Geosci. Model Dev., 7, 2435?2475, 2014
www.geosci-model-dev.net/7/2435/2014/
doi:10.5194/gmd-7-2435-2014
? Author(s) 2014. CC Attribution 3.0 License.
Simulation of tropospheric chemistry and aerosols with the climate
model EC-Earth
T. P. C. van Noije1, P. Le Sager1, A. J. Segers2, P. F. J. van Velthoven1, M. C. Krol3,4, W. Hazeleger1,3, A. G. Williams5,
and S. D. Chambers5
1Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
2Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, the Netherlands
3Wageningen University, Wageningen, the Netherlands
4Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands
5Institute for Environmental Research, Australian Nuclear Science and Technology Organisation, Lucas Heights, Australia
Correspondence to: T. P. C. van Noije (noije@knmi.nl)
Received: 20 February 2014 ? Published in Geosci. Model Dev. Discuss.: 25 March 2014
Revised: 1 September 2014 ? Accepted: 15 September 2014 ? Published: 22 October 2014
Abstract. We have integrated the atmospheric chemistry
and transport model TM5 into the global climate model
EC-Earth version 2.4. We present an overview of the TM5
model and the two-way data exchange between TM5 and
the IFS model from the European Centre for Medium-Range
Weather Forecasts (ECMWF), the atmospheric general cir-
culation model of EC-Earth. In this paper we evaluate the
simulation of tropospheric chemistry and aerosols in a one-
way coupled configuration. We have carried out a decadal
simulation for present-day conditions and calculated chem-
ical budgets and climatologies of tracer concentrations and
aerosol optical depth. For comparison we have also per-
formed offline simulations driven by meteorological fields
from ECMWF?s ERA-Interim reanalysis and output from
the EC-Earth model itself. Compared to the offline simula-
tions, the online-coupled system produces more efficient ver-
tical mixing in the troposphere, which reflects an improve-
ment of the treatment of cumulus convection. The chem-
istry in the EC-Earth simulations is affected by the fact that
the current version of EC-Earth produces a cold bias with
too dry air in large parts of the troposphere. Compared to
the ERA-Interim driven simulation, the oxidizing capacity
in EC-Earth is lower in the tropics and higher in the extrat-
ropics. The atmospheric lifetime of methane in EC-Earth is
9.4 years, which is 7 % longer than the lifetime obtained with
ERA-Interim but remains well within the range reported in
the literature. We further evaluate the model by comparing
the simulated climatologies of surface radon-222 and carbon
monoxide, tropospheric and surface ozone, and aerosol opti-
cal depth against observational data. The work presented in
this study is the first step in the development of EC-Earth into
an Earth system model with fully interactive atmospheric
chemistry and aerosols.
1 Introduction
Chemically reactive gases and aerosols play important roles
in the climate system. They affect the Earth?s energy balance
by direct interaction with radiation and in various indirect
ways.
Ozone (O3) absorbs both solar (shortwave) and terrestrial
(longwave) radiation. The absorption of ultraviolet and vis-
ible radiation by ozone causes solar heating in the strato-
sphere, and the absorption of thermal infrared radiation
makes ozone an important greenhouse gas.
Depletion of stratospheric ozone has been the main cause
for the observed cooling of the lower stratosphere since the
1980s (Forster et al., 2011). Although the impact on the
global radiation balance of the troposphere is thought to be
relatively small (Myhre et al., 2013b), stratospheric ozone
depletion has been identified as an important driver of tro-
pospheric circulation changes in the Southern Hemisphere
(e.g. Gillett and Thompson, 2003; Arblaster and Meehl,
2006; Polvani et al., 2011) and of circulation changes in the
Southern Ocean (Sigmond et al., 2011). This may also have
Published by Copernicus Publications on behalf of the European Geosciences Union.
2436 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
reduced the uptake of carbon dioxide (CO2) by the Southern
Ocean (Lenton et al., 2009). It is anticipated that the expected
recovery of the ozone layer in the coming decades will tend
to reverse these trends (Perlwitz et al., 2008; Son et al., 2008;
Sigmond et al., 2011).
Ozone is not directly emitted into the atmosphere. In
the troposphere it is produced by oxidation of carbon
monoxide (CO), methane (CH4), and other volatile organic
compounds (VOCs), in the presence of nitrogen oxides
(NOx =NO+NO2). Increases in tropospheric ozone since
the preindustrial era have contributed substantially to global
warming by direct radiative effects (Stevenson et al., 2013;
Myhre et al., 2013b), especially in the Northern Hemisphere
(Mickley et al., 2004; Shindell et al., 2006a). Moreover, in-
creases in ground-level ozone may have contributed to global
warming by reducing the CO2 uptake by vegetation (Felzer et
al., 2005; Sitch et al., 2007; Arneth et al., 2013), but the im-
portance of this effect is still uncertain (Myhre et al., 2013b).
Methane itself strongly absorbs thermal infrared radiation
and is therefore a very potent greenhouse gas. Methane is
also important as a precursor of stratospheric water vapour.
Increases in methane concentrations have contributed sub-
stantially to global warming, and increases in the anthro-
pogenic methane emissions even more so (Shindell et al.,
2009; Myhre et al., 2013b).
Aerosols affect the Earth?s radiation budget by scattering
and absorption of sunlight, by absorption of thermal infrared
radiation, and by interactions with clouds. Scattering tends
to increase the planetary albedo and has a cooling effect.
Absorption, on the other hand, causes warming in the atmo-
sphere. The importance of scattering versus absorption de-
pends on the chemical composition, mixing state, size distri-
bution, particle shapes, and vertical distribution of the aerosol
mixture, as well as on the presence of clouds and the surface
albedo.
Absorption of infrared radiation mainly takes place
through coarse-mode aerosols, and is most relevant for
stratospheric aerosols resulting from large volcanic eruptions
(Arfeuille et al., 2013), and for tropospheric aerosols contain-
ing mineral dust or sea salt (Jacobson, 2001). The shortwave
radiative effects of aerosols are generally considered to be
more important for the climate (Myhre et al., 2013a, b).
Black carbon (Petzold et al., 2013) strongly absorbs sun-
light, which makes it an important warming agent (Bond et
al., 2013). Sulfate, nitrate, and sea salt only weakly absorb
sunlight and are mainly scattering. Mineral dust and organic
aerosols vary from weakly to strongly absorbing, depending
on their composition and the wavelength of the light. Light
absorbing aerosols such as black carbon and mineral dust
also have a warming effect when deposited on snow or ice
(Myhre et al., 2013b).
Aerosol?cloud interactions include the effects of aerosols
on the albedo and the lifetime of clouds (Boucher et al.,
2013). Overall, aerosol?cloud interactions are thought to
have a cooling effect (Myhre et al., 2013b).
Due to the difficulty of characterizing the concentrations
and properties of aerosols and the complexities involved in
the processes that determine their effects on the climate,
aerosols still are a major source of uncertainty in our un-
derstanding of climate change. It is generally believed that
increases in anthropogenic aerosols since the preindustrial
era have slowed down global warming, but it is highly un-
certain by how much (Myhre et al., 2013b). Moreover, the
cooling effect of increases in sulfate and other weakly ab-
sorbing aerosols, such as organic aerosols and nitrate, has
been largely compensated by a substantial warming effect
due to increases in black carbon (Bond et al., 2013).
Aerosols and chemically reactive gases are coupled in var-
ious ways. Many aerosol components are produced from
gaseous precursors by chemical reactions and nucleation or
condensation processes in the atmosphere. Sulfate (SO4), ni-
trate (NO3) and ammonium (NH4) result from emissions of
sulfur dioxide (SO2), dimethyl sulfide (DMS), NOx, and am-
monia (NH3), and secondary organic aerosols (SOA) from
emissions of VOCs. Nitrate, ammonium and many organic
aerosol components are semi-volatile and exist in equilib-
rium with their gas-phase counterparts. Moreover, aerosols
have an influence on photolysis rates by scattering and ab-
sorption of ultraviolet light and by their impacts on clouds.
They also provide particle surfaces on which heterogeneous
chemical reactions can take place.
Deposition of chemically reactive gases and aerosols from
the atmosphere is a source of nutrients to the terrestrial and
marine biosphere. The biogeochemical cycles of nitrogen
and carbon are tightly coupled, and it is likely that the avail-
ability of reactive nitrogen will be a limiting factor for the
land carbon sink in the twenty-first century (Ciais et al.,
2013).
Despite the important role aerosols and chemically re-
active gases play in the climate system, the description of
atmospheric chemistry and aerosols varies strongly among
climate models. Most global models that participated in
the recent Coupled Model Intercomparison Project Phase 5
(CMIP5) (Taylor et al., 2012) did not include atmospheric
chemistry and many did not have fully interactive aerosols
(Flato et al., 2013). Even so, atmospheric or atmosphere?
ocean general circulation models (GCMs) are increasingly
being transformed into chemistry-climate models (CCMs)
with interactive representations of chemistry and aerosols
(e.g. Zhang, 2008; Dameris and J?ckel, 2013; Lamarque et
al., 2012).
The work presented in this paper is the first step in the de-
velopment of an interactive chemistry module in the global
climate model EC-Earth (Hazeleger et al., 2010, 2012).
EC-Earth is a relatively new climate model that has been
developed in recent years by a consortium of partner in-
stitutes from currently ten European countries, consisting
of the national meteorological services of Denmark, Ire-
land, the Netherlands, Portugal, Spain and Sweden, uni-
versities, high-performance computing centres, and other
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2437
research institutes. The atmospheric GCM of EC-Earth is
based on the Integrated Forecasting System (IFS) model
from the European Centre for Medium-Range Weather Fore-
casts (ECMWF). EC-Earth is used for seasonal to decadal
predictions as well as for long-term climate simulations (see
Hazeleger et al., 2010).
The chemistry module of EC-Earth is based on the chem-
istry and transport model TM5 (Krol et al., 2005; Huijnen
et al., 2010; Aan de Brugh et al., 2011). We have integrated
TM5 into EC-Earth by coupling it online to IFS. The model
allows for two-way exchange of fields between TM5 and IFS,
but in this paper we focus on the impact of the online integra-
tion on the performance of TM5, without feedbacks to IFS.
To this end, we have carried out a decadal simulation of
tropospheric chemistry and aerosols for present-day condi-
tions, and calculated seasonal climatologies of concentration
fields and chemical budgets for various tracers. For compari-
son, we have repeated this simulation with the stand-alone
version of TM5 driven by meteorological fields from the
ECMWF ERA-Interim reanalysis (Dee et al., 2011). We have
evaluated the results from both simulations against a number
of observational data sets.
In Sect. 2 we briefly introduce the EC-Earth model and
describe the most important aspects of TM5 and the data ex-
change between TM5 and IFS. In Sect. 3 we describe the
set-up of the online and offline simulations. An evaluation of
the results is presented in Sect. 4. We end with a discussion
and conclusions in Sect. 5.
2 Model description
2.1 EC-Earth version 2.3
The atmosphere?ocean GCM applied in this study is EC-
Earth version 2.3. It consists of an atmospheric GCM based
on the IFS model cycle 31r1 with the H-TESSEL land-
surface scheme, and an ocean GCM from the Nucleus for Eu-
ropean Modelling of the Ocean (NEMO) version 2 with the
Louvain-la-Neuve sea ice model (LIM) version 2. The ex-
change of two-dimensional fields between IFS?H-TESSEL
and NEMO?LIM takes place through the OASIS3 coupler
(Ocean Atmosphere Sea Ice Soil version 3). A description
of these components and the coupling interface is given by
Hazeleger et al. (2010, 2012).
A number of improvements in physical parameterizations
have been included from more recent cycles of IFS (see
Hazeleger et al., 2012). In particular, the convection scheme
has been updated to the formulation of cycle 32r3. A detailed
description of the changes that are involved in this update is
given by Bechtold et al. (2008). It has been shown that the
new convection scheme produces higher and more realistic
levels of convective activity over land, and leads to improve-
ments in tropical precipitation patterns and extratropical
circulation characteristics (Bechtold et al., 2008; Jung et al.,
2010).
EC-Earth version 2.3 has been used for CMIP5. Com-
pared to the version described by Hazeleger et al. (2012),
the aerosol forcings have been improved and made consis-
tent with the CMIP5 recommendations (Taylor et al., 2012).
In this study, we applied the same configuration as for the
CMIP5 long-term simulations, using the T159 spectral res-
olution (corresponding to 1.125?) with 62 vertical levels for
the atmosphere and the ORCA1 grid (about 1? horizontal res-
olution and 42 layers) for the ocean.
2.2 TM5
We have extended the atmosphere?ocean GCM version of
EC-Earth with a module for simulating atmospheric chem-
istry and transport, the Tracer Model 5 (TM5). The new
model configuration with TM5 has been released as part
of EC-Earth version 2.4. TM5 can be used for non-reactive
greenhouse gases like CO2 (Peters et al., 2010) and sul-
fur hexafluoride (SF6) (Peters et al., 2004), for diagnos-
tic radioactive tracers like radon-222 (222Rn) and lead-210
(210Pb), as well as for chemically reactive gases and aerosols.
The version used in this study is based on the tropospheric
chemistry version documented by Huijnen et al. (2010),
extended with the aerosol microphysics and optics mod-
ules described by Aan de Brugh et al. (2011) and Aan de
Brugh (2013). In this section we will give an overview of the
main characteristics of this TM5 model version and briefly
describe the most important modifications and improvements
compared to these earlier publications.
2.2.1 Resolution
The global atmospheric domain of TM5 is discretized on a
regular latitude?longitude grid. To limit the computational
costs, this grid will typically have a coarser resolution than
the grid used in IFS. In this study a horizontal resolution
of 3??2? (longitude?latitude) is used. Zoom regions with
higher horizontal resolutions can be defined and nested into
the global domain (Krol et al., 2005), but this option is not
used in EC-Earth. To avoid the use of very short time steps
near the poles, the number of grid cells in the zonal direc-
tion is gradually reduced in the polar regions. Dry deposi-
tion velocities and surface emission fluxes that depend on lo-
cal meteorological conditions and/or other surface variables
are calculated on a higher-resolution surface grid and subse-
quently coarsened to the atmospheric grid. The resolution of
the surface grid is currently 1??1?.
In the vertical direction TM5 uses the same hybrid sigma-
pressure levels as used in the IFS model version to which it
is coupled, or a subset thereof. In the EC-Earth configuration
applied in this study a selection of 31 levels is made out of the
62 levels used in IFS. Because of the relatively poor vertical
resolution of the 62-level version of IFS in the upper part of
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2438 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
the domain (e.g. compared to the 60- or 91-level versions),
no merging of levels is applied above ?100 hPa. The top of
the model is at 5 hPa.
2.2.2 Data exchange and transformations
As for the exchange between IFS and NEMO, the data ex-
change between IFS and TM5 takes place through OASIS3
(Valcke, 2013). To prevent the different components having
to wait for each other, IFS runs one exchange time interval
ahead of the other modules. In the current configuration, the
interval for the data exchange between TM5 and IFS is set
to 6 h. A more frequent exchange will be applied in future
versions of the model.
Since OASIS3 can only deal with two-dimensional (2-D)
fields, three-dimensional (3-D) fields are transferred layer by
layer. The layers that are transferred from IFS to TM5 and
vice versa correspond to the full vertical resolution of IFS.
The required merging of the layers and interpolation of the
data in the vertical direction is performed on the TM5 side.
TM5 receives both meteorological data and surface prop-
erty fields from IFS. The data sets employed in the chemistry
version of TM5 used in this study are listed in Table 1. They
include instantaneous, time-averaged, and constant fields.
Most fields are interpolated by OASIS to TM5?s regu-
lar latitude?longitude atmospheric or higher-resolution sur-
face grid. However, to avoid unnecessary interpolations of
the wind fields, the wind divergence and vorticity fields and
the concurrent surface pressure field are received in their na-
tive spectral representation and transformed into gridded air
mass fluxes following the procedure of Segers et al. (2002).
Here the vertical mass fluxes are calculated directly from the
spectral fields, and the local mass balance over the exchange
interval is closed by slightly adjusting the horizontal mass
fluxes. This method has been shown to lead to superior chem-
istry simulations compared to methods that make use of in-
terpolated wind fields (Bregman et al., 2003).
