Orbiting Carbon Observatory-2 Research Articles

Urban XCO2 Gradients From a Dense Network of Solar Absorption Spectrometers and OCO‐3 Over Mexico City

AbstractSatellite measurements of urban CO2 plumes offer a global approach to track CO2 emissions for large cities. To examine and to quantify the feasibility of space‐based monitoring, an intensive measurement campaign (MERCI‐CO2) using seven solar‐tracking Fourier transform infrared (FTIR) spectrometers has been conducted over the Mexico City Metropolitan Area (MCMA) to monitor urban emissions and to evaluate Snapshot Area Map (SAM) observations from the NASA's Orbiting Carbon Observatory‐3 (OCO‐3) mission. Once adjusted for their respective averaging kernels, we diagnosed a positive difference between OCO‐3 and FTIR column measurements (1.06 ppm). Thanks to this unprecedented amount of column observations over a large city, we demonstrate that XCO2 gradients within OCO‐3 SAMs align with the inter‐calibrated FTIR measurements (mean bias of 0.3 ppm), confirming the potential to track CO2 emissions from space over large metropolitan areas. XCO2 urban‐rural differences across the FTIR network, show a strong correlation with observed gradients, with Pearson's correlation coefficients (R) around 0.92. The correlation is significantly lower when considering intra‐urban gradients, where R drop to around 0.24. Simulated XCO2 enhancements (ΔXCO2) based on X‐STILT for both FTIR and OCO‐3 show relatively high correlations (R is around 0.6) using high‐resolution footprints and two gridded inventories. Spatial correlations with OCO‐3 improve when aggregating satellite retrievals at coarser resolutions (10 km). Our study demonstrates the capabilities of detecting urban gradients by FTIR network and OCO‐3 SAM observations over MCMA, a promising result to evaluate the evolution of MCMA's emissions over the coming decade.

Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches

Top-down constraints of CO2 emissions from coal-fired power plants are critical to improving the accuracy of CO2 emission inventory and designing carbon reduction strategies. Different top-down models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application for monitoring point-source CO2 emissions from satellite. Here, we apply three top-down models to estimate CO2 emissions from individual coal-fired power plants in the United States (US) and China in 2014–2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO2 enhancement to the observation. The second and third methods apply the same inversion framework using the maximum likelihood estimation, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are consistent with the EPA reported emission rates, with correlation coefficients (r) of 0.76 and 0.85 and mean biases (MB) of 4.06 and 3.97 ktCO2/d relative to EPA reports at all 10 power plants, respectively. The Gaussian plume model driven by wind fields from WRF-Chem model without the wind rotation shows comparable ability in reproducing the EPA reported emission rates at 7 power plants (r = 0.82, MB = 6.17), but is not applicable for the other three cases. We find that application of the high-resolution three-dimensional wind fields can better capture the shape of observed plumes, especially under complex wind conditions, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows advanced capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of <30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO2 emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO2 emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO2 emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.

Data Drought in the Humid Tropics: How to Overcome the Cloud Barrier in Greenhouse Gas Remote Sensing

AbstractDiagnosing land‐atmosphere fluxes of carbon‐dioxide (CO2) and methane (CH4) is essential for evaluating carbon‐climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel‐2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1% to 1.0%. By far the lowest yields occur in the wet season, consistent with Sentinel‐2 cloud patterns. We find that increasing the spatial resolution of observations to ∼200 m would increase yields by 2–3 orders of magnitude and allow regular measurements in the wet season. Thus, the key to effective tropical greenhouse gas observations lies in regularly acquiring high‐spatial resolution data.

Assessing the impact of global carbon dioxide changes on atmospheric fluctuations in Iran through satellite data analysis

