scholarly journals An attempt at estimating Paris area CO<sub>2</sub> emissions from atmospheric concentration measurements

2015 ◽  
Vol 15 (4) ◽  
pp. 1707-1724 ◽  
Author(s):  
F. M. Bréon ◽  
G. Broquet ◽  
V. Puygrenier ◽  
F. Chevallier ◽  
I. Xueref-Remy ◽  
...  
Keyword(s):  
Urban Area ◽  
Co2 Emissions ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Atmospheric Concentration ◽  
Paris Area ◽  
Direct Model ◽  
Concentration Measurements

Abstract. Atmospheric concentration measurements are used to adjust the daily to monthly budget of fossil fuel CO2 emissions of the Paris urban area from the prior estimates established by the Airparif local air quality agency. Five atmospheric monitoring sites are available, including one at the top of the Eiffel Tower. The atmospheric inversion is based on a Bayesian approach, and relies on an atmospheric transport model with a spatial resolution of 2 km with boundary conditions from a global coarse grid transport model. The inversion adjusts prior knowledge about the anthropogenic and biogenic CO2 fluxes from the Airparif inventory and an ecosystem model, respectively, with corrections at a temporal resolution of 6 h, while keeping the spatial distribution from the emission inventory. These corrections are based on assumptions regarding the temporal autocorrelation of prior emissions uncertainties within the daily cycle, and from day to day. The comparison of the measurements against the atmospheric transport simulation driven by the a priori CO2 surface fluxes shows significant differences upwind of the Paris urban area, which suggests a large and uncertain contribution from distant sources and sinks to the CO2 concentration variability. This contribution advocates that the inversion should aim at minimising model–data misfits in upwind–downwind gradients rather than misfits in mole fractions at individual sites. Another conclusion of the direct model–measurement comparison is that the CO2 variability at the top of the Eiffel Tower is large and poorly represented by the model for most wind speeds and directions. The model's inability to reproduce the CO2 variability at the heart of the city makes such measurements ill-suited for the inversion. This and the need to constrain the budgets for the whole city suggests the assimilation of upwind–downwind mole fraction gradients between sites at the edge of the urban area only. The inversion significantly improves the agreement between measured and modelled concentration gradients. Realistic emissions are retrieved for two 30-day periods and suggest a significant overestimate by the AirParif inventory. Similar inversions over longer periods are necessary for a proper evaluation of the optimised CO2 emissions against independent data.

scholarly journals An attempt at estimating Paris area CO<sub>2</sub> emissions from atmospheric concentration measurements

2014 ◽  
Vol 14 (7) ◽  
pp. 9647-9703 ◽  
Author(s):  
F. M. Bréon ◽  
G. Broquet ◽  
V. Puygrenier ◽  
F. Chevallier ◽  
I. Xueref-Rémy ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Current System ◽  
A Priori ◽  
Temporal Correlation ◽  
Atmospheric Concentration ◽  
Sources And Sinks ◽  
Paris Area ◽  
Concentration Measurements

Abstract. Atmospheric concentration measurements are used to adjust the daily to monthly budget of CO2 emissions from the AirParif inventory of the Paris agglomeration. We use 5 atmospheric monitoring sites including one at the top of the Eiffel tower. The atmospheric inversion is based on a Bayesian approach, and relies on an atmospheric transport model with a spatial resolution of 2 km with boundary conditions from a global coarse grid transport model. The inversion tool adjusts the CO2 fluxes (anthropogenic and biogenic) with a temporal resolution of 6 h, assuming temporal correlation of emissions uncertainties within the daily cycle and from day to day, while keeping the a priori spatial distribution from the emission inventory. The inversion significantly improves the agreement between measured and modelled concentrations. However, the amplitude of the atmospheric transport errors is often large compared to the CO2 gradients between the sites that are used to estimate the fluxes, in particular for the Eiffel tower station. In addition, we sometime observe large model-measurement differences upwind from the Paris agglomeration, which confirms the large and poorly constrained contribution from distant sources and sinks included in the prescribed CO2 boundary conditions These results suggest that (i) the Eiffel measurements at 300 m above ground cannot be used with the current system and (ii) the inversion shall rely on the measured upwind-downwind gradients rather than the raw mole fraction measurements. With such setup, realistic emissions are retrieved for two 30 day periods. Similar inversions over longer periods are necessary for a proper evaluation of the results.

