % and 20 %. As CO and precursor emissions are essentially identical, this is likely caused by differences in oxidising capacity, which is governed by OH abundance, as described below.
Zonal mean
Figure 2 also shows the annual zonal mean concentrations of OH. Overall, the magnitude of OH is largest for MOC and lowest for CBA, with MOZ in between. The largest differences in absolute terms are found in the tropics, where the concentrations are highest. Nevertheless, in relative terms the largest differences are found in the extratropics, particularly over the SH, as can be seen from Fig. 3. This figure shows the temporal evolution of the difference between MOC and MOZ simulated daily average OH at 600 hPa. This shows that differences can be up to 50 % in daily averages, in particular over the extratropics where the absolute values are lower compared to those in the tropics.
Tropospheric
Zonal annual mean
Relative differences (in percent) of OH daily averaged mixing ratios of simulation MOC with respect to MOZ at 600 hPa.
In this section we evaluate the model simulations against a range of observations, including ozone-sondes, aircraft measurements and satellite observations, for carbon monoxide and nitrogen dioxide.
Table 4 summarises the comparison of the various model results with aircraft
measurements described in Sect. 3.1 in terms of biases and correlation, in
terms of explained variance (
Also according to this analysis, the discrepancies between model results and
measurements are smaller than the uncertainties if the absolute value of the
weighted bias (i.e. in units of the normalised standard deviation, Table 4)
for a specific tracer is less than 1. A high weighted correlation in
combination with a weighted bias of [
Summary of the bias and correlation coefficients (in terms of
explained variance,
Figure 4 compares tropospheric
MOC shows positive biases over the NH mid-latitudes during winter and spring
and negative biases during Arctic winter in the lower troposphere
(< 700 hPa) as well as in the 700–300 hPa range in summer. CBA
simulates
Tropospheric ozone profiles from model versions CBA (red), MOC
(blue) and MOZ (green) against sondes (black) in volume mixing ratios (ppbv)
over six different regions (from top row to bottom row): NH polar
(90–60
Mean tropospheric ozone profiles from model versions CBA (red), MOC
(blue) and MOZ (green) against sondes (black) in volume mixing ratios (ppbv)
during DJF and JJA at selected individual stations. Error bars represent the
1
Figure 5 shows an evaluation of
Evaluation against the aircraft climatology as provided in Table 4 shows on
average a positive bias in the range of 10 (CBA and MOC) to 16 ppbv (MOZ),
while the correlation statistics show generally acceptable values
(
Mean of all model biases
Carbon monoxide is a key tracer for tropospheric chemistry, as a marker of
biomass burning and anthropogenic pollution, and provides the most important
sink for OH. Approximately half of the CO burden is directly emitted, and the
rest is formed through degradation of
Figures 7 and 8 show the monthly mean evaluation against MOPITT total CO columns for April and August 2011. Whereas generally the model versions show good agreement with the observations in terms of their spatial patterns, persistent seasonal biases remain, such as the negative bias over the NH during April (further analysed in, e.g. Shindell et al., 2006; Stein et al., 2014) and a negative bias over Eurasia during August. For all three chemistry versions the patterns of enhanced CO in the tropics, associated with biomass burning, are generally well captured, as is the magnitude of CO columns over the SH. Looking at differences between model versions, CBA shows the overall highest magnitudes, implying a smaller negative bias over the NH, particularly during April, while this simultaneously results in an emerging positive bias in the tropics.
MOPITT CO total column retrieval for April 2011
MOPITT CO total column retrieval for August 2011
In Fig. 9 the annual cycle at selected GAW stations is shown, while Fig. 10 additionally shows the corresponding temporal correlation between the simulated monthly mean CO for all stations. Even though the phase and amplitude of the annual cycle are well reproduced by the model versions at several locations (e.g. Mauna Loa, Hawaii), the concentrations tend to be overestimated in the Southern Hemisphere, particularly by CBA and to a lesser extent by the other chemistry versions, and underestimated over the remote Northern Hemisphere. This points to sensitivities due to the applied chemistry scheme mainly associated with differences in OH, which is lowest in CBA and highest in MOC (see also Sect. 4). A possible overestimation of CO over the tropics and Southern Hemisphere could relate to uncertainties in the biogenic emissions (Sindelarova et al., 2014).
The correlations (in terms of
Comparison of CO mixing ratios (ppbv) at the surface as simulated (red, blue and green are model results from CBA, MOC and MOZ, respectively) and observed (black) at 12 stations sorted by decreasing latitudes. The bars represent 1 standard deviation of the monthly average for the location of the station.
Temporal correlation (
Compared to aircraft observations (see Fig. 11), the three model versions produce similar CO mixing ratio vertical profiles, with differences among them typically within the range of 10 %–20 %, depending on the location. The biomass burning plumes are reproduced consistently (see Fig. 11, TRACE-A, West Africa coast), and all three models compare well with observations for both background conditions in the Northern Hemisphere (SONEX, Ireland) and highly polluted conditions (PEM-West-B, China coast).
Comparison of simulated CO vertical profiles by using the CBA (red solid line), MOC (blue solid line) and MOZ (green solid line) chemistry versions against aircraft data (black dots). Also shown are the modelled (dashed lines) and measured (black rectangular) standard deviations. The numbers on the right vertical axis indicate the number of available measurements.
Formaldehyde is important as one of the most ubiquitous carbonyl compounds in
the atmosphere (Fortems-Cheiney et al., 2012). It is mainly formed through
the oxidation of methane, isoprene and other VOCs such as methanol (Jacob et
al., 2005), while its oxidation and photolysis are responsible for about half
of the CO in the atmosphere. A good agreement of the simulations
with the observations can be seen from Fig. 12, where the vertical profile
from selected aircraft observations and model simulations is shown. Also
from Table 4 it is clear that all three model versions reproduce
formaldehyde accurately. The weighted bias is always well below 1 standard
deviation unit (i.e.