Most instantaneous fields transferred from IFS to TM5,
including the spectral fields mentioned above, are valid for
the middle of the exchange interval. However, for closing the
mass balance the surface pressure is also required at the be-
ginning and at the end of the interval. This is achieved by
reading the initial surface pressure field from the TM5 restart
file, and including an additional (gridded) surface pressure
field valid for the end of the interval in the transfer.
The system has also been prepared for data transfer in the
other direction. The fields that can currently be transferred
from TM5 to IFS are the ozone and methane concentra-
tions, the particle number and component-specific mass con-
centrations in the different aerosol modes (see below), and
aerosol optical property fields (extinction, single-scattering
albedo and asymmetry factor) at the wavelengths used in
the IFS shortwave radiation scheme. Because IFS runs ahead
of TM5, these forcing fields are applied with some delay in
IFS. To minimize this delay, they are treated as instantaneous
fields calculated at the end of the exchange interval. This re-
duces the delay to half an exchange time step on average.
In this paper we first evaluate the one-way coupled simula-
tion of chemistry and aerosols. In a forthcoming publication
two-way coupling will be applied, including feedbacks of the
TM5 forcing fields to the radiation and cloud scheme of IFS.
2.2.3 Transport
Tracers in TM5 are moved around by advection, cumulus
convection, and vertical diffusion. Tracer advection is de-
scribed using either the first-order moments (slopes) algo-
rithm developed by Russell and Lerner (1981) or the second-
order moments scheme by Prather (1986). Both schemes are
conserving the mass of the advected tracers. This is an im-
portant requirement especially for chemistry-climate simula-
tions, where the tracer concentrations are not constrained by
assimilation. The default option is to use the slopes scheme,
which is used in the simulations presented in this study.
Convective tracer transport in TM5 is described using a
bulk mass flux approach, in which clouds are represented by
a single pair of entraining and detraining plumes describing
the updraft and downdraft motions. The meteorological fields
involved in the calculation are the vertical air mass fluxes
and the entrainment and detrainment rates in the updrafts
and downdrafts. In EC-Earth the mass fluxes and detrain-
ment rates are taken from IFS. The corresponding entrain-
ment rates follow from mass conservation. Thus, the descrip-
tion of convective tracer transport in TM5 is fully consistent
with the representation of convection in IFS.
Vertical diffusion of tracers in TM5 is described with a
first-order closure scheme, where the diffusion coefficient is
assumed to be the same as for heat (Olivi? et al., 2004a).
In the free troposphere it is computed based on wind shear
and static stability following Louis (1979). In the bound-
ary layer it is based on the revised Louis?Tiedtke?Geleyn
(LTG) scheme of Holtslag and Boville (1993). The bound-
ary layer height is calculated following Vogelezang and Holt-
slag (1996). Details are given in Olivi? et al. (2004a).
2.2.4 Chemistry
The TM5 version applied in this study is designed to sim-
ulate the concentrations of reactive gases and aerosols in
the troposphere and their deposition to the Earth?s surface.
The model?s gas-phase, aqueous-phase, and heterogeneous
chemistry schemes are described by Huijnen et al. (2010).
Details on the aqueous-phase chemistry can be found in
Roelofs (1992) and Feichter et al. (1996).
The gas-phase reaction scheme, representing the oxidation
of CO, CH4, and non-methane volatile organic compounds
(NMVOCs) in the presence of NOx, is based on the Carbon
Bond Mechanism 4 (CBM4). It utilizes a structural-lumping
technique in which organic species are grouped into one or
more surrogate categories according to the carbon bond types
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T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2439
Table 1. Meteorological data and surface property fields transferred from IFS to TM5.
Field Type
Three-dimensional
Wind divergence/vorticity Instantaneous
Temperature Instantaneous
Specific humidity Instantaneous
Cloud liquid/ice water content Instantaneous
Cloud fraction Instantaneous
Overhead/underfoot cloud fraction Instantaneous
Updraught/downdraught convective air mass flux Average
Updraught/downdraught convective air mass detrainment rate Average
Two-dimensional
Surface pressure Instantaneous
10 m wind (west?east/south?north components) Instantaneous
2 m temperature Instantaneous
2 m dew point temperature Instantaneous
Surface east?west/north?south momentum stress Average
Surface sensible/latent heat flux Average
Surface solar radiation Average
Stratiform precipitation as rain Average
Convective precipitation as rain Average
Skin reservoir water content Instantaneous
Snow depth Instantaneous
Soil wetness in top soil layer Instantaneous
Low/high vegetation cover fractions Instantaneous
Vegetation type fractions Instantaneous
Surface roughness Instantaneous
Surface orography Constant
Land/sea fraction Constant
Sea-ice fraction Instantaneous
present in the molecule. CBM4 was originally developed for
simulating urban and regional photochemistry (Gery et al.,
1989), and was later extended to the global scale by including
reactions important under background conditions (Houwel-
ing et al., 1998). Since then, reaction rates and product dis-
tributions have been updated (see Huijnen et al., 2010). In
addition to CBM4, the gas-phase chemistry scheme in TM5
also includes reactions for the oxidation of SO2, DMS, and
NH3.
Photolysis rates are calculated based on the parameteriza-
tion of Landgraf and Crutzen (1998), using seven wavelength
bands between 202.0 and 752.5 nm. Variations due to the ef-
fects of clouds, overhead ozone, and surface albedo are in-
cluded following Krol and van Weele (1997).
Aqueous-phase chemistry in clouds is included for the ox-
idation of total dissolved sulfur dioxide, S(IV), by dissolved
hydrogen peroxide (H2O2) and O3, depending on the acid-
ity of the droplets. The sulfate production rates due to the
oxidation of S(IV) by H2O2 and O3 are calculated follow-
ing Martin and Damschen (1981), with a temperature depen-
dence from Nair and Peters (1989), and Maahs (1983), re-
spectively. The representation of heterogeneous chemistry is
currently limited to the reactive uptake of dinitrogen pentox-
ide (N2O5) at the surface of cloud droplets, cirrus particles,
and aerosols, which produces nitric acid (HNO3) (Dentener
and Crutzen, 1993).
2.2.5 Aerosols
Aerosols are represented in the model as described by Aan
de Brugh et al. (2011). Sulfate, black carbon (BC), organic
carbon (OC), sea salt and mineral dust are described with
the size-resolved modal microphysics scheme M7 (Vignati
et al., 2004). It uses seven log-normal size distributions or
modes with predefined geometric standard deviations. There
are four water-soluble modes (nucleation, Aitken, accumula-
tion, and coarse) and three insoluble modes (Aitken, accumu-
lation and coarse). The nucleation mode contains only SO4
particles with dry diameters smaller than 10 nm. The Aitken,
accumulation, and coarse modes represent particles with dry
diameters in the range 10?100 nm, 100 nm?1 ?m, and larger
than 1 ?m, respectively. The insoluble Aitken mode consists
of internally mixed particles of BC and OC, while the larger
insoluble modes contain only dust particles. The soluble
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2440 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
Aitken mode represents internal mixtures of sulfate, BC, and
OC, while the larger soluble modes also contain sea salt and
dust in the mixture. Each mode is characterized by the total
particle number and the mass of each component. With this
the total number of aerosol tracers in M7 amounts to 25. The
microphysical processes included in M7 are the formation of
new SO4 particles by nucleation from gaseous sulfuric acid
(H2SO4), condensation of H2SO4 onto existing particles, wa-
ter uptake, and intramodal and intermodal coagulation.
Of organic aerosols (OA), also known as particulate or-
ganic matter (POM), only the carbon component is included
in M7. To account for the other components that may be
present, in TM5 the dry mass of organic aerosols is assumed
to be 40 % higher than the OC mass (e.g. Dentener et al.,
2006; Kinne et al., 2006).
The current chemistry scheme does not describe the for-
mation of secondary organic aerosols. An additional source
of organic aerosols is therefore included near the sur-
face over land, representing SOA formation from biogenic
NMVOCs (mainly mono-terpenes) on timescales of a few
hours. The total mass of SOA being formed is prescribed us-
ing monthly fields from Dentener et al. (2006), which amount
to 19.1 Tg POM year?1. The freshly formed SOA particles
are assumed soluble and are added to the soluble Aitken
mode, as in Aan de Brugh et al. (2011).
The other aerosol components included in the model
are nitrate, ammonium, and methane sulfonic acid (MSA),
which is produced in the oxidation of DMS. These are repre-
sented by their total mass, i.e. using a bulk aerosol approach.
The gas?aerosol partitioning of the semi-volatile inorganic
species (i.e. the ratios between HNO3 and nitrate aerosol
and between NH3 and NH4) is described with the thermo-
dynamic equilibrium model EQSAM (Metzger et al., 2002).
The optical properties of the aerosol mixtures are calcu-
lated as a function of wavelength based on Mie theory, using
a look-up table (Aan de Brugh et al., 2011; Aan de Brugh,
2013). The optical effects of nitrate aerosol are included by
assuming that the ammonium nitrate and the water absorbed
by it are present in the soluble accumulation mode (Aan de
Brugh et al., 2011). Effective-medium approximations are
applied to calculate the refractive indices of the internally
mixed modes. Sulfate, nitrate, OC, sea salt, and water are
treated as homogeneous mixtures described by the Brugge-
man mixing rule. When BC and/or dust are present in the
mix, these are treated as inclusions in a homogeneous back-
ground medium, using the Maxwell?Garnett mixing rule. A
more detailed description of the optics module of TM5 is
given by Aan de Brugh (2013).
2.2.6 Radioactive tracers
In addition to reactive gases and aerosols, the model also in-
cludes the diagnostic radioactive tracers radon-222 and lead-
210. Radon-222 is chemically inert and insoluble in water.
It is emitted at a relatively uniform rate from the continental
crust and decays with a half-life of 3.8 days into lead-210.
Because of its short lifetime, radon-222 can be used to study
rapid vertical exchange from the continental boundary layer
to the free troposphere and further transport to more remote
parts of the atmosphere (see reviews in Zahorowski et al.,
2004; Williams et al., 2011). Lead-210 has a much longer
half-life (22.3 years). After being formed, it rapidly attaches
to submicron aerosol particles. As a consequence, lead-210
is mainly removed from the atmosphere by wet deposition
and can be used to diagnose the wet scavenging processes in
the model.
2.2.7 Dry and wet deposition
Dry deposition of gases and aerosols to the Earth?s surface
in TM5 is described using a standard resistance approach
(e.g. Seinfeld and Pandis, 2006). Dry deposition velocities
of gaseous species are calculated as the inverse of the sum
of an aerodynamic resistance, a quasi-laminar sublayer resis-
tance and a surface resistance. The surface resistance is cal-
culated following the method of Wesely (1989), which dis-
tinguishes between deposition to vegetation, bare soils, wa-
ter or wet surfaces, and snow or ice, and combines the vari-
ous deposition pathways into a total surface resistance. The
gas-phase deposition scheme is described in more detail by
Ganzeveld and Lelieveld (1995) and Ganzeveld et al. (1998).
An overview is given in Huijnen et al. (2010).
Dry deposition velocities of aerosols are determined by
the aerodynamic resistance and a quasi-laminar sublayer re-
sistance, enhanced by sedimentation of particles by gravi-
tational settling. The quasi-laminar sublayer resistance de-
pends on particle size and is calculated for land and sea sur-
faces following Slinn (1976) and Slinn and Slinn (1980),
respectively. The implementation for the M7 modes is de-
scribed by Aan de Brugh et al. (2011).
The wet deposition scheme in TM5 describes the removal
of gases and aerosols from the atmosphere by raining clouds,
and distinguishes between convective and large-scale strati-
form precipitation. Scavenging by precipitation formation in
convective clouds is included in the convective mass trans-
port operator as part of the mass fluxes entrained in the cu-
mulus updrafts (Balkansi et al., 1993; Guelle et al., 1998).
Aerosols and irreversibly soluble gases are assumed to be
completely scavenged in vigorous convective updrafts, while
the removal efficiencies of other gases are reduced depend-
ing on their solubility. Resolution dependencies are reduced
by scaling down the convective scavenging rates for all trac-
ers, depending on the grid-cell mean convective precipitation
at the surface (see Vignati et al., 2010a).
For stratiform precipitation both in-cloud and below-cloud
scavenging of gases and aerosols are considered as de-
scribed by Roelofs and Lelieveld (1995) and Jeuken et
al. (2001). Scavenging by precipitation formation inside
stratiform clouds is assumed to be five times less effective
for ice particles than for liquid droplets. In-cloud scavenging
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T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2441
of aerosols is included only for the soluble accumulation and
coarse modes, and for bulk aerosols like ammonium, nitrate,
MSA, and lead-210. The in-cloud scavenging coefficients for
the soluble accumulation and coarse modes are assumed to
be equal to the values for irreversibly soluble gases, while
for bulk aerosols they are reduced by 30 % to account for
the presence of interstitial aerosols (Vignati et al., 2010a).
Below-cloud scavenging of aerosols is modelled using es-
timates of bulk washout coefficients for the various modes
based on Dana and Hales (1976). Resolution dependencies
in wet removal by stratiform precipitation are reduced by the
introduction of a mixing timescale, which delays the subgrid-
scale mixing between cloudy and cloud-free regions (see
Vignati et al., 2010a).
2.2.8 Boundary conditions
A detailed description of stratospheric chemistry is not in-
cluded in the model. To simulate stratospheric ozone chem-
istry a parameterized linear chemistry scheme can be used
(Cariolle and Teyss?dre, 2007; McLinden et al., 2000; Van
Noije et al., 2004, 2006). Alternatively, the O3 concentrations
in the stratosphere can simply be relaxed towards observa-
tional values, as described in Huijnen et al. (2010). In the
current relaxation scheme, total O3 column estimates from
a multi-sensor reanalysis (van der A et al., 2010) are com-
bined with a climatological data set of vertical profiles con-
structed from sonde and satellite observations (Fortuin and
Kelder, 1998). A similar relaxation procedure is applied to
the CH4 concentrations in the stratosphere, while HNO3 is
constrained by prescribing the concentration ratio of HNO3
over O3 at 10 hPa. These stratospheric boundary conditions
are primarily based on satellite data (see Huijnen et al.,
2010). This is adequate for the present-day decadal simula-
tions presented in this study, but additional data sets based on
output from stratospheric chemistry models are needed for
representing the longer-term trends and variability in simu-
lations that start in the pre-satellite era or continue into the
future.
Because of the relatively long lifetime of CH4, an ad-
ditional constraint can be imposed on the CH4 concentra-
tions at the surface. This is common practice in chemistry
models in which the CH4 lifetime, which is mainly deter-
mined by the amount and distribution of the hydroxyl radical
(OH) in the troposphere, is not prescribed or tuned. It pre-
vents drifts and/or biases in the global CH4 concentration,
which would otherwise result from inconsistencies between
the CH4 sources and sinks. This constraint can be imposed
by relaxing the zonal mean surface concentrations of CH4 to
values consistent with observations, while at the same time
including the location dependent emissions of CH4. Alter-
natively, the CH4 concentrations at the surface can be pre-
scribed using zonal and monthly mean fields based on ob-
served values. In both cases, future concentration scenarios
may be imposed by scaling the target concentration fields
based on the projected evolution of the global mean concen-
tration.
2.2.9 Emissions
Emissions from anthropogenic activities and biomass burn-
ing are taken from the data set provided for the Atmospheric
Chemistry and Climate Model Intercomparison Project (AC-
CMIP), which was also used in CMIP5. The historical part of
this data set covers the period 1850?2000 (Lamarque et al.,
2010). The estimates for the year 2000 are based on a combi-
nation of regional and global inventories for the various sec-
tors. The reconstruction for earlier decades is forced to agree
with these estimates. For the 21st century emission projec-
tions from the representative concentration pathways (RCPs)
are used (Van Vuuren et al., 2011). The RCP emissions start
from the historical inventory in 2000. The RCP emissions
are provided as monthly emissions for the years 2000, 2005,
2010, 2020, etc. A linear interpolation of the seasonal cycle
is applied to obtain the emissions in the intermediate years.
