Introduction
Both the direct and semi-direct aerosol effects refer to the perturbation of
the radiation budget induced by the presence of aerosol in the atmosphere
along with the induced changes in the meteorology (e.g. surface temperature,
wind velocity, cloud coverage) . The indirect
aerosol effects refer to changes in the number of cloud condensation nuclei
along with the induced perturbations within the cloud micro-physics, and thus
of the cloud albedo and precipitation . The aerosol effect
processes are known to have a significant impact on meteorology and on
airborne aerosol concentrations . However, aerosol effects are
difficult to model precisely as studies focusing on chemistry and meteorology
usually involve two distinct models. Hence, they are neglected or simplified
through a climatology by offline models, as they are not capable of taking
aerosol feedbacks into account. Developing fully coupled online models able
to accurately take aerosol effects into account is a major scientific
challenge .
An online modelling approach enables the possibility of several models to be
run concurrently and allows them to communicate with each other. Thus, it
creates the possibility of feedback modelling, as models may interact both
ways at each time step. Online models coupling meteorological models and
chemistry-transport models (CTMs) are increasingly used .
Merging two models in order to form a unique model is one solution (e.g.
WRF-CHEM , CMCC-CESM-NEMO ,
IFS-ECWAM-NEMO ). With this method all variables are
shared; however, once models are merged it may be difficult to make each
model component evolve independently. This is an issue when several
independent modelling teams are involved or when more than two models are
coupled. Using an external coupler to handle the variable exchanges is an
alternative. Each model is interfaced with the coupler, allowing them to
retain their independent course of development. The coupler may perform some
operations on the coupling fields, such as interpolations. This approach is
also a manner of sharing new model developments among research groups while
allowing each group to continue to administrate their own model. This
approach has been applied to several online coupling platforms such as
WRF-CMAQ , CNRM-CM5 or
MPI-ESM .
OASIS is a widely used external coupler developed by the CERFACS (Centre
Européen de Recherche et de Formation Avancée en Calcul Scientifique,
Toulouse, France) . Several geoscience models such as
ECHAM , LMDz or
ORCHIDEE have been interfaced with OASIS and, therefore,
the OASIS coupler is used in several online models, such as
EC-earth , TerrSysMP , the
Met Office Unified Model or
IPSL-CM5 .
Several online-coupled regional air quality models have been
developed and many studies focused on the aerosol radiative
impacts. studied the interaction between mineral dust and
solar radiation through the inclusion of mineral dust radiative effects
within the DREAM regional atmospheric dust model . The
feedback attributed to mineral dust is negative, with a 35–45 % reduction
of the aerosol optical depth (AOD) over the Mediterranean region during a
major mineral dust outbreak. used the COSMO-ART fully
online-coupled model over western Europe and showed that aerosol particles
induce an average decrease in the 2 m temperatures (0.1 K over Germany).
showed that mineral dust particles induce a decrease of up to
90 Wm-2 in long-wave radiative forcing along with an increase of
40 Wm-2 in short-wave radiative forcing when using the
RIEMS-Chemaero online regional climate–chemistry–aerosol model over eastern
Asia.
In , aerosol radiative effects over Europe are evaluated
using both the Weather Research and Forecasting (WRF) meteorological
model and the CHIMERE regional chemistry-transport
model . Results indicate that
the presence of particles induces perturbations in both the solar radiation
(radiative forcing at the bottom of the atmosphere of -30 to
-10 Wm-2) and the near-surface temperatures (decrease of up to
0.30 ± 0.06 K). An offline coupling was made by forcing the WRF model
with aerosol optical properties computed from CHIMERE outputs. Initially, the
CHIMERE model was forced by the WRF model itself, thereby implying the need
to develop interactions between the two models. The WRF model was recently
interfaced with the OASIS coupler and coupled online to
the NEMO (Nucleus for European Modelling of the Ocean) ocean
model in order to better study air–sea
interactions . On the other hand, recent developments
within the CHIMERE CTM, made for the CHIMERE2016a
release, were related to the development of an online version of the CHIMERE
model. These developments have been pursued, leading to the creation of an
OASIS interface within the CHIMERE model. A WRF-CHIMERE online coupling was
created, allowing the two models to exchange fields at each main physical
time step (i.e a few minutes), thus enabling the possibility of the aerosol
effects modelling.
This paper aims at presenting the online coupling developments made within
the CHIMERE model along with an evaluation study of the aerosol–radiation
interactions using the WRF-CHIMERE online coupling. Section
focuses on the CHIMERE-OASIS interface that was developed within the CHIMERE
model along with the scheduling of the WRF-CHIMERE OASIS exchange operations.
An evaluation test case along with model configurations are presented in
Sect. . In Sect. , the
computational performances of the WRF-CHIMERE online coupling are compared to
the performances of both offline models. In addition, an estimation of the
OASIS exchange burden is made. Case study simulations over the summer of 2012
are evaluated in Sect. . WRF and CHIMERE offline
simulations are compared to two WRF-CHIMERE online simulations. In the first
online simulation, the CHIMERE model is forced by the WRF model, without any
feedback but at a higher rate than what is possible in offline mode. In the
second online simulation aerosol optical properties are transferred from
CHIMERE to WRF in order to take into account the aerosol–radiation
interactions. Simulated results are compared to temperatures, AOD and
concentration measurements. Note that applications presented in this paper
focus on the aerosol–radiation interactions only. The study of
cloud–aerosol interactions is currently ongoing and shall be the focus of a
forthcoming paper.
Development of the WRF-CHIMERE coupled version
The CHIMERE2016a release included preliminary technical changes for the
development of an online-coupled version of CHIMERE. CHIMERE preprocessors
(for the calculation of emissions in particular) were included in its core.
Indeed, in the case of an online simulation not all input data are known,
prior to the simulation, for the entire simulation period. In particular, in
the case of a WRF-CHIMERE online coupling, meteorological fields are received
at each time step of a simulation, thus preventing the precomputation of
meteorology-dependent variables such as mineral dust emission or biogenic
emission fluxes. Furthermore, CHIMERE held a master–worker pattern where the
master process performed all input/output operations. A more efficient
pattern was implemented in which each worker performs parallel input/output
operations, using the parallel-netcdf library , without any
master process.
