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1. Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020

Author

Anna Agustí-Panareda, Jérôme Barré, Sébastien Massart, Antje Inness, Ilse Aben, Melanie Ades, Bianca C. Baier, Gianpaolo Balsamo, Tobias Borsdorff, Nicolas Bousserez, Souhail Boussetta, Michael Buchwitz, Luca Cantarello, Cyril Crevoisier, Richard Engelen, Henk Eskes, Johannes Flemming, Sébastien Garrigues, Otto Hasekamp, Vincent Huijnen, Luke Jones, Zak Kipling, Bavo Langerock, Joe McNorton, Nicolas Meilhac, Stefan Noël, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Miha Razinger, Maximilian Reuter, Roberto Ribas, Martin Suttie, Colm Sweeney, Jérôme Tarniewicz, Lianghai Wu, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, [SDU]Sciences of the Universe [physics]
Abstract

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

Published
2023
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2. The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets

Author

Johannes Orphal, Yukio Yoshida, David Crisp, Christopher W. O'Dell, Otto Hasekamp, C. D. Crevoisier, John P. Burrows, Thorsten Warneke, Ilse Aben, M. De Mazière, Paul O. Wennberg, Günter Lichtenberg, Sergey Oshchepkov, Andrey Bril, Ralf Sussmann, Robert J. Parker, A. Chedin, Stefan Noel, Dominik Brunner, Thomas Kaminski, G. P. Stiller, Michael Buchwitz, Oliver Schneising, M. Reuter, André Butz, Justus Notholt, Hartmut Boesch, Heinrich Bovensmann, A. Laeng, Peter Bergamaschi, Brigitte Buchmann, Christian Frankenberg, Frédéric Chevallier, Frank Hase, Vanessa Sherlock, Jens Heymann, Marko Scholze, Thomas Blumenstock, Akihiko Kuze, Bart Dils, Hiroshi Suto, David W. T. Griffith, Nicholas M. Deutscher, Claus Zehner, Christoph Popp, Debra Wunch, R. Armante, S. Guerlet, Tatsuya Yokota, Institute of Environmental Physics [Bremen] (IUP), University of Bremen, University of Leicester, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Belgian Institute for Space Aeronomy / Institut d'Aéronomie Spatiale de Belgique (BIRA-IASB), SRON Netherlands Institute for Space Research (SRON), JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Institut für Meteorologie und Klimaforschung - Atmosphärische Spurengase und Fernerkundung (IMK-ASF), Karlsruher Institut für Technologie (KIT), Institut für Umweltphysik [Bremen] (IUP), Universität Bremen, Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Max-Planck-Institut für Radioastronomie (MPIFR), Laboratoire Lasers, Plasmas et Procédés photoniques (LP3), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Department of Physical Geography and Ecosystem Science [Lund], Lund University [Lund], Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), European Space Research Institute (ESRIN), Agence Spatiale Européenne = European Space Agency (ESA), Japan Aerospace Exploration Agency [Tsukuba] (JAXA), Institut of Physics, California Institute of Technology (CALTECH), Department of Earth Science and Engineering, California Institute of Technology, Pasadena, CA 91125, United States, National Institute for Environmental Studies (NIES), European Space Agency, 283576, Seventh Framework Programme, Deutsches Zentrum für Luft- und Raumfahrt, NNX11AG01G, National Aeronautics and Space Administration, U.S. Department of Energy, DP0879468, Australian Research Council, State and the University of Bremen, NASA Orbiting Carbon Observatory Program, EU projects IMECC and GEOmon, Senate of Bremen, École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), Institut für Meteorologie und Klimaforschung (IMK), Laboratoire d'étude des interactions entre sols, agrosystèmes et hydrosystèmes (LISAH), Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut national des sciences de l'Univers (INSU - CNRS), European Space Agency (ESA), Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 010504 meteorology & atmospheric sciences, Meteorology, 0211 other engineering and technologies, Soil Science, Climate change, Geology, Context (language use), 02 engineering and technology, User requirements document, 01 natural sciences, 7. Clean energy, SCIAMACHY, 13. Climate action, Core product, Greenhouse gas, Environmental science, Satellite, Computers in Earth Sciences, Total Carbon Column Observing Network, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

