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The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

Despite the importance of methane for climate change mitigation, uncertainties regarding the temporal and spatial variability of the emissions remain. Measurements of CH 4 isotopic composition are used to partition the relative contributions of different emission sources. We report continuous isotopic measurements during 5 months at the Lutjewad tower (north of the Netherlands). Time-series of χ(CH 4 ), δ 13 C-CH 4 , and δD-CH 4 in ambient air were analysed using the Keeling plot method. Resulting source signatures ranged from −67.4 to −52.4‰ vs V-PDB and from −372 to −211‰ vs V-SMOW, for δ 13 C and δD respectively, indicating a prevalence of biogenic sources. Analysis of isotope and wind data indicated that (i) emissions from off-shore oil and gas platforms in the North Sea were not detected during this period, (ii) CH 4 from fossil fuel related sources was usually advected from the east, pointing towards the Groningen gas field or regions further east in Germany. The results from two atmospheric transport models, CHIMERE and FLEXPART-COSMO, using the EDGAR v4.3.2 and TNO-MACC III emission inventories, reproduce χ(CH 4 ) variations relatively well, but the isotope signatures were over-estimated by the model compared to the observations. Accounting for geographical variations of the δ 13 C signatures from fossil fuel emissions improved the model results significantly. The difference between model and measured isotopic signatures was larger when using TNO-MACC III compared to EDGAR v4.3.2 inventory. Uncertainties in the isotope signatures of the sources could explain a significant fraction of the discrepancy, thus a better source characterisation could further strengthen the use of isotopes in constraining emissions.

This study investigates the requirements for estimating CO2 emissions at the country scale using observational data from the Integrated Carbon Observation System (ICOS) atmosphere network, taking Italy as a case study. In particular, we explore the potential expansion of Italy’s current atmospheric ICOS network by identifying additional existing and future stations that would most effectively improve the constraint of carbon flux estimations, with a focus on the southern region. Through a series of Observing System Simulation Experiments using the LUMIA regional inverse system, we evaluated 23 potential stations and identified Chieti (CHI, located in the Abruzzo region in mid-Italy) and Lecce (ECO, located in the southeastern Puglia region) as the most promising additions. These stations demonstrated significant value in recovering the annual and seasonal cycles of the assumed true CO2 fluxes (simulated by LPJ-GUESS) in southern Italy. Incorporating both CHI and ECO into the current network reduces the prior biases by approximately 82%, compared to the 48% reduction achieved when adding the CHI station alone. Our findings also suggest that adding more stations beyond CHI and ECO results in only marginal gains in flux precision. We therefore emphasize the need for targeted research funding to support the integration of these current and future stations into the ICOS atmospheric network in southern Italy, where the current network is sparse, with only Potenza as an ICOS atmospheric station. This research highlights the importance of strategic station selection to optimize network performance and improve regional carbon flux assessments, ultimately contributing to better reconciliation and understanding of discrepancies between bottom–up and top–down greenhouse gas estimation methods.

Accurate spectral calibration of satellite and airborne spectrometers is essential for remote sensing applications that rely on accurate knowledge of center wavelength (CW) positions and slit function parameters (SFP). We present a new in-flight spectral calibration algorithm that retrieves CWs and SFPs across a wide spectral range by fitting a high-resolution solar spectrum and atmospheric absorbers to in-flight radiance spectra. Using a maximum a posteriori optimal estimation approach, the quality of the fit can be improved with a priori information. The algorithm was tested with synthetic spectra and applied to data from the APEX imaging spectrometer over the spectral range of 385–870 nm. CWs were retrieved with high accuracy (uncertainty <0.05 spectral pixels) from Fraunhofer lines below 550 nm and atmospheric absorbers above 650 nm. This enabled a detailed characterization of APEX’s across-track spectral smile and a previously unknown along-track drift. The FWHMs of the slit function were also retrieved with good accuracy (<10% uncertainty) for synthetic spectra, while some obvious misfits appear for the APEX spectra that are likely related to radiometric calibration issues. In conclusion, our algorithm significantly improves the in-flight spectral calibration of APEX and similar spectrometers, making them better suited for the retrieval of atmospheric and surface variables relying on accurate calibration.

