We present a top-down approach for aerosol emission
estimation from Spectropolarimeter for Planetary
Exploration (SPEXone) polarimetric retrievals related to the aerosol
amount, size, and absorption using a fixed-lag ensemble Kalman smoother
(LETKS) in combination with the ECHAM-HAM model. We assess the system by
performing observing system simulation experiments (OSSEs) in order to
evaluate the ability of the future multi-angle polarimeter instrument,
SPEXone, as well as a satellite with near-perfect global coverage. In our
OSSEs, the nature run (NAT) is a simulation by the global climate aerosol
model ECHAM-HAM with altered aerosol emissions. The control (CTL) and the
data assimilation (DAS) experiments are composed of an ensemble of ECHAM-HAM
simulations, where the default aerosol emissions are perturbed with factors
taken from a Gaussian distribution. Synthetic observations, specifically
aerosol optical depth at 550 nm (AOD
Data assimilation methods can greatly improve the aerosol representation in the atmosphere by combining the simulated aerosol state of a model with the observed aerosol optical and microphysical properties retrieved from satellites. The accuracy of the spatiotemporal distribution of an aerosol species in a data assimilation product depends both on the accuracy of the simulated processes in the model as well as the quality and the type of the assimilated observations. Several past studies estimated aerosol emission based on remote sensing observations (Dubovik et al., 2008; Jin et al., 2019; Pope et al., 2016; Sekiyama et al., 2010; Xu et al., 2013), although only some studies assimilated size related measurements, such as aerosol optical depth (AOD) in two wavelengths or fine and coarse AOD or Ångström exponent (AE) (Escribano et al., 2017; Huneeus et al., 2012; Schutgens et al., 2012). In addition, very few recent studies assimilated absorption-related measurements like absorption aerosol optical depth (AAOD) or single-scattering albedo (SSA) to correct either the aerosol mixing ratio (Tsikerdekis et al., 2021a) or the aerosol emissions (Chen et al., 2018, 2019). Absorption observations were used by Kacenelenbogen et al. (2019) to estimate the short-wave direct aerosol effect from the A-Train satellite sensors. Further, Schutgens et al. (2021) intercompared and evaluated four AERONET satellite products (FL-MOC, OMAERUV, POLDER-GRASP, and POLDER-SRON) for AAOD and SSA and suggested that satellite absorption observations could be used to evaluate AEROCOM model biases because the diversity of model biases is larger than satellite biases.
It has been noted in the past that multi-viewing angle and multi-wavelength intensity and polarization measurements with high accuracy have the largest capability to provide the aerosol properties relevant to climate research (Hasekamp and Landgraf, 2007). Recently, Hasekamp et al. (2019b) showed that polarimetric satellite retrievals related to aerosol shape, size, and number provide a more accurate aerosol indirect radiative effect compared to previous observational-based studies. Unfortunately only one such multi-angle polarimeter (MAP) provided aerosol optical and microphysical properties from space for several years in the past (2004–2013), the Polarization and Directionality of Earth Reflectances (POLDER-3) on board the microsatellite PARASOL (Dubovik et al., 2019).
Several MAP instruments are scheduled for launch in the coming 3 years (Dubovik et al., 2019), with the NASA PACE mission (Werdell et al., 2019) hosting two MAP sensors onboard, the Spectropolarimeter for Planetary Exploration SPEXone (Hasekamp et al., 2019a) and the Hyper-Angular Rainbow Polarimeter-2 (HARP-2). Since these instruments are not yet in space, their observational capabilities for aerosol optical properties (and consequently their potential to estimate aerosol-species-specific emission fluxes) can only be theoretically predicted with observing system simulation experiments (OSSEs) (Arnold and Dey, 1986; Timmermans et al., 2015). In OSSEs a model simulation is assumed as reality, also known as the nature run (NAT), from which synthetic measurements are sampled based on the spatiotemporal coverage of an assumed satellite sensor. Subsequently, two experiments are conducted, a control (CTL) and a data assimilation (DAS) experiment, in which the sampled synthetic observations from the NAT are assimilated. Note that the NAT and the CTL simulations are different experiments, either by using a totally different model or by using the same model with different emissions and/or physics options. The ability of the instrument to estimate the aerosol state can be highlighted by evaluating the CTL and the DAS experiments with NAT.
Timmermans et al. (2008)
firstly used OSSEs with an ensemble Kalman filter to assess the ability of
assimilated AOD sampled based on an imager type instrument and assimilated
PM
In this study we quantify how well an instrument with high accuracy but
limited coverage, like SPEXone, can estimate aerosol emissions. Under the
framework of OSSEs, we implement an existing local ensemble transform Kalman
smoother (LETKS) code to operate with the global aerosol climate model
ECHAM-HAM and assimilate synthetic observations based on a future
multi-angle polarimeter instrument (SPEXone) and a theoretical satellite
with near-perfect global coverage. Following the results of our previous
work and based on the MAP observational capabilities of SPEXone, we
assimilate AOD
SPEXone is a passive remote sensing MAP instrument, part of the NASA
Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission
(Werdell et al., 2019), scheduled for
launch in 2023/2024. It was developed by the Netherlands Institute for Space
Research (SRON) and the Airbus Defense and Space Netherlands (ADS-NL) with
optical expertise from the Netherlands Organization for Applied Scientific
Research (TNO). SPEXone can measure intensity and polarization of
backscattered sunlight at multiple wavelengths and discrete viewing angles
for a specific pixel on the ground. Specifically, it can measure radiance
and polarization at five viewing angles (
The sixth generation of the general circulation model ECHAM6, developed at the Max Planck Institute for Meteorology (MPI-M) in Hamburg, Germany (Stevens et al., 2013), and the second version of the Hamburg Aerosol Model (HAM2) (Stier et al., 2005; Tegen et al., 2019; Zhang et al., 2012) are used to simulate the physical and chemical processes of aerosol in the atmosphere.
