Year after year, the effects of climate change are more dramatic and evident, and it is no longer possible to hide them, which requires immediate action against anthropogenic emissions of greenhouse gases (GHGs) in Earth’s atmosphere, mainly carbon dioxide (CO₂) and methane (CH₄). The scientific community plays an important role in continuously developing and improving current instrumentation and methods, which enable high-resolution measurements to monitor, track, quantify, and verify the concentration and emissions of GHGs in the atmosphere.

Measuring GHGs with high accuracy in the atmosphere is challenging, but achieving measurements with global coverage is even more demanding, and only satellites can provide such data sets. However, they require ground-based column-integrating measurements for validation. For this purpose, the Total Carbon Column Observing Network (TCCON) was created; however, due to its high operational costs and the need for trained expertise on-site, the number of sites is limited (~ 26). Moreover, these stations are stationary, so performing observations of selected GHG source regions with several spectrometers is impossible. The COllaborative Carbon Column Observing Network (COCCON) was initiated to overcome these limitations and to supplement the existing TCCON stations. The standard instrument used by COCCON is the portable FTIR spectrometer EM27/SUN, developed by KIT in cooperation with Bruker Optics from 2011 onwards. Although this instrument has a lower spectral resolution (0.5 cm^-1) in comparison to the TCCON instrument (0.002 cm^-1), it delivers XCO₂, XCH₄, XH₂O, and XCO with sufficient quality to complement TCCON satellite validation efforts.

COCCON data are tied to the trace gas scale as realized by the TCCON reference. COCOON ensures a high degree of internal consistency across the participating spectrometers, defines common standards for data processing, and performs quality assurance checks on individual spectrometers to ensure the quality of the network. The services of COCCON are enabled by ESA support. Meanwhile, one hundred twenty spectrometers have been optimized and characterized by the centralized testing facility operated at KIT, and many more units are expected to be commissioned in the months ahead. This contribution presents the current status of the activities of the central QA/QC facility: methods, results, and foreseen improvements.

How to cite: Alberti, C., Hase, F., Dubravica, D., Dehn, A., and Castracane, P.: The COllaborative Carbon Column Observing Network (COCCON) quality management., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8815, https://doi.org/10.5194/egusphere-egu24-8815, 2024.

Measuring the concentration of greenhouse gases (GHGs) has become a major concern in modern society because of the growing impact of human activity on the global climate system (Hui et al. 2022). Satellite observations give an unique opportunity to observe the GHGs total columns at a global scale, but these space missions need to be validated from ground-based measurements.

In order to enhance cal/val activities of current and future space missions and to better understand the spatial and vertical distributions of GHGs in different key regions for carbon and methane cycles, the MAGIC (Monitroing of Atmospheric composition and Greenhouse gases through multi-Instrument Campaigns) initiative was launched in 2018 by CNRS and CNES, brining now together more than fifteen international teams (https://magic.aeris-data.fr). Various instruments, including research aircrafts, balloons and ground-based measurements, have been used yearly over intensive measurement campaigns. The EM27/SUN Fourier Transform Spectrometer is one of the ground-based measurement instruments used as part of this initiative. This device offers the practical advantages of portability and access to  column mole fractions of dry air  of CO₂, CH₄, CO and H₂O from solar spectra

The main objective of this study is to carry out a statistical evaluation of the EM27/SUN data collected during the MAGIC measurement campaigns. The EM27/SUN devices (LERMA, LSCE, GSMA, CNES and KIT) have been deployed at various measurement sites since 2018. For the last two years, 2022 and 2023, measurements of CO₂ and CH₄ total column have been carried out in the city of Reims as a pilot site representative of medium-sized cities on a European scale, with the aim of estimating emissions of the main greenhouse gases on a city scale. The results obtained can also be compared with data from current satellite missions (S5P, IASI, GOSAT-2) for cal/val purposes.

Keywords: Climate change, Greenhouse gases, Ground-based FTIR, EM27/SUN, Reims broadcasts, Comparison

[Hui et al. 2022]: Hui, D., Deng, Q., Tian, H., & Luo, Y. (2022). Global climate change and greenhouse gases emissions in terrestrial ecosystems. In Handbook of climate change mitigation and adaptation (pp. 23-76). Cham: Springer International Publishing.

