Introduction
In recent years several global and regional-scale model systems have
been developed to take into account the feedback between natural and
anthropogenic gaseous compounds and aerosol particles and the state of
the atmosphere. At the beginning those model systems mainly covered
the global scale in general using a hydrostatic framework. Recent
developments of those hydrostatic global chemistry–climate models take
into account the dynamical and chemical coupling between troposphere
and stratosphere and partly with the mesosphere. Examples of such
model systems are ECHAM/HAMMOZ (see Glossary in Appendix A) ,
EMAC (see Glossary) , and WACCM (see Glossary) . In the meantime also regional-scale online-coupled model
systems exist . Those regional-scale models are
using a non-hydrostatic framework as this is required to resolve the
relevant processes on this scale. Examples of such model systems are
WRF–Chem (see Glossary) and COSMO–ART . Both
model systems are based on meteorological models that are applied for
operational weather forecast on timescales of a few days by national
weather services.
Regional-scale models need boundary conditions for the meteorological
as well as chemical and aerosol variables. Most often these boundary
conditions are taken from a global-scale model. Here the problem
arises that these model systems are inconsistent in model physics and
the specification of air constituents and the chemistry involved. The
global atmospheric model ICON ICOsahedral
Nonhydrostatic, offers the possibility to overcome this inconsistency as
it uses a nonhydrostatic framework already on the global scale and
allows one-way and two-way nesting in regions of interest down to
horizontal grid mesh sizes in the order of kilometers.
Based on ICON, the new model system ICON–ART is currently under
development. ART stands for Aerosols and Reactive Trace gases. The
extension ART has been previously used in COSMO–ART to study the
feedback between aerosol, trace gases and the atmosphere
e.g.,. At the final
stage of the model development ICON–ART will contain tropospheric and
stratospheric chemistry, aerosol chemistry and aerosol
dynamics. Moreover, as a fully online-coupled model system ICON–ART
will account for the impact of gases and aerosols on radiation and
clouds. By this, the feedback between gaseous and particulate matter
and the state of the atmosphere will be realized.
This paper describes the basic equations, gives an overview of the
physical parameterizations and numerical methods used in ICON–ART and
shows results of the first applications. If not stated differently,
physical parameterizations (e.g., radiation, microphysics) used for
the simulations in this paper are the same as described in
. Section
presents a short summary of ICON and the numerical methods used for
the tracer transport. Section gives the
basic equations for the treatment of gaseous and particulate
matter. Section describes the handling of
physical and chemical processes realized so
far. Section describes the temporal
discretization and the methods applied for the coupling of ICON and
ART. In Sect. , results of first
applications of ICON–ART are presented: a case study of the global
distribution of short-lived bromocarbons, the spatial and temporal
distribution of the ash cloud of the Eyjafjallajökull eruption
in 2010, and finally an estimation of the global annual sea-salt
emission.
ICON
ICON–ART is based on the nonhydrostatic model system ICON which was
developed in a joint project between the German Weather Service (DWD)
and the Max Planck Institute for Meteorology (MPI-M) as a unified
next-generation global numerical weather prediction (NWP) and climate
modeling system. The main goals which were reached during the
development of ICON are
better conservation properties than in the existing global model systems GME and ECHAM , with the obligatory requirement
of exact local mass conservation and mass-consistent transport;
better scalability on future massively parallel high-performance computing architectures; and
the availability of some means of static mesh refinement. This was subsequently concretized into the capability of
mixing one-way nested and two-way nested grids within one model application, combined with an option for vertical nesting
in order to allow the global grid to extend into the mesosphere (which greatly facilitates the assimilation of satellite data).
The nested domains extend only into the lower stratosphere in order to save computing time.
The dynamical core is based on the set of prognostic variables
suggested by , using flux-form equations for the
thermodynamic scalars density ρ and virtual potential temperature
θv. This allows for achieving local mass conservation
in a straightforward way. Mass-consistent tracer transport is
obtained by passing temporally averaged mass fluxes to the transport
scheme (see Sect. ). Compared to the
hydrostatic dynamical core developed as an intermediate step by
, several refinements of the model numerics have been
implemented in order to reduce the amount of computational diffusion
required for numerical stability. Most importantly, the velocity
components entering into the divergence operator are averaged such as
to obtain (nearly) second-order accuracy, and upwind-biased
discretization are used for the advection of the thermodynamic
scalars. Besides imposing some implicit damping on small-scale
structures, the latter reduce the numerical dispersion errors compared
to second-order centered differences.
Tracer transport
Tracer transport is accounted for in a time-split fashion, i.e., by
treating vertical and horizontal transport separately. In the
vertical, the finite-volume piecewise parabolic method (PPM)
is applied, where the tracer distribution in each
cell is reconstructed using 1-D parabolas. The specific formulation in
ICON is able to cope with large Courant-numbers (CFL > 1), following
the approach proposed by .
For horizontal transport, a simplified flux-form semi-Lagrangian
(FFSL) scheme is used, similar to and
. The basic idea for computing the horizontal flux
divergence, is to trace the area that is “swept” through an Eulerian
cell edge during 1 time step. In the current implementation, the
swept area is approximated as a rhomboid and the tracer distribution
in each cell is reconstructed using either 2-D linear, quadratic or
cubic polynomials. The polynomial coefficients are estimated using
a conservative weighted least squares reconstruction method
. The performance of the scheme with first-order
(linear) polynomials is documented in .
