Thomas Lauvaux

Bridging the gap between atmospheric concentrations and local ecosystem measurements.

This paper demonstrates that atmospheric inversions of CO2 are a reliable tool for estimating regional fluxes. We compare results of an inversion over 18 days and a 300 × 300 km2 domain in southwest France against independent measurements of fluxes from aircraft and towers. The inversion used concentration measurements from 2 towers while the independent data included 27 aircraft transects and 5 flux towers. The inversion reduces the mismatch between prior and independent fluxes, improving both spatial and temporal structures. The present mesoscale atmospheric inversion improves by 30% the CO2 fluxes over distances of few hundreds of km around the atmospheric measurement locations

Aircraft-Based Estimate of Total Methane Emissions from the Barnett Shale Region

We present estimates of regional methane (CH4) emissions from oil and natural gas operations in the Barnett Shale, Texas, using airborne atmospheric measurements. Using a mass balance approach on eight different flight days in March and October 2013, the total CH4 emissions for the region are estimated to be 76 ± 13 × 10(3) kg hr(-1) (equivalent to 0.66 ± 0.11 Tg CH4 yr(-1); 95% confidence interval (CI)). We estimate that 60 ± 11 × 10(3) kg CH4 hr(-1) (95% CI) are emitted by natural gas and oil operations, including production, processing, and distribution in the urban areas of Dallas and Fort Worth. This estimate agrees with the U.S. Environmental Protection Agency (EPA) estimate for nationwide CH4 emissions from the natural gas sector when scaled by natural gas production, but it is higher than emissions reported by the EDGAR inventory or by industry to EPAs Greenhouse Gas Reporting Program. This study is the first to show consistency between mass balance results on so many different days and in two different seasons, enabling better quantification of the related uncertainty. The Barnett is one of the largest production basins in the United States, with 8% of total U.S. natural gas production, and thus, our results represent a crucial step toward determining the greenhouse gas footprint of U.S. onshore natural gas production.

Toward quantification and source sector identification of fossil fuel CO2 emissions from an urban area: Results from the INFLUX experiment

The Indianapolis Flux Experiment (INFLUX) aims to develop and assess methods for quantifying urban greenhouse gas emissions. Here we use CO2, 14CO2, and CO measurements from tall towers around Indianapolis, USA, to determine urban total CO2, the fossil fuel derived CO2 component (CO2ff), and CO enhancements relative to background measurements. When a local background directly upwind of the urban area is used, the wintertime total CO2 enhancement over Indianapolis can be entirely explained by urban CO2ff emissions. Conversely, when a continental background is used, CO2ff enhancements are larger and account for only half the total CO2 enhancement, effectively representing the combined CO2ff enhancement from Indianapolis and the wider region. In summer, we find that diurnal variability in both background CO2 mole fraction and covarying vertical mixing makes it difficult to use a simple upwind-downwind difference for a reliable determination of total CO2 urban enhancement. We use characteristic CO2ff source sector CO:CO2ff emission ratios to examine the contribution of the CO2ff source sectors to total CO2ff emissions. This method is strongly sensitive to the mobile sector, which produces most CO. We show that the inventory-based emission product (“bottom up”) and atmospheric observations (“top down”) can be directly compared throughout the diurnal cycle using this ratio method. For Indianapolis, the top-down observations are consistent with the bottom-up Hestia data product emission sector patterns for most of the diurnal cycle but disagree during the nighttime hours. Further examination of both the top-down and bottom-up assumptions is needed to assess the exact cause of the discrepancy.

Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape.

An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 flux over the major croplands of the United States by comparing the results to an inventory of CO2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom-up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion-based mean CO2 -C sink estimates were generally slightly larger, 8-20% for PSU, 10-20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 fluxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region.

