Search

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO 2) column-averaged dry-air mole fraction (XCO 2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO 2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO 2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and - 0.3 ppm for land and ocean observations, respectively. Comparison of XCO 2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO 2 fields derived from atmospheric inversion systems that assimilated only near-surface CO 2 observations, i.e., did not assimilate satellite CO 2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO 2 over small areas, as well as XCO 2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO 2 measurements, suggesting they may be used together for scientific analyses. [ABSTRACT FROM AUTHOR]

The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (XCO2 ​​​​​​​) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO 2. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1 %–0.5 % in XCO2 , residual biases and some sources of error remain unaccounted for. Forward model and other errors can lead to systematic biases in the retrieved XCO2 , which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations. In this study we explore algorithm-induced biases through the use of simulated OCO-3 snapshot area mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vEarly product which contains artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated Level 1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the new OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g., MicroCarb, GeoCarb, GOSAT-GW, CO2M). [ABSTRACT FROM AUTHOR]

The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (XCO2) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO2. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1-0.5% in XCO2, residual biases and some sources of error remain unaccounted for (Wunch et al., 2017; Worden et al., 2017; Torres et al., 2019). Forward model and other errors can lead to systematic biases in the retrieved XCO2, which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations (Nassar et al., 2017; Kiel et al., 2021). In this study we explore algorithm-induced biases through the use of simulated OCO-3 Snapshot Area Mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vearly product which contain artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated L1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the new OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias, and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g. MicroCARB, GeoCarb, GOSAT-GW, CO2M). [ABSTRACT FROM AUTHOR]

Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO2 emissions (larger than 1.0 ktCO2 h−1 per 10−2 square degree grid cell) over the globe with a simple plume cross‐sectional inversion approach. We have compared our results with a global gridded and hourly inventory. The corresponding OCO emission retrievals explain more than one third of the inventory variance at the corresponding cells and hours. We have binned the data at diverse time scales from the year (with OCO‐2) to the average morning and afternoon (with OCO‐3). We see consistent variations of the median emissions, indicating that the retrieval‐inventory differences (with standard deviations of a few tens of percent) are mostly random and that trends can be calculated robustly in areas of favorable observing conditions, when the future satellite CO2 imagers provide an order of magnitude more data. Plain Language Summary: In the wake of the Paris Climate Agreement, there is an increasing need to monitor emissions from fossil fuel combustion around the world. For CO2 in particular, satellite imagers are being designed to observe the emission plumes from large point sources and intense urban area sources. In order to assess their potential, we have tested a simple emission retrieval scheme on the multi‐year archive of the two NASA Orbiting Carbon Observatories which provide dense observations along the orbit line, but with a narrow swath. We have compared our results with a global gridded and hourly inventory. The corresponding emission retrievals explain a large part of the inventory variability, despite uncertainty in both datasets. We also see consistent variations in the middle emission values at different time scales. These results suggest that the differences between retrievals and inventory are mostly random and that the trends can be calculated robustly in areas of favorable observation conditions, when future satellite CO2 imagers provide an order of magnitude more data. Key Points: We have retrieved some instantaneous CO2 emissions for one third of the large emission cells of a global high‐resolution hourly inventoryThe emission retrievals explain more than one third of the inventory variance at the corresponding cells and hoursConsistent temporal variations of median emissions suggest that trends can be robustly calculated when more data become available [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory 2 (OCO-2) satellite has been providing information to estimate carbon dioxide (CO2) fluxes at global and regional scales since 2014 through the combination of CO2 retrievals with top–down atmospheric inversion methods. Column average CO2 dry-air mole fraction retrievals have been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of 10 atmospheric inversions all characterized by different transport models, data assimilation algorithms, and prior fluxes using first OCO-2 v7 in 2015–2016 and then OCO-2 version 9 land observations for the longer period 2015–2018. Inversions assimilating in situ (IS) measurements have also been used to provide a baseline against which the satellite-driven results are compared. The time series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiment using either OCO-2 or IS data. We then evaluate the inversion ensemble based on the dataset from the Total Carbon Column Observing Network (TCCON), aircraft, and in situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region, which seems to show the impact of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions, and the estimation of carbon emissions over tropical Africa require further analysis. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory-3 (OCO-3) is NASA's next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52 ∘ , with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space. The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule. This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP–DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode. [ABSTRACT FROM AUTHOR]

