Abstract. The representation of river routing and floodplain dynamics remains a key limitation in many global Earth system and numerical weather prediction models, despite their importance for hydrological extremes, water resources, and land–atmosphere interactions. This paper presents the implementation of a global hydrodynamic river routing capability within the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) through the integration of the Catchment-based Macro-scale Floodplain (CaMa-Flood) model into the ecLand land surface scheme. The system enables the routine simulation of river discharge, water level, and inundation extent within a physically consistent Earth system modelling framework. We describe the scientific design and technical developments required to transition from research to operational implementation, including coupling strategies, state initialisation, parallelisation using a basin-based hybrid MPI/OpenMP approach, and integration within ECMWF's Research-to-Operations workflow. The implementation maintains consistency across multiple configurations, from offline experiments to fully coupled forecasts, while preserving operational constraints such as computational efficiency and forecast stability. The added value of the integrated system is demonstrated through a range of applications, including global water resources monitoring contributing to the World Meteorological Organisation State of Global Water Resources Report, real-time ensemble flood forecasting, high-resolution regional simulations, and land-surface diagnostics. Results illustrate that river discharge provides a powerful integrative constraint on the land water cycle and supports the identification of model deficiencies in hydrological processes. The system also enables the generation of continuous, global, high-frequency hydrological datasets suitable for both operational applications and emerging data-driven modelling approaches. This work establishes a new capability for global hydrological prediction within an operational Earth system model and provides a framework for future developments toward fully coupled atmosphere–land–river interactions.

The objective of this work is to assess the potential of the assimilation of satellite solar-induced fluorescence (SIF) retrievals at eight-day and 0.1 degrees resolutions, derived from the TROPOMI instrument on board Copernicus Sentinel-5P, in the integrated forecast system (IFS) developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), to update the IFS leaf area index (LAI) climatology and improve the IFS forecasts of gross primary productivity (GPP). This work represents a first attempt to assimilate SIF in a numerical weather prediction (NWP) framework at global scale by leveraging a machine-learning (ML)-based observation operator. Two configurations of the ML-based observation operator, based on the XGBoost gradient-boosted trees technique, were assessed: one trained using the IFS model fields which include the IFS LAI climatology, and one trained using only the actual satellite LAI observations along with spatiotemporal localization variables. The model directly trained on the satellite LAI shows the highest prediction scores. Besides, it produces more meaningful spatial patterns of LAI increments in response to climate anomalies compared to the model trained using the IFS predictors which do not include vegetation interannual variability. A key outcome is that the assimilation of SIF in the IFS improves LAI over cropland, which is promising for the monitoring of anthropogenic emissions over agriculture regions. However, at these spatial and temporal resolutions, the SIF ML model mainly resolves the seasonal variability of LAI which explains the degradations of LAI over tropical rainforests where the SIF signal is mainly driven by the variability in light use efficiency and the satellite LAI retrievals are more uncertain. Finally, the use of the updated LAI in the IFS coupled model leads to limited improvement of GPP forecasts, which is likely due to the prevailing effects of other sources of biases in the coupled IFS model.

The exchange of CO2 in the lower troposphere is governed by surface and atmospheric processes operating across a wide range of spatial and temporal scales. Over tropical regions such as the Amazon rainforest, daily occurring shallow-to-deep convective clouds are anticipated to significantly modulate the diurnal cycle and spatial variability of CO2. Yet, the physical processes controlling the exchange at the surface and across the interface of the boundary layer and free troposphere remain poorly understood and insufficiently quantified, limiting accurate estimates of CO2-exchange over the Amazon rainforest under a changing climate.To address this research gap, we examine the lower tropical troposphere CO2-exchange across shallow-to-deep convective regimes over the Amazon rainforest. More specifically, we develop an analytical framework that adopts well-mixed conditions, assuming vertical uniformity of state variables and greenhouse gases within the atmospheric boundary layer. Utilising this framework, we reconstruct and attribute the diurnal (from sunrise to sunset), day-to-day and spatial lower tropospheric CO2-exchange into seven physical components: (I) free tropospheric background mole fraction, (II) entrainment and detrainment during the night-to-day transition, (III) surface fluxes (including plant assimilation and soil respiration), (IV) entrainment and detrainment from convective boundary layer development as determined by the lapse rate of CO2, (V) boundary layer dilution associated with clouds, (VI) cloud mass flux and (VII) advection. Moreover, by inverting the expression we obtain a first-order, physics-based estimate of net ecosystem exchange that helps interpret the sensitivity of existing inverse modelling estimates. The framework is applied to the global storm-resolving Integrated Forecasting System (IFS) of the ECMWF across three horizontal resolutions (25 km, 9 km, and 4.4 km) and evaluated using comprehensive observations and turbulence and cloud resolving large-eddy simulations from the CloudRoots-Amazon22 campaign (dry season 2022).Our initial findings indicate that we satisfactorily reproduce and attribute the relative importance of each physical component to the diurnal CO2-exchange in the lower tropical troposphere under clear, shallow convective, and deep convective conditions in ECMWF-IFS. However, larger uncertainties persist in the early morning and late afternoon. Ongoing work aims to further disentangle the role of the physical processes in controlling the CO2-exchange over the Amazon rainforest across time, space and model resolutions.