High‐resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX)

Based on a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of greenhouse gases (GHGs), we developed the first comprehensive monitoring systems of CO2 emissions at high resolution over the city of Indianapolis. The urban inversion evaluated over the 2012-2013 dormant season showed a statistically significant increase of about 20% (from 4.5 to 5.7 MtC ± 0.23 MtC) compared to the Hestia CO2 emission estimate, a state-of-the-art building-level emission product. Spatial structures in prior emission errors, mostly undetermined, appeared to affect the spatial pattern in the inverse solution and the total carbon budget over the entire area by up to 15%, while the inverse solution remains fairly insensitive to the CO2 boundary inflow and to the different prior emissions (i.e., ODIAC). Preceding the surface emission optimization, we improved the atmospheric simulations using a meteorological data assimilation system also informing our Bayesian inversion system through updated observations error variances. Finally, we estimated the uncertainties associated with undetermined parameters using an ensemble of inversions. The total CO2 emissions based on the ensemble mean and quartiles (5.26-5.91 MtC) were statistically different compared to the prior total emissions (4.1 to 4.5 MtC). Considering the relatively small sensitivity to the different parameters, we conclude that atmospheric inversions are potentially able to constrain the carbon budget of the city, assuming sufficient data to measure the inflow of GHG over the city, but additional information on prior emission error structures are required to determine the spatial structures of urban emissions at high resolution.

Network design for mesoscale inversions of CO2 sources and sinks

ABSTRACT Recent instrumental deployments of regional observation networks of atmospheric CO2 mixing ratios have been used to constrain carbon sources and sinks using inversion methodologies. In this study, we performed sensitivity experiments using observation sites from the Mid Continent Intensive experiment to evaluate the required spatial density and locations of CO2 concentration towers based on flux corrections and error reduction analysis. In addition, we investigated the impact of prior flux error structures with different correlation lengths and biome information. We show here that, while the regional carbon balance converged to similar annual estimates using only two concentration towers over the region, additional sites were necessary to retrieve the spatial flux distribution of our reference case (using the entire network of eight towers). Local flux corrections required the presence of observation sites in their vicinity, suggesting that each tower was only able to retrieve major corrections within a hundred of kilometres around, despite the introduction of spatial correlation lengths (~100 to 300 km) in the prior flux errors. We then quantified and evaluated the impact of the spatial correlations in the prior flux errors by estimating the improvement in the CO2 model-data mismatch of the towers not included in the inversion. The overall gain across the domain increased with the correlation length, up to 300 km, including both biome-related and non-biome-related structures. However, the spatial variability at smaller scales was not improved. We conclude that the placement of observation towers around major sources and sinks is critical for regional-scale inversions in order to obtain reliable flux distributions in space. Sparser networks seem sufficient to assess the overall regional carbon budget with the support of flux error correlations, indicating that regional signals can be recovered using hourly mixing ratios. However, the smaller spatial structures in the posterior fluxes are highly constrained by assumed prior flux error correlation lengths, with no significant improvement at only a few hundreds of kilometres away from the observation sites.

Urban Emissions of CO2 from Davos, Switzerland: The First Real-Time Monitoring System Using an Atmospheric Inversion Technique

AbstractAnthropogenic emissions from urban areas represent 70% of the fossil fuel carbon emitted globally according to carbon emission inventories. The authors present here the first operational system able to monitor in near–real time daily emission estimates, using a mesoscale atmospheric inversion framework over the city of Davos, Switzerland, before, during, and after the World Economic Forum 2012 Meeting (WEF-2012). Two instruments that continuously measured atmospheric mixing ratios of greenhouse gases (GHGs) were deployed at two locations from 23 December 2011 to 3 March 2012: one site was located in the urban area and the other was out of the valley in the surrounding mountains. Carbon dioxide, methane, and carbon monoxide were measured continuously at both sites. The Weather Research and Forecasting mesoscale atmospheric model (WRF), in four-dimensional data assimilation mode, was used to simulate the transport of GHGs over the valley of Davos at 1.3-km resolution. Wintertime emissions prior to t...

