Significant uncertainties exist in our understanding of the CO2 and CH4 fluxes between land or ocean and atmosphere on regional and global scales. Remotely-sensed CO2 and CH4 observations provide a significant potential for improving our understanding of the natural carbon cycle and for the monitoring of anthropogenic emissions. Over the last few years, remote sensing technologies for measuring CO2 and CH4 from space, aircraft, and from the ground made great advances. New passive and active instruments from different platforms became available offering unprecedented accuracy and coverage.
This session is open to contributions related to all aspects of remote sensing of the greenhouse gases CO2 and CH4 from current, upcoming and planned satellite missions (e.g., OCO-2/3, GOSAT-2, Tansat, S5P, MERLIN, Microcarb, CO2M), as well as ground-based (e.g., TCCON, COCCON), aircraft, and other remote sensing instruments. This includes advances in retrieval techniques, instrumental concepts, and validation activities. We specifically encourage contributions that focus on the interpretation of observations with respect to natural fluxes or anthropogenic emissions.
vPICO presentations: Fri, 30 Apr
The Orbiting Carbon Observatory 3 (OCO-3) was installed on the International Space Station (ISS) in May 2019 and began routine operations in August 2019 to continue global CO2 and solar-induced chlorophyll fluorescence (SIF) observations using the flight spare instrument from OCO-2. The first version of the data, called vEarly, was released in early 2020, and an update, v10, is being prepared.
The growing OCO-3 dataset includes the standard ocean and land measurements, as well as a large set of validation measurements over TCCON stations and a new locally focused measurement. The new Snapshot Area Map (SAM) mode, where 80km by 80km areas are sampled with 2km by 2km footprints in 2 minutes is measurement approach unique to OCO-3. This is a new observation mode made possible by the agile pointing mirror assembly of OCO-3. Data has been collected over hundreds of cities, volcanos, over areas of interest to the terrestrial carbon community, and in coordination with field campaigns.
The cross comparison of OCO-3 and OCO-2 data, for radiances, XCO2, and SIF is underway to gain insights into data quality and to create and OCO-3 dataset that can be used seamlessly with OCO-2 measurements. We will discuss these intercomparisons, highlighting a few examples, such as the OCO-2 and OCO-3 target and SAM measurements in Los Angeles that were collected on the same day. Highlights from validation activities and global XCO2 data characteristics will be presented, as well as details of the SAM collection statistics and most sampled regions. The value of the OCO-3 dataset for characterization of diurnal patterns will also be shared.
Highlights of the key scientific findings from the mission to date will be included. Finally, looking forward, I will also discuss the mission status, including the expectations for the remaining mission life and progress on developing an improved data version to be released in late spring/early summer 2021.
How to cite: Eldering, A., O'Dell, C., Fisher, B., Kiel, M., Nelson, R., Taylor, T., Somkuti, P., Osterman, G., Pavlick, R., Kurosu, T., and Spiers, G.: Measuring Carbon Dioxide from the International Space Station: An Overview of the OCO-3 Mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1318, https://doi.org/10.5194/egusphere-egu21-1318, 2021.
In the southern hemisphere, the sparse coverage of in-situ CO2 measurements prevents a robust determination of regional carbon fluxes and leads to large uncertainties in inverse model results. Therefore, the extensive spatial coverage afforded by satellite CO2 measurements is especially valuable there. By analyzing satellite measurements, new insights on the carbon cycle can be derived and carbon cycle models can be validated for the southern hemisphere.
Here, we present a comparison of atmospheric CO2 data in Australia provided by the Greenhouse gases Observing SATellite (GOSAT) and the CarbonTracker (CT2019) inverse model from 2009 to 2018. We find that the seasonality of GOSAT CO2 is different from that of CarbonTracker across much of the southern hemisphere. This discrepancy follows a clear seasonal pattern with the largest difference of ~2ppm between October and December. We investigate the origin of the discrepancy by utilizing the CO2 components provided by CarbonTracker and different fire CO2 emission databases. Further, we conduct several sensitivity studies by assimilating GOSAT CO2 in the TM5-4DVar data assimilation system, and by transporting different surface fluxes through the TM5 transport model. Our results suggest that the underestimation of local and transported wildfire CO2 emissions could cause the observed discrepancy in the seasonality of column CO2 between GOSAT and inverse models such as CarbonTracker in the southern hemisphere.
How to cite: Schömann, E.-M., Basu, S., Vardag, S. N., Haun, M., Schreiner, L., and Butz, A.: The seasonal cycle of atmospheric CO2 in the southern hemisphere over the last ten years seen by GOSAT, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2044, https://doi.org/10.5194/egusphere-egu21-2044, 2021.
