The COVID-19 pandemic resulted in reduced anthropogenic
carbon dioxide (CO2) emissions during 2020 in large parts of the world.
To investigate whether a regional-scale reduction of anthropogenic CO2
emissions during the COVID-19 pandemic can be detected using space-based
observations of atmospheric CO2, we have analysed a small ensemble of
OCO-2 and GOSAT satellite retrievals of column-averaged dry-air mole
fractions of CO2, i.e. XCO2. We focus on East China and use a
simple data-driven analysis method. We present estimates of the relative
change of East China monthly emissions in 2020 relative to previous periods,
limiting the analysis to October-to-May periods to minimize the impact of
biogenic CO2 fluxes. The ensemble mean indicates an emission reduction
by approximately 10 % ± 10 % in March and April 2020. However, our
results show considerable month-to-month variability and significant
differences across the ensemble of satellite data products analysed. For
example, OCO-2 suggests a much smaller reduction (∼ 1 %–2 % ± 2 %). This indicates that it is challenging to reliably detect and
to accurately quantify the emission reduction with current satellite data
sets. There are several reasons for this, including the sparseness of the
satellite data but also the weak signal; the expected regional XCO2
reduction is only on the order of 0.1–0.2 ppm. Inferring COVID-19-related
information on regional-scale CO2 emissions using current satellite
XCO2 retrievals likely requires, if at all possible, a more
sophisticated analysis method including detailed transport modelling and
considering a priori information on anthropogenic and natural CO2 surface
fluxes.
Introduction
Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas
significantly contributing to global warming (IPCC, 2013). CO2 has many
natural and anthropogenic sources and sinks, and our current understanding of
them has significant gaps (e.g. Ciais et al., 2014; Chevallier et al.,
2014; Reuter et al., 2017c; Crisp et al., 2018; Friedlingstein et al.,
2019). Efforts are ongoing to improve the fundamental understanding of the
global carbon cycle, to improve our ability to project future changes and
to verify the effectiveness of policies such as the Paris Agreement
(https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement,
last access: 8 September 2020) aiming to reduce greenhouse gas emissions (e.g.
Ciais et al., 2014, 2015; Pinty et al., 2017, 2019; Crisp et al., 2018;
Matsunaga and Maksyutov, 2018; Janssens-Maenhout et al., 2020).
Retrievals of XCO2 from the satellite sensors SCIAMACHY/ENVISAT
(Burrows et al., 1995; Bovensmann et al., 1999; Reuter et al., 2010, 2011) and TANSO-FTS/GOSAT (Kuze et al., 2016) and from the Orbiting Carbon
Observatory-2 (OCO-2) satellite (Crisp et al., 2004; Eldering et al., 2017;
O'Dell et al., 2012, 2018) have been used in recent years to obtain
information on natural CO2 sources and sinks (e.g. Basu et al., 2013;
Chevallier et al., 2014; Chevallier, 2015; Reuter et al., 2014a, 2017c; Schneising et
al., 2014; Houweling et al., 2015; Kaminski et al., 2017; Liu et al., 2017;
Eldering et al., 2017; Yin et al., 2018; Palmer et al., 2019; Miller and
Michalak, 2020), on anthropogenic CO2 emissions (e.g. Schneising et
al., 2008, 2013; Reuter et al., 2014b, 2019; Nassar et al., 2017; Schwandner
et al., 2017; Matsunaga and Maksyutov, 2018; Miller et al., 2019; Labzovskii
et al., 2019; Wu et al., 2020; Zheng et al., 2020a; Ye et al., 2020), and for
other applications such as climate model assessments (e.g. Lauer et al.,
2017; Gier et al., 2020) or data assimilation (e.g. Massart et al., 2016).
Here we use an ensemble of satellite retrievals of XCO2 to determine
whether COVID-19-related regional-scale (here ∼ 20002 km2) CO2 emission reductions can be detected and quantified using
the current space-based observing system. This is important in order to
establish the capabilities of current satellites, which have been optimized
to obtain information on natural carbon sources and sinks but not to obtain
information on anthropogenic emissions. Nevertheless, data from existing
satellites have already been used to assess anthropogenic emissions (see
publications cited above). These assessments and the assessment presented in
this publication are relevant for future satellites focussing on
anthropogenic emissions, such as the planned European Copernicus
Anthropogenic CO2 Monitoring (CO2M) mission (e.g. ESA, 2019; Kuhlmann
et al., 2019; Janssens-Maenhout et al., 2020), which is based on the
CarbonSat concept (Bovensmann et al., 2010; Velazco et al., 2011; Buchwitz
et al., 2013; Pillai et al., 2016; Broquet et al., 2018; Lespinas et al.,
2020).
We focus on China because regional-scale COVID-19-related CO2 emission
reductions are expected to be largest there early in the pandemic (Le
Quéré et al., 2020; Liu et al., 2020). Satellite data have been used
to estimate China's CO2 emissions during the COVID-19 pandemic as shown
in Zheng et al. (2020b), but that study inferred CO2 reductions from
retrievals of nitrogen dioxide (NO2) not using XCO2. Estimates of
emission reductions have also been derived from bottom-up statistical
assessments of fossil fuel use and other economic indicators. According to
Le Quéré et al. (2020), China's CO2 emissions decreased by 242 Mt CO2 (uncertainty range 108–394 Mt CO2) during January–April
2020. As China's annual CO2 emissions are approximately 10 Gt CO2 yr-1 (Friedlingstein et al., 2019), i.e. approximately 3.3 Gt CO2 in a 4-month period assuming constant emissions, the average
relative (COVID-19 related) change during January–April 2020 is therefore
approximately 7 % ± 4 % (0.242/3.3 ± 0.14/3.3). This agrees
reasonably well with the estimate reported in Liu et al. (2020), which is
9.3 % for China during the first quarter of 2020 compared to the same
period in 2019. Liu et al. (2020) also indicate some challenges in terms of
interpreting CO2 emission reductions as being caused by COVID-19, e.g.
the fact that the first months of 2020 were exceptionally warm across much
of the Northern Hemisphere. CO2 emissions associated with home heating
may have therefore been somewhat lower than for the same period in 2019,
even without the disruption in economic activities and energy production
caused by COVID-19 and related lockdowns.
Sussmann and Rettinger (2020) studied ground-based remote-sensing XCO2
retrievals of the Total Carbon Column Observing Network (TCCON) to find out
whether related atmospheric concentration changes may be detected by the
TCCON and brought into agreement with bottom-up emission-reduction
estimates. Our study is one of the first attempts to determine whether
COVID-19-related regional-scale CO2 emission reductions can be detected
using existing space-based observations of XCO2. Tohjima et al. (2020)
inferred estimates of China's CO2 emissions from modelled and observed
ratios of CO2 and methane (CH4) surface concentrations at Hateruma
Island, Japan. They report for China fossil fuel emission reductions of 32 ± 12 % and 19 ± 15 % for February and March 2020,
respectively, which is about 10 % higher compared to the results shown in
Le Quéré et al., 2020 (see Table 1 of Tohjima et al., 2020). From
model sensitivity simulations they conclude that even a 30 % reduction of
China's fossil fuel CO2 emissions would only result in a 0.8 ppm
XCO2 reduction over China and that it therefore would be very
challenging to detect any COVID-19-related signal with the existing remote-sensing satellites GOSAT and OCO-2. Their conjecture has essentially been
confirmed by Chevallier et al. (2020). They used XCO2 from OCO-2 in
combination with other data sets and the modelling of CO2 emission
plumes of localized CO2 sources to obtain estimates of CO2
emissions focussing on several COVID-19-relevant regions such as China,
Europe, India and the USA. They concluded that these places have not been
well observed by the OCO-2 satellite because of frequent or persistent cloud
conditions and they give recommendations for future carbon-monitoring
systems. Zeng et al. (2020) used modelling, GOSAT XCO2 and other data
sets. They conclude that GOSAT is able to detect a short-term global mean
XCO2 anomaly decrease of 0.2–0.3 ppm after temporal averaging (e.g.
monthly), but for East China they could not identify a statistically robust
COVID-19-related anomaly. Satellite-derived results related to this
application are also provided in the internet (e.g. ESA-NASA-JAXA, 2020).
Regional-scale reductions of tropospheric NO2 columns have been
reported for China (e.g. Zhang et al., 2020; Bauwens et al., 2020), but for
CO2 such an assessment is more challenging because of small XCO2
changes on top of a large background. For example, over extended
anthropogenic source areas such as East China, the XCO2 enhancement due
to anthropogenic emissions is typically only approximately 1–2 ppm
(0.25 %–0.5 % of 400 ppm) or even less (see e.g. Schneising et al.,
2008, 2013; Hakkarainen et al., 2016, 2019; Chevallier et al., 2020; Tohjima
et al., 2020; and this study). A 10 % emission reduction would therefore
only change the regional XCO2 enhancement by 0.1 to 0.2 ppm. This is below the single measurement precision of current satellite XCO2 data
products (at footprint size, i.e. 10.5 km diameter for GOSAT (Kuze et al.,
2016) and 1.3 × 2.3 km2 for OCO-2 (O'Dell et al., 2018)), which is
about 1.8 ppm (1σ) (e.g. Dils et al., 2014; Kulawik et al., 2016;
Buchwitz et al., 2015, 2017a; Reuter et al., 2020) for GOSAT and around 1 ppm for OCO-2 (Wunch et al., 2017; Reuter et al., 2019). In our study we
focus on XCO2 monthly averages. Averaging reduces the noise of the
satellite retrievals (e.g. Kulawik et al., 2016) but also eliminates
day-to-day XCO2 variations (e.g. Agustí-Panareda et al., 2019),
which cannot be interpreted using our simple analysis methods. The accuracy
of the East China satellite XCO2 retrievals averaged over monthly
timescales is difficult to assess because of limited reference data. The
validation of the satellite data products is primarily based on comparisons
with ground-based XCO2 retrievals from the TCCON, a relatively sparse
network with an uncertainty of about 0.4 ppm (Wunch et al., 2010).
The purpose of this study is to investigate – using satellite XCO2
retrievals – if satellite-derived East China fossil fuel (FF) CO2
emissions in 2020 (COVID-19 period) differ significantly compared to
pre-COVID-19 periods. Ideally, we would like to know by how much emissions
have been reduced due to COVID-19. This question, however, cannot be
answered using only satellite data because they do not contain any
information on how much would have been emitted without COVID-19. Instead,
we aim at answering the following question: are satellite-derived East China
FF CO2 emissions early in the pandemic (here: January–May 2020)
significantly lower compared to pre-COVID-19 periods?
To answer this question, we analyse relative differences of estimates of
East China monthly FF emissions during different time periods. We focus on
October-to-May periods, and we refer to different periods via the year where
a period ends; i.e. we call the period October 2019 to May 2020 “year 2020
period” or simply “2020”, the period October 2018 to May 2019 is called
2019, etc. Specifically, we compute and analyse differences of monthly
emissions in the year 2020 period relative to previous year 2016 to 2019
periods; i.e. we use four periods for comparison with the year 2020 period. To
focus on the COVID-19 aspect, we subtract for each period the October-to-December (OND) mean value, and we refer to these time series as “OND
anomalies”. These OND anomalies are time series at monthly resolution of the relative emission difference between different periods relative to OND.
