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Improving the estimates of CO2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO2 variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO2 in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO2 in coarse models is quantified by using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations at a spatial resolution of 10 km × 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of 1∘ (∼ 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, for the surface and column CO2, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to ∼ 5 ppm, which is even comparable to half of the magnitude of the seasonal variability or concentration enhancement due to hotspot emissions. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo-observations from nine tall-tower sites over India, we show that the net ecosystem exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 % to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need to take account of fine-scale CO2 variability in models over the Indian subcontinent to better understand processes regulating CO2 sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.

An appropriate representation of point source emissions in atmospheric transport models is very challenging. In the Stochastic Time-Inverted Lagrangian Transport model (STILT), all point source emissions are typically released from the surface, meaning that the actual emission stack height plus subsequent plume rise is not considered. This can lead to erroneous predictions of trace gas concentrations, especially during nighttime when vertical atmospheric mixing is minimal. In this study we use two Weather Research and Forecasting (WRF)–STILT model approaches to simulate fossil fuel CO2 (ffCO2) concentrations: (1) the standard “surface source influence (SSI)” approach and (2) an alternative “volume source influence (VSI)” approach where nearby point sources release CO2 according to their effective emission height profiles. The comparison with 14C-based measured ffCO2 data from 2-week integrated afternoon and nighttime samples collected at Heidelberg, 30 m above ground level shows that the root-mean-square deviation (RMSD) between modelled and measured ffCO2 is indeed almost twice as high during the night (RMSD =6.3 ppm) compared to the afternoon (RMSD =3.7 ppm) when using the standard SSI approach. In contrast, the VSI approach leads to a much better performance at nighttime (RMSD =3.4 ppm), which is similar to its performance during afternoon (RMSD =3.7 ppm). Representing nearby point source emissions with the VSI approach could thus be a first step towards exploiting nocturnal observations in STILT. The ability to use nighttime observations in atmospheric inversions would dramatically increase the observational data and allow for the investigation of different source mixtures or diurnal cycles. To further investigate the differences between these two approaches, we conducted a model experiment in which we simulated the ffCO2 contributions from 12 artificial power plants with typical annual emissions of 1 million tonnes of CO2 and with distances between 5 and 200 km from the Heidelberg observation site. We find that such a power plant must be more than 50 km away from the observation site in order for the mean modelled ffCO2 concentration difference between the SSI and VSI approach to fall below 0.1 ppm during situations with low mixing heights smaller than 500 m.

Three-hourly net ecosystem exchange (NEE) is estimated at spatial scales of 0.25∘ over the European continent, based on the pre-operational inverse modelling framework “CarboScope Regional” (CSR) for the years 2006 to 2019. To assess the uncertainty originating from the choice of a priori flux models and observational data, ensembles of inversions were produced using three terrestrial ecosystem flux models, two ocean flux models, and three sets of atmospheric stations. We find that the station set ensemble accounts for 61 % of the total spread of the annually aggregated fluxes over the full domain when varying all these elements, while the biosphere and ocean ensembles resulted in much smaller contributions to the spread of 28 % and 11 %, respectively. These percentages differ over the specific regions of Europe, based on the availability of atmospheric data. For example, the spread of the biosphere ensemble is prone to be larger in regions that are less constrained by CO2 measurements. We investigate the impact of unprecedented increase in temperature and simultaneous reduction in soil water content (SWC) observed in 2018 and 2019 on the carbon cycle. We find that NEE estimates during these 2 years suggest an impact of drought occurrences represented by the reduction in net primary productivity (NPP), which in turn leads to less CO2 uptake across Europe in 2018 and 2019, resulting in anomalies of up to 0.13 and 0.07 PgC yr−1 above the climatological mean, respectively. Annual temperature anomalies also exceeded the climatological mean by 0.46 ∘C in 2018 and by 0.56 ∘C in 2019, while Standardised Precipitation–Evaporation Index (SPEI) anomalies declined to −0.20 and −0.05 SPEI units below the climatological mean in both 2018 and 2019, respectively. Therefore, the biogenic fluxes showed a weaker sink of CO2 in both 2018 and 2019 (−0.22 ± 0.05 and −0.28 ± 0.06 PgC yr−1, respectively) in comparison with the mean −0.36 ± 0.07 PgC yr−1 calculated over the full analysed period (i.e. 14 years). These translate into a continental-wide reduction in the annual sink by 39 % and 22 %, respectively, larger than the typical year-to-year standard deviation of 19 % observed over the full period.

As a second most abundant anthropogenic greenhouse gas, methane has been an object of intense study over the past years. Despite a good overall knowledge of the sources contributing to the increase of its atmospheric abundance, the precise constrain of the global methane budget remains elusive, with large uncertainties still characterizing both anthropogenic and natural emissions, especially in the regions poorly constrained by observations, such as tropical wetlands or northern high-latitude regions.CoMet 2.0 Arctic mission, executed in August and September 2022 aimed at characterizing the distribution of CH4 and CO2 over significant regional sources with the use of a German Research Aircraft HALO (High Altitude Long-range Observatory), as well as to validate remote sensing measurements from state-of-the-art instrumentation installed on-board against a set of independent in-situ observations. These sources included both anthropogenic (oil, gas and coal industries) as well as natural sources that represent large-scale methane-emission regions (including wetlands and major deltas in the region).We will present results of in-situ observations performed across 15 research flights performed over a variety of environments. High-precision mole fractions of CO2, CH4 and CO were measured with the the JIG (Jena In-Situ Greenhouse gas sensor, based on Picarro 2401-m) instruments, while JAS (Jena Air Sampler) allowed collection of 155 spot samples to additionally characterize N2O, H2, SF6, O2/N2, Ar/N2 and stable isotopes (not presented).Flight strategies were adopted in order to balance the needs of different types of instrumentation aboard HALO aircraft, while simultaneously sample the atmospheric constituents in the optimal manner. These strategies included a) high- to mid-altitude horizontal legs during transfers from Base of Operations (Edmonton, Alberta) towards the specific target areas, b) local vertical profiles, c) low-altitude sections with detailed scanning of the mole fractions of atmospheric constituents across the PBL and residual layers. With the appropriate selection of strategies for targeting particular emission sources, we were able to gather a rich observation suite that demonstrates importance of interplay between regional fluxes and atmospheric dynamics on spatio-temporal ranges larger than usually considered.

