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To assess global carbon cycle variability, we decompose the net land carbon sink into the sum of gross primary productivity (GPP), terrestrial ecosystem respiration (TER), and fire emissions and apply a Bayesian framework to constrain these fluxes between 1980 and 2014. The constrained GPP and TER fluxes show an increasing trend of only half of the prior trend simulated by models. From the optimization, we infer that TER increased in parallel with GPP from 1980 to 1990, but then stalled during the cooler periods, in 1990–1994 coincident with the Pinatubo eruption, and during the recent warming hiatus period. After each of these TER stalling periods, TER is found to increase faster than GPP, explaining a relative reduction of the net land sink. These results shed light on decadal variations of GPP and TER and suggest that they exhibit different responses to temperature anomalies over the last 35 years.

The global land and ocean carbon sinks have increased proportionally with increasing carbon dioxide emissions during the past decades1. It is thought that Northern Hemisphere lands make a dominant contribution to the global land carbon sink2–7; however, the long-term trend of the northern land sink remains uncertain. Here, using measurements of the interhemispheric gradient of atmospheric carbon dioxide from 1958 to 2016, we show that the northern land sink remained stable between the 1960s and the late 1980s, then increased by 0.5 ± 0.4 petagrams of carbon per year during the 1990s and by 0.6 ± 0.5 petagrams of carbon per year during the 2000s. The increase of the northern land sink in the 1990s accounts for 65% of the increase in the global land carbon flux during that period. The subsequent increase in the 2000s is larger than the increase in the global land carbon flux, suggesting a coincident decrease of carbon uptake in the Southern Hemisphere. Comparison of our findings with the simulations of an ensemble of terrestrial carbon models5,8 over the same period suggests that the decadal change in the northern land sink between the 1960s and the 1990s can be explained by a combination of increasing concentrations of atmospheric carbon dioxide, climate variability and changes in land cover. However, the increase during the 2000s is underestimated by all models, which suggests the need for improved consideration of changes in drivers such as nitrogen deposition, diffuse light and land-use change. Overall, our findings underscore the importance of Northern Hemispheric land as a carbon sink. Measurements of the interhemispheric gradient of atmospheric carbon dioxide show that the Northern Hemisphere carbon land sink remained stable between the 1960s and the late 1980s, then increased during the 1990s and 2000s. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) and methane (CH4) mole fractions were measured at four near ground sites located in and around London during the summer of 2012 in view to investigate the potential of assimilating such measurements in an atmospheric inversion system for the monitoring of the CO2 and CH4 emissions in the London area. These data were analysed and compared with simulations using a modelling framework suited to building an inversion system: a 2 km horizontal resolution South of England configuration of the transport model CHIMERE driven by European Centre for Medium-Range Weather Forecasting (ECMWF) meteorological forcing, coupled to a 1 km horizontal resolution emission inventory (the UK National Atmospheric Emission Inventory). First comparisons reveal that local sources have a large impact on measurements and these local sources cannot be represented in the model at 2 km resolution. We evaluate methods to minimise some of the other critical sources of misfits between the observation data and the model simulation that overlap the signature of the errors in the emission inventory. These methods should make it easier to identify the corrections that should be applied to the inventory. Analysis is supported by observations from meteorological sites around the city and a three-week period of atmospheric mixing layer height estimations from lidar measurements. The difficulties of modelling the mixing layer depth and thus CO2 and CH4 concentrations during the night, morning and late afternoon led us to focus on the afternoon period for all further analyses. The misfits between observations and model simulations are high for both CO2 and CH4 (i.e., their root mean square (RMS) is between 8 and 12 parts per million (ppm) for CO2 and between 30 and 55 parts per billion (ppb) for CH4 at a given site). By analysing the gradients between the urban sites and a suburban or rural reference site, we are able to decrease the impact of uncertainties in the fluxes and transport outside the London area and in the model domain boundary conditions, and to better focus attention on the signature of London urban CO2 and CH4 emissions. This considerably improves the statistical agreement between the model and observations for CO2 (model-data RMS misfit of between 3 and 7 ppm) and to a lesser degree for CH4 (model-data RMS misfit of between 29 and 38 ppb). Between one of the urban sites and either reference site, selecting the gradients during periods wherein the reference site is upwind of the urban site further decreases the statistics of the misfits in general even though not systematically. In a final attempt to focus on the signature of the city anthropogenic emission in the mole fraction measurements, we use a theoretical ratio of gradients of CO to gradients of CO2 from fossil fuel emissions in the London area to diagnose observation based fossil fuel CO2 gradients, and compare them with the modelled ones. This estimate increases the consistency between the model and the measurements when considering one of the urban sites, but not when considering the other. While this study evaluates different approaches for increasing the consistency between the mesoscale model and the near ground data, and manages to decrease the random component of the analysed model data misfits to an extent that should not be prohibitive to extracting the signal from the London urban emissions, large biases remain in the final misfits. These biases are likely to be due to local emissions, to which the urban near ground sites are highly sensitive. This questions our current ability to exploit urban near ground data for the atmospheric inversion of city emissions based on models at spatial resolution coarser than 2 km. [ABSTRACT FROM AUTHOR]