The impact of human emissions of carbon dioxide and methane on climate is an accepted central concern for current society. It is increasingly evident that atmospheric concentrations of carbon dioxide and methane are not simply a function of emissions but that there are myriad feedbacks forced by changes in climate that affect atmospheric concentrations. If these feedbacks change with changing climate, which is likely, then the effect of the human enterprise on climate will change. Quantifying, understanding, and articulating the feedbacks within the carbon-climate system at the process level are crucial if we are to employ Earth system models to inform effective mitigation regimes that would lead to a stable climate. Recent advances using space-based, more highly resolved measurements of carbon exchange and its component processes--photosynthesis, respiration, and biomass burning--suggest that remote sensing can add key spatial and process resolution to the existing in situ systems needed to provide enhanced understanding and advancements in Earth system models. Information about emissions and feedbacks from a long-term carbon-climate observing system is essential to better stewardship of the planet. [ABSTRACT FROM AUTHOR]

The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (X...) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) X... retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 um (weak CO2 band) and 2.06 um (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of ≅ 20-25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≅ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April-May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1. [ABSTRACT FROM AUTHOR]

NASA's Orbiting Carbon Observatory-3 (OCO-3) was designed to support the quantification and monitoring of anthropogenic CO 2 emissions. Its Snapshot Area Map (SAM) and target mode measurements provide an innovative dataset for carbon studies on sub-city scales. Unlike any other current space-based instrument, OCO-3 has the ability to scan large contiguous areas of emission hot spots like cities, power plants, and volcanoes. These measurements result in dense, fine-scale spatial maps of column averaged dry-air mole fractions of carbon dioxide (X CO 2). For the first time, we present and analyze X CO 2 distributions over the Los Angeles megacity (LA) derived from OCO-3 SAM and target mode observations. Urban X CO 2 enhancements range from 0 − 6 ppm (median enhancements ≃ 2 ppm) relative to a clean background and show excellent agreement with nearby ground-based TCCON measurements of X CO 2. OCO-3's dense observations reveal intra-urban variations of X CO 2 over the city that have never been observed from space before. The spatial variations are mainly driven by the complex fossil fuel emission patterns and meteorological conditions in the LA Basin and are in good agreement with those from co-located TROPOMI measurements of co-emitted NO 2. Differences between measured and simulated X CO 2 enhancements from two models (WRF-Chem and X-STILT) are typically below 1 ppm with larger differences for some sub regions. Both models capture the observed intra-urban X CO 2 gradients. Further, OCO-3's multi-swath measurements capture about three times as much of the city emissions compared to single-swath overpasses. OCO-3's frequent target and SAM mode observations will pave the way to constrain urban emissions at finer, sub-city scales. [ABSTRACT FROM AUTHOR]

