Analysis of the potential of near-ground measurements of CO2 and CH4 in London, UK, for the monitoring of city-scale emissions using an atmospheric transport model

Carbon dioxide (CO2) and methane (CH4) mole fractions were measured at four near-ground sites located in and around London during the summer of 2012 with a view to investigating the potential of assimilating such measurements in an atmospheric inversion system for the monitoring of the CO2 and CH4 emissions in the London area. These data were analysed and compared with simulations using a modelling framework suited to building an inversion system: a 2 km horizontal resolution south of England configuration of the transport model CHIMERE driven by European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological forcing, coupled to a 1 km horizontal resolution emission inventory (the UK National Atmospheric Emission Inventory). First comparisons reveal that local sources, which cannot be represented in the model at a 2 km resolution, have a large impact on measurements. We evaluate methods to filter out the impact of some of the other critical sources of discrepancies between the measurements and the model simulation except that of the errors in the emission inventory, which we attempt to isolate. Such a separation of the impact of errors in the emission inventory should make it easier to identify the corrections that should be applied to the inventory. Analysis is supported by observations from meteorological sites around the city and a 3-week period of atmospheric mixing layer height estimations from lidar measurements. The difficulties of modelling the mixing layer depth and thus CO2 and CH4 concentrations during the night, morning and late afternoon lead to focusing on the afternoon period for all further analyses. The discrepancies between observations and model simulations are high for both CO2 and CH4 (i.e. their root mean square (RMS) is between 8 and 12 parts per million (ppm) for CO2 and between 30 and 55 parts per billion (ppb) for CH4 at a given site). By analysing the gradients between the urban sites and a suburban or rural reference site, we are able to decrease the impact of uncertainties in the fluxes and transport outside the London area and in the model domain boundary conditions. We are thus able to better focus attention on the signature of London urban CO2 and CH4 emissions in the atmospheric CO2 and CH4 concentrations. This considerably improves the statistical agreement between the model and observations for CO2 (with model–data RMS discrepancies that are between 3 and 7 ppm) and to a lesser degree for CH4 (with model–data RMS discrepancies that are between 29 and 38 ppb). Between one of the urban sites and either the rural or suburban reference site, selecting the gradients during periods wherein the reference site is upwind of the urban site further decreases the statistics of the discrepancies in general, though not systematically. In a further attempt to focus on the signature of the city anthropogenic emission in the mole fraction measurements, we use a theoretical ratio of gradients of carbon monoxide (CO) to gradients of CO2 from fossil fuel emissions in the London area to diagnose observation-based fossil fuel CO2 gradients, and compare them with the fossil fuel CO2 gradients simulated with CHIMERE. This estimate increases the consistency between the model and the measurements when considering only one of the two urban sites, even though the two sites are relatively close to each other within the city. While this study evaluates and highlights the merit of different approaches for increasing the consistency between the mesoscale model and the near-ground data, and while it manages to decrease the random component of the analysed model–data discrepancies to an extent that should not be prohibitive to extracting the signal from the London urban emissions, large biases, the sign of which depends on the measurement sites, remain in the final model–data discrepancies. Such biases are likely related to local emissions to which the urban near-ground sites are highly sensitive. This questions our current ability to exploit urban near-ground data for the atmospheric inversion of city emissions based on models at spatial resolution coarser than 2 km. Several measurement and modelling concepts are discussed to overcome this challenge

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C⋅y−2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes

The CO2 Human Emissions (CHE) Project: First steps towards a European operational capacity to monitor anthropogenic CO2 emissions

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M

Caractérisation des erreurs de modélisation pour l'assimilation de données dans un modèle océanique régional du Golfe de Gascogne

A data assimilation system for ocean models, the SEEK (Singular Evolutive Extended Kalman) filter, is studied to control a Bay of Biscay configuration. This 1/15° configuration, nested in a 1/3° North Atlantic configuration, through the use of Open (sea) Boundaries Conditions, is developed using HYCOM (Hybrid Coordinate Ocean Model). This study focuses on the parametrization of the model error in the SEEK filter, and more generally in low rank Kalman filters, in order to control regional models. Classic parametrizations of these data assimilation systems, which have been developed initially for basin models, are not adapted to the regional dynamics complexity. Ensemble methods are used to get a realistic estimation of the model error due to bad determination of atmospheric and open boundary forcings. These forcings influence is supposed to be very important on regional dynamics. Model error statistics are characterized using the method of representers, which demonstrates the impact of the assimilation of various type of observations to control the oceanic state. The propagation of the error generated at open boundaries is weak. The use of the error due to atmospheric forcings to parameterize the SEEK filter for surface temperature assimilation experiments gives good results. Their comparison with those given by a more classical parametrization shows the benefits of this study on model error.Cette thèse porte sur l'application du filtre SEEK (Singular Evolutive Extended Kalman filter), un système d'assimilation de données pour les modèles océaniques, au contrôle d'une configuration du Golfe de Gascogne. Cette configuration au 1/15°, emboîtée dans une configuration au 1/3° de l'Atlantique Nord à travers l'emploi de Conditions aux Frontières Ouvertes (en mer), est développée à l'aide du modèle HYCOM (Hybrid Coordinate Ocean Model) à coordonnée verticale hybride. L'étude porte essentiellement sur la paramétrisation de l'erreur modèle dans le filtre SEEK, et plus généralement dans les filtres de Kalman de rangs réduits, pour le contrôle des modèles régionaux. Les paramétrisations classiques de ces systèmes d'assimilation, développés jusqu'à présent pour les modèles de bassin, sont inadaptées à la complexité de la dynamique régionale. On utilise des méthodes d'ensemble pour estimer de façon réaliste l'erreur modèle liée à la mauvaise détermination des forçages aux limites, forçages atmosphériques et CFO, dont l'influence est a priori très importante sur la dynamique régionale. La caractérisation des statistiques de l'erreur modèle est réalisée à l'aide de la méthode des représenteurs qui montre l'impact de l'assimilation de divers types d'observations pour le contrôle de l'état océanique. La propagation de l'erreur générée aux frontières ouvertes est faible. Les bons résultats donnés par l'emploi de l'erreur liée aux forçages atmosphériques, pour paramétrer le filtre SEEK dans des expériences d'assimilation de température de surface, que l'on compare à ceux donnés par une paramétrisation plus classique, montrent l'apport de cette étude sur l'erreur modèle

