The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal
of measuring the column-averaged dry-air mole fraction of carbon dioxide
(XCO2) with sufficient precision and accuracy to
infer regional carbon sources and sinks. One of the primary sources of error
in near-infrared measurements of XCO2 is the
scattering effects of cloud and aerosol layers. In this work, we study the
impact of ingesting better informed aerosol priors from the Goddard Earth
Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval
algorithm with the objective of reducing the error in
XCO2 from real measurements. Multiple levels of
both aerosol setup complexity and uncertainty on the aerosol priors were
tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup
to ingesting full aerosol profiles with high confidence. We find that using
co-located GEOS-5 aerosol types and AODs with low uncertainty results in a
small improvement in the retrieved XCO2 against
the Total Carbon Column Observing Network relative to V8. In contrast,
attempting to use modeled vertical information in the aerosol prior to
improve the XCO2 retrieval generally gives poor
results, as aerosol models struggle with the vertical placement of aerosol
layers. To assess regional differences in XCO2,
we compare our results to a global CO2 model validation suite. We
find that the GEOS-5 setup performs better than V8 over northern Africa and
central Asia, with the standard deviation of the
XCO2 error reduced from 2.12 to 1.83 ppm, due
to a combination of smaller prior AODs and lower prior uncertainty. In
general, the use of better informed aerosol priors shows promise but may be
restricted by the current accuracy of aerosol models.
Introduction
Earth's climate is changing and anthropogenic emissions of greenhouse gases
(GHGs) are the primary cause . In order to
quantify how the climate is changing and what might happen in the future,
society needs a reliable method of measuring GHGs. These measurements can
then be fed into models to make them more accurate. In 1958, the first
regular atmospheric measurements of the most important GHG, carbon dioxide
(CO2), were established by Charles David Keeling at the South Pole
and in Hawaii on Mauna Loa. Since then, levels of CO2 have been
observed by additional ground measurement stations and, more recently,
satellites orbiting the Earth. These CO2 measurements are then used
in carbon flux inversion models to infer regional sources and sinks. Only
about half of the carbon dioxide emitted from the burning of fossil fuels
actually remains in the atmosphere. The other half is absorbed by so-called
sinks on Earth's surface. Both the land and ocean are capable of absorbing
CO2, but the partitioning between the two surfaces is uncertain, as
well as how different land surface types absorb CO2. Space-based measurements of CO2 are one of the
best methods available to study CO2, its sources and sinks, and how
it impacts Earth's climate now and in the future. This is because space-based
measurements provide vastly more global coverage than the current network of
ground-based measurements and, when ingested into carbon flux inversion
models, could help answer carbon cycle questions (;
). However, the precision
requirements needed to gain information about the carbon cycle compared to
only having ground-based measurements is about 1–2 ppm
(; , ). The accuracy
requirements are even more demanding: a regional bias of only a few tenths of
a part per million may be detrimental to carbon flux inversion models
. Because of this, it is important to reduce
errors and biases in space-based measurements of CO2.
Currently, there is a small but growing number of satellites in orbit around
Earth designed to measure the column-averaged dry-air mole fraction of
CO2, or XCO2, using reflected, near-infrared
sunlight. They are the Greenhouse Gases Observing Satellite (GOSAT;
), the Orbiting Carbon Observatory-2 (OCO-2;
), and TanSat . Thermal infrared
instruments, such as the Atmospheric Infrared Sounder
and the Infrared Atmospheric Sounding Interferometer ,
can measure CO2 in certain parts of the atmosphere but are
typically not sensitive near the surface, where the fluctuations in
CO2 are greatest. Because of their sensitivity to the full column
of CO2, near-infrared measurements currently provide the most
valuable information on CO2 in the atmosphere. This work is
focused on the XCO2 retrieval algorithm applied to
measurements from OCO-2. OCO-2 is in a sun-synchronous orbit in the NASA
Afternoon Train and has eight adjacent footprints that
each measure approximately 1.25 km by 2 km. It takes approximately 1 000 000
measurements a day, of which on average 100 000 are cloud-free.
One of the main sources of error in space-based measurements of
XCO2 is the scattering of sunlight by clouds and aerosols.
This is because these contaminants can modify the light path seen by the
satellite's sensor in ways that are difficult to quantify. Completely
neglecting clouds and aerosols in measurements of XCO2 can
result in errors that exceed 1 % (around 4 ppm of CO2) and can be
much larger for scenes containing significant contamination
. Even when scenes are heavily
screened to remove clouds and aerosols, a non-scattering retrieval performs
20 %–40 % worse than one that includes some way to account for scattering
effects .
Typically, one or more pieces of information about clouds and aerosols are
solved for in addition to XCO2. Some common methods include
retrieving various optical properties of an aerosol type
, retrieving vertical aerosol
information , retrieving
parameters directly related to the photon path length
, and parameterizing aerosols with a single isotropic
scattering layer . All these methods are intended to act
as proxies to the real scattering effects of clouds and aerosols in the
column in order to allow an accurate XCO2 to be retrieved.
However, it is not clear that any one method is best. The current operational
OCO-2 XCO2 retrieval algorithm, known as Atmospheric Carbon
Observations from Space (ACOS; ) version 8
(V8; ), includes nine parameters related to clouds and
aerosols, which describe an ice cloud, water cloud, and three aerosol types.
However, retrieved aerosol optical depths (AODs) from ACOS generally compare
poorly to the highly accurate AErosol RObotic NETwork (AERONET,
). This indicates that the way ACOS handles
the scattering effects of clouds and aerosols can potentially be improved.
While the goal of parameterizing these scattering effects is to account for
light path modifications and not necessarily to retrieve cloud and aerosol
properties, it is hypothesized that any improvements will lead to reduced
XCO2 errors.
One choice that impacts how well the retrieved cloud and aerosol parameters
perform is the use of prior information to constrain the problem. Often, a
constant or climatological value with high uncertainty applied to it is used
for the aerosol setup (e.g. ). In this work, we
test the hypothesis that using more realistic aerosol priors will allow the
retrieved aerosol parameters to better represent the scattering of light in
the column and thus reduce the error in retrieved XCO2.
Specifically, we examine the impact of using co-located modeled aerosols from
the Goddard Earth Observing System Model, Version 5 (GEOS-5) as prior
information on the retrieved XCO2 from real OCO-2
measurements. Global atmospheric models, such as GEOS-5, are highly
sophisticated and contain many layers of complex physics to represent aerosol
processes in the atmosphere including aerosol dynamic schemes and
size-resolved aerosol microphysics . However, atmospheric
models do not perfectly represent reality. There are still large differences
among individual models, which are restricted by uncertainties in aerosol
emission source characteristics, knowledge of atmospheric processes, and the
meteorological field data used . Despite this, it is
hypothesized that these models will still be of use in the
XCO2 retrievals. We also examine the uncertainties applied to
the aerosol priors in the current OCO-2 XCO2 retrieval
algorithm to see if using a lower uncertainty, in conjunction with a more
realistic aerosol prior, results in an improvement in XCO2
against multiple validation sources. Finally, we test whether vertical
aerosol information from GEOS-5 can be successfully ingested. These results
impact not only OCO-2, but also XCO2 from GOSAT, TanSat, and
several other future space-based GHG missions that will also be significantly
influenced by the scattering effects of clouds and aerosols.
Section gives an overview of the OCO-2
ACOS XCO2 retrieval algorithm and the prior information it
uses. Section discusses the two validation datasets used in
this study along with filtering and bias correction.
Section describes our use of instantaneous
modeled aerosols as prior information in the retrieval while
Sect. presents the results.
Section 6 summarizes the study's results
and determines if the use of more realistic modeled aerosols is beneficial in
near-infrared retrievals of XCO2.