Specific care is taken to retain tracer and air mass
consistency. Firstly, mass fluxes passed to the transport scheme are
temporal averages of the mass fluxes computed within the dynamical
core (during the solution of the mass continuity equation). Secondly,
as part of the time-split approach, the mass continuity equation is
diagnostically re-integrated, as proposed by .
The transport scheme preserves linear correlations given that
a monotone flux limiter is applied.
Basic equations
In the following the basic equations for the treatment of gases and
aerosols in ICON–ART are given. We will use the so-called
barycentric mean (indicated by a hat) with respect to the density of
air ρ
Ψ^=ρΨ‾ρ‾,
where Ψ is a (mass-)specific variable and will be further
described in the following sections. A variable with a bar on top is
Reynolds-averaged. The fluctuation Ψ′′ is given by Ψ′′=Ψ-Ψ^. The total time derivative reads as
d^dt=∂∂t+v^⋅∇,
where v^ is the barycentric mean of the velocity. The
continuity equation is given by
d^ρ‾dt=-ρ‾∇⋅v^.
Gaseous tracers
For gaseous tracers, the scalar variable Ψ is given by the ratio
of the partial density of gas l and the total density. This results
in the barycentric-averaged mass mixing ratio Ψg,l^:
Ψg,l^=ρρlρ‾ρ‾=ρl‾ρ‾,
which will be used in the following equations. Within ICON–ART, the
spatiotemporal evolution of gaseous tracers is treated according to
the following equation
ρ‾d^Ψg,l^dt=-∇⋅(ρv′′Ψg,l′′)‾+Pl-Ll+El,
where ∇⋅(ρv′′Ψg,l′′)‾ is the change
due to turbulent fluxes. The production rate due to chemical reactions
is given by Pl and the loss rate by Ll. Emissions are
accounted for by El (processes are further explained within
Sect. ). Applying
Eqs. () and (),
Eq. () can be rewritten in the so-called
flux form:
∂ρ‾Ψg,l^∂t=-∇⋅(v^ρ‾Ψg,l^)-∇⋅(ρv′′Ψg,l′′)‾+Pl-Ll+El,
where the flux divergence ∇⋅(v^ρ‾Ψg,l^) includes the horizontal and
vertical advection of the gaseous compound l.
Monodisperse aerosol
For monodisperse aerosol, the scalar variable Ψ is given by the
ratio of the mass concentration Ml of aerosol l and the total
density. This results in the barycentric-averaged mass mixing ratio
Ψl^:
Ψl^=ρMlρ‾ρ‾=Ml‾ρ‾,
which will be used in the following equations. The balance equation
for a monodisperse tracer in flux form is given by
∂ρ‾Ψl^∂t=-∇⋅(v^ρ‾Ψl^)-∇⋅(ρv′′Ψl′′)‾-∂∂zvsed,lρ‾Ψl^-λlρ‾Ψ^l+El,
where ∇⋅(ρv′′Ψl′′)‾ denotes the
change due to turbulent fluxes, vsed,l is the
sedimentation velocity, λl the washout coefficient, and
El stands for the emissions flux of tracer l (processes are
further explained within Sect. ).
Polydisperse aerosol
Based on the extended version of MADEsoot Modal Aerosol
Dynamics Model for Europe, extended by soot;,
polydisperse aerosol particles are represented by several lognormal
distributions. As first example of a polydisperse aerosol, we
implemented sea-salt aerosol. Mass mixing ratio and specific number
are prognostic variables whereas the median diameter is a diagnostic
variable. The standard deviation is kept constant. We use three lognormally
distributed modes for sea-salt aerosol . A list of
modes, the according median diameters at emission and the standard deviation is given
in Table . During the simulation, some processes
(namely the diameter dependent) can change the median diameter of
a distribution.
Parameters for the lognormally distributed aerosol species. d‾0,l,E (d‾3,l,E)
is the median diameter of the specific number (mass) emission of mode l. The standard deviation of mode
l, σl, is held constant for the whole simulation.
Sea-salt aerosol
Mode A
Mode B
Mode C
d‾0,l,E [µm]
0.2
2.0
12.0
d‾3,l,E [µm]
0.69
8.45
27.93
σl
1.9
2.0
1.7
As number and mass are prognostic, we require the barycentric averages
of the (mass-)specific number Ψ0,l^ as well as the
mass mixing ratio Ψ3,l^ (the indices 0 and 3 are
chosen due to the proportionality to these moments of the distribution).
They are formed by using
Eq. () with the ratio of number (mass)
concentration Nl (Ml) to the total density for the scalar
variable Ψ. This results in
Ψ0,l^=ρNlρ‾ρ‾=Nl‾ρ‾,Ψ3,l^=ρMlρ‾ρ‾=Ml‾ρ‾.
Number
For lognormally distributed aerosol, the specific number
ψ0,l^ of mode l with diameter dp is given by
ψ0,l^(lndp)=Ψ0,l^2⋅π⋅lnσl⋅exp-(lndp-lnd‾0,l)22⋅ln2σl,
where the shape parameters of the lognormal distribution of mode l
are the standard deviation σl and the median diameter d‾0,l. As
stated before, the median diameter is a diagnostic variable and the standard deviation
is held constant during the whole simulation.