The M7 aerosol module used in HAM2 considers five aerosol species, dust
(DU), sea salt (SS), organic carbon (OC), black carbon (BC), and sulfates
(SO
All aerosol species are emitted, transported, deposited, and take part in
aerosol–radiation interactions (scattering and absorption) and
aerosol microphysical processes (e.g., nucleation, coagulation, aerosol water
uptake, and cloud activation). The natural aerosol types (DU, SS) are
introduced to the atmosphere by utilizing the simulated information of wind
and certain surface and ocean characteristics. Other aerosol species (OC,
BC) or aerosol precursor gases (SO
Two SS emission schemes are used in this study. The first and default scheme in ECHAM-HAM parameterizes sea salt emissions based on laboratory measurements (Keene et al., 2007) using the wind velocity at 10 m and the sea surface temperature (SST) (Long et al., 2011; Sofiev et al., 2011). Low SST results in lower sea salt emissions with smaller particle size (Sofiev et al., 2011). The second scheme (previously the default option) in ECHAM-HAM calculates the sea salt flux mass and number through tables of wind speed classes and fits to two lognormal distributions based on Guelle et al. (2001 and reference therein). Note that sea salt particles are emitted only in the soluble accumulation and coarse mode in both schemes.
Dust emissions are based on the dust source scheme developed by Tegen et al. (2002). Wind velocity at 10 m is the main driver of dust aerosol particle production, while soil properties are also taken into account. The preferential dust emission sources are consist of arid or sparsely vegetated areas and are predefined based on Tegen et al. (2002). Improvements in the surface aerodynamic roughness length, soil moisture, and soil properties over East Asia specifically were made by Cheng et al. (2008). The threshold friction velocity depends on the soil size distribution, vegetation cover, and soil moisture (Cheng et al., 2008). Further, updates related to the representation of Saharan dust sources were made using infrared dust index from the SEVIRI instrument on board the Meteosat second-generation satellite by Heinold et al. (2016).
The emission for the remaining aerosol types and aerosol precursors are
defined using emission inventories derived for 14 sectors. Each sector may
include one or more aerosol types or aerosol precursors
(Schultz et al.,
2018; Tegen et al., 2019). The Atmospheric Chemistry and Climate Model
Intercomparison Project (ACCMIP) dataset is used for the anthropogenic,
biomass burning, and aerosol precursor emissions, consisting of monthly mean
estimates at a horizontal resolution 0.5
The local ensemble transform Kalman smoother (LETKS) is used to estimate
aerosol emission fluxes. This method has been previously used by
Schutgens et al. (2012) for aerosols
emission estimation and earlier by Bruhwiler
et al. (2005), Peters et al. (2005), and Feng et al. (2009) for CO
The data assimilation occurs in assimilation cycles, where each cycle
contains a background and an analysis step as depicted in
Fig. 1. Dashed boxes indicate the default emission
where no assimilation took place yet, while filled boxes indicate emission
changed based on observations. The background step consists of an 8 d
(
An illustration of the data assimilation system. The horizontal
axis depicts time in segment of 2 d and the vertical axis the
assimilation cycles, where each consist of a background and an
analysis step. Boxes consist of 32 spatially correlated perturbation
maps for each perturbed parameter (DU, SS, OC, BC, SO
The assimilation window (
Note that it is assumed that the observations of a certain day contain only
a fraction of the available information to change the emissions and that the rest
is contained in observations of subsequent days. Thus, emissions should be
estimated iteratively, allowing observations up to 6 d after to correct
the emissions. The posterior emission perturbations, corrected by 6 d of
observations, are derived after three assimilation cycles and are indicated with
an asterisk (
Background emissions come with uncertainties. The uncertainty of background
emissions are represented by an ensemble that is generated by perturbing the
default emissions. The perturbations are not globally constant but vary from
grid cell to grid cell. Each grid cell has a distinct prior emission
distribution. Changes in neighboring grid cells of each member are not
abrupt but smooth. This spatial correlation of the prior perturbations was
generated using spatial smoothing, a method where data points are averaged
with their neighbors. A step-by-step description of how our spatially
correlated perturbations are created can be found at Sect. 3.2 of our
preceding work (Tsikerdekis et al., 2021a). The spatial correlation length
scale of the generated perturbations is approximately 25
More info regarding the emission perturbations and the ensemble can be found
in our preceding work (Tsikerdekis et al., 2021a). New
emission estimates are obtained by estimating new perturbed emission factors
based on the assimilated observations by solving the Kalman filter
equations:
The ensemble Kalman filter assumes that prior emissions in the model are
