GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-9-3545-2016The 1-way on-line coupled model system MECO(n) – Part 4: Chemical evaluation (based on MESSy v2.52)MertensMarianomariano.mertens@dlr.dehttps://orcid.org/0000-0003-3549-6889KerkwegAstridhttps://orcid.org/0000-0002-8378-3498JöckelPatrickhttps://orcid.org/0000-0002-8964-1394TostHolgerhttps://orcid.org/0000-0002-3105-4306HofmannChristianeDeutsches Zentrum für Luft- und Raumfahrt, Institut für Physik
der Atmosphäre, Oberpfaffenhofen, GermanyInstitut für Physik der Atmosphäre, Johannes Gutenberg-Universität Mainz, Mainz, Germanynow at: Meteorologisches Institut, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, GermanyMariano Mertens (mariano.mertens@dlr.de)4October2016910354535677December20157March20169August201617September2016This 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/9/3545/2016/gmd-9-3545-2016.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/9/3545/2016/gmd-9-3545-2016.pdf
For the first time, a simulation incorporating tropospheric and stratospheric
chemistry using the newly developed MECO(n) model system is performed.
MECO(n) is short for MESSy-fied ECHAM and COSMO models nested n times. It features an online coupling of the
COSMO-CLM model, equipped with the Modular Earth Submodel System (MESSy)
interface (called COSMO/MESSy), with the global atmospheric chemistry model
ECHAM5/MESSy for Atmospheric Chemistry (EMAC). This online coupling allows a
consistent model chain with respect to chemical and meteorological boundary
conditions from the global scale down to the regional kilometre scale.
A MECO(2) simulation incorporating one regional instance over Europe with
50 km resolution and one instance over Germany with 12 km resolution is
conducted for the evaluation of MECO(n) with respect to tropospheric
gas-phase chemistry. The main goal of this evaluation is to ensure that the
chemistry-related MESSy submodels and the online coupling with respect to
the chemistry are correctly implemented. This evaluation is a prerequisite
for the further usage of MECO(n) in atmospheric chemistry-related studies.
Results of EMAC and the two COSMO/MESSy instances are compared with
satellite, ground-based and aircraft in situ observations, focusing on
ozone, carbon monoxide and nitrogen dioxide. Further, the methane lifetimes in
EMAC and the two COSMO/MESSy instances are analysed in view of the
tropospheric oxidation capacity. From this evaluation, we conclude that the
chemistry-related submodels and the online coupling with respect to the
chemistry are correctly implemented. In comparison with observations, both
EMAC and COSMO/MESSy show strengths and weaknesses. Especially in comparison
to aircraft in situ observations, COSMO/MESSy shows very promising results.
However, the amplitude of the diurnal cycle of ground-level ozone
measurements is underestimated. Most of the differences between COSMO/MESSy
and EMAC can be attributed to differences in the dynamics of both models,
which are subject to further model developments.
Introduction
The emissions of reactive compounds are a key component for the simulation of
atmospheric chemistry processes. Many of these emissions are localised as, for
example, along ship tracks or highways. It is desirable to resolve smaller
scales because finer resolution chemistry–climate models can simulate species
like ozone or nitrogen dioxide better, as some of the relevant processes are
non-linear (for example, tropospheric ozone chemistry).
The resolution of global chemistry–climate models, however, can only be
increased to a certain degree, as current computational resources pose an
upper limit. Therefore, the new model system
MESSy-fied ECHAM and COSMO models nested n times has been developed. This system includes the
regional-scale chemistry–climate model COSMO-CLM/MESSy (from now on denoted
as COSMO/MESSy), i.e. an implementation of the Modular Earth Submodel System
MESSy, framework into the regional weather prediction
and climate model of the COnsortium for Small-scale MOdeling (COSMO,
; ; COSMO-CLM,
). The description and a meteorological evaluation of this
new model was subject to three previous publications. The implementation of
the MESSy infrastructure in COSMO/MESSy is described in detail in Part 1
. Additionally, the preprocessing tool INT2LM, which is
provided by the German Weather Service (DWD) for the calculation of the
initial and boundary data of the regional COSMO model were implemented into
MESSy as submodel INT2COSMO. The technical details about this implementation
are given in Part 2 .
The implementation of the MESSy infrastructure in COSMO (including INT2COSMO)
allows for an online coupling between the different MECO(n) instances. This
means that individual COSMO/MESSy instances can be driven online from the
global chemistry–climate model ECHAM5/MESSy for Atmospheric Chemistry
EMAC, or from coarser resolved COSMO/MESSy
instances. Especially for complex chemistry–climate applications with several
hundreds of different tracers, this online nesting is a key advantage of
MECO(n) compared to the traditional offline nesting. There is no need to
store information for the boundary conditions on disk, instead they are
interchanged using a point-to-point communication based on the message
passing interface (MPI). This direct exchange of boundary conditions allows
for a much higher update frequency of the boundary conditions, as new data
are provided at each time step of the driving model.
A second benefit is the consistency of the boundary and initial data between
the driving model and the regional refinement, as the same chemical setup
can be used in all instances. Comparable model systems without online
nesting of the regional refinement often use constant chemical boundary
conditions WRF-Chem, or use results from global models
like MOZART (e.g. COSMO-ART, , or WRF-Chem,
). In these cases not only the update frequency is limited
but also the chemical speciation between driving and regional model might be
different. Due to different chemical speciation between the driving model and
the regional model (or if realistic boundary conditions are completely
lacking) additional biases can be introduced. In addition, the meteorological
and chemical fields applied as boundary conditions might be inconsistent as
in many applications they stem from different models.
In the traditional offline nesting approach COSMO(-CLM) is usually driven by
reanalysis data. In the case of MECO(n), however, COSMO/MESSy is driven by the
meteorological fields provided by EMAC. By this, biases are potentially
introduced, which might have a negative impact on the quality of the
meteorological conditions.
To test this, compared in Part 3 the results from the
classical offline nested version of the COSMO model (using ECMWF analysis
data) to results from the online nested setup with EMAC nudged towards the
same analysis data. It was shown that for all three cases (a cold front, a
convective frontal event and a winter storm) both approaches
lead to results with a
comparable accuracy between the online and offline nesting.
Nevertheless, before MECO(n) can be used with a complex chemistry setup for
atmospheric chemistry studies, it is crucial to evaluate the model
performance with respect to gas-phase chemistry. For this reason, this paper
is dedicated to the chemical evaluation of MECO(n) with focus on tropospheric
gas-phase chemistry. Our goal is to test the implementation of the chemical
processes and the online coupling of the chemical species. In addition, we
compare the results of the coarser EMAC instance with the finer resolved
COSMO/MESSy instance to investigate the potential benefits of the increased
resolution.
The evaluation shown here is focused on June and December 2008. Results are
compared to satellite observations of tropospheric O3 and NO2
columns, ground-level observations of O3, CO and NO2
to vertical O3-profiles and to aircraft in situ measurements. In
Sect. 2, we highlight the most important aspects of the model system and focus
on differences between EMAC and COSMO/MESSy with respect to the
implementation of the chemistry-related submodels. Furthermore, the model
setup and the chemical boundary conditions are explained. An overview about
the evaluation data is given in Sect. 3. Section 4 provides the
comparison of model results to these observational data. In addition, a
comparison of the methane lifetime in both models is given. Finally, we
discuss in detail our findings about the deviations of the MECO(n) model in
comparison to the observations in Sect. 5, followed by a summary and
conclusion in Sect. 6.
Model description and setup
The MECO(n) model system benefits from a key feature in the development of
EMAC: many of the chemical processes (and also diagnostic features) described
in different submodels are formulated independent of the spatial and temporal
scale. Therefore, most of these submodels can be used with no or little
modifications in COSMO/MESSy. Readers who are not familiar with the different
MESSy submodels are referred to Appendix A, which provides a general overview
of the submodels that are most important for chemical processes. More details
about the submodels are available on the MESSy website
(http://www.messy-interface.org) or in various publications
e.g..
An important update of the MESSy infrastructure for the use of MECO(n),
however, are the new submodels IMPORT (for importing data) and GRID (for
transforming between different grids) as described by . In
this context, the old submodels ONLEM (online emissions), OFFLEM (offline
emissions) and DRYDEP (dry deposition) have been revised and renamed. The new
submodels ONEMIS, OFFEMIS and DDEP, respectively, provide the same process
parameterisations as the old process submodels, but do not include an own data
import interface any more .
Computational domains and online coupling
EMAC is used as global driving model at a resolution of T42L31ECMWF with a
time step of 720 s. The first COSMO/MESSy instance covers the European area
with a resolution of 0.44∘ (≈ 50 km) and integrates with a
time step of 240 s. The size of the inner domain (neglecting the relaxation
area at the model boundaries) is comparable with the domain of the
EURO-CORDEX project (http://euro-cordex.net). In contrast to the
EURO-CORDEX grid the domain used here is shifted and rotated slightly more to
the east. We chose this different definition to be consistent with a specific
high-resolution emission data set, which we use for a follow up study, which
is not finished yet. The second COSMO/MESSy instance covers the German area
with a resolution of 0.1∘ (≈ 12 km) and integrates with a
time step of 120 s. This results in a MECO(2) model cascade
EMAC → COSMO(50 km)/MESSy → COSMO(12 km)/
MESSy. For better readability, the two COSMO/MESSy instances hereafter are
denoted as CM50 and CM12, respectively. The regions covered by the two
COSMO/MESSy instances are shown in Fig. .