The OCO-3 instrument was launched on May 4, 2019 from Kennedy Space Center to the International Space Station. Since August 2019, the instrument has taken measurements of reflected sunlight in three near-infrared bands from which column averaged dry-air mole fractions of carbon dioxide (XCO2) are derived. The instrument was specifically designed to measure anthropogenic emissions and its snapshot area map (SAM) and target (TG) observational modes allow to scan large contiguous areas (up to 80×80 km2) on a single overpass over emission hotspots like cities, power plants, or volcanoes. These measurements result in fine-scale spatial maps of XCO2 unlike what can be done with any other current space-based instrument. Here, we present and analyze XCO2 distributions over the Los Angeles (LA) megacity derived from multiple OCO-3 TG and SAM mode observations using the vEarly data product. We find that urban XCO2 values are elevated by 2-6 ppm relative to a clean background. The dense, high resolution OCO-3 observations reveal fine-scale, intra-urban variations of XCO2 over the LA megacity that have not been observed from space before. We further analyze the intra-urban characteristics and compare the XCO2 enhancements observed by OCO-3 with simulated values from two models that can resolve XCO2 variations across the city: an Eulerian (WRF-Chem) and a Lagrangian approach (X-STILT). We show that the observed variations are mainly driven by the complex and highly variable meteorological condition in the LA Basin. Median XCO2 differences between model and observation are typically below 1.3 ppm over the entirety of the LA megacity with slightly larger differences for some sub regions. Further, we find that OCO-3’s multi-swath measurements capture about three times as much of the city emissions compared to single-swath overpasses. In the future, these observations will help to better constrain urban emissions at finer spatiotemporal scales.
How to cite: Kiel, M., Eldering, A., Roten, D. D., Lei, R., Feng, S., Lin, J. C., Lauvaux, T., Roehl, C. M., Oda, T., Iraci, L. T., and Blavier, J.-F.: Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: a first look at the Los Angeles Megacity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3006, https://doi.org/10.5194/egusphere-egu21-3006, 2021.
In this study, we analyse the capability of the Copernicus CO2 monitoring (CO2M) satellite mission to quantify the CO2 emissions of individual power plants, which is one of the prime goals of the mission. The study relies on synthetic CO2 and NO2 satellite observations over parts of the Czech Republic, Germany and Poland and quantifies the CO2 and NOx emissions of the 15 largest power plants in that region using a data-driven mass-balance approach.
The synthetic observations were generated for six CO2M satellites based on high-resolution simulations of the atmospheric transport model COSMO-GHG. To identify the emission plumes, we developed a plume detection algorithm that identifies the location, orientation and extent of multiple plumes from CO2M's NO2 observations. Afterwards, a mass-balance approach was applied to individual plumes to estimate CO2 and NOx emissions by fitting Gaussian curves to the across-plume signals. Annual emissions were obtained by interpolating the temporally sparse individual estimates applying a low-order spline fit.
Individual CO2 emissions were estimated with an accuracy <65% for a source strength >10 Mt CO2 yr-1, while NOx emissions >10 kt NO2 yr-1 were estimated with <56% accuracy. NO2 observations were essential for detecting the plume and constraining the shape of the Gaussian curve. With three CO2M satellites, annual CO2 emissions were estimated with an uncertainty <30% for source strengths larger than 10 Mt yr-1, which includes an estimate of the uncertainty in the temporal variability of emissions. Annual NOx emissions were estimated with an uncertainty <21%. Since NOx emissions can be determined with better accuracy, estimating CO2 emissions directly from the NOx emissions by applying a representative CO2:NOx emission ratio seems appealing but this approach was found to suffer significantly from the high uncertainty in the CO2:NOx emission ratios determined from the same CO2M observations.
Our study shows that CO2M should be able to quantify the emissions of the 400 largest point sources globally with emissions larger than 10 Mt yr-1 that account for about 20 % of global anthropogenic CO2 emissions. However, the mass-balance approach used here has relatively high uncertainties that are dominated by the uncertainties in the estimated CO2 background and the wind speed in the plume, and uncertainties associated with the sparse temporal sampling of the varying emissions. Estimates could be significantly improved if these parameters can be better constrained, e.g., with atmospheric transport simulations and independent observations.
How to cite: Kuhlmann, G., Henne, S., Meijer, Y., Emmenegger, L., and Brunner, D.: Quantifying CO2 emissions of power plants with the CO2M mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3260, https://doi.org/10.5194/egusphere-egu21-3260, 2021.
We conduct a global inverse analysis of 2010–2018 GOSAT satellite observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4º×5º grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil/gas), and too low for Venezuela (oil/gas) and the U.S. (oil/gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with large production or use of fossil methane, including the U.S., Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.
How to cite: Zhang, Y., Jacob, D. J., Lu, X., Maasakkers, J. D., Scarpelli, T. R., Sheng, J.-X., Shen, L., Qu, Z., Sulprizio, M. P., Chang, J., Bloom, A. A., Ma, S., Worden, J., Parker, R. J., and Boesch, H.: Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3810, https://doi.org/10.5194/egusphere-egu21-3810, 2021.
Greenhouse gases (GHGs) play a crucial role with respect to global warming. Therefore, precise and accurate observations of anthropogenic GHGs, especially carbon dioxide (CO2) and methane (CH4), are of utmost importance for the estimation of their emission strengths, flux changes and long-term monitoring. Satellite observations are well suited for this task as they provide global coverage. However, like all measurements these need to be validated. The Total Carbon Column Observing Network (TCCON) performs ground-based observations of GHGs with reference precision using high-resolution Fourier Transform infrared (FTIR) spectrometers. TCCON data are of high accuracy as TCCON uses species dependent scaling factors derived from in-situ reference measurements to be calibrated to the World Meteorological Organization (WMO) reference scale. For several satellites measuring GHGs TCCON data are the main validation source.