Oceanic emissions of DMS and NOx production by light-
ning are calculated online as in Huijnen et al. (2010). Terres-
trial DMS emissions from soils and vegetation are prescribed
following Spiro et al. (1992). Sea salt emissions are calcu-
lated online as in Vignati et al. (2010b), based on the param-
eterization by Gong (2003). The emission rate is assumed to
depend on the 10 m wind speed as a power law with expo-
nent 3.41 (Monahan and Muircheartaigh, 1980). Emissions
of mineral dust can either be calculated online based on the
parameterization by Tegen et al. (2002) or be prescribed us-
ing the monthly data set for the year 2000 from the AeroCom
project, described by Dentener et al. (2006).
Natural emissions of CO, NMVOCs, NOx, NH3, and SO2
are prescribed using a monthly varying data set compiled
for the Monitoring Atmospheric Composition and Climate
(MACC) project. It includes (1) biogenic emissions of iso-
prene and a number of other NMVOC species as well as CO
from vegetation based on the Model of Emissions of Gases
and Aerosols from Nature (MEGAN) version 2.1 (Guenther
et al., 2012) for the year 2000; (2) biogenic emissions of NOx
from soils based on Yienger and Levy (1995); (3) oceanic
emissions of CO and NMVOCs from Olivier et al. (2003);
(4) biogenic emissions of NH3 from soils under natural veg-
etation and oceanic emissions of NH3 from Bouwman et
al. (1997); and (5) SO2 fluxes from continuously emitting
volcanoes from Andres and Kasgnoc (1998). The emissions
of radon-222 are prescribed as in Dentener et al. (1999). Fol-
lowing the recommendations of Rasch et al. (2000), the emis-
sion flux density is set at 1.0 atoms cm?2 s?1 for all land ar-
eas between 60?S and 60?N and to 0.5 atoms cm?2 s?1 for
land areas between 60 and 70?N, except in Greenland. Emis-
sions in Greenland and other parts of the world are assumed
to be zero.
As in Huijnen et al. (2010), a diurnal cycle is applied to
the isoprene emissions from vegetation on top of the monthly
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2442 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
estimates provided in the data set. To account for SO4 forma-
tion in sub-grid plumes, 2.5 % of the sulfur in the SO2 emis-
sions provided for the various sources is assumed to be emit-
ted in the form of SO4 (Huijnen et al., 2010; Aan de Brugh
et al., 2011). The size distributions assumed for the differ-
ent particulate emission sources are listed in Aan de Brugh
et al. (2011).
The implementation of emission heights has been revised
compared to the description in Huijnen et al. (2010), based
on estimates from several studies (Dentener et al., 2006; De
Meij et al., 2006; Bieser et al., 2011; Simpson et al., 2012).
The vertical distributions applied to the different emission
sources are given in Table A1.
3 Simulations
In this study we present results from decadal simulations for
the years 2000?2009, using 1999 as a spin-up year for the
chemistry. The various simulations that were carried out are
listed in Table 2.
In the atmosphere?ocean GCM simulation of EC-Earth,
the historical part extends up to 2005 and is continued by
a simulation based on scenario assumptions, in accordance
with the CMIP5 experimental design for long-term climate
simulations. As a future scenario, we adopt the RCP4.5
(Thomson et al., 2011), one of the stabilization scenarios
of the representative concentration pathways. Please note
that for the period considered the simulated climate will
not be sensitive to the chosen scenario. The atmosphere?
ocean GCM was initialized on 1 January 1999 from one of
the CMIP5 20th century simulations performed by the EC-
Earth consortium. To be precise, the first ensemble member
(SHC1) provided by the Swedish Meteorological and Hydro-
logical Institute (SMHI) was used.
In TM5 the stratospheric O3 concentrations were relaxed
as described in Sect. 2.2.8, using total column estimates for
the years 2000?2009. Also, the surface CH4 concentrations
were prescribed according to observations for those years.
Emissions of CH4 were therefore not applied in these simu-
lations.
Simulations were carried out both with and without yearly
changes in the emissions from anthropogenic activities and
biomass burning in TM5. In the reference EC-Earth simu-
lation these emissions were fixed to their 2000 values. This
reference simulation is compared with a corresponding TM5
simulation driven by meteorological data from the ECMWF
reanalysis ERA-Interim (Dee et al., 2011). Both simula-
tions are also evaluated against observational data. To esti-
mate the impact of possible trends in the emissions, an ad-
ditional ERA-Interim simulation is used with anthropogenic
and biomass burning emissions varying between 2000 and
2009. In this simulation the anthropogenic and biomass burn-
ing emissions were prescribed according to the RCP4.5 sce-
nario, consistent with the set-up of the atmosphere?ocean
GCM. Please note, however, that during the RCP develop-
ment process a harmonization procedure has been applied
to ensure that the emissions in the four different RCPs are
still nearly identical in 2005. As a consequence the choice
of the RCP will only have some albeit small effect on the
chemistry during the second half of the simulation. Results
from the ERA-Interim simulation with varying emissions
have also been provided to the second phase of the AeroCom
project. Aerosol concentrations and optical property fields
from that simulation have been evaluated within that project
(see http://aerocom.met.no). In all simulations, the emissions
of mineral dust were prescribed using the AeroCom data set
for the year 2000 (see Sect. 2.2.9).
The ERA-Interim input fields for TM5 have been created
from the original ECMWF data during a pre-processing stage
(see Krol et al., 2005). In this process the required meteoro-
logical and surface property fields are retrieved at a spec-
tral resolution of T255 (corresponding to about 0.7?) and
converted into TM5 input fields at a 1??1? horizontal res-
olution, keeping the full 60-level vertical resolution of the
original data. The ERA-Interim simulation was carried out at
the same 3??2? horizontal resolution as used in EC-Earth.
However, because of the different vertical resolutions of the
ERA-Interim data set and the CMIP5 EC-Earth simulations,
the vertical grid is different. The ERA-Interim simulation
was carried out using the same selection of 34 levels out of
the original 60 levels of ERA-Interim as used in Huijnen et
al. (2010). The treatment of the meteorological fields in the
temporal dimension is also slightly different. In the ERA-
Interim simulation most meteorological fields are updated at
a 3-hourly frequency (see Huijnen et al., 2010) and a linear
interpolation is applied to the instantaneous fields.
Another difference relates to the representation of the
tracer transport by cumulus convection. Historically, the re-
quired convective air mass fluxes and entrainment and de-
trainment rates were not archived in the meteorological data
sets used to drive TM5. In the stand-alone version of TM5
these fields are therefore calculated diagnostically. This is
done in a pre-processing step according to the parameter-
ization of Tiedtke (1989). This scheme was introduced in
ECMWF?s operational forecast model in 1989. As the more
recent schemes used in later IFS cycles, the original Tiedtke
scheme already distinguished between deep, shallow and
mid-level convection.
To estimate the impact of using diagnostically calculated
convective mass fluxes and entrainment and detrainment
rates, an additional decadal simulation was performed with
the stand-alone version of TM5, but now driven by meteo-
rological output from EC-Earth. The radon-222 concentra-
tions from this offline simulation are compared with the re-
sults from the reference EC-Earth simulation, in which the
data transfer from IFS to TM5 is done online through OA-
SIS. For the offline EC-Earth simulation the driving meteo-
rological fields for the years 1999?2005 were taken from the
CMIP5 set of long-term historical simulations, which also
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T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2443
Table 2. Overview of the decadal simulations used in this study.
Simulation Focus Emissionsfrom
anthropogenic
activitiesand
biomassburning
Numberof vertical
levelsinTM5/IFS
Highestupdate
frequency of mete-
orologicalfields
inTM5
Temporal
interpolationof
instantaneous
meteorological
fieldsinTM5
Convective fields
EC-Earth Chemistryand
aerosols,radon-222
Year2000 31/62 6 hourly No ReceivedfromIFS
ERA-Interim Chemistryand
aerosols
Year2000 34/60 3 hourly Linear Diagnosedoffline
ERA-Interim,
varyingemissions
Chemistryand
aerosols
2000?2009 34/60 3 hourly Linear Diagnosedoffline
OfflineEC-Earth Radon-222 ? 31/62 6 hourly Linear Diagnosedoffline
provided the start fields for the online EC-Earth simulation.
From this ensemble the first simulation provided by the Irish
National Meteorological Service (MEI1) was selected. For
the years 2006?2009 the corresponding member (ME41) of
the ensemble of RCP4.5 simulations was used. As for ERA-
Interim, a pre-processing step was required to convert the IFS
output into input fields for TM5. The online and offline EC-
Earth simulation were performed at the same horizontal and
vertical resolutions. Also, the meteorological fields are up-
dated with the same 6-hourly frequency. The remaining mi-
nor difference between the two simulations is that in the of-
fline simulation a linear temporal interpolation is applied to
the instantaneous meteorological fields, which is not possible
in the online simulation (see Table 2).
The inclusion of atmospheric chemistry and aerosols sub-
stantially increases the computational burden of the simu-
lations. In the configuration described above with 47 pro-
cessors used for IFS and 32 for NEMO, the atmosphere?
ocean version of EC-Earth simulates one year within less
than 2 h on the ECMWF IBM POWER7 high-performance
computer facility. Adding 45 processors for TM5 slows down
the model by almost a factor 9. We like to point out that the
performance of the online-coupled IFS-NEMO-TM5 system
is similar to that of the stand-alone version of TM5, when the
same number of processors is used for TM5.
4 Evaluation
In this section an evaluation is presented of some impor-
tant aspects of the atmospheric chemistry simulation with
EC-Earth. With this objective monthly, seasonal, and/or an-
nual mean 10 year climatologies from the reference EC-Earth
simulation are compared with the corresponding climatolo-
gies from the ERA-Interim and offline EC-Earth simulations
and/or with observational data sets. The variables of inter-
est for which this has been done include temperature, humid-
ity, the concentrations of various tracers, aerosol burdens and
optical depth, and some important chemical budget terms.
When comparing the climatologies from the different simu-
lations and observational data sets, the interannual variability
in the underlying data is used to calculate standard devia-
tions and to determine the statistical significance of the dif-
ferences. Regions where the differences are not statistically
significant at the 5 % level are indicated by the stippled areas
in the figures.
When evaluating the model with ground-based observa-
tion from a particular station, we linearly interpolate the grid-
cell values from the model to the location of the station. The
station height is used to estimate the corresponding pressure
level based on a standard atmospheric profile (US Standard
Atmosphere, 1976).
4.1 Physical climate
An evaluation of the physical climate of EC-Earth version 2.2
is presented by Hazeleger et al. (2012). In general the large-
scale structures of the atmosphere, ocean, and sea ice are well
simulated and the main patterns of interannual climate vari-
ability are well represented. The climate of EC-Earth version
2.3 has qualitatively similar characteristics. In particular, the
model has a cold bias in most of the troposphere through-
out the year. A warm bias is found over the Southern Ocean,
over the stratocumulus regions west of the continents in the
subtropics, and over parts of the Northern Hemisphere (NH)
extratropics, but only in the lower troposphere or near the sur-
face. The middle and upper troposphere as well as the tropics
and most of the Arctic are on average up to 3?4?C too cold
(see Fig. 1, top panels). Consequently, the specific humid-
ity is also biased low in most of the troposphere, in partic-
ular in the tropics (Fig. 1, bottom panels). According to the
Clausius?Clapeyron relation, the saturation vapour pressure
in the lower troposphere decreases by about 7 % per degree
temperature decrease (Held and Soden, 2006). Assuming that
the relative humidity is insensitive to the temperature bias, a
cold bias of 1?C would result in a local decrease in the spe-
cific humidity of about 7 %. Such a response of specific hu-
midity to the temperature bias combined with a subsequent
redistribution of the resulting local humidity bias due to hor-
izontal and vertical transport can explain a large part of the
observed bias in the humidity field in EC-Earth compared to
ERA-Interim.
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2444 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
EC-Earth - ERA-Interim, DJF
-5 -4 -3 -2 -1 0 1 2 3 45
Temperature Difference (
o
C)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, JJA
-5 -4 -3 -2 -1 0 1 2 3 45
Temperature Difference (
o
C)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
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100
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EC-Earth - ERA-Interim, DJF
-2
-1.5
-1
-0.5
-0.2
-0.1
0
0.1
0.2
0.5
1
1.5
2
Specific Humidity Difference (g/kg)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, JJA
-2
-1.5
-1
-0.5
-0.2
-0.1
0
0.1
0.2
0.5
1
1.5
2
Specific Humidity Difference (g/kg)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Figure 1. Zonal mean bias in temperature (top) and specific humidity (bottom) in EC-Earth compared to ERA-Interim for boreal win-
ter (December?February, left) and boreal summer (June?August, right) for the period 2000?2009. Regions where the differences are not
significant at the 5 % level are indicated by the stippled areas.
Various aspects of the temperature and humidity biases in
EC-Earth are also found in other climate models (see, e.g.
Tian et al., 2013; Flato et al., 2013; Lamarque et al., 2013a).
In particular, the CMIP5 model ensemble also produces a
cold bias in most of the troposphere and a similar warm and
wet bias over the Southern Ocean (Tian et al., 2013). The
CMIP5 multi-model mean bias in annual mean temperature
maximizes at about 2?C in the extratropical upper tropo-
sphere. This is somewhat lower than the cold bias produced
by EC-Earth in this region, which goes up to about 3?C in
the NH. Moreover, EC-Earth produces a stronger cold bias in
the tropical troposphere than the CMIP5 model ensemble. As
a consequence, the wet bias in the tropical lower troposphere
is substantially stronger in EC-Earth. Finally, we note that
the spatial distribution of surface temperature biases is quali-
tatively very similar for EC-Earth and the CMIP5 ensemble,
at least in the annual mean (Flato et al., 2013).
4.2 Radon-222
Simulated radon-222 concentrations provide information
about transport in the different model configurations, in par-
ticular transport from the continental boundary layer to the
free troposphere and more remote regions. Differences be-
tween the reference, online EC-Earth simulation, and the
ERA-Interim simulation are caused by a combination of
factors: most significantly, (1) biases in the EC-Earth cli-
mate, (2) the different treatment of cumulus convection in
EC-Earth, and (3) the 6-hourly update of the meteorologi-
cal fields in EC-Earth (see Table 2). The effect of treating
convection differently is evident in the comparison between
radon concentrations from the online and offline EC-Earth
simulations (see Sect. 3).
Figure 2 shows winter and summer zonal mean radon
concentrations from the online EC-Earth simulation. Radon
concentrations are highest over the continents, where the
emissions take place. The highest zonal mean concentrations
overall are simulated in the NH lower troposphere during bo-
real winter, when the boundary layer is most stable. For both
hemispheres the concentrations in the upper troposphere are
highest in summer, when the convective activity is strongest.
Compared to both offline simulations, the online EC-Earth
simulation produces higher radon concentrations in large
parts of the upper troposphere, extending from the tropics to
mid-latitudes, and, during boreal winter, in the middle tropo-
sphere (above about 600 hPa) at northern mid-latitudes. The
online simulation generally gives lower concentrations near
the surface and in the lower troposphere, especially at north-
ern mid-latitudes during winter. These features are due to the
different treatment of convection and are in line with the fact
that the updated convection scheme produces more intense
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T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2445
EC-Earth, DJF
0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1 1.2 1.4 1.6
Rn-222 (Bq/m
3
 (STP))
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth, JJA
0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1 1.2 1.4 1.6
Rn-222 (Bq/m
3
 (STP))
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, DJF
-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
Rn-222 Difference (Bq/m
3
 (STP))
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, JJA
-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
Rn-222 Difference (Bq/m
3
 (STP))
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Offline EC-Earth - ERA-Interim, JJA
-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
Rn-222 Difference (Bq/m
3
 (STP))
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Offline EC-Earth - ERA-Interim, DJF
-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
Rn-222 Difference (Bq/m
3
 (STP))
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Figure 2. Zonal mean radon-222 concentrations in the online EC-Earth simulation (top) and the differences compared to the ERA-Interim
(middle) and offline EC-Earth (bottom) simulations for boreal winter (left) and boreal summer (right).
continental convection and stronger upper-level convective
detrainment (Bechtold et al., 2008).
Close to the equator both EC-Earth simulations produce
higher zonal mean concentrations than the ERA-Interim sim-
ulation in the lower troposphere (up to about 700?600 hPa)
and lower concentrations at higher altitudes up to about
200 hPa. The fact that this feature is common to both EC-
Earth simulations indicates that it is the result of biases in
the EC-Earth climate and/or the 6-hourly update frequency
of the meteorological fields.