XCO2 retrieval algorithm
In this section, we will discuss the current OCO-2 retrieval algorithm, ACOS
V8, and its cloud and aerosol parameterization. The remote sensing of
CO2 using reflected near-infrared sunlight is typically
accomplished by measuring absorption in an oxygen line and one or more
CO2 lines. For OCO-2, GOSAT, and TanSat, the O2 A-band at 0.76 µm is used in conjunction with a weak CO2
absorption band at 1.6 µm and a strong CO2 absorption band at
approximately 2.0 µm. The main purposes of the O2 band are to
estimate the path length of the photons detected by the satellite and to
filter out clouds and aerosols (by screening for large variations from the
expected path length). As there are only 1.5–2.0 pieces of information about
the vertical distribution of CO2 from OCO-2 ,
the column-averaged dry-air mole fraction of carbon dioxide
(XCO2) is reported:
XCO2=∫0∞NCO2(z)dz∫0∞Nd(z)dz,
where NCO2(z) is the molecular number density of carbon dioxide
and Nd(z) is the molecular dry-air number density at altitude
z. An optimal estimation technique is used to retrieve
carbon dioxide and several other parameters that the measured radiances are
sensitive to. Generally, optimal estimation takes a state vector containing
priors with associated uncertainties, solves a cost function iteratively to
find its minimum value, and outputs an optimized state vector with posterior
uncertainties for each variable. A full description of the state vector and
retrieval technique of the latest OCO-2 algorithm (V8) can be found in
. The state vector contains approximately 60 elements,
including 20 vertical levels of CO2, surface pressure,
band-dependent surface albedo, total column water vapor, a temperature
profile offset, several cloud and aerosol parameters, and others. The aerosol
setup contains five particle types: a water cloud, ice cloud, two aerosol
types from a Modern-Era Retrospective analysis for Research and Applications,
Version 2 (MERRA-2; ) climatology, and a
stratospheric aerosol type. This MERRA-2 climatology is simply the monthly
means for 1 year for each of the five MERRA-2 aerosol types (dust, organic
carbon, black carbon, sea salt, sulfate). The two types chosen to be included
in the state vector are the two with the highest climatological mean AOD for
a given month and location. For example, if dust and organic carbon are the
two largest AODs of the possible five in the month of July for a given
location, then they are selected as the two types to be retrieved for any
July OCO-2 sounding for that location. The vertical profile of these aerosol
types are described by Gaussian distributions. The height and magnitude (amplitude) of the
Gaussian distribution are retrieved for each of the two aerosol types, water cloud, and
ice cloud. The magnitude of the stratospheric aerosol type is solved for, but
the height is fixed. The widths of the five Gaussian profiles are fixed, as
it has been shown that the radiances are not especially sensitive to the
width of a cloud or aerosol layer . The
prior Gaussian profiles are shown in Fig. , from
.
Prior Gaussian profiles of the lower tropospheric aerosol types
(red), water cloud (blue), ice cloud (purple), and stratospheric aerosol
(green). The local AOD per unit of pressure at 755 nm is plotted as a function
of the relative pressure. The lower tropospheric aerosol prior AOD is not
fixed as for the other types, but rather it is taken from a climatology
described in the text. Taken from .
Thus, nine parameters are included in the state vector that directly describe
the clouds and aerosols in the scene. Additionally, the natural log of the
AOD is the parameter solved for that describes the magnitude of each
Gaussian profile. This is to prevent the algorithm from attempting to retrieve a
negative AOD, which would result in the retrieval crashing due to current
algorithmic limitations. The retrieved height of the Gaussian profile represents the
fraction of the surface pressure. For example, if the surface pressure is
1000 hPa and the retrieved height parameter is 0.5, the Gaussian profile will
be centered at 500 hPa.
Data
In this work, we use two datasets to evaluate the quality of
the OCO-2 XCO2 retrievals in the context of testing the
aerosol parameterization. While we expect the retrieved aerosol parameters to
improve with the use of a more accurate prior, the retrieved aerosols are
still only designed to be effective scattering parameters and thus we will
not evaluate their quality in this work. The first validation dataset is
32 175 retrievals co-located with 13 Total Carbon Column Observing Network
(TCCON; ) and AERONET sites across the globe. The second
is a dataset of 30 827 retrievals matched with an ensemble of global
CO2 models for which we consider the truth the median of the
CO2 models in places where they agree to within 1 ppm. These two
validation sets complement each other in that TCCON is known to be highly
accurate but with limited spatial coverage. The model validation dataset
likely has larger uncertainty than TCCON but provides excellent spatial
coverage.
TCCON–AERONET validation dataset
The TCCON validation dataset contained 32 175 OCO-2 measurements taken from
17 September 2014 to 2 May 2016. We co-located the OCO-2 measurements in time
and space with AERONET and TCCON, which were required to be both present
and operational at a given site. The co-location criteria were within
1∘
latitude–longitude and ±30 min and the sites selected for use are
shown in Fig. . As TCCON stations are all
located on land, only a small fraction of co-located soundings are over water
surfaces.
Validation locations used in this study. Every site contained both a
TCCON and AERONET station.
Table lists the TCCON sites used in this study. The
soundings were selected from a set of OCO-2 “lite” files
that had been pre-filtered (see
Sect. ). We then post-processed the
retrievals with multiple custom filters in an attempt to remove all scenes
contaminated by clouds or aerosols.
In addition to validation against the highly
accurate but sparsely located TCCON, a set of global CO2 models
was assembled in order to examine spatial errors. We co-located 30 827 OCO-2
soundings in time and space with a suite of nine global carbon models
. Only points where all
the models agreed to within 1 ppm of XCO2 were used. Work by
has shown that using this methodology produces similar
error statistics to that of the TCCON validation. The median
XCO2 of the nine models for each of the 30 827 soundings was
used as the truth metric. The OCO-2 soundings were selected by sorting all
the measurements into a 4∘× 4∘ spatial grid and filling all grid boxes
with up to 10 soundings. This allowed for excellent global coverage while
limiting the demand on the available computational resources needed to run
the retrievals.
Filtering and bias correction
As OCO-2 struggles with scenes containing clouds and aerosols, multiple
strategies are used to try and filter out any scene that is contaminated by
scattering particles. For both validation datasets, the O2 A-band
Preprocessor (ABP; ) and iterative maximum a posteriori
differential optical absorption spectroscopy (IMAP-DOAS) preprocessor (IDP;
) were applied to every measurement before being
selected to run through the retrieval. For each validation set, the
approximately 30 000 soundings used in this study were those that had
successfully passed through the preprocessors. These soundings were
determined to be clear enough to be run through the retrieval. After removing
soundings that failed to converge, post-processing filtering techniques were
applied to remove additional low-quality retrievals that were not screened
out by the preprocessors. These filters included the reduced χ2 (for
which χ2 in all three bands must be less than 2.0), a delta pressure
parameter (“dpabp” from ABP, where
dpabp<750 hPa), and the CO2 and H2O ratios
(from IDP, where 1.00<CO2 ratio <1.02 and
0.88<H2O ratio <1.01). After applying these filters, the
remaining OCO-2 measurements had co-located AERONET AODs of less than 0.25
and thus all tests are being carried out on a mostly clear-sky dataset and
conclusions cannot be drawn about how these retrieval modifications impact
the results if scenes with thick cloud or aerosol layers are present.
Additionally, as the TCCON stations are located on land, the final
post-filtered validation dataset only contained land measurements and thus no
conclusions can be made about OCO-2 measurements over ocean for the TCCON
validation study.
Despite heavy pre- and post-filtering of the dataset to remove cloud and
aerosol layers, no atmospheric column is truly free from scattering
particles. Thus, a bias correction is typically applied to the final
XCO2 in an attempt to mitigate retrieval errors caused by
remaining scattering effects and other sources of error including imperfect
spectroscopy . In the operational V8 product, considerable
effort is put into developing a multiparameter bias correction that reduces
the XCO2 bias against several independent truth metrics. In
this work, a single-parameter bias correction was selected for each
validation dataset for simplicity and to ensure a fair comparison across
different setups. The parameter chosen was that which had the largest
correlation with XCO2 error. When comparing to TCCON, the
retrieved XCO2 was bias corrected by removing a linear fit
between the XCO2 error (retrieved XCO2 – TCCON
XCO2) and the difference between the retrieved surface
pressure and the prior surface pressure (“dp”). This was the most
correlated parameter in the majority of our TCCON tests and thus was selected
as the bias correction parameter. This parameter is correlated with
XCO2 biases because any unparameterized clouds and aerosols in
the column can make the retrieval think there is a lower surface pressure
than in reality. Thus, bias correcting this mistake is designed to bring
the retrieved surface pressures back to realistic values and can
approximately account for the improperly parameterized clouds and aerosols.
In the case of the model validation dataset, the bias correction parameter
was the solar zenith angle. Physically, this represents the removal of
artificial biases induced by longer air masses. The reason why this parameter
was selected over dp is that the model dataset has excellent latitudinal
coverage and thus the air mass is weighted more than dp. TCCON, however, is
spatially limited and, despite seasonal variations in sun angle, the air mass
dependence is not as prevalent when searching for optimal bias correction
parameters.
Modeled aerosol priors
As discussed in Sect. , the OCO-2
retrieval algorithm has several aerosol parameters in its state vector. The
prior values for most of these parameters in V8 are fixed or taken from a
monthly climatology. Here, we discuss several methods in which we test the
use of instantaneous 3-D modeled aerosol data as prior information to improve
upon the current priors with the hope of increasing the precision and
accuracy of the final OCO-2 XCO2 product.
