Regional Impacts of COVID-19 on Carbon Dioxide
Detected Worldwide from Space
B Weir*1,2, D Crisp3, C W O’Dell4, S Basu2,5, A Chatterjee1,2, T Oda1,2, L E Ott2, S Pawson2, B
Poulter2, Z Zhang6, P Ciais7, S J Davis9, and Z Liu8
1Universities Space Research Association, Columbia, Maryland, USA
2NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
3Jet Propulsion Laboratory, Pasadena, California, USA
4Colorado State University, Fort Collins, Colorado, USA
5Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
6University of Maryland, College Park, Maryland, USA
7Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
8Department of Earth System Science, University of California, Irvine, USA
9Department of Earth System Science, Tsinghua University, Beijing, China
* Correspondence to: firstname.lastname@example.org
Monitoring the global distribution of greenhouse gases using spaceborne observations and
attributing regional anomalies of their surface fluxes to either climatic or human processes
represents a new frontier in Earth system science. The reduction in economic activity in early 2020
due to the Coronavirus Disease 2019 (COVID-19) pandemic led to unprecedented decreases in
monthly carbon dioxide (CO2) emissions from fossil fuel use, estimated at up to 20% for a given
day and 4–7% by April. This paper shows, for the first time, that the regional impact of COVID-
19 was observable from space. Our approach uses data assimilation to ingest satellite observations
of column average CO2 (XCO2) from NASA’s Orbiting Carbon Observatory 2 (OCO-2) into the
Goddard Earth Observing System (GEOS), an integrated Earth system model. We then quantify
regional anomalies in XCO2 and attribute them to either climate-driven variability or changes in
anthropogenic emissions. Starting in February 2020 and continuing through May, XCO2 over
many of the World’s largest emitting regions was 0.24–0.48 parts per million (ppm) less than
expected in a pandemic-free scenario, consistent with reductions of 5–10% in annual, global fossil
fuel emissions. This signal is just above the detection limit of OCO-2, but still distinguishable from
climate-driven anomalies, notably related to the 2019–2020 Indian Ocean Dipole. The timings of
the monitored reductions in CO2 growth and subsequent rebound clearly coincide with changes in
country-level activity due to COVID-19. Our results also indicate considerable remaining
uncertainties about the regrowth of CO2 emissions over China following the Lunar New Year
vacation that coincided with the COVID-19 lockdown. These results demonstrate that current
spaceborne technologies designed to monitor CO2 anomalies are approaching levels of accuracy
needed to support and inform climate mitigation strategies with future missions expected to meet
One Sentence Summary
This paper shows, for the first time, that the regional impact of COVID-19 on atmospheric carbon
dioxide was observable and quantifiable from space-based instruments.
Reductions in economic activity in response to the Coronavirus disease 2019 (COVID-19) at the
beginning of 2020 produced the largest short-term change in fossil fuel and cement carbon dioxide
(CO2) emissions since the Industrial Revolution (1). Preliminary emissions estimates for 2020
based on economic activity data suggest that, compared to 2019 emissions, daily global emissions
decreased by as much as 15–20% in April (2). Accumulated from the start of the year, these
reductions reached ~7.8% by June (3) and are expected to total ~4% (low estimate) to ~7% (high
estimate) for the year, with the exact annual decrease depending on the intensity of the reduction
during the lockdowns, and the timing of the return of economic activity to pre-pandemic levels
(2). Reductions in human activities were also indicated in satellite observations of changes in
nighttime light intensity (4), and observations of short lived combustion related pollutants, e.g.,
nitrogen dioxide (NO2; 5–7). While activity data based estimates are consistent with reductions in
satellite NO2 observations (2), the relationship of NO2 to CO2 emissions depends on combustion
efficiency which varies significantly across sectors and regions. Furthermore, CO2 emission
estimates based on recent activity data, rather than the annual reported inventories typically used
by “bottom-up” estimates, rely on different metrics and are thus subject to their own unique
uncertainties—the two most well-known products (2, 3), for example, produce estimates with
greater day-to-day variability by construction—and would benefit from independent verification
For the past two decades, space agencies from around the world have planned and launched several
satellite missions to observe vertical column average CO2 (XCO2) with a long-term goal of
quantifying anthropogenic CO2 emissions and their trends. The current constellation includes
Japan’s Greenhouse Gases Observing Satellite (GOSAT; 8), launched in 2009, NASA’s Orbiting
Carbon Observatory-2 (OCO-2; 9, 10) in 2014, Japan’s GOSAT-2 (11) in 2018 and NASA’s OCO-
3, deployed in 2019 on the International Space Station (12). These missions were all designed as
sounders that regularly sample the atmosphere at high precision, instead of mapping it in its
entirety, and had a strong focus on understanding the terrestrial biosphere. Future missions are
expected to place an increasing focus on understanding anthropogenic emissions and improve
coverage with greater swath widths and/or by sampling the atmosphere multiple times a day, e.g.,
NASA's Geostationary Carbon Observatory (GeoCarb; 13) positioned over the Americas and
many other ongoing international efforts (14).