Magic initiative group : Bruno GROUIEZ, Lilian JOLY, Abdelhamid HAMDOUNI, Yao TE, Pascal JESECK, Christel GUY, Caroline BES, Denis JOUGLET, Hervé HERBIN, Morgan LOPEZ, Josselin DOC, Simona LATCHABADY, Michel RAMONET, Christof JANSSEN, Marc DELMOTTE, Deniel CAROLE, Corinne BOURSIER, Nicole MONTENEGRO VARELA, Hao FU, Neil HUMPAGE, Carlos ALBERTI, Vincent CASSÉ, Bruna SILVEIRA, Frank HASE, Cyril CREVOISIER

How to cite: El Habchi El Fenniri, H. and the MAGIC initiative group: Statistical evaluation of the performance of EM27/SUN measurements as part of the MAGIC initiative, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20456, https://doi.org/10.5194/egusphere-egu24-20456, 2024.

As part of the UK Greenhouse gas Emissions Measurement Modelling Advancement programme, the National Centre for Earth Observation are establishing the Greenhouse gas Emissions Monitoring network to Inform Net-zero Initiatives for the UK (GEMINI-UK). The primary aim of the GEMINI-UK network, comprising ten Bruker EM27/SUN shortwave infrared spectrometers, is to help quantify regional net GHG emissions across the UK, complementing in situ measurements collected by the existing tall tower network. Collectively, these data will eventually form the backbone of a pre-operational GHG emissions monitoring framework. The GEMINI-UK instruments observe column concentrations of carbon dioxide, methane, and carbon monoxide in cloud-free conditions, which we are using in the context of Bayesian inverse methods to constrain regional flux estimates of these gases. We have designed the measurement network to deliver the biggest error reductions in carbon dioxide flux estimates, working closely with host partners that include UK universities and schools and NERC facilities to promote the open access and transparency of the collected data. Continuous and autonomous operation of these instruments at each site is achieved by an automated weatherproof enclosure, based on a design developed by University of Edinburgh researchers, which previously enabled year-round measurements to be collected during the UK DARE-UK experiment in central London. In this presentation we describe the status and longer-term goals of GEMINI-UK, which is coming online through the first half of 2024, including an ongoing evaluation of EM27/SUN with a higher specification TCCON spectrometer at Harwell. We will also report data from the DARE-UK London deployment, which demonstrates the value in using all-weather enclosures and allows comparison with coincident measurements collected by the NASA OCO-2 and OCO-3 Earth orbiting instruments.

How to cite: Humpage, N., Palmer, P., Feng, L., Kurganskiy, A., Woodwark, J., Doniki, S., Ramsay, R., and Boesch, H.: GEMINI-UK: a new UK network of ground-based greenhouse gas observing spectrometers to help track progress towards net-zero targets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15956, https://doi.org/10.5194/egusphere-egu24-15956, 2024.

To adhere to the Paris Agreement and restrict the rise in global temperatures to 1.5 degrees, it is imperative to significantly decrease anthropogenic greenhouse gas emissions, ultimately achieving net-zero emissions by the year 2050. To evaluate the reductions in CO₂ emissions, it is essential to assess the related changes in CO₂ mixing ratios in the atmosphere. An appropriate quantity to be used for this assessment in the years to come is the global annual growth rate of atmospheric CO₂. As a data basis for this, in-situ measurement station networks can be used, and annual growth rates are being inferred, e.g., from the Mauna Loa station. On the other hand, TCCON, operating 30 stations worldwide, is committed to measuring greenhouse gas total column mixing ratios through the use of ground-based solar viewing FTIR instruments. The advantage of TCCON column observations is that they are less sensitive to local emissions close to the measurement site and are more representative of regional and global scale emissions and trends in greenhouse gas mixing ratios. TCCON data, therefore, represent a potent alternative data source. A recent algorithmic approach to infer annual growth rates from TCCON data by Sussmann and Rettinger (2020) considers the temporal sampling of TCCON, accounting for data gaps due to sun-viewing geometry, and has been successfully demonstrated for selected TCCON sites. The goal of our ongoing work, presented here, is to extend the retrieval of annual growth rates to more TCCON stations and compare our TCCON results with results from the in-situ networks.

Reference: 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: Mostafavipak, S., Ralf, S., and Markus, R.: Annual Growth Rates of Column-Averaged CO2 on Global Scale Inferred from Long-Term TCCON Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16659, https://doi.org/10.5194/egusphere-egu24-16659, 2024.

