GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-9-3071-2016C-IFS-CB05-BASCOE: stratospheric chemistry in the Integrated Forecasting
System of ECMWFHuijnenVincenthuijnen@knmi.nlhttps://orcid.org/0000-0002-2814-8475FlemmingJohanneshttps://orcid.org/0000-0003-4880-5329ChabrillatSimonhttps://orcid.org/0000-0003-4378-1567ErreraQuentinChristopheYveshttps://orcid.org/0000-0003-3243-5036BlechschmidtAnne-MarleneRichterAndreashttps://orcid.org/0000-0003-3339-212XEskesHenkRoyal Netherlands Meteorological Institute, De Bilt, the NetherlandsEuropean Centre for Medium-Range Weather Forecasts, Reading, UKBelgian Institute for Space Aeronomy (BIRA-IASB), Brussels, BelgiumInstitute of Environmental Physics, University of Bremen, Bremen, GermanyVincent Huijnen (huijnen@knmi.nl)6September2016993071309117February20167March20168July201618August2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/9/3071/2016/gmd-9-3071-2016.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/9/3071/2016/gmd-9-3071-2016.pdf
We present a model description and benchmark evaluation of an extension of
the tropospheric chemistry module in the Integrated Forecasting System (IFS)
of the European Centre for Medium-Range Weather Forecasts (ECMWF) with
stratospheric chemistry, referred to as C-IFS-CB05-BASCOE (for brevity here
referred to as C-IFS-TS). The stratospheric chemistry originates from the one
used in the Belgian Assimilation System for Chemical ObsErvations (BASCOE),
and is here combined with the modified CB05 chemistry module for the
troposphere as currently used operationally in the Copernicus Atmosphere
Monitoring Service (CAMS). In our approach either the tropospheric or
stratospheric chemistry module is applied, depending on the altitude of each
individual grid box with respect to the tropopause. An evaluation of a
2.5-year long C-IFS-TS simulation with respect to various satellite retrieval
products and in situ observations indicates good performance of the system in
terms of stratospheric ozone, and a general improvement in terms of
stratospheric composition compared to the C-IFS predecessor model version.
Possible issues with transport processes in the stratosphere are identified.
This marks a key step towards a chemistry module within IFS that encompasses
both tropospheric and stratospheric composition, and could expand the CAMS
analysis and forecast capabilities in the near future.
Introduction
Existing Earth observation systems in combination with global circulation
models (GCMs) help to provide a better understanding of the Earth's
atmospheric composition and changes therein (Hollingsworth et al., 2008). For
the troposphere, hemispheric transport and chemical conversion of atmospheric
composition influence regional air quality (Pausata et al., 2012; Im et al.,
2015; Marécal et al., 2015). Also, the amount of stratospheric ozone
directly impacts the forecast capabilities of surface solar irradiance (Qu et
al., 2014), stressing the relevance of good stratospheric ozone forecasts.
Stratospheric ozone further affects the chemical composition in the
troposphere because of stratosphere–troposphere transport of ozone
(Stevenson et al., 2006; Gaudel et al., 2015) and its radiative properties
influencing the tropospheric photolysis rates. Beyond such direct
implications for the troposphere, a comprehensive description of
stratospheric composition allows a more complete understanding of processes
taking place in the stratosphere, ranging from tracking the ozone hole
(Lefever et al., 2015) and understanding the concentrations of ozone
depleting substances (Chipperfield et al., 2015) to the assessment of
dynamical effects such as the Quasi-Biennial Oscillation (QBO, Baldwin et
al., 2001), and from implications of sudden stratospheric warmings on
circulation patterns (Manney et al., 2015) to general radiative feedbacks of
ozone, water vapour and CO2 on weather and climate (Solomon et al.,
2010).
These aspects have long been studied in the framework of chemistry transport
models (CTMs) and, more recently, in GCMs; see e.g. the SPARC
Chemistry-Climate Model Validation Activity (SPARC CCMVal, 2010). In GCMs the
impact of stratospheric ozone chemistry on the tropospheric climate can
explicitly be studied (e.g. Scaife et al., 2012), but meteorological models
can also benefit from having a good representation of the stratospheric
composition and its variability, considering the radiative effects and the
resulting impact on stratospheric as well as tropospheric temperatures
(Monge-Sanz et al., 2013). This becomes relevant for tropospheric forecast
skills on long-range to seasonal timescales (Maycock et al., 2011).
Within a series of MACC (Monitoring Atmospheric Composition and Climate)
European research projects a global forecast and assimilation system has been
built, which is the core of the global system of the Copernicus Atmosphere
Monitoring Service (CAMS, http://atmosphere.copernicus.eu). In CAMS,
forecasts of atmospheric composition are carried out (Flemming et al., 2015;
Morcrette et al., 2009; Engelen et al. 2009), which benefit from assimilation
of satellite retrievals (Inness et al., 2015; Benedetti et al., 2009), to
improve the initial conditions for composition fields in terms of reactive
gases, aerosols and greenhouse gases. Here a tropospheric chemistry scheme
has been embedded in ECMWF's Integrated Forecast System, referred to as
Composition-IFS (C-IFS, Flemming et al., 2015). Even though the current
operational version of C-IFS based on the Carbon Bond chemistry scheme (CB05)
provides good model capability on tropospheric composition (Eskes et al.,
2015), the stratosphere is only realistically constrained in terms of ozone.
This is because so far the model ozone is based on a linear scheme (Cariolle
and Tyssèdre, 2007) which is suitable owing to the data-assimilation
capabilities of C-IFS of both total column and profile satellite retrievals
(Flemming et al., 2011; Inness et al., 2015; Lefever et al., 2015).
Also, it is recognized that the applicability of radiation feedbacks of trace
gases, such as ozone and water vapour, as produced through CH4
oxidation, is hampered by schemes that are based on linearizations (Cariolle
and Morcrette, 2006; de Grandpré et al., 2009). This is due to the
intrinsic dependencies on climatologies which are used to construct such
schemes, and hence they may behave poorly in anomalous situations. Having
full stratospheric chemistry available in the IFS therefore would not only
allow one to study a wider range of atmospheric composition processes, but
also have a more independent assessment of radiation feedbacks on
temperature, hence providing the potential for improvements in stratospheric
and tropospheric meteorology. These considerations drive the need for
extension of C-IFS with a module for stratospheric chemistry. For this we use
the chemistry scheme from the Belgian Assimilation System for Chemical
ObsErvations (BASCOE) (Errera et al., 2008), which was developed to
assimilate satellite observations of stratospheric composition.