To further improve the global coverage of FTIR spectrometers and complement TCCON especially in remote areas, the COllaborative Carbon Column Observing Network (COCCON) was established. Until now the focus of COCCON was on the quality control of EM27/SUN spectrometers and dedicated campaigns to estimate emission strengths of CO2 and CH4 from local and regional sources, e.g. from cities, fracking areas or mining sites.
Here we present a global validation of the Greenhouse Gases Observation Satellite GOSAT using multiple spectrometers from the COCCON network. The COCCON instruments are stationed in Finland, Germany, Greece, Japan, Namibia, Sweden and the USA. The sites span a range of different atmospheric and observing conditions, from subtropical to subpolar regions, including boreal forests and deserts, low and high albedo surfaces, polluted and clean areas. Overall, we find a good agreement between GOSAT and COCCON measurements.
How to cite: Frey, M. M., Dubravica, D., Morino, I., Ohyama, H., Hori, A., Blumenstock, T., Hase, F., Gross, J., Tu, Q., Jacobs, N., Simpson, W. R., Balis, D., Mermigkas, M., Franklin, J. E., and Gottlieb, E.: Validation of the greenhouse gases observing satellite GOSAT using an ensemble of COCCON spectrometers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6767, https://doi.org/10.5194/egusphere-egu21-6767, 2021.
Monitoring of anthropogenic carbon dioxide (CO2) emission sources with air- and space-borne remote sensing instruments relies on high-spatial resolution measurements. Such observations can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra.
In preparation for the CO2IMAGE mission (Δλ ~ 1.3 nm) – a compact satellite proposal currently in phase A at the German Aerospace Center (DLR) – we present here a dedicated study of CO2 monitoring capabilities with the airborne AVIRIS-NG sensor (Δλ ~ 5 nm). We conduct CO2 retrievals of several clear-sky AVIRIS-NG point source observations with the RemoTeC algorithm, based on the short-wave infrared absorption bands of CO2. Favorable state vector and spectral retrieval window configurations are identified that reduce correlations between the carbon dioxide and water vapor column concentrations and surface reflection properties. We also discuss the use of a posteriori correction methods to minimize biases in the retrieved CO2 fields and, finally, we carry out source rate estimates for these case studies.
How to cite: Wilzewski, J. S., Strandgren, J., Baumgartner, A., Haschberger, P., Köhler, C., Krutz, D., Paproth, C., Chapman, J. W., Thompson, D. R., Thorpe, A. K., Mayer, B., Roiger, A., and Butz, A.: Towards the CO2Image mission: performance studies using AVIRIS-NG, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7165, https://doi.org/10.5194/egusphere-egu21-7165, 2021.
How to cite: Hemmer, B., Proß, C., Sander, S. P., Pongetti, T. J., Zeng, Z.-C., Hase, F., Kostinek, J., Kleinschek, R., and Butz, A.: Toward CO2 and CH4 measurements by ground-based observations of surface-scattered sunlight, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7569, https://doi.org/10.5194/egusphere-egu21-7569, 2021.
About seventy-five percent of the global carbon dioxide emissions from fossil fuel come from cities. Reducing anthropogenic greenhouse gas emissions, in particular in developing countries, is a major concern for local, national and international policies. Different mitigation strategies are and will be implemented to reduce greenhouse gas emissions, and the evaluation of their success and their perennization depends on the ability to continuously measure and quantify the effects at different spatial and temporal scales.
Using continuous solar absorption Fourier transform Spectroscopy (FTIR) column measurements in both urban and background environments over the Mexico City metropolitan area, together with in situ datasets, we explore the spatial and temporal variability of the CO2 concentration over the 5 last years in the region. Measurements were performed from three permanent stations equipped with high and low spectral resolution FTIR spectrometers since 2012, 2016 and 2018, respectively, the first is part of the NDACC network while the other two contribute to the COCCON international initiative.
In the frame of the Mexico City’s Regional Carbon Impacts (MERCI-CO2) project, 4 complementary sites equipped with EM27/Sun instruments were temporarily implemented within the megacity since autumn 2020. In particular, our time series encompass the COVID shutdown in MCMA. In this contribution we present results of the long term measurements in background and urban environment, intercomparison measurements, and preliminary results of the temporary MERCI-CO2 stations. In addition we report about the obstacles and opportunities of this intensive measurement campaign.
How to cite: Taquet, N., Stremme, W., González del Castillo, E., Bezanilla, A., Grutter, M., Blumenstock, T., Hase, F., Dubravica, D., Blandin, E., Lopez, M., and Ramonet, M.: CO2 temporal variability over Mexico City metropolitan area from ground-based FTIR column measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7979, https://doi.org/10.5194/egusphere-egu21-7979, 2021.
Global coverage of carbon dioxide (CO2) satellite observations is necessary for accurate seasonal carbon flux estimates. Sufficient seasonal coverage is particularly important for quantifying the carbon cycle at high Northern latitudes which are sensitive to the rapidly changing climate. However, high latitudes pose significant challenges to reliable space-based observations of greenhouse gases. One reason for the shortage of good quality CO2 observations in the high latitudes is the low reflectivity of snow-covered surfaces in the CO2 absorption bands, in addition to large solar zenith angles and frequent cloud coverage over the Arctic and boreal regions. Snow surfaces are highly forward-scattering and therefore the traditional nadir-viewing geometries over land might not be optimal. In addition, the contributions from the 1.6 um and 2.0 um CO2 absorption bands need to be evaluated over snow. In this work, we present a realistic, non-Lambertian surface reflection model of snow based on snow reflectance measurements and examine results of atmospheric radiative transfer simulations in various satellite observation geometries and the contributions from different absorption bands. This research lays important ground work for a dedicated feasibility study of CO2 retrievals over snow, which would ultimately help increase the quantity and reliability of satellite observations at high latitudes from late winter to spring – an important period for the carbon cycle in the rapidly changing Arctic climate.