Developing a system that uses atmospheric CO2 observations to monitor changes in anthropogenic
emissions remains a landmark achievement needed to support the implementation of international
climate accords (15, 16). Unlike NO2 observations, which display clear plumes with high
concentrations over emitting areas, CO2 has a long lifetime in the atmosphere and is well mixed.
Furthermore, in any given month, regional terrestrial biospheric fluxes have similar or greater
magnitudes than fossil fuel emissions. This means that the CO2 signals caused by even large
emissions changes are confounded by those from long range atmospheric transport and natural
fluxes. To verify emissions changes with atmospheric CO2 observations, the eventual goal is to
sample the atmosphere as densely and frequently as possible above and downwind of emitting
areas. This is not achievable with the current sparse surface network focused primarily on
background CO2, but becomes increasingly possible with satellite observations. Below, we present
our approach for monitoring changes in atmospheric CO2, analyze the observed changes in XCO2
in 2020, and demonstrate that our system can detect and quantify the impact of COVID-19 on
XCO2, despite the significant difficulties noted in other studies (17). We conclude with a
discussion of the scientific implications of those results.
Monitoring Atmospheric CO2 in Near Real Time
The Goddard Earth Observing System (GEOS)/OCO-2 atmospheric carbon monitoring system has
several unique characteristics that enable it to capture and quantify the signal due to COVID-19
(interactive visualizations available online at Refs. 18–20). First, it takes advantage of the unique
coverage and precision of OCO-2 measurements. Mixed throughout the atmosphere, a 7%
reduction in annual fossil fuel emissions represents just a 0.33 ppm change (21, 22) against the
global marine boundary layer background concentration of 412.22 ppm in January 2020 (23).
While previous instruments have had insufficient precision and/or coverage to detect signals of
this size, they remain within the accuracy bounds of OCO-2 (24, 25). Second, it uses coupled
meteorology-carbon cycle components within GEOS (26) and data assimilation to infer three-
dimensional, gridded fields of CO2 for the entire OCO-2 data record (see Materials and Methods
and Fig. 1). By using a transport model, our approach accounts for the year-to-year variability in
CO2 due to differences in atmospheric circulation: even with no change in surface fluxes, transport
variability can cause several ppm differences in XCO2 over the same area from one year to the
next (27; see also Fig. S5). This difference is especially relevant over North America, where
passing weather systems cause sharp gradients across frontal boundaries (28). Analyses of XCO2
retrievals that do not account for transport variability (29, 30) are therefore unlikely to capture
year-to-year differences in emissions, especially given the sparse and infrequent sampling of OCO-
2 over emitting areas. Finally, our approach produces regular updates in near real time (NRT),
taken here to mean a latency of less than a month, enabling the study of changes in the carbon
cycle as they occur (31). Other common methods for inferring surface fluxes from atmospheric
observations, e.g., flux inversion systems (27, 32, 33), typically trail the current date by a year or
more and/or can be available for just a few years.
Unprecedented Carbon Dioxide Anomalies in Early 2020
Over much of the Northern Hemisphere, home to most of the World’s largest economies and more
than 95% of global total emissions, 16-day running means of XCO2 from the GEOS/OCO-2
analysis show consistent, negative anomalies compared to a pandemic-free scenario (see Materials
and Methods) beginning in February 2020 and continuing through May (Fig. 2). At the
country/regional level, these anomalies show remarkable correlation with restrictions on activity
and their subsequent relaxation (Fig. 3). Their steep initial decrease and subsequent levelling off
corroborates activity data indicators that emissions dropped precipitously during the initial
confinement and then slowly recovered or levelled off. These anomalies are well outside the
variability over the baseline period of 2017–2019 (see Materials and Methods): peak 1σ
uncertainties for February–May 2020 ranged from 0.07–0.16 ppm while peak reductions in XCO2
growth reached 0.27–0.39 ppm (Table 1). Averaging those reductions and taking the geometric
mean of the uncertainties gives us a 1σ range of 0.24–0.48 ppm for the Northern Hemisphere.