BASCOE is based on a CTM of the stratosphere which is used to investigate
stratospheric photochemistry (Theys et al., 2009; Muncaster et al., 2012). The assimilation system uses
the 4D-VAR algorithm (Talagrand and Courtier, 1987) to produce reanalyses of
stratospheric composition (Viscardy et al., 2010) which compare favourably
with similar systems (Geer et al., 2006; Thornton et al., 2009) and
facilitate detailed studies of transport processes in the stratosphere (Lahoz
et al., 2011). The photochemistry module from the BASCOE CTM was implemented
in the Canadian assimilation system GEM, demonstrating the potential of a
coupled chemical–dynamical assimilation system for stratospheric studies (de
Grandpré et al., 2009; Robichaud et al., 2010). BASCOE has been used and
evaluated within the framework of MACC as an independent system for the
provision of near real-time analyses of stratospheric ozone and for the
validation of the corresponding product by the main assimilation system
(Lefever et al., 2015; Eskes et al., 2015).
The CB05 tropospheric scheme has been combined with the stratospheric scheme
from the BASCOE CTM to form a single chemistry mechanism that encompasses
tropospheric and stratospheric chemistry throughout the atmosphere, here
referred to as C-IFS-Atmos. However, this approach appears computationally
expensive, due to the extended chemical mechanism. Therefore we have
developed an approach for an optimized merging of the CB05 tropospheric
chemistry scheme and the stratospheric chemistry scheme used in BASCOE within
C-IFS. An assessment of the two chemistry schemes showed that there is only
partial overlap in trace gases and reactions that are essential in both
regimes. For instance, 15 out of the full list of 99 trace gases need to be
treated in the chemical mechanisms for both troposphere and stratosphere.
Also, the modelling of the photolysis rates and heterogeneous reactions has
been optimized for application in troposphere and stratosphere separately. In
this optimized approach we developed a flexible set-up where – within a
single framework – either the tropospheric or stratospheric chemistry
modules are addressed, referred to as C-IFS-TS. In this approach the
parameterizations for the chemistry, including the respective chemistry
mechanisms as optimized for troposphere and stratosphere separately, are
retained.
Trace gases relevant for the stratosphere which are constrained at
the surface. The constant surface volume mixing ratios are also given.
In this paper we describe two merging approaches and provide benchmark
evaluations of the C-IFS-Atmos and C-IFS-TS systems with focus on the
stratospheric composition. The ancestor BASCOE-CTM is also included in the
comparison through a forward model run (without chemical data assimilation),
in order to provide insight into the differences caused by the treatment of
transport between C-IFS and BASCOE. The paper is organized as follows: in
Sect. 2 the chemistry modules for the stratosphere are described and the
merging with the tropospheric scheme is explained. Section 3 provides details
on the set-up of the model runs and the observational data used for the model
evaluation. Section 4 provides a basic model evaluation of the system. We
finalize this paper with conclusions and an outlook for further work.
Atmospheric chemistry in C-IFS
For general aspects related to chemistry modelling in C-IFS the reader is
referred to Flemming et al. (2015). The meteorological model in the current
version of C-IFS is based on IFS cycle 41r1 (ECMWF, 2015). The advection is
simulated with a three-dimensional semi-Lagrangian advection scheme, which
applies a quasi-monotonic cubic interpolation of the departure values.
In the following two subsections we describe the C-IFS modules for the
stratospheric (BASCOE-based) and tropospheric (CB05-based) chemistry
parameterizations, continued by a section describing the merging procedure of
these two modules to form the C-IFS-TS system. The full list of trace gases
is given in Table A1 in Appendix A, including the domains where they are
actively treated within the chemistry.
Stratospheric chemistry
From the BASCOE system (Errera et al., 2008) the chemical scheme and the
parameterization for polar stratospheric clouds (PSCs) has been implemented
in C-IFS. The BASCOE chemical scheme used here is labelled “sb14a”. It
includes 58 species interacting through 142 gas-phase, 9 heterogeneous and 52
photolytic reactions. This chemical scheme merges the reaction lists
developed by Errera and Fonteyn (2001) to produce short-term analyses, with
the list included in the SOCRATES two-dimensional model for long-term studies
of the middle atmosphere (Brasseur et al., 2000; Chabrillat and Fonteyn,
2003). The resulting list of species (see Table A1) includes all the
ozone-depleting substances and greenhouse gases necessary for multi-decadal
simulations of the couplings between dynamics and chemistry in the
stratosphere, as well as the reservoir and short-lived species necessary for
a complete description of stratospheric ozone photochemistry.
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. Look-up 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. The photolysis tables
used in chemical scheme sb14a are based on absorption cross sections from JPL
evaluation 15 (Sander et al., 2006). The kinetic rates for heterogeneous
chemistry are determined by the parameterization of Fonteyn and
Larsen (1996), using classical expressions for the uptake coefficients on
sulfate aerosols (Hanson and Ravishankara, 1994) and on PSCs (Sander et al.,
2000).
The surface area density of stratospheric aerosols uses an aerosol number
density climatology based on SAGE-II observations (Hitchman et al., 1994).
Ice PSCs are presumed to exist at any grid point in the winter/spring polar
regions where water vapour partial pressure exceeds the vapour pressure of
water ice (Murphy and Koop, 2005).
Number of trace gases, the chemistry scheme in the troposphere and
stratosphere, and the corresponding number of reactions (gas-phase,
heterogeneous and photolytic), as well as specification of the circulation
model and computational expenses of a 1-month run on T255L60 in terms of
system billing units (SBU) for various C-IFS model versions. For completeness
the BASCOE-CTM system is also indicated.
C-IFS-TC-IFS-SC-IFS-AtmosC-IFS-TSBASCOE-CTMNo. of trace gases5559999959Chemistry schemeCB05BASCOECB05 + BASCOECB05BASCOEin troposphere(P< 400 hPa)(P< 400 hPa)Chemistry schemeCB05/CariolleBASCOECB05 + BASCOEBASCOEBASCOEin stratosphereNo. of reactions93/3/18142/9/52211/11/6093/3/18 or142/9/52(gas/het/photo)142/9/52Circulation modelGCMGCMGCMGCMCTMSBU2075250045633076–*
* BASCOE does not run on the ECMWF supercomputing facility and hence
cannot be compared directly to C-IFS in terms of computational resources.
Nitric acid tri-hydrate (NAT) PSCs are assumed when the nitric acid
(HNO3) partial pressure exceeds the vapour pressure of condensed
HNO3 at the surface of NAT PSC particles (Hanson and Mauersberger,
1988). The surface area density is set to 2×10-6 cm2 cm-3 for ice PSCs and 2×10-7 cm2 cm-3 for NAT PSCs. The sedimentation of PSC
particles causes denitrification and dehydration. This process is
approximated by an exponential decay of HNO3 with a characteristic
timescale of 20 days for grid points where NAT particles are supposed to
exist, and an exponential decay of HNO3 and H2O with a
characteristic timescale of 9 days for grid points where ice particles are
supposed to exist.