Within the Integrated Greenhouse Gas Monitoring System for Germany (ITMS), a national initiative targeted at the provision of estimates of GHG fluxes for Germany, the CarboScope-Regional (CSR) inversion system operated at the MPI-BGC is envisioned as a back-bone and reference system for future developments using the ICON-ART modelling system at the German Weather Service (DWD). It utilizes ICOS atmospheric observationsAdditional focus of the research involving CSR is the evaluation and improvement of vertical transport in atmospheric models, as well as the inclusion of additional data streams (e.g. vertical profile information) to improve both, GHG flux estimates and their uncertainty characterization. In this context, the assessment of mixing heights as represented within CSR against independent information derived from radiosondes over a timespan of more than a decade has revealed problems vertical mixing during wintertime that have the potential to cause biased flux retrievals. First results related to this and other experiments will be presented.

Anthropogenic greenhouse gas (GHG) emissions remain the main concern for global climate change. To reduce and mitigate those emissions both anthropogenic and natural sources must be identified and quantified. However, high northern latitude wetland regions may also overlap with, e.g., fossil fuel extraction sites. Consequently, commonly used passive satellite sensors are often challenged to observe and disentangle those emissions due to challenging illumination conditions and their large ground scene size, respectively.To investigate anthropogenic and wetland GHG emissions, a team of scientists deployed a comprehensive suite of instruments aboard the German Research aircraft HALO (High Altitude and Long Range Research) during the CoMet 2.0 Arctic mission conducted in Canada in August and September 2022. During the campaign, passive airborne remote sensing measurements by MAMAP2D-Light (Methane airborne mapper 2D light) were combined with active airborne remote sensing measurements by CHARM-F (CH4 Airborne Remote Monitoring – Flugzeug) and in situ GHG concentration measurements, also including an extensive suite of meteorological parameters.Those column and in-situ concentration observations of CH4 and CO2 will be used to identify and quantify emissions over a wide range of source types and scales in Canada (and Europe). This comprises single point source emissions (e.g., power plants), small areal sources such as landfills (e.g, the Valdemingomez and Pinto landfills in Madrid) and opencast coal mines, and extensive oil and gas exploration sites, including oil sand areas, which might be embedded in natural wetland regions or river deltas. The imaging capabilities of the MAMAP2D-Light instrument enable precise localisation of emissions and therefore mitigation strategies in the case of, e.g., leakages. This work will summarize and present first results and emission estimates from the CoMet 2.0 Arctic mission with a focus on localised emitters observed by the airborne imaging instrument MAMAP2D-Light.

In order to reliably predict the climate of our planet, and to help inform political conventions on greenhouse gas emissions such as the Paris Agreement of 2015, adequate knowledge of both natural and anthropogenic sources of the greenhouse gases Carbon dioxide (CO2) and methane (CH4) and their feedbacks is needed. Despite the recognized importance of this issue, our current understanding about sources and sinks of CO2 and CH4 is still inadequate. This is particularly true for the Arctic, where large wetlands and permafrost areas constitute the most relevant but least quantified ecosystems for the global carbon budget.The CoMet 2.0 Arctic mission wants to help remedy this deficiency with a multi-disciplinary approach providing relevant measurements from Arctic regions using a suite of sophisticated scientific instrumentation onboard the German research aircraft HALO (High Altitude and LOng Range Research Aircraft, https://halo-research.de) to support state-of-the-art Earth System Models. At the same time, CoMet intends to support and improve current and future satellite missions, which still struggle to make high-quality measurements given the low sun elevation, low albedo, and adverse cloud conditions in the Arctic.CoMet 2.0 Arctic (https://comet2arctic.de/) has successfully been conducted within a six-week intensive operation period from August 10th to September 16th, 2022 targeting greenhouse gas emissions from boreal wetlands and permafrost areas in the Canadian Arctic, from wildfires, and from anthropogenic emission sources such oil, gas, and coal extraction sites and landfills.For that mission, HALO was equipped with a suite of remote sensing and in-situ instruments for the measurement of greenhouse gases and meteorological parameters. The remote sensing package comprised the CH4 and CO2 lidar CHARM-F (operated by DLR), the imaging spectrometer MAMAP2D-Light (operated by University of Bremen) and the hyperspectral imager specMACS (operated by LMU Munich). The remote sensors were supported by several in-situ instruments (operated by MPI Jena and DLR) to measure the main greenhouse gases and related trace species as well as an air sampler that collects air samples at flight level for later analysis (e.g. w.r.t. isotopes) in the laboratory. Furthermore, instruments to provide detailed information about the standard meteorological parameters (pressure, wind, humidity) were operated and several small meteorological sondes were launched in order to link those in-flight data to profiles.A total of 135 flight hours including a test flight to landfills in Spain and transfer flights from Europe have been performed. 16 scientific flights took place out of Edmonton, Alberta, to various regions all over Canada.CoMet 2.0 Arctic has partly been coordinated with the Arctic-Boreal Vulnerability Experiment field program by NASA (ABoVE, https://above.nasa.gov/). Both missions, ABoVE and CoMet 2.0 Arctic, are linked through the transatlantic initiative AMPAC (Arctic Methane and Permafrost Challenge, https://www.ampac-net.info/) that has recently been inaugurated by the US and European Space Agencies, NASA and ESA.Thus, a valuable data set was acquired to help better understand the methane and carbon dioxide cycles in the Arctic and emissions from natural and anthropogenic sources.

This work was developed on the scope of the national initiative of the Integrated Greenhouse Gas Monitoring System for Germany – ITMS, which focus on reducing and characterizing uncertainties/errors introduced in the full retrieval chain of models, making use of observational data. Determining the magnitude, cause, and agents of carbon fluxes is important to advance our current understanding of the carbon budget and cycle, permitting more accurate predictions of its future behaviour. Sources and sinks of CO2at the Earth's surface can, in principle, be estimated from atmospheric concentrations by inverting atmospheric transport in the atmospheric tracer inversions.Solving for CO2 fluxes in the inversions is highly desirable to better understand the carbon cycle but also to support policies aimed at reducing CO2 emissions. The Jena CarboScope inversion developed based on Bayesian inverse methods is used to obtain data-driven estimates of trace gas exchange, quantifying the large-scale sources and sinks of CO2.To make Jena CarboScope estimates more reliable in understanding sources, sinks and transport of atmospheric CO2from the surface into the troposphere, the reliability of this data product should be evaluated based on independent observations. Quantifying the quality of the inversion estimation by decomposing the inherent uncertainty components is a challenging and key component in product reliability and its use. The overall objective in validating and evaluating uncertainties of the Bayesian data product provided by Jena CarboScope, is to explicitly answer the question: How good is the inversion estimation?The assessment of these products is of special importance for further development and possibly allows for judging the trustworthiness of the inversion outcome.This study explores the potential of CarboScope to reproduce CO2 concentrations recorded during regular flights and aircraft campaigns during the past two decades. The inversion estimations are accomplished by forward runs performed with the inversion having the measurement locations as receptors.This study examines biases and uncertainties in the CarboScope estimations evaluated against flights’ data. It has been found that the CO2 simulated by CarboScope in the forward run (using the transport model TM3 for background concentration and STILT for regional signal) agree reasonably well, into the 10/90th percentile for 3-sigma of the distribution. On the other hand, the inversion exhibit some systematic biases at the edges of the distribution under and overestimating at high and lower mixing ratios, respectively. The CarboScope strength and concerns were enhanced by understanding the differences among observations and the inversion estimation. In comprehensive statistics comparing measurement data from hundreds of flights, we assess the compliance CarboScope`s CO2 estimation. Furthermore, we discuss the estimation and observation mismatches, exploiting the model constraints to reproduce atmospheric transport from the boundary layer to the upper troposphere. Understanding such constraints has the potential to reduce uncertainties of the atmospheric inversion estimates.

