Atmospheric aerosol has substantial impacts on climate, air
quality and biogeochemical cycles, and its concentrations are highly
variable in space and time. A key variability to evaluate within models that
simulate aerosol is the vertical distribution, which influences atmospheric
heating profiles and aerosol–cloud interactions, to help constrain aerosol
residence time and to better represent the magnitude of simulated impacts. To
ensure a consistent comparison between modeled and observed vertical
distribution of aerosol, we implemented an aerosol lidar simulator within
the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator
Package version 2 (COSPv2). We assessed the attenuated total backscattered
(ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S.
Department of Energy's Energy Exascale Earth System Model version 1
(E3SMv1). The simulator performs the computations at the same vertical
resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP),
making use of aerosol optics from the E3SMv1 model as inputs and assuming
that aerosol is uniformly distributed horizontally within each model
grid box. The simulator applies a cloud masking and an aerosol detection
threshold to obtain the ATB and SR profiles that would be observed above
clouds by CALIOP with its aerosol detection capability. Our analysis shows
that the aerosol distribution simulated at a seasonal timescale is generally
in good agreement with observations. Over the Southern Ocean, however, the
model does not produce the SR maximum as observed in the real world.
Comparison between clear-sky and all-sky SRs shows little differences,
indicating that the cloud screening by potentially incorrect model clouds
does not affect the mean aerosol signal averaged over a season. This
indicates that the differences between observed and simulated SR values are
due not to sampling errors, but to deficiencies in the representation of
aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.
Motivation
The role of aerosol in the Earth system has been recognized as a major
source of uncertainty for decades. Aerosol has significant impacts on the
climate system, as well as on weather and air quality, and Earth's
biogeochemical cycles (Szopa et al., 2021). They modulate the Earth's energy
budget via aerosol–radiation and aerosol–cloud interactions, exerting
radiative forcings to the climate system (Forster et al., 2021). They also
affect the Earth's water cycle by changing clouds and precipitation
characteristics (Douville et al., 2021). Due to its short lifetime (up to
several days in the troposphere) compared to long-lived greenhouse gases,
aerosol is highly variable in space and time. Obtaining appropriate
information about the spatiotemporal distribution of aerosol from satellite
measurements remains a key challenge (Constantino and Bréon, 2013).
Passive satellite measurements have been used to study column-integrated
properties of aerosol, but they are not suited for the vertical distribution
of aerosol. Nevertheless, aerosol vertical distribution is critical when it
comes to aerosol–radiation interactions (Zarzycki and Bond, 2010). This in
particular applies to the adjustments to aerosol–radiation interactions or
semi-direct effects, where the vertical alignment of clouds and aerosol is
crucial (Koch and Del Genio, 2010). Aerosol vertical distribution also
affects aerosol lifetime (e.g. Keating and Zuber, 2007) and aerosol–cloud
interactions (e.g. Waquet et al., 2009; Stier, 2016; Quaas et al., 2020).
Spaceborne lidars fill this gap by providing detailed information about the
vertical distribution of aerosol. This is particularly useful for studying
long-range transport of smoke or dust in the free troposphere and
stratosphere and for studying the interactions between aerosol and ice
clouds in the upper troposphere, because the vertically integrated aerosol
quantities retrieved from passive sensors are mostly about aerosol in the
planetary boundary layer. Furthermore, space lidars can retrieve aerosol in
regions where the surface is reflective, such as the polar regions and
desert, while passive satellite instruments only have limited capabilities
retrieving aerosol in those conditions. Over the last decade, the aerosol
profiles collected by space lidars (Winker et al., 2013) have contributed to
progress on a variety of aerosol research questions (Koffi et al., 2012,
2016; Tian et al., 2017; Ratnam et al., 2021). More advanced comparisons
between model and lidar observations have demonstrated the value of using a
lidar aerosol simulator to ensure consistent comparisons between the modeled
aerosol and the observed aerosol (Ma et al., 2018; Hodzic et al., 2004;
Watson-Parris et al., 2018). In parallel, the cloud community has developed
satellite simulators to establish a closer bridge between observed and
modeled clouds and facilitate the use of space-based data by the model
community for a variety of topics such as evaluating the model physics,
studying climate feedbacks, and inter-comparing several models in a consistent
way over short-term and long-term simulations (Konsta et al., 2016; Chepfer
et al., 2018). In particular, the active sensor satellite simulators
developed for lidars and radars have been proven to be useful tools to
properly take into account the limits of observations (e.g. cloud masking,
signal-to-noise ratio, sub-gridding) when comparing observations and models
(e.g. Ma et al., 2018).
These studies point to the potential for satellite lidars to provide
important constraints for the aerosol distributions in climate models, which are of
benefit to a range of different configurations. There is now a 15-year-record of the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and
Infrared Pathfinder Satellite Observation (CALIPSO) satellite (2006–2020).
In evaluating the simulated vertical aerosol distribution in nudged
simulations where, for example, winds are relaxed towards reanalyses, these
measurements can provide important observational constraints to improve
transport and removal processes in models. On the other hand, using
observational constraints together with a climatology statistic approach of
simulations with prescribed sea surface temperature (SST) can be beneficial to account for circulation
feedbacks to aerosol forcing. Indeed, while the transport by large-scale
circulation determines the geographical patterns of aerosol forcing, this
aerosol forcing also impacts large-scale circulation (Kim et al., 2007).
These mechanisms can be studied by making use of aerosol optical depths
(AODs) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) or
Visible Infrared Imaging Radiometer Suite (VIIRS). Finally, long-term (100 years) simulations of the coupled ocean–atmosphere system (control and
Representative Concentration Pathway 8.5, RCP8.5, type simulations) can help to understand the role of aerosol in the
context of climate change.
