ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-12845-2018Stratospheric aerosol radiative forcing simulated by the chemistry climate model
EMAC using Aerosol CCI satellite dataStratospheric aerosol radiative forcingBrühlChristophchristoph.bruehl@mpic.deSchallockJenniferKlingmüllerKlaushttps://orcid.org/0000-0002-8425-8150RobertCharleshttps://orcid.org/0000-0003-3883-8821BingenChristineClarisseLievenhttps://orcid.org/0000-0002-8805-2141HeckelAndreasNorthPeterRiegerLandonhttps://orcid.org/0000-0002-9980-7095Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, GermanyRoyal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, BelgiumFaculty of Sciences, Université Libre de Bruxelles (ULB), Brussels, BelgiumDepartment of Geography, Swansea University, Swansea, UKInstitute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, CanadaChristoph Brühl (christoph.bruehl@mpic.de)6September20181817128451285728March201823April201810August201816August2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/12845/2018/acp-18-12845-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/12845/2018/acp-18-12845-2018.pdf
This paper presents decadal simulations of stratospheric and tropospheric
aerosol and its radiative effects by the chemistry general circulation model
EMAC constrained with satellite observations in the framework of the ESA
Aerosol CCI project such as GOMOS (Global Ozone Monitoring by Occultation of
Stars) and (A)ATSR ((Advanced) Along Track Scanning Radiometer) on the
ENVISAT (European Environmental Satellite), IASI (Infrared Atmospheric
Sounding Interferometer) on MetOp (Meteorological Operational Satellite),
and, additionally, OSIRIS (Optical Spectrograph and InfraRed Imaging System).
In contrast to most other studies, the extinctions and optical depths from
the model are compared to the observations at the original wavelengths of the
satellite instruments covering the range from the UV (ultraviolet) to
terrestrial IR (infrared). This avoids conversion artifacts and provides
additional constraints for model aerosol and interpretation of the
observations.
MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) SO2 limb
measurements are used to identify plumes of more than 200 volcanic eruptions.
These three-dimensional SO2 plumes are added to the model SO2 at the
eruption times. The interannual variability in aerosol extinction in the
lower stratosphere, and of stratospheric aerosol radiative forcing at the
tropopause, is dominated by the volcanoes. To explain the seasonal cycle of
the GOMOS and OSIRIS observations, desert dust simulated by a new approach
and transported to the lowermost stratosphere by the Asian summer monsoon and
tropical convection turns out to be essential. This also applies to the
radiative heating by aerosol in the lowermost stratosphere. The existence of
wet dust aerosol in the lowermost stratosphere is indicated by the patterns
of the wavelength dependence of extinction in observations and simulations.
Additional comparison with (A)ATSR total aerosol optical depth at different
wavelengths and IASI dust optical depth demonstrates that the model is able
to represent stratospheric as well as tropospheric aerosol consistently.
Introduction
Climate effects of stratospheric aerosols can be important, as analyzed for
example by , and .
Stratospheric aerosol exerts a negative radiative forcing on the troposphere
because enhanced scattering by the particles reduces solar radiation reaching
the surface and the lower atmosphere. In addition, changes in diffuse light
fraction have shown their potential to enhance photosynthesis .
The aim of the present paper is to jointly use model simulations and
satellite observations, taking into account the multiple spectral channels of
the instruments to better understand the spatiotemporal evolution of the
stratospheric aerosol burden and the contribution of the different aerosol
types to the observed dynamical aerosol patterns at the different altitudes.
Most earlier studies focus on the effects of major volcanic eruptions like
Pinatubo e.g.,. For the post-Pinatubo period
with only medium size eruptions present
simulations with the chemistry climate model WACCM (Whole Atmosphere
Community Model) with interactive aerosol, using estimates for volcanic
injections mostly from nadir sounders. That and the present study contribute
to the SPARC/SSIRC initiative (Stratosphere-troposphere Processes And their
Role in Climate / Stratospheric Sulfur and Its Role in Climate, see for
example ), aiming at a better understanding of the
composition, microphysical and radiative properties characteristics of
stratospheric aerosols and their impact on climate . In
this work, we rely on the multiple instrument satellite dataset provided in
the Climate Change Initiative (CCI) of the European Space Agency (ESA)
, which was developed as a tool for evaluation and improvement
of the treatment of stratospheric and tropospheric aerosols in global
chemistry climate models, like the EMAC (ECHAM5/MESSy Atmospheric Chemistry)
model . The datasets providing extinctions or total optical
depth at wavelengths from the ultraviolet (UV) to terrestrial infrared (IR) are
very useful to validate and optimize assumptions on the size distribution and
on the composition of aerosol in the model, but also on aerosol sources. Some
aspects of the stratospheric part of this study follow up .