The CO2MVS Research on Supplementary Observations (CORSO) project aims at reducing the uncertainties in the predicted biogenic carbon fluxes by leveraging new types of satellite observations, machine learning and data assimilation. The objective of this work is to implement the assimilation of active microwave and solar-induced chlorophyll fluorescence (SIF) satellite observations in the Integrated Forecast System (IFS) developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) to jointly analyze soil moisture and leaf area index (LAI). The assimilation of observation requires the development of an observation operator to predict the model-simulated counterpart of the observation from the model fields. In this paper, we present the development of machine learning-based observation operators for the normalized backscatter at 40 degrees from the Advanced Scatterometer (ASCAT) instrument and SIF derived from the TROPOspheric Monitoring Instrument (TROPOMI). Results show that deep learning methods provide accurate predictions of the satellite signals from the IFS model fields. The next step will be the implementation of these operators in the IFS and the evaluation of the impacts of analyzing both soil moisture and vegetation variables on the estimation of carbon and water fluxes along with near-surface meteorological variables.

Biogenic fluxes play a fundamental role in the carbon cycle and are crucial for the land-surface water and energy cycles. These three cycles, water/energy/carbon, are coupled and interact on time-scales ranging from minutes to centuries. Among different aspects of the processes involved, Land Use and Land Cover (LULC) are extremely relevant in the estimation of biogenic carbon. Moreover, the errors found in the model’s representation of LULC effects on the lower troposphere have also been shown to limit the progress in weather and climate predictability. In this work we evaluate different configurations of two land surface models: the ECMWF ECLand and Meteo-France ISBA within the SURFEX modelling platform. The evaluation is focused on the surface energy, water and carbon fluxes using FLUXCOM as reference, as well as land surface temperature using LSA SAF satellite product. The surface offline simulations evaluation identified the added value of a revised land cover and Leaf Area Index (LAI) in ECLand in terms of Gross Primary Production (GPP) when combined with a model configuration using the Farquhar photosynthesis model. The results also suggest that time-varying LAI, prescribed in ECLand and via data assimilation in SURFEX are relevant to GPP estimates during large-scale extreme events. Limitations in the evaluation of Net Ecosystem Exchanges and terrestrial respiration arising from model uncertainties, as well as in the reference data used, suggests that flux adjustments are paramount to mitigate biases in global CO2 analysis. Finally, coupled atmosphere weather forecasts with the ECMWF model show a clear improvement of the 2-meter temperature in Eurasia during spring following the revised land cover and LAI with a negative impact during summer in the tropics, which requires further developments. This work was developed in the framework of the CoCO2 project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 958927.

