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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]

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 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 that are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of summer and winter conditions in July and in December 2007, respectively. These computations are based on a robust Observing System Simulation Experiments framework. We analyze the uncertainty in two-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 (1) the extension of the network, (2) improved prior information or (3) improved transport models. Assimilating data from 23 sites (a network comparable to present day capability) with the estimate of errors from the present prior information and transport models, the uncertainty reduction on two-week mean NEE should range between 20 and 50% for 0.5º resolution grid cells in the best sampled area encompassing eastern France and western Germany. At the European scale, the prior uncertainty in two-week mean NEE is reduced by 50% (66%), down to ~ 43 Tg C month-1 (resp. 26 Tg C month-1) in July (resp. 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 contrast. 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 stations 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 two-week mean NEE will be reduced by up to 50-80% for countries like Finland, Germany, France and Spain, which could bring a significant improvement of (and at least a high complementarity to) our knowledge about NEE derived from biomass and soil carbon inventories at multi annual scales. [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 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 that are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of summer and winter conditions in July and in December 2007, respectively. These computations are based on a robust Observing System Simulation Experiments framework. We analyze the uncertainty in two-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 (1) the extension of the network, (2) improved prior information or (3) improved transport models. Assimilating data from 23 sites (a network comparable to present day capability) with the estimate of errors from the present prior information and transport models, the uncertainty reduction on two-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 two-week mean NEE is reduced by 50% (66 %), down to ~43 TgCmonth-1 (resp. 26 TgCmonth-1) in July (resp. December). Using a larger network of 66 stations, the prior uncertainty of NEE is reduced by the inversion by 64% (down to ~3 TgCmonth-1) in July and by 79% (down to ~15 TgCmonth-1) in December. When the results are integrated over the well-observed western Euro- pean domain, the uncertainty reduction shows no seasonal contrast. 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 stations 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 two-week mean NEE will be reduced by up to 50-80%for countries like Finland, Germany, France and Spain, which could bring a significant improvement of (and at least a high complementarity to) our knowledge about NEE derived from biomass and soil carbon inventories at multi annual scales. [ABSTRACT FROM AUTHOR]

Our ability to predict future climate change relies on our understanding of current and future CO2 fluxes, particularly on a regional scale (100-1000 km). CO2 regional sources and sinks are still poorly understood. Inverse transport modeling, a method often used to quantify these fluxes, relies on atmospheric CO2 measurements. One of the main challenges for the transport models used in the inversions is to properly reproduce CO2 vertical gradients between the boundary layer and the free troposphere, as these gradients impact on the partitioning of the calculated fluxes between the different model regions. Vertical CO2 profiles are very well suited to assess the performances of the models. In this paper, we conduct a comparison between observed and modeled CO 2 profiles recorded during two CAATER campaigns that occurred in May 2001 and October 2002 over Western Europe, as described in a companion paper. We test different combinations between a global transport model (LMDZt), a mesoscale transport model (CHIMERE), and different sets of biospheric fluxes, all chosen with a diurnal cycle (CASA, SiB2 and ORCHIDEE). The vertical profile comparison shows that: 1) in most cases the influence of the biospheric flux is small but sometimes not negligible, ORCHIDEE giving the best results in the present study; 2) LMDZt is most of the time too diffuse, as it simulates a too high boundary layer height; 3) CHIMERE better reproduces the observed gradients between the boundary layer and the free troposphere, but is sometimes too variable and gives rise to incoherent structures. We conclude there is a need for more vertical profiles to conduct further studies to improve the parameterization of vertical transport in the models used for CO 2 flux inversions. Furthermore, we use a modeling method to quantify CO 2 fluxes at the regional scale from a chosen observing point, coupling influence functions from the transport model LMDZt (that works quite well at the synoptic scale) with information on the space-time distribution of fluxes. This modeling method is compared to a dual tracer method (the so-called Radon method) for a case study on 25 May 2001 during which simultaneous well-correlated in situ CO2 and Radon 222 measurements have been collected. Both methods give a similar result: a flux within the Radon 222 method uncertainty (35%), that is an atmospheric CO2 sink of -4.2 -2 -1 to -4.4 gCm-1 day-1 . We have estimated the uncertainty of the modeling method to be at least 33% on average, and even more for specific individual events. This method allows the determination of the area that contributed to the CO 2 observed concentration. In our case, the observation point located at 1700ma.s.l. in the north of France, is influenced by an area of 1500x700 km² that covers the Benelux region, part of Germany and western Poland. Furthermore, this method allows deconvolution between the different contributing fluxes. In this case study, the biospheric sink contributes 73% of the total flux, fossil fuel emissions for 27%, the oceanic flux being negligible. However, the uncertainties of the influence function method need to be better assessed. This could be possible by applying it to other cases where the calculated fluxes can be checked independently, for example at tall towers where simultaneous CO2 and Radon 222 measurements can be conducted. The use of optimized fluxes (from atmospheric inversions) and of mesoscale models for atmospheric transport may also significantly reduce the uncertainties. [ABSTRACT FROM AUTHOR]