Abstract This study aimed to examine how global CO2 changes affect atmospheric CO2 (XCO2) concentrations in Iran from 2015 to 2020. XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) satellite and CO2 surface flux data from the Copernicus Atmosphere Monitoring Service were analyzed. Monthly and annual XCO2 and surface flux values were compared. Over the 6 years, XCO2 in Iran increased steadily by 12.66 ppm, mirroring global rises. However, Iran's CO2 surface flux decreased, with slight increases in anthropogenic emissions but decreased natural and total fluxes. Monthly patterns of XCO2 and surface flux exhibited variations, with XCO2 reaching its zenith in spring and dipping to its lowest point during summer, while surface flux attained its peak during the summer months. The results reveal a significant discrepancy between Iran's surface CO2 flux and atmospheric XCO2 trends. While Iran's anthropogenic emissions increased barely from 2015–2020, its natural and total CO2 fluxes decreased. However, XCO2 increased steadily over this period, indicating the dominant impact of global rather than local factors on Iran's CO2 levels. The research emphasizes the critical need for a coordinated international effort, utilizing satellite monitoring data, to implement science-based policies that mitigate escalating global CO2 emissions. Curbing worldwide greenhouse gas.

Tracking Carbon Dioxide with Lagrangian Transport Simulations: Case Study of Canadian Forest Fires in May 2021

The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment in other parts of the world. Numerical simulations of greenhouse gas transport processes are often affected by uncertainties in the location and timing of the emission sources and local meteorological conditions, and it is difficult to obtain accurate and credible predictions by combining remote sensing satellite data with given meteorological forecasts or reanalyses. To study the regional transport processes and impacts of greenhouse gases produced by sudden large-scale forest fires, this study applies the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) to conduct forward simulations of the CO2 transport process of greenhouse gases emitted from forest fires in the central region of Saskatchewan, Canada, during the period of 17 May to 25 May 2021. The simulation results are validated with the Orbiting Carbon Observatory-2 Goddard Earth Observing System (OCO-2 GEOS) Level 3 daily gridded CO2 product over the study area. In order to leverage the high computational costs of the numerical simulations of the model, we implement the forward simulations on the Tianhe-2 supercomputer platform and the JUWELS HPC system, which greatly improves the computational efficiency through parallel computation and makes near-real-time predictions of atmospheric transport processes feasible.

The importance of digital elevation model accuracy in XCO2 retrievals: improving the Orbiting Carbon Observatory 2 Atmospheric Carbon Observations from Space version 11 retrieval product

Abstract. Knowledge of surface pressure is essential for calculating column-averaged dry-air mole fractions of trace gases, such as CO2 (XCO2). In the NASA Orbiting Carbon Observatory 2 (OCO-2) Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, the retrieved surface pressures have been found to have unacceptable errors, warranting a parametric bias correction. This correction depends on the difference between retrieved and a priori surface pressures, which are derived from a meteorological model that is hypsometrically adjusted to the surface elevation using a digital elevation model (DEM). As a result, the effectiveness of the OCO-2 bias correction is contingent upon the accuracy of the referenced DEM. Here, we investigate several different DEM datasets for use in the OCO-2 ACOS retrieval algorithm: the OCODEM used in ACOS v10 and previous versions, the NASADEM+ (a composite of SRTMv4, ASTER GDEMv3, GIMP, and RAMPv2 DEMs) used in ACOS v11, the Copernicus GLO-90 DEM (GLO-90 DEM), and two polar regional DEMs (ArcticDEM and REMA). We find that the NASADEM+ (ASTER GDEMv3) has a persistent negative bias on the order of 10 to 20 m across most regions north of 60° N latitude, relative to all the other DEMs considered (OCODEM, ArcticDEM, and GLO-90 DEM). Variations of 10 m in DEM elevations lead to variations in XCO2 of approximately 0.4 ppm, meaning that the XCO2 from OCO-2 ACOS v11 retrievals tends to be 0.4 to 0.8 ppm lower across regions north of 60° N than XCO2 from OCO-2 ACOS v10. Our analysis also suggests that the GLO-90 DEM has superior global continuity and accuracy compared to the other DEMs, motivating a post-processing update from OCO-2 v11 Lite files (which used NASADEM+) to OCO-2 v11.1 by substituting the GLO-90 DEM globally. We find that OCO-2 v11.1 improves accuracy and spatial continuity in the bias-corrected XCO2 product relative to both v10 and v11 in high-latitude regions while resulting in marginal or no change in most regions within ± 60° latitude. In addition, OCO-2 v11.1 provides increased data throughput after quality control filtering in most regions, partly due to the change in DEM but mostly due to other corrections to quality control parameters. Given large-scale differences north of 60° N between the OCODEM and NASADEM+, we find that replacing the OCODEM with NASADEM+ yields a ∼ 100 TgC shift in inferred carbon uptake for the zones spanning 30 to 60° N and 60 to 90° N, which is on the order of 5 % to 7 % of the estimated pan-Arctic land sink. Changes in inferred fluxes from replacing the OCODEM with the GLO-90 DEM are smaller, and given the evidence for improved accuracies from this DEM, this suggests that large changes in inferred fluxes from the NASADEM+ are likely erroneous.

