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The ICOS (Integrated Carbon Observation System) network of atmospheric measurement stations produces standardized data on greenhouse gas concentrations at 36 stations in 14 different European countries (November 2022). The network targets a strongly heterogeneous landscape and the placement of instruments on tall towers and mountains make for large influence regions (footprints). The combined footprints for all the individual stations create the “lens” through which the observing network sees the European flux landscape. In this study, we summarize this view using quantitative metrics of the fluxes seen by individual stations, and by the current and future ICOS network. Results are presented both from a country-level and pan-European perspective, using open-source tools that we make available through the ICOS Carbon Portal. We target anthropogenic emissions from various sectors (e.g., energy production, industrial emissions), as well as the land-cover types found over Europe (e.g., broadleaf forests, croplands) and their spatiotemporally varying fluxes. This recognizes different interests of different ICOS stakeholders. We specifically introduce “monitoring potential maps”, which quantify the sensitivity of the network with regards to specific properties of each pixel compared to the averages across all pixels, to see which regions have a relative underrepresentation of land-cover, or biospheric fluxes. This potential changes with the introduction of new stations, which we investigate for the planned ICOS expansion with 20 stations over the next few years. The monitoring potential concept is novel and a useful addition to traditional quantitative network design methods. We find that the ICOS network has limited sensitivity to anthropogenic fluxes, as was intended in the current design. Its representation of biospheric fluxes follows the fractional representation of land-cover and is generally well balanced, with exceptions for a country like Norway where the southerly station Birkenes predominantly senses coniferous forest fluxes instead of the more abundant northerly grass & shrublands. Grass & shrubland fluxes are relatively underrepresented in ICOS, with the largest monitoring potential in Scandinavia, Croatia, and Serbia. These easterly countries similarly show a relative underrepresentation of broadleaf forest fluxes, partly due to a lack of monitoring stations, and partly due to the abundant sensitivity to broadleaf forests in the most densely monitored countries such as France and Germany. We stress that this does not imply these latter countries to be fully monitored and of lesser interest for network expansion: for example, inclusion of Schauinsland in the future network expands the network lens to mostly unmonitored mixed- and broadleaf forests which are relatively underrepresented at the national level. Such considerations demonstrate the usefulness of our analyses and can readily be re-produced for any network configuration within Europe with tools offered through the Carbon Portal.

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

The unique physical and chemical characteristics of radon make it an excellent tracer of atmospheric mixing and transport processes. As such, it has long been a species of interest to the climate change research communities. However, reliable, high resolution radon flux maps are essential for the use of atmospheric radon in climate studies.Validation of the existing radon flux maps and inventories is currently limited by the availability of systematic measurements of radon fluxes and other process-relevant parameters (e.g., physical characteristics and moisture content of soil). Localised measurements of radon flux, soil moisture and soil physical properties can provide some information to validate and improve flux maps. On the other hand, high sensitivity atmospheric radon measurements in conjunction with atmospheric transport models would determine the relative representativeness of radon flux models over larger scales.To tackle the validation of radon flux maps, atmospheric radon measurements were compared to the results of modelled radon concentrations calculated using the Lagrangian particle model, the Met Office Numerical Atmospheric Modelling Environment (NAME) and two available versions of European radon flux maps1. For this purpose, radon data from three UK greenhouse gases atmospheric monitoring stations located in Heathfield (an inland, 100 m tall tower), Tacolneston (an inland, 185 m tall tower), and Weybourne (a coastal site, 10 m tower) were used. The differences between measured and modelled radon concentrations on diurnal and monthly scales will be presented and discussed. The ratio of measured-modelled radon concentrations shows the potential to objectively assess the reliability of radon flux maps under different wind directions and atmospheric mixing conditions. 1 2015: doi:10.1594/PANGAEA.854715 and 2022: ICOS data search (icos-cp.eu).

The vertical mixing state of the atmosphere, as well as the atmospheric boundary layer (ABL) height, are important atmospheric transport model parameters for the accurate simulation of greenhouse gas concentrations. In order to use tall tower greenhouse gas measurements to quantify regional scale emissions (top-down, inverse estimates) an estimate of the atmospheric transport model uncertainty across the time series of study is needed. Several methods have been used to estimate this, often relying on arbitrary thresholds or a combination of parameters (such as vertical gradients if a gas is measured at multiple points). Here we study if radon has potential as an independent measurement to assess model uncertainty.Radon is a radioactive noble gas present in our atmosphere and is a good tracer of mixing processes in the ABL due to its properties. Hence, measurements of atmospheric radon concentration can provide useful insights into the vertical mixing state of the atmosphere, and in turn may help to calibrate and validate atmospheric dispersion models.In this study, we use high temporal resolution atmospheric measurements of radon and CH4 from four tall tower sites in the UK, which are part of the Deriving Emissions linked to Climate Change (DECC) network: Heathfield (HFD), Ridge Hill (RGH), Tacolneston (TAC) and Weybourne (WAO). At each site, CH4 is measured at two or three different heights, while radon is measured at one height.To determine a metric whereby single-height measurements of radon can provide a proxy for vertical mixing states, we compare the diurnal cycle of the measured radon concentration with the modelled radon (calculated by the Met Office Numerical Atmospheric Modelling Environment (NAME) dispersion model and radon flux maps). The largest uncertainties are shown to be before sunrise and after sunset right before the inversion layer was formed/destroyed. The diurnal CH4 vertical gradient at these times is also compared with the modelled CH4 vertical gradient.

Computational notebooks (e.g. Jupyter notebook) are a popular choice for interactive scientific computing to convey descriptive information together with executable source code. The user can annotate the scientific development of the work, the methods applied, describe ancillary data or the analysis of results, with text, illustrations, figures, and equations. Such ‘executable’ documents provide a paradigm shift in scientific writing, where not only the science is described, but the actual computation and source code are openly available and can be reproduced and validated.Therefore, it is of paramount importance to preserve these documents. A unique and persistent identification (PID) is essential together with providing enough information to execute the source code. Generating a PID for a Jupyter notebook is not technically challenging. We can automatically collect system and run-time information and, with a guided workflow for the user, assemble a rich set of metadata. The collected information allows us to recreate the computational environment and run the source code, which in return (theoretically) should produce the same results as published.The importance of providing a rich set of metadata for all digital objects in a human readable and machine actionable form is well understood and widely accepted as necessity for reproducibility, traceability, and provenance. This is reflected in the FAIR principles (Wilkinson, https://doi.org/10.1038/sdata.2016.18) which are regarded as gold standard by many scientific communities.Pimentel et al. (https://doi.org/10.1109/MSR.2019.00077) analysed over 800’000 Jupyter notebooks from GitHub. 24 % executed without errors and only 4 % produced the same results. The likelihood to successfully compile and run a decade old source code is slim. Long term support for well established operating systems varies between 5 to 10 years, user software support is usually shorter and looking at free and open-source repositories there is often no support (or best effort) offered.We present an approach to safely reproduce the computational environment in the future with a focus on long-term availability. Instead of trying to reinstall the computational environment based on the stored metadata, we propose to archive the docker image, the user space (user installed packages) and finally the source code. Recreating the system in this way is more like restoring a backup, where backup is the equivalent of an entire computer system. It does not solve all the problems but removes a great deal of complexity and uncertainty.Though there are shortcomings in our approach, we believe our solution will lower the threshold for scientists to provide rich meta data, code and results attached to a publication that can be reproduced in the far future.

