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 present-day 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 Tg C month-1 (26 Tg C 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 Tg C month-1) in July and by 79 % (down to∼ 15 Tg C 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
Concentrations of atmospheric carbon dioxide (CO
2
) have continued to increase whereas atmospheric deposition of sulphur and nitrogen has declined in Europe and the USA during recent ...decades. Using time series of flux observations from 23 forests distributed throughout Europe and the USA, and generalised mixed models, we found that forest-level net ecosystem production and gross primary production have increased by 1% annually from 1995 to 2011. Statistical models indicated that increasing atmospheric CO
2
was the most important factor driving the increasing strength of carbon sinks in these forests. We also found that the reduction of sulphur deposition in Europe and the USA lead to higher recovery in ecosystem respiration than in gross primary production, thus limiting the increase of carbon sequestration. By contrast, trends in climate and nitrogen deposition did not significantly contribute to changing carbon fluxes during the studied period. Our findings support the hypothesis of a general CO
2
-fertilization effect on vegetation growth and suggest that, so far unknown, sulphur deposition plays a significant role in the carbon balance of forests in industrialized regions. Our results show the need to include the effects of changing atmospheric composition, beyond CO
2
, to assess future dynamics of carbon-climate feedbacks not currently considered in earth system/climate modelling.
The Atlantic and Arctic Oceans are critical components of the global carbon cycle. Here we quantify the net sea-air CO2 flux, for the first time, across different methodologies for consistent time ...and space scales for the Atlantic and Arctic basins. We present the long-term mean, seasonal cycle, interannual variability and trends in sea-air CO2 flux for the period 1990 to 2009, and assign an uncertainty to each. We use regional cuts from global observations and modeling products, specifically a pCO2 -based CO2 flux climatology, flux estimates from the inversion of oceanic and atmospheric data, and results from six ocean biogeochemical models. Additionally, we use basin-wide flux estimates from surface ocean pCO2 observations based on two distinct methodologies. Our estimate of the contemporary sea-air flux of CO2 (sum of anthropogenic and natural components) by the Atlantic between 40° S and 79° N is -0.49 ± 0.05 Pg C yr-1 , and by the Arctic it is -0.12 ± 0.06 Pg C yr-1 , leading to a combined sea-air flux of -0.61 ± 0.06 Pg C yr-1 for the two decades (negative reflects ocean uptake). We do find broad agreement amongst methodologies with respect to the seasonal cycle in the subtropics of both hemispheres, but not elsewhere. Agreement with respect to detailed signals of interannual variability is poor, and correlations to the North Atlantic Oscillation are weaker in the North Atlantic and Arctic than in the equatorial region and southern subtropics. Linear trends for 1995 to 2009 indicate increased uptake and generally correspond between methodologies in the North Atlantic, but there is disagreement amongst methodologies in the equatorial region and southern subtropics.
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained ...from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.
This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net ecosystem exchange (NEE) and latent heat ...(LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multisite approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model–data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model–data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modelling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP – gross primary productivity) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multisite parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modelled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global-scale evaluation with remote sensing NDVI (normalized difference vegetation index) measurements indicates an improvement of the simulated seasonal variations of the foliar cover for all considered PFTs.
This study explores the impact of the structural error of biosphere models when assimilating net ecosystem exchange (NEE) measurements or CO2 concentration measurements to optimise uncertain model ...parameters within carbon cycle data assimilation systems (CCDASs). This error has been proven difficult to identify and is often neglected in the total uncertainty budget. We propose a simple method which is derived from the model-minus-observation mismatch statistics. This diagnosis is applied to a state-of-the-art biogeochemical model using measurements of the net surface CO2 flux at twelve sites located in temperate, deciduous, broadleaf forests. We find that the structural model error in the NEE space has a standard deviation of 1.5 to 1.7 gC m−2 d−1, without a significant correlation structure beyond the lag of a few days, and a large spatial structure that can be approximated with an exponential decay of e-folding length of 500 km. In the space of concentrations, its characteristics are commensurate with the transport errors, both for surface air sample measurements and total column measurements. The inferred characteristics are confirmed by complementary optimality diagnostics performed after site-scale parameter optimisations.