Pursuing the development of an online version of CHIMERE in order to perform
a WRF-CHIMERE coupling, more developments were made since the CHIMERE2016a
release. These developments are described in Sects.
to and fulfil the implementation of an
online-coupled version of CHIMERE.
The CHISIS interface module
A Fortran module called CHISIS (CHImere/oaSIS) that interfaces CHIMERE and
OASIS was developed. This module gathers all calls to OASIS subroutines
required by CHIMERE in order to exchange fields with another model.
Furthermore, a reading routine of the OASIS configuration file (i.e. the
namcouple file) was included, thus allowing each model to be aware of
coupling parameters (e.g. exchanged variable names, time steps, partitions,
grids, models involved), leading to generic subroutines without any
hard-coded information. Even though the CHISIS module was designed for
CHIMERE, it does not contain any CHIMERE-specific material; therefore, it may
be used in other models.
An OASIS interface module already exists within WRF as a WRF-NEMO coupling
has already been implemented. However, WRF-NEMO exchanged variables were
hard-coded within this interface module, making it difficult to reuse this
module for the WRF-CHIMERE coupling. Thus, new compilation flags were added
within the WRF code (“cpl_wrf_chimere” and “cpl_wrf_chimere”) in
order to distinguish the generic OASIS interface material that may be of use
in a coupling with any model from the specific material of either WRF-NEMO or
WRF-CHIMERE coupling.
OASIS configuration
The latest OASIS release, OASIS3-MCT, internally uses the Model Coupling
Toolkit (MCT), developed by the Argonne National Laboratory in the USA, for
parallel regridding and parallel distributed exchanges of the coupling
fields . To perform a WRF-CHIMERE coupling, the
exchange of three-dimensional fields is required (e.g. temperature, wind
velocity, pressure). A previous OASIS release, OASIS4, allows one to exchange
three-dimensional variables ; however, its code was too
complex to evolve easily. Thus, OASIS developers decided to take a step back
and use MCT with the OASIS3 release, which does not include the possibility
of three-dimensional variable exchange. Therefore, three-dimensional spatial
interpolation between model grids is not possible either. To overcome this
issue, three-dimensional variables are decomposed into one-dimensional arrays
prior to the exchange. Doing so makes it impossible for OASIS to perform a
spatial interpolation between both model grids, as OASIS then considers
one-dimensional unstructured arrays instead of spatial grids. Thus, both the
WRF and CHIMERE models need to be run on the same horizontal grid in online
mode. The WRF vertical grid may be used as it is not dependent on the
sub-domain decomposition of each model.
Both the WRF and CHIMERE codes are parallelised using a decomposition into
sub-domains which may be different for both models. An OASIS partition is
required to describe each point of each sub-domain of each model within the
same global index space. The OASIS “points” partition was chosen as it
allows one to index each grid point separately, thus ensuring the
preservation of the model's sub-domain decomposition flexibility (i.e. as is
the case in offline mode), unlike other partitions that require one to index
segments of points or rectangular regions of a domain.
OASIS exchange
Exchange from WRF to CHIMERE
In order to be run in offline mode, the CHIMERE model requires 28
meteorological variables at an hourly rate. In the offline version, these
variables are read from WRF output files and include both two-dimensional
variables (e.g. 10 m wind velocities, surface pressure, 2 m air
temperature) and three-dimensional variables (e.g. base state pressure, cloud
water mixing ratio, water vapour mixing ratio). CHIMERE performs a temporal
interpolation between two sets of hourly WRF fields in order to compute
species concentrations at every physical time step (i.e. a few minutes).
The WRF-CHIMERE online coupling enables the possibility of avoiding these
sub-hourly temporal interpolations. Indeed, even though WRF output files may
be hourly, WRF computes meteorology with a finer time step that is defined in
its configuration file. Therefore, in online mode, the CHIMERE physical time
step and the OASIS exchange frequency for meteorological fields are set to
the same value. Hence, CHIMERE may receive fields at a sufficient rate to
avoid the need for a temporal interpolation of meteorological fields. The
first version of the WRF-CHIMERE online coupling includes the exchange of the
28 WRF meteorological fields from WRF to CHIMERE through the OASIS coupler.
Even though there is no feedback (i.e. exchange from CHIMERE to WRF), the use
of instant WRF fields instead of interpolated fields will have an impact on
the simulated results (see Sect. ).
Aerosol optical properties feedback
The second version of the WRF-CHIMERE online coupling includes an aerosol
optical properties feedback in order to take into account the
aerosol–radiation interactions. The feedback consists of 23
three-dimensional variables, which are the single scattering albedos (SSAs)
and the asymmetry factors (AFs) at 400 and 600 nm along with the AOD at 300,
400, 999 nm and at 16 long wavelengths ranging from 3400 to 55 600 nm.
Short-wave aerosol optical properties are already calculated within CHIMERE
using the Fast-JX model for radiative transfer and online calculation of
photo-chemical rates . The computation of long-wave
parameters is done following the same method, by extending the radiative
properties calculations within CHIMERE to the required long wavelengths.
Aerosol optical properties computed by CHIMERE from aerosol species are
interpolated over the WRF vertical grid before being sent through the OASIS
coupler. If the CHIMERE top level is lower than the WRF top level, the
optical properties climatology from is used for short-wave
aerosol optical properties, for the highest vertical levels. Long-wave
aerosol optical properties of the highest vertical levels are set to zero
above the CHIMERE top level.
Within the WRF model, short-wave AODs are interpolated over the required
wavelengths using an Ångström power law, while the SSA and AF at 440
and 600 nm are interpolated assuming a linear relation. The long-wavelength
AOD is added to the gas optical depth. Aerosol optical properties are used
within the WRF model as inputs for the RRTMG (Rapid Radiative Transfer Model
for General circulation models) scheme .