The 6 h update frequency may not always be sufficient to
capture the development of convective boundary layers over
continental areas, which may lead to an overestimation of
radon concentrations near the surface (Krol et al., 2005). The
offline EC-Earth simulation indeed produces higher concen-
trations than the ERA-Interim simulation in parts of the lower
troposphere and at higher latitudes, especially during sum-
mer in the NH. However, the online EC-Earth simulations
give lower concentrations than the ERA-Interim simulation
in these regions due to the opposing effect of stronger verti-
cal transport resulting from the different treatment of convec-
tion. The update frequency will be reduced in future versions
of the model.
We finish this section with a comparison between monthly
radon concentrations from the simulations and ground-based
observations. We present results for six stations, including
two coastal sites (Cape Grim and Richmond, both in Aus-
tralia), three island sites (Mauna Loa; Gosan on Jeju Island,
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2446 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
South Korea; and Sado Island, Japan) and one continen-
tal site (Hohenpeissenberg, Germany). Data for Hohenpeis-
senberg have been taken from the World Data Centre for
Greenhouse Gases (WDCGG); data for the other stations
have been provided by the Australian Nuclear Science and
Technology Organisation (ANSTO). All selected stations
provide multi-annual data sets of hourly radon measurements
(see Table A2). We calculated monthly climatologies based
on the full period of available measurements, and compare
these with the 10 year climatologies from the simulations.
The resulting observed and simulated seasonal cycles at
the selected stations are shown in Fig. 3. Following earlier
studies in which ground-based radon observations were used
for evaluating global models (Dentener et al., 1999; Taguchi
et al., 2002; Zahorowski et al., 2004), we applied a diurnal
sampling window to the observations. The applied window
is site specific (see Table A2), and is chosen to minimize
contributions from small-scale effects not well resolved by
the models. This is most relevant for sites like Richmond,
where global models underestimate the trapping of radon in
the nocturnal boundary layer, and mountainous and island
sites like Mauna Loa, Gosan, and Sado, where models under-
estimate the influence of local emissions for various reasons
(Zahorowski et al., 2005; Chambers et al., 2013). Application
of a diurnal sampling window vastly improves comparisons
with the simulated results for these stations (see Fig. 3). The
resulting annual mean biases, root mean square errors, and
correlation coefficients between the observed and simulated
seasonal cycles are given in Table A2.
Many of the discrepancies between the modelled and ob-
served radon seasonal cycles in Fig. 3 have also been found
in other global modelling studies. Using the same radon
emission fluxes as applied in our simulations, Dentener et
al. (1999), Tagushi et al. (2002), and Zhang et al. (2011)
reported underestimates for Mauna Loa all year round, and
overestimates for Cape Grim during the austral summer.
Moreover, the simulations reported by Zhang et al. (2008)
also failed to reproduce the concentration increase measured
at Hohenpeissenberg during boreal summer. Discrepancies
may be partially related to inaccuracies in assumed emission
fluxes. For example, emissions in continental Asia have been
estimated to be higher than previously thought (Williams
et al., 2009). Except during the summer monsoon season,
higher Asian emissions estimates would substantially in-
crease the simulated concentrations at Gosan and Sado Is-
land (Chambers et al., 2009). Zhang et al. (2011) also ob-
tained better agreements at Mauna Loa and Cape Grim using
improved emissions estimates.
It is important to note that discrepancies between simu-
lated and observed radon concentrations are generally larger
than the differences amongst the three simulations. In order
to use radon observations to evaluate the differences in the
simulated transport, in particular the impacts of the different
treatment of convection in the online EC-Earth simulation,
it will be necessary to improve the set-up of the simulations
using more accurate estimates of the spatially heterogeneous
and seasonally varying radon emission fluxes (Zhang et al.,
2011).
4.3 Oxidizing capacity and methane lifetime
The oxidizing capacity of the atmosphere is determined by
the abundance and distribution of the hydroxyl radical (OH)
in the troposphere. OH is highly reactive and initiates most of
the photochemical reaction chains that oxidize reactive gases
in the atmosphere (Levy, 1971; Lelieveld et al., 2002, 2004).
The production of OH in the troposphere is mainly governed
by the photolysis of O3,
O3 +h? ? O(1D)+O2,
followed by the reaction of the excited oxygen atom, O(1D),
with a water molecule:
O(1D)+H2O ? 2H.
The second step is limited by the availability of water
molecules in the gas phase. On average only a few percent
of the O(1D) atoms produced in the first reaction step will
encounter a water molecule to react with and produce OH
(Lelieveld et al., 2002). As a consequence, OH production
rates are highest in the tropical lower and middle tropo-
sphere, due to the relatively high amounts of both sunlight
and water vapour in those regions.
The zonal mean OH concentrations from the reference
EC-Earth simulation for winter and summer are presented
in the top panels of Fig. 4. Compared to the monthly cli-
matology presented by Spivakovsky et al. (2000), the region
of high OH concentrations extends more towards the sur-
face. In other respects, the large-scale features of the spa-
tial distributions are very similar in all seasons. The peak
concentrations in the tropics and subtropics are substantially
lower in EC-Earth, especially in boreal winter and the transi-
tion seasons. However, based on simulations of methyl chlo-
roform, it has been concluded that the OH concentrations
from Spivakovsky et al. (2000) are likely too high. A better
correspondence with the observed decay of methyl chloro-
form concentrations was obtained by reducing the climatol-
ogy of Spivakovsky et al. (2000) by 8 % (see Huijnen et al.,
2010). Compared to the optimized climatology thus obtained
(shown in the middle panels of Fig. 4), the peak concentra-
tions from the EC-Earth simulation are quantitatively similar
in boreal summer, but at least 20 % lower in the other sea-
sons.
Compared to the ERA-Interim simulation, EC-Earth pro-
duces lower OH concentrations in large parts of the tropical
and subtropical troposphere. As shown in the bottom panels
of Fig. 4, the zonal mean concentrations are lower in a region
extending from close to the surface to about 200 hPa. The
lower OH concentrations in this region are mainly caused
by lower temperatures, resulting in lower specific humidi-
ties. Because there is less water vapour available to react with
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2447
Cape Grim Station, Tasmania
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0.0
0.5
1.0
1.5
2.0
0 . 00 . 51. 01. 52. 0
Rn-222 Concentration (Bq/m
3
)
Mauna Loa Station, Hawaii
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0.00
0.05
0.10
0.15
0.20
0.25
0 . 000 . 050 .1 00 .1 50 .2 00 .2 5
Rn-222 Concentration (Bq/m
3
)
Obse rv at i o n s,  win do w edObse rv at i o n sERA-In te rimE C -E a r t h ,  o fflin eE C -E a r t h ,  o nlin e
Observations, windowed
Observations
ERA-Interim
EC-Earth, offline
EC-Earth, online
Richmond Station, Australia
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
5
10
15
051 01 5
Rn-222 Concentration (Bq/m
3
)
Sado Island Station, Japan
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
1
2
3
4
01234
Rn-222 Concentration (Bq/m
3
)
Gosan Station, Jeju Island, Korea
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
1
2
3
4
01234
Rn-222 Concentration (Bq/m
3
)
Hohenpeissenberg Station, Germany
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
1
2
3
4
5
6
7
8
012345678
Rn-222 Concentration (Bq/m
3
)
Figure 3. Comparison of monthly mean surface radon-222 concentrations from the online and offline EC-Earth simulations (solid red and
orange lines, respectively) and the ERA-Interim simulation (solid blue lines) against surface measurements (black lines). The observational
means shown by the dashed lines only include the measurements made within a certain station dependent diurnal window (see Table A2).
Table 3. Contributions to the chemical production of OH in the
troposphere (Tg OH year?1) for the reference EC-Earth simula-
tion and the corresponding ERA-Interim simulation. The contribu-
tions from 90?30? S, 30? S?30? N, and 30?90? N are given by the
numbers between parentheses. Results have been obtained from a
monthly analysis with a fixed tropopause level, set to the uppermost
model layer for which the monthly mean O3 mixing ratio is below
150 ppbv. Standard deviations are based on the simulated interan-
nual variability.
Reaction EC-Earth ERA-Interim
O(1D) + H2O 1432 1554
(112/1120/199) (102/1243/208)
NO + HO2 994 1002
(81/649/264) (65/682/255)
O3+ HO2 393 393
(42/249/102) (37/263/93)
H2O2 +h? 197 230
(18/149/29) (20/175/34)
Other 168 176
(12/139/17) (13/144/18)
Total 3184?20 3355?30
(266/2307/611) (238/2508/608)
O(1D), the production of OH via the reaction path described
above is lower than in the ERA-Interim simulation (see Ta-
ble 3).
At higher latitudes, the OH concentrations from EC-Earth
are generally higher than in the ERA-Interim simulation. As
will be shown in Sect. 4.5, EC-Earth produces higher O3
concentrations in most of the lower and middle troposphere,
especially in the extratropics. This increases the O(1D) pro-
duction rates, especially in the summer hemisphere. In the
Southern Hemisphere (SH) extratropical lower troposphere
the difference in the OH concentrations between the EC-
Earth and ERA-Interim simulations is further enhanced by
the higher levels of humidity in EC-Earth associated with the
warm bias over the Southern Ocean.
In the tropical upper troposphere (above about 200 hPa),
the OH concentrations in EC-Earth are also higher than in
the ERA-Interim simulation. This is likely related to the fact
that some of the tracers involved in the other reactions that
produce OH (see Table 3) are more efficiently transported to
higher altitudes by deep convection (see Sect. 4.2).
Differences in the amount and distribution of the NOx
production by lightning also affect the distribution of OH.
The global production of NOx by lightning is significantly
higher in EC-Earth than in the ERA-Interim simulation
(see Table A3); the production in EC-Earth is 0.84 and
0.51 Tg N year?1 higher in the NH and SH extratropics, re-
spectively, while it is 0.74 Tg N year?1 lower in the tropics
(30? S?30? N).
The lower OH concentrations in the tropical and sub-
tropical lower and middle troposphere lead to a slower re-
moval of CH4 from the atmosphere. The average chemical
lifetime of CH4 against reaction with tropospheric OH is
10.9 years in the EC-Earth simulation and 10.1 years in the
ERA-Interim simulation. Both values are within the multi-
model ranges of 9.7?1.7, 10.2?1.7, and 9.7?1.5 years
estimated by Shindell et al. (2006b), Fiore et al. (2009),
and Naik et al. (2013), respectively, from simulations for
present-day conditions (2000 or 2001). Assuming a lifetime
of 120 and 160 years, respectively, for the chemical loss in
the stratosphere and the soil sink (Ehhalt et al., 2001), the at-
mospheric lifetime of CH4 is 9.4 years in the EC-Earth sim-
ulations. This is 7 % longer than the atmospheric lifetime of
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2448 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
EC-Earth, DJF
0 0.1 0.2 0.5 1 2 5 10 15 20 25
OH (10
5
 molec/cm
3
)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth, JJA
0 0.1 0.2 0.5 1 2 5 10 15 20 25
OH (10
5
 molec/cm
3
)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Spivakovsky, DJF
0 0.1 0.2 0.5 1 2 5 10 15 20 25
OH (10
5
 molec/cm
3
)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Spivakovsky, JJA
0 0.1 0.2 0.5 1 2 5 10 15 20 25
OH (10
5
 molec/cm
3
)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, DJF
-4 -3 -2 -1 -0.5 -0.2 0 0.2 0.5 1 2 34
OH Difference (10
5
 molec/cm
3
)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, JJA
-4 -3 -2 -1 -0.5 -0.2 0 0.2 0.5 1 2 34
OH Difference (10
5
 molec/cm
3
)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Figure 4. Zonal mean OH concentrations in the reference EC-Earth simulation (top), the climatology of Spivakovsky et al. (2000) reduced
by 8 % (middle), and the differences between the reference EC-Earth simulation and the corresponding ERA-Interim simulation (bottom) for
boreal winter (left) and boreal summer (right).
8.8 years obtained in the ERA-Interim simulation. Prather et
al. (2012) recently estimated from methyl chloroform obser-
vations that the present-day atmospheric lifetime of CH4 is
in the range 9.1?0.9 years. The values obtained in the EC-
Earth and ERA-Interim simulations are both well within this
range.
4.4 Carbon monoxide
Carbon monoxide is emitted into the atmosphere by an-
thropogenic and natural sources and is chemically produced
in the atmosphere by oxidation of CH4 and NMVOCs and
by photolysis of certain NMVOCs. The oxidation of CH4
and many other hydrocarbons proceeds via the formation of
formaldehyde (CH2O), which is subsequently converted to
CO by photolysis or oxidation, mostly by reaction with OH.
CO is removed from the atmosphere by reaction with OH
and by dry deposition at the surface. The various contribu-
tions to the atmospheric budget of CO in the EC-Earth and
ERA-Interim simulations are given in Table 4.
The average lifetime of CO in the atmosphere (total burden
divided by total loss) is 54.6 days in EC-Earth compared to
52.5 days with ERA-Interim. The slightly longer lifetime in
EC-Earth is a result of a slower chemical destruction due to
the lower OH concentrations in the tropical and subtropical
troposphere. In contrast, OH levels in EC-Earth are higher in
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2449
Table 4. Contributions to the budget of CO in the atmosphere (Tg CO year?1), together with the tropospheric and atmospheric burdens of CO
(Tg CO), and the atmospheric lifetime of CO (days) for the reference EC-Earth simulation and the corresponding ERA-Interim simulation.
The contributions from 90?30? S, 30? S?30? N, and 30?90? N are given by the numbers between parentheses. Results have been obtained
from a monthly analysis with a fixed tropopause level, set to the uppermost model layer for which the monthly mean O3 mixing ratio is
below 150 ppbv. Standard deviations are based on the simulated interannual variability.
EC-Earth ERA-Interim
Emissions 1166 1166
(23/762/381) (23/762/381)
Tropospheric chemical production 1105 1170
(81/838/186) (76/905/189)
Total gain? 2284 2351
(107/1606/572) (102/1674/574)
Dry deposition 173?1.0 180?1.5
(6/107/60) (6/105/69)
Tropospheric chemical destruction 2065 2129
(191/1447/427) (166/1541/422)
Total loss? 2284 2352
(208/1570/506) (184/1662/506)
Tropospheric burden 316?2.0 317?3.3
(53/179/84) (53/175/88)
Atmospheric burden 341?2.5 338?3.7
(61/185/95) (59/181/97)
Atmospheric lifetime 54.6 52.5
? The total gain and loss also include small contributions from, respectively, chemical production
and destruction in the stratosphere.
the extratropics, causing a more efficient removal of CO at
higher latitudes, especially in the SH.
Also the production of CO in the tropics and the subtropics
is lower in EC-Earth. This is a direct consequence of a lower
yield from the oxidation of CH4, caused by the lower OH
concentrations in the tropics and subtropics. In order words,
to obtain the same CH4 concentrations lower effective CH4
emissions are needed in EC-Earth, resulting in a lower pro-
duction of CO. The total chemical production of CO in both
simulations is lower than the range of model estimates re-
ported by Shindell et al. (2006b). This is likely due to an
underestimation of the CO production from NMVOCs in the
CBM4 chemistry scheme.
The global tropospheric burden of CO is similar in both
simulations (Table 4). EC-Earth produces a lower burden
in the tropics and a higher burden in the NH extratropics,
but the differences are only a few percent. Compared to the
ERA-Interim simulation, EC-Earth gives higher CO concen-
trations in the tropical upper troposphere and in the lower
stratosphere (Fig. 5), mostly due to more efficient transport of
CO by deep convection into the tropical upper troposphere.
In the tropical lower and middle troposphere both higher and
lower concentrations are observed depending on the location
and the season. The concentrations in EC-Earth are lower
in the NH extratropics, due to the faster chemical destruc-
tion. They are also somewhat lower in the SH extratropics in
austral summer and, in the middle and upper troposphere, in
austral winter.
The simulated surface mixing ratios of CO have been eval-
uated against monthly averages from the network of sur-
face flask sampling of NOAA?s Earth System Research Lab-
oratory (ESRL) Global Monitoring Division (GMD). The
decadal monthly mean mixing ratios from the simulations
are compared with the flask measurements in Fig. 6. Since
in the reference EC-Earth simulation and the corresponding
ERA-Interim simulation the emissions from anthropogenic
activities and biomass burning were fixed to their values for
the year 2000, we have also included the results from the
ERA-Interim simulation with emissions varying from year
to year (see Table 2). The two ERA-Interim simulations give
very similar decadal mean CO concentrations at the stations
used in the evaluation (see Table A4 for a complete list).