The lidar simulator translates the vertical profiles of aerosol extinction
and backscatter coefficients computed by a model into vertical profiles of
the two key variables retrieved by a lidar: the attenuated total
backscatter (ATB) and the backscatter ratio (SR). These two lidar variables
are derived online within the model to account for the two-way attenuation
within the light's transmittance along its path from the laser to the
scattering object, as well as the return path back to the detector. The
calculations also account for the molecular backscatter (i.e. Rayleigh
backscatter), calculated from the model's air temperature and pressure
profiles. Furthermore, the model is sampled on the satellite orbital path,
the fully overcast cases are masked out to take account of the impossibility
for a space lidar to observe aerosol below optically thick clouds, and only
the signal above the instrumental noise is retained.
We incorporate modules included in previously developed simulators (Ma et
al., 2018; Vuolo et al., 2009; Hodzic et al., 2004) into the community tool
Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator
Package version 2 (COSPv2) to create a simple base on which each group can
build up its own analysis. The goal is to facilitate the comparison between
general circulation models (GCMs) and space lidar aerosol data. Besides CALIPSO operating at 532 and
1064 nm, the Atmospheric Lidar (ATLID) instrument of the EarthCARE mission
is expected to become operational in 2023. In synergy with other
instruments, it will provide vertical profiles of aerosol and thin clouds,
operating at 355 nm with a high-spectral-resolution (HSR) receiver and
depolarization channel. Moreover another HSR Lidar operating at 532 and
1064 nm is expected to be launched in the future. The COSPv2 lidar simulator
will thus be a useful tool for the exploitation of these new datasets and
the comparison with GCMs of several modeling
groups.
We have chosen to implement the lidar aerosol simulator within the COSPv2
software package to leverage all the simulator capabilities available in
COSPv2. Moreover, COSPv2 is already implemented in several GCMs (Swales et al., 2018), so the addition of the aerosol lidar simulator module should only
require a small amount of effort for the modeling groups.
Concept and design
The aerosol simulator described in this section mimics the aerosol
observations that would be observed by a space lidar overflying the
atmosphere simulated by the model (Fig. 1). Hereafter, we first define the
usual aerosol variables (specifically, the attenuated total backscattered
signal ATB and the backscatter ratio SR). Then, we describe the procedure of
the lidar aerosol simulator. Finally, we discuss its implementation and its
main differences with the cloud lidar simulator.
Schematic of the lidar aerosol COSPv2 simulator. See Table 1 for
the correspondence between the names of the variables in the code and in the
present paper.
Definitions
As defined by Stromatas et al. (2012), the attenuated total backscattered
signal (in m-1 sr-1) represents the signal backscattered towards
the lidar by aerosol and molecules and attenuated along its path by aerosol
and molecules in a cloud-free atmosphere. The ATB is integrated vertically
from the surface to the top of the atmosphere (TOA):
ATB=βm(λz)+βa(λz)⋅exp-2∫zTOAαm(λz′)+αa(λz′)dz′,
where βm and βa are the molecule and aerosol 180∘ backscatter profiles (in m-1 sr-1), respectively, and αm and αa are the extinction coefficients for molecules and aerosol
(in m-1), respectively. The 180∘ Rayleigh/molecular backscatter
coefficient depends on temperature (in K), pressure (in Pa) and on the
wavelength λ (in µm):
βm=PkT5.45×10-32λ0.55-4.09,
where k is the Boltzmann constant (k=1.38×10-23 J K-1). The extinction coefficient by molecules can be simply expressed as
αm=βm0.119
(Stromatas et al., 2012). The 180∘ backscatter and
extinction coefficients for aerosol depend on the microphysical properties
(size distribution) and chemical composition of the particles, the latter
determining its refraction index. To highlight aerosol in an atmospheric
layer versus molecular background, one often uses the backscatter ratio
(SR). The definition of SR used in CALIPSO products (e.g. Chepfer et al.,
2008, 2013) is
SR(λz)=ATBAMB,
where AMB is the attenuated molecular backscatter signal in the absence of aerosol:
AMB(λz)=βm(λz)⋅exp-2∫zTOAαmλz′dz′.
Therefore, SR=1 indicates the absence of aerosol, where the
backscatter signal is from gaseous molecules only.
Concept
The GCM provides pressure, temperature and cloud fraction at each level and
for each latitude–longitude grid cell. When the GCM includes an interactive
aerosol module, it also provides on this 3D grid the optical properties of
aerosol at a given wavelength. The simulated aerosol optical properties and
distribution depend on the aerosol parameterization in the GCM. The aerosol
optics diagnostics in GCMs vary, with some models computing
single-wavelength extinction and 180∘ backscatter, whilst others
calculate only the waveband-integrated aerosol optical properties (i.e.
extinction, absorption and phase function). In the latter case, the modeling
centers will need to implement additional aerosol optics diagnostics to
convert these optical properties into the aerosol extinction and 180∘ backscatter coefficients in order to use the lidar simulator. These
coefficients must be defined monochromatically, i.e. at specific
wavelengths, 532 and 1064 nm for CALIPSO/CALIOP, with these being standard
wavelengths for most GCMs. Coefficients defined at other wavelengths, such
as 355 nm for EarthCARE ATLID, could also be added as additional diagnostics.
In the steps listed below, it is assumed that the process applies to a
vertical profile and that it is repeated for all longitude–latitude grid
cells and for each instantaneous model output. In this study, the model
writes out at 01:30 and 13:30 local time, corresponding to the CALIPSO
overpass time.
Construct sub-grids. The ACTSIM procedure already implemented in COSP
calculates the αM(z), βm(z) and AMB(z) vertical profiles
using the GCM pressure and temperature profiles, according to the equations
of Sect. 2.1. The GCM vertical profile of cloud fraction is also passed to
the Subgrid Cloud Overlap Profile Sampler (SCOPS) (Klein and Jakob, 1999)
procedure in COSPv2 to generate sub-grid columns within a grid cell in
accordance with the simulated cloud fraction and the vertical overlap
assumption.