Ocean-driven interannual variability in atmospheric CO2 quantified using OCO-2 observations and atmospheric transport simulations

Interannual variability (IAV) in the atmospheric CO2 growth rate is caused by variation in the balance between uptake by land and ocean and accumulation of anthropogenic emissions in the atmosphere. While variations in terrestrial fluxes are thought to drive most of the observed atmospheric CO2 IAV, the ability to characterize ocean impacts has been limited by the fact that most sites in the surface CO2 monitoring network are located on coasts or islands or within the continental interior. NASA’s Orbiting Carbon-Observatory 2 (OCO-2) mission has observed the atmospheric total column carbon dioxide mole fraction (XCO2) from space since September 2014. With a near-global coverage, this dataset provides a first opportunity to directly observe IAV in atmospheric CO2 over remote ocean regions. We assess the impact of ocean flux IAV on the OCO-2 record using atmospheric transport simulations with underlying gridded air-sea CO2 fluxes from observation-based products. We use three observation-based products to bracket the likely range of ocean air-sea flux contributions to XCO2 variability (over both land and ocean) within the GEOS-Chem atmospheric transport model. We find that the magnitude of XCO2 IAV generated by the whole ocean is between 0.08-0.12 ppm throughout the world. Depending on location and flux product, between 20-80% of the IAV in the simulations is caused by IAV in air-sea CO2 fluxes, with the remainder due to IAV in atmospheric winds, which modulate the atmospheric gradients that arise from climatological ocean fluxes. The Southern Hemisphere mid-latitudes and low-latitudes are the dominant ocean regions in generating the XCO2 IAV globally. The simulation results based on all three flux products show that even within the Northern Hemisphere atmosphere, Southern Hemisphere ocean fluxes are the dominant source of variability in XCO2. Nevertheless, the small magnitude of the air-sea flux impacts on XCO2 presents a substantial challenge for detection of ocean-driven IAV from OCO-2. Although the IAV amplitude arising from ocean fluxes and transport is 20 to 50% of the total observed XCO2 IAV amplitude of 0.4 to 1.6 ppm in the Southern Hemisphere and the tropics, ocean-driven IAV represents only 10% of the observed amplitude in the Northern Hemisphere. We find that for all three products, the simulated ocean-driven XCO2 IAV is weakly anti-correlated with OCO-2 observations, although these correlations are not statistically significant (p&amp;gt;0.05), suggesting that even over ocean basins, terrestrial IAV obscures the ocean signal.

Retrieval anthropogenic CO2 emissions from OCO-2 and comparison with gridded emission inventories

Estimating anthropogenic CO2 emissions from satellite observations contributes to transparency in CO2 emissions reporting. In this study, we proposed a method for calculating CO2 emissions using Orbiting Carbon Observatory-2 (OCO-2) XCO2 (the column-averaged CO2 dry-air mole fraction) observations. We identified local XCO2 plume enhancements on the OCO-2 track and retrieved CO2 emissions through minimizing the difference between the XCO2 enhancement identified from OCO-2 and the XCO2 enhancement simulated by the Gaussian plume model based on emission rates provided by Open-Data Inventory for Anthropogenic Carbon Dioxide (ODIAC). Among 473 cases successfully retrieved from the OCO-2 from September 2014 to June 2023, the average hourly estimated CO2 emissions (OCO-2 emissions) are 6166.52 tCO2/h with the uncertainty of 1604.51 tCO2/h. Comparison with ODIAC, EDGAR (Emissions Database for Global Atmospheric Research) and MEIC (Multi-resolution Emission Inventory for China) emission inventories shows that the OCO-2 emissions are slightly overestimated. Globally, OCO-2 emissions are approximately 1.25 times that of ODIAC and 1.22 times that of EDGAR, while in China, they are 1.39 times that of MEIC. By conducting comprehensive assessments encompassing case studies in California, Riyadh, and Xinjiang, and by comparing the results with relevant investigations, this discrepancy can be attributed to missing statistics on emission inventories, actual emissions varying with the time of day, or complex terrain and other factors. This study effectively quantifies CO2 emissions using OCO-2 satellite data and provides valuable insights for monitoring significant regional CO2 emissions.