scholarly journals Quantitative evaluation of the uncertainty sources for the modeling of atmospheric CO<sub>2</sub> concentration within and in the vicinity of Paris city

2020 ◽  
Author(s):  
Jinghui Lian ◽  
François-Marie Bréon ◽  
Grégoire Broquet ◽  
Bo Zheng ◽  
Michel Ramonet ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Co2 Emissions ◽  
Large Scale ◽  
Transport Model ◽  
Co2 Concentration ◽  
Inversion Method ◽  
Different Boundary Conditions ◽  
Paris Area ◽  
The City ◽  
Atmospheric Inversion

Abstract. The top-down atmospheric inversion method that couples atmospheric CO2 observations with an atmospheric transport model has been used extensively to quantify CO2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO2 that are of different origins than the targeted CO2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of one-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations and CO2 boundary conditions. The simulated CO2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime observation-model misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Our results suggest three selection criteria for the CO2 data to be assimilated for the inversion of CO2 emissions from Paris (i) discard data that appear as statistical outliers in the model-data misfits which are interpreted as model's deficiencies under complex meteorological conditions; (ii) use only afternoon urban measurements in winter and suburban ones in summer; (iii) test the influence of different boundary conditions in inversions. If possible, using additional observations to constrain the boundary inflow, or using CO2 gradients of upwind-downwind stations, rather than absolute CO2 concentration, as atmospheric inversion inputs.

scholarly journals The potential of satellite spectro-imagery for monitoring CO<sub>2</sub> emissions from large cities

2017 ◽  
Author(s):  
Grégoire Broquet ◽  
François-Marie Bréon ◽  
Emmanuel Renault ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Measurement Errors ◽  
Transport Model ◽  
Atmospheric Transport ◽  
European Space Agency ◽  
Image Resolution ◽  
Spatial Coverage ◽  
Paris Area ◽  
The Impact ◽  
Atmospheric Transport Model

Abstract. This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the Short Wave Infra Red measurements of a space borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on Observing System Simulation Experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 hours before a given satellite overpass. These 6 hours correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge about the hourly emissions from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically-integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbing missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6-hour mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it.

scholarly journals Mesoscale circulations over complex terrain in the Valencia coastal region, Spain – Part 2: Modeling CO<sub>2</sub> transport using idealized surface fluxes

2007 ◽  
Vol 7 (7) ◽  
pp. 1851-1868 ◽  
Author(s):  
G. Pérez-Landa ◽  
P. Ciais ◽  
G. Gangoiti ◽  
J. L. Palau ◽  
A. Carrara ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Transport Model ◽  
Mesoscale Model ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Coastal Region ◽  
Net Ecosystem Exchange ◽  
Co2 Concentration ◽  
Particle Dispersion ◽  
Surface Fluxes

Abstract. Vertical profiles of CO2 concentration were collected during an intensive summer campaign in a coastal complex-terrain region within the frame of the European Project RECAB (Regional Assessment and Modelling of the Carbon Balance in Europe). The region presents marked diurnal mesoscale circulation patterns. These circulations result in a specific coupling between atmospherically transported CO2 and its surface fluxes. To understand the effects of this interaction on the spatial variability of the observed CO2 concentrations, we applied a high-resolution transport simulation to an idealized model of land biotic fluxes. The regional Net Ecosystem Exchange fluxes were extrapolated for different land-use classes by using a set of eddy-covariance measurements. The atmospheric transport model is a Lagrangian particle dispersion model, driven by a simulation of the RAMS mesoscale model. Our simulations were able to successfully reproduce some of the processes controlling the mesoscale transport of CO2. A semi-quantitative comparison between simulations and data allowed us to characterize how the coupling between mesoscale transport and surface fluxes produced CO2 spatial gradients in the domain. Temporal averages in the simulated CO2 field show a covariance between flux and transport consisting of: 1) horizontally, a CO2 deficit over land, mirrored by a CO2 excess over the sea and 2) vertically, the prevalence of a mean CO2 depletion between 500 and 2000 m, and a permanent build-up of CO2 in the lower levels.

scholarly journals The potential of satellite spectro-imagery for monitoring CO<sub>2</sub> emissions from large cities

2018 ◽  
Vol 11 (2) ◽  
pp. 681-708 ◽  
Author(s):  
Grégoire Broquet ◽  
François-Marie Bréon ◽  
Emmanuel Renault ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Measurement Errors ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Image Resolution ◽  
Spatial Coverage ◽  
Paris Area ◽  
Infrared Measurements ◽  
The Impact ◽  
Atmospheric Transport Model