The GHG-CCI project is one of several projects of the European Space Agency's (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The “ECV Greenhouse Gases” (ECV GHG) is the global distribution of important climate relevant gases – atmospheric CO 2 and CH 4 – with a quality sufficient to obtain information on regional CO 2 and CH 4 sources and sinks. Two satellite instruments deliver the main input data for GHG-CCI: SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The first order priority goal of GHG-CCI is the further development of retrieval algorithms for near-surface-sensitive column-averaged dry air mole fractions of CO 2 and CH 4 , denoted XCO 2 and XCH 4 , to meet the demanding user requirements. GHG-CCI focuses on four core data products: XCO 2 from SCIAMACHY and TANSO and XCH 4 from the same two sensors. For each of the four core data products at least two candidate retrieval algorithms have been independently further developed and the corresponding data products have been quality-assessed and inter-compared. This activity is referred to as “Round Robin” (RR) activity within the CCI. The main goal of the RR was to identify for each of the four core products which algorithms should be used to generate the Climate Research Data Package (CRDP). The CRDP will essentially be the first version of the ECV GHG. This manuscript gives an overview of the GHG-CCI RR and related activities. This comprises the establishment of the user requirements, the improvement of the candidate retrieval algorithms and comparisons with ground-based observations and models. The manuscript summarizes the final RR algorithm selection decision and its justification. Comparison with ground-based Total Carbon Column Observing Network (TCCON) data indicates that the “breakthrough” single measurement precision requirement has been met for SCIAMACHY and TANSO XCO 2 ( 4 ( 4 is 3–15 ppb for SCIAMACHY and 2–8 ppb for TANSO depending on algorithm and time period. Meeting the 0.5 ppm systematic error requirement for XCO 2 remains a challenge: approximately 1 ppm has been achieved at the validation sites but also larger differences have been found in regions remote from TCCON. More research is needed to identify the causes for the observed differences. In this context GHG-CCI suggests taking advantage of the ensemble of existing data products, for example, via the EnseMble Median Algorithm (EMMA).

Published
2015
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3. Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI

Author

Ilse Aben, Cyril Crevoisier, Anna Agusti-Panareda, Otto Hasekamp, Richard Engelen, André Butz, Christian Frankenberg, Frédéric Chevallier, Sebastien Massart, European Centre for Medium-Range Weather Forecasts (ECMWF), SRON Netherlands Institute for Space Research (SRON), Institute for Anthropomatics (KIT), Karlsruhe Institute of Technology (KIT), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, Meteorology, Atmospheric methane, High density, Infrared atmospheric sounding interferometer, Atmospheric sciences, 7. Clean energy, Methane, lcsh:QC1-999, SCIAMACHY, Atmospheric composition, Troposphere, lcsh:Chemistry, chemistry.chemical_compound, Earth sciences, chemistry, lcsh:QD1-999, 13. Climate action, ddc:550, Environmental science, Total Carbon Column Observing Network, lcsh:Physics
Abstract

The Monitoring Atmospheric Composition and Climate Interim Implementation (MACC-II) delayed-mode (DM) system has been producing an atmospheric methane (CH4) analysis 6 months behind real time since June 2009. This analysis used to rely on the assimilation of the CH4 product from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument onboard Envisat. Recently the Laboratoire de Météorologie Dynamique (LMD) CH4 products from the Infrared Atmospheric Sounding Interferometer (IASI) and the SRON Netherlands Institute for Space Research CH4 products from the Thermal And Near-infrared Sensor for carbon Observation (TANSO) were added to the DM system. With the loss of Envisat in April 2012, the DM system now has to rely on the assimilation of methane data from TANSO and IASI. This paper documents the impact of this change in the observing system on the methane tropospheric analysis. It is based on four experiments: one free run and three analyses from respectively the assimilation of SCIAMACHY, TANSO and a combination of TANSO and IASI CH4 products in the MACC-II system. The period between December 2010 and April 2012 is studied. The SCIAMACHY experiment globally underestimates the tropospheric methane by 35 part per billion (ppb) compared to the HIAPER Pole-to-Pole Observations (HIPPO) data and by 28 ppb compared the Total Carbon Column Observing Network (TCCON) data, while the free run presents an underestimation of 5 ppb and 1 ppb against the same HIPPO and TCCON data, respectively. The assimilated TANSO product changed in October 2011 from version v.1 to version v.2.0. The analysis of version v.1 globally underestimates the tropospheric methane by 18 ppb compared to the HIPPO data and by 15 ppb compared to the TCCON data. In contrast, the analysis of version v.2.0 globally overestimates the column by 3 ppb. When the high density IASI data are added in the tropical region between 30° N and 30° S, their impact is mainly positive but more pronounced and effective when combined with version v.2.0 of the TANSO products. The resulting analysis globally underestimates the column-averaged dry-air mole fractions of methane (xCH4) just under 1 ppb on average compared to the TCCON data, whereas in the tropics it overestimates xCH4 by about 3 ppb. The random error is estimated to be less than 7 ppb when compared to TCCON data.