Our ability to predict future climate change relies on our understanding of current and future CO2 fluxes, particularly at the scale of regions (100-1000 km). Nowadays, CO2 regional sources and sinks are still poorly known. Inverse transport modeling, a method often used to quantify these fluxes, relies on atmospheric CO2 measurements. One of the main challenge for the transport models used in the inversions is to reproduce properly CO2 vertical gradients between the boundary layer and the free troposphere, as these gradients impact on the partitioning ot the calculated fluxes between the different model regions. Vertical CO2 profiles are very well suited to assess the performances of the models. In this paper, we conduct a comparison between observed and modeled CO2 profiles recorded during two CAATER campaigns that occurred in May 2001 and October 2002 over western Europe, and that we have described in a companion paper. We test different combinations between a global transport model (LMDZt), a mesoscale transport model (CHIMERE), and different sets of biospheric fluxes, those latter all chosen to have a diurnal cycle (CASA, SiB2 and ORCHIDEE). The vertical profile comparison shows that: (1) in most cases the influence of the biospheric flux is small but sometimes not negligeable, ORCHIDEE giving the best results in the present study; (2) LMDZt is most of the time too diffusive, as it simulates a too high boundary layer height; (3) CHIMERE reproduces better the observed gradients between the boundary layer and the free troposphere, but is sometimes too variable and gives rise to incoherent structures. We conclude there is a need for more vertical profiles to conduct further studies that will help to improve the parameterization of vertical transport in the models used for CO2 flux inversions. Furthermore, we use a modeling method to quantify CO2 fluxes at the regional scale from any observing point, coupling influence functions from the transport model LMDZt (that works quite well at the synoptic scale) with information on the space-time distribution of fluxes. This modeling method is compared to a dual tracer method (the so-called Radon method) for a case study on 25 May 2001 during which simultaneous well-correlated in-situ CO2 and Radon 222 measurements have been collected. Both methods give a similar flux within the Radon 222 method uncertainty (35%), that is an atmospheric CO2 sink of -4.2 to -4.4 gCm-2 day-1. We have estimated the uncertainty of the modeling method to be at least 33% when considering averages, even much more on individual events. This method allows the determination of the area that contributed to the CO2 observed concentration. In our case, the observation point located at 1700ma.s.l. in the North of France, is influenced by an area of 1500×700 km² that covers the Benelux region, part of Germany and western Poland. Furthermore, this method allows deconvolution between the different contributing fluxes. In this case study, the biospheric sink contributes for 73% of the total flux, fossil fuel emissions for 27%, the oceanic flux being negligeable. However, the uncertainties of the influence function method must be better assessed. This could be possible by applying it to other cases where the calculated fluxes can be checked independantly, for example at tall towers where simultaneous CO2 and Radon 222 measurements can be conducted. The use of optimized fluxes (from atmospheric inversions) and of mesoscale models for atmospheric transport may also significantly reduce the uncertainties. [ABSTRACT FROM AUTHOR]