unbiased. In reality this is not necessarily the case, since emission
inventories or emission schemes in models may suffer from biases that are
often higher than the defined background uncertainty. Past studies have
demonstrated that optimizing prior emissions based on previous assimilation
cycles can improve data assimilation performance
(Bruhwiler
et al., 2005; Peng et al., 2017; Peters et al., 2005). Based on that we have
developed a method, hereafter called the “prior correction”. Prior
correction updates the prior emission based on estimated emissions from the
previous assimilation cycles, thus correcting biased emissions of the model
as the data assimilation experiment progresses in time. Specifically, the
ensemble mean (
Although prior correction fixes the problem of potentially biased model
prior emissions, it may introduce unwanted negative emission perturbations
when the
An example that shows how the ensemble standard deviation (
The prior correction approach has two optional settings where the background
Observing system simulation experiments (OSSEs) are data assimilation experiments in which synthetic observations are used that themselves are generated by a model. The synthetic observations of an OSSE can be modified to match the spatiotemporal coverage and observational uncertainty of any satellite sensor. Hence, with OSSEs it is possible to assess the potential impact of past, present, and future satellite missions on aerosol top-down emission estimation. The unique advantage of OSSEs is that the “truth” is perfectly known for all times, locations, and climate and aerosol components and can be used to evaluate the performance of an experiment.
There are three parts of an OSSE, (i) the nature run (NAT) that represents the “true” conditions of the aerosol state in the atmosphere, (ii) the control (CTL) run of the model, which sets the baseline performance of the model without data being assimilated, (iii) and the data assimilation run (DAS) where synthetic observations are assimilated in a model identical to the CTL model in order to improve aerosol emissions. The intercomparison of the differences between CTL and NAT and DAS and NAT can provide the added value of the assimilated observations, identify limitations of the data assimilation system, or quantify the role of some processes on the estimated emissions. The main goal of the present paper is to assess the ability of different satellite observations for quantifying aerosol emissions. Therefore, for all experiments we use the same physical model for the NAT, CTL, and DAS because otherwise we cannot attribute differences between NAT and DAS to either limitations of the satellite observations or model differences. We also perform some additional experiments with different nature runs (NAT_M, NAT_E) to assess different causes of uncertainty in emission estimation (e.g., biased meteorology) in addition to the standard nature run (NAT) and partially address the OSSE identical twin problem (Arnold and Dey, 1986; Timmermans et al., 2015). Note that the meteorology of all experiments is nudged to the ensemble mean of the 10 analysis members of ERA5 (Hersbach et al., 2020), except the nature run NAT_M (details below).
The standard nature run (NAT) only changes the emissions in comparison to
CTL, by multiplying the default emissions of DU and SS by 0.5; the default
emissions of OC and BC by 2; and the default emission of SO
Emissions inventories and schemes used per sector for all NAT experiments. Note that NAT and NAT_M use the same emissions inventories and schemes as CTL and DAS but use emission factors (per species) to scale the emissions. ACCMIP is the Atmospheric Chemistry and Climate Model Intercomparison. GFAS is the Global Fire Assimilation System. CEDS is the Community Emissions Data System. The terms ndust and nseasalt refer to the emission scheme options used by the model ECHAM-HAM.
The SPEXone spatial coverage at native resolution (
An ideal sensor in terms of spatial coverage was assumed in order to test
the data assimilation system and act as a benchmark for the SPEXone ability
to estimate aerosol emissions. This sensor, hereafter referred to as SUPER, is
able to retrieve AOD
The spatial coverage for a 2 d period for these two satellites is shown
in Fig. 3. Note that SUPER has a fixed number of
observations in time and space, while the number of SPEXone observations
fluctuates in time and space depending on cloud cover and orbit
characteristics. The total number of grid cell observations (each grid cell
includes an AOD
Red grid cells illustrate the 2 d spatial coverage of SUPER
and SPEXone instruments. SPEXone coverage is shown for the 17 and
18 August. In both cases the observation size is 1.875
An instrument and retrieval simulator was used to generate estimates of observational errors. Retrievals for 4 individual days were used for this purpose. To be more specific, the estimated uncertainty is based on the difference between the retrieved and the true values, following a similar method to that of Tsikerdekis et al. (2021a). More details can be found in Appendix C. Note that these observational uncertainties were used for both satellites (SUPER and SPEXone).