Atmospheric transport models used for nuclear emergency purposes are dedicated to simulating the atmospheric transport of radionuclides released from a damaged nuclear facility. The quality of this response is crucial and must be constantly improved. However, long-range measurement campaigns for validation are scarce, especially for radioactive pollutants. An effective way to do so is by simulating the radon-222 which is a non-reacting atmospheric tracer species with quite well-known exhalation rate and well-known nuclear transitions.Radon-222 is naturally emitted. Its flux spatial variation is mainly due to the type of soil and rocks, rather than the vegetation or land use. Temporal variations are mainly led by soil humidity, leading to a monthly variation. The monthly surface radon flux map of Karstens and Levin, 2022 is used in this study.The availability of the observations at suitable temporal and spatial scales is achieved in this study thanks to the Integrated Carbon Observation System, ICOS (Heiskanen et al. 2021). ICOS provides standardized and open data from more than 39 atmosphere stations that measures greenhouse gases concentrations in the atmosphere. Some stations also provide radon-222 concentrations measurements. Among them, some also include measurements at different heights - from ground level up to 200 meters – which is valuable to validate the vertical atmospheric transport modelling. The limited set of radon-222 stations is not a substitute for performing the comprehensive validation against a large variety of observations but gives valuable information on the performance of air concentration predictions. A previous study using the dose rate measurements network (Quérel et al. 2022), required in addition the need of an accurate deposition modelling to assess gamma dose rates at ground level due to wet deposition of radon-222 decay products.We evaluate here the overall performance of an air concentration modelling chain: Karstens radon-222 fluxes, Météo-France ARPEGE numerical weather predictions and IRSN LdX operational atmospheric transport model. Simulated radon-222 air concentrations are compared with observations from the ICOS monitoring network over Europe, on an hourly frequency basis over one year. On initial examination, the model appears to under-predict radon-222 concentrations and some possible explanations and sources of improvement are identified. References:Heiskanen, J., C. Brümmer, N. Buchmann, C. Calfapietra, H. Chen, B. Gielen, T. Gkritzalis, S. Hammer, S. Hartman, M. Herbst, et al. (2021), The Integrated Carbon Observation System in Europe, Bulletin of the American Meteorological Society, 1 - 54, doi:10.1175/bams-d-19-0364.1.Karstens, U. and Levin, I. (2022). traceRadon monthly radon flux map for Europe 2006-2022 (based on GLDAS-Noah v2.1 soil moisture), https://hdl.handle.net/11676/ge5vMeklvG_Qz43rzcS2wx0-Quérel, A., Meddouni, K., Quélo, D., Doursout, T., and Chuzel, S. (2022). Statistical approach to assess radon-222 long-range atmospheric transport modelling and its associated gamma dose rate peaks. Advances in Geosciences. 57. 109-124. 10.5194/adgeo-57-109-2022.

We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between −0.72 and 0.20 PgC yr−1, which is larger than the a priori uncertainty of 0.47 PgC yr−1. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr−1), in comparison with 0.23 and 0.10 PgC yr−1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.

We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope Regional (CSR) and LUMIA) over Europe for 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT and FLEXPART were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by applying the global transport models TM3 and TM5. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems show a relatively large spread for the annual domain wide flux ranging between -0.72 and 0.20 PgC yr-1 with a mean estimate of -0.29 PgC. The largest discrepancies resulted from varying the mesoscale transport model, which amounted to a difference of 0.51 (PgC yr-1), in comparison with 0.23 and 0.10 (PgC yr-1) that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions lead to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the models themselves. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical priors, and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.

We present the CarbonTracker Europe High-Resolution system that estimates carbon dioxide (CO2) exchange over Europe at high-resolution (0.1 x 0.2°) and in near real-time (about 2 months latency). It includes a dynamic fossil fuel emission model, which uses easily available statistics on economic activity, energy-use, and weather to generate fossil fuel emissions with dynamic time profiles at high spatial and temporal resolution (0.1 x 0.2°, hourly). Hourly net biosphere exchange (NEE) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEE is downscaled to 0.1 x 0.2° using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map, and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. An ocean flux extrapolation and downscaling based on wind speed and temperature for Jena CarboScope ocean CO2 fluxes is included in our product. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the ability of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 drought, (b) to capture the reduction of fossil fuel emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ERA5, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city centre of Amsterdam (The Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and fossil fuel emissions (COVID-19 pandemic in 2020). After transport with TM5, the CTE-HR fluxes have lower root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor-person inversion). RSMEs are close to those of the reanalysis with the data assimilation system CarbonTracker Europe (CTE). This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different sectors in summer and winter. Interestingly, in periods where synoptic scale transport variability dominates CO2 variations, the CTE-HR fluxes perform similar to low-resolution fluxes (5–10x coarsened). The remaining 10 % of simulated CO2 mole fraction differ by > 2ppm between the low-resolution and high-resolution flux representation, and are clearly associated with coherent structures ("plumes") originating from emission hotspots, such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely-populated region like the Amsterdam city centre, our fluxes underestimate the magnitude of measured eddy-covariance fluxes, but capture their substantial diurnal variations in summer- and wintertime well. We conclude that our product is a promising tool to model the European carbon budget at a high-resolution in near real-time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near real-time monitoring and modeling, for example as a-priori flux product in a CO2 data-assimilation system. The data is available at https://doi.org/10.18160/20Z1-AYJ2.

Carbonyl sulfide (COS) is used as a tracer for gross primary production (GPP) of terrestrial ecosystems and stomatal conductance of leaves. At present, sources and sinks of COS have not been fully assessed, as proven by the poor agreement between the modelled global budget and the most recent measurements. This uncertainty limits both the existing and potential future applications of COS. To understand sources and sinks of COS, the atmospheric station in Lutjewad (53°24’N, 6°21’E, 1m a.s.l.) performs continuous in situ mole fraction profile measurements. Nighttime COS fluxes of -3.0 ± 2.6 pmol m-2 s-1 were determined using the radon-tracer correlation approach. In three occasions between 2014 and 2018, COS enhancements ranging between 100 and 1000 ppt were measured in Lutjewad at 7, 40 and 60 meters above ground level. To identify the sources of these enhancements, both discrete and in situ samples were collected in the province of Groningen to be analysed with a quantum cascade laser spectrometer (QCLS). Several COS sources were identified, such as biodigesters, sugar production facilities and silicon carbide production facilities. These sources were added to the available databases, at a 0.1°x0.1° resolution. To simulate the initial dispersion, they were assumed to spread latitudinally and longitudinally over grids of 0.5°x0.5° width, as bidimensional Gaussian distributions. The updated databases were then combined with a Stochastic Time-Inverted Lagrangian Transport (STILT) model to check the influence of these sources on the Lutjewad measurements. Current results suggest a strong influence on the mole fraction of COS related to air parcels transported from known industrial sources, in particular from the Antwerp (51.2° N, 4.4° E) and Rotterdam (51.9° N, 4.5° E) regions. However, a mismatch still persists and preliminary results suggest that a local influence could explain the gap between modelled and measured COS concentrations. Possibly, COS emissions from these sources fluctuate according to different factors, such as the production rate of a specific facility or particular events. On the other hand, it is also possible that the enhancements in Lutjewad could be explained by scaling up the results to regional, national or international levels, adding similar facilities to the current databases. Nonetheless, these results could provide a useful insight about new sources of COS that could contribute to a more precise assessment of the global budget of this gas species.