Compute ATB and SR. The ATB and SR profiles are computed at model
levels. These variables are calculated according to the equations of Sect. 2.1, using the input variables αa and βa and the variables αmβm and AMB calculated in step 1. Because the
GCM does not consider sub-grid variability of aerosols, we compute the ATB
and SR for each grid cell.
Vertical regridding. The total extinction (αa+αm), ATB and SR profiles are vertically re-gridded over a standard
vertical grid having N equidistant levels to obtain profiles of total
extinction (EXT_initial), attenuated total backscatter
(ATB_initial) and backscatter ratio (SR_initial) at the vertical resolution of the space lidar observations that
would be observed in absence of instrumental noise. For consistent
comparison with CALIPSO observations, N is set to 320 levels so that each
level is 60 m thick from the surface to 19.14 km of altitude. We design the
code to allow N to be set by users so that it can be easily adapted for
other lidars. For example, the vertical resolution of EarthCARE ATLID is 100 m, so N will need to be set to 192 for the simulator to operate between the surface and 19.1 km above ground level.
Apply aerosol detection thresholds. The aerosol detection thresholds,
based on the actual space lidar capability (above instrumental noise), are
applied to the EXT_initial, ATB_initial and
SR_initial profiles, in order to get the profiles of total
extinction (EXT_detectable), attenuated total backscatter
(ATB_detectable) and backscatter ratio (SR_detectable) that would be observed by a space lidar overflying the
atmosphere simulated by the model in absence of clouds. This takes into
account the limited capability to detect aerosol when the signal-to-noise
ratio (SNR) is too low for CALIPSO. The aerosol detection threshold
considered in this study is SR = 1.2, which is different from the previous study that considered the detection threshold as a function of height (Ma et al., 2018), but we designed the code to be flexible so that it can be easily adapted for sensitivity studies or for future space lidars that have a different SNR.
Apply cloud masking. The cloud masking is applied to the initial
profiles EXT_initial, ATB_initial and
SR_initial to get the total extinction (EXT_masked), attenuated total backscatter (ATB_masked) and
backscatter ratio (SR_masked) profiles that would be observed
above clouds by a space lidar with a perfect aerosol detection capability
(no instrumental noise). This takes into account the fact that a space lidar is unable to observe aerosol below optically thick clouds (with optical depth larger than 3–5) where the laser beam is fully attenuated. To simulate
this cloud masking effect, the cloud masking in the simulator is built from
the modeled clouds (not the actual clouds) as it would be seen by a space
lidar. We take the cloud lidar simulator output called cloud fraction
profiles (CF3D). When scanning each grid point from the TOA to the surface,
the first altitude level where CF3D = 1 is called “z_bottom” and all aerosol-related output values at that altitude and below
are set to Fill_value.
Combine all factors. The cloud masking (step 5) and aerosol detection
thresholds (step 4) are applied to the initial profiles (EXT_initial, ATB_initial and SR_initial) to get
the total extinction (EXT_observable), the attenuated total
backscatter (ATB_observable) and backscatter ratio
(SR_observable) profiles that would be observed above clouds
by a space lidar with actual aerosol detection capability.
Note that in the code, the variables have different names than in this
paper. Table 1 establishes the correspondence between the names of the
variables in this text and in the code.
Translations between the name of the variables in the text and in
the code. For example, EXT_initial in the paper corresponds
to EXT0 in the code.
Variable subscriptDescription of variableEXTATBSRin articleinitialProfiles computed with aerosols + gas moleculesEXT0ATB0SR0maskedAs above but masking the highest cloud and all layers belowEXT1ATB1SR1detectableRemoving SR < 1.2 from initial profilesEXT2ATB2SR2observableRemoving SR < 1.2 from initial profiles and masking the highest cloud and all layers belowEXT3ATB3SR3Differences between the CALIPSO aerosol and cloud simulators
The aerosol lidar simulator is implemented within the COSPv2 infrastructure,
which has been optimized for computational performance so that it can be
used for long climate simulations when needed. COSPv2 already contains a
cloud lidar simulator from which several routines are used within the
aerosol lidar simulator (Chepfer et al., 2008; Cesana and Chepfer, 2012,
2013; Guzman et al., 2017; Reverdy et al., 2015). The main differences between the aerosol lidar simulator presented in this paper and the cloud lidar
simulator are described below.
The aerosol lidar simulator needs aerosol optics from the models as inputs (αa and βa profiles in each model grid box)
because those optical properties are strongly dependent on aerosol size
distribution and chemical composition. They depend on the aerosol
parametrization in the GCM, and the size of aerosol is close to the lidar
wavelength. By contrast, because cloud droplets are much larger than the
lidar wavelength, cloud optical properties can be parameterized in a simpler way than aerosol, so COSPv2 can easily compute cloud optical properties from cloud microphysical properties.
Within the aerosol lidar simulator, the computations are performed in
each grid box (with a typical grid spacing of 1∘), while the
cloud simulator computations are performed at a sub-grid scale (typically 50 sub-grid boxes in a grid box). This is consistent with the assumptions in GCMs. While GCMs represent the sub-grid variability of clouds, aerosol is
assumed to be homogeneous within a grid box. Therefore, the aerosol lidar
simulator assumes that aerosol is uniformly distributed horizontally within
a grid box while cloud simulators assume sub-grid variability according to
SCOPS.
The aerosol lidar simulator uses a higher-resolution vertical grid than the cloud simulator: e.g. 320 vertical levels (typically 60 m) instead of 40 (typically 480 m). This is because the detailed vertical structure of aerosol
is important for understanding aerosol mixing, transport and other physical
processes, especially in the atmospheric boundary layer. To be consistent
with the CALIOP aerosol data product, we use the same vertical resolution.
Note that for clouds the vertical resolution used in CFMIP experiments
(dz=480 m) results from a compromise between the wish to keep high horizontal resolution for sparse shallow clouds, the SNR of CALIOP data in daytime and the vertical resolution of CloudSat.