Exchanges from CHIMERE to WRF
Aerosol optical properties are sent from CHIMERE to WRF through the OASIS
coupler. The WRF “radt” parameter sets the frequency at which the RRTMG
scheme is called within the WRF model. The recommendation from the WRF user's
guide is to set the “radt” parameter to 1 min per kilometer of the grid
distance between each grid cell
(http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/ARWUsersGuideV3.pdf).
As this frequency may be different from the OASIS exchange frequency for
meteorological fields, “radt” is fixed to a multiple of this OASIS exchange
frequency. Therefore, whenever WRF requires the aerosol optical properties,
CHIMERE is able to send it. Regardless of the exchange frequency value, both
WRF and CHIMERE may perform sub-iterations to ensure that the
Courant–Friedrichs–Lewy condition is satisfied (see
Fig. ).
Illustration of variable exchange frequencies. The CHIMERE model
receives WRF meteorological fields and sends the aerosol optical properties.
The WRF model receives the aerosol optical properties and sends the
meteorological fields. The OASIS exchange frequency defines both the
frequency at which meteorological fields are exchanged (fixed here at 600 s) and the frequency at which aerosol optical properties are exchanged
(fixed here at 1800 s, with N3 = 3). Both models may perform
sub-iterations (here N1 = 3 and N2 = 2), in which case the previously
received data are used during the sub-iterations. Note that the OASIS
exchange frequency along with the N1, N2 and N3 integers are parameters that
may be set by users.
Operations scheduling
In the case of a WRF-CHIMERE online-coupled simulation without any feedback,
OASIS exchanges are made in one direction only (i.e.
from WRF to CHIMERE). The operations scheduling is similar to what is done in
offline mode, as CHIMERE is forced by WRF meteorological fields, but with a
higher frequency. The initial meteorological fields sent are the WRF initial
conditions. In Fig. the WRF model runs faster than
the CHIMERE model, leading to an accumulated delay between OASIS subsequent
send and receive operations. However, as OASIS send operations are
non-blocking, the WRF model may continue its calculations without having to
wait for OASIS receive instructions within the CHIMERE model. In the case of
a CHIMERE model that would run faster than the WRF model, the CHIMERE model
would wait for WRF meteorological fields.
Operations scheduling in a WRF-CHIMERE online simulation with OASIS exchange from WRF to CHIMERE only.
In the case of two-way exchanges, the aerosol optical properties exchanges
are performed right after the meteorological fields exchanges (i.e. at the
beginning of each model time iteration). This allows the two models to
perform their time iterations concurrently, thereby optimizing the overall
computational burden (Fig. ). Initial aerosol
optical properties that are sent to WRF may be provided as an input file in
CHIMERE, if available, or are set to zero otherwise. When the aerosol optical
properties feedback is activated, the two models may need to wait for each
other in order to receive the required fields that will allow them to
continue the run. In any case, the overall WRF-CHIMERE online simulation time
is expected to be close to the maximum of both WRF and CHIMERE offline run
times.
Furthermore, in offline mode CHIMERE reads WRF meteorology files every hour,
while in online mode it may receive WRF meteorology data at a higher rate.
Therefore, in online mode CHIMERE needs to perform additional calls to WRF
meteorology processing routines. In case aerosol optical properties are
exchanged, calls to optical properties computation routines are also
required. Thus, an increase in the computational time is expected within the
CHIMERE model due to these additional operations.
Operations scheduling in a WRF-CHIMERE simulation with the aerosol optical properties feedback.
Test case presentation
In order to evaluate both the computational burden and the model
performances, three simulation types are defined.
Offline: both WRF and CHIMERE are run sequentially. CHIMERE reads meteorological
fields at an hourly rate from the WRF output file and the aerosol optical
properties are not exchanged.
Online case 1: WRF and CHIMERE are run online. Meteorological fields are sent
through the OASIS coupler with a high temporal resolution (from WRF to
CHIMERE). The aerosol optical properties feedback is not exchanged.
Online case 2: WRF and CHIMERE are run online. Meteorological fields with a high
temporal resolution (from WRF to CHIMERE) along with the aerosol optical
properties (from CHIMERE to WRF) are sent through the OASIS coupler.
The simulated domain horizontal grid was built with a Lambert projection and
has 159×109 points in longitude and latitude. It covers Europe,
northern Africa, the Middle East and western Asia with a 60 km
resolution (Fig. ).
Simulated domain used in Sects.
and . AERONET stations are depicted with red
squares and temperature atmospheric sounding stations with blue triangles.
Both offline and online simulations are run with the same configuration. Note
that both the WRF and CHIMERE versions that are used to perform all
simulations presented in this paper are modified versions of the WRF 3.7.1
and CHIMERE2016a releases. These versions may be run in either offline or
online mode and modifications from the releases include exclusively the
online modelling developments described in Sect. . Both WRF
and CHIMERE configurations are presented in Sect. and
Sect. , respectively.
WRF model configuration
The WRF model is used in its non-hydrostatic
configuration and forced every 3 h by the
meteorological analysis data of NCEP/GFS provided on a
regular 1.125∘×1.125∘ grid. The model is run with 32
vertical levels, from the surface to 20 hPa, and with a 150 s
integration time step. The RRTMG scheme, mandatory for the aerosol optical
properties feedback, is used for both long- and short-wave radiations along
with the Morrison two-moment microphysics scheme . The
surface layer scheme is the MM5 similarity theory scheme
and the surface physics scheme is the unified Noah land-surface
model . The Mellor–Yamada–Nakanishi–Niino (MYNN)
planetary boundary layer's surface layer scheme is used and the cumulus parameterisation is based on the
Grell–Freitas scheme .
CHIMERE model configuration
The CHIMERE model takes into account four types of emission. Anthropogenic
emission fluxes are pre-calculated fields from the HTAP 2010 inventory
(Hemispheric Transport of Air Pollution), prepared by the EDGAR
team (http://edgar.jrc.ec.europa.eu/national_reported_data/htap.php).