The GEOS-5 Forward Processing for Instrument Teams (GEOS-5 FP-IT;
) weather forecasting model, created and maintained
by the NASA Global Modeling and Assimilation Office, is designed specifically
for instrument teams in that the entire period (2000–current) is run using
the same GEOS-5 version to maintain consistency and avoid any unwanted biases
from updates to the model. For this work, GEOS-5 FP-IT version 5.12.4,
hereafter referred to as GEOS-5, was co-located in time and space with the
OCO-2 soundings. GEOS-5 is on a 0.625∘ longitude by 0.5∘ latitude
horizontal grid with 72 vertical layers extending to 0.01 hPa with a
time step of 3 h. The model was linearly interpolated in space and the
nearest 3 h model update was chosen in time. For example, if the OCO-2
sounding was taken at 19:00 UTC, the 18:00 UTC model run was used. The GEOS-5
aerosol scheme contains 15 different types with up to five different size
bins for each type, which we aggregate into five unique types: dust, organic
carbon, black carbon, sea salt, and sulfate. The aggregation process weights by
the typical relative amount of optical depth contributed by each type at 760 nm
and uses a typical relative humidity value for the hygroscopic types. Each
type has unique optical properties, including the single-scattering albedo,
extinction coefficient, and refractive index. Further details can be found in
. GEOS-5 ingests Terra Moderate Resolution Imaging
Spectroradiometer (MODIS; ) AOD, Aqua MODIS AOD, and
Multi-angle Imaging SpectroRadiometer (MISR; )
aerosol information. AERONET measurements are not used for this product as
the data latency is unacceptably large. Figure
shows that GEOS-5 AODs correlate better with AERONET compared to both the
climatological MERRA-2 AODs and the corresponding retrieved AOD values from
OCO-2 V8. Thus, using the model and assigning it some confidence should
result in an improved correlation in retrieved OCO-2 AODs compared to
AERONET.
(a) MERRA-2 climatological AOD vs. AERONET AOD.
(b) OCO-2 V8 retrieved AOD vs. AERONET AOD. (c) GEOS-5 co-located
AOD vs. AERONET AOD. The AERONET AODs are the means of the AODs at 675 and
870 nm. Overpass means are plotted. AERONET stations used were those present
at the TCCON sites selected for this study, listed in
Table and shown in
Fig. , that had valid data between
17 September 2014 and 2 May 2016.
Our primary hypothesis in this work is that using instantaneous modeled
aerosol data as prior information will result in smaller XCO2
errors when compared to the current operational setup that uses a monthly
climatology. Figure shows
the first of the two aerosol types selected when using the MERRA-2 monthly
climatology and when using the interpolated GEOS-5 model field. Certain
features, such as Saharan dust and biomass burning, are generally
realistically placed in the climatology but the day-to-day variations of the
atmosphere are not present and thus the climatology is not representative of
the true state of the atmosphere for a given OCO-2 sounding location. For
example, dust is selected over large portions of the high northern latitudes
in the GEOS-5 model field but rarely in the MERRA-2 monthly climatology.
(a) Primary aerosol type selected for the month of July
using the MERRA-2 climatology. (b) Primary GEOS-5 aerosol type
selected for 1 July 2016 00:00 UTC. Aerosol types
are dust (DU), sea salt (SS), black carbon (BC), organic carbon (OC), and
sulfate (SU).
Three methods of varying complexity were chosen to ingest the instantaneous
model data:
using the top two aerosol types and their corresponding AODs;
using the top two aerosol types and fitting the amplitude, mean, and variance of a Gaussian distribution to the modeled vertical profile of both
types;
using the top two aerosol types and solving for a scale factor on an interpolated 20-layer modeled aerosol
profile.
The methodology for selecting which two (of the five) aerosol types to be
included in the state vector is simply sorting them by AOD at 760 nm and
selecting the two largest values. The ice cloud, water cloud, and
stratospheric aerosol type were always retrieved. The ice cloud and water
cloud characteristics were kept the same as V8, while the stratospheric
aerosol's optical depth prior and corresponding uncertainty were determined
by our setups described below.
Types and optical depths
The first method simply takes the top
two aerosol types based on sorting by each type's AOD and uses their
corresponding AODs as prior information for each type. This method is the
simplest of our tests and does not rely on any modeled vertical aerosol
information.
Types and Gaussian fits
The second method takes the largest two
aerosol types, as before. The 72-layer GEOS-5 aerosol profiles for both types
are then interpolated onto the 20-layer OCO-2 vertical grid. The amplitude,
mean, and variance of a Gaussian curve are then fit to that 20-layer profile,
and the amplitude (optical depth), height, and width of that Gaussian curve are fed
in to the retrieval as prior information. An example is shown in
Fig. . Occasionally, the fit is a poor
representation of the vertical profile. This is often the case with the
sulfate type, which can have both a lower tropospheric peak and a
stratospheric peak, resulting in a profile that cannot be represented with a
single Gaussian profile. To avoid this issue, the sulfate type, if selected, was fit
to below 400 hPa and the stratospheric aerosol type (discussed in
Sect. ) was a separate Gaussian fit for the
profile above 400 hPa. This method was chosen to test the hypothesis that
ingesting vertical information from the model will lead to an improved
parameterization of the scattering and, subsequently, a more accurate
XCO2.
Example of fitting Gaussian distributions to the
GEOS-5 AOD profiles. Panel (a) is black carbon, panel
(b) is dust, panel (c) is organic carbon, panel
(d) is sulfate, and panel (e) is sea salt. Dashed grey
lines are the Gaussian fits to the profiles.
Types and scale factors
The third and most complex method takes
the largest two aerosol types sorted by AOD, as before. The 72-layer GEOS-5
aerosol profiles for both types are then interpolated onto the 20-layer OCO-2
vertical grid. A scale factor applied to the interpolated profile is then
solved for by the retrieval. Because the ACOS retrieval solves for the
natural log of the AOD, we solve for an additive scalar which acts as a
multiplicative scaling factor when converted to AOD. This means that if the
retrieval determines that the scale factor should be 1.5, the GEOS-5 AOD
profile magnitude will be multiplied by 1.5. This method is similar to that
of , except here we are using a vertical aerosol profile
co-located in time and space as the prior, while they use a constant aerosol
profile. This method was chosen to test the hypothesis that Gaussian fits are
insufficient to realistically parameterize the shapes of true vertical
aerosol profiles in the XCO2 retrieval.
Aerosol prior uncertainties
In addition to these three techniques used to modify the aerosol priors, the
prior uncertainty must also be considered when using optimal estimation. In
the operational ACOS XCO2 retrieval algorithm, the uncertainty
on the aerosol parameters is typically very high. For example, a prior AOD of
0.1 is approximately -2.3 in ln-space. Assigning the V8 uncertainty of 2.0 to
ln(τ) gives 1σ values of -4.3 and -0.3 in ln-space, which equates
to about 0.014 and 0.74 in optical depth. This large prior uncertainty is due
to the lack of confidence in the monthly climatological priors and because it
is believed that the radiances themselves should provide enough information
to properly constrain the results. However, using this new instantaneous
setup allows us to decrease the prior uncertainty because we have more
confidence in the GEOS-5 model compared to the monthly climatology, as
demonstrated by Fig. . The three setups chosen to
test are using the operational uncertainty (“high uncertainty”), 25 % of the
operational uncertainty (“low uncertainty”), and fixing the prior aerosol
values (“no uncertainty”). For example, using our first method above
(Sect. ) with low uncertainty means solving
for the AODs of the two selected aerosol types with 25 % of the operational
uncertainty assigned to the prior values. This means that instead of a
1σ uncertainty of 2.0 on ln(τ), we assign a value of 0.5. This
equates to a 1σ uncertainty range of between 0.06 and 0.16 on a prior
AOD of 0.1. Table lists all the test
setups and the corresponding uncertainties on the retrieved ln(τ) and,
except for the scalar profile tests, the retrieved height. The third method,
solving for a 20-layer profile (Sect. ), does
not lend itself to assigning single values of uncertainty equivalent to the
other two setups. Thus, we assigned layer uncertainties of ln(10), 0.5, and
0.001 for each layer to represent the operational, low, and fixed setups. We
also ran the operational retrieval with low and no uncertainty on the AODs in
an attempt to isolate the impact of only modifying the prior uncertainties.
When ingesting vertical information in the second and third methods, we also
reduced the uncertainty of the retrieved height. The width was effectively
never retrieved, as it was always assigned an uncertainty of 0.001.
Prior uncertainties of retrieved natural log of the optical depth,
στ, and retrieved Gaussian heights, σH, for all
retrieval variants. The first entry (V8 + MERRA-2 climatological types and
AODs with high uncertainty) is equivalent to the operational ACOS retrieval
uncertainties. The prior 1σ uncertainties on the natural log of the
stratospheric AOD are slightly lower, at 1.8, 0.45, and 0.001 for the three
uncertainty levels, respectively.
Retrieval typeHigh uncertaintyLow uncertaintyNo uncertaintyV8 + MERRA-2 climatological types and AODsστ=2.0, σH=0.2στ=0.5, σH=0.2στ=0.001, σH=0.2V8 + GEOS-5 types and AODsστ=2.0, σH=0.2στ=0.5, σH=0.2στ=0.001, σH=0.2V8 + GEOS-5 types and Gaussian profilesστ=2.0, σH=0.2στ=0.5, σH=0.05στ=0.001, σH=0.0001V8 + GEOS-5 types and scalar profilesστ,layer=ln10στ,layer=0.5στ,layer=0.01
After processing all 12 retrieval variants, applying similar post-filtering,
and matching soundings to ensure a fair comparison, 19 471 soundings remained
in the TCCON validation dataset while 17 355 soundings remained in the model
validation dataset. For both datasets and all tests, there were no
significant outliers when it came to the percentage of soundings that
converged or remained after post-filtering. Typically, the retrievals with
very uncertain priors had slightly more soundings fail to converge and the
more complex retrieval schemes lost more soundings in post-filtering, but
only by a few hundred soundings.