All of the experiments span 2 months in the summer of 2006 (20 July to
20 September 2006). This year and period was chosen based on our previous work
(Tsikerdekis et al., 2021a). Prior to this period the model
was spun up for 3 months (1 April to 1 July 2006), and the ensemble background
emissions were spun up for 20 d (1 July to 20 July 2006). We employ a grid
resolution T63L31 (1.875
There are a few LETKS parameters that can be adjusted. In this study we keep
these parameters fixed in all of our experiments. The description,
discussion, and sensitivity experiments of these parameters (ensemble size,
inflation local patch size, and the horizontal localization) was presented in
our preceding study (Tsikerdekis et al., 2021a). The data
assimilation ensemble size consists of 32 members. The local patch size and
the horizontal localization are set to eight and four grid cells, respectively,
while the inflation is set to 1. The inflation parameter is essentially
deactivated with the value equal to 1, since under the emissions estimation
setup of the data assimilation system the background uncertainty remains large
enough throughout the experiment for the data assimilation to work. The
local patch size is deliberately chosen to be high (8) in order to let
observations that are far away from the source (up to 15
Table 2 shows the list of experiments related to
SPEXone. The experiment where the assimilated observations are based on the
SUPER spatiotemporal sampling is used mainly as a benchmark for the
performance of the experiments that use the SPEXone sampling. The
experiments where the assimilated observations use the SPEXone satellite
coverage intend to evaluate the added value provided by the SPEXone instrument's ability to estimate
emissions under different observational uncertainty and data assimilation
options. Specifically, the experiment SPX used the default errors estimated for SPEXone
retrievals (Appendix C). The experiment SPX_2U doubles the
uncertainty of the assimilated observations, and SPX_2URE
doubles the uncertainty and adds random errors (with standard deviation
equal to the observational uncertainty) to the assimilated observations.
Finally, SPX_W1 and SPX_W2 reduce the
List of experiments related to SPEXone.
Sensor SUPER is further used in other sensitivity experiments that aim to assess issues related to the nature run complexity and development of the data assimilation system (Table 3). The SUP0_M experiment points out the degradation in emission estimation purely due to biased wind by assimilating observation from NAT_M. SUP_E assimilates observation from NAT_E and shows that even under totally different emission schemes and emission inventories between the nature run and the data assimilation experiment, the emission errors are reduced.
List of experiments related to other uncertainty factors that can affect emission estimation.
The prior emissions may be overestimated or underestimated, and the smoother (
Figure 4 shows that the differences between DAS and NAT (solid lines) reach a value close to zero after 26 d. From that point until the end of the experiment, these differences fluctuate around zero. For comparison the emission differences of CTL–NAT (dashed lines) are also shown. Note that the day-to-day dust and sea salt emission differences can fluctuate a lot in CTL, but SUP is able to estimate them adequately.
Time series for emission fluxes differences between CTL–NAT and
SUP (DAS)–NAT for each species. The red line indicates where the
analysis emissions perturbations were estimated for the first time. Note
that SO
The duration of the initialization phase may be expected to be a multiple of the longest of two timescales: the aerosol lifetime (that determines how quickly aerosol are deposited) and the DA window (that determines how quickly we can adjust emissions based on observations).
This is shown in Fig. 5, where after approximately 26 d the differences in aerosol optical properties and column burden relative differences between DAS and NAT reach a value close to zero and start fluctuating around this value until the end of the assimilation experiment. Consequently, we choose the period of 26 d as the data assimilation initialization period, and only the remaining 36 d, spanning from 15 August 20 September 2006, are evaluated in Sect. 4. Note that the data assimilation initialization varies for each experiment depending on the amount of the assimilated observations, the differences with nature run, and the assimilation options used. Nevertheless, 26 d is sufficient as a data assimilation initialization period for all experiments (not shown) (except SUP_E for SS emissions in the coarse mode); thus, it is kept constant throughout the paper.
Time series of aerosol optical properties and column burden differences between CTL-NAT (dashed lines) and SUP (DAS)-NAT (solid lines). Column burden is depicted as relative differences. The vertical red line indicates when the analysis emissions perturbations were estimated for the first time, and the vertical purple line indicates when the plotted variables reach equilibrium with the analysis emissions. The period between the red and the purple lines indicates the lag time of the global aerosol burden's reaction to the analyzed emissions.
The ability to estimate the true aerosol state using SPEXone is compared to an experiment in which observations were assimilated based on a sensor like SPEXone (meaning that it can retrieve the same type of observations with the same accuracy) but with an almost perfect global coverage. In order to understand the simulated aerosol state for the examined period, the aerosol optical properties of the CTL experiment are shown and discussed in Fig. 6. High AOD is evident over Sahara and Arabian Peninsula mainly due to dust; over tropical forests (Amazon, Africa, Indonesia) mainly due to organic and black carbon; and over Europe, North America, and China mainly due to sulfates. AE is small over isolated ocean areas that are dominated by sea salt and shows high values over land, excluding desert areas where large dust particles prevail. High AAOD (low SSA) highlights high black carbon concentrations, either from natural (biomass burning) or anthropogenic (fossil fuel) sources, and intermediate values over high sources of dust. Note that SSA (not AAOD) is the quantity that is assimilated in our system (for details on the differences between SSA and AAOD assimilation, see Tsikerdekis et al., 2021a), but AAOD is shown in the plots since it is easier to interpret.
Aerosol optical properties for the CTL experiment. The mean stand for the global mean value is shown and is estimated by averaging all the available grid cells.