Since anthropogenic NO_x (NO_x=NO+NO₂) and CO₂ are co-emitted species for anthropogenic sources, some studies have used the NOx emissions retrieved from satellite observations to infer the anthropogenic CO₂ emissions. However, these studies did not consider the fact that satellites measure total NO₂ concentrations and their inferred emissions encompass both biogenic and anthropogenic sources. In this study, we introduce a method to distinguish soil NO_x emissions from satellite-based total NO_x emissions. The total NO_x emissions are derived by the state-of-the-art inverse algorithm DECSO (Daily Emission estimation Constrained by Satellite Observations, Mijling and van der A, 2012; Ding et al., 2017a) from TROPOMI observations. Using the characteristic seasonal cycle of soil emissions we derive these emissions for representative regions with only biogenic emissions, which are then applied to nearby regions according land-use fractions. To evaluate this approach, we compared the deviation between the tropospheric NO₂ concentration observed by satellite and two atmospheric composition model simulations: one using the satellite-derived soil NO_x emissions and another with the Copernicus Atmosphere Monitoring Service (CAMS) global soil emissions inventory (CAMS-GLOB-SOIL). Once the soil NO_x emissions are derived, they can be subtracted from the total emissions to get anthropogenic NO_x emissions. Subsequently anthropogenic CO₂ emissions can be estimated using known CO₂/NO_x factors from bottom-up inventories. The annual CO₂ emissions derived from DECSO (called DECSO-CO₂) in our study area (large part of Europe) is 3.7 Gt in 2019, which is comparable with the 3.2 Gt of the CAMS CO₂ inventory (called CAMS-CO₂). The DECSO-CO₂ and CAMS-CO₂ are comparable for most large sources and cities, but the DECSO-CO₂ show a larger number of low emission spots than the CAMS-CO₂. The results demonstrate the potential for DECSO to expand its application to other regions in the world with less information on anthropogenic CO₂ emissions.

How to cite: Lin, X., van der A, R., de Laat, J., Eskes, H., Huijnen, V., Mijling, B., Ding, J., and Liu, Z.: Using space-based NO2 observations to indirectly estimate anthropogenic CO2 emissions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1841, https://doi.org/10.5194/egusphere-egu24-1841, 2024.

Anthropogenic emissions of carbon dioxide (CO2) are the main driver of the change in climate since the industrial revolution. Policy mitigation strategies include reduction of these emissions. The Paris Agreement from 2015 requires the states to report their greenhouse gas emissions on a regular basis. Space-borne remote-sensing measurements of CO2 are considered potentially of great value  to monitor CO2 emissions, due to their better coverage in comparison to in-situ instruments. Measuring CO2  from space of an adequate quality is a challenge because of the stringent requirements related to the accuracy and precision of the data products and thus the instruments. In this study, we use the Fast atmospheric traCe gAs retrievaL (FOCAL) algorithm to retrieve maps of the column-averaged dry-air CO2 mole fraction (XCO2) with the goal of quantifying CO2 emissions for specific emission targets using Orbiting Carbon Observatory 3 (OCO-3) snapshot area maps. This data product is planned to  be used in the German national Integrated Greenhouse Gas Monitoring System (ITMS), for which an operational data assimilation system for greenhouse gases is being set up for Germany.

How to cite: Weimer, M., Hilker, M., Fuentes Andrade, B., Noël, S., Reuter, M., Buchwitz, M., Bovensmann, H., Burrows, J. P., and Bösch, H.: Towards CO2 emission monitoring from space using FOCAL XCO2 retrievals of OCO-3 satellite measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11087, https://doi.org/10.5194/egusphere-egu24-11087, 2024.

How to cite: Strupler, M., Girard, M., Jervis, D., MacLean, J.-P. W., Marshall, D., McKeever, J., Ramier, A., Tarrant, E., and Young, D.: GHGSat’s constellation: Land and offshore greenhouse gases detection and quantification , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11787, https://doi.org/10.5194/egusphere-egu24-11787, 2024.

As part of the Copernicus Programme of the European Commission, the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) are expanding the Copernicus Space Component to include additional measurements of atmospheric composition. To support the evaluation of greenhouse gas emission reductions decided during the COP21 meeting in Paris in 2015, new measurements are required with improved accuracy. Measurements from space-borne instruments are a key component of this effort – requiring improvements in both spatial and spectral resolution.

The Copernicus missions are Europe’s primary contribution to this effort. The CO2M mission – a constellation of initially 2 platforms – is due to launch at the end of 2026 and will be available to contribute to the global stocktake in 2028 and those beyond. Data from the three instruments on board each platform – CO2I/NO2I, MAP, and CLIM – will be combined to act as a single “hyper-instrument”. NO₂ is often co-emitted with CO₂ & CH₄, so the spectrometer (NO2I) will be used to detect near-surface NO₂ plumes; the impact of aerosol pollution on the XCO₂ columns will be calculated using the Multi-Angle Polarimeter (MAP); and cloud contamination will be observed and removed via data from the Cloud Imager (CLIM). These data will be used within the CO₂ and CH₄ retrievals from the CO2I spectrometer to improve the retrievals and allow for a precision of 0.7 ppm and an error of 0.5 ppm for XCO₂, and an uncertainty of ~10 ppb for CH₄.