Mass mixing ratios for N2O, CO2 and a selection of anthropogenic
and organic halogenic trace gases are constrained at the surface by a global
mean constant value (Table 1). Assuming that trace gases are well mixed in
the troposphere, this essentially serves as lower boundary conditions for the
stratospheric chemistry.
Tropospheric chemistry
The tropospheric chemistry in the C-IFS is based on 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.
The scheme has been modified and extended to include an explicit treatment of
C1 to C3 species as described in Williams et al. (2013), and SO2,
di-methyl sulfide (DMS), methyl sulfonic acid (MSA) and ammonia (NH3)
(Huijnen et al., 2010). A coupling to the MACC aerosol model is available
(Huijnen et al., 2014), but is not switched on for this study. The reaction
rates follow the recommendations given in either Sander et al. (2011) or
Atkinson et al. (2006). The modified band approach (MBA), which is adopted
for the computation of photolysis rates (Williams et al., 2012), uses seven
absorption bands across the spectral range 202–695 nm. At instances of
large solar zenith angles (71–85∘) a different set of band intervals
is used. In the MBA the radiative transfer calculation using the absorption
and scattering components introduced by gases, aerosols and clouds is
computed online for each of the predefined band intervals. The complete
chemical mechanism as applied for the troposphere is extensively documented
in Flemming et al. (2015). There a specification of the emissions and
deposition of tropospheric reactive trace gases is provided as well.
Merging procedures for the tropospheric and stratospheric
chemistry
Here we investigate two options to merge tropospheric and stratospheric
chemistry, as also summarized in Table 2. The chemistry mechanism for
C-IFS-Atmos is composed by simply combining the reaction mechanisms for
troposphere and stratosphere into one large mechanism, removing reactions
that are duplicated. In contrast to this model version, here we propose an
approach for a more efficient merging of the chemistry modules for the
troposphere and stratosphere to form the C-IFS-TS system. Key chemical cycles
differ between troposphere and stratosphere, hence allowing different
chemical mechanisms. For example, the oxidation of non-methane hydrocarbons
(NMHCs) is essentially taking place in the troposphere and represents an
important driver for tropospheric O3 production. The chemical evolution
of PAN and organic nitrate can be neglected in the stratosphere. On the other
hand, N2O and CFCs are essentially chemically inactive in the
troposphere and will only be photolysed by UV radiation in the stratosphere.
Therefore, the chemical reactions involving these gases do not need to be
accounted for in the troposphere. Also, the parameterization of the
photolysis rates leads to different requirements for the troposphere and
stratosphere, as will be discussed in the next subsection. Finally, the
numerical solver of the chemical mechanism contributes substantially to the
total costs of the model run in terms of run-time, depending on the size of
the reaction mechanism. These elements have motivated us to divide the
chemistry in the C-IFS-TS system into a tropospheric part and a stratospheric
part. Note that there is only one set of transported atmospheric trace gases,
and only the position of the grid box above or below the tropopause
determines whether the tropospheric or stratospheric chemistry is applied.
Parameterization of photolysis rates for the troposphere
(CB05-based) and stratosphere (BASCOE-based).
Troposphere (Williams et al., 2012)Stratosphere (Errera and Fonteyn, 2001)No. of J rates1852MethodTwo-stream online solver, 204 <λ< 705 nmLook-up table approach, 116 <λ< 705 nmDependenciesO3 overhead, pressure, solar zenith angle, cloud, aerosol, surface albedo, temperatureO3 overhead, pressure, solar zenith angleTerminator treatmentJ>0 for sza < 85∘J>0 for sza < 96∘, Chapman approximation
The tropopause can be defined based on different criteria. A common approach
is to use a dynamical criterion such as the isentropic potential vorticity
(e.g. Thuburn and Craig, 1997), but this fails in regions of small absolute
vorticity, notably in the tropics. A definition based on the lapse rate (WMO,
1957) is an alternative, but may not be well defined in the presence of
multiple stable layers. We therefore choose to base our criterion on the
chemical composition of the atmosphere considering that the tropopause is
associated with sharp gradients in trace gases (e.g. Gaudel et al., 2015).
This has the advantage that parcels with tropospheric/stratospheric
composition can be traced dynamically, and the most appropriate chemistry
scheme can be adopted to it. In our simulation we use a chemical definition
of the tropopause level, where tropospheric grid cells are defined at
O3< 200 ppb and CO > 40 ppb, for P> 40 hPa. With this
definition the associated tropopause pressure ranges in practice between
approx. 270 and 80 hPa for sub-tropics and tropics, respectively.
For both troposphere (CB05) and stratosphere (BASCOE) the numerical solver is
generated using the Kinetic Pre-Processor (KPP, Sandu and Sander, 2006)
software. Specifically we adopt the standard four-stage, third-order
Rosenbrock solver (Rodas3). This is different from the Eulerian backward
implicit solver as used in Flemming et al. (2015), and is motivated by the
improved coding flexibility and accuracy.
Most of the gas-phase reactions that take place both in the troposphere and
stratosphere, such as NOx and HOx reactions, are simulated in
identical ways in both chemistry schemes. It is worth mentioning that the
constituents O1D and O3P, produced from O3 and O2
photolysis, are not explicitly computed in the troposphere, as O1D and
O3P are assumed to react with O2, O3 and N2 only. This is
different for the stratosphere, where O1D and O3P are involved in
many reactions. For trace gases whose chemistry is currently neglected in the
stratosphere (the NMHCs, PAN, organic nitrate, SO2) we adopt a 10-day
decay rate to prevent their spurious accumulation in the stratosphere. Hence
these losses are currently not accounted for in the stratospheric chemical
mechanisms and do not contribute either to the load of stratospheric
aerosols. Note that tropospheric halogen chemistry, which contributes to
near-surface ozone depletion in spring-time polar regions and to changes in
oxidative capacity in the tropical marine boundary layer (von Glasow and
Crutzen, 2007), is currently neglected, even though related trace gases are
available. By inspection of individual constituent fields we have ensured
that the merging strategy does not result in spurious jumps at the interface
between troposphere and stratosphere; see also Figs. S2–S5 in the
Supplement. When the system is run with stratospheric chemistry only
(C-IFS-S), all chemistry and emissions are switched off at altitudes below
400 hPa and constrained by surface boundary conditions.
