ALBERT

Description: Three years of greenhouse gases measurements, obtained using a gas chromatograph (GC) system located at the Puy de Dôme station at 1465 m a.s.l. in Central France are presented. The GC system was installed in 2010 at Puy de Dôme and was designed for automatic and accurate semi-continuous measurements of atmospheric carbon dioxide, methane, nitrous oxide and sulfur hexafluoride mole fractions. We present in detail the instrumental set up and the calibration strategy, which together allow the GC to reach repeatabilities of 0.1 μmol mol−1, 1.2, 0.3 nmol mol−1 and 0.06 pmol mol−1 for CO2, CH4, N2O and SF6, respectively. Comparisons of the atmospheric time series with those obtained using other instruments shown that the GC system meets the World Meteorological Organization recommendations. The analysis of the three-year atmospheric time series revealed how the planetary boundary layer height drives the mole fractions observed at a mountain site such as Puy de Dôme where air masses alternate between the planetary boundary layer and the free troposphere. Accurate long-lived greenhouse gases measurements collocated with 222Rn measurements as an atmospheric tracer, allowed us to determine the CO2, CH4 and N2O emissions in the catchment area of the station. The derived CO2 surface flux revealed a clear seasonal cycle with net uptake by plant assimilation in the spring and net emission caused by the biosphere and burning of fossil fuel during the remainder of the year. We calculated a mean annual CO2 flux of 1150 t(CO2) km−2. The derived CH4 and N2O emissions in the station catchment area were 5.6 t(CH4) km−2 yr−1 and 1.5 t(N2O) km−2 yr−1, respectively. Our derived annual CH4 flux is in agreement with the national French inventory, whereas our derived N2O flux is five times larger than the same inventory.

Description: Eight surface observation sites providing quasi-continuous measurements of atmospheric methane mixing ratios have been operated since the mid-2000's in Siberia. For the first time in a single work, we assimilate all of these in situ data in an atmospheric inversion. Our objective is to quantify methane surface fluxes from anthropogenic and wetland sources at the meso-scale in the Siberian Lowlands for the year 2010. To do so, we first inquire into the way the inversion uses the observations and the fluxes are constrained by the observation sites. As atmospheric inversions at the meso-scale suffer from mis-quantified sources of uncertainties, we follow recent innovations in inversion techniques and use a new inversion approach which quantifies the uncertainties more objectively than the previous inversions. We find that, due to errors in the representation of the atmospheric transport and redundant pieces of information, only one observation every few days is found valuable by the inversion. The remaining high-resolution signals are representative of very local emission patterns. An analysis of the use of information by the inversion also reveals that the observation sites constrain methane emissions within a radius of 500 km. More observation sites are necessary to constrain the whole Siberian Lowlands. Still, the fluxes within the constrained areas are quantified with objectified uncertainties. At the end, the tolerance intervals for posterior methane fluxes are of roughly 20% (resp. 50%) of the fluxes for anthropogenic (resp. wetland) sources. About 50–70% of emissions are constrained by the inversion on average on an annual basis. Extrapolating the figures on the constrained areas to the whole Siberian Lowlands, we find a regional methane budget of 5–28 Tg CH4 for the year 2010, i.e. 1–5% of the global methane emissions. As very few in situ observations are available in the region of interest, observations of methane total columns from the Greenhouse Gas Observing SATellite (GOSAT) are used for the evaluation of the inversion results, but they exhibit marginal signal from the fluxes within the region of interest.