The concentration of atmospheric carbon dioxide (CO2) has increased since the pre-industrial era (1750) due to human activity leading to a warming of the global land and ocean surface of 1.0 ± 0.2 ºC over the last 30 years could reach 1.5 ºC between 2030 and 2052. A better understanding of the fossil fuel CO2 emission sources is essential to develop strategies to reduce these emissions, and thus trying to stop the global warming produced by the accumulation of CO2 in the atmosphere. Policies to achieve these reductions require accurate and robust estimates of these emissions by a monitoring system based on independent atmospheric observations. This system must be able to separate the impact of anthropogenic CO2 emissions from the effect of the complex natural carbon cycle, which both affect atmospheric CO2 concentrations.Radiocarbon (14CO2) measurements have been used in conjunction with total CO2 measurements on both local (e.g. Indianapolis and Heidelberg) and regional scales (e.g. North America and Europe) to separate fossil fuel CO2 fluxes from biogenic CO2. The estimation of fossil fuel emissions from atmospheric observations can, in principle, be done by inverse modeling. In this work we will use the LUMIA (Lund University Modular Inversion Algorithm) for performing a series of observation system simulation experiments (OSSEs) inverting simultaneously terrestrial CO2 and 14CO2 observations from the Integrated Carbon Observation System (ICOS) station network to solve for both the natural fluxes (mainly terrestrial) and the anthropogenic fossil fuel emissions, accounting also for the ocean and terrestrial 14C disequilibrium fluxes. The OSSEs will be performed on a spatial domain over Europe, with a spatial resolution of 0.1° for fossil fuel CO2 sources and 0.5° for natural CO2 fluxes and a weekly temporal resolution for natural and anthropogenic emissions and monthly for ocean and terrestrial disequilibrium fluxes 2009 to 2011. We will assess the suitability of the current ICOS 14CO2 observation network as well as potential extensions to estimate anthropogenic fossil fuel emissions.

In the framework of the EMPIR project 19ENV01 traceRadon(1) [1] stable atmospheres with low-level, activity concentrations of radon have to be produced for the calibration of radon detectors [2] capable of measuring the outdoor air activity concentration. The traceable calibration of these detectors at very low activity concentrations is of special interest, for the radiation protection community, as well as the climate observation community. Because radiation protection networks (like the EUropean Radiological Data Exchange Platform (EURDEP)) and climate observation networks (like the Integrated Carbon Observation System (ICOS)) need reliable, accurate radon activity concentration measurements, either for identification of Radon Priority Areas (RPA), for false alarm prevention or to apply the Radon Tracer Method (RTM) for the estimation of greenhouse gas (GHG) emissions.Radon gas is the largest source of public exposure to naturally occurring radioactivity, and concentration maps based on atmospheric measurements aid developers to comply with EU Safety Standard Regulations. Radon can also be used as a tracer to evaluate dispersal models important for supporting successful greenhouse gas (GHG) mitigation strategies. One of the recently most applied technique for this propose is the Radon Tracer Method (RTM). To reduce the uncertainty of both radiation protection measurements and those used for GHG modelling, traceability to SI units for radon exhalation rate from soil, its concentration in the atmosphere and validated models for its dispersal are needed. The project traceRadon started in 2020 to provide the necessary measurement infrastructure [3,4]. This is particularly important for GHG emission estimates that support national reporting under the Paris Agreement on climate change.As there is an overlapping need between the climate research and radiation protection communities for improved traceability at low-level outdoor radon and radon flux measurements the project traceRadon works on this aspect for the benefit of two large scientific communities. The results at midterm of the project are presented.[1] Röttger, A. et al: New metrology for radon at the environmental level 2021 Meas. Sci. Technol. 32, 124008, https://doi.org/10.1088/1361-6501/ac298d[2] Radulescu, I et al.: Inter-comparison of commercial continuous radon monitors responses, Nuclear Instruments and Methods in Physics Research Section A, Volume 1021, 2022, 165927, https://doi.org/10.1016/j.nima.2021.165927[3] Mertes, F et. al.: Approximate sequential Bayesian filtering to estimate Rn-222 emanation from Ra-226 sources from spectra, https://doi.org/10.5162/SMSI2021/D3.3[4] Mertes, F. et. al.: Ion implantation of 226Ra for a primary 222Rn emanation standard, Applied Radiation and Isotopes, Volume 181, March 2022, 110093, https://doi.org/10.1016/j.apradiso.2021.110093 (1) This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. 19ENV01 traceRadon denotes the EMPIR project reference

Quantitative understanding of the processes governing radon production and transport in soils and its exhalation rate into the atmospheric boundary layer are essential if we want to use this radioactive noble gas to assess above- and below-ground transport processes. While production of radon in soils is mainly governed by static soil properties such as texture and uranium content, the dominant parameter modulating its exhalation rate is volumetric soil moisture. Here we present an improved process-based high-resolution radon flux map for Europe, using up-to-date soil property maps, including updated uranium activity concentration data from the European Atlas of Natural Radiation. Daily radon exhalation is calculated based on high-resolution soil moisture estimates from the ERA5 and the GLDAS Noah land surface models. Depending on the soil moisture model used, estimated radon fluxes show differences as large as a factor of two, but modelled soil moisture and corresponding modelled radon fluxes also differ from observations. This highlights the importance of accurate representative soil moisture observations for model validation. Although the fluxes of biogeochemical reactive trace gases at the soil-atmosphere interface are also driven by other variable parameters, such as temperature or microbial activity, their net fluxes can often also be limited by effective diffusivity in the upper soil layers, and thus by soil moisture. Estimating variability and uncertainty of biogeochemical active trace gas fluxes such as methane or hydrogen on the regional or continental scale could therefore benefit from experience with the noble gas radon.

Radon (222Rn) gas is the largest source of public exposure to naturally occurring radioactivity and the identification of radon priority areas is required by the Council Directive 2013/59/Euratom. Radon is also used as a tracer to improve atmospheric transport models and to indirectly estimate greenhouse gas (GHG) fluxes using the Radon Tracer Method (RTM). This method is based on the correlation between atmospheric concentrations of radon and GHG, together with information on the radon flux data. For radiological data, all European countries have installed networks of automatic gamma dose rate monitoring stations and report the real-time information gathered to the European Radiological Data Exchange Platform (EURDEP). So far, atmospheric radon activity concentrations and radon fluxes are not yet reported in EURDEP, nor routinely measured within the European radiological networks although these observations could help to avoid false positives results. Due to above applications, there is a need of building a metrological chain to ensure high quality radon activity concentrations and radon flux measurements. Both climate and radiation protection research communities underline the need for improved traceability in low-level atmospheric radon measurements (Khanbabaee et al., 2021). The EMPIR project 19ENV01 traceRadon1 is aimed towards providing the necessary measurement infrastructure and transfer standards to fulfil this need. Results of this project are particularly important for improving independent GHG emission estimates that support national reporting under the Paris Agreement on climate change and for the Council Directive 2013/59/Euratom, thus benefitting two large scientific communities. In this paper, early results, such as new activity standard developments and an overview of commercial and research radon monitors are presented and discussed. These results will feed into the traceRadon project with respect to radionuclide metrology in air and its potential for the improvement of the RTM.