Sentinel-5 will fly on the EUMETSAT Polar System – Second Generation (EPS-SG) platform, due for launch in 2025. S-5 will be a push-broom hyperspectral UVN spectrometer, with a 7.5 km² spatial resolution and global daily coverage via a 2670 km wide swath. It will monitor various trace gases, including CH₄.

Comprehensive calibration and validation analysis during the satellite’s commissioning will be undertaken to ensure they meet the specified requirements, and ongoing monitoring will ensure the instruments continue to comply with the challenging requirements. External data from ground-, airborne-, and satellite-based instruments will be required, covering the whole globe and multiple parts of the EM spectrum. Calibration and Validation techniques are being developed across CO2M and S-5, as well as Sentinel-4 which is also due for launch in 2025. This inter-mission collaboration has been undertaken to reduce duplication of effort and ensure lessons are learned from the commissioning of each mission.

Here, we give an update on the current state of planning for the Calibration and Validation operations, and identify gaps in the current provision of external networks, ground-based in particular, so that alternative and additional data sources can be procured.

How to cite: Hayer, C., Lang, R., Lindstrot, R., Sierk, B., and Bojkov, B.: A harmonised approach to Calibration and Validation for upcoming Sentinel missions: updates for CO2M and Sentinel-5, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20565, https://doi.org/10.5194/egusphere-egu24-20565, 2024.

In the context of growing climate change concerns, accurate and real-time monitoring of greenhouse gases (GHGs) such as carbon dioxide (CO2) and methane (CH4) is imperative. AIRMO's innovative GHG emissions monitoring service is at the forefront of addressing this need, offering high-resolution and high-sensitivity emissions data. Local winds, along with presence of aerosol and thin clouds significantly impacts the accuracy of GHG flux measurements, hence the output data quality.

The Airmo service, under development,  is powered by a small satellite constellation equipped with 3 co-located instruments including a pushbroom SWIR spectrometer, micro LiDAR, and an RGB camera. In this context, the LiDAR emerges as a pivotal tool due to its unique capability to characterise both winds and aerosols. The major difference and mission impact brought by AIRMO relies on complementing and supplementing the column Radiance data produced by the Spectrometer with atmospheric data from the LiDAR, including data about aerosol layers and cirrus cloud’s optical properties.

This approach enables precise localization of GHG sources with spatial resolution capabilities down to 30 meters. Temporal resolution with a revisit rate of 4 hours with complete deployment ensures timely data for tracking emission changes. High spectral sensitivity in the SWIR range guarantees retrieval accuracy and spectral line characterization. The expe4cted accuracy of methane measurements lies within ±5 ppb and CO2 within ±2 ppm, offering unprecedented precision in GHG quantification.

The upcoming In-Orbit Demonstration (IoD) mission will showcase AIRMO's capability to meet stringent observation requirements and validate its operational framework in alignment with mission objectives. Two airborne campaigns are  planned for 1^st and 3^rd   Q 2024.

The paper will provide an overview of the status of the Mission and critical payload development and performance.

How to cite: Stepanova, D., Armandillo, E., Adam, M., and Ramirez, I.: GHG monitoring from microsatellite using compact micro-LiDAR and Push-broom spectrometer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4762, https://doi.org/10.5194/egusphere-egu24-4762, 2024.

CH₄ is the second anthropogenic contributor to global warming after CO₂. Monitoring methane emissions is therefore essential in order to mitigate climate change. Satellite missions have the potential to offer more spatial coverage than ground stations, however their coverage is currently insufficient. This leads to the development of satellite missions that can be envisaged as constellations. For this purpose a concept currently studied is Nanocarb, relying  on the patent  WO2018002558A1 (ImSPOC). This instrument is a compact and robust spectral imaging concept relying on a static array of Fabry-Perrot interferometers to retrieve partial interferograms of the incident radiance. This study is focused on developing and using the appropriate tools and models to simulate the flux restitution performances of a methane focused Nanocarb instrument with two intertwined inverse approaches.