Computational domain of the CM50 and CM12 instances. Depicted is the
topography (in metres) in the resolution of the corresponding instance.
Outside, the CM50 domain the values of EMAC are displayed. In both cases, the
whole computational domains, including the boundary zones, are shown.
Figure schematically illustrates the setup of this
MECO(2) system. In the first time step, the driving model EMAC provides the
necessary initial and boundary conditions for CM50. This CM50 instance
provides the initial and boundary data for the CM12 instance. For the
subsequent time steps, new boundary data are provided after every time step of
the driving model for the finer resolved instances. Consequently, CM50 is
receiving new boundary data every three time steps, while CM12 is receiving
updated data every two time steps.
The required transformation between the different grids are performed by the
MESSy sub-submodel INT2COSMO, which is an online version of the offline
preprocessing tool INT2LM provided by the DWD. A
detailed description is provided by . In both cases, the
meteorological boundary data and the boundary conditions for all chemical
species (and additional diagnostic tracers) are provided.
Illustration of the MECO(2) data exchange used in this study. The
red circles indicate the time steps, the blue arrows indicate the data
exchange. The exchange of initial data is marked with I, the exchange of
boundary data with B.
In the MECO(n) system, model instances run in parallel within the same MPI
environment. All these instances differ in their size (number of grid boxes)
and the time step length. Nevertheless, these instances have to exchange data
after certain model time intervals. Thus, it is desirable that all instances
require the same wall-clock time to simulate the time interval between two
data exchanges to avoid idle or waiting times. Therefore, it is important to
find a distribution of the MPI tasks of the participating instances on the
computing system, which minimises the waiting time between the different
model instances detailed discussion is provided by. For
the simulation setup of this study, the following distribution of MPI tasks
on the SUPERMUC phase 1 machine at the Leibniz Supercomputing Centre
(which has two 8-core processors per node) is chosen: 16 tasks for EMAC, 192
tasks for CM50 and 240 tasks for CM12. The optimal distribution, however, is
specific for the chosen setup and dependent on the architecture of the used
computing system.
Simulation period and initial conditions
The simulated period ranges from 1 July 2007 until the end of 2008. The 6
months in 2007 are used as a spin-up phase. The year 2008 is evaluated. The
initial conditions for EMAC and CM50 are taken from the
RC1SD-base-10a simulation, which is described in detail by
. Due to the high computational costs of the CM12 instance,
this nest is only employed from 1 May 2008 until 1 September 2008.
Overview of the most important submodels applied in EMAC and
COSMO/MESSy, respectively. Both COSMO/MESSy instances use the same set of
submodels. The complete list can be found in the Supplement (Sect. 6).
MMD* comprises all MMD submodels.
SubmodelEMACCOSMOShort descriptionReferencesAEROPTxcalculation of aerosol optical propertiesAIRSEAxxexchange of tracers between air and seaCH4xmethane oxidation and feedback to hydrological cycleCLOUDxcloud parameterisation, CLOUDOPTxcloud optical propertiesCONVECTxconvection parameterisationCVTRANSxxconvective tracer transportDRADONxxemission and decay of 222RadonDDEPxxdry deposition of aerosols and tracerE2COSMOxadditional ECHAM5 fields for COSMO couplingGWAVExparameterisation of non-orographic gravity wavesH2Oxstratospheric water vapour and its feedbackJVALxxcalculation of photolysis rates, LNOXxNOx production by lighting, MECCAxxtropospheric and stratospheric gas-phase chemistry, MMD*xxcoupling of EMAC and COSMO/MESSy (including libraries and all submodels)MSBMxxmultiphase chemistry of the stratosphereOFFEMISxxprescribed emissions of trace gases and aerosolsONEMISxxonline calculated emissions of trace gases and aerosolsORBITxxEarth orbit calculationsQBOxNewtonian relaxation of the quasi-biennial oscillation (QBO), RADxradiative transfer calculations calculationS4Dxxdiagnostic sampling along predefined tracksSCAVxxwet deposition and scavenging of trace gases and aerosolsSCOUTxxdiagnostic sampling at predefined locationsSEDIxxsedimentation of aerosolsSORBITxxsampling along sun synchronous satellite orbitsSURFACExsurface propertiesTNUDGExxNewtonian relaxation of tracersTROPOPxxdiagnostic calculation of tropopause height and additional diagnosticsDetails of the model setup
The model setup applied here is very similar to that of the
RC1SD-base-10a simulation in the Earth System Chemistry Integrated
Modelling (ESCiMo) project described by . Therefore,
only the most important details of the setup and the modifications compared
to the RC1SD-base-10a setup are summarised. An overview about the
used submodels is given in Table . The Supplement provides full
lists of the reaction mechanisms employed in MECCA and SCAV and the complete
namelist setup.
EMAC
In contrast to the RC1SD-base-10a setup, EMAC is applied at the
resolution T42L31ECMWF here, with 31 vertical hybrid pressure levels reaching
up to 10 hPa. To allow for further sensitivity studies with respect
to chemical perturbations, the quasi-chemistry transport model mode
(QCTM mode, ) of EMAC is used, which decouples the
chemistry and the dynamics. This is achieved by using climatologies for all
radiatively active substance (CO2, CH4, N2O,
CFC-11 and CFC-12) for the radiation calculations, nitric acid
(heterogeneous chemistry; submodel MSBM (Multiphase Stratospheric Box Model))
and for OH, O1D and Cl for methane oxidation in the
stratosphere (submodel CH4). The climatologies are monthly mean values
from the RC1SD-base-10a simulation. For lightning NOx, the
parameterisation based on is chosen, which is scaled to a
global nitrogen emission rate of ≈ 5 Tg(N)a-1 from
flashes. To facilitate a comparison with observations, EMAC is nudged by
Newtonian relaxation of temperature, divergence, vorticity and the logarithm
of surface pressure towards ERA-Interim
reanalysis data. Sea surface temperature and sea ice
coverage are prescribed as boundary conditions for the simulation setup from
this data source, too.
COSMO/MESSy
For the simulation presented here, the COSMO model in CLimate Mode
(COSMO-CLM) version 5.00 is used. COSMO-CLM is the community model of the
German regional climate research. Besides the differences regarding the
definition of the computational domain, the relaxation area and the time step
the setup of the two COSMO/MESSy instances is identical. Both instances
feature 40 vertical levels ranging up to ∼ 24 km (20 hPa). The
damping layer starts at a height of 11 km. For the time integration,
a Runge–Kutta scheme of third order with advection terms of fifth order is
chosen. The horizontal advection is calculated using a second order Bott
scheme . Most parts of the namelist setup of COSMO are
identical with the COSMO-CLM setup for the simulations within the EURO-CORDEX
framework . A detailed comparison with the CORDEX-EU setup
is part of the Supplement (Sect. S4). In COSMO, no nudging of the dynamics is
applied; instead the dynamics are relaxed towards EMAC at the five boundaries
(four lateral boundaries and damping layer above 11 km). This means
that COSMO can develop its own dynamics within the domain. As in EMAC,
COSMO/MESSy is operating in a QCTM-like mode due to the prescription of the
same nitric acid climatology for the MESSy submodel MSBM as in EMAC (in fact
dynamics and chemistry are decoupled as in EMAC; the overall approach differs
from EMAC, therefore we use the term “QCTM-like”). In contrast to EMAC, the
radiation routines of COSMO use internal climatologies. Therefore, it is not
possible to prescribe the same climatologies of the trace gases for the COSMO
radiation routines as used in the QCTM mode for the radiation routine in
EMAC. For an improved consistency between EMAC and COSMO/MESSy, it would of
course be desirable to use the same climatologies for the radiation. With the
current version this is not possible but it might be implemented for future
versions.
Chemical boundary conditions
The chemical setup of all instances is identical, which also includes the
emissions: all instances use the MACCity emissions with
0.5∘× 0.5∘ grid resolution. This approach is chosen
to yield a setup, which is highly consistent from the global to the regional
scale. As the same emissions are used, we are able to focus on the
differences due to the change of the base model (ECHAM vs. COSMO) and the
increase in the resolution. For the same reason, the lightning NOx
emissions are calculated online only on the global scale. The emissions are
then transformed to the grid of the regional instances. Only the emissions of
soil-NOx and biogenic isoprene (C5H8) are online calculated in
every instance (by the submodel ONEMIS; see Appendix A), as the land sea mask
differs between EMAC and the two COSMO/MESSy instances. Following
, the online calculated emissions of C5H8 are scaled
by a factor of 0.45 for COSMO/MESSy and 0.6 for EMAC to be in better
agreement with ground-level observations.