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

Abstract The impact of urbanization on local weather patterns affects over half the global population. Global numerical weather prediction systems have reached a resolution at which urban conurbations can be spatially resolved, justifying their representation within land surface parameterizations with the aim of improving local predictions. Additionally, real‐time atmospheric monitoring of trace gas emissions can utilize weather variables relevant for urban areas. We investigated whether a simple single‐layer urban canopy scheme can be used within a global forecast model to jointly improve predictions of near‐surface weather variables and residential CO2 emissions. The scheme has been implemented in the Integrated Forecast System used operationally at the European Centre for Medium‐Range Weather Forecasts running at ∼9 km horizontal resolution. First, we selected a suitable urban land cover map (ECOCLIMAP‐SG) based on comparisons with regional data and land surface temperature MODIS retrievals. The urban scheme is verified by providing improved 2 m temperature (∼10%) and 10 m wind (∼17%) RMSE values for both summer and winter months around urban environments. The influence of the scheme was most noticeable at night. Additionally, we have implemented a simple temperature‐dependent residential emissions model to calculate real‐time CO2 heating emissions. These were validated against existing offline products, national reporting and by comparing atmospheric simulations with total column CO2 observations. The results show an improved temporal variability of emissions, which arise from synoptic scale temperature changes. Given the improved predictability from the urban scheme for both weather and emissions, it will be operationally implemented in an upcoming model cycle.

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.

Satellite measurements of the atmospheric concentrations of carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO) require careful validation. In particular for the greenhouse gases CO2 and CH4, concentration gradients are minute challenging the ultimate goal to quantify and monitor anthropogenic emissions and natural surface-atmosphere fluxes. The upcoming European Copernicus Carbon Monitoring mission (CO2M) will focus on anthropogenic CO2 emissions, but it will also be able to measure CH4. There are other missions such as the Sentinel-5 Precursor and the Sentinel-5 series that target CO which helps attribute the CO2 and CH4 variations to specific processes. Here, we review the capabilities and use cases of a mobile ground-based sun-viewing spectrometer of the type EM27/SUN. We showcase the performance of the mobile system for measuring the column-average dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) during a recent deployment (Feb./Mar. 2021) in the vicinity of Japan on research vessel Mirai which adds to our previous campaigns on ships and road vehicles. The mobile EM27/SUN has the potential to contribute to the validation of 1) continental-scale background gradients along major ship routes on the open ocean, 2) regional-scale gradients due to continental outflow across the coast line, 3) urban or other localized emissions as mobile part of a regional network and 4) emissions from point sources. Thus, operationalizing the mobile EM27/SUN along these use cases can be a valuable asset to the validation activities for CO2M, in particular, and for various upcoming satellite missions in general.

The Copernicus Atmosphere Monitoring Service has recently produced a greenhouse gases reanalysis (version egg4) that covers almost two decades from 2003 to 2020 and will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-range Weather Forecasts’ Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gases concentrations on climate change, such as global and regional climate radiative forcing, assess intercontinental transport, and also serve as boundary conditions for regional simulations, among other applications and scientific studies. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as well as their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

The CO2 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 CO2, CH4 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 CO2 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.

Abstract. Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +14.7 ppb), the cause of which remains largely unknown. Here, we demonstrate a high-resolution (~80 km), short-window (24-hour) 4D-Var global inversion system based on the ECMWF Integrated Forecasting System (IFS) and newly available satellite observations. The largest national disagreement found between prior (63.1 Tg yr−1) and posterior (59.8 Tg yr−1) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated form our global system agree well with previous basin-wide regional studies and point source specific studies. Emission events (leaks/blowouts) >10 t hr−1 were detected, but without accurate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for 2020 were 5.7 Tg yr−1 (+1.6 %) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest 2020 increases were seen from China (+2.6 Tg yr−1, 4.3 %), with smaller increases from India (+0.8 Tg yr−1, 2.2 %) and Indonesia (+0.3 Tg yr−1, 2.6 %). Results show the rise in emissions, and subsequent atmospheric growth, would have occurred with or without the COVID-19 slowdown. During the onset of the global slowdown (March–April, 2020) energy sector CH4 emissions from China increased; however, during later months (May–June, 2020) emissions decreased below expected pre-slowdown levels. The accumulated impact of the slowdown on CH4 emissions from March–June 2020 is found to be small. Changes in atmospheric chemistry, not investigated here, may have contributed to the observed growth in 2020. Future work aims to develop the global IFS inversion system and to extend the 4D-Var window-length using a hybrid ensemble-variational method.

The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union's Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF's Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014-21) and outlines some of its new components for the second phase (2021-28), e.g., the new Copernicus anthropogenic CO2 emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases.