Negative OND anomalies during the COVID-19 period would then suggest
(depending on uncertainty) that an emission reduction during the COVID-19
period has been detected.
The structure of our paper reflects this procedure: in the Data section (Sect. 2) we present the satellite and model input data used for this study. In the
Methods section (Sect. 3) we present the analysis method, which consists of two main
steps. The purpose of the first step is to isolate the East China FF
emission signal from the XCO2 satellite retrievals. This is done by
subtracting appropriate XCO2 background values from the XCO2
retrievals to obtain XCO2 anomalies, ΔXCO2. We use two
methods to compute ΔXCO2. We describe one method, the DAM
method, in detail in Sect. 3.1 and only shortly explain the second method
(TmS method), referring for details to Appendix A. In the second step
(Sect. 3.2) we compute estimates of East China monthly FF CO2 emissions
from the XCO2 anomalies. These emission estimates are then used to
compute the OND anomalies explained above. In Results and discussion section (Sect. 4) we present
and discuss the results, i.e. the application of the described methods to
the satellite data. A summary and conclusions are given in Sect. 5.
Data
In this section, we present a short overview about the input data used for
this study.
Satellite XCO2 retrievals
This study uses four satellite XCO2 Level 2 (L2) data products. An
overview about these data sets is provided in Table 1. The first product
listed in Table 1 is the latest version of the bias-corrected OCO-2 XCO2
product delivered to the Goddard Earth Science Data and Information Services
Center (GES DISC) by the OCO-2 team (ACOS v10r Lite). The other three
satellite XCO2 data sets are different versions of the GOSAT XCO2
product derived using retrieval algorithms developed by groups at the
University of Leicester, UK (UoL-FP v7.3); the SRON Netherlands Institute
for Space Research (RemoTeC v2.3.8); and the University of Bremen, Germany
(FOCAL v1.0).
Overview of the satellite XCO2 Level 2 (L2) input data products.
SatelliteAlgorithmProductProduct IDReferencesData provider and data access informationversionOCO-2ACOSv10rCO2_OC2_ACOSO'Dell et al. (2018), Kiel et al. (2019), Osterman et al. (2020)Product OCO2_L2_Lite_FP 10r obtained from NASA's Earthdata GES DISC website: https://disc.gsfc.nasa.gov/datasets?keywords=OCO-2%20v10r&page=1 (last access: 15 August 2020)GOSATUoL-FPv7.3CO2_GOS_OCFPCogan et al. (2012), Boesch et al. (2019)Generated by Univ. Leicester (contact: Antonio Di Noia: adn9@leicester.ac.uk) and available via the CDS∗GOSATRemoTeCv2.3.8CO2_GOS_SRFPButz et al. (2011), Wu et al. (2019)Generated by SRON (contact: Lianghai Wu: l.wu@sron.nl) and available via the CDS∗GOSATFOCALv1.0CO2_GOS_FOCANoël et al. (2020)Generated by Univ. Bremen and available on request (contact: Stefan Noël: stefan.noel@iup.physik.uni-bremen.de)
∗ Products are available via the Copernicus
Climate Data Store (CDS, https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-carbon-dioxide?tab=overview (last access: 23 September 2020)) currently until end of 2019. Year 2020 data will be made available via the CDS in mid-2021 but are available from the authors on request (see contact information).
The XCO2 estimates derived from OCO-2 (e.g. O'Dell et al., 2018) and
GOSAT (e.g. Kuze et al., 2016) observations are complementary because these
two spacecraft use different sampling strategies. OCO-2 has been operating
since September 2014. Its spectrometers collect about 85 000 cloud-free
XCO2 soundings each day along a narrow (< 10 km) ground track
as it orbits the Earth 14.5 times each day from its sun-synchronous 13:36 (local time) orbit. The OCO-2 soundings provide continuous measurements with relatively
high spatial resolution (1.3 × 2.3 km2) along each track, but the
individual ground tracks are separated by almost 25∘ longitudes in
any given day. This spacing is reduced to approximately 1.5∘
longitude after a 16 d ground track repeat cycle. GOSAT has been returning
300 to 1000 cloud-free XCO2 soundings each day since April 2009. Its
TANSO-FTS spectrometer collects soundings with 10.5 km diameter surface
footprints, separated by approximately 250 km along and across its ground
track at it orbits from north to south across the sunlit hemisphere.
Model CO2 data
We use data from NOAA's (National Oceanic and Atmospheric Administration)
CO2 assimilation system, CarbonTracker (CT2019) (Jacobson et al., 2020;
Peters et al., 2007), to define the relationship between XCO2 anomalies
and fossil fuel emissions. CarbonTracker is a global atmospheric inverse
model that assimilates atmospheric CO2 measurements to produce modelled
fields of atmospheric CO2 mole fractions by adjusting land biosphere
and ocean CO2 surface fluxes. An overview about CT2019 set is provided
in Table 2, including references and access information. In short,
CarbonTracker has a representation of atmospheric transport based on weather
forecasts, as well as modules representing air–sea exchange of CO2,
photosynthesis and respiration by the terrestrial biosphere, and release of
CO2 to the atmosphere by fires and combustion of fossil fuels.
Overview of the CarbonTracker CT2019 data set. For this
study we used data from the period January 2015 to December 2018.
Model/versionDetailsReferenceAccessCarbonTracker CT2019Atmospheric CO2 mole fraction profiles (spatio-temporal sampling: 3∘× 2∘, 3-hourly) and CO2 fluxes (spatio- temporal sampling: 1∘× 1∘, 3-hourly)Jacobson et al. (2020), DOI: 10.25925/39m3-6069CarbonTracker CT2019, http://carbontracker.noaa.gov (last access: 22 July 2020)MethodsMethods to compute XCO2 anomalies (ΔXCO2)
Satellite XCO2 retrievals contain information on anthropogenic CO2
emissions (e.g. Schneising et al., 2013; Reuter et al., 2014b, 2019; Nassar
et al., 2017), but extracting this information requires appropriate data
processing and analysis. For a strong (net) source region XCO2 is
typically higher compared to its surrounding area. Our method is based on
computing and subtracting XCO2 background values. The purpose of this
background correction is to isolate the regional emission signal by removing
large-scale spatial and day-to-day temporal XCO2 variations, which
cannot be dealt with in our simple data-driven method to estimate emissions.
XCO2 varies temporally and spatially (e.g. Agustí-Panareda et
al., 2019; Reuter et al., 2020; Gier et al., 2020), for example, due to
quasi-regular uptake and release of CO2 by the terrestrial biosphere,
which results in a strong seasonal cycle, especially over northern mid- and
high latitudes. Compared to fluctuations originating from the interaction of
the terrestrial biosphere and the atmosphere, the spatio-temporal XCO2
variations due to anthropogenic fossil fuel (FF) CO2 emissions are
typically much smaller (e.g. 1 ppm compared to 10 ppm; Schneising et al.,
2008, 2013, 2014; Agustí-Panareda et al., 2019).
A method used for background correction is the one described in Hakkarainen
et al. (2019; see also Hakkarainen et al., 2016, for a first publication of
that method). We use two different methods for background correction. We
refer to these methods as “daily anomalies via (latitude band) medians”
(DAM), which is essentially identical with the method described in
Hakkarainen et al. (2019), and “target minus
surrounding” (TmS).
Hakkarainen et al. (2019) applied their method to the OCO-2 Level 2
XCO2 data product to filter out trends and seasonal variations in
order to isolate CO2 source/sink signals. For background correction,
Hakkarainen et al. (2019) calculate daily medians for 10∘ latitude
bands and linearly interpolate the resulting values to each OCO-2 data
point. Instead of interpolation, we compute the median around each latitude
(running median) using a latitude band width of ±15∘. We use a
larger width compared to Hakkarainen et al. (2019), as we also apply our
method to GOSAT data, which are much sparser than OCO-2 data. Our
investigations showed that the width of the latitude band is not critical.
The band needs to be wide enough to contain a statistically significant
sample but narrow enough to resolve large latitudinal gradients in
CO2. We subtract the corresponding median from each single XCO2
observation in the original Level 2 XCO2 data product files to obtain a
data set of XCO2 anomalies, ΔXCO2DAM.
In order to verify that our results do not critically depend on the details
of one method, we also use the second TmS method. Here we obtain the
background by averaging XCO2 in a region surrounding the target region
(see Table 3 for the latitude and longitude corner coordinates of the target
and its surrounding region).
Corner coordinates of the East China target region as
analysed in this study.
Region IDCommentsLatitudeLongituderangerange(∘ N)(∘ E)East ChinaTarget region for DAM28–44102–126and TmS methodsExtended region for18–5493–135TmS method
We call these background-corrected XCO2 retrievals XCO2 anomalies, and satellite-derived maps and time series of these XCO2 anomalies are
presented and discussed in Sect. 4.1. These XCO2 anomalies are then
used to compute East China FF CO2 emission estimates, CO2FF,
as described in the following subsection.
Computation of emission estimates (CO2FF)
To determine whether satellite XCO2 retrievals can provide information
on relative changes of anthropogenic CO2 emissions for the East China
target region, we must establish a relationship between the XCO2 anomalies (see Sect. 3.1) and the desired estimates of the target region
fossil fuel (FF) emissions. To develop a method to convert the XCO2
anomalies, ΔXCO2, to FF emission estimates, CO2FF, we
use an existing model data set, the CarbonTracker CT2019 data set described
in Sect. 2.2, which contains atmospheric CO2 fields and corresponding
CO2 surface fluxes during 2015–2018.
CT2019 XCO2 (a, c, e, in ppm)
and corresponding CO2 surface fluxes (b, d, f, in
Mt CO2 yr-1 per cell) for 15 January 2018 (a, b),
15 March 2018 (c, d) and 15 May 2018 (e, f). The red rectangle encloses the East China target region as defined for this study.
Figure 1 shows CT2019 XCO2 maps (left) and corresponding surface
CO2 flux maps (right) for selected days in the January-to-May-2018
period. The XCO2 has been computed by vertically integrating the CT2019
CO2 vertical profiles (weighted with the surface pressure normalized
pressure change over each layer). The model data are sampled at local noon,
which is close to the overpass time of the satellite data sets used here.
The spatio-temporal sampling of a specific satellite XCO2 data product
is not considered here; i.e. we use the CT2019 data set independent of any
satellite data product apart for the sampling at local noon. As can be seen
from Fig. 1, XCO2 is clearly elevated over the East China target region
(red rectangle) relative to its surrounding region on 15 January 2018 (Fig. 1a) and on 15 March 2018 (Fig. 1c). On 15 May 2018 (Fig. 1e) the
target region and parts of the surrounding region contain large areas of
lower-than-average XCO2, a pattern which primarily results from carbon
uptake by vegetation during the growing season, which starts in eastern
China around May each year. The CO2 fluxes, which are shown on the
right-hand side panels of Fig. 1, show similar spatial pattern as the
XCO2 maps, but due to atmospheric transport and the long lifetime of
atmospheric CO2 there is no one-to-one correspondence between
atmospheric XCO2 and surface emissions. The CO2 fluxes are the sum
of several contributing fluxes including FF emissions, biogenic fluxes and
other fluxes (fires, oceans).