The four options to run these types of C-IFS experiments and the
computational costs are given in Table 2. As compared to the C-IFS-T
experiments, the costs of running an experiment including full stratospheric
chemistry with the C-IFS-TS system have increased by ∼ 50 %. Most
of this increase is caused by the computation of the chemistry and not the
tracer transport due to the efficiency of the semi-Lagrangian advection
scheme for multiple tracers. The C-IFS-Atmos set-up where tropospheric
chemistry and stratospheric chemistry were merged into a single reaction
mechanism led to an increase in costs by ∼ 50 % compared to
C-IFS-TS, indicating the benefit of having separate solver codes for
tropospheric and stratospheric chemistry. The C-IFS-TS implementation allows
for an easy switch between system set-ups compared to the C-IFS-Atmos
implementation. For completeness, specifications of the BASCOE-CTM are also
provided in Table 2, which is identical in terms of stratospheric chemistry
parameterization compared to C-IFS-TS and C-IFS-S. Clearly the essential
difference compared to the C-IFS set-up refers to the fact that BASCOE is
used here as a CTM, while C-IFS is a GCM. Most notably this implies a
different advection treatment and a different horizontal grid (see Sect. 3).
Selection of photolytic reactions that are merged between the
troposphere and stratosphere. The reaction product O2 is not
shown.
* Only specified in case this is different from the
stratospheric reaction.
Merging photolysis rates
For parameterization of the photolysis rates, the modified band approach
(MBA, Williams et al., 2012) and the look-up table approach (Errera and
Fonteyn, 2001) are retained (see Table 3), as these have been optimized in
the past for applications in the troposphere and stratosphere, respectively.
While for tropospheric conditions scattering and absorption properties of
clouds and aerosol strongly impact the magnitude of photolysis rates and
hence the local chemical composition, this is of less relevance in the
stratosphere. Wavelengths shorter than 202 nm, on the other hand, are
largely blocked by stratospheric ozone and oxygen and do not contribute to
radiation in the troposphere (Williams et al., 2012). At higher altitudes
these short wavelengths contribute to the Chapman cycle and to the breakdown
of CH4, CFCs and N2O either directly or through oxidation by
O1D. Also, the presence of sunlight at solar zenith angles (SZAs) larger
than 90∘ at high altitudes needs to be accounted for in the
stratosphere due to the Earth's curvature. This plays a role in the timing of
springtime ozone depletion in the polar lower stratosphere, but may be
neglected in the troposphere.
Table 4 lists the photolysis rates that are active in both the troposphere
and stratosphere. 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. This is done by an interpolation
at four model levels around the interface level between both
parameterizations, for SZA < 85∘. For larger SZAs the original
value for the photolysis rate is retained in case of stratospheric chemistry,
while it is switched off for the troposphere.
Note that even though the reaction rates have been merged, the products from
the same photolytic reactions are sometimes different as a consequence of
the different reaction mechanisms between the troposphere and stratosphere.
An example of the merging strategy is given in Fig. 1. It shows that at the
interface for J O3 and J NO2, on average a small increase in
the merged photolysis rate is seen towards lower altitudes, with the switch
to MBA in the troposphere. Even though such jumps are undesirable, no visible
impact on local chemical composition was found, for any of the trace gases
involved in both tropospheric and stratospheric chemistry; see also
Figs. S1–S3 in the Supplement. This can be explained by the sufficiently
small difference in the photolysis rates at the merging altitude of the
photolysis and chemistry schemes, combined with the sufficiently long
lifetime of the affected trace gases.
Illustration of the merging procedure for photolysis rates between
the tropospheric and stratospheric parameterizations for the reactions
O3→O1D (left) and NO2→NO+O (right) as zonally averaged over the tropics for
1 April 2008.
Tracer transport settings
Tracer transport is treated identically for all individual chemical trace
gases. Since the semi-Lagrangian advection does not formally conserve mass
(Flemming and Huijnen, 2011; de Grandpré et al., 2016), a global mass
fixer is applied (Diamantakis and Flemming, 2014) to all but a few
constituents, including NO, NO2 and H2O. Rather than conserving
mass during the advection step of the individual components NO and NO2,
this is enforced to a stratospheric NOx tracer defined as the sum of NO
and NO2. While a chemical H2O trace gas is defined in the full
atmosphere, in the troposphere H2O mass mixing ratios are constrained by
the humidity (q) simulated in the meteorological model in IFS and provide a
boundary condition for water vapour in the stratosphere. Stratospheric
H2O (i.e. above the tropopause level) is governed by chemical production
and loss. The global advection errors in H2O essentially originate from
the tropospheric part because by far most H2O mass is located in the
troposphere and the spatial gradients are much more pronounced. This should
not affect the stratospheric H2O mass budget; therefore, the global mass
fixer for the stratospheric H2O tracer has been switched off. This
prevents spurious trends in stratospheric H2O columns over the years
(not shown), indicating that H2O mass conservation is well ensured in
the stratosphere.
Model set-up and observations used
We have executed runs with C-IFS-TS and C-IFS-Atmos for the period April 2008
until December 2010. Stratospheric ozone in C-IFS-TS is further compared to
that of the C-IFS-T system (Flemming et al., 2015). This uses the ECMWF
standard linear ozone scheme (version 2a, Cariolle and Teyssèdre, 2007)
in the stratosphere, while stratospheric HNO3 is constrained through a
climatological ratio of HNO3/ O3 at 10 hPa (Flemming et al.,
2015).
We have initialized all C-IFS runs on 1 April 2008 using assimilated
concentration fields from the BASCOE system in the stratosphere for this
date. The horizontal resolution of these runs is T255 (i.e. approx.
0.7∘ long/lat) with 60 levels in the vertical. Meteorology in the
C-IFS runs is relaxed towards ERA-Interim.
Intercomparison of runs C-IFS-TS and C-IFS-Atmos aims to provide a
justification of our approach to split the chemistry into two regions, while
intercomparison of C-IFS-TS with C-IFS-T can be used to identify the changes
to stratospheric composition modelling between full stratospheric chemistry
and the baseline approach with the linear ozone scheme.
The performance of the C-IFS runs has further been compared against the
BASCOE-CTM (without chemical data assimilation), using the same chemical
mechanism and parameterizations for photolysis and heterogeneous chemistry as
implemented in the C-IFS-TS. This serves as a model reference for the C-IFS
implementation of stratospheric chemistry. While C-IFS evaluates tracer
transport on a reduced Gaussian grid, the BASCOE-CTM uses a regular
latitude–longitude grid. It is run here with a resolution of 1.125∘
long–lat similar to the resolution chosen for the C-IFS used, and on the
same vertical grid of 60 levels. The BASCOE-CTM is driven by temperature,
pressure and wind fields simulated by the C-IFS runs. However, while BASCOE
adopts a flux-form advection scheme (Lin and Rood, 1996), the IFS uses the
semi-Lagrangian scheme for advection, accounts for vertical diffusion and
includes a parameterization for convection (ECMWF, 2015). Using essentially
the same dynamical fields together with an identical implementation of the
chemistry code should allow one to identify differences due to the different
transport schemes between C-IFS and the BASCOE-CTM. Common chemical biases
between both systems also point to issues in the chemical parameterizations
such as reaction mechanism, photolysis, heterogeneous chemistry and
sedimentation.