Correlations of night-time atmospheric methane (CH4) and 222Radon (222Rn) observations in Heidelberg, Germany, were evaluated with the Radon Tracer Method (RTM) to estimate the trend of annual CH4 emissions from 1996–2020 in the catchment area of the station. After an initial 30 % decrease of emissions from 1996 to 2004, no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg. We show that the reliability of total CH4 emission estimates with the RTM critically depends on the accuracy and representativeness of the 222Rn exhalation rate from soils in the catchment area of the site. Simply using 222Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gases (GHG) fluxes as large as a factor of two. RTM-based GHG flux estimates also depend on the parameters chosen for the night-time correlations of CH4 and 222Rn, such as the night-time period for regressions as well as the R2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station/observation footprint as their emissions are not captured by the RTM method. Long-term representative 222Rn flux observations in the catchment area of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.

Understanding of regional greenhouse gas emissions into the atmosphere is a prerequisite to mitigate climate change. In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch ("JFJ", Switzerland, 3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period of 2009–2017 with stable carbon isotope (δ13C-CO2) information. We applied two Lagrangian particle dispersion models driven by output from two different numerical weather prediction systems (FLEXPART-COSMO and STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on regional CO2 fluxes, to estimate atmospheric δ13C-CO2, and to obtain model-based estimates of the mixed source signatures (δ13Cm). Anthropogenic fluxes were taken from a fuel type-specific version of the EDGAR v4.3 inventory and ecosystem fluxes were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulations of CO2, δ13C-CO2 and δ13Cm were then compared to observations performed by quantum cascade laser absorption spectroscopy. Around 40 % of the regional CO2 variability above or below the large-scale background was captured by the models, and up to 35 % of the regional variability in δ13C-CO2. This is remarkable considering the complex Alpine topography, the low intensity of regional signals at JFJ, and the challenging measurements. Best agreement between simulations and observations in terms of short-term variability and intensity of the signals for CO2 and δ13C-CO2 was found between late autumn and early spring. The agreement was inferior in the early autumn periods and during summer. This may be associated with the atmospheric transport representation in the models. In addition, the net ecosystem exchange fluxes are a possible source of error, either through inaccuracies in their representation in VPRM for the (Alpine) vegetation or through a day (uptake) vs. night (respiration) transport discrimination to JFJ. Furthermore, the simulations suggest that JFJ is subject to relatively small regional anthropogenic contributions, due to its remote location (elevated and far from major anthropogenic sources), and the limited planetary boundary layer-influence during winter. Instead, the station is primarily exposed to summer-time ecosystem CO2 contributions, which are dominated by rather nearby sources (within 100 km). Even during winter, simulated gross ecosystem respiration accounted for approximately 50 % of all contributions to the CO2 concentrations above the largescale background. The model-based monthly mean δ13Cm ranged from −22 ‰ in winter to −28 ‰ in summer and reached the most depleted values of −35 ‰ at higher fractions of natural gas combustion, and the most enriched values of −17 to −12 ‰ when impacted by cement production emissions. Observation-based δ13Cm values derived by a moving Keeling-plot approach were in good agreement with the model-based estimates. They exhibited a larger scatter, while model-based estimates spread in a more narrow range. Overall, observation-based δ13Cm were limited to a smaller number of data points compared to model-based estimates owing to the stringent analysis prerequisites in combination with the low regional signal at JFJ.

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Estimation of greenhouse gas emissions from atmospheric measurement-based "top-down" methods is complicated by strong and uncertain fluxes from natural systems, for example carbon dioxide (CO2) sources and sinks from the terrestrial biosphere. Additional tracers such as radiocarbon are promising for disentangling the different emission contributions from human activity and natural systems. However, many open questions remain about how different uncertainties in the modeling and observation of these tracers influence the emission estimates.Here we assess the potential benefits of using radiocarbon observations to constrain global fossil fuel emissions in a Carbon Cycle Fossil Fuel Data Assimilation System (CCFFDAS). We performed sensitivity experiments to quantify how uncertainties in the observations and models affect the uncertainties in the derived emissions, including different prior assumptions about natural and anthropogenic CO2 fluxes and varying observation networks. Further, we demonstrate how radiocarbon observations can complement the existing CO2 observation network.

Carbonyl sulfide (COS) is the most abundant reduced sulfur gas in the atmosphere and is used as a tracer for gross primary production (GPP) of terrestrial ecosystems and stomatal conductance of leaves. At present, its usefulness is limited by the uncertainties in the estimation of its sources and sinks. In this study, we aim to understand the COS budget using atmospheric COS enhancements at the Lutjewad tower (53°24’N, 6°21’E, 1m a.s.l.) and atmospheric measurements of COS in the province of Groningen using a mobile van. We infer the sources and sinks of COS using continuous in situ mole fraction profile measurements of COS at Lutjewad. We determined the nighttime COS fluxes to be -3.0 ± 2.6 pmol m-2 s-1 using the radon-tracer correlation approach. We observed enhancements of COS mole fractions on the order of 100 ppt (lasting a few days) to 1000 ppt (lasting a few hours) at three occasions. To quantify potentially unidentified COS sources, we have made additional measurements by collecting air flasks that were analyzed later in the laboratory and with a continuous quantum cascade laser spectrometer. We have identified multiple COS sources, such as biodigesters, sugar production facilities and silicon carbide production facilities. Furthermore, we simulate the Lutjewad COS mole fractions in a Lagrangian model framework to quantitatively understand the COS sources and sinks. These results are useful for improving our understanding of the sources and sinks of COS, contributing to the use of COS as a tracer for GPP.

The Integrated Carbon Observation System (ICOS) provides long term, high quality observations that follow (and cooperatively set) the global standards for the best possible quality data on the atmospheric composition for greenhouse gases (GHG), greenhouse gas exchange fluxes measured by eddy covariance and CO2 partial pressure at water surfaces. The ICOS observational data feeds into a wide area of science that covers for example plant physiology, agriculture, biology, ecology, energy & fuels, forestry, hydrology, (micro)meteorology, environmental, oceanography, geochemistry, physical geography, remote sensing, earth-, climate-, soil- science and combinations of these in multi-disciplinary projects.As ICOS is committed to provide all data and methods in an open and transparent way as free data, a dedicated system is needed to secure the long term archiving and availability of the data together with the descriptive metadata that belongs to the data and is needed to find, identify, understand and properly use the data, also in the far future, following the FAIR data principles. An added requirement is that the full data lifecycle should be completely reproducible to enable full trust in the observations and the derived data products.In this presentation we will introduce the ICOS operational data repository named ICOS Carbon Portal that is based on the linked open data approach. All metadata is modelled in an ontology coded in OWL and based on a RDF triple store that is available through an open SparQL endpoint. The repository supports versioning, collections and models provenance through a simplified Prov-O ontology. All data objects are ingested under strict control for the identified data types on provision of the correct and sufficient (provenance) metadata, data format and data integrity. All data, including raw data, is stored in the long term trusted repository B2SAFE with two replicates. On top of the triple store and SparQL endpoint we have built a series of services, APIs and graphical interfaces that allow machines to machine and user interaction with the data and metadata. Examples are a full faceted search with connected data cart and download facility, preview of higher level data products (time series of point observations and spatial data), and cloud computing services like eddy covariance data processing and on demand atmospheric footprint calculations, all connected to the observational data from ICOS. Another interesting development is the community support for scientific workflows using Jupyter notebook services that connect to our repository through a dedicated python library for direct metadata and data access.