For the total specific number of mode l, Ψ0,l^, we
solve the following prognostic equation
∂ρ‾Ψ0,l^∂t=-∇⋅(v^ρ‾Ψ0,l^)-∇⋅(ρv′′Ψ0,l′′)‾-∂∂zvsed,0,lρ‾Ψ0,l^-W0,l+E0,l,
where ∇⋅(ρv′′Ψ0,l′′)‾ is the turbulent
flux of the specific number of mode l, vsed,0,l is the
sedimentation velocity of the specific number of mode l, W0,l
denotes the loss of particles of mode l due to wet
below-cloud scavenging, and E0,l denotes the number emission flux of
particles of mode l (processes are further explained within
Sect. ).
Mass
The mass mixing ratio ψ3,l^ of lognormally
distributed aerosol of mode l at diameter dp is given by
ψ3,l^(lndp)=Ψ3,l^2⋅π⋅lnσl⋅exp-(lndp-lnd‾3,l)22⋅ln2σl,
where d‾3,l denotes the median diameter of mode
l. For the emission scheme, the according median diameter of the
emissions, d‾3,l,E, is calculated with the following
relation :
lnd‾3,l,E=lnd‾0,l,E+3⋅ln2σl.
The according prognostic equation that is solved for
Ψ3,l^, is given by
∂ρ‾Ψ3,l^∂t=-∇⋅(v^ρ‾Ψ3,l^)-∇⋅(ρv′′Ψ3,l′′)‾-∂∂zvsed,3,lρ‾Ψ3,l^-W3,l+E3,l,
where ∇⋅(ρv′′Ψ3,l′′)‾ is the turbulent
flux of the mass mixing ratio of mode l, vsed,3,l is the
sedimentation velocity of the mass mixing ratio of mode l, W3,l
denotes the loss in the mass mixing ratio of mode l due to
below-cloud scavenging, and E3,l denotes the mass emission flux of
mode l (processes are further explained within
Sect. ).
Physical and chemical processes
Within this section, we want to present the physical and chemical
parameterizations we use within ICON–ART. We have ordered the
processes within this section in the same sequence as ICON–ART
computes them.
Emission
Although emissions are rather a boundary condition than a physical
process, we decided to include them at this point, as they are the
source term for primary aerosol and also (besides chemical production)
for gaseous compounds.
Very short-lived bromocarbons
In this study CHBr3 and CH2Br2 have been included as
idealized chemical tracers. Both very short-lived bromocarbons were
introduced into the model domain by prescribing
a constant volume
mixing ratio (vmr) globally for pressures greater than
950 hPa. The vmr values are taken from the WMO Ozone
Assessment 2010 and are listed in
Table .
Chemical lifetime and boundary condition of CHBr3 and CH2Br2 in the idealized chemical tracer experiment.
Substance
Chemical lifetime
Boundary condition
(p≥950 hPa)
CHBr3
24 days
1.6 pptv
CH2Br2
123 days
1.1 pptv
Volcanic ash
Usually the actual source strength during an ongoing volcanic eruption
is not known. In order to overcome this problem there is a need to
parameterize those emissions. In the following we will outline in
which way the emissions are parameterized in ICON–ART.
The idea of the currently implemented parameterization is based on the
experience we have made during the recent eruptions of the Iceland
volcanoes (Eyjafjallajökull in April 2010 and Grimsvötn in May 2011). The only information that was available within a short time
delay during these events was the height of the top of the plume of
the volcano. We assume that this information will also be available in
future events. For that reason we derived a parameterization that only
depends on the top of the plume height.
(a) Vertical profile of the emissions given in . (b) Normalized emission profiles. The blue line gives the fit with a Gaussian distribution.
![]()
used the method of inverse modeling with a Lagrangian
model to derive vertical profiles of the emissions of volcanic ash for
the Eyjafjallajökull during the 2010
eruption. Figure a shows the vertical profile of the
emissions that was derived by applying ECMWF data to
drive the dispersion model. We normalized the emission values with the
maximum value and normalized the height above surface with
12.3 km, which was the height of the top of the plume observed
in this case. The latter gives the dimensionless height z⋆:
z⋆=zh,
where z is the height in m and h the height of the top of the
plume. By this procedure we obtain a dimensionless vertical emission
profile that is shown in Fig. b. We assume that the
shape of this normalized profile is universal. The profile is fitted
with a Gaussian distribution. The result is shown by the blue curve in
Fig. b. The fit-function fe(z⋆) is given
by
fe(z⋆)=a1+a2exp-z⋆-a3a42,
where a1=0.0076, a2=0.9724, a3=0.4481, and a4=0.3078.
Assuming that we know the total emission Etot in
kgs-1, we can calculate the vertical emission profile
E(z⋆) by
E(z⋆)=Etotfe(z⋆)∫01fe(z⋆)dz⋆,
where an analytical solution of the integral in the denominator is
given by
∫01fe(z⋆)dz⋆=a1+0.5πa2a4⋅erf1-a3a4+erfa3a4.
So far we have calculated the emission terms independent of the size
class. ICON–ART simulates the size distribution of volcanic ash using
a sectional approach with six monodisperse size bins (1,3,5,10,15, and
30 µm). For distributing the total emission to the
individual size classes, we used the observed size distributions close
to the Eyjafjallajökull that are reported in
. Based on these measurements we obtain distribution
factors fl given in Table .
Size factors for the representation of volcanic ash emissions within a sectional approach.