Users can choose to run the new aerosol simulator alone, the standard cloud
simulators alone (default), or both aerosol and cloud simulators. These new
features are controlled by two new keys in the user's
configuration file in COSPv2 code. Users can set “lidar_aerosols” and “use_vgrid_aerosols” to true to invoke the aerosol simulator. The logical variable “use_obs_for_aerosols” must be set to “false”
for now as it is reserved for future feature development. Lastly, users need
to set the number of vertical levels for aerosol “nlvgrid_aerosols”, which is set to 320 by default as recommended by this study.
Observations
To facilitate fair comparisons between models and observations,
we have created an observational dataset that is consistent with the
simulator approach described in the previous section. The simulator outputs
SR_observable and ATB_observable can be
directly compared with the SR and ATB profiles above clouds observed by
CALIOP. However, it should be noted that the total extinction profile
(EXT_observable) cannot be observed directly by CALIOP, it is
an output from the simulator that can only be used to interpret the
difference between the observation and the model and simulator outputs.
We use the CALIOP L1.5 orbit file (NASA/LARC/SD/ASDC, 2019) dataset that
contains cloud-screened ATB profiles at 532 nm with 60 m vertical resolution and 20 km along-track and 90 m cross-track horizontal resolution. The CALIOP L1.5 data are built from the native L1 CALIOP data (1/3 km along-track horizontal
resolution, 90 m cross-track horizontal resolution and 30 m
vertical resolution), but a cloud-screening procedure is applied so that the
L1.5 data only contain above-cloud measurements. The cloud screening is
applied iteratively at different horizontal resolutions from 1/3 up to
80 km. When clouds are detected at a vertical level, all the data below the
cloudy level are marked as Fill_Value and all the cloud-free
and above-cloud profiles are retained below the altitude of 8 km. Then,
these cloud-free and above-cloud profiles are averaged horizontally over the
along-track 20 km grid. Since each L1.5 20 km profile represents an averaged
signal over the cloud-free profiles over 20 km, this dataset cannot be used
to study the horizontal heterogeneity of aerosol with a spatial scale
smaller than 20 km. Nevertheless, this dataset has the advantage of a much
higher SNR than the original L1 profile (1/3 km), which permits the use of a lower aerosol detection threshold in both observations and simulations, and is then able to detect optically thin aerosol layers at the 20 km spatial scale (Ma et al., 2018).
In this study, we created an example gridded data product from CALIPSO that
is consistent with the GCM grid, so that the translation from the model to
the simulator results can be more easily understood by the reader, in
relation to how it can affect the interpretation of a comparison to the
CALIOP observation profile. This dataset was created by averaging all the
L1.5 ATB cloud-screened profiles over a 1∘× 1∘
latitude–longitude grid at a given date. It is worth noting that since
CALIPSO is a polar-orbiting satellite with a relatively narrow swath, the
number of profiles at high latitudes is larger than that in the tropics, and
that not all the grid boxes contain a satellite observation in any single
day.
Similarly, we build the gridded product for SR from the orbit L1.5 ATB
dataset. We first compute the AMB profiles – the signal that would be
measured by the lidar in a cloud-free and aerosol-free atmosphere – at the 20 km along-track resolution and 60 m in the vertical from the pressure and temperature profiles from NASA Global Modeling and Assimilation Office
(GMAO) that are included in the L1.5 data. We compute the SR profiles by
dividing the L1.5 ATB with AMB. Finally, we average all the 20 km SR profiles
over 1∘× 1∘ grid boxes. Because the model SR profile is
normalized against the model pressure and temperature profiles and the
observed SR profile is normalized against the pressure and temperature from
the GMAO reanalysis, comparing SR profiles between observations and models
is more informative regarding aerosol distributions than ATB profiles which
are subject to differences in atmospheric temperature and pressure as well.
In the upper troposphere where AMB and ATB values are low, the ATB profiles
measured along the orbit have low signal-to-noise ratios, which leads to high
values of SR, even at 1∘× 1∘ resolution. To address this issue, we set ATB equal to AMB when ATB minus AMB is lower than 1 × 10-4 km-1 sr-1 and SR equal to 1
when SR is lower than 1.2. The threshold on AMB typically applies above 8 km. While this procedure removes the noise, it can also remove the signal
from the tenuous aerosol layer (e.g. Watson-Parris et al., 2018). Both threshold
values are relevant for night profiles, which are less noisy than daily ones.
We thus focus in this study on profiles observed at night only, before and
after the application of the AMB–SR thresholds. Note that the threshold on
SR is parameterized in the aerosol simulator and can be easily adjusted to
other values for various research and application purposes.
Finally, we generate daily and monthly average of the gridded data. This
enables users to perform comparisons at three different spatiotemporal
scales: (1) the instantaneous SR profiles at the resolution of 1∘
along track and 60 m in the vertical, (2) the 3D daily 1∘× 1∘ gridded SR data with a 60 m vertical resolution, and (3) the 3D monthly 1∘× 1∘ gridded SR data with a 60 m vertical resolution.
Attenuated total backscatter profiles (km-1 sr-1) before
noise filtering (a) and after noise filtering (c); backscatter ratio profiles before noise filtering (b) and after noise filtering (d); observed by CALIOP at 532 nm along the satellite orbit on 20 March 2008.
Examples of outputs of the COSPv2/lidar aerosol simulatorOrbit files
We consider the attenuated total backscatter profiles observed by CALIPSO at
532 nm along its trajectory on 20 March 2008 as an example to demonstrate
the comparison using the aerosol simulator and show the impacts of the
AMB–SR thresholds. These profiles, characterized by their latitude in
Fig. 2a and c, show missing values below the clouds with sufficient
optical thickness to fully attenuate the laser beam. Such clouds occur at
very high altitudes within the tropics, making it impossible to retrieve
significant signals below 17 km at some locations. In dry regions (e.g.
between 10 and 30∘ N, 20 and 40∘ S), however, the
absence of clouds allows the lidar to retrieve entire ATB profiles down to
the surface. The attenuated total backscatter signal, which contains the
molecular backscatter signal, shows a maximum near the surface, with a
monotonic decrease as altitude increases. The SR profiles (Fig. 2b and
d), being normalized by the molecular signal, filter out the contribution
by air molecules and are thus more appropriate to retrieve aerosol
concentrations. A large amount of SR values that were initially lower than 1
because of the instrument noise (Fig. 2b) are set to 1 by the application
of the AMB–SR thresholds (Fig. 2d). In this particular orbit, two dense
aerosol layers can be identified. One is in the polar region in the Northern
Hemisphere between 10 and 12 km, and another one is in the lower troposphere
at 30∘ S. CALIPSO also shows signals of thinner aerosol layers that are generally below 4 km.