Radon gas is the largest source of public exposure to naturally occurring radioactivity, and concentration maps based on atmospheric measurements aid developers to comply with EU Safety Standard Regulations. Atmospheric radon can also be used as a tracer to evaluate transport models important for supporting successful greenhouse gas (GHG) mitigation strategies. One of the most common techniques currently applied for this propose is the Radon Tracer Method (RTM). To increase the accuracy of both radiation protection measurements and those used for GHG modelling, traceability to SI units for radon exhalation rates from soil, its concentration in the atmosphere and validated models for its dispersal are needed. Thus, atmospheric networks such as the Integrated Carbon Observation System (ICOS) are interested in integrating atmospheric radon concentration measurements. The EMPIR project 19ENV01 traceRadon[1] started to provide the necessary measurement infrastructure for atmospheric radon activity concentration and radon flux measurements, with benefits for both large scientific communities. This is particularly important for GHG emission estimates that support national reporting under the Paris Agreement on climate change.Compared to the large spatiotemporal heterogeneity of GHG fluxes, radon is emitted almost homogeneously over ice-free land and has a negligible flux from oceans. Atmospheric measurements of radon activity concentrations can be used for the assessment and improvement of atmospheric mixing and transport models.Similarly, for radiological data, all European countries have installed networks of automatic gamma dose rate and atmospheric concentration level monitoring stations and report the information gathered to the European Radiological Data Exchange Platform (EURDEP). Currently, EURDEP exchanges real-time monitoring information from 39 countries collected from more than 5500 automatic surveillance systems. Therefore, improving contamination detection requires greater accuracy in determining environmental radon concentrations and their movement in the atmosphere.An overlapping need exists between the climate research and radiation protection communities for improved traceable low-level outdoor radon measurements, combining the challenges of collating and modelling large datasets, with setting up new radiation protection services. The project traceRadon works on this aspect for the benefit of two large scientific communities. An overview will be presented, and first results with respect to radionuclide metrology will be discussed. [1] This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. 19ENV01 traceRadon denotes the EMPIR project reference.

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO 2 ), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO 2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO 2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO 2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO 2 emissions (including cement production) account for 2624 Tg CO 2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 ( ± 463 Tg CO 2 ). The inversion reports 2700 Tg CO 2 ( ± 480 Tg CO 2 ), which is well in line with the national inventories. Over 2011–2015, the CO 2 land sources and sinks from NGHGI estimates report −90 Tg C yr −1 ± 30 Tg C yr −1 while all other BU approaches report a mean sink of −98 Tg C yr −1 ( ± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr −1 ± 400 Tg C yr −1 ). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of “CO 2 flux” obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a).

Since 1750, land-use change and fossil fuel combustion has led to a 46% increase in the atmospheric carbon dioxide (CO2) concentrations, causing global warming with substantial societal consequences. The Paris Agreement aims to limit global temperature increases to well below 2°C above preindustrial levels. Increasing levels of CO2 and other greenhouse gases (GHGs), such as methane (CH4) and nitrous oxide (N2O), in the atmosphere are the primary cause of climate change. Approximately half of the carbon emissions to the atmosphere are sequestered by ocean and land sinks, leading to ocean acidification but also slowing the rate of global warming. However, there are significant uncertainties in the future global warming scenarios due to uncertainties in the size, nature, and stability of these sinks. Quantifying and monitoring the size and timing of natural sinks and the impact of climate change on ecosystems are important information to guide policy-makers’ decisions and strategies on reductions in emissions. Continuous, long-term observations are required to quantify GHG emissions, sinks, and their impacts on Earth systems. The Integrated Carbon Observation System (ICOS) was designed as the European in situ observation and information system to support science and society in their efforts to mitigate climate change. It provides standardized and open data currently from over 140 measurement stations across 12 European countries. The stations observe GHG concentrations in the atmosphere and carbon and GHG fluxes between the atmosphere, land surface, and the oceans. This article describes how ICOS fulfills its mission to harmonize these observations, ensure the related long-term financial commitments, provide easy access to well-documented and reproducible high-quality data and related protocols and tools for scientific studies, and deliver information and GHG-related products to stakeholders in society and policy.

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results, and inverse modelling estimates, over the period 1990–2018. BU and TD products are compared with European national GHG inventories (NGHGI) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGI, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGI with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGI and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), well in line with the national inventories. Over 2011–2015, the CO2 land sources/sinks from NGHGI estimates report −90 Tg C yr−1 ± 30 Tg C while all other BU approaches report a mean sink of −98 Tg yr−1 (± 362 Tg C from DGVMs only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr−1 ± 400 T g C yr−1). This concludes that a) current independent approaches are consistent with NGHGI b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of CO2 flux obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288883 (Petrescu et al., 2020).

Evaluating atmospheric transport simulations against observations helps refining bottom-up estimates of greenhouse gas fluxes and identifying gaps in our understanding of regional and category-specific contributions to atmospheric mole fractions. This insight is critical in the efforts to mitigate anthropogenic environmental impact. Beside total mole fractions, stable isotope ratios provide further constraints on source-sink processes [1-3].Here, we present two receptor-oriented model simulations for carbon dioxide (CO2) mole fraction and δ13C-CO2 stable isotope ratios for a nine year period (2009-2017) at the High Altitude Research Station Jungfraujoch (Switzerland, 3580 m asl). The model simulations of CO2 were performed on a 3-hourly time-resolution with two backward Lagrangian particle dispersion models driven by two different numerical weather forecast fields: FLEXPART-COSMO and STILT-ECMWF. Anthropogenic CO2 fluxes were based on the EDGAR v4.3 emissions inventory aggregated into 14 source categories representing fossil and biogenic fuel uses as well as emissions from cement production. Biospheric CO2 fluxes representing the photosynthetic uptake and respiration of 8 plant functional types were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulated CO2 emissions per source and sink category were weighted with category-specific δ13C-CO2 signatures from published experimental studies. Background CO2 values at the boundaries of both model domains were taken from global model simulations and the corresponding δ13C-CO2 values were constructed as suggested in Ref. [3]. We compare the simulations to a unique data set of continuous in-situ observations of CO2 mole fractions and δ13C-CO2 stable isotope ratios by quantum cascade laser absorption spectroscopy as described in previous work [1, 4-5], available for the whole nine year period at the site.The simulated atmospheric CO2 and δ13C-CO2 time-series are in good agreement with the observations and capture the observed variability at the models' 3-hourly time-resolution. This allows for an in-depth evaluation of the contribution of different CO2 emission sources and for an allocation of source regions when Jungfraujoch is influenced by air masses from the planetary boundary layer. In brief, the receptor-oriented model simulations suggest that anthropogenic CO2 contributions are primarily of fossil origin (90%). Anthropogenic emissions contribute between 60% in February, and 20% in July/August, to the CO2 enhancements observed at Jungfraujoch. The remaining fraction is due to biosphere respiration, which thus largely dominates emissions during the summer season. However, intense photosynthetic CO2 uptake during June, July and August roughly outweighs CO2 contributions from anthropogenic activities and biosphere respiration at JFJ. REFERENCES[1] Tuzson et al., 2011. ACP, 11, 1685[2] Röckmann et al., 2016. ACP, 16, 10469[3] Vardag et al., 2016. Biogeosciences, 13, 4237[4] Tuzson et al., 2008. Appl. Phys. B, 92, 451[5] Sturm et al., 2013. AMT 6, 1659