How to cite: Mikkonen, A., Lindqvist, H., Peltoniemi, J., and Tamminen, J.: Satellite-based remote sensing of carbon dioxide over snow-covered surfaces, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8207, https://doi.org/10.5194/egusphere-egu21-8207, 2021.
The natural ecosystems of tropical Africa represent a significant store of carbon, and play an important but uncertain role in the atmospheric budgets of carbon dioxide and methane. Recent studies using satellite data have concluded that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for a third of global methane growth (Lunt et al 2019), and that the tropical Africa region dominates net carbon emission across the tropics (Palmer et al 2019). The conclusions of such studies are based on the accuracy of various satellite datasets and atmospheric transport models, over a geographical region where there are few independent observations available to check the robustness and validity of these datasets.
Here we present the first ground-based observations of greenhouse gas (GHG) column concentrations over tropical East Africa, obtained using the University of Leicester EM27/SUN spectrometer during its deployment at the National Fisheries Resources Research Institute (NaFIRRI) in Jinja, Uganda. During the deployment we were able to operate the instrument remotely, using an automated weatherproof enclosure designed by the Technical University of Munich (Heinle and Chen 2018, Dietrich et al 2020). The instrument ran near-continuously for a three month period in early 2020, observing total atmospheric column concentrations of carbon dioxide and methane, along with other gases of interest including water vapour and carbon monoxide. We describe the data obtained during this period, processed using tools developed under the COCCON project (COllaborative Carbon Column Observing Network, Frey et al 2019), and demonstrate the value of performing GHG column measurements over tropical East Africa. We then evaluate the performance of CO2 observations from OCO-2 and CH4 from Sentinel 5P TROPOMI - datasets previously used in the studies of Palmer et al 2019 and Lunt et al 2019 respectively - and interpret the comparison with the ground-based observations in the light of data from the GEOS-Chem atmospheric chemistry transport model and the CAMS (Copernicus Atmospheric Monitoring Service) reanalyses.
REFERENCES: Lunt, M. F., Palmer, P. I., Feng, L., Taylor, C. M., Boesch, H., and Parker, R. J.: An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data, Atmos. Chem. Phys., 19, 14721–14740, https://doi.org/10.5194/acp-19-14721-2019, 2019.
Palmer, P.I., Feng, L., Baker, D., Chevallier, F., Boesch, H., and Somkuti, P.: Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nat Commun 10, 3344, https://doi.org/10.1038/s41467-019-11097-w, 2019.
Heinle, L. and Chen, J.: Automated enclosure and protection system for compact solar-tracking spectrometers, Atmos. Meas. Tech., 11, 2173–2185, https://doi.org/10.5194/amt-11-2173-2018, 2018.
Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discussions, 2020, 1–24, https://doi.org/10.5194/amt-2020-300, 2020.
Frey, M. et al.: Building the COllaborative Carbon Column Observing Network (COCCON): long-term stabilityand ensemble performance of the EM27/SUN Fourier transform spectrometer, Atmos. Meas. Tech., 12, 1513–1530, https://doi.org/10.5194/amt-12-1513-2019, 2019
How to cite: Humpage, N., Boesch, H., Okello, W., Dietrich, F., Chen, J., Lunt, M., Feng, L., Palmer, P., and Hase, F.: Greenhouse gas column observations from a portable spectrometer in Uganda, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10156, https://doi.org/10.5194/egusphere-egu21-10156, 2021.
The GOSAT TANSO-FTS sensor has been collecting high spectral resolution measurements of reflected solar radiation in the Oxygen A-band (0.76 microns) and two shortwave-infrared carbon dioxide (CO2) absorption bands (1.6 and 2.0 microns) since April, 2009. The measured radiances allow for estimates of the total column carbon dioxide (XCO2) via retrieval inversion. An eleven year long record of XCO2 retrieved via NASA’s Atmospheric Carbon Observations from Space (ACOS) build 9 software suite is analyzed and discussed. The v9 XCO2 data has been publicly available on the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) since the spring of 2020.
The ACOS GOSAT v9 XCO2 is evaluated against CO2 flux inversion models, observations from the Total Carbon Column Observation Network (TCCON), as well as against collocated measurements from NASA’s OCO-2 satellite. The results indicate a product that agrees with OCO-2 and models within approximately 0.25 ppm with less than 1 ppm standard deviation (σ). Agreement with TCCON is within approximately 0.1 ppm with approximately 1 ppm σ for daily overpass mean aggregated data. The ACOS GOSAT v9 XCO2 product will allow CO2 flux inversion modelers and terrestrial ecologists to address questions about long term (decadal) carbon cycle dynamics related to net and gross carbon fluxes.