The diameters of the size bin centers are denoted by dl. The distribution factors fl are taken from .
dl [µm]
1
3
5
10
15
30
fl
0.014884
0.080372
0.186047
0.372093
0.226047
0.120558
In order to specify Etot, we once more rely on the only
information which is available within a short delay of
time. give a parameterization to calculate
Etot as a function of the height of the plume top at the
volcano:
Etot=10.3035h10.241.
In this equation, h is given in [km] and Etot results in
kgs-1. With this parameterization, we have all the
ingredients to calculate the emission term El for
Eq. ():
El=Etot⋅flV⋅flrt,
where V is the volume of a grid cell. The factor flrt is
the fraction of emitted volcanic ash which is available for long-range
transport.
Sea-salt aerosol
Emissions of sea-salt aerosol are realized as described in Sect. 2.5
of . The emission diameters
d‾0,l,emiss, d‾3,l,emiss and the
standard deviation
σl of the three lognormally distributed modes are given in
Table .
The emission fluxes E0,l and E3,l that are required for
Eqs. () and
() are calculated according
to three different emission parameterizations depending on the mode
. For the film mode (Mode A), the emission fluxes
are calculated following . This parameterization
depends on sea surface temperature and wind speed at 10 m
above ground. Emission fluxes of the spume mode (Mode B) are
parameterized as a function of wind speed at 10 m above
ground as described by . For the jet mode (Mode C),
the parameterization of is used which describes the
emission fluxes also as a function of the 10 m wind speed.
Sedimentation
Within ICON–ART, the sedimentation is treated as an additional
vertical advection with an always downward directed vertical velocity
vsed,l. For the vertical advection, a finite-volume
PPM with a quartic interpolation is used
. A monotonic flux limiter guarantees positive
definiteness. The formulation of the vertical advection is
Courant-number independent .
Monodisperse aerosol
The sedimentation velocity for the monodisperse particles
vsed,l is based on Stokes law
e.g.,:
vsed,l=4gDp,lCc,lρp3Cdρ,
where g is the gravitational acceleration [ms-2],
Dp,l is the particle diameter [m], ρp is the particle
density [kg m-3], and Cd is the drag coefficient. Cc,l
is the dimensionless slip correction factor, which depends on the
particle diameter and the mean free path of air λair
[m]:
Cc,l=1+2λDp,l1.257+0.4exp-1.1Dp,l2λair.
Polydisperse aerosol
The sedimentation velocity of polydisperse aerosol is calculated as
described in for the zeroth and third moment of
the aerosol distribution. By this approach, the size dependence of the
gravitational settling is considered.
Turbulence and dry deposition
The turbulent fluxes within ICON are treated by a one-dimensional
prognostic TKE turbulence scheme . The interface allows
for the additional vertical diffusion of tracers. As a necessary
boundary condition, a surface flux
-w′′Ψ′′‾ is required by the
turbulence scheme:
-w′′Ψ′′‾=Chd|vh|(Ψ^a-Ψ^s),
where Chd is the turbulent transfer coefficient for heat,
vh the horizontal wind velocity,
Ψ^a the value of Ψ^ in the lowest model
layer, and Ψ^s a value of Ψ^ at the
surface.
In the cases of gases and particles this surface flux is determined by the
dry deposition process. A commonly used parameterization of this
process is based on the dry deposition velocity vdep:
-w′′Ψ′′‾=vdepΨ^(zR),
with zR=10⋅z0 where z0 is the roughness length. The
deposition velocity vdep of aerosols is calculated as
described in . Combining
Eqs. () and () results in an
artificial surface value:
Ψ^s=Ψ^a1-vdepChd|vh|2⋅zRΔz1+vdepChd|vh|-vdepChd|vh|2⋅zRΔz,
which can then be used to calculate the surface flux
-w′′Ψ′′‾ in
Eq. ().
Washout
One of the major sinks of atmospheric aerosol particles is the washout
of particles by rain. We use different parameterizations depending on
the description of aerosol (monodisperse, polydisperse) which are
summarized in the following. So far, the loss of particles by
nucleation and impaction scavenging is not considered.
Monodisperse aerosol
The washout rate of monodisperse aerosol is given by
Wl=-Λlρ‾Ψ^l,
where Λl is the scavenging coefficient for particles in
mode l. With the assumptions, that size and terminal fall velocity
of aerosol particles are small compared to rain drops, Λl
for monodisperse particles is given by
Λl=∫0∞π4Dr2w(Dr)E(Dr,Dp,l)nr(Dr)dDr,
where Dr is the diameter of a rain drop,
Dp,l is the diameter of particles in mode l,
w(Dr) is the terminal fall velocity of rain drops,
E(Dr,Dp,l) is the collision efficiency
between particles of diameter Dr and Dp,l,
and nr(Dr) is the rain drop number density
size distribution.
To further simplify Eq. () we assume that the rain
drops are monodisperse. Using the definition of the rain fall
intensity
R=π6Dr3w(Dr)Nrρw,
where Nr is the total number concentration of rain
droplets and ρw the density of water, we obtain
Λl=32E(Dr,Dp,l)RρwDr.
Assuming large particles (Dp,l>1µm ) and
a constant representative rain drop size of Dr=5×10-4m we obtain E(Dr,Dp,l)≈1. This results in a scavenging coefficient of
Λl≈3R,
where Λl is given in [s-1] and R is given in
[kgm-2s-1]. The scavenging coefficient calculated
by Eq. () is then used in Eq. () to
calculate the change of Ψl^ by washout.