Assuming the average reduction over the entire atmosphere is that same number at the end of the
year, these estimates would produce 0.51–1.0 Pg C less of CO2 in the atmosphere (21, 22),
corresponding to a 5–10% reduction in the 10 Pg C global fossil fuel emissions total estimated for
2019 (34). The conversion of ppm CO2 to Pg C used above (22) is only a rough indicator of
emissions, especially since interhemispheric mixing takes over a year to transport a signal from
the Northern to Southern Hemisphere (35).
The monitored changes in XCO2 over the Northern Hemisphere in February–May 2020 are
primarily attributable to reductions in fossil fuel emissions for two reasons. First, late 2019 through
2020 saw neutral to weak La Niña conditions (36, 37) of the El Niño/Southern Oscillation (ENSO).
Globally, the annual growth rate of CO2 correlates well with a linear combination of total
anthropogenic emissions and the Niño 3 or 3.4 ENSO index (38, 39). The latter term, which serves
as a proxy for biospheric variability, is small in 2020 (36, 37), indicating a strong anthropogenic
role in the growth rate anomaly. Regionally and monthly, ENSO remains a dominant driver of
biospheric anomalies, but not without notable exceptions (40). Second, the months of February–
May occur during a “shoulder” season in which net biospheric exchange is near its smallest (Figs.
S8 and S10), making it an ideal time to capture an anomaly driven by fossil fuel emissions.
Transport simulations of 2020 anomalies from the Lund, Potsdam, Jena - Wald, Schnee und
Landscaft (LPJ-wsl; 41) dynamic global vegetation model (see Materials and Methods) also
indicate that the biospheric anomalies in the Northern Hemisphere were relatively minor in
February–May (Fig. S9).
To further evaluate the ability of our monitoring system to provide meaningful information about
country-level fossil fuel emissions estimates, we simulate the expected anomaly for 2020 based on
the daily activity data based estimates (3), indicated by the blue boxes in Fig. 3. Overall, our results
and the bottom-up simulation agree about the magnitude of reductions at a country/regional level.
One notable disagreement is in the timing of the reduction over the United States. In the bottom-
up simulations, reductions begin before activity restrictions as air with less CO2 is transported from
China, across the North Pacific, and eventually to the United States, a process that takes several
days. These reductions are not apparent in the monitoring system. Over China (Fig. 3c) and the
North Pacific (Fig. 2), where we expect to see sustained reductions in XCO2, there is almost a
complete rebound following the Lunar New Year. This is consistent with rebounds in NO2
observations from satellites (5) and in situ sensors (7). While another study (6) found a rebound in
NO2 emissions following the Lunar New Year based on satellite observations, they did not find a
complete recovery to pre-pandemic levels. There are several factors that could play a role in these
discrepancies, each of which requires further investigation. In particular, uncertainties in Chinese
emissions are greater than perhaps any other region (42, 43), preventing us from making any strong
conclusions about the magnitude of the recovery in their emissions. Nevertheless, these differences
cannot be attributed to observational coverage or the data assimilation system—an observing
system experiment (OSE) that samples the simulated values at the time and place of OCO-2
soundings and assimilates the result is able to reproduce simulated signals (Figs. S3 and S4). While
the differences between the fossil fuel simulated and GEOS/OCO-2 analysis anomalies are on the
order of the spread over previous years (indicated in grey in Fig. 3c), there are no indications of
strong positive biospheric anomalies transported over the Pacific Ocean and North America from
a companion simulation of biospheric anomalies (Fig. S9).
Starting in 2019 and continuing through February 2020, GEOS/OCO-2 captured another striking
change in XCO2, this time originating from the influence of a record-breaking climate anomaly on
the terrestrial biosphere (Fig. 4). In 2020, well before their COVID-19 related restrictions, XCO2
growth dropped over India and sub-Saharan Africa and increased over Australia (Fig. 5). At this
time, the countries surrounding the Indian Ocean were experiencing the tail end of the 2019–2020
Indian Ocean Dipole (IOD) whose Dipole Mode Index was in the greatest positive phase on record
(36, 44). The impact of the IOD on the terrestrial biosphere and atmospheric circulation began in
2019, when both sub-Saharan Africa and India had wetter than usual boreal autumns—during the
positive phase, cooler-than-normal sea surface conditions persist in the eastern Indian Ocean with
warmer-than-normal conditions in the western tropical Indian Ocean (45–47). This East-West
contrast in ocean conditions alters the wind, temperature, and rainfall patterns in the region,
typically bringing mild temperatures and floods to sub-Saharan Africa and the Indian subcontinent
(48) and high temperatures and droughts to East Asia and Australia (49), among other ecological
and socio-economic impacts. That increased rainfall over sub-Saharan Africa and the Indian
subcontinent resulted in an extremely productive agricultural year and bumper crop harvests (50),
while high temperature and drought conditions resulted in a record-setting fire season throughout
Australia (51). The impact of these extremes on the carbon cycle persisted well into 2020,
eventually falling off in early March (Figs. 4 and 5; see Fig. S9 for a comparison simulation).