(a) Vertical profiles of cloud fraction simulated by the E3SMv1 model along the satellite orbit on 20 March 2008; (b) same vertical profiles, defined by the COSPv2 simulator at the sub-grid scale and interpolated on 40 vertical levels; (c) aerosol extinction profiles (in m-1) and (d) aerosol backscatter coefficient profiles (in m-1 sr-1) calculated by E3SMv1 along the satellite orbit.
In Fig. 3, we show the results of the U.S. Department of Energy's Energy
Exascale Earth System Model version 1 (E3SMv1) (Golaz et al., 2019) with
improved calibration of cloud and sub-grid effects (Ma et al., 2022). The
model is configured to run with prescribed SST and sea ice extent. The E3SM
Atmosphere Model version 1 (EAMv1) (Rash et al., 2019) model outputs are used
to compute the ATB and SR profiles that would be seen by the lidar along its
trajectory on the same date (20 March 2008). The model horizontal winds are
nudged towards Modern-Era Retrospective analysis for Research and
Applications version 2 (MERRA-2) (Gelaro et al., 2017) reanalysis with a
relaxation timescale of 6 h (Zhang et al., 2014; Ma et al., 2015). The
simulated cloud vertical profiles (Fig. 3a) agree very well with the
observations (Fig. 2), as high cloud fractions along the satellite
trajectory coincide with the horizontal locations and altitudes of missing
data in the observations.
The vertical profiles of cloud fractions of Fig. 3a are then defined at
the horizontal sub-grid scale (with about 50 profiles being produced in each
grid box), with values of cloud fraction being equal to 0 or 1 in each
sub-grid box. Vertically, the cloud fractions are interpolated on 40 levels,
defined by their altitude. The resulting sub-profiles are shown in Fig. 3b
and are consistent with the model outputs of cloud cover of Fig. 3a.
Finally, the aerosol optical properties αa and βa
calculated by the E3SMv1 model at 532 nm along the satellite trajectory are
used as inputs to the COSPv2 simulator. These quantities are calculated by
the E3SM model at a very high vertical resolution, where the layer thickness
is about 25 m at the surface, about 90 m in the first 1.5 km above the
ground level, and about 600 m between 1.5 and 10 km (Rasch et al., 2019;
Xie et al., 2018). The aerosol extinction and backscatter profiles show a
very high correlation, with largest values below 800 hPa (Fig. 3c and
d).
Total extinction vertical profiles (m-1) defined on 320
levels and calculated by the COSPv2 simulator along the satellite orbit on
20 March 2008: (a) initial profiles; (b) profiles with the instrument aerosol detection threshold; (c) cloud-screened profiles; (d) cloud-screened profiles with the aerosol detection threshold applied.
The αa profiles are then interpolated vertically on the 320
altitude levels to produce the EXT_initial variable (Fig. 4a). The differences between the EXT_initial and
EXT_detectable fields (Fig. 4b) illustrate the effect of
applying the instrument aerosol detection threshold. In the
EXT_detectable field, the values of the extinction
coefficients that are lower than that threshold are set to zero. The
extinction profiles thus appear less noisy in the middle troposphere (for
example around 6 km at 20∘ S), whereas they remain similar in the lower troposphere. Finally, the EXT_masked field (Fig. 4c) shows
the extinction profiles when the cloud screening is applied, and the
EXT_observable field (Fig. 4d) combines the cloud
screening and the aerosol detection threshold.
Backscatter ratio vertical profiles defined on 320 levels and
calculated by the COSPv2 simulator along the satellite orbit on
20 March 2008: (a) initial profiles; (b) profiles with the instrument aerosol detection threshold; (c) cloud-screened profiles; (d) cloud-screened profiles with the aerosol detection threshold applied.
The resulting SR profiles computed by the COSPv2 simulator are shown in
Fig. 5. The obtained SR values, going up to 3 in maximum regions, agree
well with the observations. South of 20∘ N, the signal above the
detection threshold (Fig. 5b) is found below the altitude of 4 km, but
north of 20∘ N, the aerosol plume extends vertically, and a significant signal is found at altitudes as high as 12 km, in good agreement with the observations (Fig. 2b).
(a) Difference between model SR_masked and
CALIOP data before data processing; (b) difference between model
SR_observable and CALIOP data after data processing (see text
for details) along the satellite orbit on 20 March 2008.
Figure 6 shows the impacts of the AMB–SR thresholds on the comparison between the simulated and observed SR profiles. In Fig. 6a, we show the
differences between the SR_masked field (with cloud screening
only) and CALIOP profiles before applying the AMB–SR thresholds. In the
upper troposphere, the instrument noise induces differences in absolute
value that sometimes exceed 0.4. In Fig. 6b, the differences between the
SR_observable field (with cloud screening and aerosol
detection threshold) and the CALIOP profiles after applying the AMB–SR
thresholds become close to zero in the upper troposphere. In this
comparison, we find that the E3SMv1 model underestimates the aerosol
concentrations near the surface around 30∘ S but overestimates the
concentrations in the aerosol plume north of 20∘ N between 1 and 9 km.