Observational data used for validation
We evaluate the C-IFS runs in terms of stratospheric O3, NO2,
N2O, CH4, H2O and HCl, and for this purpose use a range of
observation-based products.
Model results are compared with retrievals (version 3) of O3 (Froidevaux
et al., 2008a), ClO (Santee et al., 2008), H2O (Read et al., 2007) and
HCl (Froidevaux et al., 2008b) from the Microwave Limb Sounder (MLS) onboard
the Aura satellite and with retrievals (version 6) of O3 (Ceccherini et
al., 2008), HNO3 (Wang et al., 2007) and NO2 (Wetzel et al., 2007)
from limb emission spectra recorded by the Michelson Interferometer for
Passive Atmospheric Sounding (MIPAS) onboard European satellite Envisat.
The MLS error budget is reported in Livesey et al. (2011). For HCl
observations between 1 and 20 hPa the precision and accuracy are below 10
and 15 %, respectively. Between 46 and 100 hPa, these are below 0.3 and
0.2 ppbv, respectively. For H2O between 0.46 and 100 hPa, precision
and accuracy are below 15 and 8 %. MIPAS random and systematic errors for
various trace gases are reported by Raspollini et al. (2013). For NO2
between 25 and 50 km altitude these are below 10 and 20 %, respectively.
For HNO3 between 15 and 30 km, these are below 8 and 15 %, while
for O3 between 20 and 55 km these are below 5 and 10 %. At 15 km,
these errors increase to 10 and 20 %, respectively.
Total column O3 is validated against KNMI's multi-sensor reanalysis
version 2 (MSR, van der A et al., 2015), which for the 2008–2010 time period
is based on Solar Backscattering Ultraviolet radiometer (SBUV/2), Global
Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption spectroMeter
for Atmospheric CartograpHY (SCIAMACHY) and Ozone Monitoring Instrument (OMI)
observations. The satellite retrieval products used in the MSR are
bias-corrected with respect to Brewer and Dobson spectrophotometers to remove
discrepancies between the different satellite data sets. The uncertainty in
the product, as quantified by the bias of the observation-minus-analysis
statistics, is in general less than 1 DU.
O3 profiles are compared to ozonesonde data that are acquired from the
World Ozone and Ultaviolet radiation Data Centre (WOUDC). The precision of
the ozonesondes is of the order of 5 % in the stratosphere (Hassler et
al., 2014), when based on electrochemical concentration cell (ECC) devices
(∼ 85 % of all soundings). Larger random errors (5–10 %) are
found for other sonde types, and in the presence of steep gradients and where
the ozone amount is low. Sondes at 19, 12, 2 and 1 individual stations are
used for the evaluation over Northern Hemisphere mid-latitudes, tropics,
Southern Hemisphere mid-latitudes and the Antarctic, respectively.
Stratospheric NO2 columns are compared to observational data from the
SCIAMACHY (Bovensmann et al., 1999) UV–VIS (ultraviolet–visible) and NIR
(near-infrared) sensor onboard the Envisat satellite. The satellite
retrievals are based on applying the differential optical absorption
spectroscopy (DOAS) (Platt and Stutz, 2008) method to a 425–450 nm
wavelength window. Stratospheric NO2 columns from SCIAMACHY presented
here are in fact total columns derived by dividing retrieved slant columns of
NO2 by a stratospheric air mass factor and contain data over the clean
Pacific Ocean (180–220∘ E) only (Richter et al., 2005). Although in
this region the contribution of the troposphere to total column NO2 is
small, stratospheric column NO2 from SCIAMACHY is still somewhat
positively biased by a tropospheric contribution. However, stratospheric air
mass factors for NO2 are usually large compared to tropospheric ones, so
that the uncertainty resulting from this should only have a minor impact on
the data analysis presented in this study.
Monthly mean stratospheric NO2 columns are associated with relative
uncertainties of roughly 5–10 % and an additional absolute uncertainty
of 1×1014 molec cm-2. To account for differences in
observation and model output time, simulations are interpolated linearly to
the Equator-crossing time of SCIAMACHY (10:00 LT). In addition, only model
data for which satellite observations exist are included in the corresponding
comparisons.
Furthermore, satellite-based observations are used from the Atmospheric
Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) onboard
Canadian satellite mission SCISAT-1 (first Science Satellite, Bernath et al.,
2005). This is a high spectral resolution Fourier transform spectrometer
operating with a Michelson interferometer. Vertical profiles of atmospheric
volume mixing ratios of trace constituents are retrieved from the occultation
spectra, as described in Boone et al. (2005), with a vertical resolution of 3–4 km at maximum. Here we
use level 2 retrievals (version 3.0) of N2O and CH4.
ACE-FTS N2O observations between 6 and 30 km agree to within 15 %
of independent observations, while above they agree to within ±4 ppbv
(Strong et al., 2008). The uncertainty in ACE-FTS CH4 observations is
within 10 % in the upper troposphere–lower stratosphere, and within
25 % in the middle and higher stratosphere up to the lower mesosphere
(< 60 km) (De Mazière et al., 2008).
Three-hourly C-IFS and BASCOE-CTM output has been interpolated in space and
time to match with any of these observations.
Model evaluation
Figure 2 shows the mean O3 partial columns (PCs) against observations
from Aura MLS v3.0 over the poles and the tropics. In C-IFS-T, applying the
Cariolle parameterization, the annual cycle over the Arctic is very well
simulated, but a constant overestimation of 50 DU (20 %) is found. In
the tropics the bias is much smaller, with a slight underestimation (10 DU,
5 %). In the Antarctic, the results are remarkably good during the ozone
hole episodes, but there is a serious overestimation developing from February
until the beginning of August, when it reaches 50 DU (30 %), i.e. as
much as in the Arctic. CIFS-Atmos and CIFS-TS provide very similar results
over the full time period, suggesting that our approach to keep two different
solvers in each region is valid for stratospheric ozone. Also, after an
initialization period of a few months, the model runs do not present any
obvious drift, and the differences with BASCOE-CTM are very small. This
implies that differences due to the model configuration regarding transport
are not crucial for lower stratospheric ozone at these timescales. In the
tropics the C-IFS-TS and C-IFS-Atmos results are slightly better than those
with BASCOE-CTM, potentially due to the missing parameterization for
convection. In the Antarctic, the parameterization of PSC leads to an
overestimation of spring-time ozone depletion, while the Cariolle
parameterization simulates very well the lowest columnar values observed in
September, as discussed in more detail below. The recovery of ozone is
overestimated by 20 DU (10 %) in December–January. While the amplitude
of the annual cycle is overestimated above the Antarctic, its structure
matches well with the observations.