Results obtained by applying the DAM method to CT2019 XCO2
for East China. (a) Different monthly ΔXCO2DAM components: total ΔXCO2DAM (TOT) and its FF (red) and biogenic (BIO, green) components and their sum (FF + BIO). The non-shaded time periods October to May indicate the periods analysed in this publication. (b) East China October-to-May FF CO2 emissions (red dots) and estimated emissions CO2FF(DAM) (black crosses) as obtained from total ΔXCO2DAM (TOT as shown in panel a) using the
formula shown in panel (b). (c) Scatter plot of estimated versus true (i.e. CT2019) FF emissions. (d) Relative difference of estimated and true emissions.
Figure 2a shows time series obtained by applying the DAM method to CT2019
XCO2 for the East China target region. The CT2019 data set not only
contains atmospheric CO2 concentrations but also its components due to
fossil fuel (FF) emissions and biogenic (BIO) and other fluxes. From the
CT2019 data set we computed total XCO2 (TOT) and its FF and BIO
components. From these components we subtracted the background using the DAM
method, and the corresponding monthly ΔXCO2DAM time series
are shown in Fig. 2a. As can be seen from Fig. 2a, total ΔXCO2DAM (black line) is dominated by its FF (red line) and BIO
(green line) components (their sum, i.e. FF + BIO (grey line), is close
to TOT (black line)). As can also be seen, FF emissions for East China (red
line) are larger than the BIO fluxes (green line) at least during October to
April. During May to September the BIO fluxes are negative due to uptake of
atmospheric CO2 by the terrestrial biosphere, and their absolute value
is on the same order or may even significantly exceed the FF emissions. As a
consequence, total ΔXCO2DAM (black line) gets negative.
During these months, the total anomaly (black line) is closer to BIO (green
line) than to FF (red line).
The task for the satellite inversion is to obtain estimates of East China FF
CO2 emissions from the satellite-derived (total) XCO2 anomalies,
ΔXCO2DAM (black line in Fig. 2a). To determine to what
extent this is possible, we fitted CT2019 ΔXCO2DAM (i.e.
the quantity that we can also obtain from satellites) to the East China
CT2019 FF CO2 emissions (which are the known true emissions in this
model data assessment exercise). The results are shown in Fig. 2b for October-to-May periods. The estimated emissions (black crosses) have been
obtained via a linear fit of ΔXCO2DAM to the CT2019 FF
emissions (red dots). The two parameters of the linear fit are also shown in
Fig. 2b: scaling factor A (= 0.90) and offset B (= 7.41). As can be seen,
the estimated emissions agree reasonably well with the true emissions.
The linear correlation coefficient R is 0.83 (see Fig. 2c), and the relative difference in terms of mean and standard deviation is 0.2 % ± 5 % (see Fig. 2d). However, for individual months the error can
be as large as 10 %. From this we conclude that the (approximately
2σ) uncertainty of our method is approximately 10 %.
A similar figure but generated using the TmS method is shown in Appendix A
as Fig. A1. As can be seen, the results shown in Fig. A1b to d are
similar to the ones shown in Fig. 2b to d, but the linear correlation is
slightly worse and the errors are slightly larger. In contrast, the time
series shown in panel (a) differ significantly. This is because of the
different background corrections used for the two methods. But despite these
significant differences the quality of the derived emissions is similar (the
standard deviation of the monthly biases is 5.5 % for TmS and 4.8 % for
DAM; see panel d). Nevertheless, the DAM method gives slightly better
results compared to the TmS method, and this is also confirmed by applying
both methods to the satellite data (see Sect. 4). Therefore, the DAM method
is our baseline method, and we focus on results obtained with the DAM method.
It is perhaps surprising that the offset (fit parameter B; see above) is so
large (7.41 for DAM and 7.63 for TmS). Probably one would assume that the
XCO2 anomalies ΔXCO2 are directly proportional to the
target region fossil fuel emissions, i.e. one would assume that FF is
(approximately) equal to a constant multiplied by ΔXCO2 (no
offset added) (for example, for FF = 8 Gt CO2 yr-1 and ΔXCO2= 2 ppm one would have expected that the conversion factor is 4
Gt CO2 yr-1 ppm-1). In that case, as we are only interested in relative
changes in emissions, we would not need to know the exact value of the
scaling factor. However, when analysing the satellite data, we found that
ΔXCO2 is around 2 ppm for January but decreases in subsequent
months, nearly approaching zero in May (which is consistent with the CT2019
results shown in Fig. 2a). As anthropogenic emissions are not expected to
change that much within a few months (and zero emissions around May are not
realistic at all), we concluded that the simple proportionality assumption
does not hold. To investigate this we used the CT2019 data set to test and
improve our method, and the results are reported in this section. We applied
our method to CT2019 XCO2 (as shown in Fig. 2) and compared the
retrieved FF values with the (true) CT2019 FF values. We found large
differences, which could be significantly reduced by adding an offset to the
linear fit as discussed above. The reason for the large offset is the
influence of the biosphere. Around May the uptake of atmospheric CO2
due to the biosphere is so large that ΔXCO2 is close to zero –
but the FF emissions are not – and the East China target region is
essentially carbon neutral or even a net sink (see also Fig. 1).
As explained, scaling factor A and offset B are obtained empirically via a
linear fit using CT2019 data (Fig. 2b) and used for the conversion of the
satellite XCO2 anomalies during the entire time period January 2015 to
May 2020 (as will be shown in Sect. 4). As can be seen from Fig. 2b and
c, the retrieval biases are within ±10 % during 2015–2018. We
assume in our study that the same conversion is also appropriate for 2019
and 2020. However, it cannot be ruled out that 2019 or 2020 were
significantly different compared to previous years with respect to aspects
relevant for our study. To address this, we compare the period October 2019 to
May 2020 results with the corresponding results from previous October-to-December periods to find out to what extent the period of interest, i.e.
October 2019 to May 2020, is significantly different, taking into account the
year-to-year variability, which we use to obtain uncertainty estimates.
The methods described in this section have been applied to convert
satellite-derived target region XCO2 anomalies, ΔXCO2,
into estimated target region FF CO2 emissions, CO2FF. How
this has been done using the DAM method for background correction is
explained in the following Sect. 4, where we refer for the corresponding TmS
method results to Appendix A.
Results and discussion
In this section, we present results obtained by applying the DAM method (see
Methods Sect. 3.1) to the satellite data to obtain XCO2 anomalies from
which we derive FF emission estimates (see Methods Sect. 3.2).
Application of the DAM method to satellite
XCO2 retrievals
The DAM method has been applied to the OCO-2 and GOSAT satellite XCO2
data products listed in Table 1. Figure 3 shows a global OCO-2 DAM XCO2
anomaly map at 1∘× 1∘ resolution for the period 2015–2019. A
similar map is shown in Hakkarainen et al. (2019; see their Fig. 3, top
panel). The degree of agreement confirms the finding reported in Sect. 3.1
that the generation of these anomaly maps does not critically depend on how
exactly the median is computed and used to subtract the background.
Hakkarainen et al. (2019) discuss their OCO-2-derived maps in quite some
detail also in terms of seasonal averages and comparisons with model
simulations. They show that positive anomalies correspond to fossil fuel
combustion over major industrial areas including China. Their seasonal maps
(see their Fig. 4) show a strong positive anomaly over East China (similar
to that shown here in Fig. 3) except for the June–August (JJA) summer season,
where the XCO2 anomaly can be negative. This is consistent with the
CT2019 results presented in Sect. 3.2.
DAM XCO2 anomaly map at 1∘× 1∘ resolution generated from OCO-2 Level 2 XCO2 (v10r, land) for 2015 to 2019.
As Fig. 3 but for China and surrounding areas.
As Fig. 4 but for (a) February 2015 to (f) February 2020.
A zoom into Fig. 3 is presented in Fig. 4, which shows more details for
China and its surrounding area. As can be seen from Fig. 4, ΔXCO2DAM is positive especially in the region between Beijing,
Wuhan and Hong Kong, with the highest values in the area between Beijing and
Shanghai. This positive anomaly indicates that this region is a strong
CO2 source region as also discussed in Hakkarainen et al. (2019). As
already explained, there is no one-to-one correspondence (especially not for
every grid cell) between local XCO2 anomalies and local CO2
emissions (or uptake) because the emitted CO2 is transported and mixed
in the atmosphere. Furthermore, the satellite data are typically sparse due
to strict quality filtering to avoid potential XCO2 biases, for
example, due to the presence of clouds. Cloud-contaminated ground scenes are
identified to the extent possible via the corresponding retrieval algorithm
(see references listed in Table 1) and flagged to be bad and are
therefore not used for this analysis. The sparseness of the satellite data
set is obvious from Fig. 5, which shows OCO-2 DAM XCO2 anomaly maps for
February during the 6 years 2015 to 2020.
(a) OCO-2 XCO2 (version 10r, product ID
CO2_OC2_ACOS) over land at 1∘× 1∘
resolution for February–March 2020. The red rectangle encloses the
investigated East China target region. (b–d) As panel (a) but for products CO2_GOS_OCFP (b), CO2_GOS_SRFP (c) and CO2_GOS_FOCA (d) (see Table 1 for details).
A key difference between the OCO-2 and the GOSAT data products is the
different sampling of the target region, with GOSAT having much sparser
coverage compared to OCO-2. This is illustrated in Fig. 6, which shows February-to-March-2020 averages of the OCO-2 XCO2 data product (Fig. 6a) and the three GOSAT data products (Fig. 6b–d) at 1∘× 1∘
resolution. The OCO-2 product shown in Fig. 6a is NASA's OCO-2 operational
Atmospheric CO2 Observations from Space (ACOS) algorithm version
10r bias-corrected XCO2 product (the so-called Lite product), which is
referred to in this publication via the product identifier (ID)
CO2_OC2_ACOS. The three GOSAT XCO2
products are (see details and references as given in Table 1) the University of Leicester's GOSAT product (ID CO2_GOS_OCFP; Fig. 6b), SRON Netherlands Institute for Space
Research GOSAT product (CO2_GOS_SRFP; Fig. 6c), and University of Bremen's GOSAT product (CO2_GOS_FOCA; Fig. 6d) as retrieved with the Fast atmOspheric traCe gAs
retrievaL (FOCAL) retrieval algorithm initially developed for OCO-2
(Reuter et al., 2017a, b). As can be seen from Fig. 6, the spatial
sampling of the target region is different for each product as only
quality-filtered (i.e. good) data are shown and the quality filtering
is algorithm specific (see references listed in Table 1).
Figure 6 also shows as red rectangle the East China target region as defined
for this study (the geographical coordinates are listed in Table 3). The
fossil fuel (FF) CO2 emissions of this target region are approximately
8 Gt CO2 yr-1; i.e. the selected target region covers approximately
80 % of the FF emissions of all of China, which are approximately 10 Gt CO2 yr-1 (Le Quéré et al., 2018; Friedlingstein et al.,
2019). In the following section we present East China FF emission estimates
as derived from the satellite XCO2 anomalies during and before the
COVID-19 period.
Emission estimates
Carbon dioxide fossil fuel emission estimates, CO2FF, have been
derived from the XCO2 anomalies, ΔXCO2, computed for each
of the four satellite XCO2 data products listed in Table 1. In this
section the emission results are presented and discussed. We focus on
results based on ΔXCO2 derived with the DAM method and refer to
Appendix A for results based on the TmS method.