Recent atmospheric methane concentrations show an accelerated increase, but the contributions of the underlying emitters are poorly understood. Recording the stable carbon isotope ratio of methane (δ13C(CH4)) is a powerful tool for CH4 source attribution and the understanding of the global methane budget. The airborne measurement of δ13C(CH4) provides the advantages of reaching remote areas and covering large-scale regions, but is challenging regarding sufficient precision while maintaining high spatial measurement density. The state of the art technique is to collect airborne gas samples for subsequent laboratory analysis by isotope ratio mass spectrometry, with high δ13C(CH4) precision of 0.05 ‰. Here we present an innovative in situ airborne system for the measurement of δ13C(CH4), called MIRACLE. MIRACLE consists of a conventional Picarro cavity ring down greenhouse gas analyzer (G2210-i) for the measurement of CH4 and δ13C(CH4), and a sampler unit. The sampler enables the collection of six gas samples in 2 l stainless steel tanks, in a short time (20 s each) via a metal bellows pump, which allows for the specific sampling of small-scale features, such as point source emissions. The sampling is followed by an extended period of subsequent analysis (up to 10 min). Using this setup, we achieve sufficient δ13C(CH4) precision (1σ uncertainty of 0.34 ‰) and an average of five samples per flight hour, allowing for a large number of samples for long flights. Due to the resulting dense coverage with sufficient precision, this novel approach allows for airborne δ13C(CH4) characterization of small-scale methane emitters and large-scale gradients. We employed MIRACLE aboard the research aircraft HALO during the CoMet 2.0 Arctic campaign in summer 2022, which focused on characterizing natural and anthropogenic methane sources in Canada. In this presentation, a proof of concept for the instrument is elaborated, including the investigation of sample purity and measurement comparisons with other instruments. Additionally, we show δ13C(CH4) signatures revealed by the method of Keeling analysis of measurements obtained during CoMet 2.0 and compare them to previous studies. The airborne operation of the MIRACLE instrument combines the advantages of increased precision δ13C(CH4) measurements, typically only possible under stable laboratory conditions, with the in situ, near real time data analysis and the large-scale sampling of secluded areas. MIRACLE will be deployed during the DLR GHGMon campaign (June 2023) to investigate the δ13C(CH4) ratio of agricultural sources of methane in the Netherlands.

The contribution of vegetation to the South American carbon balance is critical for understanding the regional dynamics in net carbon exchange. Of particular interest is the role of the Amazon region as a sink or source of carbon to the atmosphere. Recent evidence indicate a weakening of the Amazon carbon sink, and when taking fires into account, the region represents a source of carbon to the atmosphere. In this study we use a regional atmospheric inversion system together with data from the Amazon Tall Tower Observatory (ATTO) and airborne profiles of CO2, to constrain the Net Biome Exchange (NBE) in tropical South America. At the domain-wide scale we find that the atmospheric observations can constrain 64% of the land mass, with uncertainty reductions in most of the Amazon region, and the adjacent Cerrado and Caatinga biomes. Furthermore, we provide a sub-regional-specific analysis showing the effect of assimilating the Amazon Tall Tower Observatory CO2 time series on the mean seasonal cycle of NBE for four areas within the Amazon, the Cerrado and the Caatinga. An emerging sink-source gradient between the Amazon region (sink) and the integrated effect of the Cerrado and Caatinga (source) is found, but the source is located in the boundaries and outside the eastern border of the legal Amazon. Optimized NBE estimates at regional and subregional scales are shown and the importance of the continuous measurements at ATTO is highlighted. Finally, we indicate the areas with a limited data constraint in our system and conclude that the observational network has to be further expanded for reducing the remaining uncertainty in top-down inverse approaches for this region.

Accurate quantification of regional terrestrial fluxes is essential for improving our knowledge of the carbon sequestration potential of ecosystems, ecosystem functioning, and emission reduction demand in the context of climate change mitigation. However, the quantification is challenging owing to methodological and observational constraints, especially for regions with severe gaps in the ground-based observational network, like India. This study examines the potential of recent satellite missions, such as TROPOMI and OCO-2 providing retrievals of Solar-Induced chlorophyll Fluorescence (SIF) to improve terrestrial biosphere CO2 flux estimates over India. Here, we present high-resolution estimates of Gross Primary Productivity (GPP) and Net Ecosystem Exchange (NEE) over India on a 0.1°×0.1° grid at a temporal resolution of 1 hour from 2012 to 2020. These products can be used for various applications such as those related to the carbon cycle (e.g., inverse modelling of CO2), benchmarking terrestrial biosphere models over the region, and understanding ecosystem responses to climate change. We follow a satellite-based diagnostic data-driven approach using a biosphere model, namely the Vegetation Photosynthesis and Respiration Model (VPRM) simulating both GPP and NEE, based on light use efficiency and satellite observations of the near-infrared radiance of vegetation (NIRv). We calibrate the standard VPRM GPP estimates using SIF-GPP relationship and investigate the model performance by comparing the simulations with eddy-covariance flux tower measurements. Our best model predictions are with a mean bias error (MBE) = 2.4 µmol m-2 s-1, root mean squared error (RMSE) = 3.8 µmol m-2 s-1 and squared correlation coefficient (R2) = 0.56 when evaluating with observations at a monthly scale over the period from 2012 to 2018. The observed seasonal anomalies in NEE and GPP range from -4.9 to 8.0 µmol m-2 s-1 and -7.0 to 17.0 µmol m-2 s-1, respectively, and are well captured by our model. The model simulations are highly correlated with observations during 2018, the only common year when both EC and SIF observations are available, with R2 values of 0.68 and 0.74 for NEE and GPP, respectively. Incorporating the SIF signals in the vegetation model improves model performance in capturing the seasonality and magnitudes of GPP, thereby improving the estimates of NEE. We show the influence of soil temperature and soil moisture on ecosystem respiration and refined the VPRM's ecosystem respiration calculation to better constrain the fluxes, resulting in simulations closer to the observations. Ecosystem respiration fluxes are less well constrained than ecosystem productivity fluxes due to limited observations. Based on satellite observations and the refined model, the annual NEE and GPP estimates range from -0.38 Pg C yr-1 to -0.53 Pg C yr-1 (land C sink) and 3.39 Pg C yr-1 to 3.88 Pg C yr-1, respectively, over India for the years from 2012 to 2020. The biospheric flux distribution over the region is found to be associated with ecosystem heterogeneity, variations in precipitation, and soil characteristics at a regional scale. Overall, our results show that the satellite-based SIF data products can potentially inform ecosystem-scale vegetation responses across biomes in India. Future improvements in the terrestrial biosphere CO2 flux estimates over India can be attained through the carbon cycle data assimilation with the availability of both flux and mixing ratio observations of CO2.