NASA's Orbiting Carbon Observatory-3 (OCO-3) was installed on the International Space Station (ISS) on 10 May 2019. OCO-3 combines the flight spare spectrometer from the Orbiting Carbon Observatory-2 (OCO-2) mission, which has been in operation since 2014, with a new Pointing Mirror Assembly (PMA) that facilitates observations of non-nadir targets from the nadir-oriented ISS platform. The PMA is a new feature of OCO-3, which is being used to collect data in all science modes, including nadir (ND), sun-glint (GL), target (TG), and the new snapshot area mapping (SAM) mode. This work provides an initial assessment of the OCO-3 instrument and algorithm performance, highlighting results from the first 8 months of operations spanning August 2019 through March 2020. During the In-Orbit Checkout (IOC) phase, critical systems such as power and cooling were verified, after which the OCO-3 spectrometer and PMA were subjected to a series of rigorous tests. First light of the OCO-3 spectrometer was on 26 June 2019, with full science operations beginning on 6 August 2019. The OCO-3 spectrometer on-orbit performance is consistent with that seen during preflight testing. Signal to noise ratios are in the expected range needed for high quality retrievals of the column-averaged carbon dioxide (CO 2) dry-air mole fraction (XCO 2) and solar-induced chlorophyll fluorescence (SIF), which will be used to help quantify and constrain the global carbon cycle. The first public release of OCO-3 Level 2 (L2) data products, called "vEarly", is being distributed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). The intent of the vEarly product is to evaluate early mission performance, facilitate comparisons with OCO-2 products, and identify key areas to improve for the next data release. The vEarly XCO 2 exhibits a root-mean-squared-error (RMSE) of ≃ 1, 1, 2 ppm versus a truth proxy for nadir-land, TG&SAM, and glint-water observations, respectively. The vEarly SIF shows a correlation with OCO-2 measurements of >0.9 for highly coincident soundings. Overall, the Level 2 SIF and XCO 2 products look very promising, with performance comparable to OCO-2. A follow-on version of the OCO-3 L2 product containing a number of refinements, e.g., instrument calibration, pointing accuracy, and retrieval algorithm tuning, is anticipated by early in 2021. • Orbiting Carbon Observatory-3 has been installed on the International Space Station. • OCO-3 passed in-orbit checkout and began collecting science data August 2019. • The planned mission lifetime of OCO-3 is 3 years; August 2019 to August 2022. • OCO measures total column carbon dioxide and solar induced chlorophyll fluorescence. • The first data product release, vEarly, is publicly available on the NASA DISC. [ABSTRACT FROM AUTHOR]

With three imaging grating spectrometers, the Orbiting Carbon Observatory-2 (OCO-2) measures high spectral resolution spectra ( λ / Δ λ ≈ 19,000) of reflected solar radiation within the molecular oxygen (O 2 ) A-band at 0.765 μ m and two carbon dioxide (CO 2 ) bands at 1.61 and 2.06 μ m. OCO-2 uses onboard lamps with a reflective diffuser, solar observations through a transmissive diffuser, lunar measurements, and surface targets for radiometric calibration and validation. Separating calibrator aging from instrument degradation poses a challenge to OCO-2. Here we present a methodology for trending the OCO-2 Build 8R radiometric calibration using OCO-2 nadir observations over eight desert sites and nearly simultaneous observations from Moderate Resolution Imaging Spectroradiometer (MODIS) with sensor viewing zenith angles of 15 ± 0.5 ∘ . For the O 2 A-band, this methodology is able to quantify a drift of −0.8 ± 0.1% per year and capture a small error in correcting the aging of the solar calibrator. For the other two OCO-2 bands, no measurable changes were seen, indicating less than 0.1% and less than 0.3% per year drift in the radiometric calibration of Band 2 and Band 3, respectively. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory 3 (OCO-3) will continue global CO2 and solar-induced chlorophyll fluorescence (SIF) using the flight spare instrument from OCO-2. The instrument has been through ground testing and thermal vacuum, and will be packaged for installation on the International Space Station (ISS), currently scheduled for launch in March 2019. This talk will focus on one of the new capabilities of OCO-3, the Snapshot Area Maps.The low-inclination ISS orbit lets OCO-3 sample the tropics and sub-tropics across the full range of daylight hours with dense observations at northern and southern mid-latitudes (+/- 52º). The combination of these dense CO2 and SIF measurements provides continuity of data for global flux estimates as well as a unique opportunity to address key deficiencies in our understanding of the global carbon cycle. The instrument utilizes an agile, 2-axis pointing mechanism (PMA), providing the capability to look towards the bright reflection from the ocean and validation targets. In addition to the nadir-, glint-, and target-mode geometries familiar from OCO-2, OCO-3 includes a new observation mode dedicated to mapping out larger spatial-scale emitters like cities. This Snapshot Area Map (SAM) mode will be used to map areas of up to 100x100 km2 on the Earth surface with the standard OCO-3 ground footprints of 2x2 km2, providing unprecedented high spatial resolution coverage of large-scale CO2 emitters worldwide. Measurements over urban centers could aid in making estimates of fossil fuel CO2 emissions. Similarly, the snapshot mapping mode can be used to sample regions of interest for the terrestrial carbon cycle. This talk will focus on details of the planned observations of SAMs, including the sampling locations, details of the simulated maps, frequency of revisit over the year, hours of the day that are sampled, and the plans for making the data easily accessible to the community. [ABSTRACT FROM AUTHOR]