Reductions in economic activity in response to the Coronavirus disease 2019 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] [6] [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 and analysis.
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.
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] [19] [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 threedimensional, 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.
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. 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 bottomup 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) (46) (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., 14 C in CO2 data (52) . 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 Northern Hemisphere. 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. 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
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 other disciplines.
(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 (63) .
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 threedimensional 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.
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 multiyear 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 transportvariability-driven signal. We refer to this difference as the "analysis correction." The pandemicfree, 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 the analysis.
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) (71) (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) .
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) . 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. 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) . 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). 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, LPJwsl 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.