Results
In this section we discuss the impact of using better informed aerosol priors
in the OCO-2 XCO2 retrieval by comparing our test setups to
both TCCON and an ensemble of global XCO2 models.
TCCON validation results
Figure shows
the results of our test setups alongside the operational retrieval variants
(top row). Here, we can see the impact that different prior information and
different prior uncertainties have on the retrieved XCO2 when
compared to TCCON.
Retrieved OCO-2 XCO2 (y axes) against
TCCON XCO2 (x axes). Overpass means are
plotted. The first row is the operational retrieval (V8). The second row is
ingesting GEOS-5 types and AODs, the third row is ingesting GEOS-5 types and
Gaussian profile priors, and the fourth row is ingesting GEOS-5 types and
solving for a scalar to the prior aerosol profile. The left column is high
uncertainty, the middle column is low uncertainty, and the right column is no
uncertainty (see Table ). For each
panel, R is the correlation coefficient, σ is the standard deviation
of the error against TCCON, and MAE is the mean absolute error against TCCON.
The TCCON sites used are listed in Table , are shown in
Fig. , and were required to have valid data
between 17 September 2014 and 2 May 2016.
For our nine GEOS-5 test setups, when examining the standard deviation of the
error (σ), correlation coefficient (R), and mean absolute error
(MAE) versus TCCON, the values are typically best for the setups in which only
the types and the AODs are ingested (second row). When we apply a Gaussian
fit to the modeled profiles and use those heights and widths as priors (third
row), we see an increase in the scatter against our validation source along
with a worse MAE and R. Finally, when we solve for a scalar on the modeled
aerosol profile (bottom row), we see the largest scatter in
XCO2 against TCCON, worst correlations, and highest MAEs. It
therefore appears that trying to incorporate vertical information from the
aerosol model leads to a worse XCO2.
The only GEOS-5 test setup with errors and a correlation coefficient better
than the operational retrieval is the middle panel of the second row (green
triangles), in which the types and AODs were ingested with low uncertainty. This
may indicate that it is reasonable to assign some confidence in the modeled
AODs from GEOS-5, rather than leaving it mostly unconstrained. The
operational retrieval with low uncertainty (blue triangles) does relatively
well but slightly worse than the original V8 (blue squares).
For all retrieval setups, fixing the aerosol input (right column) results in
worse error statistics. This is likely because models are not perfect and by
entirely removing the retrieval's ability to fit for aerosol effects on the
radiances it results in large errors in XCO2. Keeping the
prior aerosol uncertainty the same as the operational retrieval (left
column), however, appears to allow too much freedom and the aerosol
parameters revert to the operational values, regardless of what the prior
values are. This is because we are assigning almost no confidence to the
prior, so all the information comes from the retrieval. This shows the
utility of having semi-constrained aerosol priors to guide the
XCO2 retrieval algorithm. The only row where the low-uncertainty test does better than the high-uncertainty test is when we ingest
types and AODs. This again indicates that it may be beneficial to use GEOS-5
modeled types and AODs and assign them some level of confidence that is
greater than the V8 constraint.
Model validation results
While TCCON gives a robust estimate of the statistical errors for our test
setups, it does not allow for regional analysis because of the sparsity of
the network. The CO2 model median validation technique, described
in Sect. , allows for an assessment of
regional errors in the test setups compared to a truth metric. For this work,
the most promising aerosol setup in the TCCON validation study was selected
for further analysis. That is, ingesting the GEOS-5 types and AODs with low
uncertainty. Figure shows
the operational (V8) XCO2 error and the GEOS-5 aerosol prior
setup XCO2 error while
Fig. a shows the difference
of the absolute value of the XCO2 errors for the two setups.
(a) The 4∘× 4∘ binned
XCO2 error against model validation for V8.
(b) The 4∘× 4∘ binned XCO2
error against model validation for the GEOS-5 types and AODs with low uncertainty. Grey bins represent no data.
(a) The 4∘× 4∘ binned absolute value of V8
XCO2 error against the model validation minus
the absolute value of the GEOS-5 types and AODs with low-uncertainty
XCO2 error against the model validation.
(b) The 4∘× 4∘ binned absolute value of V8
XCO2 error against the model validation minus
the absolute value of V8 with low-uncertainty
XCO2 error against the model validation.
(c) The 4∘× 4∘ binned absolute value of V8 with low-uncertainty XCO2 error against the model
validation minus the absolute value of GEOS-5 types and AODs with low-uncertainty XCO2 error against the model
validation. Green grid cells represent an improvement relative to the model
validation while brown grid cells represent a worse comparison to the model
validation. Grey bins represent no data.
Here, regional differences can be seen. Over northern Africa and central Asia
the operational retrieval (Fig. a) is often biased high,
which results in a large XCO2 scatter in those regions. The
GEOS-5 aerosol setup (Fig. b), however, shows that
those high-biased bins have mostly been removed. The difference plot (Fig. a)
demonstrates an improvement in the error of around 1 ppm for many grid cells
in northern Africa and central Asia. An additional regional difference is in
the Southern Ocean, where the GEOS-5 aerosol setup develops a new high bias
of 0.4 ppm in many of the bins, compared to V8, which has a bias of nearly
zero (+0.1 ppm). Over the remaining land and ocean regions there is minimal
change in XCO2 between the two datasets. While of interest,
comparing retrieval setups over the Amazon and high latitudes is difficult
due to the lack of soundings. This is because these regions are typically
cloudy around solar noon or lack the necessary amount of reflected sunlight
to make an accurate retrieval, so they have been filtered out.
Regarding the plots just discussed, three factors could be contributing to
the regional changes in XCO2 between V8 and the GEOS-5 aerosol
prior setup. First, the decrease in uncertainty to 25 % of V8. Second, the
two selected aerosol types. Third, the modified AOD priors of the two aerosol
types. In order to isolate the first effect, Fig. b shows a comparison
between V8 and V8 with low uncertainty. Here, we can see that the impact of
simply reducing the prior uncertainty on the retrieved AODs is substantial
and accounts for a considerable portion of the improvement over northern
Africa and central Asia, with the standard deviation of the error being
reduced from 2.12 to 1.92 ppm. The ocean remains nearly unchanged, along
with much of the remaining land surface.
Now that we have isolated the impact of reducing uncertainty, we can compare
V8 with low uncertainty to GEOS-5 types and AODs with low uncertainty to
determine what impact, if any, using the constrained GEOS-5 aerosol types and
priors has on the XCO2 error. Figure c shows that this
change is beneficial over central Asia (σXCO2error
reduced from 2.07 to 1.94 ppm for measurements over land between
latitudes 16 and 56∘ N and longitudes 52 and 152∘ E), has a minimal effect
over northern Africa (σXCO2error changed from 1.63
to 1.62 ppm for measurements over land between latitudes 6∘ S and 38∘ N and
longitudes 20∘ W and 52∘ E), and has a detrimental effect over the Southern Ocean
(σXCO2error increased from 1.86 to 1.95 ppm and
a positive bias of 0.4 ppm for measurements south of 45∘ S). This suggests
that the improvement in the scatter of the XCO2 error against
model validation over northern Africa was primarily due to the reduction
in uncertainty in the prior AODs and not the co-located GEOS-5 aerosol prior
types and values themselves. Over central Asia, however, the improvement
seems to be a combination of reducing the uncertainty and using instantaneous
types and/or AODs. Over the Southern Ocean, using GEOS-5 types and prior AODs
results in an increase in scatter and a high bias of 0.4 ppm.
Next, we attempt to distinguish between the impact of using GEOS-5 aerosol
types and using their corresponding AODs as prior information. The areas of
interest are central Asia, where using instantaneous priors improved the
XCO2 retrieval, and the Southern Ocean, where it worsened the
XCO2 retrieval. Figure a shows the binned prior AOD
for V8, which is derived from a monthly MERRA-2 climatology (as discussed in
Sect. ), while Fig. b shows the
binned prior AOD for our test setup, which uses the co-located GEOS-5 modeled
data. In general, slightly more variation is seen in the GEOS-5 priors, which
is to be expected, as the monthly climatology is a mean of an entire month
and thus removes most synoptic variability. The most prominent change,
however, is the significant reduction in prior AOD over northern Africa and
central Asia. The GEOS-5 aerosol setup has a 30 % lower mean prior AOD than
V8. This is likely because the MERRA-2 climatology was created by simply
averaging an entire month of data together, including all instances in which the
optical depth was large due to dust storms, pollution events, biomass
burning, etc. Those scenes, however, are typically removed by OCO-2's
pre-screeners (ABP and IDP, described in
Sect. ) before being processed through
the retrieval code, and thus the GEOS-5 prior map only includes scenes that
were determined to be sufficiently clear to perform retrievals on. This
suggests that the MERRA-2 monthly climatology aerosol prior may be
artificially high and not entirely appropriate for use in the operational
retrieval algorithm. This is also seen in Fig. ,
in which there are several MERRA-2 climatology AODs that are significantly high
biased against AERONET. Figure , however, shows only
data from 13 AERONET sites and is thus not representative of the global
differences.