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.

Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) fluxes and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021b). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven sectoral model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with European National Greenhouse Gas Inventories (NGHGIs) reported by Parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 3392 ± 49 Tg CO2 yr-1 (926 ± 13 Tg C yr-1), while eight other BU sources report a mean value of 3340 [3238,3401] [25th,75th percentile] Tg CO2 yr-1 (948 [937,961] Tg C yr-1). The sole top-down inversion of fossil emissions currently available accounts for 3800 Tg CO2 yr-1 (1038 Tg C yr-1), a value close to that of the NGHGI, but for which uncertainty estimates are not yet available. For the net CO2 land fluxes, during the most recent five-year period including the NGHGI estimates, the NGHGI accounted for -91 ± 32 Tg C yr-1 while six other BU approaches reported a mean sink of -62 [-117,-49] Tg C yr-1 and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 [-152,-5] Tg C yr-1. The five-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73 Tg C yr-1 has a slightly smaller spread (0th–100th percentile of [-135,45] Tg C yr-1), and was calculated after removing land-atmosphere CO2 fluxes caused by lateral transport of carbon (crops, wood trade and inland waters) resulting in increased agreement with the the NGHGI and bottom-up approaches. Results at the sub-sector level (Forestland, Cropland, Grassland) show generally good agreement between the NGHGI and sub-sector-specific models, but results for a DGVM are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while large uncertainties on net uptake of CO2 by the land surface prevent trend identification. In addition, a gap on the order of 1000 Tg C yr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.7365863.

Methane ( CH4 ) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than carbon dioxide ( CO2 ). Because of its short atmospheric lifetime relative to that of CO2 , the reduction of the atmospheric abundance of CH4 is an attractive target for short-term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources. For this reason, the CO2 and Methane (CoMet) campaign in May and June 2018 assessed emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the weighting-function-modified differential optical absorption spectroscopy (WFM-DOAS) method. The column anomalies, combined with wind lidar measurements, are inverted to cross-sectional fluxes using a mass balance approach. With the help of these fluxes, reported emissions of small clusters of coal mine ventilation shafts are then assessed. The MAMAP CH4 column observations enable an accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for four clusters with a total of 23 ventilation shafts, which are responsible for about 40 % of the total CH4 mining emissions in the target area. The observations were made during several overflights on different days. The final average CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 t CH4 h−1 at the time of the campaign. The fluxes observed at one cluster during different overflights vary by as much as 50 % of the average value. Associated errors (1 σ ) are usually between 15 % and 59 % of the average flux, depending mainly on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement within the uncertainties. If only emissions reported annually are available for comparison with the observations, caution is advised due to possible fluctuations in emissions during a year or even within hours. To measure emissions even more precisely and to break them down further for allocation to individual shafts in a complex source region such as the USCB, imaging remote sensing instruments are recommended.

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.

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO 2 ) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO 2 from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO 2 emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO 2 based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer “atmospheric potential oxygen” (APO). We detect and quantify ffCO 2 reductions during 2020–2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near–real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO 2 emissions reductions from three independent lower-frequency U.K. estimates.

IAGOS (In-service Aircraft for a Global Observing System) is a European Research Infrastructure for global observations of atmospheric composition using commercial aircraft. Commercial aircraft are ideal platforms for providing long-term in-situ measurements with high vertical and temporal resolution, particularly at cruise altitude (between 9 and 13 km) in the climate-sensitive region of the atmosphere known as the upper troposphere-lower stratosphere (UTLS) and throughout the depth of the troposphere during the landing and take off phases of the flights. Fully automated instruments are permanently installed on Airbus A330 aircraft operated by different airlines. Data are collected on about 500 flights per aircraft per year. The aircraft measure the abundances of many essential climate variables and the data are transmitted in near real time to weather services and are freely available for the scientific community, national air quality prediction centres and the Copernicus Atmosphere Monitoring Service (CAMS). The data are routinely used to validate the operational ECMWF chemical forecasting model through the CAMS-84 project. Up to now this effort has focused on reactive gases and water vapour. New to IAGOS are the measurements of the greenhouse gases (GHG), CO2 and CH4. We present the first comparison of the CAMS GHG system (Global analyses and high resolution forecasts of greenhouse gases) with the new IAGOS CO2 and CH4 measurements and show how this will be part of the future validation of the CAMS operational model.