Emission estimates from NASA's OCO-2 (version 10r)
XCO2
Figure 7 shows the results obtained by applying the DAM method to product
CO2_OC2_ACOS (see Table 1) for the East China
target region for the period January 2015 to May 2020 (the TmS version of
this figure is shown as Fig. A2 in Appendix A). Figure 7a shows daily DAM
XCO2 anomalies as a thin grey line and the corresponding monthly averages
as red dots. The amplitude (approximately ±1 ppm) and time dependence
(e.g. the minimum in the middle of each year) are similar to that for CT2019
(Fig. 2a black line). To ensure that there are a sufficiently large number
of observations per month, two criteria need to be fulfilled: There must be
a minimum number of days per month (here: 5) and a minimum number
observations per day (here: 30). The latter criterion is also relevant for
the daily data shown in Fig. 7(a) (grey line). We also used other
combinations of these two parameters (as shown below, e.g. Fig. 9).
DAM analysis of the OCO-2 ACOS version 10r XCO2 product
(CO2_OC2_ACOS) for the East China region from January 2015 to May 2020. (a) The thin grey line shows the daily DAM XCO2 anomalies, i.e. daily ΔXCO2DAM. The red dots are the corresponding monthly values, which are also shown in panel (b) for different October–May periods. (c) As panel (b) but for CO2FF(DAM), i.e. for the estimated East China monthly FF emissions (see main text). The data for October 2019–May 2020 (10.2019–5.2020) are shown in red (see annotation for other periods). (d) Relative CO2FF(DAM) differences for different periods. In blue, for example, the differences correspond to the period 10.2019–5.2020 (shown in red in panel c) minus 10.2018–5.2019 (shown in blue in panel c). (e) As panel (d) but after the October-to-December mean value (OND anomalies). The following parameters have been used to generate this figure: minimum number of observations per day: 30; minimum number of days per month: 5.
Figure 7b shows monthly ΔXCO2DAM for different October-to-May periods, and Fig. 7c shows the corresponding estimated FF emissions,
CO2FF(DAM). Figure 7d shows relative differences of the time
series shown in Fig. 7c. For example, the blue data are referred to as
“(2020–2019)/2019” in Fig. 7d, where 2019 refers to the blue data in
Fig. 7c, which corresponds to the period ending in May 2019. Shown are
differences of year 2020 data (red in Fig. 7c) minus data from previous
periods; i.e. Fig. 7d shows to what extent 2020 (strictly speaking the
period October 2019–May 2020, i.e. the period which ends in 2020) differs
relative to previous October-to-May periods.
Ensemble member CO2FF(DAM) OND anomalies derived from the satellite product CO2_OC2_ACOS. The thin lines
and small symbols show the same data also shown in the bottom panel of Fig. 7. The thick dots and lines show the corresponding ensemble median, mean and scatter. The following parameters have been used to generate this figure
(see also annotation): minimum number of observations per day: 30; minimum
number of days per month: 5.
To find out if we can detect a difference between the COVID-19 period and
pre-COVID-19 periods, we subtract from each time series shown in Fig. 7d
the October-to-December (OND) mean value. The corresponding time series are
shown in Fig. 7e and are referred to as OND anomalies in the
following. As can be seen from Fig. 7e, the OND anomalies vary within
±5 %. Values before January scatter around zero as the mean value
of OND anomalies is zero by definition during October to December. In
January the values also scatter around zero. After January most values are
negative, indicating reduced emissions compared to pre-COVID-19 periods. This
can be seen more clearly in Fig. 8, where the same data as in Fig. 7e are shown, but in addition the ensemble mean (light blue thick lines and dots)
and median (royal blue thick lines and dots) has been added, including uncertainty estimates as computed from the standard deviation of the
ensemble members.
The same as Fig. 8 but with additional combinations of
minimum number of observations per day (30 as in Fig. 8 and in addition 50, 15 and 10) and minimum number of days per month (5 as in Fig. 8 and in addition 10) (see annotation).
Figures 7 and 8 have been generated with the requirement that for each day
at least 30 observations need to be available in the target region and for
each month at least 5 d fulfilling this 30 observations per day requirement.
Figure 9 is similar to Fig. 8 except that also results for additional
combinations have been added, i.e. other combinations of minimum number of
observations per day and minimum number of days per month. As can be seen,
the results depend somewhat on which combination of these parameters is
used, but the ensemble median and its uncertainty (royal blue symbols and
lines) are similar. The ensemble median values are similar and negative
during February to May 2020. The large uncertainties (vertical lines;
1σ error estimates) reflect the scatter of the ensemble members. The
errors bars (1σ) overlap with the zero (i.e. no reduction) line, indicating that it cannot be claimed with confidence that a significant drop
of the emissions during the COVID-19 period has been detected.
Emission estimates from GOSAT XCO2 data
products
The same analysis method as applied to NASA's OCO-2 data product (Sect. 4.2.1) has also been applied to the three GOSAT XCO2 data products
listed in Table 1. The results are shown in Fig. 10 for product
CO2_GOS_OCFP, in Fig. 11 for product
CO2_GOS_SRFP and in Fig. 12 for product
CO2_GOS_FOCA. The month-to-month variations
are larger for these GOSAT products compared to OCO-2 product (note the
different scale of the y axes compared to Fig. 9). This is because GOSAT
products are much sparser compared to the OCO-2 product (as shown in Fig. 6)
and because the single observation random error is larger for GOSAT compared
to OCO-2. As can be seen from a comparison of the results obtained for the
three GOSAT products (Figs. 10–12), there are large differences among the
results obtained from these products. For example, product
CO2_GOS_OCFP (Fig. 10) suggests that the
largest emission reduction is in April, in contrast to the other two
products. The large spread of the GOSAT results means that no clear
conclusions can be drawn concerning East China emission reductions during
the COVID-19 period.
The same as Fig. 9 but for the product CO2_GOS_OCFP. Results are shown for several values of the
required minimum number of observations per day: 2, 4, 6, 8, 10 and 15. The
required minimum number of days per month is 5.
The same as Fig. 10 but for the product CO2_GOS_SRFP.
The same as Fig. 10 but for the product CO2_GOS_FOCA.
Ensemble mean and uncertainty
An overview about the results obtained from all four satellite data products
using the DAM method is shown in Fig. 13 (the corresponding TmS version of
this figure is shown as Fig. A3 in Appendix A). The results obtained from
the individual products (as shown in royal blue in Figs. 9–12) are shown
here using reddish colours (the corresponding numerical values are listed in
Table 4). Also shown in Fig. 13 is the mean of the ensemble members and its
estimated uncertainty (in dark blue); the corresponding numerical values are
listed in the bottom row of Table 4. The ensemble mean suggests emission
reductions by approximately 10 % ± 10 % in March and April 2020.
However, as can also be seen, there are significant differences across the
ensemble of satellite data products. For example, the analysis of the OCO-2
data suggests a much smaller emission reduction of only about 1 %–2 %.
Because of the large differences between the individual ensemble members it
is concluded that the expected emission reduction cannot be reliably
detected and accurately quantified with our method.
Overview of the ensemble-based
CO2FF(DAM) results for January–May 2020
relative to October–December 2019 and previous years (also shown in Figs. 9–12) via reddish colours for each of the four analysed satellite
XCO2 data products (see Table 1). The corresponding
ensemble mean value and its uncertainty is shown in dark blue. The
uncertainty has been computed as the standard deviation of the ensemble members.
The corresponding numerical values of the ensemble members are listed in
Table 4.
Numerical values of the ensemble-based
CO2FF(DAM) results as shown in Fig. 13.
Listed are the median values and corresponding 1σ
uncertainties (in brackets). The dimensionless values listed here represent
the relative CO2FF(DAM) change for
January–May 2020 relative to October–December 2019 and previous years
(OND anomalies; see main text).
Month Product IDOctoberNovemberDecemberJanuaryFebruaryMarchAprilMay20192019201920202020202020202020CO2_OC2_ACOS-0.0040.001-0.0100.008-0.010-0.003-0.018-0.019(0.025)(0.024)(0.015)(0.026)(0.024)(0.020)(0.023)(0.027)CO2_GOS_OCFP-0.0490.0260.071-0.110-0.055-0.151-0.281-0.141(0.046)(0.038)(0.050)(0.077)(0.087)(0.101)(0.055)(0.158)CO2_GOS_SRFP-0.0760.111-0.0610.038-0.0640.011-0.0820.024(0.031)(0.030)(0.054)(0.101)(0.053)(0.081)(0.059)(0.077)CO2_GOS_FOCA-0.0570.0530.008-0.0440.046-0.176-0.041-0.080(0.042)(0.029)(0.040)(0.062)(0.081)(0.066)(0.069)(0.064)Ensemble-0.0470.0480.002-0.027-0.021-0.085-0.106-0.054(0.031)(0.047)(0.054)(0.065)(0.050)(0.091)(0.120)(0.072)Summary and conclusions
We have analysed a small ensemble of satellite XCO2 data products to
investigate whether a regional-scale reduction of atmospheric CO2
during the COVID-19 pandemic can be detected for East China. Specifically,
we analysed four XCO2 data products from the satellites OCO-2 and
GOSAT. For this purpose, we used a simple data-driven approach, which
involves the computation of XCO2 anomalies, ΔXCO2, using a
method called DAM (daily anomalies via (latitude band) medians). This
method, which is essentially identical with the method developed at the Finnish
Meteorological Institute (FMI, Hakkarainen et al., 2019), helps to isolate
local or regional XCO2 enhancements originating from anthropogenic
CO2 emissions from large-scale daily XCO2 background variations
(note however that the FMI method is not supposed to extract exclusively
anthropogenic emission contributions to XCO2; see Hakkarainen et al.,
2019). In addition to the DAM method we also used a second method for the
computation of ΔXCO2, which is referred to as TmS (target minus
surrounding). Using model and satellite data we found that the results
obtained with the DAM method provide better results compared to the TmS
method. Therefore, we focussed on DAM-based results but also report selected
results obtained with the TmS method (reported separately in Appendix A). We analysed satellite data between January 2015 and May 2020 and compared year
2020 monthly XCO2 anomalies with the corresponding monthly XCO2
anomalies from previous periods.
In order to link the satellite-derived XCO2 anomalies to East China
fossil fuel (FF) CO2 emissions, we used output from NOAA's CO2
assimilation system CarbonTracker (CT2019) covering the years 2015 to 2018.
We focus on October-to-May periods to minimize the impact of the terrestrial
biosphere. Using CT2019, we show that ΔXCO2 can be converted to
FF emission estimates, denoted CO2FF, via a linear transformation.
The two coefficients slope and offset of this linear transformation have
been obtained empirically via a linear fit; i.e. we established a linear
empirical equation to relate the two quantities ΔXCO2 and
CO2FF. We show using CT2019 that the retrieved emissions during
October-to-May periods agree within 10 % with the CT2019 East China FF
emissions.
For the analysis of the satellite data we focus on the October-2019-to-May-2020 period, which covers months during the COVID-19 pandemic but also
pre-COVID-19 months. We compare results obtained during this period with
earlier October-to-May periods to find out to what extent year 2020 differs
from previous years. Our analysis is limited to October-to-May periods
because our simple data-driven analysis method cannot deal with the large
and highly variable terrestrial biosphere CO2 fluxes outside of this
period. On the other hand this period is challenging for satellite
retrievals because of the low sun angles especially during the winter months
and cloudiness.