(a) The 4∘× 4∘ binned prior AOD for V8, derived from a MERRA-2
monthly climatology. (b) The 4∘× 4∘ binned prior AOD for V8 + GEOS-5
types and AODs with low uncertainty, derived from co-located GEOS-5 AODs.
White bins represent no data.
In optimal estimation the final optimized state vector represents a weighted
combination of the prior information and the measurement, not just the state
of the prior. Figure a
shows the retrieved AOD for V8 while Fig. b shows the retrieved AOD
for the GEOS-5 aerosol setup. Here, we see an even more dramatic difference
in retrieved AODs over northern Africa and central Asia. Over land in
these two regions, the retrieved AOD is 52 % lower for the GEOS-5 aerosol
setup. This is partially due to the reduced uncertainty on the prior AODs for
the GEOS-5 aerosol setup, which prevents it from deviating substantially from
the prior.
(a) The 4∘× 4∘ binned retrieved AOD for V8.
(b) The 4∘× 4∘ binned retrieved AOD for V8 + GEOS-5 types
and AODs with low uncertainty. White bins represent no data.
While the differences in XCO2 over central Asia correlate with
a large difference in the magnitude of the retrieved AOD, the same cannot be
said of the XCO2 differences over the Southern Ocean, as those
differences in retrieved AOD are small (8.9 % increase in the GEOS-5 test
setup over the Southern Ocean). Additionally, Fig. b demonstrated that the
reduction in uncertainty alone had a minimal impact on this region.
The changing of one or both of the aerosol types when going from the MERRA-2
monthly climatology to the instantaneous GEOS-5 setup could be the
explanation for the positive 0.4 ppm bias in XCO2. Of the
17 355 global soundings remaining after post-filtering, 32 % show one or both
aerosol types to be different while only 0.7 % show both aerosol types to be different.
To test this hypothesis, we ran an additional set of soundings similar to the
GEOS-5 types and AODs with low uncertainty but not changing the types.
Figure
demonstrates that changing the types has almost no effect on the Southern
Ocean and overall has a small and sporadic effect (note the reduced scale).
The mean difference between ingesting GEOS-5 AODs and types and only
ingesting GEOS-5 AODs for soundings in which the type actually changed is about
0.01 ppm with a standard deviation of about 0.2 ppm. Only a small fraction of
soundings had differences larger than 0.5 ppm (2.3 % of all soundings). Thus,
the selection of an appropriate AOD prior and uncertainty is much more
important than the exact aerosol properties in our retrieval
parameterization.
The 4∘× 4∘ binned absolute value of GEOS-5 types and AODs
with low-uncertainty XCO2 error against the
model validation minus the absolute value of GEOS-5 AODs with low-uncertainty
XCO2 error against the model validation. Green
grid cells represent an improvement relative to the model validation while
brown grid cells represent a worse comparison to the model validation. Grey
bins represent no data.
After further investigation, the modification to the stratospheric aerosol
prior and its corresponding uncertainty is likely the cause of the Southern
Ocean 0.4 ppm high bias in the GEOS-5 aerosol setup. As detailed in
, ACOS V7 contained a high bias over water at high southern
latitudes due to both the presence of stratospheric aerosol from the Calbuco
eruption in 2015 and ice buildup on the OCO-2 focal plane arrays.
Here, we have unintentionally replicated this issue, as the stratospheric AOD
priors from GEOS-5 have a median of around 0.003, compared to a global prior
value of 0.006 used in V8.
Figure
demonstrates that when V8 solves for a large stratospheric AOD in the
Southern Ocean, GEOS-5 cannot because it starts with a lower prior and is
unable to sufficiently increase it. This results in a significant positive
bias in XCO2 (Fig. b). When the retrieved V8
stratospheric AOD is greater than 0.012 in the Southern Ocean, the GEOS-5
aerosol setup incurs a positive bias of approximately 1.4 ppm (compared to
the positive 0.4 ppm bias for all Southern Ocean retrievals). This indicates
that in soundings in which large stratospheric AODs are needed to fit the
radiances, the lack of AOD usually appears to lead to a high bias in
XCO2.
Retrieved stratospheric AOD from V8 versus retrieved stratospheric
AOD from V8 + GEOS-5 types and AODs with low uncertainty for the Southern
Ocean. Color represents the change in the absolute value of the
XCO2 error between the two retrieval types,
for which green demonstrates an improvement over V8 and brown represents a
worsening.
To further test this hypothesis, we ran a set of soundings in which we reverted
the stratospheric aerosol prior AOD and prior uncertainty back to V8 values
(0.006 and a 1σ uncertainty on the ln(AOD) of 1.8). This revealed that
the 0.4 ppm bias in the Southern Ocean mostly disappears, as the high bias is
reduced back to approximately +0.1 ppm, in agreement with V8.
Discussion and conclusions
In this study we investigated the impact of using better informed aerosol
priors in the OCO-2 ACOS XCO2 retrieval algorithm applied to
real measurements. We ingested aerosol information from co-located GEOS-5
model data with varying levels of uncertainty and compared to two validation
sources, TCCON and a global CO2 model suite.
We found that ingesting instantaneous AOD information with low uncertainty
slightly reduced the standard deviation of the XCO2 error
against TCCON from 1.17 to 1.13 ppm. More interestingly, we found that
attempting to ingest vertical information from GEOS-5 produced poor results
against TCCON, with mean absolute errors more than 50 % larger than V8. We
hypothesize that this is because accurate vertical placement of aerosol
layers still represents a significant challenge in global aerosol models
. This is due to the large uncertainty in processes related
to how aerosols are distributed vertically, partially because of the lack of
constraint by global observations . Additionally, fixing
the ingested aerosol information also makes the retrieval compare poorly to
TCCON. This is, again, because the priors will always be imperfect, so the
OCO-2 ACOS XCO2 retrieval algorithm needs some amount of
freedom to adjust the radiances.
Retrieved AOD from V8 versus retrieved AOD from V8 with low
uncertainty for northern Africa and central Asia. Color represents the change in
the absolute value of the XCO2 error between the
two retrieval types, for which green demonstrates an improvement over V8 and
brown represents a worsening.
A comparison to our model validation dataset reveals an improvement over
northern Africa and central Asia when using the GEOS-5 types and AODs with
low uncertainty compared to V8, with the scatter in XCO2 error
being reduced from 2.12 to 1.83 ppm. Additionally, we see a new high bias
in XCO2 of 0.4 ppm over the Southern Ocean. These regional
differences in OCO-2 V8 compared to the GEOS-5 type and AOD low-uncertainty
prior test setup are likely caused by one of the three modifications. First,
reducing the uncertainty on the prior AODs in V8 results in an improvement
over northern Africa and central Asia.
Figure shows
that by constraining the prior AODs in V8, we prevent the algorithm from
retrieving large AODs. Those soundings that retrieved large AODs in V8 but
smaller AODs in the low-uncertainty setup generally have smaller
XCO2 errors (green pixels in
Fig. ). For
soundings in which the retrieved V8 AOD is greater than 0.4 over northern Africa
and central Asia, constraining the prior AODs with low uncertainty reduces
the XCO2 bias against the validation dataset by 0.55 ppm on
average. Some soundings are outliers, in that they do worse when constrained
(brown pixels in
Fig. ). Around
8 % of the retrievals have an XCO2 error increase of greater
than 0.5 ppm. This may be because those scenes actually contain large AODs
and thus constraining the priors is hindering the retrieval's ability to
properly fit the radiances, but further investigation is needed on this
topic.
Second, some of the high bias seen in V8 (Fig. a) is likely due to the
MERRA-2 climatological priors being unrealistically high for the selected
OCO-2 soundings. Physically, when there are AODs retrieved that are too large
in magnitude, the CO2 absorption lines are filled in too much by
the retrieval because it thinks those photons are experiencing a shorter path
length than they are in reality. The retrieval then must add more
CO2 to deepen the absorption lines again to match the measured
line depths. This results in the observed high bias in V8 over central Asia,
which is mostly removed when smaller, more appropriate AOD priors are taken
from the GEOS-5 dataset (Fig. c). However, Fig. c also showed
that using lower AOD priors alone had no significant net impact on northern
Africa. This difference may be due to northern Africa having a higher surface
albedo than central Asia. This results in a larger fraction of the signal
coming from the surface and less weight placed on any aerosols present. So
long as the retrieved AODs are not very large, as discussed above, the
XCO2 results over northern Africa may be less sensitive to the
aerosol prior than other regions.
Third, the GEOS-5 types and AODs with low uncertainty resulted in a new
high bias of 0.4 ppm in XCO2 relative to V8 over the Southern
Ocean. It was initially hypothesized that the change in aerosol types may
have caused the high bias, but we showed that changing one or both of the
aerosol types has a minimal impact on the XCO2 in this setup.
After additional tests, we found that the GEOS-5 stratospheric AODs in the
Southern Ocean were too small to be able to account for actual stratospheric
aerosol in the region along with the effects of an ice accumulation artifact
in the OCO-2 radiances. Reverting the stratospheric prior and uncertainty
back to that of V8 mostly eliminated the high bias in the Southern Ocean.