Polydisperse aerosol
The removal of polydisperse aerosol by wet deposition is parameterized
as a function of the particle size distribution and the size
distribution of rain droplets as described in . In
contrast to , we use a precipitation rate
depending on height to determine the local droplet distribution
instead of using surface precipitation rate over the complete
precipitation column.
Chemical reactions
The chemical degradation of the atmospheric trace gases is
parameterized by a simplified chemistry scheme. Up to now only two
very short-lived bromocarbos (CHBr3 and CH2Br2) were included
in ICON–ART. Both substances are depleted due to chemical reactions
with OH or by photolysis with no chemical production terms. As the
total tropospheric chemical lifetimes for both species are known these
chemical lifetimes are recalculated into a total loss rate being used
in the balance equation Eq. ().
Subgrid-scale convective transport
The contribution of transport and mixing by subgrid-scale convection
on the temporal evolution of the tracer concentrations is an addition
to the advection term in
Eqs. (),
(),
(), and
() which is necessary at
coarse grid mesh sizes (≫1km). In order to account for
this additional transport and mixing, we adapted the parameterization
by , which is used operationally in ICON and the
IFS (Integrated Forecast System) model of ECMWF (European Centre for
Medium-Range Weather Forecasts). The parameterization uses a bulk mass
flux scheme and considers deep, shallow, and mid-level convection. For
a detailed description of the convection scheme we refer to
. For the convective transport of tracers only
shallow and deep convection are considered.
Numerical implementation
Within this section we give an overview of the numerical
implementation focusing on the numerical time integration and the
coupling structure of the host model ICON with the extension ART.
Temporal discretization
Numerical time integration of
Eq. () and analogously
Eqs. (),
(), and
() can be carried out
applying different methods. Following the process splitting concept
used for most processes in ICON, we carry out the numerical time
integration of the individual processes step by step. That means that
the tendency due to a certain process is calculated with the according
prognostic state variables that are already updated by the previous
processes. Within one integration over time from time level ti to
time level ti+1, several updates due to different processes may
be performed sequentially. Starting with Ψ^(ti), the
time integration scheme that leads to Ψ^(ti+1) is
outlined below:
Ψ^1(ti+1)=Ψ^(ti)+Δtρ‾⋅El(Ψ^(ti)),Ψ^2(ti+1)=Ψ^1(ti+1)+Δtρ‾⋅ADVl(Ψ^1(ti+1)),Ψ^3(ti+1)=Ψ^2(ti+1)+Δtρ‾⋅SEDl(Ψ^2(ti+1)),Ψ^4(ti+1)=Ψ^3(ti+1)+Δtρ‾⋅DIFl(Ψ^3(ti+1)),Ψ^5(ti+1)=Ψ^4(ti+1)+Δtρ‾⋅Wl(Ψ^4(ti+1)),Ψ^6(ti+1)=Ψ^5(ti+1)+Δtρ‾⋅CHEMl(Ψ^5(ti+1)),Ψ^(ti+1)=Ψ^6(ti+1)+Δtρ‾⋅CONl(Ψ^6(ti+1)),
where Δt=ti+1-ti and the process rates for emissions
El(Ψ^(ti)), advection
ADVl(Ψ^1(ti+1)), sedimentation
SEDl(Ψ^2(ti+1)), turbulent diffusion
DIFl(Ψ^3(ti+1)), washout
Wl(Ψ^4(ti+1)), chemistry
CHEMl(Ψ^5(ti+1)), and subgrid-scale convective
transport CONl(Ψ^6(ti+1)). The term for the
subgrid-scale convective transport is an addition to the advection. It
describes the temporal change caused by vertical mixing due to shallow
and deep convection. This term appears in those cases where the
horizontal grid spacing does not allow one to describe the process of
shallow and deep convection explicitly. In contrast to the uniform
Δt in Eqs. ()–(),
a subcycling for the sedimentation process (Eq. )
is carried out using the dynamics time step. Due to stability reasons
the implicit Euler solution has been taken for the very short-lived substances (VSLS) tracers in this study.
Coupling ICON and ART
Within ICON, the additional ART modules are integrated in a way that
ensures a flexible plug-in of process routines as well as an
unaffected ICON simulation in case ART is not used. This is realized
by interface modules containing a subroutine with calls to the ART
modules. These calls are separated by preprocessor
(#ifdef) structures.
Interface modules are part of the ICON code (e.g., mo_art_example_interface), whereas the routines called by the interfaces
(e.g., mo_art_example) are part of the ART code.
Schematic of the coupling of ICON–ART. The sequence in which processes
of ICON are executed is illustrated by the blue boxes. Processes of
ART are illustrated by the orange boxes. An orange frame around a blue
box indicates, that the according code is part of the ICON tracer
framework (see Sect. ) but ART tracers are treated
inside this framework. The black circle indicates the sequence of the
time integration.
![]()
The sequence of calls to the different routines in the ICON code is
illustrated in blue in Fig. . Within one
complete integration over time in the ICON model, the dynamics is
followed by the call to the tracer and hydrometeor advection
calculation. Thereafter, the so-called fast and slow physics processes
are called. Saturation adjustment, turbulent diffusion, and
microphysics are accounted for as fast physical processes. Radiation,
convection, the calculation of the cloud cover, and the gravity wave
drag are referred to as slow physics. For stability reasons,
a subcycling with a shorter time step is performed within the
dynamics.