The ability of SPEXone and SUPER sensors to recreate the NAT are summarized in Fig. 7, where the differences between the experiments CTL, SUP, and SPX from NAT are depicted for AOD, AE, and AAOD. In both data assimilation experiments the modeled aerosol is improved when compared to the CTL experiment, and the global mean error (ME) and the global mean absolute error (MAE) are almost zero. The ME and MAE equations can be found in Appendix B of our preceding publication (Tsikerdekis et al., 2021a). The performance of SPX is as good as the SUP, which suggests that the spatial coverage of SPEXone is sufficient to constrain the emissions in a similar fashion to the SUPER satellite.
Differences in aerosol optical properties of CTL–NAT
An important advantage of OSSEs is that we are able to evaluate the
estimated emissions of the data assimilation experiments with the emissions
of the nature run. Figures 8 and 9 depict the emission of aerosol species for
NAT and the emission differences for CTL, SUP, and SPX from NAT. In both data
assimilation experiments the estimated emissions are improved compared to
the emissions of the CTL. The overestimated dust emissions in the CTL are
constrained in the data assimilation experiments, and the ME is not close to
zero only in the western
part of the Sahara desert where emissions are high. For both data assimilation experiments the relative ME averaged for
the same region is lower than 10 % (not shown). The overestimated sea salt
emissions in CTL are constrained globally in both data assimilation
experiments, though in SPX the sea salt emission over the Indian Ocean shows
high ME with relative ME in some grid cells that exceeds 50 %. This is
caused by the limited observations by SPEXone due to cloudiness over India
and the surrounding seas (see Fig. B2). The ME and the
relative ME emission for organic and black carbon over high sources, mainly
over the tropics in South America, Africa, and Indonesia but also over
eastern China, reach almost zero in the data assimilation experiments.
Sulfates in the model are mainly produced from SO
Aerosol emission fluxes (kg km
The same as Fig. 8 but for the differences between SUP and NAT and SPX and NAT.
A series of data assimilation experiments were conducted in order to explore
less optimistic (SPX_2U) scenarios for the SPEXone
retrievals and also to check what the effect is of adding actual noise
to the observations (SPX_2URE) instead of relying purely on
the uncertainty descriptions of the measurements. Further, we vary the
Differences in aerosol optical properties between SPX_2U and NAT
Specifically, SPX_2U, where the assimilated observation uncertainty was doubled, shows similar results for AOD and AE, whereas the AAOD bias is increased slightly in comparison to SPX (Fig. 10a–c). SPX_2URE, where the assimilated observations uncertainty was doubled and random errors (with standard deviation equal to the observational uncertainty) were added to the assimilated observations, the bias increases over northeastern China for AOD, over the Sahara, Arabian Peninsula, and northern Indian ocean for AE, and over tropical Africa and the Amazon basin for AAOD (Fig. 10d–f). We can quantify the effect of an observation's random error on emission estimations by comparing the experiments SPX_2U and SPX_2URE. The data assimilation performance does not degrade significantly when taking into account random errors in the assimilated observations. Specifically the dust emission global MAE increases by 5 percentage points due to random errors, while for other species the increase is even lower (Fig. 13).
SPX_W1 and SPX_W2 reduce the
Differences in aerosol optical properties between SPX_W1 and NAT
Figure 12 shows the mean and standard deviation of errors per grid cell. These errors are averages for the evaluation period of the difference between an experiment (CTL or DAS) and NAT. Both SUP and SPX errors are significantly smaller than CTL in both global (mean) and local errors (spread). The global AOD MAE of SPX_2U and SPEX_2URE remains very low, while AE and AAOD global ME slightly increase. Note that SPEXone AOD uncertainty range (Appendix C) is very low (lower than 10 % over ocean and 15 % on average over land), and doubling this uncertainty only has a limited effect on the analysis. On the other hand, the uncertainty in AE and SSA observations is higher than AOD; hence, the data assimilation performance is affected to a larger extent. Overall, it can be concluded that in these less optimistic assessments (doubled uncertainty), the assimilated observations based on SPEXone spatial coverage are still able to estimate the emissions with reasonable accuracy. Further, the experiment where actual noise is added to the measurements shows similar results to the experiment where no noise was added. This illustrates that the system is not “overfitting” the observations but takes the specified uncertainty correctly into account even when there is no noise added to the measurements.
Global mean differences between CTL and several data assimilation experiments from NAT. Information in parentheses indicates the global mean relative difference. The error bar indicates the standard deviation of differences by grid for the whole globe. A larger (smaller) error bar indicates that local differences are higher (lower).
In terms of estimated emissions, the four sensitivity experiments rank a bit
lower in comparison to both SUP and SPX, as indicated in
Fig. 13, where the global relative MAE for various
species is shown. Specifically, SPX has similar emission errors to SUP but
differs in the SS-estimated emission, which is caused by the limited
observations in SPEXone due to cloudiness over India and surrounding seas
(see Fig. B2), as discussed in the previous
subsection. SPX_2U and SPX_2URE emission
biases for all species are increased by no more than 10 percentage points in
comparison to SPX, which indicates that increased (double) uncertainty and
adding random errors in the observations has a small but noticeable negative
effect on the global relative differences in the emissions. Finally,
SPX_W1 emission bias increases by no more than 6 percentage points
in comparison to SPX in all species. However, dust emission error grows to
54 % in SPX_W2 from 17 % in SPX_W1,
indicating that the information content of observations 3 and 4 d after
the emissions is very rich and should be used to correct these emissions,
especially for Saharan dust plumes that extend over the Atlantic Ocean and
last for several days. The emissions of OC, BC, and SO
Global relative MAE (%) of species-specific emission fluxes for several experiments.