The ATSR and IASI datasets provide additional constraints on tropospheric
aerosol, especially desert dust. We find in the present study that this
latter aerosol compound can penetrate the tropopause via the Asian summer
monsoon system and, to a smaller extent, via tropical convection.
The present paper is organized as follows: in Sect. 2, we briefly present
the satellite datasets used to evaluate the model, and to check for
consistency of observations at different wavelengths: GOMOS, IASI, (A)ATSR
and OSIRIS. In Sect. 3 we describe the EMAC model and the various versions
and resolutions used in our work, including the use of MIPAS SO2 for
input. In Sect. 4, we study the impact of the main aerosol sources on the
upper tropospheric and lower stratospheric aerosol burden. The influence of
volcanic sources derived from satellite data, but also of dust and organic
aerosols, is analyzed. We present examples of the constraints by satellite
observations in different spectral regions on different aerosol types with
respect to particle size and composition. We discuss the evolution of the
optical depth and radiative forcing by stratospheric aerosols, including
uncertainties introduced be horizontal model resolution. Finally, we show
that the findings concerning the importance of dust for the lower
stratosphere are consistent with observations and simulations of tropospheric
aerosol. Conclusions are drawn in Sect. 5.
Satellite data products from Aerosol CCI IIGOMOS (Global Ozone Monitoring by Occultation of Stars)
GOMOS is an instrument based on the stellar occultation technique
and provides atmospheric measurements in the
UV-visible-IR range (248–690, 755–774 and 926–954 nm). The use of stellar
occultation results in a high rate of occultation measurements, and,
consequently, a very good spatial coverage compared to solar occultation. As
a drawback, the signal-to-noise ratio of each measurement is much lower than
in the solar case, and varies with the star characteristics (especially its
magnitude and its temperature). The operational retrieval,
IPF (Instrument Processing Facility), provides density profiles for trace gases such as ozone
(O3), nitrogen dioxide (NO2) and nitrogen trioxide
(NO3) , as well as aerosol extinction. However,
the extinction shows a poor quality for the reference
wavelength at 500 nm. For this reason an alternative inverse algorithm
called AerGOM was specifically developed to optimize the aerosol retrieval
. AerGOM provides vertical profiles of the
same gas species, and the total extinction coefficient for the nongaseous
species and its spectral dependence, currently over the range 250–750 nm.
The nature of the total extinction fraction for nongaseous species is then
inferred using simple criteria based on the geolocation, associated
temperature value and extinction value, and each point of the vertical
extinction profile is attributed to aerosols, cirrus clouds, polar
stratospheric clouds or meteoritic dust.
From the AerGOM extinction, climate data records (CDRs) were developed in the
framework of the ESA Aerosol CCI project for different quantities including
the aerosol extinction and the related aerosol optical depth at several
wavelengths 355, 440, 470, 550 and 750 nm;. Particular attention was paid to the grid choice, which should optimally
render the information contained in the GOMOS measurement set. The most
important conclusions of this optimization were that grid resolutions should
be chosen to ensure a reasonable statistical sampling in most of the grid
cells, and that it should optimally reflect the typical transport of volcanic
plumes after an eruption reaching the upper troposphere or the lower
stratosphere (UTLS). Therefore, the grid should represent, in a coherent way,
the longitudinal and latitudinal air mass transport, and the time needed for
this transport. Also, the temporal resolution should be short enough to
enable the detection of volcanic signatures, taking into account the typical
lifetime of the plume. In this respect, we could verify that time intervals
of about 5 days are able to represent the signature of most of the eruptions
injecting sulfuric gases in the UTLS, while such a signature is often diluted,
underestimated or even disappears in the case of coarser grid cells. This is
the case, for instance, for monthly zonal means, even though this
representation is very commonly used in the field. The ability of the grid to
reproduce the signature of volcanic plume in a satisfactory way is of
particularly great importance when the CDRs are used to constrain climate
models. More detail about the investigations of the optimal grid choice and
all other aspects of the implementation of the CDRs can be found in
.