We have developed an end-to-end simulation chain of the flux restitution by the instrument Nanocarb. First an atmospheric situation is simulated from an inventory such as TNO's by the chemistry transport model Chimere. Then this atmospheric situation is used to simulate Nanocarb measurements, with a direct and backward ImSPOC conception and processing software called MEDOC which relies on the radiative transfer code 4A-OP. Those simulated measurements have realistic noise added and are used to recover atmospheric methane columns. Eventually this new atmospheric situation allows for the retrieval of atmospheric methane fluxes, thanks to the Community Inversion Framework (CIF), coupled with Chimere. Those fluxes can be compared to the initial situation to quantify the introduced biases.

We present this simulation chain, and specifically developments made on Medoc and columns obtained for an atmospheric simulation in the north of France. This simulation chain aims to model the flux retrieval ability of the instrument Nanocarb for point sources and area sources of methane.

How to cite: Khater, L., Croizé, L., Pison, I., and Berchet, A.: End to end simulation chain for flux restitution of the spectral imaging concept Nanocarb, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15743, https://doi.org/10.5194/egusphere-egu24-15743, 2024.

Inversion modeling is a top-down technique to infer greenhouse gas (GHG) emissions using atmospheric observations. In particular, the use of satellite retrievals have been attractive due to their advantage in dense spatial coverage compared to typically sparse surface networks.
The goal of this study is to assimilate satellite data at high spatial resolution to independently locate and quantify GHG sources and sinks, which can be used as a reference for carbon budget studies and policy makers. The findings also contribute as groundwork for the development of the Integrated GHG Monitoring System for Germany (ITMS).
For this purpose, we coupled the numerical weather prediction and atmospheric transport model ICON-ART with an Ensemble Kalman Filter (EnKF) based inversion system, using the CarbonTracker Data Assimilation Shell (CTDAS). We use column data (XCH4) measured by the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, targeting anthropogenic CH₄ fluxes over Europe. Prior anthropogenic emissions are taken from the EDGARv4.3.2 inventory, while natural fluxes were derived from peatlands, mineral soils, lakes, oceans, biofuels, biomass burning, termites, and geology. 
We first present a synthetic study of our system by performing an ensemble simulation forward in time with randomly perturbed emission fields. CH₄ fluxes were retrieved at 0.25° x 0.25° resolution, and prior emissions are scaled to optimally fit the measured values. With this idealized experiment, we demonstrate that the system is capable of capturing the prescribed spatial pattern applied onto the emission field, using pseudo-observations of satellite retrievals with realistic coverage. In addition, we report on the results of assimilating real observations into the system, including emission estimates and their associated uncertainties. 
This study demonstrates the potential of incorporating satellite retrievals into inverse modeling, enabling us to extend its application to other GHG species. It also serve as a preparation work for the planned Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) satellite mission.

How to cite: Ho, D., Steiner, M., Koene, E., Gałkowski, M., Marshall, J., and Gerbig, C.: Towards regional CH4 inversions with ICON-ART assimilating satellite TROPOMI data over Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1768, https://doi.org/10.5194/egusphere-egu24-1768, 2024.

Carbon dioxide (CO₂) and methane (CH₄) are the most important anthropogenic greenhouse gases because they are the main drivers of climate change. Monitoring their concentrations from space can help to detect and quantify anthropogenic emissions, supporting the mitigation efforts urgently needed to fulfill the Paris Agreement. Additionally, it can help to better understand the processes of the carbon cycle and thus allow better climate projections.

These are key objectives of the European Copernicus CO₂ Monitoring Mission CO2M, scheduled for launch in 2026, for which three retrieval algorithms are currently being developed and implemented in the EUMETSAT ground segment. These are so called conventional retrieval techniques that base on radiative transfer calculations. Despite shortcuts and approximations, the vast amount of satellite data makes them computationally expensive, requiring thousands of CPU cores. Although conventional retrieval methods base on physical principles, they typically require empirical data-driven methods to correct for biases in order to meet the demanding accuracy and precision requirements. The biases arise, e.g., from inaccuracies of the radiative transfer computations or unknown instrumental issues. Machine learning methods have the potential to combine both steps into a single data-driven retrieval algorithm, reducing the computational cost by several orders of magnitude.

We used the radiative transfer model SCIATRAN to simulate two years (2015 and 2020) of sub-sampled realistic radiances of three instruments on board CO2M: the main instrument CO2I (CO₂ imager), MAP (multi angle polarimeter), and CLIM (cloud imager). We use data from the first year of this data set to train artificial neural networks (ANNs) to retrieve XCO2 and XCH4 (the column-average dry-air mole fraction of atmospheric CO₂ and CH₄, respectively) plus related uncertainties and column averaging kernels. We will introduce a method which allows us to modify the training data making it representative for a wider range of atmospheric states. This ensures that the ANNs learn from the spectral signatures of CO₂ and CH₄ and that learning from spurious correlations is minimized. Despite the annual growth of CO₂ and CH₄, we will show that the ANNs trained with data from 2015 have almost the same quality when applied to data from 2020. We will analyze and compare the performance of different input vector settings, e.g., with and without MAP data and will discuss potential advantages or disadvantages of our ANN approach.