OSSEs also allow us to quantify the uncertainty due to assumptions in
nudging meteorology or emission source locations. The first relates to the
assumption that the meteorological parts of the model and specifically the
wind components (
The OSSEs in previous subsections implicitly assumed that the data assimilation experiment would have perfect knowledge of the NAT meteorology. Since even reanalysis datasets of wind speeds have errors, we test their impact here. Simulations that were nudged to different reanalysis datasets (e.g., ERA-Interim and ERA-5) reveal very dissimilar results in terms of AOD, AE, and SSA for specific regions (Fig. 14g, h, i).
Differences in aerosol optical properties between CTL and NAT_M
In this subsection we explore the effect of biased meteorology in the aerosol emission estimation by nudging the wind components of the nature run (NAT_M) to ERA-Interim and the wind components of the data assimilation (SUP0_M) experiment to ERA-5. The sampled observations of NAT_M are based on the SUPER sensor; hence, the observational coverage is optimal in space and continuous in time. Note that the emissions of NAT_M are scaled with the same scale factor as NAT (Table 1). Further, prior correction is not used in SUP0_M.
The evaluation of SUP0_M modeled aerosol against NAT_M reveals high errors in some regions (Fig. 14d, e, f). Unsurprisingly, AOD differences between SUP0_M and NAT_M and NAT and NAT_M shown in Fig. 14 display striking similarities for subtropical and tropical Africa and the Atlantic Ocean, as well as over East China Sea and Philippine Sea, which suggests that the remaining aerosol biases on SUP0_M are mostly related to the biased meteorology that affects aerosol transport paths.
In terms of the estimated emissions, SS is negatively affected the most by
the effect of biased meteorology. Figure 15 shows
that the relative MAE in SS emissions increases by 24 percentage points in
SUP0_M (42 %) compared to SUP0 (18 %), while the estimated
emissions of DU, OC, BC, and SO
Global relative MAE (%) of species-specific emission fluxes for several experiments. The information in parentheses indicates the nature run, which is used as a reference in each case.
Transport deviations (vertically and horizontally) between ERA-5 and ERA-Interim were assessed using Lagrangian transport simulations by Hoffmann et al. (2019). In that study differences of Lagrangian simulations based on the two reanalysis products were up to 2 to 3 orders of magnitude compared to differences caused by parameterized diffusion and subgrid-scale wind fluctuation after 10 d. Some of the main simulation improvements of ERA-5 compared to ERA-Interim are its higher spatial (31 km) and temporal (hourly analysis) resolution as well as its 4D-Var uncertainty estimate, which comes from a 10-member ensemble of data assimilation in a coarser resolution (63 km). Considering the improvements of ERA-5 compared to its predecessor, we assume that the aerosol differences (Fig. 14g, h, i) caused by nudging ECHAM-HAM to ERA-5 or ERA-interim represent a worst-case scenario and that the differences between ERA-5 and the real wind are not greater than that scenario.
Our nature run (NAT) has emissions that are simply scaled for the different species compared to the control and data assimilation runs. To investigate whether this scaling represents a too simple difference between nature and data assimilation run, we conduct OSSEs with a new nature run (NAT_E). In this new nature run we change the emission inventories and emission schemes (Table 1) compared to the control and data assimilation runs. This creates a more realistic emission differences between NAT_E and CTL that fluctuate in time and space. The CTL to NAT_E differences in Fig. 16 illustrate an overestimation of AOD and AAOD over the tropics in South America and Africa. An underestimation of AOD is apparent in Southeast Asia and over the deserts in the western Sahara and Taklamakan. In addition, a strong global overestimation (0.46) of AE, mainly over the ocean, is observed due to a high amount of SS coarse particles emitted by the scheme selected in NAT_E.
Differences in aerosol optical properties between CTL and NAT_E
In a new assimilation experiment (SUP_E) we used some new options. Emission estimation was conducted by mode and not only by species (separately for accumulation and coarse) for the SS and DU aerosol species. In addition, prior correction was used (without the prior max option). Both of these changes were introduced for the SUP_E experiment in order to create more variation in AE and let emissions of SS in the coarse mode match those in NAT_E, which are much higher than the background uncertainty for midlatitudes and high latitudes. Results of the data assimilation experiments, where we applied these two changes one at a time, are shown in Fig. S3.
In SUP_E, we perform a data assimilation experiment using the CTL baseline prior emissions with observations drawn from NAT_E. The data assimilation system was able to adjust model emissions in order to match the observations of NAT_E. Specifically, the global ME for SUP_E is zero for AOD and AAOD, while AE global ME is reduced from 0.46 to 0.11 (Fig. 16), with the highest local errors still persisting over high latitudes (Fig. S4 and explanation in caption).