We applied our method to each of the four satellite XCO2 data products
to obtain monthly emission estimates, CO2FF, for East China. We
focus on changes relative to pre-COVID-19 periods. Our results show
considerable month-to-month variability (especially for the GOSAT products)
and significant differences across the ensemble of satellite data products
analysed. The ensemble mean suggests emission reductions by approximately
10 % ± 10 % in March and April 2020. This estimate is dominated by
the GOSAT ensemble members. Analysis of the OCO-2 product yields smaller
values, indicating a reduction of only about 1 %–2 % with an uncertainty of
approximately ±2 %.
The large uncertainty, which is on the order of the derived reduction (i.e.
100 %, 1σ), and the large spread of the results obtained for the
individual ensemble members indicate that it is challenging to reliably
detect and to accurately quantify the emission reduction using the current
generation of space-based methods and the simple DAM-based analysis strategy
adopted here.
These findings, which are consistent with other recent studies (e.g.
Chevallier et al., 2020; Zeng et al., 2020), are not unexpected. Regional
XCO2 enhancements due to fossil fuel emissions are typically only 1 to
2 ppm and even a 10 % emission reduction would therefore only correspond
to a reduction of the fossil-fuel-related regional XCO2 enhancement by
0.1 to 0.2 ppm. XCO2 variations as small as 0.2 ppm are below the
estimated uncertainty of the single footprint satellite XCO2
retrievals. The uncertainty of single observations, which is typically
around 0.7 ppm (e.g. Buchwitz et al., 2017a; Reuter et al., 2020), has been
obtained by comparisons with ground-based Total Carbon Column Observing
Network (TCCON) XCO2 retrievals, which have an uncertainty of 0.4 ppm
(1σ, Wunch et al., 2010). In this study we focus on monthly averaged
data because our analysis method cannot properly deal with day-to-day
variability and because of the sparseness of the satellite data. Averaging
results in the reduction of the random error, but investigations have shown that
random errors do not simply scale with the inverse of the square root of
number of observations added due to (unknown) systematic errors and error
correlations (Kulawik et al., 2016). Of course also other sources of
uncertainty are relevant in this context, in particular time-dependent
atmospheric transport and varying biogenic CO2 contributions (e.g.
Houweling et al., 2015, and references given therein).
We conclude that inferring COVID-19-related information on regional-scale
CO2 emissions using current (quite sparse) satellite XCO2
retrievals requires, if at all possible, a more sophisticated analysis
method including the use of detailed a priori information and atmospheric transport
modelling.
The extent to which COVID-19-related emission reductions can be resolved on
smaller scales – such as power plants or cities (e.g. Nassar et al., 2017;
Reuter et al., 2019; Zheng et al., 2020a; Wu et al., 2020) has not been
investigated in this study. For this purpose, XCO2 retrievals from
NASA's OCO-3 mission are promising, especially because of its Snapshot Area
Map (SAM) mode, which permits the mapping of XCO2 over
∼ 80 km by 80 km areas around localized anthropogenic CO2 emission
sources (see https://ocov3.jpl.nasa.gov/, last access:
28 August 2020). Even more complete coverage is planned for the Copernicus
CO2M mission in the future (e.g. Janssens-Maenhout et al., 2020).
As explained in the main text, a second method has been applied to the
CT2019 and the satellite data. This method is called “target minus
surrounding” (TmS) and differs from the DAM method in the approach to determine the XCO2 background. Whereas the DAM method computes the
(daily) background as the median of the XCO2 values in latitude bands,
the TmS background is computed from the XCO2 values in an area
surrounding the target region (the coordinates are listed in Table 3).
The TmS results are discussed in the main text. Here we only show three
figures. Figure A1 is the same as Fig. 2 but using the TmS method instead of
the DAM method. Figure A2 is the TmS version of Fig. 7, and Fig. A3 is the TmS version of Fig. 13.
The same as Fig. 2 but using the target minus surrounding
(TmS) method.
The same as Fig. 7 but using the TmS method.
The same as Fig. 13 but using the TmS method.
Data availability
The key results of this study are listed in this paper in numerical form (Table 4). Access information for the satellite
data used as input for this study is provided in Table 1. The CT2019 data are
available from NOAA (see access information given in Table 2).
Author contributions
MB designed the study, performed the analysis and led the writing of this paper in close cooperation with MR, SN, BFA, HeB, JPB, OS, KB
and MH. Input data and corresponding advice has been provided by MR,
SN, ADN, HaB, LW, JL, IA, CWO'D, DC and CR. All authors
contributed to significantly improve the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study has been funded in parts by the European Space Agency (ESA) via
projects ICOVAC (Impacts of COVID-19 lockdown measures on Air quality and
Climate) and GHG-CCI+ (http://cci.esa.int/ghg, last access:
13 August 2020) and the University and the State of Bremen. We acknowledge
financial support for the generation of several data sets used as input for this study from the following: (i) European Commission via Copernicus Climate Change Service
(C3S, https://climate.copernicus.eu/, last access:
22 July 2020) project C3S_312b_Lot2, (ii) the
Japanese space agency JAXA (contract 19RT000692) and (iii) EUMETSAT
(contract EUM/CO/19/4600002372/RL). Hartmut Boesch, Univ. Leicester, was funded as
part of NERC's support of the National Centre for Earth Observation
(NE/R016518/1).
We also acknowledge access to OCO-2 XCO2 data product
OCO2_L2_Lite_FP 10r
obtained from NASA's Earthdata GES DISC website (https://disc.gsfc.nasa.gov/datasets?keywords=OCO-2%20v10r&page=1, last access: 15 August 2020).
We thank JAXA and the National Institute for Environmental Studies (NIES),
Japan, for access to GOSAT Level 1 (L1) data and ESA for making the GOSAT L1
products available via the ESA Third Party Mission (TPM) archive.
We acknowledge CarbonTracker CT2019 results provided by NOAA ESRL, Boulder,
Colorado, USA, from the website at http://carbontracker.noaa.gov (last access: 22 July 2020). We also
acknowledge feedback from Andy Jacobson on an early draft of the manuscript.
Some of the work reported here was conducted by the Jet Propulsion
Laboratory, California Institute of Technology, under contract to NASA.
Government sponsorship is acknowledged.
Financial support
This research has been supported by the
European Space Agency via the projects ICOVAC (contract no. 4000127610/19/I-NS) and GHG-CCI+ (contract no. 4000126450/19/I-NB).The article processing charges for this open-access publication were covered by the University of Bremen.
Review statement
This paper was edited by Ralf Sussmann and reviewed by three anonymous referees.
ReferencesAgustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D.: Modelling CO2 weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, 10.5194/acp-19-7347-2019, 2019.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.Bauwens, M., Compernolle, S.,
Stavrakou, T., Müller, J.‐F., van Gent,
J., Eskes, H., Levelt, P. F., van der A, R., Veefkind, J. P., Vlietinck, J., Yu, H., and Zehner, C.: Impact of
coronavirus outbreak on NO2 pollution
assessed using TROPOMI and OMI
observations, Geophys. Res.
Lett., 47, e2020GL087978, 10.1029/2020GL087978, 2020.Boesch, H., Anand, J., and Di Noia, A.: Product User Guide and Specification
(PUGS) – ANNEX A for products CO_2_GOS_OCFP,
CH4_GOS_OCFP & CH4_GOS_OCPR (v7.2, 2009–2018),
available at: http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/PUGS/C3S_D312b_Lot2.3.2.3-v1.0_PUGS-GHG_ANNEX-A_v3.1.pdf (last access: 17 August 2020), 2019.
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S.,
Rozanov, V. V., Chance, K. V., and Goede, A. H. P.: SCIAMACHY – Mission
objectives and measurement modes, J. Atmos. Sci., 56, 127–150, 1999.Bovensmann, H., Buchwitz, M., Burrows, J. P., Reuter, M., Krings, T., Gerilowski, K., Schneising, O., Heymann, J., Tretner, A., and Erzinger, J.: A remote sensing technique for global monitoring of power plant CO2 emissions from space and related applications, Atmos. Meas. Tech., 3, 781–811, 10.5194/amt-3-781-2010, 2010.Broquet, G., Bréon, F.-M., Renault, E., Buchwitz, M., Reuter, M., Bovensmann, H., Chevallier, F., Wu, L., and Ciais, P.: The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities, Atmos. Meas. Tech., 11, 681–708, 10.5194/amt-11-681-2018, 2018.Buchwitz, M., Reuter, M., Bovensmann, H., Pillai, D., Heymann, J., Schneising, O., Rozanov, V., Krings, T., Burrows, J. P., Boesch, H., Gerbig, C., Meijer, Y., and Löscher, A.: Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization, Atmos. Meas. Tech., 6, 3477–3500, 10.5194/amt-6-3477-2013, 2013.Buchwitz, M., Reuter, M., Schneising, O., Boesch, H., Guerlet, S., Dils, B.,
Aben, I., Armante, R., Bergamaschi, P., Blumenstock, T., Bovensmann, H.,
Brunner, D., Buchmann, B., Burrows, J. P., Butz, A., Chédin, A.,
Chevallier, F., Crevoisier, C. D., Deutscher, N. M., Frankenberg, C., Hase,
F., Hasekamp, O. P., Heymann, J., Kaminski, T., Laeng, A., Lichtenberg, G.,
De Mazière, M., Noël, S., Notholt, J., Orphal, J., Popp, C., Parker,
R., Scholze, M., Sussmann, R., Stiller, G. P., Warneke, T., Zehner, C.,
Bril, A., Crisp, D., Griffith, D. W. T., Kuze, A., O'Dell, C., Oshchepkov,
S., Sherlock, V., Suto, H., Wennberg, P., Wunch, D., Yokota, T., and
Yoshida, Y.: The Greenhouse Gas Climate Change Initiative (GHG-CCI):
comparison and quality assessment of near-surface-sensitive
satellite-derived CO2 and CH4 global data sets, Remote Sens.
Environ., 162, 344–362, 10.1016/j.rse.2013.04.024, 2015.Buchwitz, M., Reuter, M., Schneising, O., Hewson, W., Detmers, R. G.,
Boesch, H., Hasekamp, O. P., Aben, I., Bovensmann, H., Burrows, J. P., Butz,
A., Chevallier, F., Dils, B., Frankenberg, C., Heymann, J., Lichtenberg, G.,
De Maziere, M., Notholt, J., Parker, R., Warneke, T., Zehner, C., Griffith,
D. W. T., Deutscher, N. M., Kuze, A., Suto, H., and Wunch, D.: Global
satellite observations of column-averaged carbon dioxide and methane: The
GHG-CCI XCO2 and XCH4 CRDP3 data set, Remote Sens.
Environ., 203, 276–295, 10.1016/j.rse.2016.12.027,
2017a.Burrows, J. P., Hölzle, E., Goede, A. P. H., Visser, H., and Fricke, W.:
SCIAMACHY – Scanning Imaging Absorption Spectrometer for Atmospheric
Chartography, Acta Astronaut., 35, 445–451,
10.1016/0094-5765(94)00278-t, 1995.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.: On the statistical optimality of CO2 atmospheric inversions assimilating CO2 column retrievals, Atmos. Chem. Phys., 15, 11133–11145, 10.5194/acp-15-11133-2015, 2015.Chevallier, F., Palmer, P. I., Feng, L., Boesch, H., O'Dell, C. W., and
Bousquet, P.: Towards robust and consistent regional CO2 flux estimates
from in situ and space-borne measurements of atmospheric CO2, Geophys.