How to cite: Reuter, M., Hilker, M., Noël, S., Di Noia, A., Weimer, M., Buchwitz, M., Bovensmann, H., Bösch, H., and Burrows, J. P.: A data-driven method to retrieve XCO2 and XCH4 using artificial neural networks in preparation for the European Copernicus CO2 Monitoring Mission CO2M, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10179, https://doi.org/10.5194/egusphere-egu24-10179, 2024.

After selection as the second Earth Venture Mission in 2016, the Geostationary Carbon Observatory (GeoCarb), led by the University of Oklahoma PI Dr. Berrien Moore III, was developed until its cancellation during Phase C in November 2022. The GeoCarb PI was directed by NASA to complete as much of the instrument as remaining funding permitted.  The GeoCarb team successfully completed alignment and focus of the spectrograph before verification through a sequence of thermal vacuum campaigns, during which other characteristics of the instrument were determined (e.g., SNR, spectral range, stray light). These measurements suggested that the GeoCarb integrated instrument would have sufficient performance to deliver on the promise of the originally proposed greenhouse gas observing mission.  As a result, the project continued with integration of the optical subassemblies and electronics into a fully integrated instrument as of August 2023.  The instrument underwent a final thermal vacuum campaign during the fall of 2023 and was shipped to NASA for storage in November 2023.  Since that time, the GeoCarb science team has been analyzing the test data to determine the capabilities of the integrated instrument with positive results.  All indications are that the GeoCarb instrument will meet its key performance requirements.

In this presentation, we will discuss the spectral, radiometric, and polarimetric performance of the GeoCarb instrument from the measurement campaigns, including the spectral and spatial image quality, polarization response, and SNR.  We will address the implications for the scientific capabilities of the fully characterized and calibrated observatory should NASA restart the program.

How to cite: Crowell, S., Moore III, B., Burgh, E., Adamkovicz, M., Miller, T., Somkuti, P., Webb, A., Spiers, G., Mentzell, E., O'Dell, C., and McGarragh, G.: Expected Performance of the GeoCarb Integrated Instrument from Thermal Vacuum Measurements During a Limited Performance Test, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12350, https://doi.org/10.5194/egusphere-egu24-12350, 2024.

The world is facing a serious warming crisis, highlighting the need for monitoring greenhouse gases such as carbon dioxide (CO₂). In 2016, China launched its first satellite mission for measuring atmospheric CO₂, i.e., TanSat. Previous TanSat retrievals of the column-averaged dry air mole fraction of CO₂ (XCO₂) have a moderate precision of 1.47–2.45 ppm, with only limited spatial coverage over the land surface by using TanSat nadir-mode (ND) spectroscopy. These existing gaps are affected by the poor signal-to-noise ratio of glint mode (GL) or over the oceanic surface. However, CO₂ measurements over the ocean, a major source of global carbon sinks, are often lacking and subject to significant uncertainties but are nevertheless quite important. To increase the spatial coverage and retrieval accuracy and precision of TanSat CO₂, we further improve XCO₂ retrieval by introducing spectral recalibration, spectral window optimization, and explicit radiative transfer simulation. Thus, universal CO₂ retrieval with high precision over land and ocean is realized by using both ND and GL spectra. Ground-based comparisons using the total carbon column observing network (TCCON) indicate that the standard deviations of the bias-corrected XCO₂ retrievals from the ND and GL modes were 1.28 and 1.19 ppm, respectively. Consistent spatial and temporal distributions of satellite XCO₂ retrievals can also be found among TanSat, the Greenhouse gases Observing SATellite (GOSAT), and the Orbiting Carbon Observatory-2 (OCO-2) satellites. The updated TanSat XCO₂ retrievals have ~3.5 times the seasonal spatial coverage of GOSAT, while the highest difference between TanSat and OCO-2 is only +0.63 ppm. In addition, TanSat XCO₂ retrievals over the ocean successfully captured enhanced CO₂ plumes from the neighboring Yasur volcano, with an estimated emission flux of 32.1 kilotons per day. These results indicate that the improved TanSat XCO₂ retrieval is useful for understanding and quantifying global land and ocean carbon emissions.