This conclusion highlights the severe impact that even a small amount of
aerosol can have on the retrieved XCO2.
This work is relevant for several missions set to launch in the near future,
including OCO-3, GOSAT-2, MicroCarb, TanSat-2, and GeoCarb. Algorithm
development teams will have to decide how they will account for clouds and
aerosols in order to minimize errors in their CO2 measurements.
While we have shown the benefits and limitations of using instantaneous
modeled aerosol data to inform the OCO-2 XCO2 retrieval, more
work is needed to determine the optimal aerosol parameterization for
near-infrared measurements of CO2. These results may be of use in
guiding development for the next version of the OCO-2 ACOS
XCO2 retrieval algorithm.
Data availability
The OCO-2 L2 Full Physics Code is open source and available
on GitHub https://github.com/nasa/RtRetrievalFramework (last access:
27 February 2019), and the user's guide for it is available at
http://nasa.github.io/RtRetrievalFrameworkDoc/ (last access:
27 February 2019). All of the OCO-2 data products are publicly available
through the NASA Goddard Earth Science Data and Information Services Center
(GES DISC) for distribution and archiving
(http://disc.sci.gsfc.nasa.gov/OCO-2; last access: 27 February 2019).
TCCON data were obtained from the TCCON data archive hosted by CaltechDATA
and are available from https://tccondata.org/ (last access:
27 February 2019).
Author contributions
RN and CO developed
the hypotheses and designed the experiments, RN performed the retrievals, and RN and CO analyzed the results.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors would like to thank Thomas E. Taylor of Colorado State University
and Aronne Merrelli of the University of Wisconsin-Madison for their
contributions to this work. This work was funded by NASA Jet Propulsion
Laboratory OCO-2 subcontract no. 5359007. TCCON data were obtained from the
TCCON Data Archive, hosted by the Caltech Library Research Data Repository – https://tccondata.org/ (last access: 27 February 2019). The Caltech, Lamont, and Park Falls TCCON
stations are funded by NASA grants NNX14AI60G, NNX11AG01G, NAG5-12247, and
NNG05-GD07G and the NASA Orbiting Carbon Observatory. The Darwin TCCON station
is funded by NASA grants NAG5-12247 and NNG05-GD07G and the Australian
Research Council grants DP140101552, DP110103118, DP0879468, and LP0562346.
The Orléans TCCON station is funded by the EU projects InGOS and
ICOS-INWIRE, by the Senate of Bremen and by the RAMCES team at LSCE. The
Paris TCCON station is supported by UPMC.
Edited by: Piet Stammes Reviewed by: François-Marie
Bréon and one anonymous referee
ReferencesAben, I., Hasekamp, O., and Hartmann, W.: Uncertainties in the space-based
measurements of CO2 columns due to scattering in the Earth's
atmosphere, J. Quant. Spectrosc. Ra., 104, 450–459, 10.1016/j.jqsrt.2006.09.013, 2007.Baker, D. F., Bösch, H., Doney, S. C., O'Brien, D., and Schimel, D. S.:
Carbon source/sink information provided by column CO2 measurements
from the Orbiting Carbon Observatory, Atmos. Chem. Phys., 10, 4145–4165,
10.5194/acp-10-4145-2010, 2010.Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I.,
Krummel, P., Steele, P., Langenfelds, R., Torn, M., Biraud, S., Stephens, B.,
Andrews, A., and Worthy, D.: Global CO2 fluxes estimated from GOSAT
retrievals of total column CO2, Atmos. Chem. Phys., 13, 8695–8717,
10.5194/acp-13-8695-2013, 2013.Butz, A., Hasekamp, O. P., Frankenberg, C., and Aben, I.: Retrievals of
atmospheric CO2 from simulated space-borne measurements of
backscattered near-infrared sunlight: accounting for aerosol effects,
Appl. Optics, 48, 3322–3336, 10.1364/AO.48.003322, 2009.Butz, A., Guerlet, S., Hasekamp, O., Schepers, D., Galli, A., Aben, I.,
Frankenberg, C., Hartmann, J.-M., Tran, H., Kuze, A., Keppel-Aleks, G., Toon,
G., Wunch, D., Wennberg, P., Deutscher, N., Griffith, D., Macatangay, R.,
Messerschmidt, J., Notholt, J., and Warneke, T.: Toward accurate
CO2 and CH4 observations from GOSAT, Geophys.
Res. Lett., 38, L14812, 10.1029/2011GL047888, 2011.Chevallier, F., Bréon, F.-M., and Rayner, P. J.: Contribution of the
Orbiting Carbon Observatory to the estimation of CO2 sources and
sinks: Theoretical study in a variational data assimilation framework,
J. Geophys. Res., 112, D09307,
10.1029/2006JD007375, 2007.Chevallier, F., Maksyutov, S., Bousquet, P., Bréon, F.-M., Saito, R.,
Toshida, Y., and Yokota, T.: On the accuracy of the CO2 surface
fluxes to be estimated from the GOSAT observations, Geophys. Res.
Lett., 36, L19807, 10.1029/2009GL040108, 2009.Chevallier, F., Palmer, P. I., Feng, L., Boesch, H., O'Dell, C. W., and
Bousquet, P.: Toward robust and consistent regional CO2 flux
estimates from in situ and spaceborne measurements of atmospheric
CO2, Geophys. Res. Lett., 41, 1065–1070,
10.1002/2013GL058772, 2014.Connor, B. J., Boesch, H., Toon, G., Sen, B., Miller, C., and Crisp, D.:
Orbiting Carbon Observatory: Inverse method and prospective error analysis,
J. Geophys. Res., 113, D05305,
10.1029/2006JD008336, 2008.Crisp, D., Miller, C. E., and DeCola, P. L.: NASA Orbiting Carbon
Observatory:
measuring the column averaged carbon dioxide mole fraction from space,
J. Appl. Remote Sens., 2, 023508,
10.1117/1.2898457, 2008.Crisp, D., Boesch, H., Brown, L., Castano, R., Christi, M., Connor, B.,
Frankenberg, C., McDuffie, J., Miller, C. E., Natraj, V., O'Dell, C.,
O'Brien, D., Polonsky, I., Oyafuso, F., Thompson, D., Toon, G., and Spurr,
R.: OCO (Orbiting Carbon Observatory)-2 Level 2 Full Physics Retrieval
Algorithm Theoretical Basis, Tech. Rep. OCO D-65488, Tech. rep., NASA Jet
Propulsion Laboratory, California Institute of Technology, Pasadena, CA,
version 1.0 Rev 4, available at:
https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO-2_L2_FP_
ATBD_v1_rev4_Nov10.pdf (last access: 27 February 2019), 2010.Crowell, S. M. R., Kawa, S. R., Browell, E. V., Hammerling, D. M., Moore, B.,
Schaefer, K., and Doney, S. C.: On the Ability of Space-Based Passive and
Active Remote Sensing Observations of CO2 to Detect Flux
Perturbations to the Carbon Cycle, J. Geophys. Res.-Atmos.,
123, 1460–1477, 10.1002/2017JD027836, 2018.Dubey, M., Parker, H., Henderson, B., Green, D., Butterfield, Z.,
Keppel-Aleks,
G., Allen, N., Blavier, J.-F., Roehl, C., Wunch, D., and Lindenmaier, R.:
TCCON data from Manaus (BR), Release GGG2014.R0, TCCON Data Archive,
hosted by CaltechDATA, 10.14291/tccon.ggg2014.manaus01.R0/1149274,
2014.Dubovik, O., Lapyonok, T., Kaufman, Y. J., Chin, M., Ginoux, P., Kahn, R. A.,
and Sinyuk, A.: Retrieving global aerosol sources from satellites using
inverse modeling, Atmos. Chem. Phys., 8, 209–250,
10.5194/acp-8-209-2008, 2008.Feist, D. G., Arnold, S. G., John, N., and Geibel, M. C.: TCCON data from
Ascension Island (SH), Release GGG2014.R0, TCCON Data Archive, hosted by
CaltechDATA, 10.14291/tccon.ggg2014.ascension01.R0/1149285,
2014.Feng, L., Palmer, P. I., Yang, Y., Yantosca, R. M., Kawa, S. R., Paris,
J.-D., Matsueda, H., and Machida, T.: Evaluating a 3-D transport model of
atmospheric CO2 using ground-based, aircraft, and space-borne data, Atmos.