How to cite: Taylor, T., O'Dell, C., Eldering, A., Crisp, D., Gunson, M., Fisher, B., Nelson, R., Payne, V., Lindqvist, H., Rigel, K., Griffith, D., Osterman, G., Wunch, D., and Kuze, A.: An 11 year record of GOSAT XCO2 measurements from NASA's ACOS version 9 retrieval algorithms: comparisons to models, TCCON, and OCO-2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10193, https://doi.org/10.5194/egusphere-egu21-10193, 2021.
While initial plans for measuring carbon dioxide from space hoped for 1-2 ppm levels of accuracy (bias) and precision in the CO2 column mean dry air mole fraction (XCO2), in the past few years it has become clear that accuracies better than 0.5 ppm are required for most current science applications. These include measuring continental (1000+ km) and regional scale (100s of km) surface fluxes of CO2 at monthly-average timescales. Considering the 400+ ppm background, this translates to an accuracy of roughly 0.1%, an incredibly challenging target to hit.
Improvements in both instrument calibration and retrieval algorithms have led to significant improvements in satellite XCO2 accuracies over the past decade. The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, including post-retrieval filtering and bias correction, has demonstrated unprecedented accuracy with our latest algorithm version as applied to the Orbiting Carbon Observatory-2 (OCO-2) satellite sensor. This presentation will discuss the performance of the v10 XCO2 product by comparisons to TCCON and models, and showcase its performance with some recent examples, from the potential to infer large-scale fluxes to its performance on individual power plants. The v10 product yields better agreement with TCCON over land and ocean, plus reduced biases over tropical oceans and desert areas as compared to a median of multiple global carbon inversion models, allowing better accuracy and faith in inferred regional-scale fluxes. More specifically, OCO-2 has single sounding precision of ~0.8 ppm over land and ~0.5 ppm over water, and RMS biases of 0.5-0.7 ppm over both land and water. Given the six-year and growing length of the OCO-2 data record, this also enables new studies on carbon interannual variability, while at the same time allowing identification of more subtle and temporally-dependent errors. Finally, we will discuss the prospects of future improvements in the next planned version (v11), and the long-term prospects of greenhouse gas retrievals in the coming years.
How to cite: ODell, C., Eldering, A., Gunson, M., Crisp, D., Fisher, B., Kiel, M., Kuai, L., Laughner, J., Merrelli, A., Nelson, R., Osterman, G., Payne, V., Rosenberg, R., Taylor, T., Wennberg, P., Kulawik, S., Lindqvist, H., Miller, S., and Nassar, R.: Improvements in XCO2 accuracy from OCO-2 with the latest ACOS v10 product, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10484, https://doi.org/10.5194/egusphere-egu21-10484, 2021.
Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Using the satellite observations as reference, the CMIP6 models have a lower bias in the the multi-model mean than CMIP5, but the spread remains large. The satellite data are a combined data product covering the period 2003–2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003–2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009–2014) instruments. While the combined satellite product shows a strong negative trend of decreasing seasonal cycle amplitude (SCA) with increasing XCO2 in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (mean XCO2, growth rate, SCA and trend in SCA). This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.
How to cite: Gier, B. K., Buchwitz, M., Reuter, M., Cox, P. M., Friedlingstein, P., and Eyring, V.: Spatially resolved evaluation of Earth system models with satellite column-averaged CO2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11848, https://doi.org/10.5194/egusphere-egu21-11848, 2021.
The COVID-19 pandemic caused annual CO2 emission reductions estimated up to -8 % for 2020. This approximately matches reductions required year-on-year to fulfill the Paris agreement. We pursue the question whether related atmospheric concentration changes may be detected by the Total Carbon Column Observing Network (TCCON), and brought into agreement with bottom-up emission-reduction estimates. We present an original mathematical framework to derive annual growth rates from TCCON observations. Our approach guarantees robust results for the non-equidistant (clear-sky) sampling of solar absorption measurements of column-averaged carbon dioxide (XCO2) and it includes for the first time a mathematically rigorous uncertainty calculation for annual growth rates. The min-max range of TCCON growth rates for 2012-2019 is [2.00, 3.27] ppm/yr with a largest one-year increase of 1.07 ppm/yr for 2015/16 caused by El Niño. Uncertainties are 0.38 [0.28, 0.44] ppm/yr limited by synoptic variability, including a 0.05 ppm/yr contribution from single-measurement precision. TCCON growth rates are linked to a UK Met Office forecast of a COVID-19 related reduction of -0.32 ppm yr-2 in 2020 for Mauna Loa. The separation of TCCON-measured growth-rates vs the reference forecast (without COVID-19) is discussed in terms of detection delay. A 0.6 [0.4, 0.7]-yr delay is caused by the impact of synoptic variability on XCO2, including a »1-month contribution from single-measurement precision. A hindrance for detection of the COVID-19 related growth-rate reduction in 2020 is the ±0.57 ppm/yr uncertainty for the forecasted reference case (without COVID-19). Assuming ongoing growth-rate reductions increasing year-on-year by -0.32 ppm yr-2 would allow a discrimination of TCCON measurements vs the unperturbed forecast and its uncertainty – with a 2.4 [2.2, 2.5]-yr delay. Using no forecast but the max-min range of the TCCON-observed growth rates for discrimination only leads to a factor »2 longer delay. Therefore, forecast uncertainties for annual growth rates must be reduced. This requires improved terrestrial ecosystem models and ocean observations to better quantify the land and ocean sinks dominating interannual variability. The paper highlights the results of our first published study based on 4 midlatitude TCCON sites and gives an outlook to our ongoing work including all TCCON sites. TCCON will be a valuable basis to monitor the Paris process in the years to come.