Observation data
For a qualitative evaluation of the simulated tropospheric ozone and
NO2 columns, a comparison to satellite observations is performed. For
ozone, the tropospheric ozone columns (TOCs) as described by
are used. For the TOCs, the stratospheric ozone columns measured by the
Microwave Limb Sounder (MLS) are
subtracted from the measured total ozone column of the Aura Ozone Monitoring
Instrument (OMI). For simplicity,
this data product is hereafter called OMI TOC. The data are available as
monthly mean values at a resolution of 1.00∘× 1.25∘
(latitude × longitude).
For the calculation of the OMI TOC, the definition of the tropopause
according to the World Meteorological Organisation (WMO) has been used.
Therefore, the TOC of the simulation data are also calculated using the
online diagnosed tropopause height (by the submodel TROPOP) according to the
WMO definition. The temperature fields employed for the calculation of the
tropopause height for OMI and the simulated data are different, which can
lead to differences of the diagnosed tropopause height. Differences in the
definition of the tropopause can cause differences of up to 4 DU, even for
multi-annual averages as discussed by. This uncertainty
is in a similar range as the difference of up to 5 DU given by
for the comparison of the OMI TOC climatology with other
climatologies derived from ozonesondes and satellite products.
For the comparison with NO2 data, the satellite-derived NO2
measurements from the SCanning Imaging Absorption spectroMeter for
Atmospheric CHartographY (SCIAMACHY) instrument with a
resolution of 0.25∘× 0.25∘ are used. Similar to
the ozone columns, the online diagnosed tropopause following the WMO
definition is used as the upper limit for the vertical integration of the
simulation data. The comparison performed by between
SCIAMACHY NO2 measurements, ground-level observations and model
simulations showed in general a good agreement between the observations and
SCIAMACHY results. However, local hotspots, which are not well resolved by
the resolution of the measurements, are underestimated.
In both cases, the averaging kernels of the measurements are not taken into
account. Therefore, only a qualitative comparison of the data is possible. A
quantification of biases is rather based on the comparison with the
ground-level observations.
For a more quantitative inter-comparison at ground level, the simulations are
compared with observations of O3, NO2 and CO from the
EBAS database (http://ebas.nilu.no). The choice is restricted to the
data which are available for the year 2008 from the European Monitoring and
Evaluation Programme EMEP, http://www.emep.int,.
In addition, only those stations are selected which are within the CM50
domain. For O3, the selection is further restricted to those stations
which offer observations with hourly resolution. For CO and
NO2, stations with daily resolution are additionally used. The
simulated vertical ozone profiles are compared with data from the world ozone
database (WOUDC, http://woudc.org, last access: 28 September 2016). All
vertical profiles available in the CM50 domain for the year 2008 are
compared.
All observations are checked for a plausible range of the reported values.
Finally, only data from stations with at least 75 % time coverage for the
analysed period are employed. A detailed list of all station data, which are
used for the evaluation, is part of the Supplement (Sect. S5). At the
latitude and longitude position of the stations, the simulation data have been
online sampled with the MESSy submodel SCOUT , which
samples the vertical column (of different species) at every given station.
The hourly averaged SCOUT output is used for the comparison with ground-level
measurements.
To allow for a fair comparison between EMAC, COSMO/MESSy and the observations
a “height correction” of the model results from EMAC and COSMO/MESSy is
applied. For the EMAC data, the geometric height of each station is compared
with the geopotential height of the individual model levels at the
corresponding grid box in which the station is located. For the COSMO data, the
procedure is analogous to EMAC, but the height of the model level instead of
the geopotential height is chosen.
We pick the model results at the vertical level, where the geopotential
height (EMAC)/model level height (COSMO) is nearest to the geometric height
of the station. No interpolation of the model results between different
levels is performed. However, this option only works if the station is
located higher than the ground of the lowest model layer. In the opposite
case, the values of the lowest model layer are chosen and no extrapolation of
the simulated data is performed. This height correction is very important,
especially over mountainous terrain, as the topography is much finer resolved
by COSMO/MESSy. In other words, if the observations would always be compared
to the model values at the lowest model level, COSMO/MESSy would outperform
EMAC solely because of the finer resolved topography. The usage of these
height-corrected values is indicated in the corresponding sections.
For a comparison with aircraft in situ observations (CO and
O3), measurements from the IAGOS-CARIBIC In-service Aircraft
for a Global Observing System – Civil Aircraft for Regular Investigation of
the Atmosphere Based on an Instrument Container,
project are used. For the comparison with IAGOS-CARIBIC, the simulation data
have been sampled online along the flightpaths using the submodel S4D
.
Evaluation
For the evaluation, we focus on the results for June 2008 and December 2008,
as examples for summer conditions (with strong photochemical activity) and
winter conditions. First, we compare the model with results from satellite
measurements of the tropospheric ozone and NO2 columns (Sect. 4.1).
Subsequently, the differences between the simulation data and the
ground-level observations (Sect. 4.2), the vertical ozone profiles
(Sect. 4.3) and aircraft in situ observations (Sect. 4.4) are investigated.
Finally, the simulated methane lifetimes are analysed in view of the
tropospheric oxidation capacity (Sect. 4.5).
The simulated meteorology of EMAC and the two COSMO/MESSy instances is also
compared to ERA-Interim data and to the vertical temperature
profiles from the ozone sonde data, which are used in Sect. 4.3. We do not
focus on the discussion of meteorology in this study, as the meteorological
evaluation of MECO(n) has already been performed by , but
rather provide the main results.
In general, a cold bias exists throughout the year in both COSMO/MESSy domains
in the troposphere, which is a known problem of COSMO-CLM during winter
. EMAC shows only a little or no cold bias in the lower
troposphere. A strong cold bias is present in EMAC in the upper troposphere,
which is not that prominent in COSMO/MESSy. The cold bias of COSMO/MESSy
results in a slightly enhanced positive bias of the mean sea level pressure
compared to EMAC. For the 10 m wind speed, EMAC shows a small negative bias,
while COSMO/MESSy mainly shows a positive bias near the coastlines. The
corresponding figures are part of the Supplement (Sects. S2 and S3).
Comparison with satellite observations
Figure shows the ozone columns of OMI (top), EMAC (middle)
and CM50 (bottom) for June 2008. Please note that the OMI data are scaled
for a better comparability. For the reasons discussed in Sect. ,
it is not possible to derive the magnitude of the bias for ozone from this
scaling factor. The bias for ozone is quantified in the following sections.
However, it is known that EMAC simulates a positive ozone bias
.
Tropospheric ozone columns in Dobson units
(DU) of OMI (top), EMAC (middle)
and CM50 (bottom) for June 2008. Please note that the OMI values are scaled
with 1.45 for a better comparability (allowing the same colour bar).
The overall patterns of all three ozone columns look very similar with a
strong north–south gradient. With further investigation, some differences
are apparent. COSMO simulates the maximum ozone column mainly along the
coastline of Turkey. Compared to this the maximum in EMAC extents further to
the west and south. This corresponds better to the satellite measurements,
which show the largest values in the whole south-eastern part of the
Mediterranean Sea. The low values over the Alps or the Atlas mountains in
Morocco found in the OMI data are well reproduced by CM50. Also, the higher
ozone values in south-west France, which are present in the OMI data, are
better reproduced by CM50 in comparison to EMAC. Over Poland, the Baltic Sea
and east Germany, CM50 shows higher values compared to EMAC and OMI. For
December 2008, the OMI data are very noisy over Europe; therefore, we do not
present a comparison for this month.
Figure a shows the monthly averaged tropospheric
NO2 columns for June 2008. In general, CM50 captures the hotspot
regions much better than EMAC due to the higher resolution. Some examples are
the Po Basin, Paris, Madrid, Moscow, eastern Ukraine and the coastal
regions of the Middle East. Striking is the overestimation of NO2 in
CM50 in south-east Europe. Furthermore, some other hotspots might be
overestimated by the MACCity emission database, e.g. the region around
Helsinki or the harbour area around Marseilles. As discussed in
Sect. , especially these localised hotspots could also be
underestimated by SCIAMACHY.
Tropospheric NO2 columns (in
1015moleccm-2) of SCIAMACHY (top), EMAC (middle) and CM50
(bottom) for (a) June and (b) December 2008.
For December 2008 (Fig. b), we see overall a similar
picture. Due to the coarse resolution of EMAC, the emissions are spread over
large gridboxes, which renders it hard to resolve individual hotspots. A good
example is the Po Basin region, which is not resolved by EMAC. In CM50 this
hotspot is underestimated. The NO2 columns simulated by CM50 over the
Atlantic Ocean between Spain and England are overestimated, possibly due to
overestimated ship emissions in this area. Additionally, CM50 overestimates
most hotspots in England and Germany. This overestimation indicates that the
NOx emissions are too high in these regions or that too much NO is
converted to NO2 by reaction with O3 or HO2.
Root mean square error (RMSE, in µg m-3 for
O3 and NO2, in nmolmol-1 for CO) and
normalised mean bias error (MBE, in %) for EMAC and CM50 in comparison to
ground-level observations. Shown are the values for June and December 2008.