We found that satellite monitored changes in early 2020 XCO2 due to the COVID-19 pandemic
were small (0.24–0.48 ppm), negative, and consistent with country-level activity data. The United
States, Europe, and Asia each saw noticeable reductions in XCO2 growth coinciding with
restrictions placed on economic activity and a rebound in emissions as those restrictions were
lifted. Attribution of these signals to changes in anthropogenic emissions remains challenging:
interannual variability in transport and biospheric carbon-climate teleconnections both drive
concentration changes many times greater than the record-setting changes in regional
anthropogenic emissions due to COVID-19. For example, increased net vegetation growth in India
and Africa and fires and respiration in Australia driven by the record-setting 2019–2020 IOD
produced the greatest XCO2 anomaly of early 2020. The ability to detect fossil fuel CO2 emissions
changes in the midst of climate variability is a significant milestone toward the long-term goal of
monitoring future emissions reductions, especially given the planned increase in space-based
observing capability. Despite these advances, land and ocean flux variations related to ENSO and
IOD, and their related uncertainties, limit our ability to monitor and understand changes in
anthropogenic emissions. Attribution of CO2 anomalies to individual surface flux components, and
not their total, remains an active area of research with growing importance due to the societal need
to reduce and monitor emissions. This effort will benefit in the future by improvements in
terrestrial biospheric models, significant planned future increases in the space-based coverage of
CO2 observations from NASA’s Geostationary Carbon Observatory, Japan’s Greenhouse gases
Observing SATellites (GOSATs), and the European CO2 Monitoring mission with a greater
emphasis on monitoring fossil fuel emissions, colocation with other remote-sensing observations,
e.g., NO2, and in situ measurements, e.g., 14C in CO2 data (52).
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The authors dedicate this article to the memory of Michael Freilich, the former director of the
Earth Science Division of NASA. His skill, dedication, bravado, and commitment were critical to
ensuring that the Orbiting Carbon Observatory-2 even existed. The authors would also like to thank
Kenneth W. Jucks for his invaluable scientific insights and guidance as program manager, Peter
H. Jacobs for comments and suggestions, the NASA Center for Climate Simulation (NCCS),
where all of the computing for this project was conducted, and the Total Carbon Column Observing
Network (TCCON) and NOAA for providing data. This work was supported by NASA’s Carbon
Monitoring System (NNH16DA001N-CMS 16-CMS16-0054), Science Team for the OCO
Missions (NNH17ZDA001N-OCO2 17-OCO2-17-0010), and Modeling, Analysis, and Prediction
(NNH16ZDA001N-MAP 16-MAP16-0165) projects. Some of the work described here was
performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract to
the National Aeronautics and Space Administration. Government sponsorship acknowledged. All
authors contributed to the development of the ideas described within this manuscript, the data
collection, and the manuscript’s composition. B. Weir led the writing and development of the data
assimilation system. D. Crisp and C. O’Dell led the development and processing of OCO-2
retrievals. T. Oda led the development of the ODIAC fossil fuel emissions, B. Poulter and Z. Zhang
the LPJ-wsl biospheric model, and P. Ciais, Z. Liu, and S. Davis the NRT activity data based fossil
fuel emissions. The authors declare no competing interests. OCO-2 data were produced by the
OCO-2 project at JPL and were obtained from the free data archive maintained at the Goddard
Earth Sciences Data and Information Services Center (GES DISC;
https://disc.gsfc.nasa.gov/OCO-2). The GEOS/OCO-2 analyzed fields used in this study will be
made available at GES DISC as well.