Reliable estimates of fossil fuel CO2 (ffCO2) emissions from high-emission regions or urban areas are currently in demand from a wide range of players. On the one hand, cities and municipalities themselves are interested in an independent validation of their ffCO2 emissions. On the other hand, there is an increased interest in atmospheric science to merge independent emission estimate methods over different scales [Pinty et al. 2019]. 14CO2 has become the gold standard when it comes to the experimental splitting of atmospheric CO2 concentration into its biogenic and fossil components [e.g. Levin et al. 2003; 2011 or Turnbull et al. 2009].Here we report on the identification of ffCO2 emitted from the Mannheim/Ludwigshafen metropolitan region in the upper Rhine valley, Germany. Quantification of the regional ffCO2 component requires knowledge of the composition of the background air. Thus, the emission area has been sampled by an upwind and a downwind station. We will discuss the advantages and disadvantages of using local background measurements conducted at a dedicated upwind station of the emission area and compare this realisation of background estimate to regional background estimates derived from measurements at classical remote background sites. All CO2 and 14CO2 observations have been performed as part of the European RINGO project. Furthermore, we investigate the suitability of using the total-CO2 difference between the two stations as a proxy for fossil fuel CO2 and the seasonal applicability of such a surrogate tracer. Finally, the observations of the total-CO2 surrogate tracer will be compared with the predictions from STILT forward model runs. Ref.:Levin, I., B. Kromer, M. Schmidt and H. Sartorius, 2003. A novel approach for independent budgeting of fossil fuels CO2 over Europe by 14CO2 observations. Geophys. Res. Lett. 30(23), 2194, doi. 10.1029/2003GL018477.Levin, I., S. Hammer, E. Eichelmann, F. Vogel, 2011. Verification of greenhouse gas emission reductions: The prospect of atmospheric monitoring in polluted areas. Philosophical Transactions A 369, 1906-1924, doi:10.1098/rsta.2010.0249.Pinty B., P. Ciais, D. Dee, H. Dolman, M. Dowell, R. Engelen, K. Holmlund, G. Janssens-Maenhout, Y. Meijer, P. Palmer, M. Scholze, H. Denier van der Gon, M. Heimann, O. Juvyns, A. Kentarchos and H. Zunker (2019) An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity – Needs and high level requirements for in situ measurements, doi: 10.2760/182790, European Commission Joint Research Centre, EUR 29817 ENTurnbull, J., Rayner, P., Miller, J., Naegler, T., Ciais, P., & Cozic, A. (2009). On the use of 14CO2 as a tracer for fossil fuel CO2: Quantifying uncertainties using an atmospheric transport model. Journal of Geophysical Research: Atmospheres, 114(D22).

The rather short life time of 222Radon of 5.5 days makes this radioactive noble gas an almost ideal tracer of atmospheric transport processes. 222Radon, the gaseous progeny of 226Radium, which is a trace constituent of all soils, can escape the soil grains and make its way from the unsaturated soil zone into the atmosphere. The exhalation rate of 222Radon from continental surfaces depends on soil type and permeability, but is orders of magnitude larger than that from ocean surfaces. Therefore, the atmospheric 222Radon activity concentration can be used as a measure of the residence time of air over continental surfaces or to distinguish continental from marine air masses. At continental sites, the short-term variability of 222Radon is mainly determined by diurnal or synoptic-scale boundary layer mixing processes. If its continental exhalation rate is known, 222Radon can even be applied as a quantitative tracer for evaluating regional scale transport model performance. In the present study we use 222Radon activity concentration measurements from the ICOS atmospheric station network and STILT transport model results to assess the ability of this routinely used model to correctly simulate the (diurnal) variation of boundary layer transport. This uncertainty assessment is an important step towards reliable estimates of the contribution of transport model error in GHGs inversion studies that aim at providing accurate fluxes from inversion of atmospheric GHGs observations in ICOS.

In situ CO2 and CO measurements from five atmospheric ICOS (Integrated Carbon Observation System) stations have been analysed together with footprint model runs from the regional transport model STILT, to develop a dedicated strategy for flask sampling with an automated sampler. Flask sampling in ICOS has three different purposes: (1) Provide an independent quality control for in situ observations, (2) provide representative information on atmospheric components currently not monitored in situ at the stations, (3) collect samples for 14CO2 analysis that are significantly influenced by fossil fuel CO2 (ffCO2) emission areas. Based on the existing data and experimental results obtained at the Heidelberg pilot station with a prototype flask sampler, we suggest that single flask samples should be collected regularly every third day around noon/afternoon from the highest level of a tower station. Air samples shall be collected over one hour with equal temporal weighting to obtain a true hourly mean. At all stations studied, more than 50 % of flasks to be collected around mid-day will likely be sampled during low ambient variability (

In order to effectively tackle the on-going changes in global climate, stakeholders and policy makers across the globe need timely and accurate information they can trust on a wide range of topics. Here we briefly describe how observational data on greenhouse gas concentrations collected from the ICOS network of measurement stations in Europe is transformed via atmospheric modelling into information on how emission and uptake of greenhouse gases vary over time and space, and interpreted by experts to create knowledge usable by decision makers.

The 2018 drought was one of the worst European droughts of the twenty-first century in terms of its severity, extent and duration. The effects of the drought could be seen in a reduction in harvest yields in parts of Europe, as well as an unprecedented browning of vegetation in summer. Here, we quantify the effect of the drought on net ecosystem exchange (NEE) using five independent regional atmospheric inversion frameworks. Using a network of atmospheric CO 2 mole fraction observations, we estimate NEE with at least monthly and 0.5° × 0.5° resolution for 2009–2018. We find that the annual NEE in 2018 was likely more positive (less CO 2 uptake) in the temperate region of Europe by 0.09 ± 0.06 Pg C yr −1 (mean ± s.d.) compared to the mean of the last 10 years of −0.08 ± 0.17 Pg C yr −1 , making the region close to carbon neutral in 2018. Similarly, we find a positive annual NEE anomaly for the northern region of Europe of 0.02 ± 0.02 Pg C yr −1 compared the 10-year mean of −0.04 ± 0.05 Pg C yr −1 . In both regions, this was largely owing to a reduction in the summer CO 2 uptake. The positive NEE anomalies coincided spatially and temporally with negative anomalies in soil water. These anomalies were exceptional for the 10-year period of our study. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.