We present a performance assessment of the European Integrated Carbon Observing System (ICOS) atmospheric network for constraining European biogenic CO2 fluxes (hereafter net ecosystem exchange, NEE). The performance of the network is assessed in terms of uncertainty in the fluxes, using a state-of-the-art mesoscale variational atmospheric inversion system assimilating hourly averages of atmospheric data to solve for NEE at 6 h and 0.5° resolution. The performance of the ICOS atmospheric network is also assessed in terms of uncertainty reduction compared to typical uncertainties in the flux estimates from ecosystem models, which are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of northern summer and winter conditions in July and in December 2007, respectively. These computations are based on a observing system simulation experiment (OSSE) framework. We analyzed the uncertainty in a 2-week-mean NEE as a function of the spatial scale with a focus on the model native grid scale (0.5°), the country scale and the European scale (including western Russia and Turkey). Several network configurations, going from 23 to 66 sites, and different configurations of the prior uncertainties and atmospheric model transport errors are tested in order to assess and compare the improvements that can be expected in the future from the extension of the network, from improved prior information or transport models. Assimilating data from 23 sites (a network comparable to presentday capability) with errors estimated from the present prior information and transport models, the uncertainty reduction on a 2-week-mean NEE should range between 20 and 50% for 0.5° resolution grid cells in the best sampled area encompassing eastern France and western Germany. At the European scale, the prior uncertainty in a 2-week-mean NEE is reduced by 50% (66%), down to ~43 TgC month-1 (26 TgC month-1) in July (December). Using a larger network of 66 stations, the prior uncertainty of NEE is reduced by the inversion by 64% (down to ~33 TgC month-1) in July and by 79%(down to ~15 TgC month-1) in December. When the results are integrated over the well-observed western European domain, the uncertainty reduction shows no seasonal variability. The effect of decreasing the correlation length of the prior uncertainty, or of reducing the transport model errors compared to their present configuration (when conducting real-data inversion cases) can be larger than that of the extension of the measurement network in areas where the 23 station observation network is the densest. We show that with a configuration of the ICOS atmospheric network containing 66 sites that can be expected on the long-term, the uncertainties in a 2-week-mean NEE will be reduced by up to 50-80% for countries like Finland, Germany, France and Spain, which could significantly improvement (and at least a high complementarity to) our knowledge of NEE derived from biomass and soil carbon inventories at multi-annual scales. [ABSTRACT FROM AUTHOR]

Cities, currently covering only a very small portion (< 3 %) of the world's land surface, directly release to the atmosphere about 44% of global energy-related CO2, and are associated with 71-76% of CO2 emissions from global final energy use. Although many cities have set voluntary climate plans, their CO2 emissions are not evaluated by Monitoring, Reporting and Verification (MRV) procedures that play a key role for market- or policy-based mitigation actions. Here we propose a monitoring tool that could support the development of such procedures at the city scale. It is based on an atmospheric inversion method that exploits inventory data and continuous atmospheric CO2 concentration measurements from a network of stations within and around cities to estimate city CO2 emissions. We examine the cost-effectiveness and the performance of such a tool. The instruments presently used to measure CO2 concentrations at research stations are expensive. However, cheaper sensors are currently developed and should be useable for the monitoring of CO2 emissions from a megacity in the near-term. Our assessment of the inversion method is thus based on the use of several types of hypothetical networks, with a range of numbers of sensors sampling at 25ma.g.l. The study case for this assessment is the monitoring of the emissions of the Paris metropolitan area (12 million inhabitants and 11.4 TgC emitted in 2010) during the month of January 2011. The performance of the inversion is evaluated in terms of uncertainties in the estimates of total and sectoral CO2 emissions. These uncertainties are compared to a notional ambitious target to diagnose annual total city emissions with an uncertainty of 5% (2-sigma). We find that, with 10 stations only, which is the typical size of current pilot networks that are deployed in some cities, the uncertainty for the 1-month total city CO2 emissions is significantly reduced by the inversion by 42% but still corresponds to an annual uncertainty that is two times larger than the target of 5%. By extending the network from 10 to 70 stations, the inversion can meet this requirement. As for major sectoral CO2 emissions, the uncertainties in the inverted emissions using 70 stations are reduced significantly over that obtained using 10 stations by 32% for commercial and residential buildings, by 33% for road transport and by 18% for the production of energy by power plants, respectively. With 70 stations, the uncertainties from the inversion become of 15% 2-sigma annual uncertainty for dispersed building emissions, and 18% for emissions from road transport and energy production. The inversion performance could be further improved by optimal design of station locations and/or by assimilating additional atmospheric measurements of species that are co-emitted with CO2 by fossil fuel combustion processes with a specific signature from each sector, such as carbon monoxide (CO). Atmospheric inversions based on continuous CO2 measurements from a large number of cheap sensors can thus de liver a valuable quantification tool for the monitoring and/or the verification of city CO2 emissions (baseline) and CO2 emission reductions (commitments). [ABSTRACT FROM AUTHOR]