Temporal variability of atmospheric columnar CO2, CH4, CO and N2O concentrations using ground-based remote sensing FTIR Spectrometer

Solar absorption spectra collected using a ground-based Fourier Transform Infrared (FTIR) Spectrometer were used to retrieve the column-averaged concentrations (X) of CO2, CH4, CO, and N2O during the years 2016–2019 in the Suburban region of Shadnagar, Telangana, India. For the research period, the diurnal mean amplitudes of XCO2, XCH4, XCO, and XN2O are ∼ 13 ppmv, ∼50 ppbv, ∼13 ppbv, and ∼ 19 ppbv respectively. Strong seasonality at the study site is indicated by the seasonal variations of XCO2 and XCO of 18.3 ppmv and 39.2 ppbv, respectively. Additionally, we compared FTIR data to XCO2 detected by Orbiting Carbon Observatory-2 (OCO-2) and CO columns from Measurements of Pollution in the Troposphere (MOPITT). For the XCO2 and CO columns, the comparison research shows a mean relative bias between FTIR data and satellite data of 0.92 % and 0.71 %, respectively. The FTIR and satellite data show a Pearson correlation coefficient (r) of 0.71 (XCO2, N = 26 co-located) and 0.60 (CO columns, N = 44), respectively. The findings demonstrate a good agreement between our data and satellite data.

A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images

Abstract. Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas. Its atmospheric concentration has increased by almost 50 % since the beginning of the industrial era, causing climate change. Fossil fuel combustion is responsible for most of the atmospheric CO2 increase, which originates to a large extent from localized sources such as power stations. Independent estimates of the emissions from these sources are key to tracking the effectiveness of implemented climate policies to mitigate climate change. We developed an automatic procedure to quantify CO2 emissions from localized sources based on a cross-sectional mass-balance approach and applied it to infer CO2 emissions from the Bełchatów Power Station (Poland) using atmospheric observations from the Orbiting Carbon Observatory 3 (OCO-3) in its snapshot area map (SAM) mode. As a result of the challenge of identifying CO2 emission plumes from satellite data with adequate accuracy, we located and constrained the shape of emission plumes using TROPOspheric Monitoring Instrument (TROPOMI) NO2 column densities. We automatically analysed all available OCO-3 overpasses over the Bełchatów Power Station from July 2019 to November 2022 and found a total of nine that were suitable for the estimation of CO2 emissions using our method. The mean uncertainty in the obtained estimates was 5.8 Mt CO2 yr−1 (22.0 %), mainly driven by the dispersion of the cross-sectional fluxes downwind of the source, e.g. due to turbulence. This dispersion uncertainty was characterized using a semivariogram, made possible by the OCO-3 imaging capability over a target region in SAM mode, which provides observations containing plume information up to several tens of kilometres downwind of the source. A bottom-up emission estimate was computed based on the hourly power-plant-generated power and emission factors to validate the satellite-based estimates. We found that the two independent estimates agree within their 1σ uncertainty in eight out of nine analysed overpasses and have a high Pearson's correlation coefficient of 0.92. Our results confirm the potential to monitor large localized CO2 emission sources from space-based observations and the usefulness of NO2 estimates for plume detection. They also illustrate the potential to improve CO2 monitoring capabilities with the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) satellite constellation, which will provide simultaneously retrieved XCO2 and NO2 maps.