In their current version (version 3.0), these CDRs are defined on a grid with
a resolution of 5∘ in latitude, 60∘ in longitude, 1 km in
altitude and 5-day time period. The records cover the whole ENVISAT period
(March 2002–April 2012) and include the total extinction of nongaseous
species, but also the polar stratospheric cloud (PSC) fraction and the
cloud-free aerosol fraction which is dominated by sulfate aerosols below an
altitude of 32 km. It is important to mention that cloud detection is not yet
optimal, and that cloud contamination of the aerosol fraction is possible in
the UTLS region. This issue is still under investigation.
The IASI dust dataset of the Université Libre de Bruxelles (ULB) was
generated in the context of ESA CCI's project . It is based
on a statistical regression technique and the use of a neural network trained
on synthetic IASI data. A similar scheme has already been applied for the
retrieval of NH3 (ammonia; ). As input
variables it uses the IASI L2 pressure, humidity and temperature information,
as well as spectral information and a CALIPSO (Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation) derived dust altitude climatology. The main
output variables are dust optical depth at 10 and 11 µm (and 550 nm).
Initial results and validation performance are provided in .
(A)ATSR ((Advanced) Along Track Scanning Radiometer)
The ATSR (SU) algorithm has been
developed at Swansea University for estimation of atmospheric aerosol and
surface reflectance for the ATSR-2, AATSR sensors and SLSTR (Sea and Land
Surface Temperature Radiometer) on Sentinel-3. Over land, the algorithm
employs a parameterized model of the surface angular anisotropy
, and uses the dual-view capability of the instrument to
allow aerosol property estimation without a priori assumptions on surface
spectral reflectance. Over ocean, the algorithm uses a simple a priori model
of ocean surface reflectance at both nadir and along-track view angles. A
climatology is used to constrain chemical composition of
the aerosol components at 1∘×1∘ latitude–longitude
grid, while the method retrieves aerosol size and optical thickness on a
10 km grid. Both optical thickness and size are retrieved as vertical column
values. Size is not resolved vertically, but is represented by fraction of
fine and coarse mode aerosol in total. The algorithm has been developed from
initial prototype under the Aerosol CCI program, and
results and validation performance for version 4.21 are provided in
. The version used here (v 4.3) differs from that summarized
in by improvements in retrieval of coarse/fine mode
fraction, and improved cloud screening over ocean in the region of dense
plumes, resulting in approximately 10 % greater coverage, with small
improvement in correlation against AERONET (AErosol RObotic NETwork) values.
AERONET is recognized as a reference dataset for validation of satellite data
products .
OSIRIS (Optical Spectrograph and InfraRed Imager) – an additional instrument
OSIRIS was launched on board the Odin satellite, and has provided vertical
profiles of limb scattered radiance between 280 and 810 nm since 2001
. The radiance profiles are inverted to provide aerosol
extinction measurements at 750 nm at altitudes between 10 and 35 km with a
vertical resolution of approximately 2 km . This
technique provides high sampling rates with hundreds of measurements per day
over the sunlit portion of the globe, enabling excellent spatial and temporal
sampling of short-lived events. OSIRIS aerosol extinction retrievals agree
well with coincident occultation measurements from Stratospheric Aerosol and
Gas Experiments II and III during background periods but have known low
biases above approximately 25 km, and will have some cloud contamination
near and below the tropopause . Additionally,
seasonal biases are possible due to the orbital geometry and changes in
aerosol optical properties such as after volcanic eruptions may also bias the
retrievals. These effects are described in more detail by
. This work uses the OSIRIS version 5.10 aerosol
retrieval averaged into daily, 5∘ latitude by
30∘ longitude bins for comparisons.
Model setup
For the simulations of the radiative and chemical effects of stratospheric
aerosol, the ECHAM5 (5th generation of European Centre Hamburg) general
circulation model coupled to the Modular
Earth Submodel System Atmospheric Chemistry (EMAC) was used
, updated to the version of . In contrast
to – who use stratospheric aerosol extinction
climatologies derived from observations – our model setup aerosol and its
optical properties are calculated from precursor gases and emissions. As dust
reaching the UTLS region turned out to be sensitive to model resolution, we
used different model resolutions: the T42 resolution (spectral, 2.75∘
in latitude and longitude) of the previous studies, T63 resolution
(1.88∘), the standard resolution for the stratosphere used in this
study and T106 resolution (1.1∘) for a 1-year sensitivity test. The
vertical grid has 90 layers from the surface up to 0.01 hPa (80 km
altitude, short L90) with finest resolution in the boundary layer and near
the tropopause. For T106 only simulations with the low top model version with
31 levels up to 30 km altitude (L31), the setup used by
, which is well tested regarding the representation of
tropospheric aerosol, are discussed here in detail. In all simulations,
except the T42L90 one of the previous studies, the meteorology below about
the 100 hPa level is nudged to the reanalysis ERA-Interim
. The simulations were performed for the ENVISAT time
period from July 2002 to March 2012 to allow for the use of data from MIPAS
for input, and GOMOS and ATSR for validation. The period from 1997 to 2002
using SAGE II (Stratospheric Aerosol and Gas Experiment) was simulated first
to get consistent initial conditions.