Daily averages of O3 partial columns (10–100 hPa) for the
Arctic (60–90∘ N), tropics (30∘ S–30∘ N) and
Antarctic (60–90∘ N) over the period April 2008–December 2010.
Data sets are averaged in 5-day bins and model output is interpolated to the
location and time of Aura MLS v3 retrievals (black dots). Blue line: C-IFS-T;
green line: BASCOE-CTM; red dashed line: C-IFS-Atmos; red solid line:
C-IFS-TS.
An evaluation of O3 total columns (TCs) against the MSR at various
latitude bands is given in Fig. S6 in the Supplement. Considering the missing
tropospheric chemistry in the BASCOE-CTM, this system is not well constrained
in terms of the O3 TC, which implies that it is not useful to include
its results here. The TC comparison confirms the evaluation with PC from Aura
MLS observations, showing a strong positive bias over the NH mid-latitudes
and Arctic for C-IFS-T, which is reduced for C-IFS-Atmos and C-IFS-TS. These
model versions do not show a significant trend during the 2009–2010 period.
For the tropical and Southern Hemisphere mid-latitudes, all C-IFS versions
show a similar performance, with C-IFS-Atmos showing a small positive offset
with respect to C-IFS-TS of approx. 2–8 DU, depending on the latitude band
and season.
Top row: evaluation of ozone against WOUDC sondes over SH
mid-latitudes (60–30∘ S, left), tropics
(30∘ N–30∘ S, middle) and NH mid-latitudes
(30–60∘ N, right) for December–January–February 2009 and 2010 in
units ppmv. Black: WOUDC observations; red dashed: C-IFS-Atmos; red solid:
C-IFS-TS; blue: C-IFS-T. Error bars denote the 1σ spread in the models
and observations. Bottom row: corresponding mean biases.
Same as Fig. 3 but for June–July–August 2009 and 2010.
Evaluation of ozone in units mPa against WOUDC ozone sondes at Syowa
station during August–December 2009. Black: ozone sonde; red dashed:
C-IFS-Atmos; red solid: C-IFS-TS; blue: C-IFS-T. Error bars denote the
1σ spread in the models and observations.
Closer inspection of O3 profiles against sondes averaged over the NH
mid-latitudes, tropics and SH mid-latitudes for the DJF and JJA seasons in
2009 and 2010 (Figs. 3 and 4) shows biases in a generally similar order of
magnitude, although frequently with opposite sign, for C-IFS-TS and
C-IFS-Atmos compared to C-IFS-T. Especially over the extra-tropics the
C-IFS-TS and C-IFS-Atmos model versions show lower mixing ratios than C-IFS-T
at the middle stratosphere (∼ 10 hPa), generally leading to an
improvement compared to the observations. For the NH mid-latitudes this also
explains the improved O3 TC and O3 PC in these runs compared to
C-IFS-T as discussed above. Nevertheless, these experiments still show a
positive bias near the ozone maximum in terms of partial pressure
(∼ 50 hPa) and also at lower altitudes during the northern hemispheric
spring season. Furthermore, in the tropics the use of the full stratospheric
chemistry implies a slight degradation against the linear scheme around the
ozone maximum, where the Cariolle parameterization is very well tuned. The
negative bias in the lower stratosphere as found in C-IFS-TS is not improved
compared to C-IFS-T. These alternating biases in CIFS-TS and C-IFS-Atmos are
due to corresponding biases in chemically related species such as NOx
and also due to transport issues, as discussed in more detail below. The very
similar performance of C-IFS-TS with respect to C-IFS-Atmos, especially in
this altitude range, once again gives confidence in our approach to split
chemistry schemes for tropospheric or stratospheric conditions. A similar
evaluation against MLS observations, but for the period
September–October–November 2009, provides very similar conclusions
(Fig. S7, Supplement). For the 2009 Antarctic ozone hole season (Fig. 5) the
C-IFS-TS and C-IFS-Atmos show a positive bias at ∼ 100 hPa for August
and September, and a negative bias at higher altitudes (50–10 hPa), where
C-IFS-T shows a positive bias. Still, the depth of the ozone hole is well
modelled in October. During the closure phase in November and December the
O3 variability with altitude is better captured in C-IFS-TS than in
C-IFS-T.
Daily averages of O3 partial columns (10–100 hPa) over the
Antarctic (90–60∘ S) for the period April–November 2009 for
HNO3 (top), ClO (middle), and O3 (bottom) against MLS
observations.
A closer analysis of the processes responsible for spring-time polar ozone
depletion is given in Fig. 6. In both the C-IFS-TS and C-IFS-Atmos runs as
well as BASCOE-CTM there is an HNO3 deficit at the beginning of the
winter. Denitrification, which is not modelled in C-IFS-T, starts at the
correct time in the models with stratospheric chemistry, indicating that NAT
PSCs appear at about the right time. However, denitrification proceeds more
slowly and ends 1 month later than observed by Aura-MLS. We attribute this
shortcoming to the crude modelling of NAT PSCs, which does not calculate the
amount of condensed nitric acid and water, keeps the surface area densities
of PSC particles fixed at an arbitrary value, and parameterizes sedimentation
through irreversible removal. Chlorine activation starts at exactly the right
time and is as strong in the C-IFS runs as in the Aura-MLS observations until
the beginning of September, but starts decreasing afterwards, while it lasts
2 more weeks in the observations. Hence the overestimation of ozone depletion
during August and September in the models with explicit stratospheric
chemistry is probably not due to an overestimation of chemical removal. This
feature is more pronounced in CIFS-TS and CIFS-Atmos than in the BASCOE-CTM,
suggesting that it may be associated with differences in the modelling of
transport.
Zonal mean stratospheric O3 (top row, units ppmv), daytime
NO2 (second row), night-time NO2 (third row) and HNO3 (bottom
row, all in units ppbv) for October 2009 using MIPAS observations (first
column) and co-located output of BASCOE-CTM (second), C-IFS-TS (third),
C-IFS-Atmos (fourth) and C-IFS-T (fifth).
The evaluation of the zonal mean ozone mixing ratios against MIPAS
observations shows good general agreement (Fig. 7), with all four modelling
experiments providing similar features. The tropical maximum of the O3
mixing ratio at 10 hPa is underestimated in all experiments but to a larger
extent in those which model stratospheric photochemistry explicitly (BASCOE
CTM, C-IFS-TS, C-IFS-Atmos) than in C-IFS-T, in line with the evaluation
against O3 sondes for June–July–August (Fig. 4). The same evaluation
against MLS observations provides exactly the same conclusions (Fig. S8,
Supplement).