Exploring CO2 anomalies in Brazilian biomes combining OCO-2 & 3 data: Linkages to wildfires patterns

Climate change is a challenge to the global community and one of the causes is the increase of greenhouse gases (GHG) in the atmosphere, especially carbon dioxide (CO2). The major emission source of this gas into the atmosphere comes from the burning of fossil fuels and biomass burning, on the other hand, the main sink comes from biochemical processes such as photosynthesis. Thus, the observation of CO2 is a key point to understanding sources and sinks. In this context, The Orbiting Carbon Observatory 2 (OCO-2) and 3 (OCO-3), are a NASA dedicated mission to monitor the column-averaged dry-air mole fraction of carbon dioxide (XCO2) on a global scale. We combined the OCO-2 and OCO-3 observations to study the spatial distribution of XCO2 anomalies and how some of these anomalies are related to fire occurrence in the Brazilian Biomes during 2020 and 2021 considering two different seasons, Dry and Wet. The fire occurrence was obtained from Fire Information for Resource Management System (FIRMS) that provides the data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) product of active fires and thermal anomalies at Near-Real Time (MCD14DL, collection 6). The OCO-2/3 observations are affected by cloud formations in wet seasons, we observe that the dry period has more observations. The XCO2 anomaly values range from ∼ 7.0 ppm to −7.0 ppm and mostly positive anomalies occur in Amazon Biome, and this ecosystem has higher average values for all periods (∼0.9 ppm), compared to the other biomes. The fire occurrence was higher in dry periods, especially in 2020 when unprecedented fire outbreaks were registered in Brazil. The most affected biomes were Pantanal, Cerrado, and Amazon. XCO2 positive anomalies spatially agree with fire foci over some areas, and the correlation values between them ranged from 0.2 to 0.5 depending on the biome and season, and when considering observations with clouds the correlation is slightly higher. We point out for the first time the possibility of using OCO-2 and 3 combined, also, how positive XCO2 anomalies are related to fire occurrence in different ecosystems and periods, and the role of cloud detection in this relationship.

Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

The Middle East has major sources of anthropogenic carbon dioxide (CO2) emissions, but a dearth of ground-based measurements precludes an investigation of its regional and temporal variability. This is achieved in this work with satellite-derived estimates from the Orbiting Carbon Observatory-2 (OCO-2) and OCO-3 missions from September 2014 to February 2023. The annual maximum and minimum column (XCO2) concentrations are generally reached in spring and autumn, respectively, with a typical seasonal cycle amplitude of 3–8 ± 0.5 ppmv in the Arabian Peninsula rising to 8–10 ± 1 ppmv in the mid-latitudes. A comparison of the seasonal-mean XCO2 values with the CO2 emissions estimated using the divergence method stresses the role played by the sources and transport of CO2 in the spatial distribution of XCO2, with anthropogenic emissions prevailing in arid and semi-arid regions that lack persistent vegetation. In the 8-year period 2015–2022, the XCO2 concentration in the United Arab Emirates (UAE) increased at a rate of about 2.50 ± 0.04 ppmv/year, with the trend empirical orthogonal function technique revealing a hotspot over northeastern UAE and southern Iran in the summer where anthropogenic emissions peak and accumulate aided by low-level wind convergence. A comparison of the satellite-derived CO2 concentration with that used to drive climate change models for different emission scenarios in the 8-year period revealed that the concentrations used in the latter is overestimated, with maximum differences exceeding 10 ppmv by 2022. This excess in the amount of CO2 can lead to an over-prediction of the projected increase in temperature in the region, an aspect that needs to be investigated further. This work stresses the need for a ground-based observational network of greenhouse gas concentrations in the Middle East to better understand its spatial and temporal variability and for the evaluation of remote sensing observations as well as climate models.

Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America.

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.

Improved estimation of CO2 emissions from thermal power plants based on OCO-2 XCO2 retrieval using inline plume simulation

CO2 emissions from power plants are the dominant source of global CO2 emissions, thus in the context of global warming, accurate estimation of CO2 emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO2 emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions. Here, based on 6 cases of power plant plumes observed by the OCO-2 satellite over the Yangtze River Delta, China, we use an inline plume rise module coupled in the Community Multi-scale Air Quality model (CMAQ) to simulate the plumes and invert the emissions, and compare the simulated plumes and inverted emissions using the GPM model. We found that CO2 emissions can be significantly overestimated or underestimated based on the GPM simulations, and that the CMAQ inline plume simulation could significantly improve the estimates. However, the simulation bias in wind speed can significantly affect the inversion results. These results indicate that accurate meteorological field and plume simulations are critical for future inversion of point source emissions.