Fig. 1. Snapshots OCO-2 data and assimilated GEOS/OCO-2 fields. (A) 16 days of OCO-2
XCO2 soundings over April 1–16, 2020, (B) transects of 3-hourly, 3-dimensional GEOS/OCO-2
CO2 on April 9, 2020 at 00:00 UTC at the surface (bottom), 15 km above sea level (top,
transparent), and along the International Dateline (right), and (C) the 16-day average of
GEOS/OCO-2 assimilated XCO2 over the same period. In the data assimilation system, satellite
observations (A) are used to update the instantaneous, gridded GEOS fields (B), from which
vertical and temporal averages follow (C). OCO-2 sounding coverage for each month is shown in
Fig. S1 and the impact of assimilation on the fits to observations in Fig. S2.
Fig. 2. Anomalies of GEOS/OCO-2 16-day average XCO2 in ppm over the Northern
Hemisphere starting on March 1, 2020 and continuing through June 5, 2020. Blue colors
indicate decreases in XCO2 growth compared to a pandemic-free scenario (see Materials and
Methods), while red colors indicate increases. Reductions surpassing 1 ppm, depicted in deep blue,
developed over North America and Europe in March through May as COVID-19 related
restrictions on activity were put in place. Afterwards, in late May to early June, mixing by
atmospheric transport, rebounds in emissions, and variability in the terrestrial biosphere diminish
the magnitude and coherence of the COVID-19 signal.
Fig. 3. Time series of regional average anomalies in GEOS/OCO-2 running 16-day XCO2
anomalies. (A) the United States, (B) Western Europe, (C) Russia, and (D) China. The grey shaded
area indicates the spread of anomalies across the nominal years (2017–2019) and the stem plot
indicates the 2020 anomaly. Simulations for 2020 using activity based emissions estimates (3) are
depicted in blue with asterisks. Dot-dashed lines indicate a rough beginning and end (when
appropriate) to confinements for each area and are provided in Table 1. For the corresponding
histograms of daily sounding counts, see Fig. S3.
Table 1. GEOS/OCO-2 February–May regional reductions in XCO2 growth and associated
uncertainties. Reductions and uncertainties are calculated from the data depicted in Fig. 3 (stem
plot and grey shading) as the peak 2020 reduction and peak 1
uncertainty in the 2017–2019
spreads for February–May (see Materials and Methods). Start dates and end dates are taken from
activity data (3). The average reduction over all four regions is 0.36 ppm and the geometric mean
of the uncertainties is 0.12 ppm, giving a 1
range of 0.24–0.48 ppm for the reduction over the
Fig. 4. Identical to Fig. 2, but over the Indian ocean before the start of COVID-19 related
confinements. The dominant signal is from the carbon-climate teleconnection between the 2019–
2020 Indian Ocean Dipole (IOD), the strongest on record, and terrestrial biospheric exchange. In
January and February 2020, greater than average precipitation over India and Africa drove
increased biospheric uptake, while higher than average temperatures over Australia and Southeast
Asia drove increased respiration and biomass burning.
Fig. 5. Identical to Fig. 3, but for different regions. (A) India, (B) Australia, and (C) Southern
Africa. The dominant signal is that of the IOD impact over India, but most of the anomalies are
within the range of typical changes. As opposed to the Northern Hemisphere (Fig. 3), early in the
calendar year is a time of significant biospheric activity in the Tropics and Southern Hemisphere
(Figs. S8–S10), complicating the interpretation of any anthropogenic variability. For the
corresponding histograms of daily sounding counts, see Fig. S4.
Materials and Methods
The GEOS/OCO-2 atmospheric carbon monitoring system tracks changes in CO2 throughout the
atmosphere every three hours by ingesting OCO-2 Build 10 XCO2 full-physics retrievals (53, 54)
into GEOS using a statistical technique commonly referred to as data assimilation (used here)
and/or state estimation (55, 56). Data assimilation synthesizes simulations and observations,
adjusting the atmospheric state of atmospheric constituents like CO2 to reflect observed values,
thus gap-filling observations when and where they are unavailable. These features are particularly
appealing given the narrow, 10 km wide swath and 16-day repeat time of OCO-2 (Fig. 1).
