Abstract. A reliable and precise in situ CO2 and CO analysis system has been developed and deployed at eight sites in the NOAA Earth System Research Laboratory's (ESRL) Global Greenhouse Gas Reference Network. The network uses very tall (> 300 m) television and radio transmitter towers that provide a convenient platform for mid-boundary-layer trace-gas sampling. Each analyzer has three sample inlets for profile sampling, and a complete vertical profile is obtained every 15 min. The instrument suite at one site has been augmented with a cavity ring-down spectrometer for measuring CO2 and CH4. The long-term stability of the systems in the field is typically better than 0.1 ppm for CO2, 6 ppb for CO, and 0.5 ppb for CH4, as determined from repeated standard gas measurements. The instrumentation is fully automated and includes sensors for measuring a variety of status parameters, such as temperatures, pressures, and flow rates, that are inputs for automated alerts and quality control algorithms. Detailed and time-dependent uncertainty estimates have been constructed for all of the gases, and the uncertainty framework could be readily adapted to other species or analysis systems. The design emphasizes use of off-the-shelf parts and modularity to facilitate network operations and ease of maintenance. The systems report high-quality data with > 93% uptime. Recurrent problems and limitations of the current system are discussed along with general recommendations for high-accuracy trace-gas monitoring. The network is a key component of the North American Carbon Program and a useful model for future research-grade operational greenhouse gas monitoring efforts.

Abstract. Atmospheric inversion models have the potential to quantify CO2 fluxes at regional, sub-continental scales by taking advantage of near-surface CO2 mixing ratio observations collected in areas with high flux variability. This study presents results from a series of regional geostatistical inverse models (GIM) over North America for 2004, and uses them as the basis for an inter-comparison to other inversion studies and estimates from biospheric models collected through the North American Carbon Program Regional and Continental Interim Synthesis. Because the GIM approach does not require explicit prior flux estimates and resolves fluxes at fine spatiotemporal scales (i.e. 1° × 1°, 3-hourly in this study), it avoids temporal and spatial aggregation errors and allows for the recovery of realistic spatial patterns from the atmospheric data relative to previous inversion studies. Results from a GIM inversion using only available atmospheric observations and a fine-scale fossil fuel inventory were used to confirm the quality of the inventory and inversion setup. An inversion additionally including auxiliary variables from the North American Regional Reanalysis found inferred relationships with flux consistent with physiological understanding of the biospheric carbon cycle. Comparison of GIM results with bottom-up biospheric models showed stronger agreement during the growing relative to the dormant season, in part because most of the biospheric models do not fully represent agricultural land-management practices and the fate of both residual biomass and harvested products. Comparison to earlier inversion studies pointed to aggregation errors as a likely source of bias in previous sub-continental scale flux estimates, particularly for inversions that adjust fluxes at the coarsest scales and use atmospheric observations averaged over long periods. Finally, whereas the continental CO2 boundary conditions used in the GIM inversions have a minor impact on spatial patterns, they have a substantial impact on the continental carbon budget, with a difference of 0.8 PgC yr−1 in the total continental flux resulting from the use of two plausible sets of boundary CO2 mixing ratios. Overall, this inter-comparison study helps to assess the state of the science in estimating regional-scale CO2 fluxes, while pointing towards the path forward for improvements in future top-down and bottom-up modeling efforts.

The stratospheric aerosol layer has been monitored with lidars at Mauna Loa Observatory in Hawaii and Boulder in Colorado since 1975 and 2000, respectively. Following the Pinatubo volcanic eruption in June 1991, the global stratosphere has not been perturbed by a major volcanic eruption providing an unprecedented opportunity to study the background aerosol. Since about 2000, an increase of 4–7% per year in the aerosol backscatter in the altitude range 20–30 km has been detected at both Mauna Loa and Boulder. This increase is superimposed on a seasonal cycle with a winter maximum that is modulated by the quasi‐biennial oscillation (QBO) in tropical winds. Of the three major causes for a stratospheric aerosol increase: volcanic emissions to the stratosphere, increased tropical upwelling, and an increase in anthropogenic sulfur gas emissions in the troposphere, it appears that a large increase in coal burning since 2002, mainly in China, is the likely source of sulfur dioxide that ultimately ends up as the sulfate aerosol responsible for the increased backscatter from the stratospheric aerosol layer. The results are consistent with 0.6–0.8% of tropospheric sulfur entering the stratosphere.