How to cite: Zhang, C., Hong, X., and Liu, C.: First TanSat CO2 retrieval over land and ocean using both nadir and glint spectroscopy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14028, https://doi.org/10.5194/egusphere-egu24-14028, 2024.

Methane is known to be the second largest contributor to greenhouse gas induced warming after carbon dioxide. However, we know much less about its sources and sinks on global to regional scales and their sensitivity to climate change. Emissions of methane from permafrost and from the abundant number of wetlands, lakes, and rivers located in arctic and tropic regions are expected to substantially increase during this century due to the rapid climate warming. Therefore, disentangling natural and anthropogenic methane fluxes is a key scientific task.

The French-German Methane Remote Sensing LIDAR Mission MERLIN is designed to measure highly accurate atmospheric columns of methane to identify natural fluxes and emissions to better quantify global and regional sources and sinks, aiming at - reducing uncertainties on the global methane budget. To accomplish this, MERLIN will be relying on its Integrated Path Differential Absorption (IPDA) lidar to access methane atmospheric concentration at all latitudes and in all seasons. Especially, MERLIN will be able to provide spaceborne methane observations also in regions with high cloud cover and at high latitudes in winter time and in the so-called shoulder seasons. The IPDA measurements are insensitive to ground albedo variations and atmospheric aerosol load and will therefore achieve a level of accuracy not possible with the current available passive measurement techniques in space but will provide highly valuable information for closing knowledge gaps concerning global to regional methane distributions. The launch date of the MERLIN satellite is expected in 2029. Here we want to provide an overview of the MERLIN mission together with some discussion on the validation needs.

How to cite: Feist, D. G., Zechlau, S., Ehret, G., and Bousquet, P.: MERLIN – Measuring methane with lidar from space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19932, https://doi.org/10.5194/egusphere-egu24-19932, 2024.

Atmospheric methane (CH₄) is measured continuously from space, providing valuable information at global scales for atmospheric monitoring. CH₄ measurements from space can be based on observations in the shortwave infrared (SWIR), leading to a more uniform sensitivity to the atmospheric column, as well as thermal infrared observations (TIR) which provide useful information on the CH₄ content in the upper troposphere and lower stratosphere.

Among the various instruments measuring in the TIR, the Infrared Atmospheric Sounding Interferometer (IASI) series of instruments onboard the METOP satellites have been observing the Earth’s atmosphere for more than 15 years. Also, the Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) on-board the GOSAT 1 and 2 satellites monitor the atmosphere in the SWIR and TIR since 2009. Both instruments provide valuable information for the retrieval atmospheric CH₄.

CH4 retrievals in the TIR can be demanding in terms of computational time. The large number of species in the CH₄ region, and the high radiometric accuracy of current and upcoming instruments (e.g. IASI, IASI-NG) demand highly accurate radiative transfer modelling (RTM), to be carried out on a fine spectral and vertical grid. These constraints usually lead to long processing time when using full-physics RTMs (e.g. ASIMUT-ALVL). Other fast RTMs exists (e.g. RTTOV), but they often cannot be easily modified to include a specific species or spectroscopy, and do not support all instruments.

To allow for faster processing of the already large datasets available, we developed a model based on the Principal Component-based Radiative Transfer Model (PCRTM) approach (Liu et al., 2006) to perform CH₄ inversion in the TIR with IASI and TANSO-FTS. Instead of predicting channel radiance directly, the Principal Component-based Radiative Transfer Model (PCRTM) predicts the Principal Component (PC) scores of these quantities parameterized by a relatively small number of monochromatic RT simulations, leading to significant savings in computational time. The model returns the channel radiances and the jacobians for all species of interest.

In this work, we will detail the approach that was taken for the development of the fast RTM and will compare the results with a full-physics RTM. The impact of some internal parameters on the current model will also be discussed, as well as possible improvements in the future.

Reference:

Xu Liu, William L. Smith, Daniel K. Zhou, and Allen Larar, "Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept," Appl. Opt. 45, 201-209 (2006)

How to cite: Robert, C., Vandenbusshe, S., Vandaele, A. C., Erwin, J., and De Mazière, M.: A fast forward model for IASI and TANSO-FTS CH4 retrievals , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12720, https://doi.org/10.5194/egusphere-egu24-12720, 2024.

Thanks to its continuous spectral coverage of the whole thermal infrared domain, the IASI sounder offers the possibility to monitor on the long term several essential climate variables, including mid-tropospheric columns of the 3 major greenhouse gases influenced by human activitie: carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O).