Published
2014
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4. The effect of sensor resolution on the number of cloud-free observations from space

Author

M. van Weele, J. M. Krijger, Richard A. Frey, Ilse Aben, and EGU, Publication

Subjects
Troposphere, Footprint, Geolocation, Cardinal point, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, Cloud cover, Resolution (electron density), Environmental science, Satellite, Air quality index, Remote sensing
Abstract

Air quality and surface emission inversions are likely to be focal points for future satellite missions on atmospheric composition. Most important for these applications is sensitivity to the atmospheric composition in the lowest few kilometers of the troposphere. Reduced sensitivity by clouds needs to be minimized. In this study we have quantified the increase in number of useful footprints, i.e. footprints which are sufficient cloud-free, as a function of sensor resolution (footprint area). High resolution (1 km×1 km) MODIS TERRA cloud mask observations are aggregated to lower resolutions. Statistics for different thresholds on cloudiness are applied. For each month in 2004 two days of MODIS data are analyzed. Globally the fraction of cloud-free observations drops from 16% at 100 km2 resolution to only 3% at 10 000 km2 if not a single MODIS observation within a footprint is allowed to be cloudy. If up to 5% or 20% of a footprint is allowed to be cloudy, the fraction of cloud-free observations is 9% or 17%, respectively, at 10 000 km2 resolution. The probability of finding cloud-free observations for different sensor resolutions is also quantified as a function of geolocation and season, showing examples over Europe and northern South America.

Published
2006

5. Temporal and spectral variation of desert dust and biomass burning aerosol scenes from 1995?2000 using GOME

Author

M. de Graaf, Piet Stammes, Ilse Aben, and EGU, Publication

Subjects
Ozone, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, Spectral variation, Albedo, Seasonality, Atmospheric sciences, medicine.disease, Spectral line, Aerosol, chemistry.chemical_compound, chemistry, medicine, Environmental science, Biomass burning, Desert dust, Remote sensing
Abstract

Global Ozone Monitoring Experiment (GOME) Absorbing Aerosol Index (AAI) and AAI-related residue data were used to investigate areas with UV-absorbing aerosols. Time series of regionally averaged residues show the seasonal variation and trends of aerosols and clouds in climatologically important parts of the globe. GOME spectra were used to study scenes containing specific types of aerosols. AAI data are specifically sensitive to biomass burning aerosols (BBA) and desert dust aerosols (DDA). Areas where these aerosols are regularly found were analysed to find spectral fingerprints in the ultraviolet (UV), visible and near-infrared (near-IR), to establish an aerosol type classification of BBA and DDA. Spectral residues are different for BBA and DDA, but over deserts the surface albedo is dominant beyond the UV and spectral residues cannot be used over land. Over oceans, about half of the BBA scenes show a very high reflectance that is never observed for DDA scenes. However, in the case of low reflectance scenes BBA and DDA cannot be distinguished. This is in part due to the microphysical and optical properties of biomass burning aerosols, which are highly variable in time, making it difficult to specify them spectrally as one type. Because of their high hygroscopicity BBA are often found in the presence of clouds, which disturb the spectrum of the scenes. Desert dust aerosols are much less hygroscopic and behave spectrally more uniformly.

Published
2006

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