The ART processes are marked by orange boxes within the ICON–ART
sequence in Fig. . Directly at the
beginning, the emissions of aerosols and gaseous species are
calculated. The advection of ART tracers is done within the ICON
tracer framework followed by the sedimentation where a subcycling is
used for stability reasons. Within the fast physics, turbulent
diffusion, washout, and chemical reactions are treated. Finally, within
the slow physics, vertical transport due to subgrid-scale convection
is performed. Advection, turbulence, and convection are marked by
orange frames to illustrate that these are processes that are extended
inside the ICON code for the treatment of aerosol particles and trace
gases.
Zonal mean of temperature (left column) and zonal wind (right column) at 1 October 2012 as given by ERA-Interim reanalysis (top row) and ICON–ART (bottom row).
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The aerosol and gaseous concentrations treated by ART are updated
directly within the according process routines or interfaces (see
Sect. ).
We performed tests with different numbers of cores (powers of two
between 64 and 1024) and found roughly a factor of 3 for an
ICON–ART simulation compared to an ICON simulation without ART. The
ART simulation for this purpose was performed with volcanic ash and
sea-salt aerosol switched on. This shows that the scalability of
ICON applies also to ICON–ART.
First applications
In this section we present examples of first applications of the model
system ICON–ART. According to the processes implemented so far, we
focus on very short-lived bromocarbons, aerosol from volcanic
eruptions, and sea salt. The forcing of dynamics and transport in these
simulations was done by parameterized processes of the NWP version of ICON
and namelist parameters were set accordingly. A R2B06 grid (about
40 km horizontal grid spacing; for a detailed description of ICON grids see Zängl et al., 2014) with no
nested domain has been chosen with 90 non-equidistant vertical levels up
to 75 km together with a time step of 72 s. The vertical
thickness of the lowest model layer is 20 m, the maximum thickness
of about 2600 m is reached at the top of the model domain.
Simulation of very short-lived bromocarbons
Simulated zonal mean of CHBr3 (top) and CH2Br2 (bottom) volume mixing ratio at 1 October 2012.
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Biogenic emitted VSLS have a short
chemical lifetime in the atmosphere compared to tropospheric transport
timescales . As the ocean is the main source of the most
prominent VSLS, bromoform (CHBr3) and dibromomethane (CH2Br2),
this leads to large concentration gradients in the troposphere. The
tropospheric depletion of CHBr3 is mainly due to photolysis, whereas
for CH2Br2 the loss is dominated by oxidation by the hydroxyl
radical (OH) both contributing to the atmospheric inorganic bromine
(Bry) budget.
Once released active bromine radicals play a significant role in
tropospheric as well as stratospheric chemistry as it is in particular
involved in ozone destroying catalytic cycles. Although the bromine
budget in the stratosphere is dominated by release of Bry from
long-lived source gases (e.g., halons) which is relatively well
understood the contribution of biogenic VSLS to stratospheric bromine
is still uncertain e.g.,.
Thus, it is important to simulate the emissions, chemistry and
transport of VSLS from the surface to the lower stratosphere
reasonably having in mind that due to the short chemical lifetime in
the troposphere the transport of CHBr3 and CH2Br2 is mainly
expected in regions of deep convection taking place frequently as,
e.g., the tropical western Pacific e.g.,.
To test the ability of ICON–ART to simulate this fast transport from
the ocean surface into the lower stratosphere CHBr3 and
CH2Br2 have been included as idealized chemical tracers. The
boundary conditions and the chemical lifetimes are taken from the WMO
Ozone assessment 2010 . They are recalculated into
a destruction frequency for the implicit solution of the balance
equation Eq. () (see
Table ).
The simulation was initialized with
data from the ECMWF Integrated Forecast System (IFS) for 1 June 2012,
00:00 UTC. The sea surface temperature was initialized with the skin
temperature from the IFS initialization data and kept constant during
the simulation. The output was interpolated on a regular
latitude–longitude grid with 0.5∘×0.5∘
resolution on pressure levels.
To estimate the ability of ICON–ART in the NWP mode to simulate longer
timescales necessary for the diffusion of the very short-lived
bromocarbons into the upper troposphere–lower stratosphere (UTLS) the
zonal mean of the simulated temperature and of the zonal wind are
shown in Fig. and compared to
ERA-Interim data at 1 October 2012. In this model setup
ICON–ART is able to reproduce the main characteristics of the
reanalyzed meteorology 122 days after initialization in the
UTLS. For example, the simulated temperature agrees well with the
ERA-Interim data in absolute temperature values in the tropical lower
stratosphere as well as in the minimum temperatures within the
stratospheric polar vortex in the Southern Hemisphere. A good
agreement with the ERA-Interim data is also found for the wind fields
revealing that ICON–ART in the NWP mode is suitable for the
investigation of the tracer transport of the VSLS from the surface
into the UTLS region.
In Fig. the zonal mean of the simulated
distribution of CHBr3 and CH2Br2 at 1 October 2012 is
shown. Therein the fixed boundary condition for p≥950 hPa is visible together with the fast upward transport
into the UTLS in the tropics. Due to
its longer lifetime CH2Br2 is transported quasi-horizontally in
the upper troposphere into the mid-latitudes and also slightly
higher up into the lower stratosphere compared to the about 5 times
shorter lived CHBr3.