The global relative MAEs for emissions are depicted by species in
Fig. 17 for SUP_E and CTL. The
emission errors of SUP_E for all species are reduced or
remain almost unchanged (SO
Global relative MAE (%) of species-specific emission fluxes for several experiments. The information in parentheses indicates the nature run, which is used as a reference in each case. Note that statistics were calculated for sources that are active on NAT_E.
We focus on the Sahara region and the estimated DU emissions to highlight an important issue of any data assimilation system for emission estimation. Figure 18 depicts the dust emission fluxes over the western Sahara for the NAT_E, CTL, and SUP_E. Although the dust emission fields are similar, the spatial distribution of the dust sources differs. There are some grid cells where dust emissions are zero (not considered as sources by the model) in the control and the data assimilation experiment (highlighted with the red polygon at Fig. 18d), while the same locations are active sources in the nature run. These differences are caused by the setup of each dust scheme, where the preferential dust sources can differ (Schepanski et al., 2007). These contrasting assumptions can negatively impact the estimated emissions, since our data assimilation setup adjusts existing sources and does not introduce new sources. Dust emission differences between CTL and NAT_E (Fig. 18d) show an underestimation over these grid cells and the surrounding area in question. Differences between SUP_E and NAT_E (Fig. 18e) reveal that dust emissions remained underestimated over the same grid cells but that the surrounding emissions (especially westward) were increased (overestimated) to compensate for the lack of dust in the area. Hence, the data assimilation system not only underestimated these specific grid cells but ended up overestimating all of the surrounding area as well in order to compensate for the missing aerosol in the atmosphere. On the other hand, for emissions in areas where the location of preferential dust emission sources is the same, data assimilation did not have a problem estimating the correct emissions (highlighted with the orange polygon at Fig. 18c). These examples show that it is possible for a data assimilation system to reduce source strengths in the model, whereas it is not possible (under the current dust scheme and data assimilation setup) to start emitting dust in grid cells specified as non-sources. Consequently, dust schemes with spatially broader and continuous sources may provide a more flexible way to adjust the emissions based on observations. Note that although these examples reside in the modeling world of an OSSE, the same problem can affect the dust emission estimation of non-OSSE data assimilation studies since source location in models can differ from the source location in nature.
Dust emission fluxes (kg km
In this study we have quantified SPEXone ability to estimate aerosol emissions using a fixed-lag ensemble Kalman smoother (LETKS) in combination with the ECHAM-HAM aerosol–climate model. SPEXone is a passive remote sensing multi-angle polarimeter part of the NASA PACE missions scheduled to be launched in 2023. The system is tested using observing system simulation experiments where the nature run is created by an ECHAM-HAM simulation with altered aerosol emissions from the standard model setup. Synthetic observations of aerosol optical depth, Ångström exponent, and single-scattering albedo are sampled from this nature run according to the spatiotemporal coverage of SPEXone or a theoretical sensor with almost perfect global coverage.
The data assimilation experiments based on SPEXone or the theoretical sensor
provide similar results in terms of the estimated emissions and the
simulated observations, which is very encouraging since it shows that
spatially limited SPEXone observational coverage will be able to constrain
emissions almost as well as the theoretical satellite setup. Note that we
assume that the 1.875
Specifically, the initial global prior emissions errors in the control run that ranged from 33 % to 117 % (depending on the species) drop to a range of 0 % to 5 % for the theoretical sensor and 0 % to 11 % for SPEXone. The highest difference between the two sensors is observed on the SS-estimated emissions mainly due to the lack of observations for SPEXone over India caused by cloudy conditions. An observational uncertainty scenario for SPEXone that doubles the uncertainty of the assimilated observations leads to reasonably good emission estimates. Further, we show the information of observations on days 5 and 6 after emission is not that important for the estimation of emissions (for all species), but the information of observations on days 3 and 4 after dust emissions is very important and should be used for the estimation of dust emissions. Note that in all of these experiments the nature run was created using the same model and the same physics options as the data assimilation run, with their only difference being that the emissions of the nature run were multiplied with emission factors that are globally constant and distinct for each aerosol species. Hence, the results of these data assimilation experiments may be too optimistic, since they do not account for any other uncertainty factor that would affect emissions estimation (e.g., meteorology biases, complexity in emission sources) in reality.
Therefore, additional experiments were conducted using the theoretical sensor in order to quantify the impact of other uncertainty factors that can affect the estimation of aerosol emissions. The role of biased meteorology is tested by nudging the wind components of the nature run to ERA-interim and the data assimilation run to ERA-5. Biased meteorology mostly increases global error in sea salt emissions in comparison to the data assimilation experiment where meteorology was not biased. The estimated emissions of the other species are negatively affected to a smaller extent.