Thus, also the simulations with fixed emissions can be di-
rectly compared with the measurements.
At the measurement locations the concentration differ-
ences between the EC-Earth and ERA-Interim simulation
are generally small in the SH and in the tropics and be-
come larger in the NH extratropics (see Fig. 6 and Ta-
ble A2). For the majority of stations the seasonal cycle is
very well simulated, as expressed by a high correlation be-
tween the simulated and the measured monthly values. The
simulated CO concentrations are generally in good quanti-
tative agreement with the measurements in the SH. At the
tropical stations in the NH both simulations underestimate
the measurements by about equal amounts. At northern mid-
latitudes, both simulations underestimate the measurements,
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2450 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
EC-Earth, DJF
0 10 20 30 40 50 60 70 80 90 100 110 120
CO (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth, JJA
0 10 20 30 40 50 60 70 80 90 100 110 120
CO (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, DJF
-15 -10 -5 -2 -1 0 1 2 5 10 15
CO Difference (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, JJA
-15 -10 -5 -2 -1 0 1 2 5 10 15
CO Difference (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Figure 5. Zonal mean CO mixing ratios in the reference EC-Earth simulation (top) and the differences compared to the corresponding
ERA-Interim simulation (bottom) for boreal winter (left) and boreal summer (right).
but the concentrations in EC-Earth are lower than with ERA-
Interim, especially outside of the summer season. In the an-
nual mean the difference can be up to about 11 ppbv. The dif-
ferences between the two simulations are generally smaller
than the amounts by which the measurements are underes-
timated. We note that other modelling studies have shown
similar CO biases in the NH, using identical anthropogenic
and biomass burning emissions (Lamarque et al., 2010; Naik
et al., 2013).
4.5 Ozone
The sources of ozone in the troposphere are chemical produc-
tion by the oxidation of CO, CH4, and NMVOCs in the pres-
ence of NOx, and net transport from the stratosphere. Ozone
is removed from the troposphere by chemical destruction and
by dry deposition at land surfaces. The chemical destruction
of tropospheric O3 occurs mainly through the photolysis of
O3 followed by the reaction of the produced excited oxy-
gen atom with a water molecule (see Sect. 4.3) and through
the reaction of O3 with the peroxy radical (HO2) and with
OH. The main contributions to the tropospheric O3 budget in
the EC-Earth and ERA-Interim simulations are given in Ta-
ble 5. These numbers can be directly compared with the AC-
CENT and ACCMIP multi-model results for the present day
reported by Stevenson et al. (2006) and Young et al. (2013),
who use a similar method for defining the tropopause. Note
that Stevenson et al. (2006) give ranges based on the full en-
semble of models participating in that study as well as on a
subset of models that were selected based on criteria related
to the simulation of O3 and the CH4 lifetime for the present
day.
In EC-Earth the average lifetime of tropospheric O3 is
25.5 days, which is outside the ranges 22.3?2.0 days and
22.2?2.2 days estimated by Stevenson et al. (2006) for the
full ensemble of ACCENT models and a subset of models,
respectively, and corresponds to the highest value out of the
six individual model results reported by Young et al. (2013).
With ERA-Interim a lifetime of 23.9 days is obtained, which
is within the ranges reported by these authors. The longer
lifetime in EC-Earth is caused by a slower chemical destruc-
tion. The cold bias that exists in most of the troposphere
slows down the destruction of O3 by photolysis because of
the lower specific humidity. Lower concentrations of OH and
HO2 in large parts of the tropical and subtropical troposphere
(see Sect. 4.3) further slow down the destruction of O3.
EC-Earth produces a tropospheric O3 burden of 327 Tg,
which is well within the ranges 344?39 Tg and 336?27 Tg
reported by Stevenson et al. (2006) and the range
337?23 Tg reported by Young et al. (2013). With ERA-
Interim a tropospheric burden of 309 Tg is obtained, which
is at the low side of the ranges estimated by Stevenson
et al. (2006) and below the range estimated by Young et
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T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2451
Mauna Loa Station, Hawaii
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Terceira Island Station, Azores
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Ragged Point Station, Barbados
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Tutuila Station, American Samoa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
South Pole Station
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Obse rv at i o n sERA-In te rim ,  v a r. e mi s .ERA-In te rimE C -E a r t h
Observations
ERA-Interim, var. emis.
ERA-Interim
EC-Earth
Cape Grim Station, Tasmania
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Mace Head Station, Ireland
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Alert Station, Canada
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Storhofdi Station, Iceland
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
CO Mixing Ratio (ppbv)
Figure 6. Comparison of monthly mean surface CO mixing ratios from the reference EC-Earth simulation (solid red lines) and the two ERA-
Interim simulations (solid and dotted blue lines) against flask measurements at a number of stations selected from the NOAA GMD network.
The results from the ERA-Interim simulation with interannual variations in the emissions from anthropogenic activities and biomass burning
(dotted blue lines) nearly coincide with those from the ERA-Interim simulation where these emissions are fixed to their values for the year
2000 (solid blue lines).
Table 5. Contributions to the budget of O3 in the troposphere (Tg O3 year?1), together with the tropospheric O3 burden (Tg O3), and the
tropospheric O3 lifetime (days) for the reference EC-Earth simulation and the corresponding ERA-Interim simulation. The contributions
from 90?30? S, 30? S?30? N, and 30?90? N are given by the numbers between parentheses. Results have been obtained from a monthly
analysis with a fixed tropopause level, set to the uppermost model layer for which the monthly mean O3 mixing ratio is below 150 ppbv.
Standard deviations are based on the simulated interannual variability.
EC-Earth ERA-Interim
Chemical production 4328?17 4419?38
(339/2890/1099) (278/3070/1071)
Chemical destruction 3698?24 3873?39
(327/2656/714) (291/2896/687)
Dry deposition 978?3.2 851?4.1
(115/471/392) (84/425/341)
Stratosphere?troposphere exchange 349?10 306?15
Burden 327?1.3 309?1.7
(66/161/100) (59/162/88)
Lifetime 25.5 23.9
al. (2013). The higher burden in EC-Earth compared to
the ERA-Interim simulation is mainly due to the slower
chemical destruction of O3 in the troposphere and a higher
net influx of O3 from the stratosphere. The influx from
the stratosphere is 349 Tg year?1 in EC-Earth compared to
306 Tg year?1 with ERA-Interim. Both values are below the
ranges 552?168 Tg year?1 and 556?154 Tg year?1 esti-
mated by Stevenson et al. (2006) and the model results
reported by Young et al. (2013). Other model studies of
stratosphere?troposphere exchange also found the net O3
www.geosci-model-dev.net/7/2435/2014/ Geosci. Model Dev., 7, 2435?2475, 2014
2452 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
flux to be higher than about 400 Tg year?1, in line with esti-
mates based on observations (Olsen et al., 2004; Hsu et al.,
2005). Thus, compared to the ERA-Interim simulation, the
higher net stratosphere?troposphere exchange flux simulated
in EC-Earth is likely an improvement.
Overall, the total chemical destruction of O3 in the tropo-
sphere is lower in EC-Earth than in the ERA-Interim sim-
ulation. The chemical production of O3 in the troposphere
is also somewhat lower, but the net chemical production of
O3 in the troposphere is still higher in EC-Earth. Combined
with the higher net influx from the stratosphere this is con-
sistent with a higher deposition of O3. The total deposition is
978 Tg year?1 in EC-Earth, while 851 Tg year?1 is obtained
in the ERA-Interim simulation. Both results are within the
ranges 1003?200 Tg year?1 and 953?154 Tg year?1 esti-
mated by Stevenson et al. (2006) and the model results re-
ported by Young et al. (2013); however, the higher value ob-
tained with EC-Earth is closer to the central estimates ob-
tained by these authors.
EC-Earth produces higher zonal mean O3 concentrations
than the ERA-Interim simulation in large parts of the tropo-
sphere, including most of the NH and the lower and middle
parts of the SH (Fig. 7). Lower zonal mean concentrations are
simulated in the tropical and subtropical upper troposphere
and parts of the tropical and subtropical middle troposphere,
and in parts of the lower stratosphere of the SH.
Differences in the contribution from O3 originating from
the stratosphere explain part of the differences in the sim-
ulated O3 concentrations in the troposphere (compare the
lower and middle panels of Fig. 7). This contribution was di-
agnosed using a stratospheric O3 tracer, O3S. As in Lelieveld
and Dentener (2000), O3S is subject to the same strato-
spheric boundary conditions and removal processes as reg-
ular O3, but is not produced below a certain pressure level,
?140 hPa in our model set-up. Since only small amounts
of O3 are produced in the region between this level and the
tropopause, effectively the chemical production is switched
off in the troposphere. The O3S tracer therefore provides
a robust method for estimating the contribution of O3 pro-
duced in the stratosphere to the tropospheric budget. The to-
tal chemical destruction and deposition of O3S in the tropo-
sphere is 351 Tg year?1 in EC-Earth and 305 Tg year?1, very
close to the estimates of the net stratosphere?troposphere ex-
change flux quoted above, which were obtained by closing
the tropospheric budget of O3.
EC-Earth gives higher O3 concentrations than the ERA-
Interim simulation in the lowermost stratosphere at high
northern latitudes (Fig. 7); this, combined with the slower
chemical destruction in the troposphere, leads to higher O3S
concentrations in most of the NH. In the SH, the lower zonal
mean O3 concentrations simulated with EC-Earth in the sub-
tropical upper and middle troposphere are partly due to a
lower contribution from O3S, especially in austral winter. At
higher latitudes, on the other hand, the contribution from
O3S in the troposphere is higher than in the ERA-Interim
simulation. Concentration differences in the lower strato-
sphere between the EC-Earth and ERA-Interim simulations
are the combined effect of differences in the large-scale
stratospheric circulation, stratosphere?troposphere exchange
and vertical resolution.
The slower chemical destruction due to the cold bias in
EC-Earth increases the lifetime of O3 in the troposphere, and
tends to increase the concentration of both O3S and O3. The
resulting concentration increase is larger for O3 than for O3S,
but the increase in the O3 concentration is partly compen-
sated by a reduced chemical production in the troposphere.
Differences in vertical exchange are also important to ex-
plain the differences in the O3 concentrations between the
two simulations. The different treatment of convection in EC-
Earth leads to more efficient convection to the upper parts
of the tropical troposphere (see Sect. 4.2). This tends to de-
crease the O3 concentrations in the tropical and subtropical
upper troposphere. In the extratropics enhanced vertical mix-
ing in EC-Earth tends to increase the O3 concentrations in
the lower parts of the troposphere by bringing down more
O3 from higher altitudes. Enhanced mixing similarly tends
to increase the O3S concentrations in the lower extratropical
troposphere. The latter effects are unique features of the on-
line EC-Earth simulation, which are not reproduced in the
offline EC-Earth simulation (not shown).
The simulated O3 concentrations have been evaluated
against a vertically resolved, zonal, and monthly mean data
set based on the O3 profile measurements from the Binary
DataBase of Profiles (BDBP) of Hassler et al. (2008), which
includes both satellite observations and ozonesondes. The
data set used in the evaluation was constructed for the years
1979?2007 by Bodeker Scientific (www.bodekerscientific.
com) in a similar way as described in Hassler et al. (2009).
In addition, the monthly mean O3 data set from Cionni
et al. (2011) was included in the evaluation. This data set
provided the O3 distribution for the CMIP5 climate models
that did not calculate O3 interactively. The historical part of
this data set extends to 2009. In the stratosphere it consists
of zonal mean fields derived from a multiple linear regres-
sion analysis of satellite observations and polar ozonesonde
data. In the troposphere it is based on simulations with the
chemistry-climate models CAM3.5 (Lamarque et al., 2010)
and GISS-PUCCINI (Shindell et al., 2006c) with prescribed
sea surface temperatures. The anthropogenic and biomass
burning emissions used in these simulations are the same as
in the simulations presented here, and are also kept constant
from 2000 to 2009.
In Figs. 8 and 9 the monthly mean O3 mixing ratios from
the EC-Earth and ERA-Interim simulations are compared
with the observational and CMIP5 data sets in latitude bands
of 30? at 750, 500, and 250 hPa. In these figures, the 2000?
2009 means from the simulations are compared with the
2000?2007 means from the observational data set and the
2000?2009 means from the CMIP5 data set.
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2453
EC-Earth, DJF
0 10 20 30 40 50 60 70 80 90 100 150
Ozone (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth, JJA
0 10 20 30 40 50 60 70 80 90 100 150
Ozone (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, DJF
-25 -20 -15 -10 -5 -2 0 2 5 10 15 20 25
Ozone Difference (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, JJA
-25 -20 -15 -10 -5 -2 0 2 5 10 15 20 25
Ozone Difference (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, DJF
-25 -20 -15 -10 -5 -2 0 2 5 10 15 20 25
O
3S
 Difference (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
EC-Earth - ERA-Interim, JJA
-25 -20 -15 -10 -5 -2 0 2 5 10 15 20 25
O
3S
 Difference (ppbv)
-90 -60 -30 0 30 60 90
Latitude (deg)
1000
900
800
700
600
500
400
300
200
100
1 0009008007 006005004 003002 001 00
Pressure (hPa)
Figure 7. Zonal mean O3 mixing ratios in the reference EC-Earth simulation (top), the differences compared to the corresponding ERA-
Interim simulation (middle), and the contribution from the stratospheric O3 tracer (O3S) to these differences (bottom) for boreal winter (left)
and boreal summer (right).
We first look at the extratropical upper troposphere?lower
stratosphere (250 hPa). Here EC-Earth gives higher O3 con-
centrations in the NH and lower concentrations in the SH
than the ERA-Interim simulation. At high northern latitudes
(60?90? N,), the higher concentrations obtained with EC-
Earth are in better agreement with the observational data set.
However, EC-Earth underestimates the observational data in
boreal spring and summer, which indicates that the down-
ward transport in the lower stratosphere may still be too
slow. Between 30 and 60? N the higher values obtained with
EC-Earth lead to a somewhat stronger overestimation of the
observational data. On the other hand, the CMIP5 data set
shows even higher concentrations in this region during boreal
winter and spring. At high southern latitudes (60?90? S) both
simulations underestimate the observational data, but the
agreement is worse for EC-Earth. EC-Earth also underesti-
mates the observational data between 30 and 60? S, where
the ERA-Interim simulation overestimates the observational
data, especially in austral spring and summer. In this region
the CMIP5 data set gives similar values as obtained with EC-
Earth.
In the extratropical middle and lower troposphere (500 and
750 hPa, respectively), EC-Earth gives significantly higher
concentrations than the ERA-Interim simulation. In the NH,
EC-Earth agrees well with both the observational data and
the CMIP5 data set in boreal winter. In boreal summer, the
www.geosci-model-dev.net/7/2435/2014/ Geosci. Model Dev., 7, 2435?2475, 2014
2454 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
60-90
o
S, 250 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
02 04 060801 0012 014 01 601 80
Ozone Mixing Ratio (ppbv)
C MIP 5  D atasetObs . D atasetERA-In te rimE C -E a r t h
CMIP5 Dataset
Obs. Dataset
ERA-Interim
EC-Earth
0-30
o
S, 250 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
01 02 0304 05060
Ozone Mixing Ratio (ppbv)
0-30
o
S, 750 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
01 02 0304 050
Ozone Mixing Ratio (ppbv)
30-60
o
S, 750 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
01 02 0304 0
Ozone Mixing Ratio (ppbv)
60-90
o
S, 750 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
01 02 0304 0
Ozone Mixing Ratio (ppbv)
0-30
o
S, 500 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
01 02 0304 05060
Ozone Mixing Ratio (ppbv)
30-60
o
S, 500 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
01 02 0304 050
Ozone Mixing Ratio (ppbv)
60-90
o
S, 500 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
01 02 0304 050
Ozone Mixing Ratio (ppbv)
30-60
o
S, 250 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
02 04 060801 0012 014 0
Ozone Mixing Ratio (ppbv)
Figure 8. Monthly mean O3 mixing ratios at 750, 500, and 250 hPa averaged over different latitude bands in the SH. The results from the
reference EC-Earth simulation (solid red lines) and the corresponding ERA-Interim simulation (solid blue lines) are compared against the
observational data set (solid black lines) constructed from the Binary Database of Profiles (BDBP). The CMIP5 data set from Cionni et
al. (2011) is indicated by the dashed black lines. The contributions from O3S for the EC-Earth and ERA-Interim simulations are shown by
the dashed red and blue lines, respectively.
concentrations from EC-Earth are significantly higher than
in the observational data set. In this season, the concen-
trations from the ERA-Interim simulation are either very
close to (30?60? N) or slightly lower than the observational
estimates (60?90? N). In the SH, EC-Earth shows a much
smaller bias relative to the observational data set. At high
southern latitudes (60?90? S), the concentrations from EC-
Earth are lower than the observational estimates during aus-
tral winter and higher during austral summer. EC-Earth is in
excellent agreement with the observational data in the lower
troposphere between 30 and 60? S.