Res. Lett., 41, 1065–1070, 10.1002/2013GL058772, 2014.Chevallier, F., Zheng, B., Broquet, G., Ciais, P., Liu, Z., Davis, S. J.,
Deng, Z., Wang, Y., Bréon, F.-M., and O'Dell, C. W.: Local anomalies in
the column-averaged dry air mole fractions of carbon dioxide across the
globe during the first months of the coronavirus recession, Geophys.
Res. Lett., 47, e2020GL090244, 10.1029/2020GL090244, 2020.Ciais, P., Dolman, A. J., Bombelli, A., Duren, R., Peregon, A., Rayner, P. J., Miller, C., Gobron, N., Kinderman, G., Marland, G., Gruber, N., Chevallier, F., Andres, R. J., Balsamo, G., Bopp, L., Bréon, F.-M., Broquet, G., Dargaville, R., Battin, T. J., Borges, A., Bovensmann, H., Buchwitz, M., Butler, J., Canadell, J. G., Cook, R. B., DeFries, R., Engelen, R., Gurney, K. R., Heinze, C., Heimann, M., Held, A., Henry, M., Law, B., Luyssaert, S., Miller, J., Moriyama, T., Moulin, C., Myneni, R. B., Nussli, C., Obersteiner, M., Ojima, D., Pan, Y., Paris, J.-D., Piao, S. L., Poulter, B., Plummer, S., Quegan, S., Raymond, P., Reichstein, M., Rivier, L., Sabine, C., Schimel, D., Tarasova, O., Valentini, R., Wang, R., van der Werf, G., Wickland, D., Williams, M., and Zehner, C.: Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system, Biogeosciences, 11, 3547–3602, 10.5194/bg-11-3547-2014, 2014.Ciais, P., Crisp, D., Denier van der Gon, H., Engelen, R.,
Janssens-Maenhout, G., Heimann, H., Rayner, P., and Scholze, M.: Towards a
European Operational Observing System to Monitor Fossil CO2 emissions,
Final Report from the expert group, European Comission, 68 pp., available at:
https://edgar.jrc.ec.europa.eu/news_docs/CO2_report_22-10-2015.pdf (last access:
26 August 2020), 2015.Cogan, A. J., Boesch, H., Parker, R. J., Feng, L., Palmer, P. I., Blavier,
J.-F. L., Deutscher, N. M., Macatangay, R., Notholt, J., Roehl, C., Warneke,
T., and Wunsch, D.: Atmospheric carbon dioxide retrieved from the Greenhouse
gases Observing SATellite (GOSAT): Comparison with ground-based TCCON
observations and GEOS-Chem model calculations, J. Geophys. Res., 117,
D21301, 10.1029/2012JD018087, 2012.
Crisp, D., Atlas, R. M., Bréon, F.-M., Brown, L. R., Burrows, J. P.,
Ciais, P., Connor, B. J., Doney, S. C., Fung, I. Y., Jacob, D. J., Miller,
C. E., O'Brien, D., Pawson, S., Randerson, J. T., Rayner, P., Salawitch, R.
S., Sander, S. P., Sen, B., Stephens, G. L., Tans, P. P., Toon, G.
C., Wennberg, P. O., Wofsy, S. C., Yung, Y. L., Kuang, Z., Chudasama, B.,
Sprague, G., Weiss, P., Pollock, R., Kenyon, D., and Schroll, S.: The
Orbiting Carbon Observatory (OCO) mission, Adv. Space Res., 34,
700–709, 2004.Crisp, D., Meijer, Y., Munro, R., Bowman, K., Chatterjee, A., Baker, D.,
Chevallier, F., Nassar, R., Palmer, P. I., Agusti-Panareda, A., Al-Saadi,
J., Ariel, Y., Basu, S., Bergamaschi, P., Boesch, H., Bousquet, P.,
Bovensmann, H., Bréon, F.-M., Brunner, D., Buchwitz, M., Buisson, F.,
Burrows, J. P., Butz, A., Ciais, P., Clerbaux, C., Counet, P., Crevoisier,
C., Crowell, S., DeCola, P. L., Deniel, C., Dowell, M., Eckman, R., Edwards,
D., Ehret, G., Eldering, A., Engelen, R., Fisher, B., Germain, S.,
Hakkarainen, J., Hilsenrath, E., Holmlund, K., Houweling, S., Hu, H., Jacob,
D., Janssens-Maenhout, G., Jones, D., Jouglet, D., Kataoka, F., Kiel, M.,
Kulawik, S. S., Kuze, A., Lachance, R. L., Lang, R., Landgraf, J., Liu, J.,
Liu, Y., Maksyutov, S., Matsunaga, T., McKeever, J., Moore, B., Nakajima,
M., Natraj, V., Nelson, R. R., Niwa, Y., Oda, T., O'Dell, C. W., Ott, L.,
Patra, P., Pawson, S., Payne, V., Pinty, B., Polavarapu, S. M., Retscher,
R., Rosenberg, R., Schuh, A., Schwandner, F. M., Shiomi, K., Su, W.,
Tamminen, J., Taylor, T. E., Veefkind, P., Veihelmann, B., Wofsy, S.,
Worden, J., Wunch, D., Yang, D., Zhang, P., and Zehner, C.: A Constellation Architecture for Monitoring Carbon Dioxide and Methane from Space,
CEOS Atmospheric Composition Virtual Constellation Greenhouse Gas
Team, Committee on Earth Observation Satellites, Version 1.0, 173 pp., available at: https://ceos.org/document_management/Virtual_Constellations/ACC/Documents/CEOS_AC-VC_GHG_White_Paper_Version_1_20181009.pdf (last access: 26 August 2020), 2018.Dils, B., Buchwitz, M., Reuter, M., Schneising, O., Boesch, H., Parker, R., Guerlet, S., Aben, I., Blumenstock, T., Burrows, J. P., Butz, A., Deutscher, N. M., Frankenberg, C., Hase, F., Hasekamp, O. P., Heymann, J., De Mazière, M., Notholt, J., Sussmann, R., Warneke, T., Griffith, D., Sherlock, V., and Wunch, D.: The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO2 and CH4 retrieval algorithm products with measurements from the TCCON, Atmos. Meas. Tech., 7, 1723–1744, 10.5194/amt-7-1723-2014, 2014.Eldering, A., Wennberg, P. O., Crisp, D., Schimel, D. S., Gunson, M. R.,
Chatterjee, A., Liu, J., Schwandner, F. M., Sun, Y.,
O'Dell, C. W., Frankenberg, C., Taylor, T., Fisher, B., Osterman, G. B.,
Wunch, D., Hakkarainen, J., Tamminen, J., and
Weir, B.: The Orbiting Carbon Observatory-2 early science investigations
of regional carbon dioxide fluxes, Science, 358, eaam5745, 10.1126/science.aam5745, 2017.ESA: European Space Agency, Copernicus CO2 Monitoring
Mission Requirements Document, version 2.0 of 27/09/19, ESA Earth and
Mission Science Division document ref. EOP-SM/3088/YM-ym, available at:
https://esamultimedia.esa.int/docs/EarthObservation/CO2M_MRD_v2.0_Issued20190927.pdf (last access: 15 July 2020), 2019.ESA-NASA-JAXA: ESA, NASA and JAXA COVID-10 Dashboard, available at: https://www.esa.int/ESA_Multimedia/Images/2020/06/COVID-19_Earth_Observation_Dashboard2 (last access: 15-March-2021), 2020.Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Hauck, J., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, D. C. E., Canadell, J. G., Ciais, P., Jackson, R. B., Anthoni, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., Bopp, L., Buitenhuis, E., Chandra, N., Chevallier, F., Chini, L. P., Currie, K. I., Feely, R. A., Gehlen, M., Gilfillan, D., Gkritzalis, T., Goll, D. S., Gruber, N., Gutekunst, S., Harris, I., Haverd, V., Houghton, R. A., Hurtt, G., Ilyina, T., Jain, A. K., Joetzjer, E., Kaplan, J. O., Kato, E., Klein Goldewijk, K., Korsbakken, J. I., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Marland, G., McGuire, P. C., Melton, J. R., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Neill, C., Omar, A. M., Ono, T., Peregon, A., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Séférian, R., Schwinger, J., Smith, N., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Werf, G. R., Wiltshire, A. J., and Zaehle, S.: Global Carbon Budget 2019, Earth Syst. Sci. Data, 11, 1783–1838, 10.5194/essd-11-1783-2019, 2019.Gier, B. K., Buchwitz, M., Reuter, M., Cox, P. M., Friedlingstein, P., and Eyring, V.: Spatially resolved evaluation of Earth system models with satellite column-averaged CO2, Biogeosciences, 17, 6115–6144, 10.5194/bg-17-6115-2020, 2020.Hakkarainen, J., Ialongo, I., and Tamminen, J.: Direct space-based
observations of anthropogenic CO2 emission areas from OCO-2, Geophys.
Res. Lett., 43, 11400–11406, 10.1002/2016GL070885, 2016.Hakkarainen, J., Ialongo, I., Maksyutov, S., and Crisp, D.: Analysis of Four
Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2,
Remote Sens., 11, 850, 10.3390/rs11070850, 2019.Houweling, S., Baker, D., Basu, S., Boesch, H., Butz, A., Chevallier, F.,
Deng, F., Dlugokencky, E. J., Feng, L., Ganshin, A., Hasekamp, O., Jones,
D., Maksyutov, S., Marshall, J., Oda, T., O'Dell, C. W., Oshchepkov, S.,
Palmer, P. I., Peylin, P., Poussi, Z., Reum, F., Takagi, H., Yoshida, Y.,
and Zhuralev, R.: An intercomparison of inverse models for estimating
sources and sinks of CO2 using GOSAT measurements, J. Geophys. Res.-Atmos., 120, 5253–5266, 10.1002/2014JD022962, 2015.IPCC: Climate Change 2013: The Physical Science Basis, Working Group I
Contribution to the Fifth Assessment Report of the Intergovernmental Report
on Climate Change, Cambridge University Press, Cambridge, UK, available at: http://www.ipcc.ch/report/ar5/wg1/ (last access: 21 February 2019), 2013.Jacobson, A. R., Schuldt, K. N., Miller, J. B., Oda, T., Tans, P., Andrews,
A., Mund, J., Ott, L., Collatz, G. J., Aalto, T., Afshar, S., Aikin, K.,
Aoki, S., Apadula, F., Baier, B., Bergamaschi, P., Beyersdorf, A., Biraud,
S. C., Bollenbacher, A., Bowling, D., Brailsford, G., Abshire, J. B., Chen,
G., Chen, H., Chmura, L., Colomb, A., Conil, S., Cox, A., Cristofanelli, P.,
Cuevas, E., Curcoll, R., Sloop, C. D., Davis, K., Wekker, S. D., Delmotte,
M., DiGangi, J. P., Dlugokencky, E., Ehleringer, J., Elkins, J. W.,
Emmenegger, L., Fischer, M. L., Forster, G., Frumau, A., Galkowski, M.,
Gatti, L. V., Gloor, E., Griffis, T., Hammer, S., Haszpra, L., Hatakka, J.,
Heliasz, M., Hensen, A., Hermanssen, O., Hintsa, E., Holst, J., Jaffe, D.,
Karion, A., Kawa, S. R., Keeling, R., Keronen, P., Kolari, P., Kominkova,
K., Kort, E., Krummel, P., Kubistin, D., Labuschagne, C., Langenfelds, R.,
Laurent, O., Laurila, T., Lauvaux, T., Law, B., Lee, J., Lehner, I.,
Leuenberger, M., Levin, I., Levula, J., Lin, J., Lindauer, M., Loh, Z.,
Lopez, M., Lund Myhre, C., Machida, T., Mammarella, I., Manca, G., Manning,
A., Manning, A., Marek, M. V., Marklund, P., Martin, M. Y., Matsueda, H.,
McKain, K., Meijer, H., Meinhardt, F., Miles, N., Miller, C. E., Mölder,
M., Montzka, S., Moore, F., Morgui, J.-A., Morimoto, S., Munger, B., Necki,
J., Newman, S., Nichol, S., Niwa, Y., O'Doherty, S., Ottosson-Löfvenius,
M., Paplawsky, B., Peischl, J., Peltola, O., Pichon, J.-M., Piper, S.,
Plass-Dölmer, C., Ramonet, M., Reyes-Sanchez, E., Richardson, S., Riris,
H., Ryerson, T., Saito, K., Sargent, M., Sasakawa, M., Sawa, Y., Say, D.,
Scheeren, B., Schmidt, M., Schmidt, A., Schumacher, M., Shepson, P., Shook,
M., Stanley, K., Steinbacher, M., Stephens, B., Sweeney, C., Thoning, K.,
Torn, M., Turnbull, J., Tørseth, K., Bulk, P. V. D., Laan-Luijkx, I. T.