Abstract. This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ∼ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6 h mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular, the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it.

scholarly journals Technical Note: A new coupled system for global-to-regional downscaling of CO<sub>2</sub> concentration estimation

2009 ◽  
Vol 9 (6) ◽  
pp. 23187-23210 ◽  
Author(s):  
K. Trusilova ◽  
C. Rödenbeck ◽  
C. Gerbig ◽  
M. Heimann
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Near Field ◽  
Coupled System ◽  
Technical Note ◽  
Surface Fluxes ◽  
Global Model ◽  
Atmospheric Concentration ◽  
Mixing Ratios ◽  
Measured Time

Abstract. We introduce a global-to-regional nesting scheme for atmospheric transport models that are used for simulating concentrations of green house gases from globally distributed surface fluxes. The coupled system of the regional Stochastic Time-Inverted Lagrangian Transport (STILT) model and the global atmospheric transport model (TM3) is designed to resolve atmospheric trace gas concentrations at high temporal and spatial resolutions in a specified domain e.g. for regional inverse applications. The nesting technique used for the coupling is based on a decomposition of the atmospheric concentration signal into a far-field and a near-field contribution that allows global and regional models being of different type, i.e. Eulerian (grid) and Lagrangian (trajectory). For illustrating the performance of the coupled TM3-STILT system we compare simulated mixing ratios of carbon dioxide with available observations at 10 sites in Europe. For all chosen sites the TM3-STILT provided higher correlations between the modelled and the measured time series than the TM3 global model. The autocorrelation analysis showed that in contrast to the global model TM3-STILT model is capable to represent the variability of the measured tracer concentrations.

scholarly journals Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO<sub>2</sub> surface flux inversions

2015 ◽  
Vol 15 (6) ◽  
pp. 8883-8932 ◽  
Author(s):  
A. Babenhauserheide ◽  
S. Basu ◽  
S. Houweling ◽  
W. Peters ◽  
A. Butz
Keyword(s):  
Data Assimilation ◽  
Co2 Flux ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Filter Method ◽  
Operational Parameters ◽  
Atmospheric Concentration ◽  
The North ◽  
Concentration Measurements

Abstract. Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on assimilation of more than one year of atmospheric in-situ concentration measurements, we compare the performance of two established data assimilation models, CarbonTracker and TM5-4DVar, for CO2 flux estimation. CarbonTracker uses an Ensemble Kalman Filter method to optimize fluxes on ecoregions. TM5-4DVar employs a 4-D variational method and optimizes fluxes on a 6° × 4° longitude/latitude grid. Harmonizing the input data allows analyzing the strengths and weaknesses of the two approaches by direct comparison of the modelled concentrations and the estimated fluxes. We further assess the sensitivity of the two approaches to the density of observations and operational parameters such as temporal and spatial correlation lengths. Our results show that both models provide optimized CO2 concentration fields of similar quality. In Antarctica CarbonTracker underestimates the wintertime CO2 concentrations, since its 5-week assimilation window does not allow for adjusting the far-away surface fluxes in response to the detected concentration mismatch. Flux estimates by CarbonTracker and TM5-4DVar are consistent and robust for regions with good observation coverage, regions with low observation coverage reveal significant differences. In South America, the fluxes estimated by TM5-4DVar suffer from limited representativeness of the few observations. For the North American continent, mimicking the historical increase of measurement network density shows improving agreement between CarbonTracker and TM5-4DVar flux estimates for increasing observation density.

scholarly journals Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO<sub>2</sub> surface flux inversions

2015 ◽  
Vol 15 (17) ◽  
pp. 9747-9763 ◽  
Author(s):  
A. Babenhauserheide ◽  
S. Basu ◽  
S. Houweling ◽  
W. Peters ◽  
A. Butz
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Time Window ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Variational Model ◽  
Filter Method ◽  
Operational Parameters ◽  
Atmospheric Concentration ◽  
Concentration Measurements