The applied aerosol module GMXE accounts for seven modes
using lognormal size distributions (nucleation mode, soluble and insoluble
Aitken, accumulation and coarse modes). The boundary between accumulation
mode and coarse mode, a model parameter, is set at a dry particle radius of
1.6 µm to avoid too fast sedimentation of a too large coarse mode
fraction in case of major volcanic eruptions. For dust sensitivity studies in
T106 which focus on the troposphere, a boundary of 1.0 µm is also used.
The mode parameters are used for every aerosol type and listed for
convenience in Table S1 of the Supplement. Optical properties for the types
sulfate, dust, organic carbon and black carbon (OC and BC), sea salt, and aerosol
water are calculated using Mie-theory-based lookup tables consistent with the
selected size distribution widths of the modes. The resulting optical depths,
single scattering albedos and asymmetry factors are used in radiative
transfer calculations which (except for the T106 low top sensitivity studies)
feedback to atmospheric dynamics. The contribution of stratospheric aerosol
to (instantaneous) radiative forcing and heating is calculated online via
multiple calls of the radiation module.
The mineral dust emissions are calculated online using the emission scheme of
which builds on previous studies by ,
, , ,
and . The emission scheme parameterizes saltation bombardment and
aggregate disintegration by sand blasting, combining the surface friction
velocity with descriptions of land cover type, clay fraction of the soil and
vegetation cover. For an improved representation of dust at higher
resolution, we adopted the updates presented by in
the T106L31 simulation.
Aerosol module parameters, for example the composition of sea salt,
were optimized on the basis of the satellite data. We apply the chemical
speciation of the sea salt emission flux used by as
listed in Table S2 of the Supplement. The sea salt composition affects the
hygroscopic growth and thereby the AOD. The setting of ,
dominated by Na and Cl ions, which we initially applied in our simulations
produced very high AOD levels over the North Pacific which are not consistent
with the satellite observations.
SO2 plumes (sulfur dioxide) from about 230 explosive volcanic
eruptions into the stratosphere were derived from 3-dimensional data fields
of MIPAS and, in case of data gaps, of GOMOS on ENVISAT
with a temporal resolution of 5 days, and added as volume mixing ratio to the
simulated SO2 at the time of the eruption. Each identified volcanic
eruption (with names from the Smithsonian volcanic database,
http://www.volcano.si.edu, last access: 31 August 2018) is listed in an
emission inventory published recently , which provides an
estimate of the altitude and the amount of SO2 injected into the
atmosphere. The table and the 3-D fields of volcanic SO2 are
available at 10.1594/WDCC/SSIRC_1. These data were derived from MIPAS
within the uncertainty range but nearer the upper end for best results with
the model resolution T42L90 and free running mode, which has some artifacts
from the convection scheme and a dry bias at the tropical tropopause. For the
nudged T63L90 simulation, the volcanic SO2 data of the inventory
have to be downscaled by about a factor of 0.7 which is actually closer to
the most likely MIPAS measurements. The actual values for each injection,
which depend on the time span between the eruptions and on corrections for
data gaps, are given in the Supplement (Table S3). Boundary conditions for
background concentrations of SO2 from outgassing volcanoes into the
troposphere are taken from the monthly climatology of
truncated at 200 hPa to avoid double counting in the stratosphere. The
sulfur source gas OCS (carbonyl sulfide) is constrained by observed monthly
zonal average surface volume mixing ratios update of the data
by. Marine DMS (dimethyl sulfide) as a natural sulfur source
is also included in the model, using a module for exchange fluxes between
seawater and atmosphere by and the
climatology. For anthropogenic emissions of CO (carbon monoxide),
NOx (nitrogen oxides), sulfur, OC and BC the DLR- MACCity
emission inventory is used. Biomass burning is based on ACCMIP-MACCity and
GFEDv2, OC-SOA (secondary organic aerosol) on AEROCOM_UMZ1. For details on
these emission inventories selected for the Chemistry Climate Model
Initiative (CCMI) see .