V. D., Dinther, D. V., Vermeulen, A., Viner, B., Vitkova, G., Walker, S.,
Weyrauch, D., Wofsy, S., Worthy, D., Young, D., and Zimnoch, M.:
CarbonTracker CT2019, NOAA Earth System Research Laboratory, Global Monitoring Division, 10.25925/39m3-6069, 2020.Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E.,
Balsamo, G., Bezy, J.-L., Brunhes, T., Boesch, H., Bojkov, B., Brunner, D.,
Buchwitz, M., Crisp, D., Ciais, P., Counet, P., Dee, D., Denier van der Gon,
H., Dolman, H., Drinkwater, M., Dubovik, O., Engelen, R., Fehr, T.,
Fernandez, V., Heimann, M., Holmlund, K., Houweling, S., Husband, R.,
Juvyns, O., Kentarchos, A., Landgraf, J., Lang, R., Loescher, A., Marshall,
J., Meijer, Y., Nakajima, M., Palmer, P. I., Peylin, P., Rayner, P.,
Scholze, M., Sierk, B., Tamminen, J., and Veefkind P.: Towards an
operational anthropogenic CO2 emissions monitoring and verification
support capacity, B. Am. Meteorol. Soc.,
101, E1439–E1451,
10.1175/BAMS-D-19-0017.1, 2020.
Kaminski, T., Scholze, M., Voßbeck, M., Knorr, W., Buchwitz, M., and
Reuter, M.: Constraining a terrestrial biosphere model with remotely sensed
atmospheric carbon dioxide, Remote Sens. Environ., 203, 109–124,
2017.Kiel, M., O'Dell, C. W., Fisher, B., Eldering, A., Nassar, R., MacDonald, C. G., and Wennberg, P. O.: How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates, Atmos. Meas. Tech., 12, 2241–2259, 10.5194/amt-12-2241-2019, 2019.Kuhlmann, G., Broquet, G., Marshall, J., Clément, V., Löscher, A., Meijer, Y., and Brunner, D.: Detectability of CO2 emission plumes of cities and power plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission, Atmos. Meas. Tech., 12, 6695–6719, 10.5194/amt-12-6695-2019, 2019.Kulawik, S., Wunch, D., O'Dell, C., Frankenberg, C., Reuter, M., Oda, T., Chevallier, F., Sherlock, V., Buchwitz, M., Osterman, G., Miller, C. E., Wennberg, P. O., Griffith, D., Morino, I., Dubey, M. K., Deutscher, N. M., Notholt, J., Hase, F., Warneke, T., Sussmann, R., Robinson, J., Strong, K., Schneider, M., De Mazière, M., Shiomi, K., Feist, D. G., Iraci, L. T., and Wolf, J.: Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON, Atmos. Meas. Tech., 9, 683–709, 10.5194/amt-9-683-2016, 2016.Kuze, A., Suto, H., Shiomi, K., Kawakami, S., Tanaka, M., Ueda, Y., Deguchi, A., Yoshida, J., Yamamoto, Y., Kataoka, F., Taylor, T. E., and Buijs, H. L.: Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space, Atmos. Meas. Tech., 9, 2445–2461, 10.5194/amt-9-2445-2016, 2016.Labzovskii, L. D., Jeong, S.-J., and Parazoo, N. C.: Working towards
confident spaceborne monitoring of carbon emissions from cities using
Orbiting Carbon Observatory-2, Remote Sens. Environ., 233, 111359,
10.1016/j.rse.2019.111359, 2019.Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M.,
Friedlingstein, P., de Jeu, R., de Leeuw, G., Loew, A., Merchant, C. J.,
Müller, B., Popp, T., Reuter, M., Sandven, S., Senftleben, D., Stengel,
M., Van Roozendael, M., Wenzel, S., and Willén, U.: Benchmarking CMIP5
models with a subset of ESA CCI Phase 2 data using the ESMValTool, Remote
Sens. Environ., 203, 9–39, 10.1016/j.rse.2017.01.007, 2017.Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Pongratz, J., Manning, A. C., Korsbakken, J. I., Peters, G. P., Canadell, J. G., Jackson, R. B., Boden, T. A., Tans, P. P., Andrews, O. D., Arora, V. K., Bakker, D. C. E., Barbero, L., Becker, M., Betts, R. A., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Cosca, C. E., Cross, J., Currie, K., Gasser, T., Harris, I., Hauck, J., Haverd, V., Houghton, R. A., Hunt, C. W., Hurtt, G., Ilyina, T., Jain, A. K., Kato, E., Kautz, M., Keeling, R. F., Klein Goldewijk, K., Körtzinger, A., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lima, I., Lombardozzi, D., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., Nojiri, Y., Padin, X. A., Peregon, A., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Reimer, J., Rödenbeck, C., Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., van Heuven, S., Viovy, N., Vuichard, N., Walker, A. P., Watson, A. J., Wiltshire, A. J., Zaehle, S., and Zhu, D.: Global Carbon Budget 2017, Earth Syst. Sci. Data, 10, 405–448, 10.5194/essd-10-405-2018, 2018.Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J. P.,
Abernethy, S., Andrew, R. M., De-Gol, A. J., Willis, D. R., Shan, Y.,
Canadell, J. G., Friedlingstein, P., Creutzig, G., and Peters, G. P.:
Temporary reduction in daily global CO2 emissions during the COVID-19
forced confinement, Nat. Clim. Change, 10, 647–653,
10.1038/s41558-020-0797-x,
2020.Lespinas, F., Wang, Y., Broquet, G., Breon, F.-M., Buchwitz, M., Reuter, M.,
Meijer, Y., Loescher, A., Janssens-Maenhout, G., Zheng, B., and Ciais, P.:
The potential of a constellation of low earth orbit satellite imagers to
monitor worldwide fossil fuel CO2 emissions from large cities and point
sources, Carbon Balance Manage., 15, 18,
10.1186/s13021-020-00153-4,
2020.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, eaam5690, 10.1126/science.aam5690, 2017.Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., Zheng, B.,
Cui, D., Dou, X., He, P., Zhu, B., Lu, C., Ke, P., Sun, T., Wang, Y., Yue,
X., Wang, Y., Lei, Y., Zhou, H., Cai, Z., Wu, Y., Guo, R., Han, T., Xue, J.,
Boucher, O., Boucher, E., Chevallier, F., Wei, Y., Zhong, H., Kang, C.,
Zhang, N., Chen, B., Xi, F., Marie, F., Zhang, Q., Guan, D., Gong, P.,
Kammen, D. M., He, K., and Schellnhuber, H. J.: Near-real-time-data captured
record decline in global CO2 emissions due to COVID-19, arXiv [preprint], arXiv:2004.13614, 28 April 2020.Massart, S., Agustí-Panareda, A., Heymann, J., Buchwitz, M., Chevallier, F., Reuter, M., Hilker, M., Burrows, J. P., Deutscher, N. M., Feist, D. G., Hase, F., Sussmann, R., Desmet, F., Dubey, M. K., Griffith, D. W. T., Kivi, R., Petri, C., Schneider, M., and Velazco, V. A.: Ability of the 4-D-Var analysis of the GOSAT BESD XCO2 retrievals to characterize atmospheric CO2 at large and synoptic scales, Atmos. Chem. Phys., 16, 1653–1671, 10.5194/acp-16-1653-2016, 2016.Matsunaga, T. and Maksyutov, S.: A Guidebook on the Use of Satellite
Greenhouse Gases Observation Data to Evaluate and Improve Greenhouse Gas
Emission Inventories, Satellite Observation Center, National Institute for
Environmental Studies, Japan,
available at: https://www.nies.go.jp/soc/doc/GHG_Satellite_Guidebook_1st_12d.pdf (last access: 26 August 2020), 2018.Miller, S. M. and Michalak, A. M.: The impact of improved satellite retrievals on estimates of biospheric carbon balance, Atmos. Chem. Phys., 20, 323–331, 10.5194/acp-20-323-2020, 2020.Miller, S. M., Michalak, A. M., Detmers, R. G., Hasekamp, O. P., Bruhwiler,
L. M. P., and Schwietzke, S.: China's coal mine methane regulations have not
curbed growing emissions, Nat. Commun., 10, 303, 10.1038/s41467-018-07891-7, 2019.Nassar, R., Hill, T. G., McLinden, C. A., Wunch, D., Jones, D. B. A., and
Crisp, D.: Quantifying CO2 emissions from individual power plants
from space, Geophys. Res. Lett., 44, 10045–10053, 10.1002/2017GL074702, 2017.Noël, S., Reuter, M., Buchwitz, M., Borchardt, J., Hilker, M., Bovensmann, H., Burrows, J. P., Di Noia, A., Suto, H., Yoshida, Y., Buschmann, M., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Podolske, J. R., Pollard, D. F., Sha, M. K., Shiomi, K., Sussmann, R., Té, Y., Velazco, V. A., and Warneke, T.: XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm, Atmos. Meas. Tech. Discuss. [preprint], 10.5194/amt-2020-453, in review, 2020.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.Osterman, G., O'Dell, C., Eldering A., Fisher, B., Crisp, D., Cheng, C.,
Frankenberg, C., Lambert, A., Gunson, M., Mandrake, L., and Wunch, D.: Orbiting
Carbon Observatory-2 and 3 (OCO-2 and OCO-3) Data Product User's Guide,
Operational Level 2 Data Versions 10 and Lite File Version 10 and VEarly,
Technical Report National Aeronautics and Space Administration, Jet
Propulsion Laboratory, California Institute of Technology, Pasadena,
USA, available at: https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_OCO3_B10_DUG.pdf, last access: 17 August
2020.Palmer, P. I., Feng, L., Baker, D., Chevallier, F., Bösch, H., and
Somkuti, P.; Net carbon emissions from African biosphere dominate
pan-tropical atmospheric CO2 signal, Nat. Commun., 10,
3344,
10.1038/s41467-019-11097-w, 2019.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.Pillai, D., Buchwitz, M., Gerbig, C., Koch, T., Reuter, M., Bovensmann, H., Marshall, J., and Burrows, J. P.: Tracking city CO2 emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany, Atmos. Chem. Phys., 16, 9591–9610, 10.5194/acp-16-9591-2016, 2016.Pinty, B., Janssens-Maenhout, G., Dowell, M., Zunker, H., Brunhes, T.,
Ciais, P., Dee, D., Denier van der Gon, H., Dolman, H., Drinkwater, M.,