Within European Research Infrastructure IAGOS (In-service Aircraft for a Global Observing System) regular observations of atmospheric greenhouse gases have started in 2018 onboard a Lufthansa Airbus A330. The aircraft is based in Frankfurt with service to destinations in central Africa, the Middle East, and North America. During the initial deployment periods, each lasting from several months to more than a year, various lessons have been learned related to the robustness and resilience of the autonomous operation of the IAGOS-core system, its maintenance in the laboratory, as well as the data transmission and regular automated processing of near-real-time (NRT) data with provision to users through the IAGOS data centre. Equipped with a two-standard in-flight calibration system, trace gas measurements could be made fully traceable to WMO calibration scales. The Covid-19 Pandemic had a significant impact on aviation, and thus on the IAGOS operation, but the aircraft carrying the IAGOS GHG equipment was flying throughout, with services to China and South Korea for medical supplies during the initial phase of the pandemic, allowing for frequent profile observations in the Far East. We present an overview of the observations collected so far, and an outlook on the future expansion of the IAGOS-core GHG measurements

Studies have shown that the uncertainty of methane emission estimates over India is as high as 40-60%, largely due to the lack of observations. In India, measurements are limited to a few locations, with the majority of them being flask measurement stations. The observational constraint of the measurements could be greatly improved with the development of a network of continuous measurement stations at well-chosen locations. For this study, we have designed an atmospheric methane measurement network for India using transport modeling techniques and a scaling-factor-based inversion approach. A network optimization algorithm selects the combination of observation locations that gives the most uncertainty reduction in the estimates of posterior methane emission fluxes over India. The backbone of this study is a simple analytical inversion setup that utilizes the STILT (Stochastic Time Inverted Lagrangian Transport) model, a sectorial emission model based on EDGAR, as well as fluxes from wetlands and biomass burning. The state space of the inversion consists of monthly emissions, separated by sector, aggregated spatially to the level of political states.The challenge in network design is to formulate an appropriate target quantity, which the network will be optimized to constrain. Using the annual total emissions as the single target results in a network that will optimally constrain the largest sources, irrespective of their spatial location or the seasonality of the source. Thus, we also included other targets, such as political-state-level emissions, sectoral emissions, and seasonality. For the study, we used a base network of existing stations (“base”) and added further stations from a candidate set (“extended”) on the basis of the incremental uncertainty reduction they provide. We found that more measurement stations along the Indo-Gangetic Plains and North-Eastern India are required. An optimized network was also designed from scratch using the same strategy and it was found to yield similar uncertainty reduction compared to the “base” + “extended” network despite having fewer stations. The effectiveness of the optimal network and the base network in reducing the uncertainties of the different emission categories is assessed and discussed.

Forecasting atmospheric CO2 concentrations on synoptic timescales (∼ days) can benefit the planning of field campaigns by better predicting the location of important gradients. One aspect of this, accurately predicting the day-to-day variation in biospheric fluxes, poses a major challenge. This study aims to investigate the feasibility of using a diagnostic light-use-efficiency model, the Vegetation Photosynthesis Respiration Model (VPRM), to forecast biospheric CO2 fluxes on the timescale of a few days. As input, the VPRM model requires downward shortwave radiation, 2 m temperature, and enhanced vegetation index (EVI) and land surface water index (LSWI), both of which are calculated from MODIS reflectance measurements. Flux forecasts were performed by extrapolating the model input into the future, i.e., using downward shortwave radiation and temperature from a numerical weather prediction (NWP) model, as well as extrapolating the MODIS indices to calculate future biospheric CO2 fluxes with VPRM. A hindcast for biospheric CO2 fluxes in Europe in 2014 has been done and compared to eddy covariance flux measurements to assess the uncertainty from different aspects of the forecasting system. In total the range-normalized mean absolute error (normalized) of the 5 d flux forecast at daily timescales is 7.1 %, while the error for the model itself is 15.9 %. The largest forecast error source comes from the meteorological data, in which error from shortwave radiation contributes slightly more than the error from air temperature. The error contribution from all error sources is similar at each flux observation site and is not significantly dependent on vegetation type.

High-quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014–2019) of continuous, high-precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal (urn:x-wiley:13541013:media:gcb15905:gcb15905-math-1802) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between urn:x-wiley:13541013:media:gcb15905:gcb15905-math-0001 and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of urn:x-wiley:13541013:media:gcb15905:gcb15905-math-0002. In addition, we present how the 2015–2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of urn:x-wiley:13541013:media:gcb15905:gcb15905-math-0004 together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.

IAGOS (www.iagos.org) is a European Research Infrastructure using commercial aircraft (Airbus A340, A330, and soon A350) for automatic and routine measurements of atmospheric composition including reactive gases (ozone, carbon monoxide, nitrogen oxides, volatile organic compounds), greenhouse gases (water vapour, carbon dioxide, methane), aerosols and cloud particles along with essential thermodynamic parameters. The main objective of IAGOS is to provide the most complete set of high-quality essential climate variables (ECV) covering several decades for the long-term monitoring of climate and air quality. The observations are stored in the IAGOS data centre along with added-value products to facilitate the scientific interpretation of the data. IAGOS began as two European projects, MOZAIC and CARIBIC, in the early 1990s. These projects demonstrated that commercial aircraft are ideal platforms for routine atmospheric measurements. IAGOS then evolved as a European Research Infrastructure offering a mature and sustainable organization for the benefits of the scientific community and for the operational services in charge of air quality and climate change issues such as the Copernicus Atmosphere Monitoring Services (CAMS) and the Copernicus Climate Change Service (C3S). IAGOS is also a contributing network of the World Meteorological Organization (WMO). IAGOS provides measurements of numerous chemical compounds which are recorded simultaneously in the critical region of the upper troposphere – lower stratosphere (UTLS) and geographical regions such as Africa and the mid-Pacific which are poorly sampled by other means. The data are used by hundreds of groups worldwide performing data analysis for climatology and trend studies, model evaluation, satellite validation and the study of detailed chemical and physical processes around the tropopause. IAGOS data also play an important role in the re-assessment of the climate impact of aviation. Most important in the context of weather-related research, IAGOS and its predecessor programmes provide long-term observations of water vapour and relative humidity with respect to ice in the UTLS as well as throughout the tropospheric column during climb-out and descending phases around airports, now for more than 25 years. The high quality and very good resolution of IAGOS observations of relative humidity over ice are used to better understand the role of water vapour and of ice-supersaturated air masses in the tropopause region and to improve their representation in numerical weather and climate forecasting models. Furthermore, CAMS is using the water vapour vertical profiles in near real time for the continuous validation of the CAMS atmospheric models.