Chem. Phys., 11, 2789–2803, 10.5194/acp-11-2789-2011, 2011.Frankenberg, C., Platt, U., and Wagner, T.: Iterative maximum a posteriori
(IMAP)-DOAS for retrieval of strongly absorbing trace gases: Model studies
for CH4 and CO2 retrieval from near infrared spectra of SCIAMACHY onboard
ENVISAT, Atmos. Chem. Phys., 5, 9–22, 10.5194/acp-5-9-2005,
2005.Frankenberg, C., Hasekamp, O., O'Dell, C., Sanghavi, S., Butz, A., and
Worden, J.: Aerosol information content analysis of multi-angle high spectral
resolution measurements and its benefit for high accuracy greenhouse gas
retrievals, Atmos. Meas. Tech., 5, 1809–1821,
10.5194/amt-5-1809-2012, 2012.Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Wennberg, P. O., Yavin,
Y., Keppel-Aleks, G., Washenfelder, R., Toon, G. C., Blavier, J.-F.,
Paton-Walsh, C., Jones, N. B., Kettlewell, G. C., Connor, B., Macatangay,
R. C., Roehl, C., Ryczek, M., Glowacki, J., Culgan, T., and Bryant, G.:
TCCON data from Darwin (AU), Release GGG2014.R0, TCCON Data Archive,
hosted by CaltechDATA, 10.14291/tccon.ggg2014.darwin01.R0/1149290,
2014.Hase, F., Blumenstock, T., Dohe, S., Gross, J., and Kiel, M.: TCCON data
from
Karlsruhe (DE), Release GGG2014.R1, TCCON Data Archive, hosted by
CaltechDATA, 10.14291/tccon.ggg2014.karlsruhe01.R1/1182416, 2015.Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J., Setzer, A.,
Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and
Data Archive for Aerosol Characterization, Remote Sens. Environ., 66,
1–16, 10.1016/S0034-4257(98)00031-5, 1998.Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H.,
Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George,
M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L.,
Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M.,
Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O.,
Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC
team: The MACC reanalysis: an 8 yr data set of atmospheric composition,
Atmos. Chem. Phys., 13, 4073–4109, 10.5194/acp-13-4073-2013,
2013.Iraci, L. T., Podolske, J., Hillyard, P. W., Roehl, C., Wennberg, P. O.,
Blavier, J.-F., Allen, N., Wunch, D., Osterman, G., and Albertson, R.: TCCON
data from Edwards (US), Release GGG2014.R1, TCCON Data Archive, hosted by
CaltechDATA, 10.14291/tccon.ggg2014.edwards01.R1/1255068,
2016.Kaufman, Y. J., Tanré, D., Remer, L. A., Vermote, E., Chu, A., and
Holben,
B.: Operational remote sensing of tropospheric aerosol over land from EOS
moderate resolution imaging spectroradiometer, J. Geophys.
Res.-Atmos., 102, 17051–17067, 10.1029/96JD03988, 1997.Kawakami, S., Ohyama, H., Arai, K., Okumura, H., Taura, C., Fukamachi, T.,
and
Sakashita, M.: TCCON data from Saga (JP), Release GGG2014.R0, TCCON Data
Archive, hosted by CaltechDATA,
10.14291/tccon.ggg2014.saga01.R0/1149283, 2014.Kipling, Z., Stier, P., Johnson, C. E., Mann, G. W., Bellouin, N., Bauer, S.
E., Bergman, T., Chin, M., Diehl, T., Ghan, S. J., Iversen, T., Kirkevåg,
A., Kokkola, H., Liu, X., Luo, G., van Noije, T., Pringle, K. J., von Salzen,
K., Schulz, M., Seland, Ø., Skeie, R. B., Takemura, T., Tsigaridis, K.,
and Zhang, K.: What controls the vertical distribution of aerosol?
Relationships between process sensitivity in HadGEM3-UKCA and inter-model
variation from AeroCom Phase II, Atmos. Chem. Phys., 16, 2221–2241,
10.5194/acp-16-2221-2016, 2016.Kivi, R. and Heikkinen, P.: Fourier transform spectrometer measurements of
column CO2 at Sodankylä, Finland, Geosci. Instrum. Method. Data
Syst., 5, 271–279, 10.5194/gi-5-271-2016, 2016.Koffi, B., Schulz, M., Bréon, F.-M., Dentener, F., Steensen, B. M.,
Griesfeller, J., Winker, D., Balkanski, Y., Bauer, S. E., Bellouin, N.,
Bernsten, T., Bian, H., Chin, M., Diehl, T., Easter, R., Ghan, S.,
Hauglustaine, D. A., Iversen, T., Kirkevåg, A., Liu, X., Lohmann, U.,
Myhre, G., Rasch, P., Seland, Ø., Skeie, R. B., Steenrod, S. D., Stier,
P., Tackett, J., Takemura, T., Tsigaridis, K., Vuolo, M. R., Yoon, J., and
Zhang, K.: Evaluation of the aerosol vertical distribution in global aerosol
models through comparison against CALIOP measurements: AeroCom phase II
results, J. Geophys. Res.-Atmos., 121, 7254–7283,
10.1002/2015JD024639, 2016.Kuang, Z., Margolis, J., Toon, G., Crisp, D., and Yung, Y.: Spaceborne
measurements of atmospheric CO2 by high-resolution NIR
spectrometry of reflected sunlight: An introductory study, Geophys. Res.
Lett., 15, 11-1–11-4, 10.1029/2001GL014298, 2002.L'Ecuyer, T. S. and Jiang, J. H.: Touring the atmosphere aboard the A-Train,
Phys. Today, 63, 36–41, 10.1063/1.3463626, 2010.Le Quéré, C., Raupach, M. R., Canadell, J. G., Marland, G., Bopp,
L.,
Ciais, P., Conway, T. J., Doney, S. C., Feely, R. A., Foster, P.,
Friedlingstein, P., Gurney, K., Houghton, R. A., House, J. I., Huntingford,
C., Levy, P. E., Lomas, M. R., Majkut, J., Metzl, N., Ometto, J. P., Peters,
G. P., Prentice, I. C., Randerson, J. T., Running, S. W., Sarmiento, J. L.,
Schuster, U., Sitch, S., Takahashi, T., Viovy, N., van der Werf, G. R., and
Woodward, F. I.: Trends in the sources and sinks of carbon dioxide, Nat.
Geosci., 2, 831–836, 10.1038/ngeo689, 2009.Liu, J., Bowman, K. W., Schimel, D. S., Parazoo, N. C., Jiang, Z., Lee, M.,
Bloom, A. A., Wunch, D., Frankenberg, C., Sun, Y., O'Dell, C. W., Gurney,
K. R., Menemenlis, D., Gierach, M., Crisp, D., and Eldering, A.: Contrasting
carbon cycle responses of the tropical continents to the 2015–2016 El
Niño, Science, 358, 191, 10.1126/science.aam5690, 2017.Mann, G. W., Carslaw, K. S., Reddington, C. L., Pringle, K. J., Schulz, M.,
Asmi, A., Spracklen, D. V., Ridley, D. A., Woodhouse, M. T., Lee, L. A.,
Zhang, K., Ghan, S. J., Easter, R. C., Liu, X., Stier, P., Lee, Y. H., Adams,
P. J., Tost, H., Lelieveld, J., Bauer, S. E., Tsigaridis, K., van Noije, T.
P. C., Strunk, A., Vignati, E., Bellouin, N., Dalvi, M., Johnson, C. E.,
Bergman, T., Kokkola, H., von Salzen, K., Yu, F., Luo, G., Petzold, A.,
Heintzenberg, J., Clarke, A., Ogren, J. A., Gras, J., Baltensperger, U.,
Kaminski, U., Jennings, S. G., O'Dowd, C. D., Harrison, R. M., Beddows, D. C.
S., Kulmala, M., Viisanen, Y., Ulevicius, V., Mihalopoulos, N., Zdimal, V.,
Fiebig, M., Hansson, H.-C., Swietlicki, E., and Henzing, J. S.:
Intercomparison and evaluation of global aerosol microphysical properties
among AeroCom models of a range of complexity, Atmos. Chem. Phys., 14,
4679–4713, 10.5194/acp-14-4679-2014, 2014.Martonchik, J. V., Diner, D. J., Kahn, R. A., Ackerman, T. P., Verstraete,
M. M., Pinty, B., and Gordon, H. R.: Techniques for the retrieval of aerosol
properties over land and ocean using multiangle imaging, IEEE T.
Geosci. Remote, 36, 1212–1227, 10.1109/36.701027, 1998.Miller, C. E., Crisp, D., DeCola, P. L., Olsen, S. C., Randerson, J. T.,
Michalak, A. M., Alkhaled, A., Rayner, P., Jacob, D. J., Suntharalingam, P.,
Jones, D. B. A., Denning, A. S., Nicholls, M. E., Doney, S. C., Pawson, S.,
Boesch, H., Connor, B. J., Fung, I. Y., O'Brien, D., Salawitch, R. J.,
Sander, S. P., Sen, B., Tans, P., Toon, G. C., Wennberg, P. O., Wofsy, S. C.,
Yung, Y. L., and Law, R. M.: Precision requirements for space-based
XCO2 data, J. Geophys. Res., 112,
D10314, 10.1029/2006JD007659, 2007.Nelson, R. R., O'Dell, C. W., Taylor, T. E., Mandrake, L., and Smyth, M.: The
potential of clear-sky carbon dioxide satellite retrievals, Atmos. Meas.
Tech., 9, 1671–1684, 10.5194/amt-9-1671-2016, 2016.O'Brien, D. M. and Rayner, P. J.: Global observations of the carbon budget 2.