Description: Water stable isotopes provide integrated tracers of the atmospheric water cycle, affected by changes in air mass origin, non-convective and convective processes and continental recycling. Novel remote sensing and in situ measuring techniques have recently offered opportunities for monitoring atmospheric water vapour isotopic composition. Recently developed infrared laser spectrometers allow for continuous in situ measurements of surface water vapour δDv and δ18Ov. So far, very few intercomparison of measurements conducted using different techniques have been achieved at a given location, due to difficulties intrinsic to the comparison of integrated with local measurements. Nudged simulations conducted with high resolution isotopically enabled GCMs provide a consistent framework for comparison with the different types of observations. Here, we compare simulations conducted with the ECHAM5-wiso model with three types of water vapour isotopic data obtained during summer 2012 at the forest site of Kourovka, Western Siberia: daily mean GOSAT δDv soundings, hourly ground-based FTIR total atmospheric columnar δDv amounts, and in situ hourly Picarro δDv measurements. There is an excellent correlation between observed and predicted δDv at surface while the comparison between water column values derived from the model compares well with FTIR and GOSAT estimates. This research was supported by the grant of Russian government under the contract 11.G34.31.0064.

Description: Since September 2011, a Wavelength-Scanned Cavity Ringdown Spectroscopy analyzer has been remotely operated in Ivittuut, southern Greenland, providing the first continuous record of surface water vapour isotopic composition (δ18O, δD) in South Greenland and the first record including the winter season in Greenland. This record depicts small summer diurnal variations. Measurements of precipitation isotopic composition suggest equilibrium between surface vapour and precipitation. The vapour data show large synoptic and seasonal variations corresponding to shifts in moisture sources estimated using a quantitative moisture source diagnostic. The arrival of low pressure systems towards south Greenland leads to δ18O enrichment (+5‰) and deuterium excess depletion (−15‰), coupled with moisture sources shifts. Monthly δ18O is minimum in November–December and maximum in June–July, with a seasonal amplitude of ~10‰. The strong correlation between δ18O and the logarithm of local surface humidity is consistent with Rayleigh distillation processes. The relationship with local surface air temperature is associated with a slope of ~0.4‰ °C−1. During the summer 2012 heat waves, the observations display a divergence between δ18O and local climate variables, probably due to the isotopic depletion associated with long distance transport from subtropical moisture sources. Monthly deuterium excess is minimum in May–June and maximum in November, with a seasonal amplitude of 20‰. It is anti-correlated with δ18O, and correlated with local surface relative humidity (at the station) as well as surface relative humidity in a North Atlantic sector, south of Greenland and Iceland. While synoptic and seasonal variations are well represented by the Atmospheric General Circulation Model LMDZiso for Ivittuut δ18O, the model does not capture the magnitude of these variations for deuterium excess.

Description: Eight surface observation sites providing quasi-continuous measurements of atmospheric methane mixing ratios have been operated since the mid-2000's in Siberia. For the first time in a single work, we assimilate 1 year of these in situ observations in an atmospheric inversion. Our objective is to quantify methane surface fluxes from anthropogenic and wetland sources at the mesoscale in the Siberian lowlands for the year 2010. To do so, we first inquire about the way the inversion uses the observations and the way the fluxes are constrained by the observation sites. As atmospheric inversions at the mesoscale suffer from mis-quantified sources of uncertainties, we follow recent innovations in inversion techniques and use a new inversion approach which quantifies the uncertainties more objectively than the previous inversion systems. We find that, due to errors in the representation of the atmospheric transport and redundant pieces of information, only one observation every few days is found valuable by the inversion. The remaining high-resolution quasi-continuous signal is representative of very local emission patterns difficult to analyse with a mesoscale system. An analysis of the use of information by the inversion also reveals that the observation sites constrain methane emissions within a radius of 500 km. More observation sites than the ones currently in operation are then necessary to constrain the whole Siberian lowlands. Still, the fluxes within the constrained areas are quantified with objectified uncertainties. Finally, the tolerance intervals for posterior methane fluxes are of roughly 20 % (resp. 50 %) of the fluxes for anthropogenic (resp. wetland) sources. About 50–70 % of Siberian lowlands emissions are constrained by the inversion on average on an annual basis. Extrapolating the figures on the constrained areas to the whole Siberian lowlands, we find a regional methane budget of 5–28 TgCH4 for the year 2010, i.e. 1–5 % of the global methane emissions. As very few in situ observations are available in the region of interest, observations of methane total columns from the Greenhouse Gas Observing SATellite (GOSAT) are tentatively used for the evaluation of the inversion results, but they exhibit only a marginal signal from the fluxes within the region of interest.