Engelen, R., Heimann, M., Holmlund, K., Husband, R., Kentarchos, A., Meijer,
Y., Palmer, P., and Scholze, M.: An Operational Anthropogenic CO2 Emissions
Monitoring and Verification Support capacity – Baseline Requirements, Model
Components and Functional Architecture, European
Commission Joint Research Centre, 10.2760/08644, 2017.Pinty, B., Ciais, P., Dee, D., Dolman, H., Dowell, M., Engelen, R.,
Holmlund, K., Janssens-Maenhout, G., Meijer, Y., Palmer, P., Scholze, M.,
Denier van der Gon, H., Heimann, M., Juvyns, O., Kentarchos, A., and Zunker,
H.: An Operational Anthropogenic CO2 Emissions Monitoring and Verification
Support Capacity – Needs and high level requirements for in situ
measurements, European Commission Joint Research
Centre, 10.2760/182790, 2019.Reuter, M., Buchwitz, M., Schneising, O., Heymann, J., Bovensmann, H., and Burrows, J. P.: A method for improved SCIAMACHY CO2 retrieval in the presence of optically thin clouds, Atmos. Meas. Tech., 3, 209–232, 10.5194/amt-3-209-2010, 2010.Reuter, M., Bovensmann, H., Buchwitz, M., Burrows, J. P., Connor, B. J.,
Deutscher, N. M., Griffith, D. W. T., Heymann, J., Keppel-Aleks, G.,
Messerschmidt, J., Notholt, J., Petri, C., Robinson, J., Schneising, O.,
Sherlock, V., Velazco, V., Warneke, W., Wennberg, P. O., and Wunch, D.:
Retrieval of atmospheric CO2 with enhanced accuracy and precision from
SCIAMACHY: Validation with FTS measurements and comparison with model
results, J. Geophys. Res., 116, D04301, 10.1029/2010JD015047, 2011.Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Schneising, O., Pillai, D., Bovensmann, H., Burrows, J. P., Bösch, H., Parker, R., Butz, A., Hasekamp, O., O'Dell, C. W., Yoshida, Y., Gerbig, C., Nehrkorn, T., Deutscher, N. M., Warneke, T., Notholt, J., Hase, F., Kivi, R., Sussmann, R., Machida, T., Matsueda, H., and Sawa, Y.: Satellite-inferred European carbon sink larger than expected, Atmos. Chem. Phys., 14, 13739–13753, 10.5194/acp-14-13739-2014, 2014a.Reuter, M., Buchwitz, M., Hilboll, A., Richter, A., Schneising, O., Hilker,
M., Heymann, J., Bovensmann, H., and Burrows, J. P.: Decreasing emissions of
NOx relative to CO2 in East Asia inferred from satellite observations,
Nat. Geosci., 7, 792–795, 10.1038/ngeo2257, 2014b.Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Rozanov, V.,
Bovensmann, H., and Burrows, J. P.: A Fast Atmospheric Trace Gas Retrieval
for Hyperspectral Instruments Approximating Multiple Scattering – Part 1:
Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote
Sens., 9, 1159, 10.3390/rs9111159, 2017a.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 Sens., 9, 1102,
10.3390/rs9111102, 2017b.Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Bovensmann, H., Burrows,
J. P., Houweling, S., Liu, Y., Nassar, R., Chevallier, F., Ciais, P.,
Marshall, J., and Reichstein, M.: How much CO2 is taken up by the
European terrestrial biosphere?, B. Am. Meteorol. Soc., 98, 665–671, 10.1175/BAMS-D-15-00310.1, 2017c.Reuter, M., Buchwitz, M., Schneising, O., Krautwurst, S., O'Dell, C. W., Richter, A., Bovensmann, H., and Burrows, J. P.: Towards monitoring localized CO2 emissions from space: co-located regional CO2 and NO2 enhancements observed by the OCO-2 and S5P satellites, Atmos. Chem. Phys., 19, 9371–9383, 10.5194/acp-19-9371-2019, 2019.Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., Burrows, J. P., Boesch, H., Di Noia, A., Anand, J., Parker, R. J., Somkuti, P., Wu, L., Hasekamp, O. P., Aben, I., Kuze, A., Suto, H., Shiomi, K., Yoshida, Y., Morino, I., Crisp, D., O'Dell, C. W., Notholt, J., Petri, C., Warneke, T., Velazco, V. A., Deutscher, N. M., Griffith, D. W. T., Kivi, R., Pollard, D. F., Hase, F., Sussmann, R., Té, Y. V., Strong, K., Roche, S., Sha, M. K., De Mazière, M., Feist, D. G., Iraci, L. T., Roehl, C. M., Retscher, C., and Schepers, D.: Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications, Atmos. Meas. Tech., 13, 789–819, 10.5194/amt-13-789-2020, 2020.Schneising, O., Buchwitz, M., Burrows, J. P., Bovensmann, H., Reuter, M., Notholt, J., Macatangay, R., and Warneke, T.: Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite – Part 1: Carbon dioxide, Atmos. Chem. Phys., 8, 3827–3853, 10.5194/acp-8-3827-2008, 2008.Schneising, O., Heymann, J., Buchwitz, M., Reuter, M., Bovensmann, H., and Burrows, J. P.: Anthropogenic carbon dioxide source areas observed from space: assessment of regional enhancements and trends, Atmos. Chem. Phys., 13, 2445–2454, 10.5194/acp-13-2445-2013, 2013.Schneising, O., Reuter, M., Buchwitz, M., Heymann, J., Bovensmann, H., and Burrows, J. P.: Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability, Atmos. Chem. Phys., 14, 133–141, 10.5194/acp-14-133-2014, 2014.Schwandner, F. M., Gunson, M. R., Miller, C. E., Carn, S. A., Eldering, A.,
Krings, T., Verhulst, K. R., Schimel, D. S., Nguyen, H. M., Crisp, D.,
O'Dell, C. W., Osterman, G. B., Iraci, L. T., and Podolske, J. R.:
Spaceborne detection of localized carbon dioxide sources, Science, 358,
eaam5782, 10.1126/science.aam5782, 2017.Sussmann, R. and Rettinger, M.: Can We Measure a COVID-19-Related Slowdown
in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column
Observations, Remote Sens., 12, 2387,
10.3390/rs12152387, 2020.Tohjima, Y., Patra, P. K., Niwa, Y., Mukai, H., Sasakawa, M., and Machida,
T.: Detection of fossil-fuel CO2 plummet in China due to COVID-19 by
observation at Hateruma, Sci. Rep., 10, 18688,
10.1038/s41598-020-75763-6, 2020.Velazco, V. A., Buchwitz, M., Bovensmann, H., Reuter, M., Schneising, O., Heymann, J., Krings, T., Gerilowski, K., and Burrows, J. P.: Towards space based verification of CO2 emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation, Atmos. Meas. Tech., 4, 2809–2822, 10.5194/amt-4-2809-2011, 2011.Wu, D., Lin, J., Oda, T., and Kort, E.: Space-based quantification of per
capita CO2 emissions from cities, Environ. Res. Lett., 15, 035004, 10.1088/1748-9326/ab68eb,
2020.Wu, L., Aben, I., and Hasekamp, O. P.: Product User Guide and Specification
(PUGS) – ANNEX B for products CO2_GOS_SRFP,
CH4_GOS_SRFP (v2.3.8, 2009–2018), available at: http://wdc.dlr.de/C3S_312b_Lot2/Documentation/GHG/PUGS/C3S_D312b_Lot2.3.2.3-v1.0_PUGS-GHG_ANNEX-B_v3.1.pdf (last access: 17 August 2020), 2019.Wunch, D., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Stephens, B. B., Fischer, M. L., Uchino, O., Abshire, J. B., Bernath, P., Biraud, S. C., Blavier, J.-F. L., Boone, C., Bowman, K. P., Browell, E. V., Campos, T., Connor, B. J., Daube, B. C., Deutscher, N. M., Diao, M., Elkins, J. W., Gerbig, C., Gottlieb, E., Griffith, D. W. T., Hurst, D. F., Jiménez, R., Keppel-Aleks, G., Kort, E. A., Macatangay, R., Machida, T., Matsueda, H., Moore, F., Morino, I., Park, S., Robinson, J., Roehl, C. M., Sawa, Y., Sherlock, V., Sweeney, C., Tanaka, T., and Zondlo, M. A.: Calibration of the Total Carbon Column Observing Network using aircraft profile data, Atmos. Meas. Tech., 3, 1351–1362, 10.5194/amt-3-1351-2010, 2010.Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, C. M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P. W., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON, Atmos. Meas. Tech., 10, 2209–2238, 10.5194/amt-10-2209-2017, 2017.Ye, X., Lauvaux, T., Kort, E. A., Oda, T., Feng, S., Lin, J. C., Yang, E.
G., and Wu, D.: Constraining fossil fuel CO2 emissions from urban area
using OCO-2 observations of total column CO2, J. Geophys.
Res.-Atmos., 125, e2019JD030528, 10.1029/2019JD030528, 2020.Yin, Y., Ciais, P., Chevallier, F., Li, W., Bastos, A., Piao, S., Wang, T.,
and Liu, H.: Changes in the response of the Northern Hemisphere carbon
uptake to temperature over the last three decades, Geophys. Res.
Lett., 45, 4371–4380, 10.1029/2018GL077316,
2018.Zhang, R., Zhang, Y., Lin, H., Feng, X., Fu, T.-M., and Wang, Y.: NOx
Emission Reduction and Recovery during
COVID-19 in East China, Atmosphere, 11, 433,
10.3390/atmos11040433, 2020.Zheng, B., Chevallier, F., Ciais, P., Broquet, G., Wang, Y., Lian, J., and Zhao, Y.: Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2, Atmos. Chem. Phys., 20, 8501–8510, 10.5194/acp-20-8501-2020, 2020a.Zheng, B., Geng, G., Ciais, P., Davis, S. J., Martin, R. V., Meng, J., Wu,
N., Chevallier, F., Broquet, G., Boersma, F., van der A, R., Lin, J., Guan,
D., Lei, Y., He, K., and Zhang, Q.: Satellite-based estimates of decline and
rebound in China's CO2 emissions during COVID-19 pandemic, Sci. Adv., 6,
eabd4998, 10.1126/sciadv.abd4998, 2020b.Zeng, N., Han, P., Liu, D., Liu, Z., Oda, T., Martin, C., Liu, Z., Yao, B.,
Sun, W., Wang, P., Cai, Q., Dickerson, R., and Maksyutov, S.: Global to
local impacts on atmospheric CO2 caused by COVID-19 lockdown,
arXiv [preprint], arXiv:2010.13025, 25 October 2020.