Stratospheric aerosol and its radiative effectVolcanic eruptions
Volcanic emissions have a large impact on the stratospheric aerosol burden.
Even small and moderate eruptions contribute to the stratospheric aerosol
load due to convective transport of SO2 and its gradual uplift to
the upper troposphere and the lower stratosphere, and resulting accumulation
of sulfate aerosol. Volcanic SO2 injections explain most of the
interannual variability of stratospheric aerosol extinction (decadal
logarithm) observed by GOMOS, as depicted in Fig. at three
wavelengths. For each wavelength (350 nm in Fig. 1a, b; 550 nm in Fig. 1c,
d and 750 nm in Fig. 1e, f), the GOMOS time series (Fig. a, c, e)
showing the altitude dependence in the tropics, is compared with the EMAC
simulation in resolution T63L90 including the dust contribution
(Fig. b, d, f; see Sect. 4.2 for more detail). Figure
shows, at all three wavelengths, that an enhancement of the extinction value
is observed around 16–18 km, corresponding to the aerosol load resulting
from a succession of volcanic eruptions during the whole period 2002–2012.
The eruptions of Nabro in June 2011 and the successive eruptions of Soufriere
Hills and Rabaul in 2006 have the largest effects on extinction in the lower
stratosphere in the observations and the simulation. The best agreement
between GOMOS and EMAC is found in the case of the extinction at 550 nm
(Fig. 1c, d), where the quality of the GOMOS retrieval is the best. At
750 nm (Fig. 1e, f) also, GOMOS measurements agree well with EMAC for the
aerosol layer (16–22 km) where measured extinction values exceed ≈2×10-4 km-1. At lower
altitudes (14–16 km), rather unstructured patterns of enhanced extinction
are found by GOMOS, probably corresponding to cloud contamination. At
350 nm, where a decrease in the GOMOS quality is expected due to a loss in
signal-to-noise ratio obtained in the UV spectral region while using cold
stars, still the volcanic events stick out. More details over these aspects
can be found in references .
also present the latitude dependence of 550 nm aerosol extinction at 17 km
altitude as observed by GOMOS and simulated by EMAC in the coarse resolution
T42L90 in their Fig. 10.
GOMOS and EMAC extinctions (log) in the tropics as a function of
altitude for different wavelengths: (a, b) UV 350 nm,
(c, d) visible 550 nm and (e, f) near-infrared 750 nm;
resolution T63L90.
Observed (a, b) and simulated (c, d, EMAC T63L90)
extinction in the Asian sector (60–120∘ E,
20∘ S–60∘ N) for 550 nm (a, c) and
750 nm (b, d). Contribution of wet dust (e, f) and wet
sulfate (g, h) to extinction for 550 nm (e, g) and
750 nm (f, h). (i) Median wet radius in accumulation mode
(for effective radius multiply by 1.4).
Dust and organics from the troposphere in the UTLS)
Extinction in the lowermost stratosphere and upper troposphere is to a large
fraction due to desert dust and organic carbon aerosol. These contributions
were strongly underestimated in due to a crude
parameterization in the used model version based on , but
overestimated in . Both simulations were performed in the
relatively coarse resolution T42L90. Dust reaching the UTLS is sensitive to
model resolution, mostly via the convection parameterization
. In Fig. the simulated extinction at
resolution T63L90 fits well to the GOMOS observations which appear to have a
seasonal contribution from the Asian summer monsoon. For more detailed
analysis, Fig. shows observed and simulated extinction in the
Asian sector at 17 km in the visible and the near-IR. The largest extinction
values are indeed found at the location and time of the Asian summer monsoon
at the altitude of outflow. This feature is clearest in years not perturbed
by medium strength volcanic eruptions, for example 2010. For a clear
separation, the contributions of wet dust and wet sulfate to extinction are
displayed separately (Fig. 2e–h). The wet dust particles in the monsoon
region have a larger median wet radius than the volcanic sulfate particles
(e.g., from Sarychev in 2009, Fig. 2i) which is consistent with a relatively
larger extinction in the infrared compared to the visible in the monsoon
region in observations and simulations. Figure 2a–d demonstrates that dust
is essential to reproduce the observations. Total extinction without wet dust
in T63L90 is shown in the Supplement. Comparing Fig. S1b with Fig. 2g shows a
small contribution of organics from biomass burning in northern spring (for
volume mixing ratios see Fig. S2). Figure S1 also contains results from the
T42L90 simulation of , showing that for this resolution the contribution of wet dust to extinction has to be
downscaled (i.e., divided) by a factor of 2 to get agreement (Fig. S1d,
factor of 3 if only dry dust is considered).