Atmospheric transport models are often used to simulate the distribution of Greenhouse Gases (GHGs) for atmospheric inverse modeling. However, errors in simulated transport are often neglected in the context of inverse flux estimation. We coupled the commonly used Weather Research and Forecasting (WRF) model with the greenhouse gas module (WRF-GHG), to enable passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF’s transport is only constrained by global meteorological fields via initialization and at the lateral boundaries; over time the winds in the center of the domain can deviate considerably from these (re-)analysis fields that are constrained by observations. The aim of this study is to have the WRF-simulated transport represent reality as closely as possible, which in this case means staying consistent with the (ERA5) reanalysis fields used as boundary conditions. Therefore, two ways of blending ERA5 with WRF-GHG were tested: (a) regularly restarting the model with fresh initial conditions from ERA5, and (b) nudging the atmospheric winds, temperatures, and moisture to those from ERA5 continuously, using the built-in FDDA option (four-dimensional data assimilation). FDDA constantly forces the model towards the physical reference state (ERA5) by adding an additional tendency term at each grid point and time step. Meteorological variables, as well as the concentrations of CO2 and CH4, were analyzed by comparing with observations. We also compared mixed layer heights (PBLH) with radiosonde-derived observation. We found that performance in horizontal winds and PBLH are slightly better in the nudged simulation (NS) compared to the simulation incorporating frequent restarts (RS). The advantage of grid-nudging is notable when comparing CH4 with aircraft measurements from the CoMet campaign. However, differences in soil moisture increase over time, as soil moisture is not used for nudging. The consequence is a change in the Bowen ratio and thus in vertical mixing, impacting the distribution of GHG tracers in general. To preserve the benefits of nudging and avoid the divergence of soil moisture, we recommend a hybrid approach, combining nudging with daily re-initializations. This technique will be used in an ensemble-based regional inversion system currently under development to make use of satellite-based measurements of GHGs.

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.

High-quality atmospheric CO

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.

Though they cover less than 3 % of the global land area, urban areas are responsible for over 70 % of the global greenhouse gas (GHG) emissions and contain 55 % of the global population. A quantitative tracking of GHG emissions in urban areas is therefore of great importance, with the aim of accurately assessing the amount of emissions and identifying the emission sources. The Weather Research and Forecasting model (WRF) coupled with GHG modules (WRF-GHG) developed for mesoscale atmospheric GHG transport can predict column-averaged abundances of CO2 and CH4 (XCO2 and XCH4). In this study, we use WRF-GHG to model the Berlin area at a high spatial resolution of 1 km. The simulated wind and concentration fields were compared with the measurements from a campaign performed around Berlin in 2014 (Hase et al., 2015). The measured and simulated wind fields mostly demonstrate good agreement. The simulated XCO2 shows quite similar trends with the measurement but with approximately 1 ppm bias, while a bias in the simulated XCH4 of around 2.7 % is found. The bias could potentially be the result of relatively high background concentrations, the errors at the tropopause height, etc. We find that an analysis using differential column methodology (DCM) works well for the XCH4 comparison, as corresponding background biases are then canceled out. From the tracer analysis, we find that the enhancement of XCH4 is highly dependent on human activities. The XCO2 enhancement in the vicinity of Berlin is dominated by anthropogenic behavior rather than biogenic activities. We conclude that DCM is an effective method for comparing models to observations independently of biases caused, e.g., by initial conditions. It allows us to use our high-resolution WRF-GHG model to detect and understand major sources of GHG emissions in urban areas.

A crucial issue in atmospheric studies on greenhouse gas content involves assessing the representativeness (footprint) having influence on their concentrations measured by tall towers. In this study, the Stochastic Time-Inverted Lagrangian Transport (STILT) model was used to estimate seasonal cumulative footprint climatology for greenhouse gases measurements obtained on the 301-meter-high Zotino Tall Tower Observation Facility (ZOTTO) for the growing seasons (May-September) from 2008 to 2012 (with the exception of 2011). Results showed that the ZOTTO seasonal concentration cumulative footprint climatology for four years reached 6.9×106 km2 and the 75% cumulative footprints varied from 1.9 to 2.3×106 km2. For the same period, the Russian Land Cover map based on MODIS data for 2014 was used to estimate the impact of land cover surrounding the ZOTTO tower on concentration measurements. The analysis showed that in the 75% seasonal cumulative footprint the largest area is occupied by bogs, followed (in decreasing order) by larch, mixed, light-coniferous evergreen forests, grassland, and by other classes. Furthermore, analysis of the contributions from individual cells making up a footprint showed that the largest influence on formation of greenhouse gas concentrations as recorded by ZOTTO comes from the types of vegetation growing in the immediate vicinity of the tall tower, namely bogs, mixed forests, and light and dark coniferous forest stands.

The prospect of improving the estimates of CO2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO2 variability. In the context of expanding atmospheric CO2 measurement networks over India, this study aims to investigate the importance of a high-resolution modelling framework to utilize these observations and to quantify the uncertainty due to the misrepresentation of fine-scale variability of CO2 in the employed model. The spatial variability of atmospheric CO2 is represented by implementing WRF-Chem at a spatial resolution of 10 km × 10 km. We utilize these high-resolution simulations for sub-grid variability calculation within the coarse model grid at a horizontal resolution of one degree (about 100 km). We show that the unresolved variability in the coarse model reaches up to a value of 10 ppm at the surface, which is considerably larger than the sampling errors, even comparable to the magnitude of mixing ratio enhancements in source regions. We find a significant impact of monsoon circulation in sub-grid variability, causing ~3 ppm average representation error between 12–14 km altitude ranges in response to the tropical easterly jet. The cyclonic storm Ockhi during November 2017 generates completely different characteristics in sub-grid variability than the rest of the period, whose influence increases the average representation error by ~1 ppm at the surface. By employing a first-order inverse modelling scheme using pseudo observations from nine tall tower sites over India and a constellation of satellite instruments, we show that the Net Ecosystem Exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 6.3 to 16.2 % of the total NEE. We illustrate an example to test the efficiency of a simple parameterization scheme during non-monsoon periods to capture the unresolved variability in the coarse models, which reduces the bias in flux estimates from 9.4 % to 2.2 %. By estimating the fine-scale variability and its impact during different seasons, we emphasise the need for implementing a high-resolution modelling framework over the Indian subcontinent to better understand processes regulating CO2 sources and sinks.