Both biogenic and mineral dust emission fluxes are computed within the
CHIMERE model using the MEGAN emissions scheme for the
biogenic emissions and the dust production model described
in for the mineral dust emissions. Finally, emissions
related to biomass burning are pre-calculated using the model described
in . The LMDZ-INCA global model
climatology is used for aerosol and gas boundary
conditions, while the GOCART model is used for mineral dust boundary
conditions . The MELCHIOR2 chemical mechanism and
the aerosol module are used. The Fast-JX module,
version 7.0b , was included in the CHIMERE model in
order to compute photolysis rates along with aerosol optical
depth . Dry and wet depositions are treated as described
in and ; 20 pressure-dependent
vertical levels are used, from the surface up to 200 hPa. The WRF
model fields computed on 32 σ levels, which are either received via
the OASIS coupler (online mode) or read from the WRF output files (offline
mode), are linearly interpolated over the 20 CHIMERE vertical levels.
studied the contribution of the different aerosol sources to
surface particulate matter (PM), using the CHIMERE model with a similar
configuration and over a similar domain during the summer of 2012. Results
showed that both mineral dust and anthropogenic sources are the main
contributors of PM over Europe and the Mediterranean region. Daily
exceedances of the PM10 European Union limit (50 µgm-3)
are captured at the right time. However, the number of exceedances is
generally overestimated by the model, particularly in the northern part of
the domain.
WRF-CHIMERE computational performances
WRF and CHIMERE offline simulation times along with WRF-CHIMERE online
simulation times are compared in this section. Tests consist of 24 h
simulations that are run on a 64-core server using the simulation domain and
model configurations presented in Sect. . The exchange
frequency is set to 15 min for both ways' exchanges; therefore, a total of
96 exchange time steps is performed. Several test simulations are run with
different numbers of cores, which are equally distributed between WRF and
CHIMERE models.
Considering the size of the domain and the variable's dimensions, the total
number of exchanged points per iteration is over 6.4 million for
WRF-to-CHIMERE exchanges. When adding the aerosol optical properties feedback
it leads to a total of 19.2 million exchanged points per time iteration
between the two models for both ways of exchanges. An estimation of the
computational burden of these variable exchanges is made in
Sect. , calculation and waiting times are studied
using the LUCIA utility (Load-balancing Utility and Coupling Implementation
Appraisal) that is distributed together with OASIS , in
Sect. , and the load balance of each model is discussed in
Sect. .
Comparison of both offline and online simulation times
The total online simulation durations are compared here to the offline
simulation times. Time measurements were made using the Linux command
“time”. There is an uncertainty regarding these measurements that is not
fully known, as it depends on the load of the computer that is used, which
may vary during the simulations. However, simulations are long enough for the
average times per iteration along with the trend to be significant.
Evolution of the computational time per iteration as a function of
the number of cores per model. Online case 1 refers to the online simulation
without the aerosol optical properties feedback and online case 2 refers to
the online simulation with the aerosol optical properties feedback.
Average simulation times per iteration are shown in
Fig. as a function of the number of cores per model.
As the WRF model is much faster than the CHIMERE model, the maximum of both
WRF and CHIMERE offline run times is equal to the CHIMERE offline run time.
As expected, the CHIMERE model parallelisation induces a decrease in the
overall computational time with the increase in the number of cores. The
decrease tendency is preserved in both online simulations; however, both
online cases require more computational resources. The time increase is
higher for online case 2 simulation than for online case 1 simulation, as
more variables are exchanged and more computations are made (see
Sect. ). The highest time increase occurs when a
lower number of cores is used (an up to 170 s increase using one core per
model in the case 2 simulation). On the other hand, the percentage of the
time increase from the offline simulation increases with the number of cores
and reaches a 42 % increase when using 32 cores per model in the case 2
simulation (Fig. ). A gradual increase is observed when
more than 12 cores per model are used. Indeed, the additional burden due to
the coupling does not decrease as steadily as the offline CHIMERE model
burden, when increasing the number of cores. Part of the additional burden
may be attributed to the OASIS exchange along with the variable formatting
routines. The other part is related to additional calls to some CHIMERE
routines that are made in online mode (i.e. a more frequent meteorology
treatment subroutine in case 1 along with optical properties computations in
case 2). A measure of the computational burden that may be attributed to
variable exchange subroutines has been made using the “cpu_time” Fortran
routine.
These subroutines are responsible for less than 3 % of the time increase
for both online cases when using 32 cores per model. Therefore the increase
in the computational burden that may be attributed to the OASIS exchange is
not significant compared to the model computations.
Evolution of the time increase from the offline simulation of both
online simulations, as a function of the number of cores per model. Online
case 1 refers to the online simulation without the aerosol optical properties
feedback and online case 2 refers to the online simulation with the aerosol
optical properties feedback.
Calculation and waiting times
Results presented in this section were obtained using the LUCIA utility on
the 32 cores per model simulations, which provide the total calculation and
waiting times for both models and for both the case 1 and case 2 simulations.
Online case 1 results indicate that WRF performs fewer calculations than
CHIMERE, i.e. 770 s for WRF vs. 3630 s for CHIMERE. This is consistent with
the fact that there is almost no waiting time for the CHIMERE model (i.e.
10 s), as WRF meteorological fields are always available when CHIMERE
required them. Even though OASIS send operations are non-blocking, the WRF
model waits for CHIMERE for 2890 s. A possible explanation is that WRF is so
far ahead of CHIMERE that its sending buffer is full. Thus, WRF needs to wait
for CHIMERE receive instructions to empty its buffer and to continue the run.
Nevertheless, as CHIMERE is computationally more costly, WRF waiting times do
not induce any additional burden on the overall WRF-CHIMERE online
simulation. Similar results are observed for the case 2 simulation. As both
model iterations are done in parallel, the aerosol optical properties
feedback does not induce a significant change in the overall balance between
the two models.