Compared to interpolation, Kriging, and machine learning methods, data assimilation has the
advantage that it makes estimates based on our collective scientific understanding of Earth’s
carbon cycle, as encapsulated within GEOS, rather than relying on functional relationships that
rarely hold in nature. The value of relying on forecasted fields instead of functional relationships
in data analysis has been understood in the numerical weather prediction community since at least
1954 (57), almost a decade before E. Lorenz’s seminal work (58), yet receives less attention in
GEOS/OCO-2 applies an implementation of the GEOS Constituent Data Assimilation System
(CoDAS), a high-performance computing implementation of the Gridpoint Statistical Interpolation
(GSI; 59) technique for finding the analyzed state that minimizes the three-dimensional variational
(3D-Var) cost function formulation of its differences with observed and simulated values. In
particular, it ingests column retrievals of trace gas abundances taking into account both their
vertical sensitivity/averaging kernel and a priori assumptions. While current versions of GSI
include the ability to use four-dimensional variational (4D-Var) and hybrid ensemble-variational
formulations (60), this application relies on the simpler 3D-Var technique. In GEOS CoDAS, the
atmospheric circulation is constrained by the millions of remote sensing and in situ observations
every hour included in the Modern Era Retrospective analysis for Research and Application,
version 2 (MERRA-2; 61). This accurate representation of transport patterns is critical for
interpreting measured variations that reflect a combination of nearby and distant surface fluxes
due to the long lifetime of CO2. In other applications, GEOS CoDAS has been used to analyze
multi-decadal trends of stratospheric ozone (62) and the anomalously small ozone hole of 2019
GEOS/OCO-2 runs on a horizontal grid with a nominal horizontal resolution of 50 km and 72
terrain-following vertical levels from the surface up to 0.01 hPa. Fig. 1 demonstrates the impact
of data assimilation on OCO-2 coverage for April 2020. Before assimilation (top panel), there are
notable patches of missing data where either clouds (e.g., the Amazon), aerosols (China), and high
solar zenith angles (the poles) prevent reliable measurements. Assimilation produces three-
dimensional CO2 fields with global coverage that are updated every three hours (middle panel).
Values in missing areas are inferred from nearby observational data and model relationships. The
16-day running means (bottom panel) and monthly means analyzed in this paper follow from
simple averaging of the assimilated CO2 fields. Data processing is divided into six separate streams
covering 2015–2020. Each stream begins on October 31 of the previous year to allow some
equilibration of the analysis prior to the period of interest beginning on January 1. Differences
between overlapping streams are less than 10% of the magnitude of the anomalies analyzed in this
paper, and thus can be safely ignored.
GEOS/OCO-2 has been previously documented for an earlier version of OCO-2 data (10), and has
been available to the public through May 2020 on the NASA/ESA/JAXA trilateral Earth
Observing Dashboard (18) since late July. Its fine spatial resolution enables it to reproduce
atmospheric observations with high fidelity (31, 64), especially over North America, where there
is a wealth of data, e.g., airborne in situ measurements from NASA’s Atmospheric Carbon and
Transport - America (ACT-America) campaign (64). Its extensive evaluation makes us confident
in its ability to reproduce regional signals with small magnitudes. A recent study (65) claims to
have found a COVID-19 signal on the global scale (50°N to 50°S), but most likely mistook it for
the record-setting IOD signal, since they find reductions starting in January (before lockdowns)
concentrated mostly in the Tropics and Southern Hemisphere, emphasizing the need for regional
and monthly resolution and careful consideration in the analysis of anthropogenic CO2 signals.
A unique feature of GEOS CoDAS is its ability to process data in NRT, as retrievals become
available to assimilate. This is accomplished primarily through the use of a surface flux collection,
the Low-order Flux Inversion (LoFI; 31) with distinct modes for retrospective and NRT
simulations. In retrospective simulations, the system uses surface fluxes informed by several
remote sensing datasets that include fire radiative power, nighttime lights, and vegetation
properties like leaf area index (26) and atmospheric growth rate estimates derived from surface
observations. In near real time, before many of these datasets become available, LoFI uses fluxes
and a projected atmospheric growth rate based on data from previous years and the current ENSO
phase (38, 39). This dual capability ensures a strong, multi-platform data constraint on XCO2 on
previous years for computing anomalies while the products for the current year highlight areas
where land, ocean, and fossil fuel fluxes deviate from expectations. For fossil fuel emissions, we
use the 2018 version of the Open-source Data Inventory for Anthropogenic CO2 (ODIAC; 66),
which estimates emissions by tracking fossil fuel consumption (i.e., barrels of oil, tons of coal,
etc.) and cement production. The ODIAC 2018 product ends in 2018. In 2019, we rescale the 2018
monthly gridded maps to match the Global Carbon Project (GCP) 2019 global emissions estimate
(34), and for 2020 we simply repeat 2019 emissions.