Comparison of simulated
Comparison of simulated
Considering the short lifetimes for
Ethene is the smallest alkene which is primarily emitted from biogenic
sources. In our configuration, biogenic
The three chemical mechanisms produce mostly very similar mixing ratios of
Furthermore, it is interesting to note the comparatively large difference present
between the simulations at high latitudes (e.g. SONEX, Newfoundland), where
the largest relative differences in modelled OH have been found (see also
Sect. 4), illustrating the importance of OH for explaining inter-model
differences. CBA indeed shows the largest values for
Comparison of simulated
Ethane (
Compared to aircraft observations, all three model versions significantly
underestimate the
Comparison of simulated
Nitrogen dioxide is a trace gas difficult to compare with in situ
observations due to its photochemical balance with nitric oxide. Nitrogen
dioxide shows a strong diurnal cycle, mainly due to the fast photolysis rate.
Here only daytime values have been used to construct the model averages
because the observations from the various field campaigns were equally
conducted in daylight conditions. Figure 15 shows the strong variability in
daytime
Comparison of daytime
Monthly mean tropospheric
Monthly mean tropospheric
Figures 16 and 17 evaluate tropospheric
Another interesting finding is a relatively strong negative bias over the
Eurasian and North American continents in April for CBA, which is stronger than
modelled in MOZ and MOC. In contrast, MOC in particular (but also MOZ)
overestimates
Compared to several of the trace gases previously analysed, nitric acid is
not primary emitted but is purely photochemically formed in the atmosphere.
It has a very high solubility and therefore tends to be scavenged by
precipitation very efficiently, providing an effective sink for the
Comparison of simulated
Comparison of simulated
Similar to
We have reported on an extended evaluation of tropospheric trace gases as modelled in three largely independent chemistry configurations to describe ozone chemistry, as implemented in ECMWF's Integrated Forecasting System of cycle 43R1. These configurations are based on IFS(CB05BASCOE), IFS(MOZART) and IFS(MOCAGE) chemistry versions. While the model versions were forced with the same overall emissions and adopt the same parameterisations for transport and dry and wet deposition, they largely vary in their parameterisations describing atmospheric chemistry. In particular their VOC degradation, treatment of heterogeneous chemistry and photolysis, and the adopted chemical solver vary strongly across model versions. Therefore, this evaluation provides a quantification of the overall model uncertainties in the CAMS system for global reactive gases, which are due to these chemistry parameterisations, compared to other common uncertainties such as emissions or transport processes.
Overall the three chemistry versions implemented in the IFS produce similar
patterns and magnitudes for CO,
The comparison of the model simulations of NMHCs against a selection of
aircraft observations reveals two major issues. First, the evaluation shows
that large uncertainties remain in current and widely used emission
estimates. For instance, the MACCity ethane emissions are likely
underestimated by at least a factor of 2 (Hausmann et al., 2016; Monks et al.,
2018) and were shown to lead to significantly lower coupling of the heterogeneous reactions in the troposphere with
CAMS aerosol in IFS(MOCAGE); implementations of more accurate solvers for atmospheric chemistry based on
Rosenbrock (Sandu and Sander, 2006) or alternatively ASIS (Cariolle et al.,
2017) in IFS(MOCAGE); revisions in the atmospheric chemistry scheme in IFS(MOZART) by revising
assumptions in the heterogeneous chemistry, expending the complexity of the
scheme with additional species, detailed aromatic speciation instead of
lumped toluene and updated reaction products following recent developments
in CAM-Chem; updates to the lookup table for photolysis rate determination in
IFS(MOZART); and updates of the reaction rate coefficients in any of the chemistry schemes to
follow the latest recommendations from IUPAC or JPL.
An update of the emission inventories is also foreseen for the near future. All these updates should tend to narrow the spread between the three model versions and bring them closer to observations. This suggests that the present estimates of uncertainties in atmospheric chemistry parameterisations are on the conservative side. Still, the diversity of chemistry versions will be useful to provide a quantification of uncertainties in key CAMS products due to the chemistry module compared to other sources of uncertainties.
The source codes of the chemistry modules are integrated
into ECWMF's IFS code, which is only available subject to a licence agreement
with ECMWF. The IFS code without modules for Research Atmospheric Science Data Center assimilation and chemistry can
be obtained for educational and academic purposes as part of the openIFS
release (
The model simulation datasets used in this work are archived on the ECMWF archiving system (MARS) under the experiment IDs listed in Table 3. Readers with no access to this system can freely obtain these datasets from the corresponding author upon request.
VH designed the study, contributed to the evaluations against sondes and satellite retrievals, and wrote large parts of the paper. VH, SC, YC and JF developed the IFS(CB05BASCOE) chemistry module; VM, JA, TD, JG, BJ and SP developed the IFS(MOCAGE) chemistry module; IB and GB contributed to the development of the IFS(MOZART) chemistry module; and AP and VK performed the evaluation against aircraft observations and contributed to the writing.
The authors declare that they have no conflict of interest.
We acknowledge funding from the Copernicus Atmosphere Monitoring Service
(CAMS), which is funded by the European Union's Copernicus Programme. We are
grateful to the World Ozone and Ultraviolet Radiation Data Centre (WOUDC)
for providing ozone-sonde observations. We thank the Global Atmospheric
Watch programme for the provision of CO surface observations. MOPITT data were obtained from the NASA Langley
Research Atmospheric Science Data Center. We acknowledge the free use of
tropospheric
This paper was edited by Jason Williams and reviewed by Carlos Ordóñez and one anonymous referee.