In the tropical upper troposphere (30? S?30? N, 250 hPa),
EC-Earth produces lower values than the ERA-Interim simu-
lation and is in fairly good agreement with the observational
data set. The CMIP5 data set gives significantly lower values
in this region.
In the tropical middle and lower troposphere, the differ-
ences between the EC-Earth and ERA-Interim simulation are
relatively small. In the NH tropics (0?30? N), EC-Earth gives
somewhat higher concentrations. In the middle troposphere
(500 hPa), EC-Earth reproduces the observational data very
well during boreal summer and fall, but gives lower values
during winter and spring. In any case, EC-Earth is closer
to the observational data than both the ERA-Interim sim-
ulation and the CMIP5 data set. In the lower troposphere
(750 hPa), EC-Earth agrees well with the observational data
set in boreal spring and gives slightly higher values in the
other seasons. Both simulations are in fairly good agreement
with the observational data set. The CMIP5 data set gives
significantly lower concentrations in this region. In the SH
tropics (0?30? S), both simulations underestimate the obser-
vation data, especially in the middle troposphere (500 hPa).
Here EC-Earth gives somewhat lower values than the ERA-
Interim simulation during austral winter and spring. The
CMIP5 data set underestimates the observational data more
strongly in the lower troposphere (750 hPa), and also gives
somewhat lower values than EC-Earth in the middle tropo-
sphere (500 hPa) during austral winter and fall.
The results presented in Figs. 8 and 9 can be compared
with the evaluation presented by Young et al. (2013), in
which present-day tropospheric O3 concentrations from the
ACCENT and ACCMIP model ensembles are compared with
monthly climatological data sets based on ozonesonde mea-
surements as well as satellite retrievals from the Tropo-
spheric Emission Spectrometer (TES). In general, our sim-
ulations are within the range of concentrations simulated by
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2455
60-90
o
N, 250 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
50
100
150
200
250
300
350
400
0501 001 502 002 503003504 00
Ozone Mixing Ratio (ppbv)
C MIP 5  D atasetObs . D atasetERA-In te rimE C -E a r t h
CMIP5 Dataset
Obs. Dataset
ERA-Interim
EC-Earth
60-90
o
N, 500 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
70
80
01 02 0304 050607 080
Ozone Mixing Ratio (ppbv)
30-60
o
N, 500 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
70
80
01 02 0304 050607 080
Ozone Mixing Ratio (ppbv)
0-30
o
N, 500 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
01 02 0304 05060
Ozone Mixing Ratio (ppbv)
30-60
o
N, 250 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
20
40
60
80
100
120
140
160
180
200
02 04 060801 0012 014 01 601 802 00
Ozone Mixing Ratio (ppbv)
0-30
o
N, 250 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
70
01 02 0304 050607 0
Ozone Mixing Ratio (ppbv)
30-60
o
N, 750 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
70
01 02 0304 050607 0
Ozone Mixing Ratio (ppbv)
60-90
o
N, 750 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
01 02 0304 05060
Ozone Mixing Ratio (ppbv)
0-30
o
N, 750 hPa
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
01 02 0304 050
Ozone Mixing Ratio (ppbv)
Figure 9. Same as Fig. 8, but for the NH.
the ACCMIP models. In particular, most ACCMIP models
also underestimate the O3 concentrations in the middle and
lower troposphere between 0 and 30? S. Likewise they also
underestimate the observed seasonal cycle in the upper and
middle troposphere between 0 and 30? N.
The surface O3 concentrations simulated with EC-Earth
and the differences compared to the ERA-Interim simulation
are presented in Fig. 10. EC-Earth gives higher surface con-
centrations in most of the world, with the exception of some
regions located in the tropics and subtropics. In the mid- to
high latitudes of the NH, differences up to about 10 ppbv are
simulated during the winter season, while even larger differ-
ences are found during summer.
The simulated surface O3 concentrations have been evalu-
ated against in situ surface measurements. The stations used
for the evaluation of surface O3 are listed in Table A5. They
include stations from the NOAA GMD network and a se-
lection of stations included in the World Data Centre for
Greenhouse Gases (WDCGG). Data for Mace Head were
taken from the European Monitoring and Evaluation Pro-
gramme (EMEP). Monthly averages were calculated from
the hourly mixing ratio measurements and then averaged
over the available years in the simulation period. Figure 11
shows the resulting monthly mixing values for a subset of
stations spanning a broad range of latitudes, together with
the decadal mean simulation results obtained at the corre-
sponding locations. Simulation results are included for the
reference EC-Earth simulation and the corresponding ERA-
Interim simulation, as well as for the ERA-Interim simula-
tion with yearly changes in the emissions from anthropogenic
activities and biomass burning (see Table 2). As for CO, the
effect of these emission variations on the simulated decadal
mean O3 concentrations at the stations used in the evalua-
tion is very small, and sometimes barely visible. The sim-
ulations with fixed emissions can therefore again be directly
compared with the measurements (see Fig. 11 and Table A5).
EC-Earth produces higher monthly mean surface concen-
trations than the ERA-Interim simulation at all stations, ex-
cept at Mauna Loa (Hawaii), Tutuila (American Samoa) and
Pyramid on Mount Everest (Nepal).
At the Antarctic stations, both simulations underestimate
the measurements. Here EC-Earth is closer to the observa-
tions than the ERA-Interim simulation, but this is achieved
at the expense of the correlation between the measured and
simulated monthly concentrations. At Cape Grim (Tasma-
nia), the seasonal cycle in both simulations is weaker than
in the observations. EC-Earth is in excellent agreement with
the observations during the winter months, but overestimates
the observations during summer. The ERA-Interim simula-
tion, on the other hand, is in good agreement during summer,
but underestimates the observations during winter.
The ERA-Interim simulation also underestimates the mea-
surements at high northern latitudes, especially during win-
ter. Here EC-Earth is on average closer to the observations.
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2456 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, DJF
0 5 10 15 20 25 30 35 40 45 50 55 60
Surface Ozone (ppbv)
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, JJA
0 5 10 15 20 25 30 35 40 45 50 55 60
Surface Ozone (ppbv)
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, DJF
-15
-12.5
-10
-7.5
-5
-2.5
0
2.5
5
7.5
10
12.5
15
Surface Ozone Difference (ppbv)
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, JJA
-15
-12.5
-10
-7.5
-5
-2.5
0
2.5
5
7.5
10
12.5
15
Surface Ozone Difference (ppbv)
Figure 10. Surface O3 mixing ratios in the reference EC-Earth simulation (top) and the differences compared to the corresponding ERA-
Interim simulation (bottom) for boreal winter (left) and boreal summer (right).
At Summit (Greenland), EC-Earth is in excellent agreement
with the measurements during summer and underestimates
the measurements in the other seasons. At Storhofdi (Ice-
land), on the other hand, EC-Earth very well reproduces the
measured concentrations in winter, but overestimates them in
the other seasons.
The picture is different at the tropical and subtropical sta-
tions, where both simulations show an average positive bias.
At Tutuila station, located in the tropics in the SH, the sim-
ulations give similar results and systematically overestimate
the observations. At Mauna Loa, EC-Earth is in reasonable
agreement with the observations during spring and summer
and overestimates the observations during fall and winter.
Here the difference with the ERA-Interim simulation is rel-
atively small compared to the interannual variability in the
results. At the other tropical and subtropical stations, EC-
Earth shows worse agreement with the observations than the
ERA-Interim simulation, depending on the location.
EC-Earth also shows an average positive bias at the NH
mid-latitude stations. Here the model overestimates the mea-
surements especially during summer. With the exception of
the high-altitude station Hohenpeissenberg (Germany), the
ERA-Interim simulation is closer to the observations at these
stations.
4.6 Aerosols
In this section the model is evaluated with regard to the sim-
ulation of aerosols. Maps of the atmospheric loads of the
different aerosol components from the EC-Earth and ERA-
Interim simulations are presented in Fig. 12. In Table 6 we
compare the corresponding global burdens, lifetimes, and dry
and wet deposition rates as well as the emissions of sea salt
and mineral dust with results from recent modelling stud-
ies, in particular multi-model estimates from AeroCom and
ACCMIP. Results on the global budgets of the sulfate pre-
cursors DMS, MSA, and SO2 are given in Table A6.
Compared to the ERA-Interim simulation, the sulfate load
in EC-Earth is on average lower in the tropics and, especially
in boreal winter, higher in the extratropics. The decrease in
the tropics is mainly caused by a lower chemical production
(see Table A6). The increase in the extratropics is the com-
bined effect of a higher chemical production and a longer
lifetime. The loads of black carbon, organic aerosols, and ni-
trate are also higher at high northern latitudes in boreal win-
ter.
Furthermore, EC-Earth gives higher sea salt loads at high
latitudes, especially during local winter and spring. This is
mainly due to higher emissions from the oceans (see Ta-
ble 6). Since the emission rate calculated in the model de-
pends strongly on the 10 m wind speed (Sect. 2.2.9), the
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2457
Cape Grim Station, Tasmania
JanFebMarApr
May
Jun
Jul
AugSep
Oct
NovDec
0
10
20
30
40
50
60
70
80
01 02 0304 050607 080
Ozone Mixing Ratio (ppbv)
Hohenpeissenberg Station, Germany
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Obse rv at i o n sERA-In te rim ,  v a r. e mi s .ERA-In te rimE C -E a r t h
Observations
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Figure 11. Comparison of monthly mean surface O3 mixing ratios from the reference EC-Earth simulation (solid red lines) and the two ERA-
Interim simulations (solid and dotted blue lines) against in situ measurements at a number of stations included in the NOAA GMD, WDCGG,
and/or EMEP databases (solid black lines). The results from the ERA-Interim simulation with interannual variations in the emissions from
anthropogenic activities and biomass burning (dotted blue lines) nearly coincide with those from the ERA-Interim simulation where these
emissions are fixed to their values for the year 2000 (solid blue lines). The contributions from O3S are shown by the dashed lines at the
bottom of each panel (red for EC-Earth, blue for ERA-Interim).
emissions at high and mid-latitudes are highest during the
winter season. Moreover, small differences in surface winds
over the oceans may introduce substantial differences in the
emissions. A comparison of the sea salt emissions in both
simulations shows that EC-Earth produces higher emissions
over large parts of the northern Pacific and the Southern
Ocean (not shown).
In the tropics, the differences in the aerosol distributions
between the two simulations also reflect a shift in the location
of the inter-tropical convergence zone (ITCZ). In particular,
during boreal winter a southward shift in the aerosol loads
can be observed over central Africa and the tropical Atlantic
for all components except sea salt. As the mineral-dust and
biomass burning emissions are the same in both simulations,
this shift in the aerosol distributions is likely caused by dif-
ferences in the location of the ITCZ and in the associated
transport.
The global burdens of the different aerosol components
from both simulations are generally on the low side of the
ranges obtained in recent model intercomparison studies (see
Table 6). On the other hand, their atmospheric lifetimes and
the relevant dry and wet deposition rates are well within the
multi-model estimates from these studies. This indicates that
the emissions of aerosols and/or aerosol precursors are lower
in our simulations. As can be verified in Tables 6 and A6, the
emissions of sea salt and DMS in our simulations are indeed
on the low side of the ranges used in AeroCom and ACCMIP,
respectively. Moreover, the volcanic sulfur emissions applied
in our model are substantially lower than in the ACCMIP
models.
The aerosol optical depth (AOD) field from the EC-Earth
simulation is quantitatively similar to the result obtained with
ERA-Interim (see Fig. 13). The spatial correlation between
the multi-annual mean AOD fields from the two simulations
is 0.97 and the global mean AOD values differ by only 3 %.
EC-Earth gives somewhat higher values at high latitudes,
especially during winter and spring. This is primarily due to
a higher contribution from sea salt. The higher AOD values
simulated by EC-Earth in the Arctic are in somewhat better
agreement with ground-based and satellite measurements as
well as reanalysis data (see von Hardenberg et al., 2012).
In the tropics and at mid-latitudes the AOD differences be-
tween the two simulations can be positive or negative, de-
pending on the location and the season. During boreal winter
www.geosci-model-dev.net/7/2435/2014/ Geosci. Model Dev., 7, 2435?2475, 2014
2458 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
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2
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Sulfate Load (mg/m
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Sulfate Load Difference (mg/m
2
)
Figure 12. Total loads of the different aerosol components in the reference EC-Earth simulation and the differences compared to the corre-
sponding ERA-Interim simulation for boreal winter (left two columns) and boreal summer (right two columns). The global mean values of
the displayed fields are indicated at the top of each panel.
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2459
Table 6. Global budgets, burdens, and lifetimes of the different aerosol components for the reference EC-Earth simulation and the corre-
sponding ERA-Interim simulation. Estimates of their contributions to the optical depth at 550 nm are also included. The results are compared
with estimates from other studies.
EC-Earth ERA-Interim Otherstudies
Sulfate
Emissions(TgSyear?1) 1.47a
Chemicalproduction(TgSyear?1)
SO2 +OH 7.80 8.24
S(IV)+H2O2 23.9 24.2
S(IV)+O3 5.42 4.58
Burden(TgS) 0.522 0.498 0.67?0.17d
Lifetime(days) 4.93 4.73 5.0?2.0d
4.1?0.7e
Drydepositionrate(day?1) 4.68?10?3 4.57?10?3 0.03?0.02e
Wetdepositionrate(day?1) 0.198 0.207 0.22?0.05e
Opticaldepth 2.13?10?2 2.08?10?2 0.044f
Blackcarbon
Emissions(Tgyear?1) 7.77
Burden(Tg) 0.145 0.149 0.16?0.07d
Lifetime(days) 6.81 6.99 7.4?3.4d
7.1?2.3e
Drydepositionrate(day?1) 6.17?10?3 5.65?10?3 0.03?0.02e
Wetdepositionrate(day?1) 0.141 0.137 0.12?0.04e
Opticaldepth 1.11?10?3 1.15?10?3 0.0085f
Organicaerosols
Emissions(Tgyear?1) 69.5b
Burden(Tg) 1.18 1.16 1.6?0.8g
Lifetime(days) 6.18 6.08 5.7?1.6g
Drydepositionrate(day?1) 5.23?10?3 4.69?10?3 0.029?0.046g
Wetdepositionrate(day?1) 0.157 0.160 0.16?0.04g
Opticaldepth 9.28?10?3 9.29?10?3 0.024f
Nitrate
Burden(TgN) 2.29?10?2 1.27?10?2 0.1?0.0h
Opticaldepth 6.82?10?4 3.99?10?4 0.007?0.001h
Seasalt
Emissions(Pgyear?1) 7.35?0.11c 6.83?0.09c 8.2?8.2i
16.6?33.0e
Burden(Tg) 6.81 6.17 7.9?5.5i
7.5?4.1e
Lifetime(days) 0.338 0.330 0.48?0.28e
Drydepositionrate(day?1) 2.42 2.40 4.3?9.4e
Wetdepositionrate(day?1) 0.538 0.630 0.79?0.61e
Opticaldepth 2.66?10?2 2.35?10?2 0.055?0.016j
Mineraldust
Emissions(Pgyear?1) 1.78 1.84?0.90e
Burden(Tg) 12.1 13.4 19.2?7.7e
Lifetime(days) 2.48 2.75 4.1?1.8e
Drydepositionrate(day?1) 0.311 0.287 0.23?0.19e
Wetdepositionrate(day?1) 9.20?10?2 7.60?10?2 0.08?0.03e
Opticaldepth 1.55?10?2 1.71?10?2 0.043?0.014j
a Includes0.12TgSyear?1 fromvolcanoes.b Includes19.1Tgyear?1 representingSOA(seeSect.2.2.5).