Direct and Indirect Measurements and Modeling of Methane Emissions in Indianapolis, Indiana

This paper describes process-based estimation of CH4 emissions from sources in Indianapolis, IN and compares these with atmospheric inferences of whole city emissions. Emissions from the natural gas distribution system were estimated from measurements at metering and regulating stations and from pipeline leaks. Tracer methods and inverse plume modeling were used to estimate emissions from the major landfill and wastewater treatment plant. These direct source measurements informed the compilation of a methane emission inventory for the city equal to 29 Gg/yr (5% to 95% confidence limits, 15 to 54 Gg/yr). Emission estimates for the whole city based on an aircraft mass balance method and from inverse modeling of CH4 tower observations were 41 ± 12 Gg/yr and 81 ± 11 Gg/yr, respectively. Footprint modeling using 11 days of ethane/methane tower data indicated that landfills, wastewater treatment, wetlands, and other biological sources contribute 48% while natural gas usage and other fossil fuel sources contribute 52% of the city total. With the biogenic CH4 emissions omitted, the top-down estimates are 3.5-6.9 times the nonbiogenic city inventory. Mobile mapping of CH4 concentrations showed low level enhancement of CH4 throughout the city reflecting diffuse natural gas leakage and downstream usage as possible sources for the missing residual in the inventory.

Planetary boundary layer errors in mesoscale inversions of column-integrated CO2 measurements

Observing platforms of greenhouse gas column mole fractions using remote sensing instruments have enhanced the capability of carbon data assimilation systems at large scales and helped improve our understanding of the underlying processes involved in the exchange of carbon at the surface of the globe. In this study, we quantify the additional information carried by these measurements at finer scales and consider the impact of vertical transport errors in current modeling systems, one of the main sources of uncertainties in regional inverse flux estimates. Surface-based column-integrated sensors are shown to be a significant source of information to constrain local surface fluxes at fine scales. Gain and error reduction are only about 20% lower than for in situ instruments. Column measurements show less dependence on near-field surface fluxes compared to in situ, with the error reduction being more homogeneously distributed. However, vertical transport errors still impact the flux retrievals, as with in situ measurements but to a lesser extent. Inverse fluxes from both types of measurements were affected by errors in vertical mixing and mean horizontal winds, with a larger impact on the inverse carbon balance using in situ measurements. The use of remote sensing measurements also appeared to constrain significantly the boundary concentrations, a critical limitation in current regional inversions. We finally performed a pseudo-data experiment combining both types of instruments, creating an optimal observing network with a lower impact of planetary boundary layer transport errors on the surface fluxes and the boundary concentrations, and a more widely distributed reduction of the errors over the boundaries.

Hyperparameter estimation for uncertainty quantification in mesoscale carbon dioxide inversions

Uncertainty quantification is critical in the inversion of CO2 surface fluxes from atmospheric concentration measurements. Here, we estimate the main hyperparameters of the error covariance matrices for a priori fluxes and CO2 concentrations, that is, the variances and the correlation lengths, using real, continuous hourly CO2 concentration data in the context of the Ring 2 experiment of the North American Carbon Program Mid Continent Intensive. Several criteria, namely maximum likelihood (ML), general cross-validation (GCV) and χ 2 test are compared for the first time under a realistic setting in a mesoscale CO2 inversion. It is shown that the optimal hyperparameters under the ML criterion assure perfect χ 2 consistency of the inverted fluxes. Inversions using the ML error variances estimates rather than the prescribed default values are less weighted by the observations, because the default values underestimate the model-data mismatch error, which is assumed to be dominated by the atmospheric transport error. As for the spatial correlation length in prior flux errors, the Ring 2 network is sparse for GCV, and this method fails to reach an optimum. In contrast, the ML estimate (e.g. an optimum of 20 km for the first week of June 2007) does not support long spatial correlations that are usually assumed in the default values.