For the comparison, the height-corrected values are always used. The values
are calculated from the monthly averaged values for all stations with
observations for the given variable.
Figure a shows the monthly averaged ozone concentrations of
EMAC (left) and CM50 (right) for June 2008. The ozone concentrations of the
lowest model layer are displayed as coloured contours. The coloured symbols
indicate the positions of the observations and compare simulated (height-corrected)
and observed ozone concentrations at the measurement sites.
Monthly averaged ozone concentrations (µg m-3) at the
lowest model layer. The inner parts of the coloured dots show the monthly
mean values measured at the corresponding stations, while the outer parts
depict the simulated value corrected for the station elevation. Triangles
indicate stations with an elevation higher than 800 m, circles
stations below that height. Panel (a) shows ozone concentration from
EMAC (left) and CM50 (right) in June 2008, (b) ozone concentration
for CM50 (left) and CM12 (right) in June 2008 and (c) ozone
concentration for EMAC (left) and CM50 (right) in
December 2008.
In comparison to EMAC, CM50 shows a better agreement with the observations
over Germany, France and Spain. Comparing the monthly averaged values at all
measurement sites, both models show an overall positive ozone bias with a
normalised mean bias error (MBE) of around 16 % for EMAC and 20 % for
CM50 (see Table ). Compared to EMAC, this positive bias in CM50
is more pronounced over north-east Europe than over central Europe. This
bias is further discussed in Sect. .
In general, this bias (all metrics are calculated using the height-corrected
model data) is slightly lower than the MBE of around 30 %
(1.875∘× 1.25∘ resolution) and 33 %
(0.56∘× 0.375∘ resolution) found by
over Europe using the UKCA model. As they calculated the MBE for July 2005,
we additionally calculated the MBE for July 2008, which is 18 % for EMAC
and 17 % for CM50. In comparison, found negative values
of the MBE between -3 and -15 % for summer conditions in June 2006
using the COSMO-ART model.
RMSE (in µg m-3) and MBE (in %) for EMAC, CM50 and
CM12 in comparison to ground-level observations. Shown are the values of
O3 and NO2 for June 2008. The values are calculated for the
subset of measurement sites which are located in the CM12 domain and from
the monthly averaged values for all stations with observations for the
selected variable.
Figure b displays the simulated ozone concentrations for June
2008 zooming in over Germany. While the values for CM50 are shown on the
left, the values for CM12 are shown on the right. In general, the
ground-level ozone distribution is very similar, though many more details are
revealed by the enhanced resolution of the second COSMO instance. As the same
0.5∘× 0.5∘ emission database is used, the
differences are due to the more realistic topography (e.g. the Rhine valley
or the Eifel region). Compared to the measurements, the root mean square
error (RMSE) slightly decreases with finer resolution, from
15 µg m-3 in EMAC to 12 µg m-3 in CM50 and to
11 µg m-3 in CM12. The MBE is decreasing from 10 % in
EMAC to 4 % in CM50 and increases again to 7 % in CM12
(Table ). While the benefit of the increased resolution
(detected in a decreased RMSE and MBE) compared to EMAC is obvious, it is
important to note again that both COSMO/MESSy instances are using the same
emissions with 0.5∘× 0.5∘ resolution. A detailed
investigation of the effect of the finer resolution of CM12 compared to CM50
is beyond the scope of this study and requires a different experimental
setup, with adequately resolved emissions and an intercomparison with a dense
local measurement network like AirBase (European Air quality dataBase,
http://acm.eionet.europa.eu/databases/airbase, last access:
28 September 2016).
Monthly averaged nitrogen dioxide concentrations
(µg (N) m-3) at the lowest model layer in (a) June
and (b) December 2008 from EMAC (left) and CM50 (right). The inner
parts of the coloured dots show the monthly mean values measured at the
corresponding stations, while the outer parts depict the simulated value
corrected for the station elevation. Triangles indicate stations higher than
800 m, circles stations below that height.
The ground-level ozone concentrations in CM50 for December 2008
(Fig. c) show more details compared to EMAC. Examples are the
higher values in the mountainous areas (Alps, Pyrenees) and lower values in
hotspot regions like the Po Valley or around Paris. Comparing the height-corrected
values at the mountain stations, EMAC and COSMO/MESSy show
comparable results.
The reason for these differences between the ground-level concentrations and
the height-corrected concentrations are the finer resolved topography in
COSMO/MESSy compared to EMAC. The enhanced positive bias of
COSMO/MESSy over central and north-eastern Europe is also apparent: the MBE is around 20 %
in EMAC and 28 % in CM50. In comparison to this, found a
negative bias of an approximately similar amplitude (22 %) for winter
conditions in COSMO-ART.
As already seen from the comparison with the SCIAMACHY NO2 columns,
the increased resolution of CM50 shows the largest benefit when comparing
ground-level NO2 concentrations of EMAC and CM50 with observations.
The monthly mean nitrogen dioxide concentrations for June 2008 are shown in
Fig. a. Comparing the simulated concentrations from EMAC
(left) and CM50 (right) to measurements, the highly variable regional
distribution, with higher concentrations near the hotspots and lower
concentrations in the remote areas, is better represented by CM50. The RMSEs
(Table ) of EMAC and CM50 are similar (≈1µg (N) m-3). According to the MBE, both models show a
negative bias. This bias is ≈16 % larger in CM50 than in EMAC.
However, this quantity does only compare the average over all stations;
positive and negative biases at different stations cancel out.
Monthly averaged carbon monoxide mixing ratios
(nmolmol-1) at the lowest model layer in (a) June and
(b) December 2008 from EMAC (left) and CM50 (right). The inner parts
of the coloured dots show the monthly mean values measured at the
corresponding stations, while the outer parts depict the simulated value
corrected for the station elevation. Triangles indicate stations higher than
800 m, circles stations below that height.
Taylor diagram of ground-level ozone concentrations for
June (a) and December (b) 2008. The results for EMAC are
shown in red, for CM50 in blue. The mean over all stations is coloured in
green for EMAC and in yellow for COSMO/MESSy. The size of the symbols
indicate the bias in percent; upward symbols signify a positive bias,
downward symbols a negative bias. The symbols below the horizontal axis
indicate the stations which are out of range. The coloured number provides
the number of the station, the upper black number depicts the normalised
standardised deviation and the lower number shows the correlation coefficient
at the station.
For the stations located in the CM12 domain, similar results are found. The
RMSEs between EMAC and the two COSMO/MESSy instances are similar, while the
negative biases of the MBE are larger in both COSMO/MESSy instances compared
to EMAC. The corresponding figure displaying the ground-level concentrations
is part of the Supplement (Sect. S1.5).
A similar picture as for June 2008 is found for December 2008
(Fig. b). Comparing first the ground-level concentrations
between EMAC and CM50, a higher contrast between remote areas and hotspot
regions is present in CM50. In comparison to the measurements, the strong
contrast between hotspot and remote regions is simulated better by CM50 than
by EMAC (e.g. Norway and south of Spain). As for June, both models show negative
MBEs (-42 % for EMAC, -46 % for CM50), the RMSEs are similar
(3 µg (N) m-3).
Despite the better representation of hotspots in CM50, some measured
concentrations are underestimated in CM50 (and EMAC). These hotspots may not
be covered by the emission database, or local effects, which cannot be
resolved by or are missing in the model, play an important role.
The MBE for CM50 is -34 % (June) and -46 % (December) which is of
a similar order of magnitude as reported by for NO2
using COSMO-ART. However, they report a positive, not a negative bias. The
difference of the sign might be explained by the different emission data sets,
as they used the emission data set provided by TNO (Netherlands) with
an hourly time curve , while the MACCity data set with a
constant emission flux for the whole month is used here.
Simulated ground-level CO mixing ratios in June 2008
(Fig. a) and December 2008 (Fig. b) show a
negative bias in EMAC and in CM50. Again the larger regional variation of the
ground-level mixing ratio with lower values over the Alps, as well as the
larger values over the largely polluted Po Valley, can be resolved much
better by CM50. Comparing the height-corrected values, the MBE is around
-20 % for EMAC (independent of the season) and between -25 %
(December) and -28 % (June) for CM50 (Table ). The
differences of the RMSE between EMAC and COSMO are similar for June (around
4 nmolmol-1) and in December (6 nmolmol-1).
Additional comparisons of simulated ground-level concentrations with
observation of isoprene (C5H8) and nitric acid (HNO3) are
part of the Supplement (Sect. S1.6, S1.7). Both species are simulated well
in CM50 compared to the observations. Especially for C5H8, the
benefit of the increased resolution is obvious, because the larger spatial
variability of the observations is captured much better by CM50 than by EMAC.
Taylor diagrams
For a more quantitative comparison, Taylor diagrams details are given
by are calculated. These diagrams combine the (normalised)
standard deviation (as radius) and the correlation between the observed and
the simulated time series (as angle). The observational reference point is
marked with REF on the x axis. The calculations are based on hourly
averaged model output and observations, respectively. The bias in percent
between the simulated and observed ozone concentration is displayed by the
size of the symbols. The dashed circles indicate the root mean square error.