Regional-scale inverse modeling of atmospheric carbon dioxide (CO 2 ) holds promise to determine the net CO 2 fluxes between the land biosphere and the atmosphere. This approach requires not only high fidelity of atmospheric transport and mixing, but also an accurate estimation of the contribution of the anthropogenic and background CO 2 signals to isolate the biospheric CO 2 signal from the atmospheric CO 2 variations. Thus, uncertainties in any of these three components directly impact the quality of the biospheric flux inversion. Here, we present and evaluate a carbon monoxide (CO)-based method to reduce these uncertainties solely on the basis of co-located observations. To this end, we use simultaneous observations of CO 2 and CO from a background observation site to determine the background mole fractions for both gases, and the regional anthropogenic component of CO together with an estimate of the anthropogenic CO/CO 2 mole fraction ratio to determine the anthropogenic CO 2 component. We apply this method to two sites of the CarboCount CH observation network on the Swiss Plateau, Beromünster and Lägern-Hochwacht, and use the high-altitude site Jungfraujoch as background for the year 2013. Since such a background site is not always available, we also explore the possibility to use observations from the sites themselves to derive the background. We contrast the method with the standard approach of isolating the biospheric CO 2 component by subtracting the anthropogenic and background components simulated by an atmospheric transport model. These tests reveal superior results from the observation-based method with retrieved wintertime biospheric signals being small and having little variance. Both observation- and model-based methods have difficulty to explain observations from late-winter and springtime pollution events in 2013, when anomalously cold temperatures and northeasterly winds tended to bring highly CO-enriched air masses to Switzerland. The uncertainty of anthropogenic CO/CO 2 emission ratios is currently the most important factor limiting the method. Further, our results highlight that care needs to be taken when the background component is determined from the site's observations. Nonetheless, we find that future atmospheric carbon monitoring efforts would profit greatly from at least measuring CO alongside CO 2 .

Estimating biogenic CO2 fluxes is essential to quantify urban anthropogenic emissions, yet urban vegetation heterogeneity presents a significant challenge to making accurate estimations. We have developed an hourly temporal, 10‐m spatial resolution biogenic CO2 flux estimation framework based on the Vegetation Photosynthesis and Respiration Model (VPRM) and its variants (UrbanVPRM and VPRM‐modified). Unlike lower‐resolution models, our approach captures finer‐scale variability, particularly in fragmented urban green spaces like street trees and lawns. Results show that vegetation in Munich offsets 2.0%–2.8% of annual anthropogenic CO2 emissions in the study domain, with tree‐covered areas as primary sinks and grasslands as net sources. During summer, daytime CO2 uptake can match or exceed anthropogenic emissions. Evaluations employing city park field measurements and eddy covariance towers confirm strong performance of our models, while highlighting VPRM‐modified's advantage in grasslands and croplands, and UrbanVPRM's improvements in urban areas via impervious surface correction. These findings highlight the value of high‐resolution modeling in improving urban carbon flux assessments. Plain Language Summary Cities are emitters of carbon dioxide (CO2) from human activities like traffic and heating, but green spaces like lawns and street trees also absorb and release CO2. To accurately calculate how much CO2 humans are truly responsible for, we need to precisely estimate the CO2 exchange from the city's complex and varied vegetation. To solve this, we developed a framework to map this vegetation CO2 exchange in Munich and Zurich, creating hour‐by‐hour estimates at a fine 10‐m resolution, which can resolve the impact of small green patches, like individual street trees and lawns. This high‐resolution approach is further validated against our field measurements from city parks and tower measurements from rural areas. Results show that Munich's vegetation offsets about 2.0%–2.8% of its annual human‐caused CO2 emissions. Tree‐covered areas are the primary sinks that absorb CO2, while grasslands tend to be minor sources that release it. Remarkably, the greenery's CO2 absorption on a summer day can temporarily outweigh Munich's entire human emissions. This transferable framework enables an accurate evaluation of the carbon sequestration benefits of urban green infrastructure, ultimately providing policymakers with precise data to support more effective climate strategies and urban planning policies. Key Points High‐resolution modeling is crucial for accurately estimating the heterogeneous urban biogenic and anthropogenic CO2 fluxes Urban vegetation offers significant carbon‐buffering potential. In Munich, trees are primary CO2 sinks, grasslands are net sources The model's promising performance was validated by our urban biogenic measurements, showing that our model is applicable in urban scenarios