c Standarddeviationscalculatedfromthesimulatedinterannualvariability. d ACCMIPmulti-modelmeansand
standarddeviationsfortheyear2000fromShindelletal.(2013).e AeroComphase-Imulti-modelmeansand
standarddeviationsfromTextoretal.(2006). f MACCreanalysis(Benedettietal.,2009)resultsfortheyear2003
asprovidedontheAeroComphase-IIwebinterface(http://aerocom.met.no/cgi-bin/aerocom/surfobs_annualrs.pl;
simulationlabelled?ECMWF_FBOV?). g AeroComphase-IImulti-modelmeansandstandarddeviationsfrom
Tsigaridisetal.(2014).h Resultsfor1998?2002froma CMIP5simulationwiththeHadley Centreclimatemodel
HadGEM2-ESbyBellouinetal.(2011).i AeroComphase-Imulti-modelmeansandstandarddeviationsfrom
Textoretal.(2007),basedona selectionofsevenmodelsfromTextoretal.(2006).j MACCreanalysisresultswith
uncertaintyestimatesfromBellouinetal.(2013).
www.geosci-model-dev.net/7/2435/2014/ Geosci. Model Dev., 7, 2435?2475, 2014
2460 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
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Figure 13. Aerosol optical depth at 550 nm in the reference EC-Earth simulation (top) and the differences compared to the corresponding
ERA-Interim simulation (bottom) for boreal winter (left) and boreal summer (right). The global mean values of the displayed fields are
indicated at the top of each panel.
a southward shift in the AOD pattern can be observed over
central Africa and the tropical Atlantic. This is primarily due
to a shift in the contributions from mineral dust and biomass
burning (see Fig. A1 in the Appendix), and reflects a shift
in the location of the ITCZ. During boreal summer, EC-
Earth gives smaller AOD values over large parts of northern
Africa and the tropical Atlantic. In the NH this can mainly
be attributed to a lower contribution from mineral dust, over
the southern equatorial Atlantic to a lower contribution from
biomass burning. In most of western Africa and the Sahel,
EC-Earth produces lower AOD values than the ERA-Interim
simulation in all seasons. EC-Earth also produces somewhat
lower AOD values over India and the Arabian Sea from sum-
mer to winter, especially during the summer season. This is
mainly caused by lower contributions from mineral dust and
sulfate (see Fig. A1).
We have evaluated the AOD fields from both simulations
against remote-sensing data from the Moderate Resolution
Imaging Spectroradiometer (MODIS) instrument aboard the
Terra and Aqua satellites, part of NASA?s Earth Observing
System (EOS). For this analysis, we have used the Level-
3 monthly gridded AOD fields from MODIS collection 5.1,
which are provided at 1??1? resolution. We have included
the data for the years 2000?2009 and averaged the results
from the Terra and Aqua data products. The resulting mean
fields were subsequently coarsened to the 3??2? grid of
TM5.
Both simulations strongly underestimate the AOD values
retrieved from MODIS over almost the entire globe (Fig. 14).
The mean bias is ?0.083 for EC-Earth and ?0.086 for
the ERA-Interim simulation, which is 53 and 54 %, respec-
tively, of the retrieved mean value of 0.158. EC-Earth gives
a smaller mean bias than the ERA-Interim simulation from
boreal autumn to spring, especially in the winter season (see
Table A7).
There are some land regions where the simulations pro-
duce higher values than observed. This is for instance the
case over large parts of Australia, which also include desert
areas, in all seasons. As it is difficult to accurately retrieve
AOD over deserts because of the high reflectivity of the sur-
face, biases in these regions can be due to model biases as
well as errors in the MODIS retrieval. The simulations also
give higher AOD values over the southeastern United States
during boreal winter and autumn, over southern parts Cen-
tral America during boreal winter, over eastern parts of South
America during austral winter and autumn, and over parts of
South Africa during austral summer and autumn. We have
checked that these biases are not caused by the fact that
in the reference EC-Earth simulation and the corresponding
ERA-Interim simulation the emissions from anthropogenic
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T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2461
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Figure 14. Difference in aerosol optical depth at 550 nm from the reference EC-Earth simulation compared to the MODIS Level-3 product
coarsened to the same resolution and averaged over the years 2000?2009 for boreal winter (left) and boreal summer (right). Grid areas
where, for all consecutive years, the MODIS data are missing for one or more months during the presented seasons are not included. The
mean values of the displayed fields are indicated at the top of each panel.
activities and biomass burning were fixed to their values for
the year 2000. Biases of similar magnitude are found in the
ERA-Interim simulation with emissions varying from year to
year. In other parts of the world, the simulated AOD values
are generally much lower than the MODIS values.
In all seasons, the AOD fields from the simulations cor-
relate well with the distributions observed by MODIS (Ta-
ble A7). The spatial correlation between the simulated and
observed multi-annual mean AOD fields is 0.80 for EC-Earth
and 0.79 for ERA-Interim, which means that 65 and 62 %, re-
spectively, of the observed spatial variability is captured by
the simulations. The observed spatial distribution is slightly
better represented by EC-Earth than by the ERA-Interim sim-
ulation during boreal winter and autumn, and slightly worse
during boreal summer.
We have also calculated the temporal correlation between
the simulated and observed monthly mean AOD values as
a function of geographical location. As we have only in-
cluded locations where observations are available for every
month of the year, the analysis is restricted to the tropics and
mid-latitudes. The resulting seasonal correlation map shows
strong spatial variability where regions with high correlations
are intermixed with regions with low correlation (Fig. 15, left
panel). Moreover, in some regions the observed seasonal cy-
cle is better represented by EC-Earth, in other regions by the
ERA-Interim simulation (Fig. 15, right panel). For instance,
EC-Earth gives higher correlations over the southern equato-
rial Atlantic and the southern Indian Ocean, but lower cor-
relations over the northern equatorial Atlantic, the Arabian
Sea, the Bay of Bengal, and Indonesia.
Finally, we compare the contributions from the individual
aerosol components to the global mean optical depth in our
simulations with estimates from an aerosol reanalysis pro-
duced within the MACC project. The MACC reanalysis sys-
tem for aerosols is based on the IFS meteorological assimila-
tion system with an integrated aerosol module (Morcrette et
al., 2009) and uses MODIS retrievals of total AOD at 550 nm
to further constrain the aerosol simulation (Benedetti et al.,
2009). Because total AOD is assimilated in the reanalysis, it
provides a valuable reference data set for evaluating global
aerosol models.
Compared to the results from the MACC reanalysis, our
simulations underestimate the global mean AOD contribu-
tions for all components (see Table 6). Both in the reanal-
ysis and in our simulations, the components that contribute
most to the global mean AOD at 550 nm are sea salt, sulfate,
and mineral dust, followed by organic aerosols. Compared
to the reanalysis results, the contributions from these compo-
nents are underestimated by a factor 2 to 3. It is important
to note, however, that the distribution of AOD over the indi-
vidual aerosol components in the reanalysis is also subject to
model uncertainty.
5 Discussion and conclusions
We have integrated the atmospheric chemistry and transport
model TM5 into the global climate model EC-Earth. The sys-
tem allows for two-way exchange of fields between TM5 and
IFS, the atmospheric GCM of EC-Earth. Here we have tested
the system in one-way coupled configuration. We have car-
ried out a decadal simulation of tropospheric chemistry and
aerosols for present-day conditions, and calculated chemi-
cal budgets and climatologies of tracer concentrations and
aerosol optical depth. We have evaluated the results against
corresponding TM5 simulations driven offline by meteoro-
logical fields from (1) the ERA-Interim reanalysis and (2) the
EC-Earth model itself, as well as against various observa-
tional data sets.
Differences in transport have been diagnosed from the
simulated radon-222 concentrations. Compared to the of-
fline simulations, the online-coupled system exhibits more
efficient vertical mixing in the troposphere. This is due to
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2462 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth vs. MODIS
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
AOD Correlation Coefficient
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim
-1
-0.8
-0.6
-0.4
-0.2
-0.1
0
0.1
0.2
0.4
0.6
0.8
1
AOD Correlation Coefficient Difference
Figure 15. Linear correlation coefficient between the decadal monthly mean aerosol optical depths at 550 nm from the reference EC-Earth
simulation and the MODIS Level-3 product coarsened to the same resolution (left), and the difference compared to the corresponding
correlation coefficient for the ERA-Interim simulation (right). Grid areas where, for all consecutive years, the MODIS data are missing for
one or more months are not included.
the different treatment of cumulus convection in the online-
coupled system, in which the relevant convective fields are
passed from IFS to TM5. In the offline simulations these
fields are calculated diagnostically based on a somewhat out-
dated parameterization (Tiedtke, 1989). The stronger mixing
characteristics seen in the coupled system likely reflect im-
provements in the convection scheme made in more recent
cycles of IFS (Bechtold et al., 2008; Jung et al., 2010).
Compared to the ERA-Interim simulation, the oxidizing
capacity in EC-Earth is lower in large parts of the tropi-
cal and subtropical troposphere, and higher in the extratrop-
ics. The lower oxidizing capacity in the tropics and subtrop-
ics is primarily driven by the model?s cold bias in these re-
gions, which results in a lower specific humidity. As a con-
sequence, the atmospheric lifetime of CH4 is 7 % longer
in EC-Earth than in the ERA-Interim simulation: EC-Earth
gives a lifetime of 9.4 years, while the ERA-Interim simu-
lation gives 8.8 years. Both values are well within the range
9.1?0.9 years, recently estimated by Prather et al. (2012).
Differences in vertical mixing and oxidizing capacity also
affect the distribution of other chemically reactive gases such
as CO and O3. Compared to the ERA-Interim simulation, the
total chemical production and destruction of CO are lower in
EC-Earth, resulting in very similar total amounts and a 4 %
longer lifetime. On the other hand, EC-Earth gives lower CO
concentrations in the NH extratropics, due to faster chemical
destruction in this region. This leads to a somewhat stronger
underestimation of the surface concentrations measured by
the NOAA GMD flask sampling network. In both configu-
rations, the total chemical production of CO in TM5 is be-
low the range of model estimates reported by Shindell et
al. (2006b), suggesting that the CBM4 chemistry scheme un-
derestimates the production of CO from NMVOCs.
The influx of O3 from the stratosphere to the troposphere
is 14 % higher in EC-Earth than in the ERA-Interim simula-
tion, and is in slightly better agreement with other modelling
studies as well as observational estimates. Moreover, the cold
bias in EC-Earth tends to slow down the chemical destruc-
tion of O3 in the troposphere, resulting in an increase in the
net chemical production of O3 in the troposphere. Further-
more, enhanced vertical mixing tends to redistribute O3 from
higher to lower parts of the troposphere. Overall, the total
amount of O3 in the troposphere is 6 % higher in EC-Earth, in
better agreement with the ranges reported in the model inter-
comparison studies by Stevenson et al. (2006) and Young et
al. (2013). The average lifetime of tropospheric O3 increases
by 7 % from 23.9 days with ERA-Interim to 25.5 days in EC-
Earth. The latter value is higher than the multi-model range
reported by Stevenson et al. (2006) and is on the high side of
the model results reported by Young et al. (2013).
Overall, EC-Earth produces lower concentrations in the
upper parts of the tropical and subtropical troposphere and
higher concentrations in most of the lower and middle tro-
posphere, especially in the extratropics. Similarly, EC-Earth
gives higher surface concentrations in most of the world, with
the exception of some regions located in the tropics and sub-
tropics. This results in a 15 % higher total deposition of O3
compared to the ERA-Interim simulation.
The simulated O3 concentrations have been evaluated
against a vertically resolved, zonal, and monthly mean data
set produced from satellite and ozonesonde observations
(Hassler et al., 2009; www.bodeker.com) and against sur-
face measurements from various networks. Both simulations
show reasonable agreement with the observational data sets
and both have their relative strengths and weaknesses de-
pending on the location and the season. EC-Earth tends to
overestimate the O3 concentrations in the lower troposphere
and at the surface in the NH extratropics during boreal sum-
mer and fall. Note that this is partly a resolution effect.
It is well known that the relatively coarse horizontal res-
olutions applied in the current generation of global chem-
istry models tends to overestimate the production of O3 in
the boundary layer (Wild and Prather, 2006). During bo-
real winter and spring, on the other hand, as well as in the
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2463
SH extratropics, the higher surface and lower-tropospheric
concentrations produced by EC-Earth are generally in better
agreement with the measurements than the results from the
ERA-Interim simulation.
The aerosol climatologies from both simulations are quan-
titatively very similar. EC-Earth gives somewhat higher AOD
values at high latitudes especially during winter and spring,
mainly due to a higher contribution from sea salt. The global
burdens of the various aerosol components are generally on
the low side of the ranges obtained in recent model intercom-
parison studies like AeroCom and ACCMIP. This is partly
caused by lower natural emissions, in particular of sea salt,
DMS and volcanic sulfur. However, previous studies indicate
that the wet removal of aerosols in TM5 may also be too fast
(see Aan de Brugh et al., 2011). A comparison with AOD
fields retrieved from MODIS shows that the simulations cap-
ture a large part of the spatial variability, but strongly under-
estimate the observed values over almost the entire globe.
In the system we have developed, the description of atmo-
spheric chemistry and aerosols is not integrated into IFS. In-
stead, it is taken care of by a separate module, TM5, which is
coupled to IFS through OASIS. Previously, a coupled TM5-
IFS system using OASIS was developed within the GEMS
project (Flemming et al., 2009). However, the data exchange
in that system was designed specifically for short-term fore-
casts and reanalysis purposes, in which chemical data assim-
ilation plays a central role. We also needed to completely
redo the technical implementation of the coupling, because
the GEMS system made use of an OASIS version that is in-
compatible with the version used in EC-Earth.
A more recent activity is the development of the
Composition-IFS (C-IFS) model within the MACC project.
In C-IFS the description of atmospheric chemistry is inte-
grated into the IFS model (Flemming et al., 2012). As part
of this development, the aerosol scheme of IFS (Morcrette
et al., 2009) is also being upgraded and coupled to the gas-
phase chemistry. The main advantages of C-IFS compared
to the system we have developed are (1) that the description
of chemistry and aerosols can be more tightly coupled to the
relevant dynamical and physical processes described in IFS,
and (2) that there is no external exchange of data. Conversely,
the main advantages of our system compared to C-IFS are
(1) that the tracer transport in TM5 is locally mass conserv-
ing, and (2) that TM5 can be run at a lower resolution than
IFS. The latter points are crucial to enable long-term climate
integrations, but are less relevant for the short-term forecast
and reanalysis simulations which C-IFS has been developed
for, and (3) that the TM5 module can be kept more easily up
to date with the offline version.
The work presented in this study is the first step in the
development of EC-Earth into an Earth system model with
fully interactive chemistry and aerosols. A number of de-
velopments are planned for the near future. First, to im-
prove the simulation of aerosol burdens and optical depths,
we intend to improve the representation of natural emissions
of aerosols and aerosol precursors, and revisit the descrip-
tion of the wet removal processes in TM5. At the same
time, the calculation of photolysis rates will be updated
following Williams et al. (2006, 2012) and coupled to the
simulated aerosols. Moreover, the representation of hetero-
geneous chemistry will be improved following Huijnen et
al. (2014). Another line of work focuses on improving the
representation of stratospheric chemistry through simplified
schemes, but this is more a long-term project.
Meanwhile, the performance and scalability of TM5 has
recently been strongly improved, and will be further im-
proved in the near future. The new, massively parallel model
(named TM5-mp) is currently being implemented into the
latest version of EC-Earth (version 3.0). In this system the
couplings with the radiation and cloud schemes of IFS will
be made, and the exchange period will be reduced from 6 to
3 h or less.
A parallel development is the introduction of a carbon cy-
cle in EC-Earth, based on the dynamic vegetation model LPJ-
GUESS (Smith et al., 2001; Weiss et al., 2014) and the bio-
geochemical component of NEMO. As part of these devel-
opments, various couplings will be made between TM5 and
the terrestrial and marine biosphere.