Load balance of each model
Results show an imbalance in the load of the two models as in both online
cases the WRF model performs fewer calculations than the CHIMERE model. As
the load of each model may depend on criteria such as the selected options
within both WRF and CHIMERE configuration files or the geometry of the
domain, the ratio of cores that will optimise the computational burden is not
unique, and it would not be fair to give one specific ratio. Therefore, here,
the same number of cores was attributed to both models. This is an arbitrary
choice made in order not to favour either WRF or CHIMERE in the study.
Ultimately, this choice needs to be revised using iterative methods to
estimate the optimum ratio of the number of cores for each model. In our
case, attributing a lower number of cores to WRF and a higher number of cores
to CHIMERE will reduce the overall computational time. Based on the
Sect. results, using 4 or 5 times more cores with CHIMERE
than with WRF may be an efficient ratio.
WRF-CHIMERE evaluation study during a mineral dust event
WRF-CHIMERE online simulations are confronted in this section with both
measurements and a corresponding offline simulation. The simulated period
starts on 15 May 2012 and ends on 14 July 2012, thereby covering the June
2012 mineral dust outbreak event . Simulated results from 15
until 31 May are considered to be spin-up time. Thus only the simulated
results from 1 June are considered for the evaluations made in the following
sections. The OASIS exchange frequency for meteorological fields, thus the
CHIMERE physical time step, is set to 15 min, and the WRF “radt” parameter
is set to 30 min. WRF meteorological fields and CHIMERE output
concentrations are stored every hour for the analysis.
Simulated radiation budgets, surface temperatures and wind velocities are
compared in Sect. . Simulated results are
then successively evaluated against University of Wyoming vertical
temperature atmospheric soundings (Sect. ),
MODIS AOD (Sect. ), AERONET AOD (Sect. ) and
AirBase PM10 concentration data (Sect. ).
Feedback impact on radiation budget, surface temperatures and wind velocities
The radiative forcing is defined as the difference in the net radiation flux
(down–up) between both online simulations. Changes in the radiation budget
induced by the optical properties feedback are studied here through the
radiative forcing induced by the aerosol optical properties feedback.
Figure shows difference maps between the two
online cases of the average radiation budget at the ground surface for both
long waves and short waves. Long-wave radiative forcing attributed to the
optical properties feedback in the case 2 simulation is positive, up to
35 Wm-2, and is mainly located over desert areas (i.e. the
Saharan region and the Arabian Peninsula). A negative forcing observed over
the Atlantic Ocean is of lesser importance, less than 5 Wm-2. An
opposite behaviour is obtained for short-wave radiation fluxes, as there is a
negative forcing, up to 55 Wm-2 over the Saharan region and the
Arabian Peninsula, and a positive forcing of a lesser importance over the
Atlantic Ocean, less than 28 Wm-2. The average forcing over the
simulated domain is a cooling of 4.8 Wm-2 (i.e. radiative
forcing of 5.8 Wm-2 for long waves and -10.7 Wm-2
for short waves).
Difference in the WRF radiation budget at the ground surface between
both online simulations (all-sky fluxes). Fluxes are in Wm-2 and
are averaged in time over the period ranging from 1 June to 14 July.
CHIMERE mineral dust emission fluxes (in gm-2s-1)
averaged over the period ranging from 1 June to
14 July (online case 1 simulation).
The perturbation of the WRF radiative scheme outputs depends on the CHIMERE
aerosol optical properties, and thus on the CHIMERE aerosol load. In our
case the perturbation in the optical properties is dominated by mineral dust,
as observed changes occur over regions where mineral dust constitutes the
main aerosol type (i.e. the Saharan region and the Arabian Peninsula).
Mineral dust emission fluxes computed by the CHIMERE during the online case 1
simulation model are shown in Fig. in order to
visualise the locations of the main mineral dust sources. Mineral dust both
absorbs and scatters solar radiation, leading to both negative and positive
radiative forcing, depending on the radiation wavelength and on the mineral
dust size distribution . Aerosol absorption of solar
radiation induces a heating of the atmosphere, and thus a reduction of the
cloud coverage. This effect is referred to as the aerosol semi-direct
effect and is responsible for part of the
changes in the radiative forcing. Off the western Saharan coast, high mineral
dust concentrations cause a reduction of the cloud coverage, thereby inducing
an increase in the short-wave radiative forcing in the online case 2
simulation.
In the mineral dust impacts on the regional precipitation and
summer circulation in eastern Asia are studied. A negative short-wave
radiative forcing along with a positive long-wave radiative forcing induced
by the presence of mineral dust particles are observed. The long-wave
radiative forcing is less than 50 Wm-2 and the short-wave
radiative forcing is less than -70 Wm-2. Even though the
simulated areas are different, the impacts of mineral dust on the radiative
forcing are in accordance with the results presented in the current paper.
A direct consequence of the changes in the radiative forcing is a
perturbation of the surface temperatures. Figure maps
show a moderate decrease in the surface temperatures (i.e. less than
0.4∘) over Sub-Saharan Africa and Europe and over the northern part
of the Atlantic Ocean, where the radiative forcing (short-wave + long-wave)
is negative. Over the Saharan region, the Arabian Peninsula and off the
western Saharan coast, temperatures increase, where the radiative forcing
(short-wave + long-wave) is positive. The maximum increase is 2.6∘
over a grid cell located in north-eastern Niger.
Figure a presents a 4-day time series (1 to 4
June) of surface temperatures over the north-eastern Niger grid cell in which
the maximum differences in average surface temperatures occur. The diurnal
profile shows that an increase in temperatures occurs during nighttime (up to
5∘), while a slight decrease in temperatures occurs during daytime
(less than 1∘). Figure b shows that
the short-wave effect prevails during daytime, thus creating a decrease in
the surface temperatures, while the long-wave effect alone contributes at
night due to the earth outgoing long-wave radiations, inducing an increase in
the temperatures. This is also observed in .
Difference map of WRF temperature at 2 m from the surface averaged
over the simulated period ranging from 1 June to 14 July (in Kelvin).