Observations by IASI and ATSR indicate a maximum in dust aerosol optical
depth (DAOD) in early Northern Hemisphere summer over the Asian deserts
located in the inflow regions of the monsoon (see Sect. 4.4). A similar
feature is found in the simulations by EMAC. This supports our findings that
desert dust is also important for the UTLS.
(a) Stratospheric aerosol radiative forcing,
(b, c) stratospheric AOD for tropics and midlatitudes. Red lines and
crosses: EMAC, resolution T63L90, current version; black: EMAC T42L90
; blue: T63L90 without downscaling the SO2
injections for T42L90; green: from observations (crosses annual mean for
forcing; ; SAGE II, CALIPSO, OSIRIS).
(a, b) Stratospheric AOD at 550 nm observed by GOMOS
(green) and simulated by EMAC in resolutions T42L90 (black) and T63L90 (red).
(c, d) Stratospheric AOD at 750 nm in the northern tropics and
subtropics (SAOD above 15 km), additionally with OSIRIS observations (light
blue).
Simulated aerosol radiative heating in the tropics
(solar + infrared, T63L90).
Observed (left) and simulated (right).
(a, b) 10 µm dust AOD (DAOD) for IASI and EMAC;
(c, d) 0.55 µm DAOD from ATSR and EMAC;
(e, f) fine mode AOD; (g, h) absorbing AOD (AAOD) and
(i, j) total AOD for ATSR (SU) and EMAC in T63L90 resolution, annual
mean 2011.
Desert dust transported to the UTLS mostly via the Asian summer monsoon
contributes significantly to the seasonal cycle of total stratospheric
aerosol optical depth (SAOD) in satellite observations and the EMAC
simulations shown in Fig. 3b for the tropics (vertical integral of extinction
above about 16 km) and in Fig. c for midlatitudes (above about
14 km). Global radiative forcing at the tropopause is depicted in
Fig. a. The figure contains in black results from the T42L90
simulation of and in blue the T63L90 simulation with the
high volcanic sulfur input derived for the coarse resolution. Green lines and
symbols show estimates derived from satellite observations SAGE II,
OSIRIS and CALIPSO;. Red
shows results of the current model version in T63L90 with the
dust scheme and corrected SO2 input (see
Sect. 3 and Supplement). Concerning global radiative forcing, the volcanoes
are the dominating effect with up to 0.13 W m-2 for Rabaul and Nabro
compared to the volcanically quiet period in 2002. Here the use of the
SO2 inventory for T42L90 in the T63L90 simulation (blue) causes an
overestimate of up to 50 % in 2006 and 2007 due to accumulation effects
of eruptions following in short sequence. This is visible in the overestimate
of tropical SAOD depicted by the blue curve in Fig. 3b.
Especially in northern midlatitude summer SAOD in T42L90 appears to be high
because at that resolution the convective transport of dust to the UTLS in
the Asian monsoon region is overestimated (Fig. c). This is clearly
seen in Fig. which shows in black the T42L90 simulation, in green
the observations of 550 and 750 nm SAOD by GOMOS, and in light blue (Fig. 4c, d
only) by OSIRIS in different latitude bands, including the monsoon region.
For the narrow latitude bands in Fig. 4c and d, inclusion of OSIRIS data is
important because GOMOS coverage is often too low. Nevertheless, for a
lot of features the two satellite datasets agree well. Using the higher
resolution T63L90, for which the convection parameterization was developed,
the agreement with the satellite observations is much better (Figs. 3 and 4,
red) than with T42L90, especially at midlatitudes and in the subtropics. In the
subtropics (Fig. 4d), the simulation with low resolution (black) always
overestimates the monsoon peaks in August compared to the ones seen in the
observations. Comparing the model results with OSIRIS in the northern tropics
(Fig. 4c) indicates that some volcanic events are still missing in the
inventory, for example in spring 2007 and 2010. This would also explain the
differences in radiative forcing (indicated by crosses in Fig. 3a) in these
years.