Sussmann, R., and Rettinger, M.: Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations, Remote Sens., 12, 2387, https://doi.org/10.3390/rs12152387, 2020.
How to cite: Sussmann, R., Rettinger, M., Hase, F., and Roehl, C.: Can We Measure a COVID-19 Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12332, https://doi.org/10.5194/egusphere-egu21-12332, 2021.
The high-resolution Tropospheric Monitoring Instrument (TROPOMI) satellite observations of atmospheric methane offer a powerful tool to identify emission hot spots and quantify regional emissions. The divergence of horizontal fluxes of NO2 has already been proven to be an efficient way to resolve and quantify high sources on a global scale. Since the lifetime of CH4 is in the order of 10 years, the sinks can be ignored at the synoptic time scale which makes the divergence method even more applicable to CH4 than to short-lived NO2.
Because plumes of newly emitted CH4 disperse within the Planetary Boundary Layer (PBL), we first convert the satellite observed total column average (XCH4) to a regional enhancement of methane in the PBL (∆XCH4_PBL) by using the CAMS global methane background reanalysis fields above the PBL. These model fields represent the transport- and chemically-modulated large-scale distribution of methane. Secondly, the divergence of ∆XCH4_PBL is derived by the use of the wind speeds halfway within the PBL. Based on the divergence, methane emissions are estimated on a 0.25°× 0.25° grid. We tested our new method for Texas in the United States and quantified methane emissions from the well-known oil-gas fields in the Permian Basin, as well as from – less well quantitatively established – oil-gas fields located in southern coastal areas.
Compared to traditional inverse methods, our method is not restricted by an a priori emission inventory and so far unidentified local sources (i.e. emissions from livestock in feed yards) may be found. Due to its computational efficiency, the method might be applied in the near future globally on the current spatial resolution.
How to cite: Liu, M., Van der A, R., Van Weele, M., Eskes, H., Lu, X., de Laat, J., Kong, H., Sun, J., Ding, J., Zhao, Y., and Weng, H.: Using a new divergence method to quantify methane emissions with TROPOMI on Sentinel-5p, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12565, https://doi.org/10.5194/egusphere-egu21-12565, 2021.
The Mexico City Metropolitan Area (MCMA), located in proximity to an active volcano, is the largest urban center in North America and there is great interest to better characterize carbon emissions of this and other major urban centers in the country. NASA’s Orbiting Carbon Observatory (OCO-3) was installed in the International Space (ISS) in 2019. The inclusion of a Pointing Mirror Assembly (PMA) in this third iteration allows for a new mode of data collection that samples an area of ~80 x 80 km in approximately 2 minutes. This mode is used to collect map-like data, called Snapshot Area Maps (SAMs), over areas of interest (e.g. volcanos or urban areas). The OCO-3 module has collected SAMs over the MCMA (and the Popocatépetl volcano) throughout 2020, and also of the metropolitan areas of Guadalajara and Monterrey throughout the second half of 2020.
Using data from the public release of OCO-3 Level 2 (L2) “Lite EarlyR” product, available at the Goddard Earth Sciences Data and Information Services Center (GES DISC), we have built maps of the spatial distribution of xCO2 for these regions. Data is filtered according to the reported quality flag in the data product, compared with ground-based FTIR measurements of column xCO2 over the MCMA region and averaged with an oversampling method. Surface pressure data with the averaged xCO2 is used to calculate the concentrations within the mixed layer (xCO2ML) in order to compensate for the effects of the complex terrain. This product is also used for comparison with CO spatial distributions obtained from TROPOMI data products and a simple xCOML/xCO2ML ratio is obtained and mapped for the three urban centers. This work showcases the utility of SAMs in cooperation with ground-based measurements to produce detailed descriptions of the spatial distribution of CO2 for a wide variety of applications, as well as the importance of frequent soundings over important emission sources around the world.
How to cite: Campos-Pineda, M., Taquet, N., Stremme, W., Bezanilla, A., Lauvaux, T., Ramonet, M., and Grutter, M.: CO2 spatial distribution over Mexican urban centers from satellite observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12572, https://doi.org/10.5194/egusphere-egu21-12572, 2021.
An increasingly-large number of cities have designed ambitious climate mitigation plans to contribute to national GHG emission reduction objectives, typically starting with city-scale accountability of their direct and indirect fossil fuel emissions (Self Reported Inventories). Several concepts of spaceborne instruments providing high resolution 2D view of CO2 total column concentrations (XCO2) have been developed to monitor the CO2 anthropogenic emissions. Those images target mainly the CO2 atmospheric plumes from cities and large power plants, expecting that their study may quantify the emissions of those sources. However, there is still a need to develop and asses estimation methods which could process a large number of images in a robust way for such quantifications.
In this study, we evaluate the ability to quantify CO2 urban emissions from XCO2 2D images by conducting sensitivity experiments with synthetic images over the Paris area during the winter 2019/2020. Synthetic data were simulated using state-of-the-art mesoscale model simulations at 1km resolution coupled to a high-resolution inventory, all validated against in situ CO2 tower measurements. We compared multiple direct flux calculation methods as described in various studies including Source Pixel, Integrated Mass Enhancement and Cross-sectional methods [Varon et al.,2018], further examined with various configurations, in addition to several formulations of Gaussian plume inversion techniques. These methods are computationally affordable compared to mesoscale inversions based on Eulerian or Lagrangian models, hence able to process rapidly a large amount of data over various cities in the future.