CO2 column from differential absorption of reflected sunlight in
the 1.61 µm band of CO2, J. Geophys. Res., 107, 4354, 10.1029/2001JD000617, 2002.O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C.,
Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J.,
Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T.,
Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval
algorithm – Part 1: Description and validation against synthetic
observations, Atmos. Meas. Tech., 5, 99–121,
10.5194/amt-5-99-2012, 2012.O'Dell, C. W., Eldering, A., Wennberg, P. O., Crisp, D., Gunson, M. R.,
Fisher, B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L., Merrelli,
A., Natraj, V., Nelson, R. R., Osterman, G. B., Payne, V. H., Taylor, T. E.,
Wunch, D., Drouin, B. J., Oyafuso, F., Chang, A., McDuffie, J., Smyth, M.,
Baker, D. F., Basu, S., Chevallier, F., Crowell, S. M. R., Feng, L., Palmer,
P. I., Dubey, M., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L.
T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Roehl, C. M.,
Sha, M. K., Strong, K., Sussmann, R., Te, Y., Uchino, O., and Velazco, V. A.:
Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with
the version 8 ACOS algorithm, Atmos. Meas. Tech., 11, 6539–6576,
10.5194/amt-11-6539-2018, 2018.Oshchepkov, S., Bril, A., and Yokota, T.: PPDF-based method to account for
atmospheric light scattering in observations of carbon dioxide from space,
J. Geophys. Res., 113, D23210, 10.1029/2008JD010061, 2008.Osterman, G., Eldering, A., Mandrake, L., O'Dell, C., Wunch, D., Wennberg,
P. O., Fischer, B., and Marchetti, Y.: Orbiting Carbon Observatory–2
(OCO-2) Lite Files, Bias Correction, and Warn Level, Tech. rep., NASA Jet
Propulsion Laboratory, California Institute of Technology, Pasadena, CA,
version 2.0, available at:
https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_XCO2_Lit
e_Files_and_Bias_Correction.pdf (last access: 27 February 2019),
2017.Parker, R., Bösch, H., and Cogan, A.: Algorithm Theoretical Basis
Document
Version 2 (ATBDv2) The University of Leicester Full-Physics Retrieval
Algorithm for the retrieval of XCO2 and XCH4,
Tech. rep., University of Leicester, Leicester, UK, version 2, 2011.Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway,
T. J., Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Pétron,
G., Hirsch, A. I., Worthy, D. E. J., van der Werf, G. R., Randerson,
J. T., Wennberg, P. O., Krol, M. C., and Tans, P. P.: An atmospheric
perspective on North American carbon dioxide exchange: CarbonTracker,
P. Natl. Acad. Sci. USA, 104, 18925–18930, 10.1073/pnas.0708986104, 2007.Pougatchev, N., August, T., Calbet, X., Hultberg, T., Oduleye, O.,
Schlüssel, P., Stiller, B., Germain, K. St., and Bingham, G.: IASI
temperature and water vapor retrievals – error assessment and validation,
Atmos. Chem. Phys., 9, 6453–6458, 10.5194/acp-9-6453-2009,
2009.Rayner, P. J. and O'Brien, D. M.: The utility of remotely sensed
CO2 concentration data in surface source inversions,
Geophys. Res. Lett., 28, 175–178, 10.1029/2000GL011912,
2001.Reuter, M., Bovensmann, H., Buchwitz, M., Burrows, J. P., Heymann, J., and
Schneising, O.: Algorithm Theoretical Basis Document Version 5 (ATBDv5) – The
Bremen Optimal Estimation DOAS (BESD) algorithm for the retrieval of
XCO2, Tech. rep., Institute of Environmental Physics (IUP)
University of Bremen, Bremen, Germany, version 5, 2016.Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., and
Burrows, J. P.: A fast atmospheric trace gas retrieval for hyperspectral
instruments approximating multiple scattering – Part 2: Application to
XCO2 retrievals from OCO-2, Remote Sensing, 9, 1102,
10.3390/rs9111102, 2017.Rienecker, M. M., Suarez, M. J., Todling, R., Bacmeister, J., Takacs, L.,
Liu,
H.-C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelaro, R., Stajner, I., and
Nielsen, J. E.: The GEOS-5 Data Assimilation System-Documentation of Versions
5.0.1, 5.1.0, and 5.2.0, Tech. rep., NASA Goddard Space Flight Center,
Greenbelt, MD, available at:
https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120011955.pdf (last access: 27 February 2019), 2008.Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu,
E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S.,
Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster,
R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder,
C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and
Woollen, J.: MERRA: NASA's modern-era retrospective analysis for research and
applications, J. Climate, 24, 3624–3648,
10.1175/JCLI-D-11-00015.1, 2011.Rödenbeck, C.: Estimating CO2 sources and sinks from
atmospheric mixing ratio measurements using a global inversion of atmospheric
transport, Tech. Rep. 6, Max Planck Institue für Biogeochemie, Jena, Germany, technical
Report 6, 2005.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and
Practice, World Scientific, Singapore, 2000.Schuh, A., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K.,
Chevallier, F., Crowell, S., Davis, K., Deng, F., Denning, S., Feng, L.,
Jones, D., Liu, J., and Palmer, P.: Quantifying the Impact of Atmospheric
Transport Uncertainty on CO2 Surface Flux Estimates, Global
Biogeochem. Cy., in review, 2019.Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung,
J.,
Nauels, A., Xia, Y., Bex, V., and Midgley, P.: IPCC, 2013: Climate Change
2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, 10.1017/CBO9781107415324, 2014.Susskind, J., Barnet, C. D., and Blaisdell, J. M.: Retrieval of atmospheric
and
surface parameters from AIRS/AMSU/HSB data in the presence of clouds, IEEE
T. Geosci. Remote, 41, 390–409,
10.1109/TGRS.2002.808236, 2003.Sussmann, R. and Rettinger, M.: TCCON data from Garmisch (DE), Release
GGG2014.R0, TCCON Data Archive, hosted by CaltechDATA,
10.14291/tccon.ggg2014.garmisch01.R0/1149299, 2014.Taylor, T. E., O'Dell, C. W., Frankenberg, C., Partain, P. T., Cronk, H. Q.,
Savtchenko, A., Nelson, R. R., Rosenthal, E. J., Chang, A. Y., Fisher, B.,
Osterman, G. B., Pollock, R. H., Crisp, D., Eldering, A., and Gunson, M. R.:
Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation
against collocated MODIS and CALIOP data, Atmos. Meas. Tech., 9, 973–989,
10.5194/amt-9-973-2016, 2016.Te, Y., Jeseck, P., and Janssen, C.: TCCON data from Paris (FR), Release
GGG2014.R0, TCCON Data Archive, hosted by CaltechDATA,
10.14291/tccon.ggg2014.paris01.R0/1149279, 2014.Warneke, T., Messerschmidt, J., Notholt, J., Weinzierl, C., Deutscher, N. M.,
Petri, C., Grupe, P., Vuillemin, C., Truong, F., Schmidt, M., Ramonet, M.,
and Parmentier, E.: TCCON data from Orléans (FR), Release GGG2014.R0,
TCCON Data Archive, hosted by CaltechDATA,
10.14291/tccon.ggg2014.orleans01.R0/1149276, 2014.Wennberg, P. O., Roehl, C., Wunch, D., Toon, G. C., Blavier, J.-F.,
Washenfelder, R., Keppel-Aleks, G., Allen, N., and Ayers, J.: TCCON data
from Park Falls (US), Release GGG2014.R0, TCCON Data Archive, hosted by
CaltechDATA, 10.14291/tccon.ggg2014.parkfalls01.R0/1149161, 2014.Wennberg, P. O., Wunch, D., Roehl, C., Blavier, J.-F., Toon, G. C., and
Allen,
N.: TCCON data from Caltech (US), Release GGG2014.R1, TCCON Data Archive,
hosted by CaltechDATA, 10.14291/tccon.ggg2014.pasadena01.R1/1182415,
2015.Wennberg, P. O., Wunch, D., Roehl, C., Blavier, J.-F., Toon, G. C., and
Allen,
N.: TCCON data from Lamont (US), Release GGG2014.R1, TCCON Data Archive,
hosted by CaltechDATA, 10.14291/tccon.ggg2014.lamont01.R1/1255070,
2016.Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J.,
Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The
Total Carbon Column Observing Network, Philos. T.
Roy. Soc. A, 369, 2087–2112, 10.1098/rsta.2010.0240, 2011.
Yang, D., Liu, Y., Cai, Z., Chen, X., Yao, L., and Lu, D.: First Global
Carbon
Dioxide Maps Produced from TanSat Measurements, Adv. Atmos.
Sci., 35, 621–623, 10.1007/s00376-018-7312-6, 2018.Yokota, T., Yoshida, Y., Eguchi, N., Ota, Y., Tanaka, T., Watanabe, H., and
Maksyutov, S.: Global Concentrations of CO2 and CH4
Retrieved from GOSAT: First Preliminary Results, Scientific Online Letters on
the Atmosphere, 5, 160–163, 10.2151/sola.2009-041, 2009.