The simulated aerosol radiative heating, derived from radiation calls with
and without aerosol, reflects the medium volcanic eruptions with the largest
effects near 18 km (Fig. ). There, desert dust causes additional
heating at the time of the Asian summer monsoon. In the UTLS, below, every year
in September, a clear signal from biomass burning organic aerosol – its volume
mixing ratio is shown in Fig. S2 of the Supplement – is visible. Above, around
22 km, the dust below in Northern Hemisphere summer causes a reduction of
absorption of terrestrial radiation by ozone.
Constraints from total aerosol optical depth in different spectral regions and for different aerosol
subsets
The first comparisons are carried out for EMAC in T63L90, the standard
resolution used in the previous sections. Here AOD refers to the troposphere
and stratosphere. The DAOD (dust AOD) in terrestrial infrared is most
sensitive to the coarse mode of tropospheric dust. Figure a, b
shows that the model reproduces most of the IASI features. DAOD in the
visible spectral region (Fig. 6c, d) is too high over central Asia, pointing
to an overestimate of dust in the accumulation mode near the Taklamakan
Desert. The patterns in the IR and visible spectral range are different
despite considering the factor 2 often applied by the AEROCOM/AEROSAT
(Aerosol Comparison between Observations and Models) community for conversion
in the color scales of Fig. 6a, b and c, d. This holds for model and observations. The fine mode AOD fraction,
which is dominated by the accumulation mode, is slightly overestimated over
Europe and underestimated in the biomass burning regions in Africa (Fig. 6e,
f). In the model this is sensitive to the way the extinction of aerosol water
is attributed to the soluble aerosol species, especially sea salt. Absorbing
AOD, i.e., AOD × (1-ω) with ω representing single scattering albedo, agrees
surprisingly well (Fig. 6g, h). In the total AOD (Fig. 6i, j) there appears
to be too much sea salt in the model, or still suboptimal parameters for the sea
salt composition which controls water uptake (see Sect. 3).
Annual mean for 2011 of the DAOD at 10 µm wavelength
observed by IASI (b, IASI ULB dataset version 8) and simulated by
EMAC (a) at T106L31 resolution.
Annual mean for 2011 of the AOD at (from left to right) 550, 670 and
870 nm wavelength observed by AATSR (d, e, f; SU-ATSR algorithm
version 4.3) and simulated by EMAC (a, b, c) at T106L31 resolution.
Figure compares the annual average for 2011 of the 10 µm
DAOD observed by IASI and simulated by EMAC in the low top
version with high horizontal resolution (T106L31, about
1.1∘). The satellite retrievals are taken from version 8 of the ULB
dataset. The simulation uses the dust emission scheme of
which calculates the emissions online considering the
meteorological conditions. To extract the DAOD from the total EMAC AOD at
10 µm, we apply a filter nullifying sea-salt-dominated AOD values.
To identify the latter, we compare the AOD weighted with the volume of sea
salt and dust.
The observed and modeled global DAOD distributions shown in Fig. 7 agree
remarkably well. The pixel values of each map are strongly correlated with a
correlation coefficient of 0.91. The overall AOD level is consistent as well,
so that a similar variance in the pixel values is obtained for the observed
(0.00038) and the modeled (0.00041) DAOD distribution. Interestingly, the
DAOD from the older version 7 of the ULB dataset yields a pixel by pixel
correlation coefficient of only 0.89 and a pixel value variance of only
0.00029. We conclude that the agreement of EMAC and IASI has improved with
the update from version 7 to version 8 of the IASI ULB dataset.
The main disagreement of the two maps in Fig. 7 is the less pronounced
maximum over the Taklamakan Desert in central Asia in the model result. This
underestimation is related to the model surface friction velocity in
mountainous regions like the surroundings of the Taklamakan Desert, which
tends to be lower in simulations at higher horizontal resolution (e.g., T106)
than at lower resolution (e.g., T63), possibly resulting in an underestimation
of the dust emissions.