The assessment of NO2 against MIPAS daytime NO2 observations,
acquired by sampling the ascending orbits from Envisat, shows good agreement
with the models, although C-IFS-TS and C-IFS-Atmos tend to show a positive
bias. The C-IFS-TS and C-IFS-Atmos runs describe well the seasonal variation
in zonal mean stratospheric NO2 columns at different latitude bands
(Fig. 8), with monthly mean biases with respect to the SCIAMACHY observations
of less than 1×1015 molec cm-2 in the tropics and at
mid-latitudes. The positive bias is larger in C-IFS-Atmos than C-IFS-TS. In
contrast, poor performance can be seen for C-IFS-T, due to the lack of
stratospheric NOx chemistry in that version.
However, a positive NO2 bias with respect to night-time MIPAS NO2
observations appears larger for C-IFS-TS and C-IFS-Atmos than for the
BASCOE-CTM (Fig. 7). In contrast, this figure also shows a negative bias in
HNO3 with respect to MIPAS observations in the BASCOE-CTM, and
C-IFS-Atmos, and is even more marked in the C-IFS-TS experiment. Even though
a clear improvement compared to run C-IFS-T is found, further investigation
is necessary to diagnose the origins of the biases in night-time NO2
above 10 hPa and in HNO3 between 10 and 70 hPa.
Time series of total column NO2 above the clean Pacific Ocean
(180–220∘ E) for April 2008–December 2010, in units
1015 molec cm-2 for NH mid-latitudes (left), tropics (middle) and
SH mid-latitudes (right). Black: SCIAMACHY observations; red dashed:
C-IFS-Atmos; red solid: C-IFS-TS; blue: C-IFS-T.
Figure 9 shows an evaluation of N2O and CH4 profiles during
September 2009 against observations by ACE-FTS. Owing to their long lifetimes
these trace gases are good markers for the model's ability to describe
transport processes – i.e. not only the Brewer–Dobson circulation, but also
isentropic mixing, mixing barriers, descent in the polar vortex, and
stratosphere–troposphere exchange (Shepherd, 2007). Moreover, N2O is
the main source of reactive nitrogen in the stratosphere, while CH4 is
one of the main precursors of stratospheric water vapour. The figure suggests
reasonable profile shapes for both CH4 and N2O in the upper
stratosphere (10 hPa and higher), where their abundance is more strongly
influenced by chemical loss but at lower altitudes (100–10 hPa). C-IFS-TS
and C-IFS-Atmos show larger discrepancies in the observations than the
BASCOE-CTM run, with weaker vertical gradients in the tropics and SH
mid-latitudes and a sharper gradient in the extra-tropical Northern
Hemisphere.
This discrepancy cannot be due to different wind fields because the
BASCOE-CTM experiment is driven by 3-hourly output of the C-IFS experiment.
We attribute it instead to the different numerical schemes for advection
and/or to differences in the representation of sub-grid transport processes
in the GCM and in the CTM. Convection and diffusion are indeed explicitly
modelled in C-IFS but neglected in the BASCOE CTM, which relies on the
implicit diffusion properties of its flux-form advection scheme to represent
sub-grid mixing (Lin and Rood, 1996; Jablonowski and Williamson, 2011). Since
lower stratospheric ozone is strongly determined by both chemistry and
transport, the transport issue indicated by Fig. 9 could also contribute
directly to the ozone biases seen below 10 hPa in Figs. 3 and 4.
Figure 10 shows a good consistency between H2O modelled by C-IFS-TS and
the BASCOE-CTM results, albeit with a slight negative bias with respect to
MLS observations above 5 hPa and a positive bias around 30 hPa in the
tropics, associated with corresponding biases in CH4. This figure also
shows globally a good agreement between HCl modelled by C-IFS-TS and MLS
observations, although with a positive bias of 0.8 ppbv confined in the
region of ozone depletion above Antarctica.
Conclusions
We have presented a model description and benchmark evaluation of an
extension of the C-IFS system with stratospheric ozone chemistry of the
BASCOE model added to already existing tropospheric scheme CB05. We refer to
this system as C-IFS-CB05-BASCOE, or C-IFS-TS for short. In our approach we
have retained a separate treatment for tropospheric and stratospheric
chemistry, and select the most appropriate scheme depending on the altitude
with respect to the tropopause level. This has the advantage that mechanisms
which are optimized for tropospheric and stratospheric chemistry,
respectively, can be retained, which also substantially reduces the
computational costs of the chemical solver compared to an approach where all
reactions are activated in the whole atmosphere, referred to as C-IFS-Atmos.
Also, it allows for an easy switch between system set-ups. To avoid jumps in
trace gas concentrations at the interface, the consistency in gas-phase
reaction rates has been verified, while the photolysis rates from the two
parameterizations are interpolated across the interface. We showed that
differences between C-IFS-TS and C-IFS-Atmos are overall small; hence, our
basic assumption of having different chemistry solvers for troposphere and
stratosphere is valid for our applications.
Zonal mean profiles of stratospheric N2O (top) and
CH4 (bottom) for September–October–November 2009 using ACE-FTS
observations (black symbols) and co-located output of BASCOE-CTM (green
lines), C-IFS-TS (red solid lines) and C-IFS-Atmos (red dashed lines). The
zonal means are shown separately in five columns corresponding to the
latitude bands 90–60∘ S, 60–30∘ S,
30∘ S–30∘ N, 30–60∘ N and 60–90∘ N,
respectively.
An evaluation of a 2.5-year simulation of C-IFS-TS indicates good performance
of the system in terms of stratospheric ozone, of similar quality as its
ancestor BASCOE-CTM model results, and a considerable general improvement in
terms of stratospheric composition compared to the C-IFS-T predecessor model
version which applied a linear ozone scheme in the stratosphere.
The O3 partial columns (10–100 hPa) show biases mostly smaller than
±20 DU when compared to the Aura MLS observations. Also, the profiles
were generally well captured, and show an improvement with respect to the
C-IFS-T linear ozone scheme in the stratosphere over mid-latitudes. The depth
and variability of the ozone hole over Antarctica is modelled well. While the
C-IFS-T also shows a remarkably good agreement with the observations during
the ozone hole episodes, it develops a significant overestimation of the
partial columns during other months. The tropical maximum of the mixing
ratio, around 10 hPa, is the only stratospheric region where C-IFS-T agrees
better all-year-long with observations.
Zonal mean stratospheric H2O (top, units ppmv) and HCl (bottom,
units ppbv) for October 2009 using Aura/MLS observations (first column) and
co-located output of BASCOE-CTM (second), C-IFS-TS (third) and C-IFS-Atmos
(fourth).
Also, evaluation of other trace gases (NO2, HNO3, CH4,
N2O, HCl) against observations derived from various satellite retrievals
(SCIAMACHY, ACE-FTS, MIPAS, MLS) illustrates the clear improvements obtained
with C-IFS-TS compared to C-IFS-T, even though C-IFS-TS still suffers from
positive biases in stratospheric NO2, whereas HNO3 is biased low.