The human-made pollution of surface and ground waters is becoming an inevitable and persistently urgent problem for humankind and life in general, as these pollutants are also distributed by their natural circulation. For example, from mining activities and metallurgy, toxic heavy metals pollute the environment and present material risk for human health and the environment. Bioadsorbers are an intriguing way to efficiently capture and eliminate these hazards, as they are environmentally friendly, cheap, abundant, and efficient. In this study, we present brewers’ spent grain (BSG) as an efficient adsorber for toxic heavy metal ions, based on the examples of iron, manganese, cadmium, and nickel ions. We uncover the adsorption properties of two different BSGs and investigate thoroughly their chemical and physical properties as well as their efficiency as adsorbers for simulated and real surface waters. As a result, we found that the adsorption behavior of BSG types differs despite almost identical chemistry. Elemental mapping reveals that all components of BSG contribute to the adsorption. Further, both types are not only able to purify water to reach acceptable levels of cleanness, but also yield outstanding adsorption performance for iron ions of 0.2 mmol/g and for manganese, cadmium, and nickel ions of 0.1 mmol/g.

The CO 2 Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO 2 , CH 4 and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO 2 Monitoring and Verification Support system. The relative contribution of different emissions and natural fluxes towards observed atmospheric variability is diagnosed by additional tagged tracers in the simulations. The evaluation of such high-resolution model simulations can be used to identify model deficiencies and guide further model improvements. Measurement(s) atmospheric carbon dioxide, methane and carbon monoxide Technology Type(s) numerical simulation Factor Type(s) None Sample Characteristic - Organism long-lived greenhouse gases Sample Characteristic - Environment atmosphere Sample Characteristic - Location global atmosphere

Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using an ensemble Kalman smoother system. We use the estimation of European CH4 emissions from the in situ measurement network as an example, but we also demonstrate the additional benefits for trace gases with more localized sources, such as SF6. The uncertainty in flow-dependent transport is determined using meteorological ensemble simulations that are perturbed by physics and driven at the boundaries by an analysis ensemble from a global meteorology and a CH4 simulation. The impact of direct representation of temporally varying transport uncertainties in atmospheric inversions is then investigated in an observation system simulation experiment framework in various setups and for different flux signals. We show that the uncertainty in the transport model varies significantly in space and time and that it is generally highest during nighttime. We apply inversions using only afternoon observations, as is common practice, but also explore the option of assimilating hourly data irrespective of the hour of day using a filter based on transport uncertainty and taking into account the temporal covariances. Our findings indicate that incorporating flow-dependent uncertainties in inversion techniques leads to more accurate estimates of GHG emissions. Differences between estimated and true emissions could be reduced more effectively by 9 % to 82 %, with generally larger improvements for the SF6 inversion problem and for the more challenging setup with small flux signals.

Lagrangian particle dispersion models (LPDMs) are frequently used for regional-scale inversions of greenhouse gas emissions. However, the turbulence parameterizations used in these models were developed for coarse resolution grids, hence, when moving to the kilometre-scale the validity of these descriptions should be questioned. Here, we analyze the influence of the turbulence parameterization employed in the LPDM FLEXPART-COSMO model. Comparisons of the turbulence kinetic energy between the turbulence schemes of FLEXPART-COSMO and the underlying Eulerian model COSMO suggest that the dispersion in FLEXPART-COSMO suffers from a double-counting of turbulent elements when run at a high resolution of 1×1km2. Such turbulent elements are represented in both COSMO, by the resolved grid-scale winds, and FLEXPART, by its stochastic parameterizations. Therefore, we developed a new parametrization for the variations of the winds and the Lagrangian time scales in FLEXPART in order to harmonize the amount of turbulence present in both models. In a case study for a power plant plume, the new scheme results in improved plume representation when compared with in situ flight observations and with a tracer transported in COSMO. Further in-depth validation of the LPDM against methane observations at a tall tower site in Switzerland shows that the model’s ability to predict the observed tracer variability and concentration at different heights above ground is considerably enhanced using the updated turbulence description. The high-resolution simulations result in a more realistic and pronounced diurnal cycle of the tracer concentration peaks and overall improved correlation with observations when compared to previously used coarser resolution simulations (at 7 km × 7 km). Our results indicate that the stochastic turbulence schemes of LPDMs, developed in the past for coarse resolution models, should be revisited to include a resolution dependency and resolve only the part of the turbulence spectrum that is a subgrid process at each different mesh size. Although our new scheme is specific to COSMO simulations at 1×1km2 resolution, the methodology for deriving the scheme can easily be applied to different resolutions and other regional models.