Further, to investigate whether the creation of a nature run with emission scaling represents a too simple difference between nature and data assimilation run, an experiment where emissions in a new nature run are altered by changing the emission inventories and emission schemes. Data assimilation successfully reduced the global emission errors of all species, with the exception of dust at some locations. Dust emission errors are not reduced because the preferential dust sources of the nature run are greater compared to the data assimilation run. This complicates the emission estimation since dust is emitted from different locations in the nature run and the data assimilation run. Specifically, in the western Sahara data assimilation increases dust emission extensively in its available dust sources based on the assimilated observations (sampled from the nature run) in order to compensate for the lack of dust that originated from dust sources only available in the nature run. This OSSE demonstrates that a data assimilation system may not provide the desirable results in cases where the locations of emission sources are more sparse than nature.
This work highlights that the upcoming SPEXone sensor will provide high-accuracy observations with sufficient coverage that contains information about the mass, size, and absorption of the aerosol particles in order to estimate aerosol emission accurately using our data assimilation system. Using the full observational information of the PACE mission (SPEXone, HARP-2 and OCI), as well as using more retrieved aerosol properties (effective radius, refractive index), can potentially provide even better results.
The Kalman filter assumes that the emissions do not have persistent errors or, in other words, that the emissions are not constantly biased (low or high) in time. Unfortunately, emissions in models can be biased; hence, we developed a prior correction method to account for this phenomenon. The effect of prior correction is tested by comparing the performance of the experiments with (SUP) and without (SUP0) prior correction. The simulated aerosols in the SUP0 experiment become almost identical to NAT, although a small bias remained in all variables (Fig. A1). This is due to the setup of our OSSE, where the prior emissions of all the species are biased either low or high in comparison to NAT. In other words, although the uncertainty of prior emissions describe the prior emission errors well, the biased prior ensemble mean has a small toll on data assimilation performance. With prior correction (SUP) this issue is resolved, and we get a better fit to the observations for all variables as shown in Fig. A1. The global error of the estimated emission is improved due to prior correction by 18 % for SS and by up to 7 % for the other species (not shown). Although the effect of prior correction is small for SUP and SUP0, in the case where the prior emissions error differs a lot from the uncertainty of prior emissions, the effect of prior correction would be much more significant, since it will adjust the ensemble mean of the emission perturbations and correct the bias of the model. An example of this is presented in Sect. 4.3.2 for the estimate of SS emissions.
AOD
We want a realistic cloud mask that is nevertheless determined from the ECHAM cloud mask. The way we achieve this is by setting an ECHAM cloud fraction threshold for all the grid cells that coincide with the cloud-free SPEXone spatiotemporal coverage. When ECHAM cloud fraction of a grid cell is lower than the cloud fraction threshold, we assume that at least some observations could be retrieved over the cloud-free part of that grid cell. In order to make our results more realistic, we further change the cloud fraction threshold in each grid cell (in a statistical sense, by random draws) to make it appear more like MODIS cloud mask.
Specifically, the grid cells of the cloud-free SPEXone mask were filtered out based on ECHAM cloud fraction greater than 0.7 (ECHAM-CloudMask1 red points in Fig. B1). Although ECHAM and MODIS cloud-based SPEXone masks almost matched in the total number of observations, they differed in the latitudinal and temporal distribution of observations (especially at high latitudes and the subtropics) (black and red points in Fig. B1). Thus, we allowed the 0.7 cloud fraction threshold to change depending on how much the ECHAM and MODIS cloud-based SPEXone masks differ per latitude and time. This resulted in a SPEXone mask based on ECHAM cloud fraction but with the more realistic sampling that MODIS provides, specifically regarding time (ECHAM-CloudMask2 blue points in Fig. B1). The total number of observations retrieved by SPEXone based on MODIS and ECHAM cloud masks is depicted in Fig. B2.
Number of observations by latitude, longitude, and time for the
SPEXone mask based on MODIS cloudiness (black; MODIS-CloudMask), ECHAM cloud
fraction
Number of observations for the MODIS and ECHAM-HAM cloud-based
SPEXone masks. Each gridded observation includes an AOD
We need to estimate the observational uncertainty for SPEXone, which is a sensor that is not yet launched. The retrievals errors of SPEXone are simulated as in Hasekamp et al. (2019a). The uncertainty of the retrieved parameters are propagations of uncertainties in both measured radiance (and DoLP) and the prior of the retrieved parameters.
Based on synthetic retrievals performed globally for 4 individual days,
the standard deviation of the differences between the truth and the
retrieved values were calculated for several AOD
Retrievals over land have higher uncertainty than retrievals over ocean for
almost all AOD
Defined uncertainty of SPEXone observations. Each point
represents the standard deviation of the differences between truth and retrieved values for a specified AOD
The model simulations and the SPEXone simulated retrievals are available from Zenodo at
the following link:
The supplement related to this article is available online at:
AT designed the experiments with the help of NAJS and OPH and carried them out. GF prepared SPEXone-simulated retrievals. AT performed the analysis and prepared the manuscript with contributions from all co-authors.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative.
This research has been supported by the Dutch Research Council (NWO) and Netherlands Space Office (NSO) (grant no. 2017.008). Athanasios Tsikerdekis is funded by a NWO/NSO project “AEROSOURCE: Estimation of Aerosol Emissions from Polarization Data” (grant no. ALWGO.2017.008).
This paper was edited by Samuel Remy and reviewed by two anonymous referees.