For the long-lived tracers CH4 and N2O, larger errors with respect
to limb-sounding retrievals were found between 10 and 100 hPa than with the
BASCOE-CTM, suggesting difficulties in representing slow transport processes.
The BASCOE-CTM experiment shown here was driven by 3-hourly wind field output
of the C-IFS experiments. Hence this discrepancy is due to a difference in
the representation of the transport processes between the GCM and the CTM,
i.e. the numerical scheme used for advection (semi-Lagrangian vs. flux-form),
the convection (parameterized in C-IFS but neglected in BASCOE CTM) or the
diffusion (parameterized in C-IFS but not explicitly considered in the CTM).
Hence, stratospheric transport in C-IFS will be an area for further
evaluation and developments.
This benchmark model evaluation of C-IFS-TS marks a key step towards merging
tropospheric and stratospheric chemistry within IFS, aiming at a possible
configuration for daily operational forecasts of lower and middle atmospheric
composition in the near future. Future work could focus on the following
aspects.
Chemical data assimilation: initial tests with data assimilation of
O3 total column and profile retrievals suggest that stratospheric ozone
is successfully constrained in C-IFS-TS. However, observational constraints
on other components driving ozone chemistry are currently lacking in the
assimilation system. Our extension opens the possibility of assimilation of
additional trace gases such as N2O and HCl. However, for the 4D-VAR
assimilation of short-lived species such as NO2 and ClO, an adjoint
chemistry module would likely be required as implemented in the BASCOE data
assimilation system.
Alignment of the reaction mechanism and photolysis rates: while at
the current stage the gas-phase and photolytic reaction rates of the parent
schemes are retained, we foresee a further integration to ensure better
alignment of the chemical mechanisms. Especially the existing jumps in
photolysis rates as a consequence of the different parameterizations are not
desirable, even though they are not harmful to model stability or visibly
lead to any degradation in model performance. The alignment in terms of
gas-phase reaction rate expressions can be achieved by the introduction of
the KPP solver in C-IFS, for both tropospheric and stratospheric chemistry,
which allows for a better traceable model development than the hard-coded
Euler backward integration (EBI) solver as adopted in Flemming et al. (2015).
Improvement of the representation of stratospheric sulfate aerosols and
polar stratospheric clouds: the current climatology for these aerosols and
parameterization for PSCs could easily be improved. While the current results
are satisfactory for a general-purpose monitoring system, these improvements
would especially allow better simulations of the composition in the polar
lower stratosphere during springtime.
Extension of tropospheric and stratospheric chemistry schemes: the
availability of a comprehensive set of trace gas fields allows for a
relatively easy extension of the tropospheric reaction mechanism by including
selective reactions originating from the stratospheric chemistry, and vice
versa. Examples are the introduction of halogen chemistry into the
troposphere (von Glasow and Crutzen, 2007), or SO2 conversion to sulfate
aerosol in the stratosphere, relevant in case of strong volcanic events
(Bândă, et al., 2016).
Optimization of solver efficiency: even though the use of the KPP has
simplified the code maintenance and may result in a higher numerical accuracy
of the solution, it also caused a considerable slow-down of the numerical
efficiency as compared to the EBI solver, as that solver had been optimized
for tropospheric ozone chemistry in C-IFS-CB05. Solutions could be an
optimization of the initial chemical time step for the KPP solver, depending
on prevailing chemical and physical conditions, and an optimization of the
automated solver code, which allows for a more efficient code structure (KP4,
Jöckel et al., 2010).
In summary, the extension towards stratospheric chemistry in C-IFS broadens
its ability for forecast and assimilation of stratospheric composition, which
is beneficial to the monitoring capabilities in CAMS, and may also contribute
to advances in meteorological forecasting of the ECMWF IFS model in the
future.
Code availability
The C-IFS source code is integrated into ECWMF's IFS code, which is available
subject to a licence agreement with ECMWF; see also Flemming et al. (2015)
for details. The stratospheric chemistry module of C-IFS was originally
developed in the framework of BASCOE. Readers interested in the BASCOE code
can contact the developers through http://bascoe.oma.be (BASCOE code, 2016).
Trace gases in C-IFS-TS, along with their chemically active domain:
troposphere (Trop), stratosphere (Strat) or whole atmosphere
(WA).
Short nameLong nameActivedomainIC3H7O2IC3H7O2TropHYPROPO2HYPROPO2TropRORorganic ethersTropRXPARPAR budget correctorTropXO2NO to NO2 operatorTropXO2NNO to alkyl nitrate operatorTropOoxygen atom (ground state)StratO1Doxygen atom (first excited) state)StratHhydrogen atomStratH2hydrogenStratH2OwaterStratCH3methyl radicalStratCH3Omethoxy radicalStratHCOformyl radicalStratCO2carbon dioxideStratNnitrogen atomStratN2Onitrous oxideStratCLchlorine atomStratCL2chlorineStratHCLhydrogen chlorideStratHOCLhypochlorous acidStratCH3CLmethyl chlorideStratCH3CCL3methyl chloroformStratCCL4tetrachloromethaneStratCLONO2chlorine_nitrateStratCLNO2chloro(oxo)azane oxideStratCLOchlorine monoxideStratOCLOchlorine dioxideStratCLOOasymmetric chlorine dioxide radicalStratCL2O2dichlorine_dioxideStratBRbromine atomStratBR2bromine atomic ground stateStratCH3BRmethyl bromideStratCH2BR2dibromomethaneStratCHBR3bromoformStratBRONO2bromine nitrateStratBRObromine monoxideStratHBRhydrogen bromideStratHOBRhypobromous acidStratBRCLbromine monochlorideStratHFhydrofluoric acidStratCFC11trichlorofluoromethaneStratCFC12dichlorodifluoromethaneStratCFC113trichlorotrifluoroethaneStratCFC1141,2-dichlorotetrafluoroethaneStratCFC115chloropentafluoroethaneStratHCFC22chlorodifluoromethaneStratHA1301bromotrifluoromethaneStratHA1211bromochlorodifluoromethaneStrat
The Supplement related to this article is available online at doi:10.5194/gmd-9-3071-2016-supplement.
Acknowledgements
MACC III was funded by the European Union's Seventh Framework Programme (FP7)
under grant agreement no. 283576. We are grateful to the World Ozone and
Ultraviolet Radiation Data Centre (WOUDC) for providing ozone sonde
observations and to the GOME-2, MIPAS, ACE-FTS and MLS teams for providing
satellite observations. We thank two anonymous reviewers for their useful
comments on the original manuscript. Edited
by: V. Grewe Reviewed by: two anonymous
referees
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