Again, only the height-corrected values are used, which improve the results
of EMAC considerably. The Taylor diagrams for the uncorrected cases are part
of the Supplement (Sect. S1.4).
Mean values which are subtracted from the diurnal cycle (in
µg m-3) for EMAC and CM50. The given uncertainty is the
standard deviation over all stations.
Jun non-mountainJun mountainDec non-mountainDec mountainEMAC88.8±19.2103.4±8.557.5±11.672.6±9.3CM5095.3±12.195.3±8.768.2±9.777.1±6.0Observations74.2±11.495.5±7.239.1±13.264.2±11.3
The resulting Taylor diagrams for June and December 2008 are shown in
Fig. . In addition to the individual stations for
EMAC and CM50, the mean over all stations for every model is depicted.
The symbols below the horizontal axis indicate stations with a correlation or
standard deviation out of the range displayed in the corresponding diagrams.
For June 2008, both models underestimate the variability of the observations.
The mean values for the normalised standard deviation are larger in EMAC
(0.74) compared to CM50 (0.65). The same is true for the correlation
coefficient which is 0.48 for EMAC and 0.34 for CM50. In general, the results
at different stations in both models are similarly scattered. The biases of
EMAC (17 %) and CM50 (22 %) are positive.
For December 2008, both models show a better agreement with the observed
normalised standard deviations. For EMAC, the mean normalised standard
deviation increases to 0.97, while the normalised standard deviation for CM50
increases to 0.78. The mean correlation coefficients for both models decrease
to 0.45 for EMAC and 0.38 for CM50, respectively. As for June, the results at
different stations in EMAC and CM50 are similarly scattered.
The overall better results for EMAC compared to COSMO are likely caused by
the deficits in the representation of the diurnal cycle in COSMO as discussed
in Sect. . A more detailed discussion about potential
reasons for this is provided in Sect. .
We also calculate the Taylor diagrams for the entire year 2008
(Fig. ). In this case, the correlation is higher
than 0.50 (0.63 for EMAC and 0.55 for CM50). The standard deviation is 0.84
for EMAC and 0.73 for CM50. This indicates that the amplitude of the annual
cycle is underestimated by both models, while the general shape is well
simulated by both models. Some exemplary figures comparing the annual cycle
of EMAC and CM50 with the observation are part of the Supplement (Sect. S1.3)
The same as Fig. but for the whole year 2008.
Diurnal cycles
To compare the diurnal cycle at the different stations, we calculate average
diurnal cycles for all non-mountain stations (stations with an elevation
lower than 800 m) and all mountain stations. Again, the height-corrected model data are
used. For a more quantitative analysis, we split these
averaged diurnal cycles into mean values and the amplitudes. For this we
calculate first the monthly averaged diurnal cycle at every station. From
this cycle, the mean value is calculated, which is subtracted from the
diurnal cycle to get the amplitude of the diurnal cycle. These values are
averaged in a second step over all non-mountain and mountain stations,
respectively.
Figure a shows the averaged amplitude of
the diurnal cycle of the non-mountain stations for June 2008, the
corresponding mean values are listed in Table .
Comparing the mean values of EMAC and CM50 the positive ozone bias is
apparent; however, the differences are within 1 standard deviation of the
observations. The amplitude, however, is underestimated in CM50. While the
amplitude of the observations is ±18 µg m-3, CM50
simulates an amplitude of only ±5 µg m-3 and EMAC
simulates an amplitude of ≈±12µg m-3. Comparing
not the amplitude, but the complete diurnal cycle (not shown), both EMAC and
CM50 simulate an identical noon peak of ≈100µg m-3
(the observations show a peak of ≈93µg m-3).
Obviously, CM50 underestimates the decrease of O3 during night (which
is mainly due to chemical destruction and dry deposition). This issue is
discussed in detail in Sect. .
For the mountain stations in June 2008, CM50 simulates mean values, which are
comparable with the observations, while EMAC shows a positive ozone bias
(≈ 7 µg m-3). However, the small amplitude of the
observed diurnal cycle (±4 µg m-3) is underestimated by
both models, which show hardly any amplitude.
Diurnal cycle amplitude of ozone in µg m-3 for
(a) all non-mountain stations and (b) all mountain stations
for June 2008. The observations are shown in black, while EMAC is shown in
red and CM50 in blue. The dashed lines indicate the standard deviation over
all stations of the observations, while the coloured polygons display the
standard deviation of the simulation data.
The same as Fig. but for the
subset of stations which are located in all three model instances.
The same as Fig. but for
December 2008.
Figure displays the averaged
amplitude of the diurnal cycle for the subset of stations, which are located
in both COSMO/MESSy instances. The corresponding mean values are listed in
Table . Overall, the results are similar to
all stations in the CM50 domain. For the non-mountain stations, EMAC and the
two COSMO/MESSy instances underestimate the observed amplitude of the diurnal
cycle (≈±19µg m-3). Especially, the two
COSMO/MESSy instances reach smaller (≈±5µg m-3)
values compared to EMAC (±12 µg m-3). The absolute values
of the observed noon peak (not shown) are well simulated by both COSMO/MESSy
instances (≈95µg m-3) and overestimated by EMAC
(≈102µg m-3). Again, the conclusion that the loss
overnight is underestimated in CM50. For the mountain stations, EMAC and the
two COSMO/MESSy instances do not reproduce the small amplitude of the
observations (≈±10µg m-3). From the results of
the models, CM12 shows the largest amplitude (≈±2µg m-3), which is still much lower compared to the
observed amplitude. The mean values have a negative bias for both COSMO/MESSy
instances (≈-5µg m-3) and a positive bias for EMAC
(≈5µg m-3).
The same as Table but only for the stations located
in the CM12 domain.
Jun non-mountainJun mountainEMAC89.6±17.399.1±1.6CM5087.2±10.388.3±8.2CM1289.9±7.489.4±0.9Observations79.2±8.994.4±2.2
For the non-mountain stations in December 2008, both models in general
simulate a similar amplitude compared to the observations
(Fig. a). However, the (small) noon peak is
underestimated, yet all differences are within 1 standard deviation of the
observations. The mean values show a positive bias of ≈19µg m-3 for EMAC and ≈29µg m-3
for CM50 (Table ).
This bias for ozone exists also at the mountain stations, but smaller in
magnitude (8 µg m-3 for EMAC and 13 µg m-3
for CM50); the absence of a diurnal cycle is represented by both models
(Fig. b).
Vertical ozone profiles
In order to check if the vertical distribution of ozone is well simulated, we
compare the simulation results with ozone sonde data. For this, the ozone
sonde data are transformed to a fixed pressure grid. The ozone sonde data are
not continuous measurements in time, but represent distinct points in time
(and space). To simplify the comparison with the simulated data, all
measurements within 1 month are averaged, without any weighting of the
individual measurements. From the simulations, we use the hourly averaged
model data at the location of every station, which are averaged over the
month. Therefore, the simulated and observed data are co-located in space, but
not necessarily in time.
Exemplarily, the ozone profiles of the observations and from the simulation
data at De Bilt (Fig. ) are displayed. For June 2008,
also the vertical profiles for CM12 are shown. Profiles at more
stations can be found in the Supplement (Sect. S1.8). The vertical ozone
distribution is captured well by EMAC and COSMO/MESSy instances. For most
profiles, the mean of the simulated ozone mixing ratios lies within 1
standard deviation around the mean of the observations. However, in the
boundary layer we note a positive bias of COSMO/MESSy at most stations. This
bias is in line with the results already presented above. The large
variability of the observations in the upper troposphere/lower stratosphere
(UTLS) is captured much better by COSMO/MESSy than by EMAC, as COSMO/MESSy
resolves intrusion of stratospheric air into the troposphere better. However,
while comparing the variability, it is again important to note that the
number of data points of the observations is much lower than for the
simulated data. The results of CM12 (Fig. ) are very
similar to CM50, but the variability is slightly larger due to the finer
horizontal resolution.
Despite the good representation of the measured ozone mixing ratios in the
free troposphere, ozone is overestimated within the planetary boundary layer
(PBL) at most stations, which is more pronounced in COSMO/MESSy than in EMAC.
For some stations (e.g. Payerne, Legionowo) only a small or even no gradient
of the mixing ratio within the PBL is simulated by COSMO/MESSy. This problem
is discussed in detail in Sect. .
In addition, the RMSE between the monthly average simulation data and the
monthly mean of the observation is calculated. For this, the observations are
transformed on the vertical grid of the respective simulation. The RMSE for
all profiles in June 2008 is shown in Fig. a. In general,
the RMSEs of EMAC and CM50 look very similar. From the bottom to roughly
800 hPa, the RMSEs are between 0 and 20 nmolmol-1. From 800
to 600 hPa, the RMSEs increase to 5–25 nmolmol-1. At
600 hPa they drop back to 0–20 nmolmol-1. In the UTLS
the variability of the RMSE is increasing again. In this area, the variability
and the absolute ozone values are very large.