Anomalies and pandemic-free baseline atmospheric CO2 fields
Even after obtaining realistic, gap-filled XCO2 maps, defining 2020 anomalies for CO2 is more
challenging than for most other species. For NO2, which is short-lived, simply subtracting a multi-
year mean from the 2020 values is often sufficient for highlighting recent emissions changes (5),
although recent research suggests that such methods can ignore important meteorological
variations (7, 67). For CO2 and other long-lived species, this simple anomaly calculation reveals a
strong imprint of circulation anomalies, which can have a greater impact than and obscure the
spatial signature of emissions changes (Fig. S5). To minimize the circulation influence, at the
beginning of each year we start a companion GEOS simulation that is identical to the analyzed
product, except that OCO-2 data are not assimilated. By subtracting the simulated anomaly from
the analysis anomaly, we isolate the flux-driven signal observed by OCO-2 from the transport-
variability-driven signal. We refer to this difference as the “analysis correction.” The pandemic-
free, baseline scenario is then the average of all analysis corrections for 2017–2019 plus the GEOS
simulation for 2020. This represents 2020 transport while applying the mean analysis corrections
due to assimilating OCO-2. Subtracting the 2020 analysis from the pandemic-free field then gives
the flux driven GEOS/OCO-2 anomaly depicted in Figs. 2–5 and in the supplemental figures.
We omit 2015 and 2016 from our baseline years because they contain one of the strongest ENSOs
on record and are not representative of 2020, which was neutral in the first three months of 2020,
and transitioned to a moderate La Nina in April 2020 (36, 37). Strong ENSO signals produce
significant inter-annual variability in carbon fluxes over ocean and land (32, 68) as well as
atmospheric circulation patterns (69). Figs. S6 and S7 add the 2015 and 2016 anomalies onto the
plots from Figs. 3 and 5. The ENSO years (red) are clear outliers, supporting their exclusion from
As an indicator of uncertainty, we use the range of analysis corrections for individual years in
2017–2019 depicted as the grey shading in Figs. 3 and 5. From the ranges, we calculate the 2
uncertainty as half the min-to-max range of the grey shaded area, corresponding to an assumption
that the 2017–2019 range represents about 95% of year-to-year variability in neutral ENSO
conditions. The uncertainty ranges reported in Table 1 are consistent with evaluations of
GEOS/OCO-2 against independent surface in situ and remote sensing observations and a posteriori
tests of the statistical consistency of the data assimilation system (see Supplement). They are
smaller than, but the same order as the errors reported by the analyses in several previous studies
(17, 70–72). This is to be expected as our uncertainty estimate does not include persistent biases,
while those estimates do. They also coincide with a 0.15 ppm standard deviation of the analysis
error uncertainty for the GEOS/OCO-2 fields calculated from an a posteriori diagnostic (73).
Separating the COVID-19 atmospheric CO2 signal from natural variability
In order to help separate anthropogenic from natural variability, we perform two supplementary
GEOS CO2 simulations. The first transports the difference in 2020 and 2019 emissions from the
daily activity data based fossil fuel estimates (3) through the atmosphere using the same settings
as the GEOS/OCO-2 assimilation run (see the monthly global maps in Fig. S9). For these
simulations, daily country-level emissions totals for 2019 and 2020 are spatially downscaled using
2015 monthly EDGAR v5.0 sector totals (74) for power generation, ground transportation,
industry, aviation, residential energy usage, and international shipping. The second simulation
aims to represent the difference in 2020 biospheric flux by transporting the difference between
2020 and the 2017–2019 average calculated using the LPJ-wsl dynamic global vegetation model
(Figs. S8–S10). While LPJ-wsl is a different model of the terrestrial biosphere than we use for our
prior fluxes, it is useful as a prognostic, independent method of identifying regional biospheric
anomalies and has been demonstrated to realistically reproduce interannual variations in global net
flux (34). For consistency, we apply the same MERRA-2 meteorological data used to force our
transport simulations and as inputs to LPJ-wsl (75).
Fig. S1. Monthly maps of OCO-2 B10 XCO2 retrieval coverage. The OCO-2 satellite flies in a
sun-synchronous, low-Earth orbit with a local overpass time of 1:30 PM in NASA’s Afternoon
Train (A-Train) formation.
Fig. S2 (continued below ...). Monthly maps of OCO-2 minus GEOS/OCO-2 background
(before assimilation; top rows) and analysis (after assimilation; bottom rows) XCO2.