Global statistics
To have an overview of the aerosol distribution at the seasonal timescale,
we average the observed and simulated ATB and SR profiles over 3 months:
March, April and May (MAM) 2008. As aforementioned, the thresholds on AMB and SR are applied to observations. The profiles are further averaged over
all longitudes for each 1∘ latitude bin and are represented in Fig. 7. The attenuated total backscatter signal, as the molecular backscatter
signal (not shown), shows a decrease with altitude in the lower troposphere.
The SR ratio, directly depending on aerosol concentrations, shows maxima
reaching the value of 3 in the 2 km layer above the surface, indicating a
very dense aerosol layer in the boundary layer. The ratios are especially
large at 10∘ N and between 40 and 60∘ S, which can be
attributed to the presence of dust and sea spray aerosol. At 10∘ N, dust is the predominant component of aerosol over northern Africa, the Arabian Peninsula and western China (Yu et al., 2010). Between 40 and 60∘ S, the main aerosol contribution during the MAM season is sea spray,
as biomass burning over South America and southern Africa occurs mainly
between June and November. The maximum between 40 and 60∘ S also
appears within the first kilometer above the surface on zonal-mean 532 nm
aerosol extinction profiles retrieved from CALIOP over the whole year during
nighttime by Winker et al. (2013). The vertical extension of the aerosol
plume seems to be largest in the Northern Hemisphere, where convection is
the most active in MAM, whereas it is limited to the top of the boundary
layer in the Southern Hemisphere, consistently with the scale heights
retrieved by Yu et al. (2010).
(a) Attenuated total backscatter profiles (km-1 sr-1) and backscatter ratio profiles (b) observed by CALIOP at 532 nm at night and averaged over longitudes and time during MAM 2008.
SR profiles simulated by E3SMv1 at 532 nm and averaged over
longitudes and time during MAM 2008: (a) initial profiles; (b) profiles with the instrument aerosol detection threshold; (c) cloud-screened profiles; (d) cloud-screened profiles with the aerosol detection threshold applied.
The simulated SR_observable profiles computed for the same
period by the COSPv2 simulator are shown in Fig. 8d. The maximum at
10∘ N is well reproduced, but the maximum in the Southern Hemisphere
does not appear, which might be due to an inaccurate simulation of sea spray
aerosol in the model at this time and location. As in the observations, the
aerosol plume shows a larger vertical extension in the Northern Hemisphere
than in the Southern Hemisphere, which validates the convective transport of
aerosol in the model. Yu et al. (2010) raised the issue that the convective
transport of aerosol could not be well observed by CALIOP because it is not
possible to retrieve aerosol in the presence of thick convective clouds.
However, the comparison between the SR_initial (Fig. 8a)
and SR_masked fields (Fig. 8c) shows minor differences,
indicating that, at least in this particular model simulation, cloud
screening does not affect dramatically the mean aerosol concentrations and
does not modify significantly the amount of aerosol transported upward.
(a) CALIOP SR after data processing (see text for details); (b) model SR_observable; (c) difference
between model SR_observable and CALIOP SR. All fields are
shown between 0 and 4 km and are averaged over all longitudes and time
during MAM 2008.
Finally, we compare the simulated and observed SR values to identify model
biases. Figure 9 shows the differences between the SR_observable profiles and the CALIOP SR profiles after the application of the
AMB–SR thresholds (see Sect. 3) in the first 4 km above the surface. The
SR maxima are underestimated by 1 to 1.5 in the model from the surface to
500–800 m and are slightly overestimated above this level up to 1.5–1.8 km.
The underestimation of SR in the surface layer corresponds to a relative
model error on the aerosol optical depth of approximately 50 %. This
vertical distribution bias revealed by the simulator could have several
causes that need to be investigated further, such as overly efficient vertical
mixing or incorrect wet scavenging in the E3SMv1 model.
Validity of the comparison between CALIOP data and simulator outputs
A cause of the discrepancy between simulated SR_observable
fields and SR fields retrieved from CALIOP observations can be the
differences between model and observed clouds. For those two fields
corresponding to cloud-free conditions only, the differences in the
occurrences of cloud-free scenes in the model and observations can affect
the sampling of aerosol concentrations. If those aerosol plumes show a large
spatiotemporal variability, differences in sampling can induce differences
in the seasonal or zonal-mean concentrations and thus in the mean SR.
Probabilities of (a) cloud-free, (b) partially cloud covered, and (c) totally cloud covered 1∘× 1∘ horizontal grid cells as a function of latitude, during the MAM period (nighttime), in CALIOP and E3SMv1.
To compare the sampling induced by the cloud screening in E3SMv1 and in
CALIOP, we consider the probability of having cloud-free conditions during
the night at a daily scale in 1∘× 1∘ horizontal grid cells at a
given latitude, during the MAM period (Fig. 10a). In the observations, the
total cloud cover (CLT) is estimated in the 532 nm channel of CALIOP. The
probability for cloud-free conditions (CLT = 0 %) at nighttime is
extremely low in CALIOP for all latitudes, except for polar regions that are
dry and less cloudy than the rest of the globe (especially over land). The
cloud-free probability is much higher in E3SMv1, with a maximum value of
70 % in the Southern Hemisphere polar region and about 40 % and 50 % at 25∘ S and 25∘ N, respectively.
However, the cloud-free grid cells are not the only ones to be sampled for
the estimation of the mean SR. SR can still be obtained in grid cells with
partial cloud cover (0 % < CLT < 100 %), as the SR will be
computed in the clear-sky sub-columns of the considered grid cell in E3SMv1
and retrieved in the cloud-free pixels belonging to the grid cell by CALIOP.
Making the reasonable assumption that aerosol concentrations are homogeneous
within the 1∘× 1∘ grid, this local estimation of SR can be considered to be representative of the whole grid cell.
The probability of partially covered grid cells (shown in Fig. 10b) is
generally higher in CALIOP observations that in the E3SMv1 model. In CALIOP,
the probability shows two maxima of about 70 % in the subtropical regions, while it is not above 50 % in E3SMv1 at these latitudes.