The simulated fast transport into the lower stratosphere occurs mainly
in the tropical western Pacific region (see Fig. )
which is in agreement with previous studies as the preferred region of
the transport of VSLS into the lower stratosphere
e.g.,. For CHBr3 the distribution at
150 hPa is more inhomogeneous than for CH2Br2 due to its
shorter life time pointing to the regions of fast vertical transport
into the lower stratosphere. Consequently, the advection into the
mid-latitudes is more visible in the longer lived CH2Br2
compared to CHBr3.
Simulated distribution of CHBr3 (top) and CH2Br2 (bottom) volume mixing ratio at 150 hPa at 1 October 2012. Please note the different color scales.
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Zonal mean profiles of the simulated CHBr3 and CH2Br2
averaged between 20∘ S and 20∘ N are compared to
mean tropical observations in Fig. . The mean
observations of CHBr3 and CH2Br2 are based on a compilation
of data from different projects and campaigns on different platforms:
the CARIBIC (see Glossary) project between 2009 and 2013 , the
campaigns TRACE-A in 1992, STRAT in 1996, PEM-Tropics in 1996 and
1999, ACCENT in 1999, TRACE-P in 2001, Pre-AVE, AVE and CR-AVE in 2004
and 2006, TC4 in 2007, HIPPO-1 to HIPPO-5 between 2009 and 2011, SHIVA
in 2011, and TACTS/ESMVal in 2012 (G. Krysztofiak, personal
communication, 2014). The observed mean
tropical profiles have been multiplied by 1.70 for CHBr3 and 1.15
for CH2Br2, respectively, for an easier comparison with the
modeled profiles. This is justified as the boundary value of
differs significantly from the observed value and as the
calculation of the concentrations of the VSLS is linearized due to the
lifetime approach and therefore depends linearly on the concentration
of the VSLS themselves. The ICON–ART results of both brominated
substances exhibit the characteristic C-shape profile form and more
pronounced for the short-lived CHBr3 than for CH2Br2, both
also being observed. The volume mixing ratios of about 1 pptv
at about 200 hPa (about 11 km) for the longer-lived
CH2Br2 is in good agreement with the mean observations as well
as other model studies which are in the range of about
0.9 pptv for CH2Br2. For the shorter-lived CHBr3, the
observations are in the range of 0.3–1.1 with a mean of about
0.6 pptv , and thus slightly lower than the
simulated volume mixing ratio. This discrepancy might be caused to
a possible sampling bias of the highly variable CHBr3 in that
altitude region due to its short lifetime e.g.,.
Mean vertical profiles between 20∘ S and 20∘ N of CHBr3 (red) and CH2Br2 (blue) volume mixing ratio simulated by ICON–ART for 1 October 2012 (solid lines) and observed during different campaigns (dashed lines). Please note that, the observed mean tropical profiles have been multiplied by 1.70 for CHBr3 and 1.15 for CH2Br2. See text for more details.
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Volcanic ash
The forecast of volcanic ash particles is of great interest for
aviation. Moreover, the impact of volcanic ash particles on radiation
and cloud properties is of importance for climate change and most
probably also for weather forecast. In order to test the capability of
ICON–ART to simulate the spatial and temporal distribution of volcanic
ash particles, we performed a simulation of the ash cloud of the
Eyjafjallajökull eruption in April 2010. This eruption led to
a shutdown of civil aviation over large parts of Europe. In response,
high efforts have been undertaken to improve the prediction of the
volcanic ash plume by deriving the volcanic eruption source strength
and vertical emission profile from direct observations
e.g.,. With ICON–ART's predecessor, COSMO–ART, time
lagged ensembles have been produced to assess the uncertainties of the
volcanic ash forecast due to meteorology . Based on
both studies, we developed a new parameterization of the source term as
outlined in Sect. . We performed a 5 days
continuous forecast, that means ICON–ART was initialized only at the
beginning of the forecast period. The simulation starts at 14 April
2010, 00:00 UTC. We use a R2B06 grid that results in a horizontal grid
mesh size of about 40 km and specified the observed emission
heights based on .
The emission fluxes for the different size bins are calculated using
Eq. (). It is assumed that a significant fraction
of the total emitted mass is deposited close to the source due to the
gravitational settling of large particles, aggregation of small particles
and organized downdrafts. Common values for the ash fraction available
for long-range transport lie between 1 and 10 %
e.g.,. We chose an appropriate value of
flrt=0.04 in Eq. () which lies well
in that range.
Simulated and observed number concentrations of particles with a diameter of 3 µm at Hohenpeissenberg, Germany.
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Figure shows time series of the simulated
and the observed number concentrations of particles with a diameter of
3 µm at the meteorological observatory Hohenpeissenberg,
Germany. The arrival of the ash plume is captured quite well by the
simulation keeping in mind that we have used a relatively coarse grid
mesh size and that the forecast lead time is already 4 days when
the plume reaches the station.
Lidar measurements of the volcanic ash plume were carried out by
at Maisach, Germany. The quantities derived from
those measurements are not directly comparable to modeled variables of
ICON–ART. Therefore, we calculated the cross sections of all size bins
as a proxy. A comparison of the observed range-corrected signal and
the simulated cross section of the ash particles is given in
Fig. . It is very promising that the model simulations
capture some of the main features of the observed plume. This includes
the time when the maximum at higher levels occurs, as well as the
descent of the plume. The thickness of the simulated plume differs
from the observations which can be explained by the vertical grid
spacing used for the simulations. In the height range where the
maximum occurs the vertical grid spacing is in the order of
300 m.