This paper reports a comparison between large-scale simulations of three different land surface models (LSMs), ORCHIDEE, ISBA-A-gs and CTESSEL, forced with the same meteorological data, and compared ...with the carbon fluxes measured at 32 eddy covariance (EC) flux tower sites in Europe. The results show that the three simulations have the best performance for forest sites and the poorest performance for cropland and grassland sites. In addition, the three simulations have difficulties capturing the seasonality of Mediterranean and sub-tropical biomes, characterized by dry summers. This reduced simulation performance is also reflected in deficiencies in diagnosed light-use efficiency (LUE) and vapour pressure deficit (VPD) dependencies compared to observations. Shortcomings in the forcing data may also play a role. These results indicate that more research is needed on the LUE and VPD functions for Mediterranean and sub-tropical biomes. Finally, this study highlights the importance of correctly representing phenology (i.e. leaf area evolution) and management (i.e. rotation-irrigation for cropland, and grazing-harvesting for grassland) to simulate the carbon dynamics of European ecosystems and the importance of ecosystem-level observations in model development and validation.
Measurements of CO2, CO, NOx and selected Volatile Organic Compounds (VOCs) mole fractions were performed continuously during a 10-day period in the Guy Môquet tunnel in Thiais, a peri-urban area ...about 15 km south of the centre of Paris, between 28 September and 8 October 2012. This data set is used here to identify the characteristics of traffic-emitted CO2 by evaluating its ratios to co-emitted species for the first time in the Paris region. High coefficients of determination (r2 > 0.7) are observed between CO2 and certain compounds that are characteristic of the traffic source (CO, NOx, benzene, xylenes and acetylene). Weak correlations (r2 < 0.2) are found with species such as propane, n-butane and i-butane that are associated with fuel evaporation, an insignificant source for CO2. To better characterise the traffic signal we focus only on species that are well-correlated with CO2 and on rush-hour periods characterised by the highest traffic-related mole fractions. From those mole fractions we remove the nighttime-average weekday mole fraction obtained for each species that we infer to be the most appropriate background signal for our study. Then we calculate observed Δspecies / ΔCO2 ratios, which we compare with the ones provided by the 2010 bottom–up high-resolved regional emission inventory from Airparif (the association in charge of monitoring the air quality in Île-de-France), focusing on local emission data for the specific road of the tunnel. We find an excellent agreement (2%) between the local inventory emission CO / CO2 ratio and our observed ΔCO / ΔCO2 ratio. Former tunnel experiments carried out elsewhere in the world provided observed ΔCO / ΔCO2 ratios that differ from 49 to 592% to ours. This variability can be related to technological improvement of vehicles, differences in driving conditions, and fleet composition. We also find a satisfactory agreement with the Airparif inventory for n-propylbenzene, n-pentane and xylenes to CO2 ratios. For most of the other species, the ratios obtained from the local emission inventory overestimate the observed ratios to CO2 by 34 to more than 300%. However, the emission ratios of NOx, o-xylene and i-pentane are underestimated by 30 to 79%. One main cause of such high differences between the inventory and our observations is likely the obsolete feature of the VOCs speciation matrix of the inventory that has not been updated since 1998, although law regulations on some VOCs have occurred since that time. Our study bears important consequences, discussed in the conclusion, for the characterisation of the urban CO2 plume and for atmospheric inverse modelling of urban CO2 emissions.
The Global and Regional Earth System Monitoring Using Satellite and In Situ Data (GEMS) project is combining the manifold expertise in atmospheric composition research and numerical weather ...prediction of 32 European institutes to build a comprehensive monitoring and forecasting system for greenhouse gases, reactive gases, aerosol, and regional air quality. The project is funded by the European Commission as part of the Global Monitoring of Environment and Security (GMES) framework. GEMS has extended the data assimilation system of the European Centre for Medium-Range Weather Forecasts (ECMWF) to include various tracers for which satellite observations exist. A chemical transport model has been coupled to this system to account for the atmospheric chemistry. The GEMS system provides lateral boundary conditions for a set of 10 regional air quality forecast models and global atmospheric fields for use in surface flux inversions for the greenhouse gases. Observations from both in situ and satellite sources are used as input, and the output products will serve users such as policy makers, environmental agencies, the science community, and providers of end-user services for air quality and health. This article provides an overview of GEMS and uses some recent results to illustrate the current status of the project. It is expected that GEMS will grow into a full operational service for the atmospheric component of GMES in the next decade. Part of this transition will be the merge with the Protocol Monitoring for the GMES Service Element: Atmosphere (PROMOTE) GMES project into the Monitoring of Atmospheric Composition and Climate (MACC) project.
PDF