We quantified the uncertainties and accuracy for these methods using different combinations of assumptions to i) identify the plume from the city, ii) to determine the corresponding background concentrations from natural and anthropogenic sources outside the city, and iii) to estimate the effective wind speed and direction of the plume. From this large ensemble of approaches and configurations, we identified the most robust methods and parametrizations with their corresponding precisions under various meteorological conditions and specifications of the XCO2 images (esp. spatial resolution and measurement errors).
Starting with ideal cases without measurement noise and with perfectly known transport, we further increase the complexity of the experiments towards more realistic conditions in order to quantify the impact of the various sources of uncertainties (i.e. measurement errors, uncertainties in background conditions, uncertain plume detection, transport uncertainties). We show that most methods have to be adapted to handle the spatial extent of the targeted sources and that their performance are good in near steady state conditions. The source pixel method seems to be the less suited for extended source estimation. However, the final uncertainty is mainly driven by the pre-processing steps (background, plume limits and effective wind estimations).
How to cite: Danjou, A., Broquet, G., Lian, J., Bréon, F.-M., Eldering, A., Utard, H., and Lauvaux, T.: Evaluation of light atmospheric plume inversion methods using synthetic XCO2 satellite images to compute Paris CO2 emissions., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12639, https://doi.org/10.5194/egusphere-egu21-12639, 2021.
Remote sensing measurements of carbon dioxide and methane at the Sodankylä facility in northern Finland cover a 12-year time period. The measurements have been taken by a Fourier Transform Spectrometer (FTS), operating in the near-infrared spectral region. The Sodankylä site is participating in the Total Carbon Column Observing Network (TCCON). Here we present long-term measurements of column-averaged, dry-air mole fractions of carbon dioxide and methane and comparisons with satellite borne measurements. The relevant satellite missions include the TROPOspheric Monitoring Instrument (TROPOMI) on board of the Copernicus Sentinel-5 Precursor satellite, the Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse Gases Observing Satellite (GOSAT). We have performed AirCore observations in the vicinity of the TCCON instrument at Sodankylä during all seasons. AirCore measurements are directly related to the World Meteorological Organization in situ trace gas measurement scales. The AirCore data are used in this study to provide comparisons with remote sensing retrievals.
How to cite: Kivi, R., Hatakka, J., Heikkinen, P., Laurila, T., Lindqvist, H., and Chen, H.: Remote Sensing Measurements of Carbon Dioxide and Methane over Northern Finland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13073, https://doi.org/10.5194/egusphere-egu21-13073, 2021.
A large fraction of global anthropogenic greenhouse gas emissions originates from localized point sources. International climate treaties foresee their independent monitoring. Given the high number of point sources and their global spatial distribution, local monitoring is challenging, whereas a global satellite-based observing system is advantageous. In this perspective, a promising measurement approach is active remote sensing by airborne lidar, such as provided by the integrated-path differential-absorption lidar CHARM-F. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO2 and CH4 below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken at the largest European point sources of anthropogenic CO2 and CH4 emissions, i.e. coal-fired power plants and ventilation shafts of coal mines. The measurement flights aimed to transect isolated exhaust plumes, in order to derive the corresponding emission rates from the resulting enhancement in concentration, along the plume crossing. For the first time, multiple measurements of power plant emissions were made using airborne lidar. On average, we find that our measurements are consistent with reported numbers, but observe high discrepancies between successive plume crossings of up to 50 %. As an explanation for these high discrepancies, we assess the influence of inhomogeneity in the exhaust plume, caused by atmospheric turbulence. This assessment is based on the Weather Research and Forecasting Model (WRF). We find a pronounced diurnal cycle of plume inhomogeneity associated with local turbulence, predominately driven by midday solar irradiance. Our results reveal that periods of high turbulence, specifically during midday and afternoon, should be avoided whenever possible. Since lidar is intrinsically independent of sun light, measurements can be performed under conditions of weak turbulence, such as at night or in the early morning.
How to cite: Wolff, S., Ehret, G., Kiemle, C., Amediek, A., Quatrevalet, M., Wirth, M., and Fix, A.: Quantification of CO2 Emission Rates from Large Coal-Fired Power Plants Using Airborne Lidar during CoMet, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14303, https://doi.org/10.5194/egusphere-egu21-14303, 2021.
This study investigates the use of total column methane measurements from the TROPOMI satellite instrument for estimating the global sources and sinks of methane. A bias correction method has been developed based on a comparison between the satellite measurements and an inversion using surface measurements only, building on the experience using GOSAT data. The bias correction is applied to the satellite measurements prior to the use of the data in the inversion. Results will be shown of inversions using the TM5 4D-VAR and CarboScope inverse modelling systems applied to two years of TROPOMI data. The inversion-optimized methane mixing ratios are inter-compared and validated against independent surface (WMO-GAW), Aircraft (ATom) and total column (TCCON) observations. The derived methane fluxes are aggregated over selected geographic regions, to compare the optimised methane emissions from TM5-4DVAR, CarboScope, and GOSAT inversions from the Copernicus Atmospheric Monitoring Service.