Top: logarithm of range-corrected signal of multi-wavelength lidar system (MULIS) at = 1064 nm at Maisach from 16 April 2010, 17:00 UTC
to 17 April 2010, 17:00 UTC and from 0 to 10 km above ground; white areas denote periods without measurements
taken from. The thick white line shows the hand drawn border of the top of the ash plume based on the
observations. Bottom: simulated cross sections in µm2m-3 for the size bins 1, 3, 5, 10, and 15 µm.
The brownish line is a copy of the white line drawn in the measurements into the graph of the model results.
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In Fig. , the horizontal distribution of the
total volcanic ash concentration (sum of all six size bins) is shown
at four reference heights at 16 April 2010, 12:00 UTC. The shape of the
ash plume is very characteristic. It spans a horizontally thin band
across the northern coasts of France, Germany, Poland, and the
Baltic. This band is partly connected to Iceland by an area of low
concentrations over the North Sea. Although further investigation and
validation is needed concerning the absolute values of the ash
concentration, the spatial pattern of the simulated ash plume is in
very good agreement with previous studies including model results and
observations (e.g., , Fig. 9; ,
Fig. 12; , Fig. 11).
Sea-salt aerosol
Sea-salt aerosol is directly emitted into the atmosphere as a results
of the wind stress at the sea surface. The earth's surface is roughly
70 % covered by oceans and sea salt is probably the key
aerosol constituent over large parts of the oceanic
regions. Consequently, sea salt plays a major role for atmospheric
processes from weather to climate timescales. For this reason sea
salt has to be taken into account in online-coupled weather forecast
and climate models. In order to test the emission parameterization used
in ICON–ART (see Sect. ), a 1-year simulation was
performed starting at 29 March 2014 using a R2B06 grid (about
40km horizontal grid spacing).
The total global mass production rate of sea-spray aerosol varies
strongly between different parameterizations as highlighted by the
review paper of . For sea-salt particles with
a diameter of less than 10 µm they found a range of
3–70 Pgyr-1 for the global annual
emissions. estimated 3.9–8.1 Pgyr-1
for a size range of 0.1 to 15 µm. Using our
parameterization as described in Sect. and
simulating 1 year, we obtain a global mass production for particles
smaller than 10 µm of 7.36 Pgyr-1. For particles
with a diameter below 15 µm, we obtain
10.86 Pgyr-1. This number is somewhat higher than the
range given by . Summing up the total emissions of
all three modes contained in ICON–ART, we obtain
26.0 Pgyr-1. The results are summarized in
Table .
Annual sea-salt aerosol emissions obtained within this study compared to ranges given by different reviews of existing sea-salt aerosol source functions.
Study
Emission [Pgyr-1]
Size Range
3–70
<10µm
3.9–8.1
0.1–15 µm
This study
7.36
<10µm
This study
10.86
<15µm
This study total
26.0
Mode A + B + C
Horizontal distribution of the volcanic ash concentrations in µgm-3 at 16 April 2010, 12:00 UTC. Top left: at about flight
level F350 (10 668 m). Top right: at about flight level F200 (6096m). Bottom left: at about 2300m. Bottom right: at about 1160m.
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Horizontal distribution of the mass production rate of sea-salt aerosol in kgm-2yr-1. Top:
only modes A and B are considered to be comparable to . Bottom: sum of all three modes, A, B and C.
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The horizontal distribution of the annual emissions in
kgm-2yr-1 is presented in
Fig. . The strongest source regions can be found
around Antarctica between 45 and 60∘ S and in the
northern Pacific and northern Atlantic between 30 and
60∘ N. This is in line with recent studies concerning global
sea-salt emissions e.g.,.
Summary
We presented a first version of the extended modeling system
ICON–ART. The goal of developing ICON–ART is to account for the
interactions of atmospheric trace substances (gases and particles) and
the state of the atmosphere within a numerical weather prediction
model from the global to regional scale. This first version contains
the numerical treatment of the balance equations for gaseous
compounds, monodisperse particles and polydisperse particles. We
presented these equations as well as the physical parameterizations
and numerical methods we use to solve these equations.
Two VSLS, CHBr3 and
CH2Br2, were simulated with ICON–ART. Their
volume mixing ratio of about 1 pptv in the tropical upper
troposphere as well as the regional distribution with the tropical
western
Pacific region as the main source region of stratospheric VSLS is in
good agreement with observations and previous model studies.
We simulated the spatial and temporal concentration distribution for
the Eyjafjallajökull eruption that occurred in March 2010. Using
a novel approach, we parameterize the emissions source strength as
well as the vertical distribution in dependence on the plume height
which is commonly the first available information during an ongoing
eruption. We tuned the emission parameterization based on
observations. A preliminary comparison with observed lidar profiles
shows that ICON–ART reproduces main features as plume height and
temporal development. Moreover, the simulation shows the
characteristic shape of the Eyjafjallajökull ash plume as seen in
previous publications and observations.
Sea salt is treated as a polydisperse aerosol with prognostic mass and
number and diagnostic diameter. We conducted a 1-year simulation in
order to compare our mass emission fluxes with those derived in other
studies. We obtain a total emission flux of 26.0 Pgyr-1
and an emission flux of particles with diameter less than
10 µm of 7.36 Pgyr-1. This is within the
range given in the review paper by .
The first version of ICON–ART, which is presented in this publication,
is the basis for a comprehensive modeling framework capable of
simulating secondary aerosol formation and the impact of aerosol on
clouds as well as on radiation.