Caractérisation des erreurs de modélisation pour l'assimilation de données dans un modèle océanique régional du Golfe de Gascogne

Cette thèse porte sur l'application du filtre SEEK (Singular Evolutive Extended Kalman filter), un système d'assimilation de données pour les modèles océaniques, au contrôle d'une configuration du Golfe de Gascogne. Cette configuration au 1/15, emboîtée dans une configuration au 1/3 de l'Atlantique Nord à travers l'emploi de Conditions aux Frontières Ouvertes (en mer), est développée à l'aide du modèle HYCOM (Hybrid Coordinate Ocean Model) à coordonnée verticale hybride. L'étude porte essentiellement sur la paramétrisation de l'erreur modèle dans le filtre SEEK, et plus généralement dans les filtres de Kalman de rangs réduits, pour le contrôle des modèles régionaux. Les paramétrisations classiques de ces systèmes d'assimilation, développés jusqu'à présent pour les modèles de bassin, sont inadaptées à la complexité de la dynamique régionale. On utilise des méthodes d'ensemble pour estimer de façon réaliste l'erreur modèle liée à la mauvaise détermination des forçages aux limites, forçages atmosphériques et CFO, dont l'influence est a priori très importante sur la dynamique régionale. La caractérisation des statistiques de l'erreur modèle est réalisée à l'aide de la méthode des représenteurs qui montre l'impact de l'assimilation de divers types d'observations pour le contrôle de l'état océanique. La propagation de l'erreur générée aux frontières ouvertes est faible. Les bons résultats donnés par l'emploi de l'erreur liée aux forçages atmosphériques, pour paramétrer le filtre SEEK dans des expériences d'assimilation de température de surface, que l'on compare à ceux donnés par une paramétrisation plus classique, montrent l'apport de cette étude sur l'erreur modèleA data assimilation system for ocean models, the SEEK (Singular Evolutive Extended Kalman) filter, is studied to control a Bay of Biscay configuration. This 1/15 configuration, nested in a 1/3 North Atlantic configuration, through the use of Open (sea) Boundaries Conditions, is developed using HYCOM (Hybrid Coordinate Ocean Model). This study focuses on the parametrization of the model error in the SEEK filter, and more generally in low rank Kalman filters, in order to control regional models. Classic parametrizations of these data assimilation systems, which have been developed initially for basin models, are not adapted to the regional dynamics complexity. Ensemble methods are used to get a realistic estimation of the model error due to bad determination of atmospheric and open boundary forcings. These forcings influence is supposed to be very important on regional dynamics. Model error statistics are characterized using the method of representers, which demonstrates the impact of the assimilation of various type of observations to control the oceanic state. The propagation of the error generated at open boundaries is weak. The use of the error due to atmospheric forcings to parameterize the SEEK filter for surface temperature assimilation experiments gives good results. Their comparison with those given by a more classical parametrization shows the benefits of this study on model errorGRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Quantifying CO2 emissions of a city with the Copernicus Anthropogenic CO2 Monitoring satellite mission

International audienc

Probabilistic global maps of the CO 2 column at daily and monthly scales from sparse satellite measurements

International audienceThe column-average dry air-mole fraction of carbon dioxide in the atmosphere (XCO 2) is measured by scattered satellite measurements like those from the Orbiting Carbon Observatory (OCO-2). We show that global continuous maps of XCO 2 (corresponding to level 3 of the satellite data) at daily or coarser temporal resolution can be inferred from these data with a Kalman filter built on a model of persistence. Our application of this approach on 2 years of OCO-2 retrievals indicates that the filter provides better information than a climatology of XCO 2 at both daily and monthly scales. Provided that the assigned observation uncertainty statistics are tuned in each grid cell of the XCO 2 maps from an objective method (based on consistency diagnostics), the errors predicted by the filter at daily and monthly scales represent the true error statistics reasonably well, except for a bias in the high latitudes of the winter hemisphere and a lack of resolution (i.e., a too small discrimination skill) of the predicted error standard deviations. Due to the sparse satellite sampling, the broad-scale patterns of XCO 2 described by the filter seem to lag behind the real signals by a few weeks. Finally, the filter offers interesting insights into the quality of the retrievals, both in terms of random and systematic errors