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2464 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
Appendix A
Table A1. Vertical distributions applied to the different emission sources. The emissions from aviation are distributed as provided in the
CMIP5 emission data set.
Verticaldistributiontype Emissionsector/source Fractionperheightrange(%)
0?30m 30?100m 100?300m 300?600m 600?1000m 1000?2000m
Energy Energyproductionanddistribution 0 10 70 20 0 0
Industrial Industrialprocessesandcombustion 10 20 60 10 0 0
Residential Residentialandcommercialcombustion 40 40 20 0 0 0
Waste Wastetreatmentanddisposal 10 20 40 30 0 0
Near-surfacea 80 20 0 0 0 0
Surfaceb 100 0 0 0 0 0
Volcanic VolcanicSO2 0 0 10 30 40 20
0?100m 100?500m 500?1000m 1000?2000m 2000?3000m 3000?6000m
Forestfires
Tropical(30?S?30?N) 20 20 20 40 0 0
Temperate(30?60?S/N) 20 20 20 40 0 0
High-latitude(60?90?S/N) 10 10 20 20 40 0
a Includessolventproductionandusesmaritimetransport,agriculturalwasteburning,grasslandfires,SOA,andmineraldust.b Includeslandtransport,agriculture,biogenicemissionsfromsoilsandvegetation,
oceanicemissions,andradon-222.
Table A2. List of stations used for the evaluation of the simulated surface radon-222 concentrations. The mean bias and root mean square
errors (RMSE) in the simulated monthly mean concentrations are indicated for the online and offline EC-Earth simulations and the ERA-
Interim simulation. Also given are the linear correlation coefficients between the simulated and measured monthly mean concentrations.
A diurnal window has been applied to the measurements, except for Cape Grim. The results for the online EC-Earth simulation and the
ERA-Interim simulation are separated by a slash; the results for the offline EC-Earth simulation are shown between parentheses.
Station Lat. Long. Height Period Window Bias RMSE Correlation
(deg) (deg) (m) (years) (hoursLT, inclusive) (Bqm?3) (Bqm?3) coefficient
CapeGrim,Tasmania ?40.68 144.69 90 1991?2012 Notapplied 0.11/0.30 0.27/0.33 0.47/0.93
(0.19) (0.28) (0.79)
Richmond,Australia ?33.62 150.75 24 2007?2012 14?18 ?0.10/0.33 0.50/0.53 0.81/0.84
(0.07) (0.59) (0.62)
MaunaLoa,Hawaii 19.54 ?155.58 4170 2004?2012 8?10 ?0.03/?0.04 0.04/0.04 0.78/0.78
(?0.03) (0.04) (0.78)
Gosan,JejuIsland,Korea 33.29 126.16 70 2001?2010 13?17 ?0.97/?0.82 1.07/0.95 0.66/0.59
(?0.96) (1.10) (0.45)
SadoIsland,Japan 38.25 138.40 130 2002?2005 11?16 ?0.77/?0.43 0.94/0.73 ?0.70/?0.71
(?0.62) (0.84) (?0.64)
Hohenpeissenberg, Germany 47.80 11.02 985 1999?2005 14?18 ?1.30/0.12 1.45/0.83 0.26/0.52
(?0.56) (0.97) (0.13)
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T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2465
Table A3. Global budgets of oxidized and reduced reactive nitrogen (Tg N year?1) for the reference EC-Earth simulation and the correspond-
ing ERA-Interim simulation, compared with the ACCMIP multi-model means and standard deviations for the year 2000 from Lamarque et
al. (2013b). The standard deviations indicated for the NOx production by lightning in our simulations are calculated from the interannual
variability.
EC-Earth ERA-Interim ACCMIP
Oxidized nitrogen (NOy)
Total NOx emissions 49.6 49.0 49?3
NOx emissions by soils 4.95
NOx production by lightning 6.45?0.06 5.83?0.21 6?2
NOy dry deposition 21.1 20.9 21?7
NOy wet deposition 29.6 28.8 29?5
NOy net chemical production 1.02 0.76 ?1
Reduced nitrogen (NHx)
NH3 emissions 48.5 49?2
NH3 dry deposition 21.9 22.3 15?6
NH4 dry deposition 0.0 0.0 5?1
NH3 wet deposition 8.23 8.14 7?4
NH4 wet deposition 18.1 17.8 23?6
NHx net chemical destruction 0.24 0.27
Table A4. List of stations from the NOAA GMD network of flask measurements used for the evaluation of the simulated surface CO mixing
ratios. The mean bias and root mean square errors (RMSE) in the simulated monthly mean mixing ratios are indicated for the reference
EC-Earth simulation and the corresponding ERA-Interim simulation. Also given are the linear correlation coefficients between the simulated
and measured monthly mean concentrations. The results for the EC-Earth and ERA-Interim simulations are separated by a slash.
Station Lat. Long. Height Period Bias RMSE Correlation
(deg) (deg) (m) (years) (ppbv) (ppbv) coefficient
South Pole ?89.98 ?24.80 2810 2000?2009 ?0.2/0.1 2.9/3.7 0.94/0.91
Halley Station, Antarctica ?75.58 ?26.5 30 2000?2009 ?1.2/?0.6 2.4/2.7 0.97/0.94
Cape Grim, Tasmania ?40.68 144.69 94 2000?2009 6.5/6.4 8.8/8.7 0.70/0.67
Tutuila, American Samoa ?14.25 ?170.56 42 2000?2009 ?3.9/?4.3 4.6/4.9 0.90/0.86
Ascension Island ?7.97 ?14.40 85 2000?2009 ?5.2/?3.1 9.3/7.9 0.69/0.83
Ragged Point, Barbados 13.16 ?59.43 15 2000?2009 ?18.3/?18.2 18.6/18.7 0.97/0.96
Mariana Islands, Guam 13.39 144.66 0 2000?2009 ?19.5/?15.9 19.6/16.9 0.99/0.96
Mauna Loa, Hawaii 19.54 ?155.58 3397 2000?2009 ?18.5/?18.4 18.8/18.8 0.98/0.98
Iza?a, Tenerife 28.31 ?16.50 2373 2000?2009 ?25.3/?23.9 25.9/24.6 0.96/0.94
Tudor Hill, Bermuda 32.27 ?64.88 30 2002?2009 ?29.3/?23.2 30.9/24.0 0.97/0.99
Terceira Island, Azores 38.77 ?27.38 19 2000?2009 ?33.9/?27.6 34.5/28.1 0.95/0.96
Mace Head, Ireland 53.33 ?9.90 5 2000?2009 ?30.2/?19.9 31.2/20.7 0.97/0.97
Cold Bay, Alaska 55.21 ?162.72 21 2000?2009 ?36.7/?29.2 39.0/31.5 0.92/0.90
Storhofdi, Iceland 63.40 ?20.29 118 2000?2009 ?34.0/?23.9 35.3/24.9 0.96/0.97
Barrow, Alaska 71.32 ?156.61 11 2000?2009 ?38.1/?28.7 41.0/31.3 0.94/0.96
Ny-?lesund, Spitsbergen 78.91 11.89 474 2000?2009 ?39.0/?28.0 41.0/29.5 0.97/0.98
Alert, Canada 82.45 ?62.51 200 2000?2009 ?38.9/?29.4 41.4/31.5 0.95/0.97
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2466 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
Table A5. List of stations used for the evaluation of the simulated surface O3 mixing ratios. The mean bias and root mean square errors
(RMSE) in the simulated monthly mean mixing ratios are indicated for the reference EC-Earth simulation and the corresponding ERA-
Interim simulation. Also given are the linear correlation coefficients between the simulated and measured monthly mean concentrations. The
results for the EC-Earth and ERA-Interim simulations are separated by a slash.
Station Lat. Long. Height Period Bias RMSE Correlation
(deg) (deg) (m) (years) (ppbv) (ppbv) coefficient
South Pole ?89.98 ?24.80 2810 2000?2009 ?11.2/?17.1 11.6/17.3 0.70/0.94
Arrival Heights, Antarctica ?77.83 166.20 250 2000?2008 ?6.1/?13.3 8.0/14.1 0.86/0.89
Neumayer, Antarctica ?70.65 ?8.25 42 2000?2009 ?3.3/?10.5 5.6/11.1 0.86/0.97
Syowa, Antarctica ?69.00 39.58 16 2000?2009 ?2.8/?11.1 4.7/11.4 0.91/0.99
Cape Grim, Tasmania ?40.68 144.68 94 2000?2009 3.5/?3.3 5.0/4.9 0.88/0.89
Tutuila, American Samoa ?14.25 ?170.56 42 2000?2009 6.6/5.8 6.7/5.8 0.99/0.99
Ragged Point, Barbados 13.16 ?59.43 15 2006?2009 12.8/8.0 13.1/8.3 0.79/0.91
Cape Verde 16.85 ?24.87 10 2006?2009 11.1/5.9 12.5/7.3 0.31/0.68
Mauna Loa, Hawaii 19.54 ?155.58 3397 2000?2009 2.6/2.7 3.0/3.1 0.95/0.96
Assekrem, Algeria 23.27 5.63 2710 2000?2001, 2003?2009 9.0/5.1 9.5/5.6 0.66/0.78
Mount Everest, Nepal 27.96 86.82 5079 2006?2009 10.3/8.9 12.1/10.5 0.78/0.81
Iza?a, Tenerife 28.30 ?16.50 2367 2000?2009 10.6/2.7 11.6/3.9 0.76/0.83
Tudor Hill, Bermuda 32.27 ?64.88 30 2003?2009 6.2/1.7 7.9/5.8 0.87/0.85
Trinidad Head, California 41.05 ?124.15 107 2002?2009 11.0/2.9 11.9/5.9 0.44/0.38
Jungfraujoch, Switzerland 46.55 7.99 3580 2000?2009 6.9/?1.5 7.7/2.8 0.93/0.94
Payerne, Switzerland 46.82 6.95 490 2000?2009 9.4/1.8 10.1/4.3 40.93/0.93
Zugspitze, Germany 47.42 10.98 2960 2000?2002 7.9/0.1 8.7/3.5 0.90/0.90
Hohenpeissenberg, Germany 47.80 11.02 985 2000?2007 2.8/?10.2 5.5/11.6 0.88/0.91
Mace Head, Ireland 53.33 ?9.90 25 2000?2009 9.3/0.0 10.2/4.1 0.39/0.49
Storhofdi, Iceland 63.40 ?20.29 118 2003?2009 5.5/?6.2 7.2/7.1 0.40/0.69
Barrow, Alaska 71.32 ?156.61 11 2000?2009 2.5/?4.5 8.3/8.0 ?0.26/?0.16
Summit, Greenland 72.58 ?38.48 3216 2000?2009 ?4.0/?13.1 5.2/13.5 0.63/0.68
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2467
Table A6. Global budgets of DMS, MSA, SO2 and total reactive sulfur together with the global burden and lifetime of SO2 for the reference
EC-Earth simulation and the corresponding ERA-Interim simulation. For comparison we have also included ACCMIP multi-model means
and standard deviations of emissions and depositions for the year 2000 from Lamarque et al. (2013b). For the gas-phase SO2 and aqueous-
phase S(IV) oxidation reactions, which result in the production of SO4, the numbers between parentheses give the separate contributions
from 90?30? S, 30? S?30? N, and 30?90? N.
EC-Earth ERA-Interim ACCMIP
DMS
Emissions (Tg S year?1) 19.4?0.2? 19.1?0.3? 23?5
Chemical destruction (Tg S year?1)
DMS + OH 14.4 14.5
DMS + NO3 4.93 4.60
MSA
Chemical production (Tg S year?1)
DMS + OH 1.87 1.79
Wet deposition (Tg S year?1) 1.87 1.79 ?2
SO2
Total emissions (Tg S year?1) 57.2 65?2
Volcanic emissions (Tg S year?1) 4.67 ?12?2
Chemical production (Tg S year?1)
DMS + OH 12.6 12.7
DMS + NO3 4.93 4.60
Chemical destruction (Tg S year?1)
SO2+ OH 7.80 8.24
(0.43/3.84/3.53) (0.34/4.39/3.52)
S(IV) + H2O2 23.9 24.2
(2.75/12.7/8.46) (2.63/13.4/8.11)
S(IV) + O3 5.42 4.58
(1.01/2.58/1.83) (0.91/2.02/1.64)
Dry deposition (Tg S year?1) 26.1 26.3
Wet deposition (Tg S year?1) 11.5 11.2
Burden (Tg S) 0.317 0.261
Lifetime (days) 1.55 1.28
Total reactive sulfur
Emissions (Tg S year?1) 78.1 77.8 89?6
Dry deposition (Tg S year?1) 27.0 27.2 37?10
Wet deposition (Tg S year?1) 51.1 50.7 52?8
? Standard deviations calculated from the simulated interannual variability.
Table A7. Mean biases and linear correlation coefficients between the time averaged AOD fields from the simulations and the MODIS
Level-3 product coarsened to the same resolution and averaged over the years 2000?2009. Grid areas where, for all consecutive years, the
MODIS data are missing for one or more months during the year or season of interest have not been included in the calculations. The results
for the EC-Earth and ERA-Interim simulations are separated by a slash.
Season Mean bias Correlation coefficient Observed area (%)
Annual ?0.083/?0.086 0.80/0.79 71.8
DJF ?0.073/?0.080 0.76/0.72 82.2
MAM ?0.093/?0.095 0.78/0.78 82.1
JJA ?0.087/?0.085 0.72/0.78 84.1
SON ?0.078/?0.080 0.62/0.60 84.8
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2468 T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, DJF 0.0011
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Black Carbon AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, DJF  0.0000
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Black Carbon AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, JJA 0.0012
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Black Carbon AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, JJA -0.0001
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Black Carbon AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, DJF 0.0084
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Organic Aerosol AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, DJF  0.0004
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Organic Aerosol AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, JJA 0.0110
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Organic Aerosol AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, JJA -0.0004
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Organic Aerosol AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, DJF 0.0014
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Nitrate AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, DJF  0.0007
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Nitrate AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, JJA 0.0002
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Nitrate AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, JJA  0.0000
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Nitrate AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, DJF 0.0275
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Sea Salt AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, DJF  0.0031
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Sea Salt AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, JJA 0.0266
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Sea Salt AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, JJA  0.0034
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Sea Salt AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, DJF 0.0149
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Mineral Dust AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, DJF  0.0006
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Mineral Dust AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, JJA 0.0147
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Mineral Dust AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, JJA -0.0038
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Mineral Dust AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, DJF 0.0183
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Sulfate AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, DJF  0.0018
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Sulfate AOD Difference at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth, JJA 0.0254
0
0.01
0.02
0.04
0.06
0.08
0.1
0.15
0.2
0.3
0.4
0.5
0.6
Sulfate AOD at 550 nm
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
-90
-60
-30
0
30
60
90
EC-Earth - ERA-Interim, JJA -0.0009
-0.2
-0.15
-0.1
-0.05
-0.02
-0.01
0
0.01
0.02
0.05
0.1
0.15
0.2
Sulfate AOD Difference at 550 nm
Figure A1. Contributions from the different aerosol components to the aerosol optical depth at 550 nm in the reference EC-Earth simulation
and the differences compared to the corresponding ERA-Interim simulation for boreal winter (left two columns) and boreal summer (right
two columns). The global mean values of the displayed fields are indicated at the top of each panel.
Geosci. Model Dev., 7, 2435?2475, 2014 www.geosci-model-dev.net/7/2435/2014/
T. P. C. van Noije et al.: Simulation of chemistry and aerosols with EC-Earth 2469
Acknowledgements. The authors thank Klaus Wyser from the
Swedish Meteorological and Hydrological Institute (SMHI) for
providing the IFS-NEMO restart fields used to initialize the
online EC-Earth simulations and Tido Semmler for providing IFS
model-level output from an EC-Earth simulation performed by
the Irish National Meteorological Service. The authors also thank
Greg Bodeker (Bodeker Scientific) and Birgit Hassler (NOAA)
for providing the combined vertical ozone profile database, and
the MODIS science team for providing the Level-3 monthly
AOD fields from MODIS collection 5.1. The EC-Earth and
TM5 simulations for this study were performed on ECMWF?s
high-performance computing facility.
Edited by: O. Morgenstern
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