Another consequence of the perturbation of the radiative forcing is the
alteration of the wind velocities. Figure shows that the
use of the aerosol optical properties feedback in the online case 2
simulation induces both an increase (up to 0.5 ms-1) and a
decrease (up to 0.4 ms-1) in the wind module over part of the
Saharan region and the Arabian Peninsula. As the wind velocity is the main
parameter influencing mineral dust emissions, changes in CHIMERE aerosol
content are also observed. The perturbation in the mineral dust emission
fluxes is sporadic, due to the non-linear property of mineral dust emissions,
and is less than 0.1 % of the total mineral dust emission fluxes over the
simulated domain.
Comparison with the University of Wyoming atmospheric sounding vertical temperature data
Atmospheric sounding temperature data were gathered at five stations over the
Saharan region and the Arabian Peninsula (see Fig. for
station locations), from the University of Wyoming website
(http://weather.uwyo.edu/upperair/sounding.html). Differences in
temperature vertical profiles between sounding and online modelled values are
displayed in Fig. at selected times. Results are
interpolated over the soundings' vertical levels. Stations are located in the
western Sahara (Tambacounda, Abidjan, Nouakchott and Niamey), where the
impact of mineral dust emissions, and thus the differences in solar
radiation, are important. The profiles are shown for 23 June, during the end
of June mineral dust outbreak (i.e. from 21 to 23 June). In addition,
temperature vertical profiles are shown at the Casablanca station for both 23
and 26 June. Therefore, the two profiles at the Casablanca station allow one
to compare vertical temperature profiles with a low and high level of mineral
dust.
Surface temperature and downwelling radiative forcing 4-day time
series (1 to 4 June) over a grid cell in north-eastern Niger (GMT time).
Differences between observations and modelled values lie between
-3.1∘ at the Tambacounda station and 6.5∘ at the Nouakchott
station. Differences between modelled values are small at higher levels
(roughly above 5000 m, where mineral dust concentrations are low) and are
less than 0.12∘ at the highest level at all stations, except for
Casablanca on 26 June, where the temperature difference between both online
cases at the highest level is 0.5∘. Therefore, only the lower part of
the vertical profiles is shown in Fig. .
The online case 2 simulation yields the temperature generally closer to
observations at altitudes of up to 1–2 km, compared to case 1. At
Tambacounda, for instance, the case 2 simulation reduces the underestimation
of measurements by 0.6∘.
The differences are higher, however, at Nouakchott on 23 June and Casablanca
on 26 June between 1.5 and 4.5 km. The atmospheric cooling with height is
already overestimated within this layer in case 1, by up to -2.5∘.
This overestimation becomes slightly higher in case 2, with an additional
0.5∘. The cooling overestimation can be related to excessive cloud
formation in the WRF model in this region, which is reinforced through the
aerosol–radiation interactions in case 2. In general, the impact of the
aerosol optical properties feedback can lead to differences of up to
1.7∘.
The 10 m high wind module difference map between online case 1 and
case 2 (in ms-1) averaged in time over the period ranging from 1
June to 14 July.
Difference in vertical profiles of temperature (modelled
values-radiosounding values).
AOD and AOD difference maps at 550 nm, averaged in time over the
period ranging from 1 June to 14 July.
AERONET and modelled AOD time series.
Performance indicators of WRF-CHIMERE modelled values against
daily AERONET AOD measurements over the period ranging from 1 June to 14
July.
Meas, Off, On1 and On2 correspond to measurements, offline simulation, online
case 1 simulation and online case 2 simulation, respectively. N is the
number of observations and RMSE is the root mean square
error.
Station names
N
Mean values
RMSE
Correlation
Bias
Meas
Off
On1
On2
Off
On1
On2
Off
On1
On2
Off
On1
On2
Izana
44
0.12
0.28
0.29
0.29
0.25
0.26
0.24
0.94
0.94
0.95
0.16
0.17
0.16
Lampedusa
44
0.18
0.32
0.33
0.33
0.23
0.25
0.25
0.77
0.77
0.75
0.14
0.15
0.15
Granada
44
0.18
0.24
0.24
0.25
0.14
0.14
0.15
0.85
0.85
0.84
0.05
0.06
0.06
Lecce University
44
0.12
0.15
0.16
0.16
0.08
0.08
0.09
0.72
0.72
0.71
0.03
0.04
0.04
Santa Cruz Tenerife
42
0.26
0.3
0.31
0.3
0.21
0.21
0.2
0.86
0.87
0.89
0.03
0.04
0.04
Evora
42
0.11
0.12
0.13
0.13
0.09
0.1
0.1
0.95
0.95
0.94
0.02
0.02
0.02
Rome Tor Vergata
41
0.13
0.18
0.19
0.19
0.14
0.14
0.14
0.82
0.82
0.82
0.05
0.06
0.06
Banizoumbou
37
0.69
0.65
0.69
0.68
0.29
0.29
0.3
0.44
0.41
0.37
-0.04
0.0
-0.01
Cinzana
35
0.7
0.62
0.68
0.68
0.45
0.45
0.45
0.29
0.25
0.23
-0.08
-0.02
-0.02
Capo Verde
31
0.53
0.87
0.91
0.88
0.53
0.55
0.51
0.47
0.48
0.51
0.35
0.38
0.36
La Laguna
31
0.29
0.34
0.35
0.34
0.2
0.2
0.18
0.89
0.89
0.91
0.05
0.06
0.06
Athenes
30
0.11
0.14
0.14
0.14
0.05
0.06
0.06
0.74
0.74
0.72
0.02
0.03
0.03
Leipzig
27
0.11
0.11
0.13
0.12
0.11
0.12
0.11
0.5
0.5
0.5
0.0
0.01
0.01
Cabauw
23
0.12
0.06
0.06
0.06
0.09
0.08
0.08
0.2
0.19
0.19
-0.06
-0.06
-0.06
Palaiseau
22
0.11
0.07
0.08
0.08
0.08
0.07
0.08
0.67
0.67
0.67
-0.04
-0.03
-0.03
Lille
21
0.1
0.07
0.07
0.07
0.09
0.09
0.09
0.48
0.48
0.48
-0.03
-0.03
-0.03
Barcelona
20
0.2
0.23
0.25
0.24
0.17
0.18
0.19
0.8
0.8
0.77
0.03
0.04
0.04
Comparison with MODIS AOD
The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are
compared to the modelled AOD . MODIS Dark-Target and
Deep-Blue products at 550 nm are merged in order to form a single map. The
Deep-Blue product is preferred when both products are available at a given
point, as it is more accurate over desert areas . Data are
averaged over the period ranging from 1 June to 14 July. Modelled values were
also averaged in time, using only modelled values at times at which a MODIS
observation is available. As CHIMERE aerosol optical depth is calculated at
fixed wavelengths (i.e. 200, 300, 400, 600 and 999 nm), the AOD is
interpolated at 550 nm following an Ångström power law. The
corresponding MODIS AOD map is displayed in the top left corner of
Fig. and the difference between MODIS and offline AOD is shown
in the top right corner of Fig. . In addition, both online AODs
are shown as the difference between modelled values rather than the
difference with the MODIS AOD (bottom of Fig. ).