Atmospheric inversions have been used for the past two decades to derive large scale constraints on the sources and sinks of CO2 into the atmosphere. The development of high density in-situ surface observation networks, such as ICOS in Europe, enables in theory inversions at a resolution close to the country scale in Europe. This has led to the development of many regional inversion systems capable of assimilating these high-resolution data, in Europe and elsewhere. The EUROCOM project (EUROpean atmospheric transport inversion COMparison) is a collaboration between seven European research institutes, which aims at producing a collective assessment of the net carbon flux between the terrestrial ecosystems and the atmosphere in Europe for the period 2006–2015. It aims in particular at investigating the capacity of the inversions to deliver consistent flux estimates from the country scale up to the continental scale. The project participants were provided with a common database of in-situ observed CO2 concentrations (including the observation sites that are now part of the ICOS network), and were tasked with providing their best estimate of the net terrestrial carbon flux for that period, and for a large domain covering the entire European Union. The inversion systems differ by the transport model, the inversion approach and the choice of observation and prior constraints, enabling us to widely explore the space of uncertainties. This paper describes the intercomparison protocol and the participating systems, and it presents the first results from a reference set of inversions, at the continental scale and in four large regions. At the continental scale, the regional inversions support the assumption that European ecosystems are a relatively small sink (−0.21 ± 0.2 PgC/year). We find that the convergence of the regional inversions at this scale is not better than that obtained in state-of-the-art global inversions. However, more robust results are obtained for sub-regions within Europe, and in these areas with dense observational coverage, the objective of delivering robust country scale flux estimates appears achievable in the near future.

Atmospheric inversions are widely used in the optimization of surface carbon fluxes on a regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not directly reflect the true flux uncertainties but is used to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model–data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, in which the flux constraint is derived following the same residual analysis but applied to the model–model mismatch. The synthetic study showed a quite good agreement between posterior and true fluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the known truth by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006–2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3–8.2) Tg CH4 yr−1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between −40 and 20 % at the aircraft profile sites in France, Hungary and Poland.

An optimal network design was carried out to prioritise the installation or refurbishment of greenhouse gas (GHG) monitoring stations around Africa. The network was optimised to reduce the uncertainty in emissions across three of the most important GHGs: CO2, CH4, and N2O. Optimal networks were derived using incremental optimisation of the percentage uncertainty reduction achieved by a Gaussian Bayesian atmospheric inversion. The solution for CO2 was driven by seasonality in net primary productivity. The solution for N2O was driven by activity in a small number of soil flux hotspots. The optimal solution for CH4 was consistent over different seasons. All solutions for CO2 and N2O placed sites in central Africa at places such as Kisangani, Kinshasa and Bunia (Democratic Republic of Congo), Dundo and Lubango (Angola), Zoétélé (Cameroon), Am Timan (Chad), and En Nahud (Sudan). Many of these sites appeared in the CH4 solutions, but with a few sites in southern Africa as well, such as Amersfoort (South Africa). The multi-species optimal network design solutions tended to have sites more evenly spread-out, but concentrated the placement of new tall-tower stations in Africa between 10ºN and 25ºS. The uncertainty reduction achieved by the multi-species network of twelve stations reached 47.8% for CO2, 34.3% for CH4, and 32.5% for N2O. The gains in uncertainty reduction diminished as stations were added to the solution, with an expected maximum of less than 60%. A reduction in the absolute uncertainty in African GHG emissions requires these additional measurement stations, as well as additional constraint from an integrated GHG observatory and a reduction in uncertainty in the prior biogenic fluxes in tropical Africa. This work was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement 730995 and by Natural Environment Research Council (NERC) Methane Observations and Yearly Assessments programme (MOYA, NE/N016548/1). ALB was supported by a Juan de la Cierva-Formaci?n postdoctoral contract from the Spanish Ministry of Science, Innovation and Universities (FJC2018-038192-I). The authors would like to thank Alistair Manning for useful tea-time discussions on biomass burning emissions.

We present inverse modelling (top-down) estimates of European methane (CH4) emissions for 2006–2012 based on a new quality-controlled and harmonized in-situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.7 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom-up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) total wetland emissions of 4.3 (2.3–8.2) CH4 yr−1 from EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. This analysis identifies regional biases for several models at the aircraft profile sites in France, Hungary and Poland.

Inverse modelling is a useful tool for retrieving CH4 fluxes; however, evaluation of the applied chemical transport model is an important step before using the inverted emissions. For inversions using column data one concern is how well the model represents stratospheric and tropospheric CH4 when assimilating total column measurements. In this study atmospheric CH4 from three inverse models is compared to FTS (Fourier transform spectrometry), satellite and in situ measurements. Using the FTS measurements the model biases are separated into stratospheric and tropospheric contributions. When averaged over all FTS sites the model bias amplitudes (absolute model to FTS differences) are 7.4 +/- 5.1, 6.7 +/- 4.8, and 8.1 +/- 5.5 ppb in the tropospheric partial column (the column from the surface to the tropopause) for the models TM3, TM5-4DVAR, and LMDz-PYVAR, respectively, and 4.3 +/- 9.9, 4.7 +/- 9.9, and 6.2 +/- 11.2 ppb in the stratospheric partial column (the column from the tropopause to the top of the atmosphere). The model biases in the tropospheric partial column show a latitudinal gradient for all models; however there are no clear latitudinal dependencies for the model biases in the stratospheric partial column visible except with the LMDz-PYVAR model. Comparing modelled and FTS-measured tropospheric column-averaged mole fractions reveals a similar latitudinal gradient in the model biases but comparison with in situ measured mole fractions in the troposphere does not show a latitudinal gradient, which is attributed to the different longitudinal coverage of FTS and in situ measurements. Similarly, a latitudinal pattern exists in model biases in vertical CH4 gradients in the troposphere, which indicates that vertical transport of tropospheric CH4 is not represented correctly in the models.

Optimized biogenic carbon fluxes for Europe were estimated from high resolution regional scale inversions, utilizing atmospheric CO2 measurements at 16 stations for the year 2007. Additional sensitivity tests with different data-driven error structures were performed. As the atmospheric network is rather sparse and consequently contains large spatial gaps, we use a priori biospheric fluxes to further constrain the inversions. The biospheric fluxes were simulated by the Vegetation Photosynthesis and Respiration Model (VPRM) at a resolution of 0.1° and optimized against Eddy covariance data. Overall we estimate an a priori uncertainty of 0.54 GtC y−1 related to the poor spatial representation between the biospheric model and the ecosystem sites. The sink estimated from the atmospheric inversions for the area of Europe (as represented in the model domain) ranges between 0.23 and 0.38 GtC y−1 (0.30 and 0.49 GtC y−1 up-scaled to geographical Europe). This is within the range of posterior flux uncertainty estimates of previous studies using ground based observations.