Figure compares results from the T106L31 EMAC simulation for the
annual average of the total AOD at visible and near-infrared wavelengths with
AASTR retrievals using the ATSR (SU) algorithm version 4.3. Generally good
agreement is obtained at 550 nm which is consistent with the good agreement
between the 550 nm MODIS (Moderate-resolution Imaging Spectroradiometer) AOD
and model results based on the same EMAC version . As
for the T63L90 simulation, the model yields higher sea-salt-related AOD
levels over the oceans. In contrast, the model AOD over the Sahara is lower
than the satellite retrieved values. This becomes even more evident at larger
wavelengths (Fig. 8c, f): the model AOD over the Sahara, in contrast to most
other regions, has a stronger wavelength dependence than the observed AOD,
corresponding to a larger Ångström exponent. This discrepancy might be
resolved by adjusting the dust particle size distribution in the model under
the constraint of not sacrificing the good agreement of model and observed
AOD at 550 nm and at 10 µm. This could involve modifying the parameters
of the log-normal modes, i.e., their widths and boundaries, but also
reassessing the parameterization of relevant processes such as emission,
deposition, coagulation and hygroscopic growth, or even adding an extra mode
for extremely coarse particles which can be relevant close to dust sources.
Over South America, the biomass burning regions of Africa, and India and China
the wavelength dependence of model and observed AOD is largely consistent.
Conclusions
Satellite data are not only important to constrain model parameters but they
are also very important for model improvement. Comparing satellite data with model
results at different wavelengths simultaneously provides additional
information and is also valuable for the satellite community to check
internal consistency, as in our case for GOMOS and OSIRIS.
Sophisticated modeling of dust and organic aerosol as well as a detailed
volcano dataset are necessary to reproduce the seasonal cycle and the
interannual variability in extinction in the lowermost stratosphere observed
by GOMOS at different wavelengths. From the wavelength dependence in
observations and simulations, aerosol in the UTLS with enhanced particle size
due to water uptake can be identified as aged dust in the Asian monsoon
region. Convective transport of
dust into the UTLS is resolution dependent because of differences in
convection top height and overshooting convection. A resolution of T63L90
(1.88∘ in longitude and latitude, 90 vertical layers) fits best to
the observations. For the low resolution T42L90 (2.75∘), dust SAOD
(and stratospheric mixing ratio) has to be downscaled by a factor of about
0.33; for higher resolutions (e.g., T106L90), upscaling is required. The
resolution dependent differences in convection also modify the residence time
of sulfur species in the lowermost stratosphere, and especially at low
latitudes, at resolution T42L90, it appears to be too short.
The total AOD in the visible spectral range is very sensitive to aerosol
water and the composition of sea salt. In the modal model, the bulk fraction
has to be increased compared to ions to reduce artifacts of too much water
uptake by sea salt. The satellite data helped to identify a preferred
parameter set for the sea salt emission composition.
Our simulated dust total aerosol optical depth agrees with satellite data in
the visible (ATSR SU) and the infrared (IASI ULB, version 8). The combined
comparison at visible and infrared wavelengths provides strong constraints on
the modeled particle size distribution. The direct comparison of
observations and model reveals different structures in the extinction
patterns at both spectral ranges. From this, we conclude that simply assuming
a spatially constant factor of (about) 2 for conversion of DAOD from 10 µm to 550 nm, as commonly applied in the AEROCOM/AEROSAT community, is
too crude.
Satellite datasets identifying volcanic SO2, including its vertical
distribution or enhanced extinction by aged dust enable the model to get
closer to observationally based estimates for radiative forcing, showing the
interest of a close interaction between modeling and observation research
teams.
The Aerosol CCI satellite data are available at ICARE,
Lille. All model output of EMAC used here is stored at DKRZ, Hamburg, and
available on request. This includes 5-day averages and 10-hourly values.
Volcanic SO2 input data are available at
10.1594/WDCC/SSIRC_1.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-12845-2018-supplement.
CBr wrote the paper and performed the stratospheric simulations, supported by JS. KK performed the tropospheric simulations
and provided code for the stratospheric part. CBi and CR provided the GOMOS
data and the corresponding text, LC the IASI data; PN and AH provided the ATSR data,
and LR the OSIRIS data.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “The Modular Earth
Submodel System (MESSy) (ACP/GMD inter-journal SI)”. It is not associated
with a conference.
Acknowledgements
This study was funded by the Aerosol CCI project, phase II, of the ESA Climate Change
Initiative, as a user option, and by the EU FP7 project STRATOCLIM.
Supporting work for the development of GOMOS datasets was performed in the
framework of a Marie Curie Career Integration Grant within the 7th European
Community Framework Programme under grant agreement no. 293560. The satellite
data, except OSIRIS, were provided via the Aerosol CCI database at ICARE,
Lille, France; the model simulations were performed at DKRZ, Hamburg,
Germany, where the results are also stored.
The article processing charges for this open-access publication were covered by the Max Planck Society.
Edited by: Farahnaz Khosrawi
Reviewed by: two anonymous referees
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