Over major sources of mineral dust, such as the Saharan region and the
Arabian Peninsula, the MODIS AOD values are high (up to 1.3). However, even
higher values are observed over the eastern side of the Caspian Sea, the Red
Sea and the Zagros Mountains (up to 3). Both offline and online simulations
failed to detect these high values over those three regions. Such
CHIMERE/MODIS AOD differences were already observed in and
in . Over the eastern part of the Caspian Sea, those
differences may be attributed to missing mineral dust as it is an arid
region. In the MODIS data overestimate the MISR (Multiangle
Imaging SpectroRadiometer ) satellite product and AERUS-GEO
(Aerosol and surface albedo Retrieval Using a directional Splitting method;
application to GEO data by ) over the Red Sea and on the
eastern side of the Caspian Sea. This suggests that the high MODIS AOD values
may be attributed to an overestimation made by the MODIS aerosol retrieval
algorithm.
Over Europe, North Africa and the Atlantic Ocean, differences between MODIS
and the offline simulated AOD are less than 0.4. Major differences occur in
the western part of the Sahara (south of Mauritania) and in the southern part
of the Arabian Peninsula, where the CHIMERE model overestimates the MODIS AOD
by up to 1.4. Differences are most likely due to an overestimation of mineral
dust emissions, which are the main AOD contributors in those areas.
The more resolved meteorology in online case 1 simulation mainly induces
higher AOD than the offline simulation. The AOD increase ranges from 0.03
over Europe up to 0.08 over southern Mauritania and western Mali. Changes
induced by the aerosol optical properties feedback are more important
(difference of up to 0.25); however, it induces both increases and decreases,
principally over both Africa and the Arabian Peninsula. Differences may be
explained by the alteration of the wind velocities in these areas, thus
inducing alterations of the mineral dust emissions, as the wind velocity is
the main parameter influencing mineral dust production
(Fig. ).
Comparison with AERONET AOD
Daily AOD at 675 nm of both level 2.0 quality assured AERONET
data and CHIMERE AOD are compared in this section. The
locations of AERONET stations are shown in Fig. .
A mineral dust outbreak occurred over western Africa between 21 and 23
June . Due to a lack of data during this period, this
mineral dust outbreak is not visible in the AOD time series at the Capo Verde
and Cinzana stations. However, particles have been transported along the
African coast up to southern Spain; therefore, it is visible in the AOD time
series at the Izana, La Laguna, Santa Cruz Tenerife (24 to 30 June), Granada
(24 to 30 June), Evora (25 to 29 June) and Barcelona (27 June to 1 July)
stations (Fig. ). Even though AOD peak intensities tend
to be overestimated, models manage to predict efficiently the times at which
high AOD events occur. Although high AOD events are detected at the same
moment in each simulation, variations in the peak intensities appear.
However, time series alone are not sufficient to infer whether or not one
simulation performs better than another because the three simulation results
are close to each other.
AOD performance indicators over the period ranging from 1 June to 14 July are
shown in Table and are defined as
correlation,∑i=1N(Oi-O‾)(Mi-M‾)∑i=1N(Oi-O‾)2∑i=1N(Mi-M‾)2,
RMSE (root mean square error),1N∑i=1N(Mi-Oi)2,
bias,1N∑i=1N(Mi-Oi),
where Mi and Oi are the modelled and observed values, respectively, and
x‾=1N∑i=1Nxi.
Apart from the Lampedusa station, RMSE is less than 0.19 at all European
stations, while at African stations it ranges from 0.18 in La Laguna up to
0.55 in Capo Verde. Six stations have a particularly low correlation (less
than 0.5), Cabauw (0.19 to 0.2), Cinzana (0.23 to 0.29), Banizoumbou (0.37 to
0.44), Lille (0.48), Capo Verde (0.47 to 0.51) and Leipzig (0.5), while
correlations at other stations are higher, ranging from 0.67 at Palaiseau up
to 0.95 at Evora and Izana. Bias is higher at the Izana, Capo Verde and
Lampedusa stations (from 0.14 to 0.38) and is less than 0.08 elsewhere. The
three African stations located near major mineral dust sources (i.e.
Banizoumbou, Cinzana and Capo Verde) present lower performances. This may be
explained by the difficulty in reproducing mineral dust events within the
model, as mineral dust is the main AOD contributor at these stations. If a
mineral dust event is not detected or if it is wrongly detected by the model,
the impact on the AOD may be important.
Models overestimate measurements at 12 out of 17 stations. Furthermore,
average AOD is higher in both online simulations than with the offline
simulation. The offline simulation performs equivalently or better at
European stations (higher correlation and lower RMSE and bias); however,
simulated results are close to each other. Differences between modelled
values are higher at African stations (mean value differences of up to 0.6)
than at European stations (mean value differences of up to 0.2 at the
Barcelona station). The online case 2 has higher correlations (up to 0.4
higher) and a lower RMSE (up to 0.2 lower) at the Izana, Santa Cruz Tenerife,
Capo Verde and La Laguna stations than the other simulations. At both the
Banizoumbou and Cinzana stations the offline simulation presents higher
correlations and lower negative biases than the online simulations.