Several ambient air quality records corroborate the severe and persistent degradation of air quality over northern India during the winter months, with evidence of a continued, increasing trend of pollution across the Indo-Gangetic Plain (IGP) over the past decade. A combination of atmospheric dynamics and uncertain emissions, including the post-monsoon agricultural stubble burning, make it challenging to resolve the role of each individual factor. Here we demonstrate the potential use of an atmospheric transport model, the Weather Research and Forecasting model coupled with chemistry (WRF–Chem) to identify and quantify the role of transport mechanisms and emissions on the occurrence of the pollution events. The investigation is based on the use of carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite and the surface measurement network, as well as the WRF–Chem simulations, to investigate the factors contributing to CO enhancement over India during November 2018. We show that the simulated column-averaged dry air mole fraction (XCO) is largely consistent with TROPOMI observations, with a spatial correlation coefficient of 0.87. The surface-level CO concentrations show larger sensitivities to boundary layer dynamics, wind speed, and diverging source regions, leading to a complex concentration pattern and reducing the observation-model agreement with a correlation coefficient ranging from 0.41 to 0.60 for measurement locations across the IGP. We find that daily satellite observations can provide a first-order inference of the CO transport pathways during the enhanced burning period, and this transport pattern is reproduced well in the model. By using the observations and employing the model at a comparable resolution, we confirm the significant role of atmospheric dynamics and residential, industrial, and commercial emissions in the production of the exorbitant level of air pollutants in northern India. We find that biomass burning plays only a minimal role in both column and surface enhancements of CO, except for the state of Punjab during the high pollution episodes. While the model reproduces observations reasonably well, a better understanding of the factors controlling the model uncertainties is essential for relating the observed concentrations to the underlying emissions. Overall, our study emphasizes the importance of undertaking rigorous policy measures, mainly focusing on reducing residential, commercial, and industrial emissions in addition to actions already underway in the agricultural sectors.

During the COVID-19 pandemic lockdowns, human activities are strongly restricted, which results in a reduction in greenhouse gas (GHG) emissions associated with changes in energy consumptions. The Copernicus Atmosphere Monitoring Service (CAMS) reported a 10.3% decrease in CO2 fossil fuel emissions during the first lockdown (February-July, 2020) of the COVID-19 pandemic throughout Europe. Using our WRF modeling framework built for the Munich area [1,3] and the column measurements from our automated Munich Urban Carbon Column network (MUCCnet, [2]), we aim to quantify the reduction of GHG emissions within Munich during the COVID-19 pandemic.Our high-resolution modeling framework can simulate the sources, sinks, and emissions of CO2 and CH4 at a spatial resolution of up to 400m. The initial and boundary conditions for meteorological fields are taken from ERA5 and CAMS data is used for initializing the initial and lateral tracer boundary conditions. Anthropogenic emissions below ~1 km altitude above the ground level are obtained from TNO-GHGco v1.1 at a resolution of 1 km2. Various tagged tracers are included to quantify the contribution from different emission categories (such as biogenic emissions from wetlands, emissions from road transport, industry, etc). By refining the vegetation classification using the Dynamic Land Cover map of the Copernicus Global Land Service at 100 m resolution (CGLS-LC100), the urban biogenic signals of CO2 can be well captured using the diagnostic light-use-efficiency biosphere model VPRM (Vegetation Photosynthesis and Respiration Model), which is driven by MODIS indices. Moreover, we integrate urban canopy information derived from World Urban Database and Access Portal Tools (WUDAPT) classified by local climate zones (LCZs) [4] into our model infrastructure. Incorporating precise urban land use data in WRF helps to capture more urban transport features, improving the model behavior within urban areas.We targeted the pandemic period from February to July 2020 and the same period in 2019 to make a comparison. Thanks to our nearly continuous column measurements during the COVID-19 pandemic, we are able to evaluate our simulated GHG concentrations by comparing them to the measurement results. Furthermore, an estimation of GHG emissions reduction in Munich during the targeted period will be obtained by performing a Bayesian inversion approach incorporating the simulated concentration enhancements from tagged tracers in WRF.[1] Zhao, X., Chen, J., Marshall, J., Galkowski, M., Gerbig, C., Hachinger, S., Dietrich, F., Lan, L., Knote, C., and van der Gon, H.D., 2020. A semi-operational near-real-time Modelling Infrastructure for assessing GHG emissions in Munich using WRF-GHG. In EGU General Assembly 2020.[2] Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discuss. https://doi.org/10.5194/amt-2020-300, in review, 2020.[3] Zhao, X., Marshall, J., Hachinger, S., Gerbig, C., Frey, M., Hase, F., and Chen, J.: Analysis of total column CO2 and CH4 measurements in Berlin with WRF-GHG, Atmos. Chem. Phys., 19, 11279–11302, https://doi.org/10.5194/acp-19-11279-2019, 2019.[4] Demuzere, M., Bechtel, B., Middel, A., & Mills, G. (2019). Mapping Europe into local climate zones. PLOS ONE, 14(4), e0214474. https://doi.org/10.1371/journal.pone.0214474.

Climate change mitigation strategies require regional-scale estimations of CO2 fluxes, but estimates are often tampered with a significant amount of uncertainties due to multiple factors. In the case of the top-down approach, the error associated with the forward transport model is one of the major causes of uncertainty. Current generation global atmospheric transport models are more often simulated at a horizontal resolution of one degree or less, which omits a large number of small-scale processes and creates a significant amount of uncertainty in estimations. Attempts for the estimation of CO2 fluxes at fine scales over India using the top-down approach is essentially new compared to some other parts of the globe like North America or Europe. The study focuses on implementing an inverse modelling system, by considering high-resolution atmospheric transport and flux distribution, to retrieve the fluxes at spatial scales relevant for ecosystem and policy-making. Using WRF-Chem (GHG) model, we estimate the transport error in CO2 simulations over India during July and November 2017 which is associated with different processes whose scale is smaller than the resolution of current-generation global transport models. We show that the overall sub-grid variability (or representation error) at the surface can go up to ~10 ppm for the surface CO2, which is markedly higher than the sampling errors. Total column-averaged CO2 also shows similar variability in the spatial pattern though the magnitude is less compared to the surface, which indicates the prominence of heterogeneity at surface flux in modulating the entire column variability. Our results show that there exist regional differences in sub-grid scale variability for both surface and column CO2 with very high magnitude observed at coastal and mountain regions and at emission hot spots. In addition to spatial variability, the sub-grid variability of CO2 over India exhibits seasonal variations as well. The vertical distribution of sub-grid variability during July shows its association with the monsoon circulations, which is much different than those during November. Our estimates suggest that the terrain heterogeneity alone can explain about 53-63% of the surface representation errors over the domain, which shows the importance of using accurate Digital Elevation Models in the atmospheric transport model simulations. With underlying assumptions, the total flux uncertainty solely due to the unresolved sub-grid scale variations is estimated to be 8.1 to 14.4% of the total NEE. These results will be discussed and presented in the context of our attempts towards estimating the source-sink distribution of CO2 over India.