Abstract. Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on the assimilation of more than 1 year of atmospheric in situ concentration measurements, we compare the performance of two established data assimilation models, CarbonTracker and TM5-4DVar (Transport Model 5 – Four-Dimensional Variational model), for CO2 flux estimation. CarbonTracker uses an ensemble Kalman filter method to optimize fluxes on ecoregions. TM5-4DVar employs a 4-D variational method and optimizes fluxes on a 6° × 4° longitude–latitude grid. Harmonizing the input data allows for analyzing the strengths and weaknesses of the two approaches by direct comparison of the modeled concentrations and the estimated fluxes. We further assess the sensitivity of the two approaches to the density of observations and operational parameters such as the length of the assimilation time window. Our results show that both models provide optimized CO2 concentration fields of similar quality. In Antarctica CarbonTracker underestimates the wintertime CO2 concentrations, since its 5-week assimilation window does not allow for adjusting the distant surface fluxes in response to the detected concentration mismatch. Flux estimates by CarbonTracker and TM5-4DVar are consistent and robust for regions with good observation coverage, regions with low observation coverage reveal significant differences. In South America, the fluxes estimated by TM5-4DVar suffer from limited representativeness of the few observations. For the North American continent, mimicking the historical increase of the measurement network density shows improving agreement between CarbonTracker and TM5-4DVar flux estimates for increasing observation density.

scholarly journals Biases in regional carbon budgets from covariation of surface fluxes and weather in transport model inversions

2014 ◽  
Vol 14 (3) ◽  
pp. 1571-1585 ◽  
Author(s):  
I. N. Williams ◽  
W. J. Riley ◽  
M. S. Torn ◽  
S. C. Biraud ◽  
M. L. Fischer
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Interannual Variability ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Carbon Uptake ◽  
Atmospheric Transport Model

Abstract. Recent advances in atmospheric transport model inversions could significantly reduce uncertainties in land carbon uptake through the assimilation of CO2 concentration measurements at weekly and shorter timescales. The potential of these measurements for reducing biases in estimated land carbon sinks depends on the strength of covariation between surface fluxes and atmospheric transport at these timescales and how well transport models represent this covariation. Daily to seasonal covariation of surface fluxes and atmospheric transport was estimated in observations at the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, and compared to an atmospheric transport model inversion (CarbonTracker). Covariation of transport and surface fluxes was stronger in CarbonTracker than in observations on synoptic (daily to weekly) timescales, with a wet year (2007) having significant covariation compared to a dry year (2006). Differences between observed and CarbonTracker synoptic covariation resulted in a 0.3 ppm CO2 enhancement in boundary layer concentrations during the growing season, and a corresponding enhancement in carbon uptake by 13% of the seasonal cycle amplitude in 2007, as estimated by an offline simplified transport model. This synoptic rectification of surface flux variability was of similar magnitude to the interannual variability in carbon sinks alone, and indicates that interannual variability in the inversions can be affected by biases in simulated synoptic rectifier effects. The most significant covariation of surface fluxes and transport had periodicities of 10 days and greater, suggesting that surface flux inversions would benefit from improved simulations of the effects of soil moisture on boundary layer heights and surface CO2 fluxes. Soil moisture remote sensing could be used along with CO2 concentration measurements to further constrain atmospheric transport model inversions.

scholarly journals Technical Note: A new coupled system for global-to-regional downscaling of CO<sub>2</sub> concentration estimation

2010 ◽  
Vol 10 (7) ◽  
pp. 3205-3213 ◽  
Author(s):  
K. Trusilova ◽  
C. Rödenbeck ◽  
C. Gerbig ◽  
M. Heimann
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Near Field ◽  
Coupled System ◽  
Technical Note ◽  
Surface Fluxes ◽  
Atmospheric Concentration ◽  
Mixing Ratios ◽  
Concentration Estimation ◽  
Measured Time

Abstract. We introduce a global-to-regional nesting scheme for atmospheric transport models used in simulating concentrations of green house gases from globally distributed surface fluxes. The coupled system of the regional Stochastic Time-Inverted Lagrangian Transport (STILT) model and the global atmospheric transport model (TM3) is designed to resolve atmospheric trace gas concentrations at high temporal and spatial resolutions in a specified domain e.g. for regional inverse applications. The nesting technique used for the coupling is based on a decomposition of the atmospheric concentration signal into a far-field and a near-field contribution enabling the usage of different model types for global (Eulerian) and regional (Lagrangian) scales. For illustrating the performance of the coupled TM3-STILT system we compare simulated mixing ratios of carbon dioxide with available observations at 10 sites in Europe. For all chosen sites the TM3-STILT provided higher correlations between the modelled and the measured time series than the TM3 global model. Autocorrelation analysis demonstrated that the TM3-STILT model captured temporal variability of measured tracer concentrations better than TM3 at most sites.

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