Comparison with AirBase PM10 concentrations
Hourly PM10 measurement from the European Air quality dataBase (AirBase)
of the European Environment Agency
(http://acm.eionet.europa.eu/databases/airbase) are used in this
section for comparison with CHIMERE PM10 concentrations. As
in , only rural and background stations are considered for the
comparison in order to avoid sites which are strongly influenced by local
sources. In addition, stations with a minimum of 300 measurements during the
period ranging from 1 June to 14 July are selected, leading to a total of
more than 940 remaining stations located over Europe.
Averaged performance indicators show that all simulations overestimate
measurements and that the overestimation is higher with both online
simulations (6.8 µgm-3) than with the offline simulation
(1.7 µgm-3). Correlations are lower (differences of up to
0.17) and the RMSE is higher (differences of up to 22 µgm-3)
at most stations for both online simulations. The increase in PM10
concentrations in online simulations is consistent with the results of
Sect. and , in which the more resolved
meteorology in the online case 1 simulation induces higher AOD over Europe.
Indeed, higher-frequency meteorological fields, received by CHIMERE from WRF,
in the online simulations are associated with higher temporal variability,
which are smoothed out through the temporal interpolation in the offline
simulation. In the case of the wind velocity for instance it can lead to
higher mineral dust emissions, which is a threshold process, and/or
particulate matter resuspension, thus increasing the PM10 concentrations
in online mode. A deeper analysis is needed, using PM10 concentration
measurements over Africa, in order to assess the overall impact of the
WRF-CHIMERE coupling on PM10 concentrations.
Discussion and conclusions
An online coupling between the WRF and CHIMERE models through the OASIS
coupler has been developed. WRF meteorological fields along with CHIMERE
aerosol optical properties are exchanged in order to simulate the
aerosol–radiation interactions.
The WRF-CHIMERE online model requires more computational resources than the
offline models, mainly due to the CHIMERE model, as the WRF model is less
demanding. The computational time increase within the online model is mostly related to additional calls to the routines added to prepare the fields before being sent through the coupler and to process the received fields. On the other hand, the increase in computational
time related to OASIS exchanges is not significant. Therefore, increasing the
amount of OASIS exchange in future development would not be an issue.
Both offline and online simulations of 2 months of the summer of 2012 are
compared. The use of the optical properties feedback induces a
5.8 Wm-2 average increase in long-wave radiative forcing and a
10.7 Wm-2 decrease in short-wave radiative forcing. Consequences
of the radiative forcing perturbation are changes in the averaged surface
temperatures (i.e. an increase of up to 2.6∘ over desert areas and a
moderate decrease of less then 0.4∘ elsewhere) and wind velocities
(i.e. averaged differences ranging from -0.4 to 0.5 ms-1).
Diurnal profiles over the grid cell where the average temperature difference
is maximum show that temperatures decrease slightly during daytime, when the
short-wave effect prevails. On the other hand, temperatures increase at
night, when the long-wave effect alone contributes due to the earth outgoing
long-wave radiations. Therefore, the modelling of the aerosol–radiation
interactions, through the aerosol optical properties feedback, is not
negligible. Observed AOD by the AERONET network is compared to modelled AOD,
leading to higher correlations and lower RMSE at African stations when using
the aerosol optical properties feedback while simulating a dust event. Over
Europe, differences between simulations are of minor importance.
The aerosol–radiation coupling is found to better simulate the temperature
in the lower layer of the atmosphere (1 to 2 km). At the same time it can
amplify the overestimation of the cooling in the middle troposphere, through
the aerosol–radiation interactions. Specific studies are needed, beyond the
scope of this model presentation article, in order to evaluate these effects
using more available data on the atmospheric vertical structure.
The evaluations of the AQMEII project performed 10 online- and
1 offline-coupled model simulation for Europe and have not clearly concluded
on the impact of the online coupling on aerosol simulation performance. The
offline model (BG2) showed the highest AOD555 over Europe (their Fig. 13c),
but the differences between the online-coupled models, attributed to their
parameterisations, emission and boundary condition treatment, appear to be
similar to the difference between their median and the offline-coupled
simulation. As for the PM10 and PM2.5, the offline simulation
results have not shown any particular difference from the online simulations.
This is in agreement with the findings of the present study. Our results
suggest that the online coupling between meteorology and aerosols, taking
into account aerosol–radiation interactions, might be only beneficial for
model performance for sufficiently large aerosol loads. Further evaluation
studies are needed, relating the observed aerosol loads and their chemical
compositions to the model performance improvements due to the
meteorology–aerosol coupling.
Even though the radiative coupling between WRF and CHIMERE does not
necessarily improve the model performances in terms of biases and
correlations of PM10 aerosols in Europe, these results open the
possibility of using the WRF-CHIMERE coupled system to simulate cases where
the radiative effects of optically thick aerosol plumes on the atmosphere are
significant, and to examine the impact of these dense plumes on meteorology
and their feedbacks on the advected plumes themselves. Results presented in
this paper emphasise that using the aerosol optical properties feedback
induces non-negligible changes in model outputs. In addition, up to now
emissions have been designed for offline models, and some modifications
within emission parameterisations, mineral dust in particular, may be
required in online mode. For instance, the more resolved meteorology in
online simulation induces an increase in the wind velocity variability. A
Weibull distribution is used to account for the wind variability within the
mineral dust emission parameterisation , and its parameters
might need to be adjusted to yield better model performance in the
online-coupled case.
Online modelling developments presented in this paper will be made publicly
available through a future CHIMERE release. The development of the
WRF-CHIMERE online coupling continues with the implementation of another
WRF-CHIMERE feedback, aiming at modelling the aerosol–cloud microphysical
interactions. In addition, as the CHIMERE model is now interfaced with the
OASIS coupler, future work may involved online coupling with other models.