Assimilation clearly improves the fits to the assimilated data, as intended. Differences after
assimilation have O(0.1 ppm) magnitudes, further supporting the uncertainty quantification in the
Fig. S2 (... continued from above).
Fig. S3. Identical to Fig. 3 with OSE results (red open squares) and daily soundings (lower
panel) for 2020 (black lines) and the 2017–2019 average (grey shading). The proximity of the
OSE results to the simulated values (blue dots) demonstrates that the data coverage and
assimilation system can reliably capture the signal of activity-data emissions reduction estimates,
i.e., differences between the analyzed 2020 values (stem plot) and simulated values (blue dots)
cannot be attributed to the data constraint alone.
Fig. S4. Identical to Fig. 5 with OSE results (red open squares) and daily soundings (lower
panel) for 2020 (black lines) and the 2017–2019 average (grey shading). While the OSE results
reproduce the general features of the simulated values (blue dots), there is a noticeable offset in all
three regions. This is to be expected given the greater biospheric influence than in the Northern
Hemisphere (Fig. S9) represented by the greater width of the grey shaded area and the low
sounding coverage over India during their monsoon.
Fig. S5. Anomalies of GEOS/OCO-2 monthly average XCO2 in ppm during early 2020
computed with two different pandemic-free baseline scenarios. The top row, used in the text,
makes an adjustment for year-to-year variability in atmospheric transport by subtracting out
simulated values. The bottom row does not make this adjustment, and much of its variability is
thus dominated by that due to transport. In particular, the large positive/negative anomaly over
Western/Eastern Europe in May 2020 in the calculation without the adjustment (bottom right) is
almost entirely due to an anomaly in atmospheric circulation patterns (Fig. S11).
Fig. S6. Identical to Fig. 3, but with the range of the 2015–2016 ENSO years included. ENSO
anomalies are negative because our surface fluxes underpredict the Northern Hemisphere land sink
in these years.
Fig. S7. Identical to Fig. 5, but with the range of the 2015–2016 ENSO years included.
Fig. S8. Cumulative NBE anomalies from LPJ-wsl terrestrial biospheric model over different
countries/regions. Each line represents a different year with years before the OCO-2 launch
(2014) drawn in grey, years after the OCO-2 launch drawn in black, 1982 drawn in green (most
recent IOD of comparable magnitude), and 2020 drawn in red. The year 2020 is a clear outlier in
South Asia and Oceania/Australia, indicating strong biospheric anomalies. Over North America,
Europe, and East Asia (viz., China), 2020 is a typical year for the biosphere, if not more of a source
(positive values) of carbon to the atmosphere.
Fig. S9 (continued below ...). Monthly maps of XCO2 in ppm from the GEOS/OCO-2
anomaly analysis (top), simulation of LPJ-wsl biospheric flux anomalies (middle), and
simulation of fossil fuel emissions (3) anomalies (bottom). In January through February, the
GEOS/OCO-2 anomaly captures the IOD signal over India, Australia, and Africa, showing
remarkable agreement with the spatial patterns from the LPJ-wsl simulation. The difference in
amplitudes is because LPJ-wsl tends to produce a far greater seasonal cycle amplitude over these
regions than we would expect from LoFI (Fig. S10).
Fig. S9 (... continued from above and below ...). In February–May, the biospheric variability
over the United States, Europe, Russia, and China simulated from LPJ-wsl remains relatively small
and, if anything, net positive, supporting the attribution of the analysis anomalies changes in fossil
fuel emissions. In June, biospheric variability begins to dominate in the Northern Hemisphere,
complicating the interpretation of any anthropogenic signals (cf. the growth into June and July of
the grey shaded regions in Fig. 3), e.g., the large negative analysis anomaly over Siberia in June
(top right) is most likely due to the biosphere (middle right).
Fig. S9 (... continued from above).
Fig. S10. NBE from our a priori surface fluxes (LoFI, black) and the LPJ-wsl model (red)
used for simulating biospheric anomalies. There is striking similarity between the two globally
and over much of the world, e.g., Boreal Eurasia and Europe. Over North and South Africa, LPJ-
wsl produces a seasonal cycle that is about twice as great as LoFI, while over Boreal North
America the difference in amplitude is roughly a factor of three. The difference in Temperate North
America is likely due to significant differences in the representation of corn and soybean harvest
between the two products.
Fig. S11 (continued below …). Meteorological anomalies for 2020 in percent for sea-level
pressure (top) and the geopotential height at 500 hPa (bottom). The strong correlation between
the two is due to hydrostatic balance.
Fig S11 (... continued from above).