Emissions from fossil fuels are one of the primary sources of atmospheric methane (CH4) growth. However, estimates of anthropogenic CH4 emissions still show large uncertainties on global and regional scales. Differences in CH4 isotopic source signatures δ13C and δD can help to constrain different source contributions (e.g. fossil, thermogenic, or biogenic).The Upper Silesian Coal Basin (USCB) represents one of the largest European CH4 emission source regions, with more than 500 Gg CH4 yr-1 released by more than 50 coal mine ventilation shafts. During the CoMet (Carbon Dioxide and Methane Mission) campaign in June 2018 methane observations were conducted from a variety of platforms including aircraft and cars. Beside the continuous sampling of atmospheric methane concentration, numerous air samples were taken from inside the ventilation shafts, around the ventilation shafts (1‑2 km distance) and aboard the DLR Cessna Caravan aircraft and analyzed in the laboratory for the isotopic composition of CH4.The ground-based samples allowed determining the source signatures of individual ventilation shafts. These signatures displayed a considerable range between different shafts and also varied from day to day. The airborne samples contained a mixture of methane emissions from several mines and thus enabled accurately determining the signature of the entire region. The mean isotopic signature of methane emissions over the USCB derived from the aircraft samples was -51.9 ± 0.5 ‰ for δ13C and -233 ± 6 ‰ for δD. This is in between the range of other microbial and thermogenic coal reservoirs, but more depleted in δD than previous USCB studies reported based on samples taken within the mines. Signatures of methane enhancements sampled upwind of the mines and in the free troposphere clearly showed the presence of methane of biogenic origin (e.g. wetlands, waste, ruminants).Furthermore, we simulated the methane isotopologues using the on-line three-times nested global regional chemistry climate model MECO(n). We implemented a submodel extension, which includes the kinetic fractionation and uses the isotopic source signatures determined by the ground-based observations. We compare the regional simulations to flask samples taken during CoMet.

The boreal biome is an important component of the global carbon (C) cycle. However, current estimates of its sink-source strength at regional scales and its responses to climate change rely primarily on models and thus remain uncertain. We investigated the C balance over a north Scandinavian boreal region by integrating observations of land-atmosphere fluxes and atmospheric CO2 concentrations at landscape to regional scales. We also placed a special focus to understand the impact of 2018 drought on the region. Flux estimates can be obtained through various techniques such as in-situ flux measurements, eddy covariance (EC) observations, vegetation modelling and inverse modelling of CO2 observations. These techniques are however typically relevant at very different spatial scales ranging from plot scale to country-scale, which makes it difficult to compare them. The -Svartberget site (SVB), an established ICOS (Integrated Carbon Observation System) station in Northern Sweden offers a unique range of observations, from in-situ flux measurements to EC fluxes and tall-tower concentration measurements. Here we used several vegetation models and an atmospheric transport model to connect the different scales for the period 2016-2018. The land-atmosphere carbon fluxes are from four different vegetation models (VPRM, LPJ-GUESS, ORCHIDEE and SiBCASA) and are used in the LUMIA/FLEXPART atmospheric transport model (Lund University Modular Inversion Algorithm) to generate estimates of atmospheric CO2 concentration. We found that the northern Sweden region remained as a C sink for the study period with models differed in sink strength. It was also noticed that the site SVB can be taken as a representative for the northern Sweden region. All models indicate similar but small reductions in the net CO2 uptake for the drought year 2018 in northern Sweden except LPJ-GUESS that reveal limitations which call for further model improvement. Our work highlights the interest of using combined ecosystem,-atmosphere ICOS sites such as SVB in the Scandinavian region and shows that it is a promising way forward to monitor CO2 fluxes at the regional scale.

Methane (CH4) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than carbon dioxide (CO2). Because of its short atmospheric lifetime relative to that of CO2, the reduction of the atmospheric abundance of CH4 is an attractive target for short term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources is essential. For this reason, the CO2 and Methane (CoMet) campaign in early summer of 2018 was initiated with the primary goal of assessing emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this campaign, a variety of instruments (both in situ and remote sensing) and platforms (e.g., ground-based and airborne) were deployed, which were supplemented by modeling activities supporting the flight planning and the interpretation of the observations. Consequently, CH4 emissions originating from ~54 coal mine ventilation shafts distributed over an area of around 60 × 40 km2 could be investigated on different scales, ranging from single shafts over smaller clusters up to the entire basin. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) method. The column anomalies are combined with wind lidar measurements and inverted to cross-sectional fluxes for different flight tracks making use of a mass balance approach. These fluxes are subsequently used to assess the reported emissions of small clusters of ventilation shafts. The MAMAP CH4 column observations allow for accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for 4 clusters comprising 23 ventilation shafts in total, which are responsible for about 40 % of the total CH4 emissions from mining in the target area. The observations used were made during multiple overflights on different days between 28 May and 7 June 2018. The final averaged CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 t CH4 hr−1 at the time of the campaign. The range of fluxes observed at one cluster during different overflights can vary by as much as 50 % of the respective averaged value. Associated errors (1-σ) are usually between 15 % and 59 % of the averaged flux, mainly depending on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement with the computed fluxes within the uncertainties. In the case that only annually reported emissions are available for comparison with the observations, caution is required due to potential fluctuations of the emissions during one year or even within hours. To measure emissions even more precisely and to further unravel them for allocation to individual shafts in a complex source region as encountered in the USCB, imaging remote sensing instruments are recommended.

International audience; The boreal biome plays an important role in the global carbon cycle. However, current estimates of its sink-source strength and responses to changes in climate are primarily derived from models and thus remain uncertain. A major challenge is the validation of these models at a regional scale since empirical flux estimates are typically confined to ecosystem or continental scales. The Integrated Carbon Observation System (ICOS)-Svartberget atmospheric station (SVB) provides observations including tall tower eddy covariance (EC) and atmospheric concentration measurements that can contribute to such validation in Northern Sweden. Thus, the overall aim of this study was to quantify the carbon balance in Northern Sweden region by integrating land-atmosphere fluxes and atmospheric carbon dioxide (CO2) concentrations. There were three specific objectives. First, to compare flux estimates from four models (VPRM, LPJ-GUESS, ORCHIDEE, and SiBCASA) to tall tower EC measurements at SVB during the years 2016–2018. Second to assess the fluxes' impact on atmospheric CO2 concentrations using a regional transport model. Third, to assess the impact of the drought in 2018. The comparison of estimated concentrations with ICOS observations helped the evaluation of the models' regional scale performance. Both the simulations and observations indicate there were similar reductions in the net CO2 uptake during drought. All the models (except for SiBCASA) and observations indicated the region was a net carbon sink during the 3-year study period. Our study highlights a need to improve vegetation models through comparisons with empirical data and demonstrate the ICOS network's potential utility for constraining CO2 fluxes in the region.