Description: Three years of greenhouse gas measurements, obtained using a gas chromatograph (GC) system located at the Puy de Dôme station at 1465 m a.s.l. in central France, are presented. The GC system was installed in 2010 at Puy de Dôme and was designed for automatic and accurate semicontinuous measurements of atmospheric carbon dioxide, methane, nitrous oxide and sulfur hexafluoride mole fractions. We present in detail the instrumental setup and the calibration strategy, which together allow the GC to reach repeatabilities of 0.1 μmol mol−1, 1.2 nmol mol−1, 0.3 nmol mol−1 and 0.06 pmol mol−1 for CO2, CH4, N2O and SF6, respectively. The analysis of the 3-year atmospheric time series revealed how the planetary boundary layer height drives the mole fractions observed at a mountain site such as Puy de Dôme where air masses alternate between the planetary boundary layer and the free troposphere. Accurate long-lived greenhouse gas measurements collocated with 222Rn measurements as an atmospheric tracer allowed us to determine the CO2, CH4 and N2O emissions in the catchment area of the station. The derived CO2 surface flux revealed a clear seasonal cycle, with net uptake by plant assimilation in the spring and net emission caused by the biosphere and burning of fossil fuel during the remainder of the year. We calculated a mean annual CO2 flux of 1310 ± 680 t CO2 km−2. The derived CH4 and N2O emissions in the station catchment area were 7.0 ± 4.0 t CH4 km−2 yr−1 and 1.8 ± 1.0 t N2O km−2 yr−1, respectively. Our derived annual CH4 flux is in agreement with the national French inventory, whereas our derived N2O flux is 5 times larger than the same inventory.

Description: Classical Bayesian atmospheric inversions process atmospheric observations and prior emissions, the two being connected by an observation operator picturing mainly the atmospheric transport. These inversions rely on prescribed errors in the observations, the prior emissions and the observation operator. At the meso-scale, inversion results are very sensitive to the prescribed error distributions, which are not accurately known. The classical Bayesian framework experiences difficulties in quantifying the impact of mis-specified error distributions on the optimized fluxes. In order to cope with this issue, we rely on recent research results and enhance the classical Bayesian inversion framework through a marginalization on all the plausible errors that can be prescribed in the system. The marginalization consists in computing inversions for all possible error distributions weighted by the probability of occurence of the error distributions. The posterior distribution of the fluxes calculated by the marginalization is complicated and not explicitly describable. We then carry out a Monte-Carlo sampling relying on an approximation of the probability of occurence of the error distributions. This approximation is deduced from the well-tested algorithm of the Maximum of Likelihood. Thus, the marginalized inversion relies on an automatic objectified diagnosis of the error statistics, without any prior knowledge about the matrices. It robustly includes the uncertainties on the error distributions, contrary to what is classically done with frozen expert-knowledge error statistics. Some expert knowledge is still used in the method for the choice of emission aggregation pattern and sampling protocol in order to reduce the computation costs of the method. The relevance and the robustness of the method is tested on a case study: the inversion of methane surface fluxes at the meso-scale with real observation sites in Eurasia. Observing System Simulation Experiments are carried out with different transport patterns, flux distributions and total prior amounts of emitted gas. The method proves to consistently reproduce the known "truth" in most cases, with satisfactory tolerance intervals. Additionnaly, the method explicitly provides influence scores and posterior correlation matrices. An in-depth interpretation of the inversion results is then possible. The more objective quantification of the influence of the observations on the fluxes proposed here allows us to evaluate the impact of the observation network on the characterization of the surface fluxes. The explicit correlations between emission regions reveal the mis-separated regions, hence the typical temporal and spatial scales the inversion can analyze. These scales proved to be consistent with the chosen aggregation patterns.