If the probability of CLT < 100 % was equal to 100 % both in
model and observations (i.e. no overcast grid boxes in both model and
observations), then the sampling would be perfect, with the totality of the
grid cells equally contributing to the estimations of the observed and
modeled mean SR values for the MAM period. However, we find that the sum of
the cloud-free probability (Fig. 10a) and the partial cloud cover
probability (Fig. 10b) is lower than 100 % in both E3SMv1 and CALIOP.
Figure 10c shows the probability of fully overcast grid cells
(CLT = 100 %) as a function of latitude. Aerosol in these grid cells is totally filtered out and thus does not contribute to the mean SR. The
overcast probability is highest at 60∘ S in both E3SMv1 (80 %) and CALIOP observations (65 %) during the MAM period. Maxima of lower
amplitude are also found in the equatorial region and in middle and high
latitudes in the Northern Hemisphere. The model overestimates the overcast
probability almost everywhere in the globe, producing either cloud-free or
fully overcast conditions most of the time, which is not found in
observations.
The large occurrences of overcast cases at 60∘ S suggest that the SR
values estimated in both simulations and in the real world might not be
representative of the true aerosol distribution due to the cloud-screening
procedure. Large sampling errors can then be introduced to the mean SR at
60∘ S. Similarly, sampling errors might also exist in the equatorial
region and in the Northern Hemisphere mid-latitudes, where the occurrence of
fully overcast cases is high, or in the northern polar region, where
occurrence of fully overcast cases in the model is significantly different
from that in observations.
The occurrence of overcast cases depends on the size of the horizontal grid
cells and decreases with a coarser resolution. For example, the probability
of having CLT = 100 % does not exceed 5 % at 60∘ S for
10∘× 10∘ horizontal grid cells (not shown). Choosing a coarser
resolution might then ensure a better temporal sampling, but on the other
hand, taking account of the partially covered 10∘× 10∘ grid
cells for the mean SR estimation would be based on the implicit assumption
that the aerosol concentrations are homogeneous over these grid cells of
large horizontal surfaces, which is probably not realistic in the vicinity
of the source regions.
To assess the impact of the cloud screening on the mean SR values in E3SMv1
simulations, we compute the relative difference between the
SR_observable field (with both aerosol detection threshold
and cloud screening applied) and the SR_detectable field
(with the detection threshold applied and no cloud screening). This relative
difference, shown in Fig. 11 as a function of altitude and latitude, is
lower than 10 % everywhere. In regions where cloud screening is large in
the model (e.g. near 60∘ S and in the equatorial region)
SR_observable values tend to be larger than SR_detectable values, probably because most of the SR_detectable
profiles coincide with cloud and rainfall conditions, while
SR_observable profiles contain dry cases only, and thus
cloud-screened aerosol concentrations are higher because wet scavenging does
not occur. Furthermore, the low absolute values of relative differences in
Fig. 11 imply that the intra-seasonal variability of aerosol emissions
might be low in the model. This variability depends on the emissions of
anthropogenic aerosol, which are monthly mean averaged, consistently across
all CMIP6 models (Hoesly et al., 2018; van Marle et al., 2017). It also
depends on the variability of sea spray aerosol emissions, which somewhat
follows the variability of surface winds and sea surface temperature (SST).
Relative difference (in %) between the SR_observable field and the SR_detectable field both computed by E3SMv1, as a function of latitude and altitude.
Overall, the sampling bias introduced by the cloud-screening procedure does
not significantly affect the mean SR values in E3SMv1. Therefore, errors in
E3SMv1 clouds are not likely the primary reason for the differences in the
aerosol seasonal comparison between E3SMv1 and CALIOP observations. In
particular, the large difference observed at 60∘ S between the observed and simulated mean SR values cannot be explained by the large
cloud screening in E3SMv1 at this latitude.
Nevertheless, cloud screening might have a larger impact on the mean aerosol
CALIOP retrievals. Winker et al. (2013) found a lack of correlation between
high semi-transparent cloud and aerosol in the lower troposphere in most
regions in CALIOP data, implying that the screening of thin clouds does not
significantly impact the retrieved values of aerosol optical depth or
aerosol extinction coefficients. However, this result has to be extended to
opaque cloud screening and has to be examined over a 3-month period at
the specific locations that exhibit large cloud covers. To get an insight
into the representativeness of our SR values retrieved from CALIOP, we
computed the zonal-mean SR values over the MAM period by only considering
one-third of the CALIOP data. We find that the relative difference between
these SR values and those obtained by using the full CALIOP data is highest
in covered regions, but it never exceeds 15 % (not shown). This gives us
confidence about the robustness of our results retrieved by CALIOP over a
3-month period. An alternative approach would be to extend the analysis
to cover multiple years, but the results would then be affected by the inter-annual variability of aerosol.
We can thus conclude that
the SR maxima retrieved by CALIOP over 3 months are robust and
the method of comparing modeled and retrieved SR is robust, although the modeled and observed clouds show large differences.
Therefore, the differences between observed and simulated SR values should
be attributed to the representation of aerosol in the model.
Discussion
Aerosol modeling basically consists of the representation of aerosol
sources, optics, chemistry, micro-physics, aerosol–cloud interactions and
transport. In the E3SMv1 model, aerosol optics is parameterized in terms of
wet refractive index and wet surface mode radius of each mode (Ghan and
Zaveri, 2007). It assumes volume mixing to compute the wet refractive index
for mixtures of insoluble and soluble particles. The parameterization
provides the aerosol extinction αa. We apply the same Ghan and Zaveri (2007) methodology and add the diagnostic variable of the
180∘ backscatter βa, as the aerosol lidar simulator
requires these two input variables. Most GCMs compute the aerosol
extinction, but not many of them routinely compute the aerosol
180∘ backscatter βa. Hence, more work has to be done
so that other GCMs also diagnose their aerosol 180∘ backscatter
βa in a way that is consistent with their aerosol optics
parameterization. For future comparisons between CALIOP data and other GCMs,
or for model-to-model comparisons, one might find it useful to use one single
optics module to eliminate aerosol optics as a potential source of
discrepancy in the comparisons. This is beyond the scope of this study and
requires future investigation.
