The greenhouse gas emissions from anthropogenic sectors

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Table of contents

Chapter I: Introduction
I.1Global radiative balance:
I.2Role of the greenhouse gases in global warming
I.3Carbon budget
I.3.1Carbon dioxide cycle
I.3.2Methane cycle
I.4CO2 and CH4 atmospheric measurerments
I.5Flux estimation approaches :
I.5.1Bottom-up approach:
I.5.2Top-down approach
I.6Estimation of the regional fluxes
I.6.1Some techniques for flux optimization
I.6.2Estimation of CO2 fluxes
I.6.1Estimation of CH4 fluxes
I.7Objective and structure of this thesis
Chapter II: Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4
II.1Summary
II.1.1Context of the study
II.1.2Material and methods
II.1.3Selection and the optimization of the spike detection methods
II.1.4Principal results
II.1.5Conclusions and implications
II.2Introduction
II.3Methodology
II.3.1Measurement sites and methods
II.3.1.1Measurement sites
II.3.1.2Measurement methods
II.3.2Spike detection algorithms
II.3.2.1Coefficient of variation (COV) method
II.3.2.2Standard deviation of the background (SD)
II.3.2.3Robust extraction of baseline signal (REBS)
II.4Results
II.4.1Optimization of the SD and REBS methods
II.4.1.1Sensitivity to the parameters of the SD method
II.4.1.2Sensitivity to the parameters of the REBS method
II.4.2Statistics of the three spike detection methods
II.4.1Comparison of SD and REBS methods to detect CH4 spikes at the PDM clean-air mountain station
II.4.2Comparison between automatic and manual spike detection
II.4.3Influence of the spike detection on hourly averages:
II.5Conclusion
Chapter III: Evaluation of the sensitivity of the transport model CHIMERE using different meteorological fields and surface fluxes for simulating the CO2 and the CH4 concentrations
III.1Introduction
III.2Methods
III.2.1CHIMERE atmospheric transport model
III.2.2Meteorological fields
III.2.2.1AROME
III.2.2.2ECMWF
III.2.1CO2 and CH4 surface fluxes
III.2.1.1Anthropogenic emissions
III.2.1.2Vegetation – atmosphere CO2 fluxes
III.2.2Atmospheric concentration measurements
III.2.3Ecosystem measurements
III.3results
III.3.1Comparison of the national totals and temporal distribution of IER and EDGAR anthropogenic fluxes
III.3.2Spatial differences between IER and EDGAR totals
III.3.3Temporal differences between IER and EDGAR
III.3.4Comparison of the biogenic CO2 fluxes between CTESSEL and VPRM
III.3.4.1Spatial distribution of the modeled fluxes for January and July
III.3.4.2Comparison between the modeled and the simulated NEE
III.3.5Sensitivity of the concentrations to the meteorological forcing
III.3.6Spatial distribution of the AROME/ECMWF differences
III.3.7Sensitivity of the concentrations to the surface fluxes
III.3.8Spatial distribution of the surface flux differences
III.4Conclusions
Chapter IV: The potential of a European network for the optimization the CO2 and the CH4 surface fluxes in France
IV.1Introduction
IV.2Methods
IV.2.1Inverse problem formalism
IV.2.1.1Inversion formalism
IV.2.1.2Inverse problem constraints
IV.2.1.3Regularization of the inverse problem
IV.2.2The solution of the inverse problem:
IV.2.3The inversion setup:
IV.2.3.1Estimation of the observations and prior variance-covariance matrices
IV.2.4The definition of the inverse problem
IV.2.4.1Control vector
IV.2.4.2Observation vector
IV.2.4.3Surface fluxes
IV.2.4.4Observation operator
IV.3Results
IV.3.1Inversion of the CH4 fluxes
IV.3.1.1Weight of the CH4 atmospheric observations in the inversion
IV.3.1.2Comparison of observation and prior flux errors with independent empirical estimates
IV.3.1.3Fit of posterior concentrations to observations
IV.3.1.4Emission regions constrained by the inversion
IV.3.1.5Spatial correlation of the flux errors
IV.3.1.6The spatio-temporal scales resolved by the inversion
IV.3.1.7Optimized fluxes
IV.3.2Inversion of the CO2 fluxes
IV.3.2.1Weight of the CO2 atmospheric observations in the inversion
IV.3.2.2Investigation of the observation and the prior flux errors
IV.3.2.3 Fit of posterior concentrations to observations
IV.3.2.4Flux regions constrained by the inversion
IV.3.2.5Spatial correlation of the anthropogenic and biogenic flux errors
IV.3.2.6The spatio-temporal scales resolved by the inversion
IV.3.2.7Optimized fluxes
IV.4Conclusions
Chapter V: Conclusions and perspectives :
V.1Conclusion
V.1.1Spike detection algorithms
V.1.2Evaluation of the simulated CO2 and CH4 concentrations
V.1.3Estimation of the CO2 and CH4 fluxes in France
V.2Perspectives
V.2.1Identification of the local contamination sources
V.2.2Atmospheric modeling
V.2.3Inverse modeling
Chapter VI: References