Assessment of the Emission Characteristics of Major States in the United States using Satellite Observations of CO2, CO, and NO2

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO2 from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO2, CO, and NO2 across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO2 and CO2 emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO2 and CO emissions and a weaker correlation of 0.37 between CO and NO2 emissions. Notably, we identify the NO2 concentration as a reliable indicator of CO2 emission levels, in which a 1% increase in NO2 concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO2 emissions. Enhancement ratios among NO2, CO, and XCO2 are also calculated, uncovering that high ΔNO2: ΔXCO2 ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO2 ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making.

Neutral Tropical African CO2 Exchange Estimated From Aircraft and Satellite Observations

AbstractTropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric carbon dioxide (CO2) have greatly increased spatial coverage over tropical regions, providing the potential for improved estimates of terrestrial fluxes. Despite this advancement, the spread among satellite‐based and in‐situ atmospheric CO2 flux inversions over northern tropical Africa (NTA), spanning 0–24°N, remains large. Satellite‐based estimates of an annual source of 0.8–1.45 PgC yr−1 challenge our understanding of tropical and global carbon cycling. Here, we compare posterior mole fractions from the suite of inversions participating in the Orbiting Carbon Observatory 2 (OCO‐2) Version 10 Model Intercomparison Project (v10 MIP) with independent in‐situ airborne observations made over the tropical Atlantic Ocean by the National Aeronautics and Space Administration (NASA) Atmospheric Tomography (ATom) mission during four seasons. We develop emergent constraints on tropical African CO2 fluxes using flux‐concentration relationships defined by the model suite. We find an annual flux of 0.14 ± 0.39 PgC yr−1 (mean and standard deviation) for NTA, 2016–2018. The satellite‐based flux bias suggests a potential positive concentration bias in OCO‐2 B10 and earlier version retrievals over land in NTA during the dry season. Nevertheless, the OCO‐2 observations provide improved flux estimates relative to the in situ observing network at other times of year, indicating stronger uptake in NTA during the wet season than the in‐situ inversion estimates.

Solar‐Induced Fluorescence Helps Constrain Global Patterns in Net Biosphere Exchange, as Estimated Using Atmospheric CO2 Observations

AbstractSolar‐induced chlorophyll fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO2 observations, and thus global variability in NBE. We do so using a 4‐year record of CO2 observations from NASA's Orbiting Carbon Observatory 2 satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space‐time variability in atmospheric CO2 observations, particularly in the extratropics. In the extratropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO2 observations compared to an ensemble of process‐based CO2 flux models and other vegetation indicators. With that said, other vegetation indicators, when multiplied by photosynthetically active radiation, yield similar results as SIF and may therefore be an effective structural SIF proxy at regional to global spatial scales. Furthermore, we find that using SIF as a predictor variable in the geostatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. These results highlight how SIF can help constrain global‐scale variability in NBE.

A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10

Abstract. Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.

Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Abstract. Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (&lt;15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.

Spatio-temporal variability of Atmospheric CO2 and CH4 concentrations over Antarctica using Ground and Space-based measurements

The present study reports on the variability of atmospheric carbon dioxide (CO2) and methane (CH4) concentrations over Antarctica using data collected during the 2015–2016 and 2016–2017 as part of the Indian Scientific Expedition to Antarctica (ISEA). During the austral summer (January–March), this study examined the spatio-temporal variability in atmospheric CO2 and CH4 concentrations at the Indian Antarctic Stations (IAS). An average atmospheric CO2 concentration was observed to be 396.25 ± 4.20 ppm at Bharati station during 2015-16 austral summer period, whereas CO2 (CH4) concentrations were 398.7 ± 0.46 ppm (1780 ± 2 ppb) and 399.1 ± 0.41 ppm (1780 ± 3 ppb) at Bharati and Maitri stations, respectively during 2016–17. During the study period, the Orbiting Carbon Observatory-2 (OCO-2) based column averaged CO2 concentration shows similar values against records. At Bharati and Maitri, the diurnal amplitudes of CO2 (CH4) are varied from 0.11 ppm (0.37 ppb) to 8.90 ppm (11.44 ppb) and 0.54 ppm (1.96 ppb) to 7.30 ppm (16 ppb), respectively. In the austral summer, dominant winds at the IAS are from the east, northeast, and southeast.