To tackle the very small seasonal variability of these gases compared to their background values, combined to the strong dependence of IR radiances to atmospheric temperature and the simultaneous sensitivity of the channels to several gases, a non-linear inference scheme has been developed at LMD. Since 2007, mid-tropospheric columns of methane have been derived for both day and night conditions, over land and over sea. The retrieval scheme strongly relies on careful validation of level1c spectra, characterization of systematic radiative biases and severe cloud and aerosol screening. CH₄ fields are delivered on ‘near real time’ (D-1) basis to the Copernicus Atmosphere Monitoring Service (CAMS) and are assimilated in ECMWF C-IFS system, along with total columns from GOSAT, to produce forecast of vertical profiles of atmospheric concentration. Owing to its 20 year-program, IASI also participates to the establishment of long time series in the Copernicus Climate Change Service (C3S). The retrievals are thus used for a variety of purpose: assimilation to produce CH₄/CO₂ profile forecasts; estimation of surface fluxes using “top-down” atmospheric inversions; characterization of specific emissions such as biomass burnings.

In this talk we will present the latest development of the retrieval and application of methane. In particular, we will present the extension and validation of the retrieval to the high latitude regions achieved during the ESA MethaneCAMP project. By using AirCore 0-30 km profiles of methane concentration acquired at Sodankylä and Kiruna and several stations of the French AirCore network, we will also highlight the crucial need to better understand the variation of stratospheric methane in order to combine satellite-derived methane columns with simulations from atmospheric transport models. Finally, we will present long-term and interannual variability of methane as seen by IASI, with a focus of 2020-2021 methane anomaly and the characterization of specific emissions such as biomass burnings or NordStream leakage.

How to cite: Meilhac, N., Crevoisier, C., Orset, R., Armante, R., Kivi, R., and Chen, H.: Global distribution of methane in the mid-troposphere as seen by IASI onboard three successive Metop platforms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17815, https://doi.org/10.5194/egusphere-egu24-17815, 2024.

Carbon dioxide (CO₂) is the primary greenhouse gas emitted into the atmosphere due to anthropogenic activities. While it is naturally present as a part of Earth’s carbon cycle, human activities impact the ability of natural sinks to reduce CO₂ from the atmosphere and thus alter the carbon cycle. It thus becomes pertinent to focus on the long-term monitoring of atmospheric CO₂ and the ability to make precise, accurate, and continuous CO₂ measurements.

The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014. It is NASA’s first Earth-orbiting satellite dedicated to making observations of CO₂ in the atmosphere and measuring its column-averaged dry-air mole fraction (X_CO2). The primary goal of the OCO-2 mission is to provide X_CO2 measurements with sufficient precision and accuracy alongside quantifying its seasonal and interannual variability. While OCO-2 provides global coverage and consistently measures at high latitudes, the remote sensing measurement of CO₂ from space can be challenging. This is because the goal is to resolve inter-annual CO₂ deviations to subcontinental scales, alongside capturing the known seasonal cycle and trends. Further, the X_CO2 data is susceptible to location- and surface-property-dependent biases that must be corrected. Thus, validation of the X_CO2 data from OCO-2 becomes necessary to ensure a high degree of retrieval accuracy on a global scale.

This study uses the new and improved OCO-2 V11.1 dataset and compares coincident X_CO2 measurements against three independent datasets. The Total Carbon Column Observing Network (TCCON) is a network of solar-viewing ground-based Fourier Transform Spectrometers and the primary validation source for X_CO2 from OCO-2. TCCON measurements are unaffected by surface properties and are minimally sensitive to aerosols. The COllaborative Carbon Column Observing Network (COCCON) is a network of portable ground-based Fourier Transform Infrared spectrometers that are less expensive than full TCCON sites and have lower spectral resolution, but are similarly insensitive to surface properties and aerosols. Comparison of coincident OCO-2 measurements against selected TCCON and COCCON sites indicate that the absolute average bias values are close to 0 ppm for TCCON and less than 0.7 ppm for COCCON in the Land Nadir/Glint and Target mode observations.

Finally, we compare coincident OCO-2 measurements to the airborne Atmospheric Tomography Mission (ATom) when ATom conducted around-the-world flights in each of the four seasons between 2016 and 2018. This study bridges the gap between satellite, ground-based, and airborne X_CO2 measurements and aids the improvement of the OCO-2 X_CO2 data product. Further, it provides the latest validation analysis for OCO-2, providing the most up-to-date information on biases and uncertainty in the OCO-2 data.

How to cite: Das, S., Kiel, M., Laughner, J., and Osterman, G.: Three ways to validate the XCO2 product from the Orbiting Carbon Observatory-2 , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1267, https://doi.org/10.5194/egusphere-egu24-1267, 2024.