In December 2008 (Fig. b) too-high values within the PBL in
CM50 show up by higher values of the RMSE (up to 25 nmolmol-1),
while EMAC exhibits a maximum RMSE of 15 nmolmol-1. At roughly
800 hPa both models show a decreased RMSE of ≈10nmolmol-1 at maximum, before the spread of the RMSE is again
increasing in the UTLS.
Vertical ozone profile (in nmolmol-1) at De Bilt
(Netherlands) for (a) June, (b) December 2008. In
(a) results for all EMAC and the two COSMO/MESSy instances are
shown, while (b) shows the results for EMAC and CM50. The standard
deviation of the temporal mean is indicated by the error bars for the
observations and by the shaded area for the simulation data.
In situ observations
Here, we compare the simulation results of EMAC and CM50 with
measurements of the IAGOS-CARIBIC flight 240 from Frankfurt (Germany) to
Chennai (India) and the flight 243 from Denver (USA) to Frankfurt (both
July 2008). The flight was sampled in EMAC and in CM50 using the MESSy
submodel S4D , which online samples the model data along
the flight path with model time step resolution. For a better comparison
between simulated and measured data, the measurements are aggregated on the
same time step as the model output (720 s for EMAC and 240 s for CM50).
Ozone and carbon monoxide mixing ratios from the simulation and the
measurements are compared in Fig. . For the
simulation data, additionally the potential vorticity (PV) is displayed. In
general, both models underestimate carbon monoxide and overestimate ozone in
the troposphere. This is in line with the findings of the previous sections.
However, the intrusion of stratospheric air at the beginning of the flight 240
is captured much better by CM50. This is visible from the high values of the
ozone mixing ratios, where the observed magnitude is nearly perfectly
reproduced by CM50. Flight 243 resides in stratospheric air masses most of
the time. Here the carbon monoxide mixing ratios are well simulated by both
models. However, the huge fluctuations of the ozone mixing ratios along the
flight track are not captured by the models. To achieve this, maybe a higher
vertical resolution is necessary to account for the steep vertical gradients
in the UTLS area. Also note that parts of the flight may already be within
the upper damping zone (starting at 11 km) of CM50. For future comparisons,
the use of a grid with a higher vertical extent in COSMO/MESSy e.g.
is envisaged.
Vertical profile showing the RMSE of the model data compared to the
ozone sonde data (in nmolmol-1) for (a) June and
(b) December 2008.
Comparison between IAGOS-CARIBIC measurements of ozone, carbon
monoxide (left axis in nmolmol-1) for EMAC (left side) and CM50
(right side). The upper row shows the results for the IAGOS-CARIBIC flight
240 and the lower row for the flight 243. For both models, also the potential
vorticity (right axis in PVU) is displayed as a proxy for tropospheric or
stratospheric air masses.
Tropospheric oxidation capacity
To compare if EMAC and the two COSMO/MESSy instances simulate different
oxidation capacities of the troposphere, the lifetime of methane against
OH (τCH4+OH) is calculated according to
as
τCH4+OH(t)=∑b,tMCH4b(t)∑b,tκCH4+OHb(t)⋅cairb(t)⋅OHb(t)⋅MCH4b(t),
with MCH4b(t) the mass of CH4 in every gridbox (b) at
a respective time step (t), κCH4+OHb(t) the reaction
coefficient of the reaction CH4+OH (which depends on the
temperature), cairb(t) the concentration of air and
OHb(t) the mole fraction of OH.
Usually, the lifetime of methane is calculated in global models. In this
case, the methane lifetime can be calculated at every time step. As we
calculate here the lifetime only for a fraction of the globe, it is important
to sum the numerator and denominator first over all time steps of a certain
period (> 1 day) before the calculation of τ. The reason for this is
that during night OH is virtually absent and the denominator becomes
arbitrarily small. As shown by , the methane lifetime against
OH of the RC1SD-base-10a simulation, which has a very similar setup
as used in the present study (see Sect. ), is around
7.7 a for the year 2008. As analysed
in detail by this is at the lower end compared to results
from other models which are mainly in the range from 8–9 a. The values we
present here are not directly comparable to these global estimates of the
methane lifetime, as we calculate the lifetime only for a part of the globe.
Here, for a more detailed comparison of the results from EMAC and the two
COSMO/MESSy instances we further calculate the lifetime separately for three
different vertical layers of the atmosphere: from the ground to 850 hPa,
from 850 to 500 hPa and finally from 500 to 200 hPa. For this, we sum up
all grid boxes within the respective area.
Average values for June–August 2008 of the CH4 mass
(MCH4), the OH mass (MOH) and the methane
lifetime against OH (τ) for EMAC, CM50 and CM12.
All values are computed for the area of the CM12 instance. The mass of
CH4 and OH are the time-averaged values. The uncertainty range
is the standard deviations with respect to time (based on the monthly mean
values). The subscripts on the individual variables indicate the different
vertical layers.
First, we compare τ for the German region, which is covered by EMAC,
CM50 and CM12 (Table ). For the layer from the bottom up to
850 hPa, EMAC calculates the shortest average lifetime
(2.7 a), which is due to a larger OH mass (60 kg). In the
CM12 instance the lifetime is considerably shorter (2.9 a) than in CM50
(3.4 a), as more OH is present in the finer resolved instance. In the
second vertical layer (850–500 hPa), both COSMO/MESSy instances show
comparable results (3.5 a). The CH4 mass is smaller compared to
EMAC, while the OH mass is larger, which leads to a shorter average
CH4 lifetime in both COSMO/MESSy instances compared to EMAC. For the
highest vertical layer (500–200 hPa), all instances show comparable
OH masses, the lifetime of methane, however, is longer for EMAC
(12.4 a) compared to CM50 (11.3 a) and CM12 (11.2 a). This difference is
mainly caused by the lower temperatures in EMAC in this vertical layer.
The methane lifetimes in the European domain (Table ) show
similar results as over Germany. In the lowest vertical layer EMAC simulates
a shorter methane lifetime (mainly due to more OH). In the second vertical
layer both models simulate very similar methane lifetimes, while the lifetime
in the upper layer is again larger in CM50. The shorter lifetime in EMAC
compared to COSMO/MESSy is due to more OH in EMAC.
Area-averaged ground-level concentrations (for a box from
5∘ W–20∘ E, 20–55∘ N) in µg m-3
of various chemical species. The second through fourth columns display the values for
CM50 , EMAC and CM50 with changed temperature for the submodel MECCA
(CM50T*), respectively. The fifth column indicates if the differences
between EMAC and CM50 are positive (+), negative (-) or if there is only
a minor difference (≈). The last column indicates the corresponding
differences between CM50 and CM50 with changed temperature field for MECCA.
CM50EMACCM50T*Diff 2 and 1Diff 3 and 1HO20.004530.005630.00492++OH4.53×10-55.67×10-54.75×10-5++CHBr30.003880.004020.00387+≈CH3Br0.03080.03200.0308+≈CH3I0.002610.004120.00261+≈NO30.008660.009140.0126++NH32.044.002.03+≈NO0.1780.4010.164+-NO23.515.253.55+≈C5H80.08550.1440.0818+-HCHO0.851.140.938++CO149169149+≈O311199.0114-+Discussion on deviations from observations
By comparing the COSMO/MESSy results with observations in the previous
section, we find some remarkable deviations. First of all, the simulated
ground-level mixing ratios of carbon monoxide are too low, while the ozone
concentrations are too high. In particular, the north-east European area is
affected by too-high ground-level ozone concentrations during April (not
shown) to June. In addition, not only are the monthly mean ground-level
concentrations of ozone too high but also the amplitude of the diurnal cycle
is underestimated showing too-large values in CM50 at night.
To investigate the influence of the cold bias of COSMO/MESSy which is
a known problem of COSMO-CLM during winter, e.g., we conduct
a short sensitivity study with a modified temperature field of CM50 for the
calculation of the reaction kinetics in the submodel MECCA (see Appendix A).
For this, the temperature field of EMAC is transformed using INT2COSMO to
CM50. This transformed temperature field is then used within MECCA in CM50.
All other dynamical and chemical processes (like the online calculation of
emissions) use the original temperature field of CM50. Resulting area-averaged
ground-level concentrations for a small subset of all chemical
species over Europe (defined as a box from 5∘ W–20∘ E,
20–55∘ N) are summarised in Table .
Comparing first the area-averaged concentrations between EMAC and CM50, we see
for all species, except for ozone, a positive difference which means higher
values in EMAC compared to CM50. This includes short-lived tracers like
OH or NO3 and longer-lived tracers like bromoform
(CHBr3) and CO. Comparing further the results between CM50 and
CM50T* (with the changed temperature field) we see that the
concentrations of most short-lived species (like OH, NO3 or
HCHO) increase. These differences are due to the temperature
dependence of most reaction rates. The magnitude of these increases can,
however, not fully explain the observed differences between CM50 and EMAC,
but are an important contributor to the difference of the short-lived tracers
between EMAC and CM50.