Description: Classical Bayesian atmospheric inversions process atmospheric observations and prior emissions, the two being connected by an observation operator picturing mainly the atmospheric transport. These inversions rely on prescribed errors in the observations, the prior emissions and the observation operator. When data pieces are sparse, inversion results are very sensitive to the prescribed error distributions, which are not accurately known. The classical Bayesian framework experiences difficulties in quantifying the impact of mis-specified error distributions on the optimized fluxes. In order to cope with this issue, we rely on recent research results to enhance the classical Bayesian inversion framework through a marginalization on a large set of plausible errors that can be prescribed in the system. The marginalization consists in computing inversions for all possible error distributions weighted by the probability of occurrence of the error distributions. The posterior distribution of the fluxes calculated by the marginalization is not explicitly describable. As a consequence, we carry out a Monte Carlo sampling based on an approximation of the probability of occurrence of the error distributions. This approximation is deduced from the well-tested method of the maximum likelihood estimation. Thus, the marginalized inversion relies on an automatic objectified diagnosis of the error statistics, without any prior knowledge about the matrices. It robustly accounts for the uncertainties on the error distributions, contrary to what is classically done with frozen expert-knowledge error statistics. Some expert knowledge is still used in the method for the choice of an emission aggregation pattern and of a sampling protocol in order to reduce the computation cost. The relevance and the robustness of the method is tested on a case study: the inversion of methane surface fluxes at the mesoscale with virtual observations on a realistic network in Eurasia. Observing system simulation experiments are carried out with different transport patterns, flux distributions and total prior amounts of emitted methane. The method proves to consistently reproduce the known "truth" in most cases, with satisfactory tolerance intervals. Additionally, the method explicitly provides influence scores and posterior correlation matrices. An in-depth interpretation of the inversion results is then possible. The more objective quantification of the influence of the observations on the fluxes proposed here allows us to evaluate the impact of the observation network on the characterization of the surface fluxes. The explicit correlations between emission aggregates reveal the mis-separated regions, hence the typical temporal and spatial scales the inversion can analyse. These scales are consistent with the chosen aggregation patterns.

Description: Since September 2011, a wavelength-scanned cavity ring-down spectroscopy analyser has been remotely operated in Ivittuut, southern Greenland, providing the first record of surface water vapour isotopic composition based on continuous measurements in South Greenland and the first record including the winter season in Greenland. The comparison of vapour data with measurements of precipitation isotopic composition suggest an equilibrium between surface vapour and precipitation. δ18O and deuterium excess are generally anti-correlated and show important seasonal variations, with respective amplitudes of ~10 and ~20‰, as well as large synoptic variations. The data depict small summer diurnal variations. At the seasonal scale, δ18O has a minimum in November–December and a maximum in June–July, while deuterium excess has a minimum in May–June and a maximum in November. The approach of low-pressure systems towards South Greenland leads to δ18O increase (typically +5‰) and deuterium excess decrease (typically −15‰). Seasonal and synoptic variations coincide with shifts in the moisture sources, estimated using a quantitative moisture source diagnostic based on a Lagrangian back-trajectory model. The atmospheric general circulation model LMDZiso correctly captures the seasonal and synoptic variability of δ18O, but does not capture the observed magnitude of deuterium excess variability. Covariations of water vapour isotopic composition with local and moisture source meteorological parameters have been evaluated. δ18O is strongly correlated with the logarithm of local surface humidity, consistent with Rayleigh distillation processes, and with local surface air temperature, associated with a slope of ~0.4‰ °C−1. Deuterium excess correlates with local surface relative humidity as well as surface relative humidity from the dominant moisture source area located in the North Atlantic, south of Greenland and Iceland.