To evaluate the representation of aerosol composition in the model, the NASA
product providing aerosol types from CALIPSO data is of particular interest.
Indeed, CALIOP level 2 data include seven aerosol classes: clean marine,
dust, polluted continental, clean continental, polluted dust, smoke and other.
This classification utilizes the depolarization ratio, integrated attenuated
backscatter coefficient, altitude, and land vs. ocean (Kim et al., 2018). The
aerosol subtypes of CALIOP measurements have been shown to be in good
agreement with the daily aerosol types derived from AERONET level 2.0
inversion data (Mielonen et al., 2009).
The CALIOP classification might be useful to provide insights into the model
deficiency in representing aerosol composition in the model. According to
this classification, the aerosol observed at 60∘ S during MAM is mostly
clean marine aerosol. The large differences observed between CALIOP and
E3SMv1 at this latitude may then be due to model biases in simulating marine
aerosol in this region. Figure 9 in Rasch et al. (2019) and Fig. 11
in Wang et al. (2020) also show an aerosol bias over the Southern Ocean.
There are certainly many possible reasons. The E3SMv1 model has both sea
salt and marine organics as marine aerosol. Their “emissions” are function
of surface winds and SST, based on Martensson et al. (2003). If the model
has significant surface wind bias, that may thus impact the marine aerosol
sources. Furthermore, McCoy et al. (2021) show that new particle formation
(NPF) might be important in that region when they contrast SOCRATES field
campaign measurements and Community Atmosphere Model version 6 (CAM6)
simulations. This process is not well represented in the CAM6 model or in
the E3SM model. We demonstrate here that the aerosol lidar simulator can be
very useful in revealing these model biases, providing insights into future
model development directions.
Perspectives
The validation of aerosol simulated by GCMs with space lidar data will be
expanded to other lidars and to other GCMs. We plan to perform studies with
the Laboratoire de Météorologie Dynamique Zoom (LMDZ) model, the
European Center–Hamburg (ECHAM) model and the Icosahedral Nonhydrostatic
(ICON) model. The modal aerosol module “HAM” that employs seven log-normal
aerosol modes has been used interactively in the ECHAM model since almost
two decades (Zhang et al., 2012; Tegen et al., 2019). Recently it is also
implemented in the successor of ECHAM, the ICON model (Salzmann et al.,
2022). The two models with profoundly different dynamical cores share the
same physics package. It will be interesting to evaluate the differences
induced by the two numerical representations of the atmospheric dynamics
with the satellite retrievals.
Note that for a multi-model comparison, it is necessary to use a standard
vertical grid with a coarser vertical resolution than N= 320 levels and Δz=60 m, as traditional climate models do not reach such a fine resolution. For the comparison of these models with CALIOP observations,
data interpolation is needed on the same vertical coarser grid. Vertically
averaging the CALIOP data would enhance the SNR and consequently would
allow us to lower the aerosol detection threshold and make use of the more
noisy CALIOP daily data. For each model it is important to check that the
errors in the model clouds do not significantly impact the model–observation
aerosol comparison over the considered period.
Since 2018, the Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) has been operating the first
high-spectral-resolution lidar (HSRL) in space. Although primarily dedicated
to wind measurements, the HSRL capability in the UV allows the separation of
the molecular and particulate contributions and enables the measurements of
the particulate backscatter and extinction coefficients. These measurements
provide new insight into very thin aerosol layers and can be very useful for
the validation of models that directly compute these quantities. Later in
2023, the EarthCARE mission will also provide data from the HSRL lidar ATLID
at 355 nm. The COSPv2 simulator can be easily adapted to other wavelengths,
which opens the way to the determination of new diagnostics for cloud
susceptibility, aerosol typing and aerosol–cloud proximity metrics.
Code availability
The aerosol lidar simulator presented in this paper is available at 10.5281/zenodo.7418199 (Bonazzola and Chepfer, 2022) and is incorporated in the COSPv2 infrastructure at https://github.com/CFMIP/COSPv2.0 (last access: 9 December 2022).
Data availability
The CALIPSO L1.5 data are available at
10.5067/CALIOP/CALIPSO/CAL_LID_L15-Standard-V1-01 (NASA/LARC/SD/ASDC, 2019). The processed gridded CALIOP ATB and SR data files used in this study are available at 10.5281/zenodo.7107232 (Bonazzola, 2022a) and
10.5281/zenodo.7107162 (Bonazzola, 2022b).
Author contributions
MB wrote the first version of this paper and conducted the analysis of this study; DMW, NS, JQ, and HC contributed to the design of the science objectives and the method of the study; PLM provided the GCM simulations and contributed to the code of the lidar aerosol module; AF gave technical support; and HC contributed to the code of the interface of the lidar aerosol simulator with the COSPv2 infrastructure. All authors contributed to the writing of the paper.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Rodrigo Guzman for his work on the development of the aerosol lidar simulator and on its interface with the COSPv2 infrastructure and Steve Klein for his inputs on the improvements of the vertical
re-gridding within COSPv2. Po-Lun Ma was supported by the “Enabling
Aerosol–cloud interactions at GLobal convection-permitting scalES
(EAGLES)” project (no. 74358), funded by the U.S. Department of
Energy (DOE), Office of Science, Office of Biological and Environmental Research,
Earth System Model Development (ESMD) program area. The Pacific Northwest
National Laboratory (PNNL) is operated for DOE by Battelle Memorial
Institute under contract DE-AC05-76RL01830.
Financial support
This research has been supported by the Centre National d'Etudes Spatiales (EECLAT) and the U.S. Department of
Energy, Office of Science, Office of Biological and Environmental Research,
Earth System Model Development (ESMD) program area (project no. 74358).
Review statement
This paper was edited by Graham Mann and reviewed by Duncan Watson-Parris and one anonymous referee.
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