The differences of longer-lived species like ozone, carbon monoxide or
bromoform can not be explained by the temperature differences. For further
analysis, we compare vertical profiles of 222Radonusing the
MESSy submodel DRADON, in CM50 and EMAC. This submodel emits
222Radon as purely diagnostic species on all land surfaces not
covered by ice or snow. The emission rate is
10 000 atomsm-2s-1 and the only sink in the atmosphere is
radioactive decay with a half-life of 3.8 days.
The vertical profiles of 222Radon (not shown) show smaller
concentrations in the PBL in COSMO/MESSy than in EMAC, even though the
sources are identical. This difference can only be explained by a stronger
vertical mixing (vertical diffusion) within the PBL in COSMO/MESSy compared
to EMAC. This stronger mixing explains also the differences for the longer-lived
trace gases like ozone, carbon monoxide or bromoform. For CO and
bromoform, the high concentrations near the surface are more quickly reduced
through upward transport in COSMO/MESSy than in EMAC. The concentration of
ozone increases with height, meaning that the lower values at the surface are
faster mixed with air containing more ozone. This is in agreement with the
vertical ozone profiles of CM50 (see Sect. ) showing too-large
ozone mixing ratios in the PBL.
In addition to this stronger mixing, there is yet another cause for the too-high
ozone concentration in COSMO/MESSy over north-east Europe: COSMO/MESSy
uses different soil types in some areas over north-eastern Europe. This
affects, for example, the stomata resistance determined by the different base
models, which subsequently affects the dry deposition velocities. This leads
to a reduced dry deposition velocity over parts of north-eastern Europe in
COSMO/MESSy compared to EMAC (additional figures are part of the Supplement
in Sect. 1.1). Moreover, found higher ground-level
concentrations of ozone over north-eastern Europe, when increasing the
resolution of their simulations. As they are using the same MACCity emissions
as we do, we speculate that the too-large ground-level mixing ratios of ozone
might also be influenced by too-large emissions of ozone precursors in this
area. As the ozone chemistry is strongly non-linear, even a small amount of
higher NOx emissions would lead to an increased ozone production in the
NOx-limited regime.
So far, this discussion focused on the differences of the monthly mean
ground-level concentrations, but not on the underestimation of the amplitude
of the diurnal cycle. The underestimation of the amplitude of the diurnal
cycle in COSMO/MESSy has several reasons. The most important difference is
the dynamics of the PBL. The diurnal cycle of the PBL is more pronounced in
EMAC compared to CM50, showing higher values around noon and smaller values
during night (Fig. ).
The lower height of the PBL in EMAC during night leads to a much smaller
“reservoir” from which ozone can be deposited or chemically destroyed
(e.g. via reaction with NO). Nevertheless, the amount of ozone which
is removed by dry deposition depends on the concentration of ozone, which is
smaller in EMAC compared to CM50, the concentration in EMAC can be reduced faster
as in CM50. This leads in general to a more efficient destruction of
ground-level ozone during night, when no photochemical production of ozone
takes place. In addition, the more efficient vertical diffusion in COSMO/MESSy
(as discussed above) leads to more efficient downward transport of air with
higher ozone concentration.
This is intensified by two additional differences between EMAC and
COSMO/MESSy leading to a more pronounced diurnal cycle in EMAC. First of all,
the dry deposition velocities during noon are comparable between COSMO/MESSy
and EMAC. During night this changes and EMAC simulates slightly larger dry
deposition velocities as COSMO/MESSy. In addition, the net ozone production
in the lowermost model layer (production - loss) is more negative during
night in EMAC compared to COSMO/MESSy.
To investigate if we can improve the vertical ozone profiles and the
amplitude of the diurnal cycle of ozone in COSMO/MESSy by changes to the
COSMO setup, we conducted further sensitivity studies. The main aim of these
studies was to investigate the effect of changing parameters affecting
vertical mixing (diffusion).
Focusing on the vertical ozone profiles in comparison to ozone sonde
observations and the amplitude of the diurnal cycle of ozone, none of these
simulations show substantial improvements compared to the observations.
One simulation, however, slightly improves the amplitude of the diurnal cycle
and shows a decreased cold bias. Compared to the reference setup, the
minimum diffusion coefficient for temperature (tkhmin = 0.1) and momentum
(tkmmin = 0.1) is decreased. Further, the factor for diffusion of
turbulent kinetic energy (TKE, c_diff = 0.05), the length scale for
subscale surface pattern (pat_len = 100) and the maximal turbulent
length scale (tur_len = 150) are decreased. In addition, the explicit
corrections of implicitly calculated turbulent heat and moisture fluxes due
to effects from subgrid-scale condensation is switched off
(lexpcor = false, which is also set to false for COSMO-DE and COSMO-EU at
the DWD or in the CORDEX-EU setup). We recommend these settings for further
simulations using COSMO/MESSy at least over Europe and with a resolution
comparable to the simulations performed here. Using an increased resolution
or a domain in different regions of the world might require other parameters.
To improve the results with respect to the too-small amplitude of the diurnal
cycle of the PBL and the too-strong mixing within the PBL, further model
developments are necessary. For example, the turbulence scheme and thus the
vertical diffusion parameterisation were recently further developed for the
ICON model (M. Raschendorfer, personal communication, DWD). These
developments become available in the COSMO model from version 5.3 on.
Further testing of the additional options available within this newer COSMO version
are planned as soon as these are available. In this context, a detailed
comparison with observed diurnal cycles for temperature and relative humidity
between COSMO/MESSy and observations are required.
Furthermore, it is well known that the soil moisture has an important
influence on the boundary layer dynamics. Therefore, a better initialisation
of the soil moisture could very well yield an improved diurnal cycle and more
realistic vertical profiles. In future, additional tests with a nudging of
the mean temperature in EMAC as done in some of the simulations
described by would be interesting to test whether the cold
bias in the upper troposphere can be reduced.
Height of the planetary boundary layer for June 2008 in m
averaged over all non-mountain stations.
Summary and conclusion
For the first time, we performed model simulations using complex tropospheric
and stratospheric chemistry with the newly developed model system MECO(n).
MECO(n) features an online coupling between the global chemistry–climate
model EMAC and the regional chemistry–climate model COSMO/MESSy. The main
purpose of the simulations is the evaluation of MECO(n) with respect to
gas-phase chemistry. This evaluation is a prerequisite for further studies
focusing on the analysis of atmospheric chemistry. Therefore, we perform a
simulation covering the period from July 2007 to December 2008, from which we
compare the results for June and December 2008 to observations. We use a
MECO(2) setup with one regional instance covering Europe (0.44∘) and
a second instance covering Germany (0.1∘). Because of the high
computational demands, the finer nest was applied only during the summer
period of 2008. The chemical boundary conditions of EMAC and the two
COSMO/MESSy instances were as consistent as possible. This means that we use
the same emission data set with a resolution of
0.5∘× 0.5∘ for all instances and the same lightning
NOx emissions as calculated by EMAC in all instances. This setup allows
us to focus on the difference due to the changes of the base model (ECHAM vs.
COSMO) and the increased resolution.
We focus on the evaluation of ozone, carbon monoxide and nitrogen dioxide and
compare the simulated values with satellite observations, in situ
ground-level data, vertical profiles and aircraft in situ measurements. This
comparison shows that the increased resolution of COSMO/MESSy allows for a
more detailed representation of the hotspot regions. In particular, the
spatial representation of highly variable trace gases like nitrogen dioxide
are improved. The annual cycles of the investigated trace gases are
represented well by COSMO/MESSy and by EMAC. Especially for the German area
we found a better agreement with observations using COSMO/MESSy instead of
EMAC. The same is true for the representation of ozone at mountain stations.
COSMO/MESSy shows a positive bias for ozone and a negative bias for nitrogen
dioxide. The magnitude of the bias is in the same range as that of
comparable model systems. In addition, a negative bias for carbon
monoxide is apparent. The vertical profiles of COSMO/MESSy are in agreement
with observations from ozone sonde data within the free troposphere, showing
a RMSE between 0 and 20 nmolmol-1. In particular, the large
variability in the UTLS region is captured much better by COSMO/MESSy than by
the coarser resolved EMAC model. This shows the high potential of MECO(n) for
the preparation and wrap-up of aircraft measurement campaigns helping to
interpret the measurements.
The diurnal cycle of ozone is not as well represented in COSMO/MESSy as in
EMAC. The main reasons for this are differences in the dynamics of the models.
The amplitude of the diurnal cycle of the PBL is smaller in COSMO/MESSy
compared to EMAC. The comparison of the vertical profiles from COSMO/MESSy to
observations shows that the profiles within the PBL at some stations in
COSMO/MESSy are too steep. The COSMO/MESSy profiles are also steeper compared
to EMAC, explaining the increased positive ozone and negative carbon monoxide
bias in COSMO/MESSy. In order to overcome these problems, further model
improvements are necessary, e.g. the improvement of the PBL turbulence
scheme.