The exchanges of carbon, water and energy between the atmosphere and the Amazon basin have global implications for the current and future climate. Here, the global atmospheric inversion system of the Monitoring of Atmospheric Composition and Climate (MACC) service is used to study the seasonal and interannual variations of biogenic CO2 fluxes in Amazonia during the period 2002-2010. The system assimilated surface measurements of atmospheric CO2 mole fractions made at more than 100 sites over the globe into an atmospheric transport model. The present study adds measurements from four surface stations located in tropical South America, a region poorly covered by CO2 observations. The estimates of net ecosystem exchange (NEE) optimized by the inversion are compared to an independent estimate of NEE upscaled from eddy-covariance flux measurements in Amazonia. They are also qualitatively evaluated against reports on the seasonal and interannual variations of the land sink in South America from the scientific literature. We attempt at assessing the impact on NEE of the strong droughts in 2005 and 2010 (due to severe and longer-thanusual dry seasons) and the extreme rainfall conditions registered in 2009. The spatial variations of the seasonal and interannual variability of optimized NEE are also investigated. While the inversion supports the assumption of strong spatial heterogeneity of these variations, the results reveal critical limitations of the coarse-resolution transport model, the surface observation network in South America during the recent years and the present knowledge of modelling uncertainties in South America that prevent our inversion from capturing the seasonal patterns of fluxes across Amazonia. However, some patterns from the inversion seem consistent with the anomaly of moisture conditions in 2009. [ABSTRACT FROM AUTHOR]

This study presents two methods for estimating methane emissions from a waste water treatment plant (WWTP) along with results from a measurement campaign at a WWTP in Valence, France. These methods, chamber measurements and tracer release, rely on Fourier transform infrared spectroscopy and cavity ring-down spectroscopy instruments. We show that the tracer release method is suitable for quantifying facility-and some process-scale emissions, while the chamber measurements provide insight into individual process emissions. Uncertainties for the two methods are described and discussed. Applying the methods to CH4 emissions of the WWTP, we confirm that the open basins are not a major source of CH4 on the WWTP (about 10% of the total emissions), but that the pretreatment and sludge treatment are the main emitters. Overall, the waste water treatment plant is representative of an average French WWTP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper describes different methods to estimate methane emissions at different scales. These methods are applied to a waste water treatment plant (WWTP) located in Valence, France. We show that Fourier Transform Infrared (FTIR) measurements as well as Cavity Ring Down Spectroscopy (CRDS) can be used to measure emissions from the process to the regional scale. To estimate the total emissions, we investigate a tracer release method (using C2H2) and the Radon tracer method (using 222Rn). For process-scale emissions, both tracer release and chamber techniques were used. We show that the tracer release method is suitable to quantify facility- and some process scale emissions, while the Radon tracer method encompasses not only the treatment station but also a large area around. Thus the Radon tracer method is more representative of the regional emissions around the city. Uncertainties for each method are described. Applying the methods to CH4 emissions, we find that the main source of emissions of the plant was not identified with certainty during this short campaign, although the primary source of emissions is likely to be from solid sludge. Overall, the waste water treatment plant represents a small part (3 %) of the methane emissions of the city of Valence and its surroundings,which is in agreement with the national inventories. [ABSTRACT FROM AUTHOR]

The Bayesian framework of CO2 flux inversions permits estimates of the retrieved flux uncertainties. Here, the reliability of these theoretical estimates is studied through a comparison against the misfits between the inverted fluxes and independent measurements of the CO2 Net Ecosystem Exchange (NEE) made by the eddy covariance technique at local (few hectares) scale. Regional inversions at 0.5° resolution are applied for the western European domain where 50 eddy covariance sites are operated. These inversions are conducted for the period 2002-2007. They use a mesoscale atmospheric transport model, a prior estimate of the NEE from a terrestrial ecosystem model and rely on the variational assimilation of in situ continuous measurements of CO2 atmospheric mole fractions. Averaged over monthly periods and over the whole domain, the misfits are in good agreement with the theoretical uncertainties for prior and inverted NEE, and pass the chi-square test for the variance at the 30% and 5% significance levels respectively, despite the scale mismatch and the independence between the prior (respectively inverted) NEE and the flux measurements. The theoretical uncertainty reduction for the monthly NEE at the measurement sites is 53% while the inversion decreases the standard deviation of the misfits by 38 %. These results build confidence in the NEE estimates at the European/monthly scales and in their theoretical uncertainty from the regional inverse modelling system. However, the uncertainties at the monthly (respectively annual) scale remain larger than the amplitude of the inter-annual variability of monthly (respectively annual) fluxes, so that this study does not engender confidence in the inter-annual variations. The uncertainties at the monthly scale are significantly smaller than the seasonal variations. The seasonal cycle of the inverted fluxes is thus reliable. In particular, the CO2 sink period over the European continent likely ends later than represented by the prior ecosystem model. [ABSTRACT FROM AUTHOR]

A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The goal of this study is to identify the current state of carbon observations and needs for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion (by several orders of magnitude) of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over remote areas such as the southern oceans, tropical forests and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in-situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases inter-operable, and on the calibration of each component of the system to agreed-upon international scales. [ABSTRACT FROM AUTHOR]