Atmospheric inversions are widely used in the optimization of surface carbon fluxes at regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not reflect directly the true flux uncertainties but it is used in such a way to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model – data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, where the flux constraint is derived following the same residual analysis but applied to the model-model mismatch. The synthetic study showed a quite good agreement between posterior and “true” fluxes at European/Country and annual/monthly scales. Posterior monthly and country aggregated fluxes improved their correlation coefficient with the “known truth” by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. Respectively, the ratio of the standard deviation between posterior/reference and prior/reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales where the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

We evaluate the capability of the global atmospheric transport model TM5 to reproduce observations of the boundary layer dynamics and the associated variability of trace gases close to the surface, using radon (222Rn), which is an excellent tracer for vertical mixing owing to its short lifetime (half-life) of 3.82 days. Focusing on the European scale, we compare the boundary layer height (BLH) in the TM5 model with observations from the NOAA Integrated Global Radiosonde Archive (IGRA) and in addition with ceilometer measurements at Cabauw (The Netherlands) and lidar BLH retrievals at Trainou (France). Furthermore, we compare TM5 simulations of 222Rn activity concentrations, using a novel, process-based 222Rn flux map over Europe (Karstens et al., 2015), with quasi-continuous 222Rn measurements from 10 European monitoring stations. The TM5 model reproduces relatively well the daytime BLH (within ~ 10–20 % for most of the stations), except for coastal sites, for which differences are usually larger due to model representation errors. During night, TM5 overestimates the shallow nocturnal BLHs, especially for the very low observed BLHs (< 100 m) during summer. The 222Rn activity concentration simulations based on the new 222Rn flux map show significant improvements especially regarding the average seasonal variability, compared to simulations using constant 222Rn fluxes. Nevertheless, the (relative) differences between simulated and observed daytime minimum 222Rn activity concentrations are larger for several stations (on the order of 50 %) compared to the (relative) differences between simulated and observed BLH at noon. Although the nocturnal BLH is often higher in the model than observed, simulated 222Rn nighttime maxima are larger at several continental stations, which points to potential deficiencies of TM5 to correctly simulate the vertical gradients within the nocturnal boundary layer, limitations of the 222Rn flux map, or issues related to the definition of the nocturnal BLH. At several stations the simulated decrease of 222Rn activity concentrations in the morning is faster than observed. In addition, simulated vertical 222Rn activity concentration gradients at Cabauw decrease faster than observations during the morning transition period, and are in general lower than observed gradients during daytime, which points to too fast vertical mixing in the TM5 boundary layer during daytime. Furthermore, the capability of the TM5 model to simulate the diurnal BLH cycle is limited due to the current coarse temporal resolution (3 hr/6 hr) of the TM5 input meteorology. Additionally, we analyze the impact of a new treatment of convection in TM5, based on the ECMWF reanalysis, leading to overall significantly lower (on the order of ~ 20 %) surface 222Rn activity concentrations during daytime compared to the current default convection scheme based on Tiedtke (1989). However, the performance of the model simulations compared to the 222Rn observations is very similar in terms of root mean square and correlation coefficient for both convection schemes.

We evaluate the capability of the global atmospheric transport model TM5 to reproduce observations of the boundary layer dynamics and the associated variability of trace gases close to the surface, using radon (222Rn), which is an excellent tracer for vertical mixing owing to its short lifetime (half-life) of 3.82 days. Focusing on the European scale, we compare the boundary layer height (BLH) in the TM5 model with observations from the NOAA Integrated Global Radiosonde Archive (IGRA) and in addition with ceilometer measurements at Cabauw (The Netherlands) and lidar BLH retrievals at Trainou (France). Furthermore, we compare TM5 simulations of 222Rn activity concentrations, using a novel, process-based 222Rn flux map over Europe (Karstens et al., 2015), with quasi-continuous 222Rn measurements from 10 European monitoring stations. The TM5 model reproduces relatively well the daytime BLH (within ~ 10–20 % for most of the stations), except for coastal sites, for which differences are usually larger due to model representation errors. During night, TM5 overestimates the shallow nocturnal BLHs, especially for the very low observed BLHs (< 100 m) during summer. The 222Rn activity concentration simulations based on the new 222Rn flux map show significant improvements especially regarding the average seasonal variability, compared to simulations using constant 222Rn fluxes. Nevertheless, the (relative) differences between simulated and observed daytime minimum 222Rn activity concentrations are larger for several stations (on the order of 50 %) compared to the (relative) differences between simulated and observed BLH at noon. Although the nocturnal BLH is often higher in the model than observed, simulated 222Rn nighttime maxima are larger at several continental stations, which points to potential deficiencies of TM5 to correctly simulate the vertical gradients within the nocturnal boundary layer, limitations of the 222Rn flux map, or issues related to the definition of the nocturnal BLH. At several stations the simulated decrease of 222Rn activity concentrations in the morning is faster than observed. In addition, simulated vertical 222Rn activity concentration gradients at Cabauw decrease faster than observations during the morning transition period, and are in general lower than observed gradients during daytime, which points to too fast vertical mixing in the TM5 boundary layer during daytime. Furthermore, the capability of the TM5 model to simulate the diurnal BLH cycle is limited due to the current coarse temporal resolution (3 hr/6 hr) of the TM5 input meteorology. Additionally, we analyze the impact of a new treatment of convection in TM5, based on the ECMWF reanalysis, leading to overall significantly lower (on the order of ~ 20 %) surface 222Rn activity concentrations during daytime compared to the current default convection scheme based on Tiedtke (1989). However, the performance of the model simulations compared to the 222Rn observations is very similar in terms of root mean square and correlation coefficient for both convection schemes.

Inverse modeling techniques used to quantify surface carbon fluxes commonly assume that the uncertainty of fossil fuel CO2 (FFCO2) emissions is negligible and that intra-annual variations can be neglected. To investigate these assumptions, we analyzed the differences between four fossil fuel emission inventories with spatial and temporal differences over Europe and their impact on the model simulated CO2 concentration. Large temporal flux variations characterize the hourly fields (~40 % and ~80 % for the seasonal and diurnal cycles, peak-to-peak) and annual country totals differ by 10 % on average and up to 40 % for some countries (i.e., the Netherlands). These emissions have been prescribed to seven different transport models, resulting in 28 different FFCO2 concentrations fields. The modeled FFCO2 concentration time series at surface sites using time-varying emissions show larger seasonal cycles (+2 ppm at the Hungarian tall tower (HUN)) and smaller diurnal cycles in summer (−1 ppm at HUN) than when using constant emissions. The concentration range spanned by all simulations varies between stations, and is generally larger in winter (up to ~10 ppm peak-to-peak at HUN) than in summer (~5 ppm). The contribution of transport model differences to the simulated concentration std-dev is 2–3 times larger than the contribution of emission differences only, at typical European sites used in global inversions. These contributions to the hourly (monthly) std-dev's amount to ~1.2 (0.8) ppm and ~0.4 (0.3) ppm for transport and emissions, respectively. First comparisons of the modeled concentrations with 14C-based fossil fuel CO2 observations show that the large transport differences still hamper a quantitative evaluation/validation of the emission inventories. Changes in the estimated monthly biosphere flux (Fbio) over Europe, using two inverse modeling approaches, are relatively small (less that 5 %) while changes in annual Fbio (up to ~0.15 % GtC yr−1) are only slightly smaller than the differences in annual emission totals and around 30 % of the mean European ecosystem carbon sink. These results point to an urgent need to improve not only the transport models but also the assumed spatial and temporal distribution of fossil fuel emission inventories.

We report results from our atmospheric flask sampling network for three European sites: Lutjewad in the Netherlands, Mace Head in Ireland and the North Sea F3 platform. The air samples from these stations are analyzed for their CO2 and O2 concentrations. In this paper we present the CO2 and O2 data series from these sites between 1998 and 2009, as well as the atmospheric potential oxygen (APO). The seasonal pattern and long term trends agree to a large extent between our three measurement locations. We however find a changing gradient between Mace Head and Lutjewad, both for CO2 and O2. To explain the potential contribution of fossil fuel emissions to this changing gradient we use an atmospheric transport model in combination with CO2 emission data and information on the fossil fuel mix per region. Using the APO trend from Mace Head we obtain an estimate for the global oceanic CO2 uptake of 1.8 ± 0.8 PgC/year.