It is also important to note that the potential of the increased resolution
(especially for the finest instance) is not fully exploited in the simulation
presented here, as a coarse emission data set is used in all instances. Usage
of coarse emission data sets can lead to deterioration of the results on
finer scales, as the emissions are already blurred out due to the coarse
resolution of the emission data and small peaks on a scale smaller than the
emission data can not be resolved. A finer resolved emission data set is
expected to reveal many more benefits of the increased resolution.
This, however, is not the intention of the
simulation presented here. The purpose of this study is a first evaluation of
the MECO(n) model system with respect to tropospheric chemistry. This
evaluation is an important step in the model development. We show that both
models have strengths and weaknesses. Even with coarse emission data
COSMO/MESSy shows its strength in particular in the comparison with in situ
aircraft observations. Besides further model improvements, the next step will
be a detailed evaluation using high-resolution emissions and comparison with
regional observation networks.
Code availability
The Modular Earth Submodel System (MESSy) is continuously further developed
and applied by a consortium of institutions. The usage of MESSy and access to
the source code is licensed to all affiliates of institutions which are
members of the MESSy Consortium. Institutions can become a member of the
MESSy consortium by signing the MESSy memorandum of understanding. The legacy
model ECHAM5 is licensed by the Max Planck Institute for Meteorology in
Hamburg (Germany). The COSMO code is available under two different licenses:
either an individual user license granted by the CLM-Community or by an institutional
license granted by the German Weather Service (DWD). More information can be
found on the MESSy consortium website (http://www.messy-interface.org).
Description of gas-phase chemistry-related submodels
Due to the modular MESSy infrastructure, we can use most of the submodels of
the MESSy framework simultaneously in EMAC and COSMO/MESSy. This is
especially the case for all submodels, which are important for the
calculation of atmospheric chemistry. Below we provide a short overview of
the submodels which are most important for the calculation of atmospheric
chemistry processes. We restrict this overview to the submodels (with the
exception of MECCA), where differences between EMAC and COSMO/MESSy exist.
In the beginning, we would like to highlight one general important difference
between COSMO/MESSy and EMAC with respect to the submodels DDEP (dry
deposition), OFFEMIS (offline emissions) and ONEMIS (online emissions). In
general, these submodels have two options to handle the deposition and
emissions: the tracer tendency in the respective model box can be directly
changed or a lower boundary condition for the vertical flux can be
calculated. In the latter case, the emission is treated by the vertical
diffusion operator (VDIFF; more details can be found in
). In general, both options would be available for use in
COSMO/MESSy. However, as using the lower boundary flux can lead to problems
in closing the budgets of the trace species in COSMO/MESSy only the option to
change the tracer tendencies directly has been implemented so far.
DDEP
The submodel DDEP handles the dry deposition of trace gases and aerosols.
Following the approach of the dry deposition velocities of
ozone and sulfur dioxide are calculated explicitly, as these dry deposition
velocities are relatively well known. The velocities of the other trace gases
are calculated in relation to the velocities for ozone and sulfur dioxide
depending on their solubility and reactivity. The only exceptions are
nitrogen oxide, nitrogen dioxide and nitric acid, where most of the surface
resistances are prescribed too. A detailed description of the submodel can be
found in (, named DRYDEP). In
COSMO/MESSy the dry deposition is applied (as described above) only as tracer
tendency in the lowermost grid layer.
The necessary offline fields for the dry deposition parameterisation (e.g.
soil pH, leaf area index, drag coefficient) are currently only available at a
horizontal resolution of 0.5∘× 0.5∘.
JVAL
To calculate the photolysis rate coefficients the submodel JVAL is used,
which is based on . The current version of this submodel is
described by . In COSMO/MESSy, the required ozone input data,
providing the ozone column above the model domain top, is downscaled from
EMAC using the MMD (multi-model driver) submodels.
LNOX
The submodel LNOX described by calculates the NOx
emissions due to lightning. However, up to now no detailed comparison of the
results from the different lightning NOx parameterisations in COSMO/MESSy
with observations has been conducted. This needs to be done in the near
future. This is not relevant for this study as, for comparison reasons, the
downscaled lightning NOx fluxes (from EMAC) have been the means of choice.
MECCA
The submodel MECCA Module Efficiently Calculating the Chemistry of the
Atmosphere, comprises the atmospheric reaction mechanism used
to calculate the chemical kinetics. As described by the
submodel was recently revised with updated rate coefficients according to the
Jet Propulsion Laboratory (JPL) recommendations described by .
For the simulations performed here the mechanism
CCMI-base-01-tag.bat is used. This mechanism includes the chemistry
of ozone, methane and odd nitrogen. While alkynes and aromatics are not
considered, alkenes and alkanes are considered up to C4. We use the
Mainz Isoprene Mechanism
MIM1, for the chemistry of isoprene and some
non-methane hydrocarbons (NMHCs). The detailed mechanism is part of the
Supplement.
MSBM
For the consistent calculation of the heterogeneous reaction rates on polar
stratospheric clouds (PSCs), the Multiphase Stratospheric Box
Model MSBM; see
is used. Additionally, this submodel determines the
partitioning of H2O between gas, liquid and ice phase, which affects
the hydrological cycle and feedbacks on the dynamics.
OFFEMIS
For the emissions described by prescribed fluxes, the submodel OFFEMIS is used
described as OFFLEM by . The prescribed fields are
transformed on the computational grid using the submodel IMPORT
. Similar to DDEP, the emissions in COSMO/MESSy are
applicable only as a tracer tendency.
ONEMIS
The submodel ONEMIS described as ONLEM by calculates
different emission fluxes of selected chemical species online. In this
study, we use ONEMIS to calculate soil/biogenic emission of NO and biogenic
emissions of isoprene (C5H8). For NO, the algorithm is based
on and on for isoprene. The same data
(for the leaf area index and the soil fertiliser classes) as for EMAC are
used in COSMO/MESSy. These data have a resolution of
0.5∘× 0.5∘ and should be updated to a higher
resolution in the near future. For COSMO/MESSy, only the option to add the
emissions as tracer tendencies is available.
SCAV
The scavenging of trace gases (and aerosols) by clouds and precipitation is
treated by the submodel SCAV . As COSMO/MESSy operates
on shorter time steps, the equilibrium between gas and cloud phase can not be
reached within each model time step in contrast to the EMAC application
where this can be considered a valid assumption. Therefore, additional
tracers for the chemical species in the cloud phase (liquid and ice) have
been added, which allow for transport of in-cloud tracers and consistent
uptake (release) into (out of) the cloud droplets depending on the
microphysical processes and thermodynamic conditions in the simulated clouds.
TNUDGE
The submodel TNUDGE allows a relaxation of tracers to
specific mixing ratios and is mainly used for species with long but uncertain
lifetimes, uncertain emission fluxes but well-observed mixing ratios. In our
simulations, TNUDGE mainly prescribes CH4, CO2 and the CFCs
mixing ratios at the surface. So far, the fields which are used in
COSMO/MESSy by TNUDGE can be downscaled from EMAC using MMD submodels or
imported using IMPORT.
The Supplement related to this article is available online at doi:10.5194/gmd-9-3545-2016-supplement.
Acknowledgements
Mariano Mertens acknowledges funding by the DLR project “Verkehr in
Europa”. Christiane Hofmann and Astrid Kerkweg additionally like to
acknowledge funding by the German Ministry of Education and Research (BMBF)
in the framework of the MiKlip (Mittelfristige Klimaprognose/Decadal
Prediction) subproject FLAGSHIP (Feedback of a Limited-Area model to the
Global-Scale implemented for HIndcasts and Projections, funding ID
01LP1127A). Furthermore, this work is based on work funded by the German
Science Foundation (DFG) under the project name MACCHIATO (WE 2943/4-1). We
thank M. Raschendorfer (DWD) for helpful comments on the turbulence
parameterisation in COSMO. We also thank U. Blahak (DWD) and the COSMO-CLM
community for their support. In addition, we are thankful for very helpful
comments from A. Lauer (DLR) and two anonymous referees, which improved this
manuscript.
We acknowledge the EBAS platform (http://ebas.nilu.no) for providing a
broad range of observational data used in this study. We also acknowledge the
World Ozone and Ultraviolet Radiation Data Centre (WOUDC) for the access to
the vertical ozone profiles, retrieved from http://woudc.org. We
further acknowledge the IAGOS-CARIBIC team for providing the aircraft in situ
observations. Further, we acknowledge the free use of tropospheric NO2 column
data from the SCIAMACHY sensor from http://www.temis.nl.
Analysis and graphics of the used data was performed using the NCAR command
language (version 6.2.0) software developed by UCAR/NCAR/CISL/TDD and
available online: http://dx.doi.org/10.5065/D6WD3XH5. For the model
development and the simulations presented here, a lot of computational
resources were needed. Therefore, we acknowledge the computational resources
provided by the Leibniz Supercomputing Centre (LRZ) in Garching and the
German Climate Computing Centre (DKRZ) in Hamburg. The article processing charges for this open-access
publication were covered by a Research Centre
of the Helmholtz Association. Edited by: R.
Neale Reviewed by: two anonymous referees
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