Methane surface mixing ratios derived from the TROPOMI inversion show a good agreement with the surface measurements in general. Near areas with high aerosol optical thickness (e.g. the Sahara) we see significant adjustments in the surface fluxes, compensating for model-data differences, pointing to influences of residual uncorrected systematic errors in the data. The total column comparison with TCCON measurements shows a slight North-South bias gradient. These finding are investigated in further detail by comparing results using the operational retrieval product to the use of the scientific RemoTeC and WFMD retrievals. Encouragingly, both the TM5 and CarboScope inversions show similar increments in the aggregated fluxes over time. The seasonal cycle in the posterior fluxes is different from that of the a a priori fluxes, which were the same for both inversion systems.
How to cite: van Peet, J., Houweling, S., Marshall, J., Nunez Ramirez, T., and Segers, A.: Inverse modelling of global methane emissions using TROPOMI, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14510, https://doi.org/10.5194/egusphere-egu21-14510, 2021.
Imaging spectroscopy, also known as hyperspectral imaging, is a remote sensing technique in which images of the solar radiation reflected by the Earth are produced in hundreds of spectral channels between the visible and the shortwave infrared part of the electromagnetic spectrum (roughly 400–2500 nm). The 2100-2450 nm spectral window can be used for methane retrievals, as it has been demonstrated over the last years with airborne imaging spectrometers, and very recently also with space-based instruments. Satellite-based hyperspectral images are acquired with a typical spatial sampling for satellite data of 30 m, a spatial coverage between 30x30 and 60x60 km per scene, and a spectral sampling of 10 nm. In this work, we will present an overview of the state-of-the-art of methane mapping with imaging spectroscopy missions. We will review the characteristics of the available missions, the main retrieval approaches, and will present examples of methane emission detection from a number of missions and locations around the Earth.
How to cite: Guanter, L., Irakulis-Loitxate, I., Sánchez-García, E., Gorroño, J., Zhang, Y., Liu, Y., Maasakkers, J. D., and Aben, I.: Mapping methane point emissions with imaging spectroscopy satellite missions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14811, https://doi.org/10.5194/egusphere-egu21-14811, 2021.
Atmospheric methane is measured continuously from space, providing valuable information at global/regional scales for atmospheric monitoring as well as for surface flux estimates. However, as shown by several studies, CH4 atmospheric concentration retrievals from thermal infrared (TIR) nadir sensors exhibit significant biases compared to independent observations, or when intercompared between different TIR and SWIR/TIR sensors products. It is necessary to analyse the possible causes of biases and to investigate potential retrieval improvements and/or bias-correction for proper and consistent CH4 measurements in the TIR: this is the objective of the ESA CH4TIR project.
The CH4TIR project brings together the expertise of researchers using different state-of-the-art forward/inverse model and retrieval schemes, namely ASIMUT-ALVL and σ-δ-IASI, to identify the possible causes for the observed biases and quantify the uncertainties linked with the forward modelling and inversion of CH4 in the TIR region. Due to its accurate measurements and well-characterized noise, IASI observations are used as the main data source for the project, but TANSO-FTS observations are also used for comparison.
First, we present a sensitivity analysis carried out for the CH4 retrievals performed with IASI and TANSO-FTS data in the TIR region using ASIMUT. We assess the impact of the retrieval spectral range, the measurement uncertainty, uncertainties in the spectroscopic data, and the inclusion of different species in the retrieval. An analysis of the IASI spectral residuals from both ASIMUT-ALVL and σ-δ-IASI retrievals shows that residuals are largest in the strongest part of the Q branch (1300-1310 cm-1), where line mixing effects are most significant. Dedicated laboratory measurements of CH4 lines in this spectral domain are being performed and analysed in the frame of this project.
An important feature of the project is to call on two different approaches and tools for the retrieval of CH4 from IASI observations. We therefore characterize the differences between the σ-IASI and ASIMUT-ALVL radiative transfer modelling in the 1190 - 1350 cm-1 region based on 6 AFGL atmospheres. To further assess the error from the forward/inverse model, the results of a round robin exercise is also presented, where the output from one RTM is used as input for the other RTM/inversion scheme.
Finally, we explore how critical a priori temperature and H2O profiles are to the accuracy of the CH4 inversion. To investigate this effect, a two-step retrieval approach is used where a first retrieval by σ-δ-IASI exploits the entire IASI spectral range and is used as a priori for the CH4 retrieval performed on a narrower spectral range (1190 – 1350 cm-1).
This ongoing work already provides a comprehensive analysis and prioritisation of error sources for the retrieval of CH4 from TIR hyperspectral measurements, emphasizing the critical need of spectroscopy measurements and line interference modelling in the Q branch around 1300 cm-1 for reducing CH4 retrieval biases and improving the retrieval sensitivity in the lowermost levels of the atmosphere.
How to cite: Robert, C., Camy-Peyret, C., Prunet, P., Vandael, A. C., Erwin, J., Vispoel, B., De Mazière, M., Kangah, Y., Lezeaux, O., Serio, C., Masiello, G., Lepère, M., and Straume, A. G.: Error source analysis for CH4 retrievals in the TIR, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15638, https://doi.org/10.5194/egusphere-egu21-15638, 2021.
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