Long-term atmospheric CO2 mole fraction and δ13CO2 observations over North America document persistent responses to the El Niño–Southern Oscillation. We estimate these responses corresponded to 0.61 (0.45 to 0.79) PgC year−1 more North American carbon uptake during El Niño than during La Niña between 2007 and 2015, partially offsetting increases of net tropical biosphere-to-atmosphere carbon flux around El Niño. Anomalies in derived North American net ecosystem exchange (NEE) display strong but opposite correlations with surface air temperature between seasons, while their correlation with water availability was more constant throughout the year, such that water availability is the dominant control on annual NEE variability over North America. These results suggest that increased water availability and favorable temperature conditions (warmer spring and cooler summer) caused enhanced carbon uptake over North America near and during El Niño.

A robust in situ CO₂ and CO analysis system has been developed and deployed at eight sites in the NOAA Earth System Research Laboratory's (ESRL) Tall Tower Greenhouse Gas Observing Network. The network uses very tall (> 300 m) television and radio transmitter towers that provide a convenient platform for mid-boundary layer trace gas sampling. Each analyzer has three sample inlets for profile sampling, and a complete vertical profile is obtained every 15 min. The instrument suite at one site has been augmented with a cavity ring-down spectrometer for measuring CO₂ and CH₄. The long-term stability of the systems in the field is typically better than 0.1 ppm for CO₂, 6 ppb for CO, and 0.5 ppb for CH₄, as determined from repeated standard gas measurements. The instrumentation is fully automated and includes sensors for measuring a variety of status parameters, such as temperatures, pressures and flow rates that are inputs for automated alerts and quality control algorithms. These algorithms provide detailed and time-dependent uncertainty estimates for all of the gases and could be adapted to other species or analysis systems. The design emphasizes use of off the shelf parts and modularity to facilitate network operations and ease of maintenance. The systems report high-quality data with > 93% uptime. Recurrent problems and limitations of the current system are discussed along with general recommendations for high accuracy trace-gas monitoring. The network is a key component of the North American Carbon Program and a useful model for future research-grade operational greenhouse gas monitoring efforts

Robust estimates of regional-scale terrestrial CO₂ exchange are needed to support carbon management policies and to improve the predictive ability of models representing carbon-climate feedbacks. Large discrepancies remain, however, both among and between CO₂ flux estimates from atmospheric inverse models and terrestrial biosphere models. Improved atmospheric inverse models that provide robust estimates at sufficiently fine spatial scales could prove especially useful for monitoring efforts, while also serving as a validation tool for process-based assumptions in terrestrial biosphere models. A growing network of continental sites collecting continuous CO₂ measurements provides the information needed to drive such models. This study presents results from a regional geostatistical inversion over North America for 2004, taking advantage of continuous data from the nine sites operational in that year, as well as available flask and aircraft observations. The approach does not require explicit prior flux estimates, resolves fluxes at finer spatiotemporal scales than previous North American inversion studies, and uses a Lagrangian transport model coupled with high-resolution winds (i.e. WRF-STILT) to resolve near-field influences around measurement locations. The estimated fluxes are used in an inter-comparison with other inversion studies and a suite of terrestrial biosphere model estimates collected through the North American Carbon Program Regional and Continental Interim Synthesis. Differences among inversions are found to be smallest in areas of the continent best-constrained by the atmospheric data, pointing to the value of an expanded measurement network. Aggregation errors in previous coarser-scale inversion studies are likely to explain a portion of the remaining spread. The spatial patterns from a geostatistical inversion that includes auxiliary environmental variables from the North American Regional Reanalysis were similar to those from the median of the biospheric model estimates during the growing season, but diverged more strongly in the dormant season. This could be due to a lack of sensitivity in the inversion during the dormant season, but may also point to a lack of skill in the biospheric models outside of the growing season, particularly in agricultural areas. For the annual continental budget, the boundary conditions used as an input into the inversions were seen to have a substantial impact on the estimated net flux, with a difference of ~0.8 PgC yr⁻¹ associated with results using two different plausible sets of boundary conditions

An infrared absorption spectrometer has been constructed to measure the stable isotopic composition of atmospheric methane samples. The spectrometer employs periodically poled lithium niobate to generate 15 W of tunable difference-frequency radiation from two near-infrared diode lasers that probe the 3 rotational-vibrational band of methane at 3.4 m. To enhance the signal, methane is extracted from 25 l of air by use of a cryogenic chromatographic column and is expanded into the multipass cell for analysis. A measurement precision of 12‰ is demonstrated for both ␦ 13 C and ␦D.

Abstract. Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly used approaches because they do not require a single emissions inventory or a bottom–up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using a synthetic CO2 case study based upon NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4×106 unknown fluxes using 9.9×104 synthetic observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximations are, typically, an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.