The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is explained by instrument and retrieval algorithm similarities. VIIRS AOD is frequently lower over the ocean background and higher over biomass burning and dust source land regions compared to MODIS. VIIRS shows larger spatial coverage over land and resolves finer spatial structures such as the transport of Australian biomass burning smoke over the Pacific, which can be explained by the use of a heavy aerosol detection test in the retrieval algorithm. Our results confirm the positive offset over ocean (i) between Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration degradation of Terra/MODIS in the Dark Target algorithm and (ii) between SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the rest of the products, which is mainly related to differences in spatial representativity at the IFS grid spatial resolution due to the stringent cloud filtering applied to the SLSTR radiances. Finally, the geometry characteristics of the instrument, which drive the range of scattering angles sampled by the instrument, can explain a large part of the differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A.

The Paris Agreement establishes a transparency framework for anthropogenic carbon dioxide (CO2) emissions. It's core component are inventory-based national greenhouse gas emission reports, which are complemented by independent estimates derived from atmospheric CO2 measurements combined with inverse modelling. It is, however, not known whether such a Monitoring and Verification Support (MVS) capacity is capable of constraining estimates of fossil-fuel emissions to an extent that is sufficient to provide valuable additional information. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of such an MVS capacity. Here we provide a novel assessment of the potential of a comprehensive data assimilation system using simulated XCO2 and other observations to constrain fossil fuel CO2 emission estimates for an exemplary 1-week period in 2008. We find that CO2M enables useful weekly estimates of country-scale fossil fuel emissions independent of national inventories. When extrapolated from the weekly to the annual scale, uncertainties in emissions are comparable to uncertainties in inventories, so that estimates from inventories and from the MVS capacity can be used for mutual verification. We further demonstrate an alternative, synergistic mode of operation, with the purpose of delivering a best fossil fuel emission estimate. In this mode, the assimilation system uses not only XCO2 and the other data streams of the previous (verification) mode, but also the inventory information. Finally, we identify further steps towards an operational MVS capacity.

Abstract Vegetation regulates land‐atmosphere, water, and energy exchanges and is an essential component of land‐surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to the environment with the fast responses observable in the laboratory. The effects of acclimation can be taken into account by including PFT‐specific values of photosynthetic parameters, but at the cost of increasing parameter requirements. Here, we develop an alternative approach for including acclimation in LSMs by adopting the P model, an existing light‐use efficiency model for gross primary production (GPP) that implicitly predicts the acclimation of photosynthetic parameters on a weekly to monthly timescale via optimality principles. We demonstrate that it is possible to explicitly separate the fast and slow photosynthetic responses to environmental conditions, allowing the simulation of GPP at the sub‐daily timesteps required for coupling in an LSM. The resulting model reproduces the diurnal cycles of GPP recorded by eddy‐covariance flux towers in a temperate grassland and boreal, temperate and tropical forests. The best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. Comparison between this model and the operational LSM in the European Centre for Medium‐range Weather Forecasts climate model shows that the new model has better predictive power in most of the sites and years analyzed, particularly in summer and autumn. Our analyses suggest a simple and parameter‐sparse method to include both instantaneous and acclimated responses within an LSM framework, with potential applications in weather, climate, and carbon‐cycle modeling.

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand, which would facilitate modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, for example, Copernicus, is presented. This paper introduces recent examples of novel ECLand developments on (i) vegetation; (ii) snow; (iii) soil; (iv) open water/lake; (v) river/inundation; and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF's NWP, climate and atmospheric composition monitoring services using an Earth system assimilation and prediction framework.

The societal benefits of numerical weather prediction (NWP) forecasts are most evident in populated areas. An urban representation within NWP models should provide improved forecast accuracy. Here, we present the preliminary implementation of an urban scheme within the Integrated Forecasting System (IFS) using a simplified single‐layer urban canopy model. The scheme makes assumptions of canyon geometry and considers fluxes from roads, walls, and roofs. Temperature observations were used to optimize single‐column model (SCM) parameters using the Gauss‐Newton method. Observation comparisons over six European cities, show a 2‐m temperature root‐mean‐squared error reduction from 1.85 to 1.75 K with the urban scheme. Optimized parameters were used globally at kilometric scale in a land surface model. A sensitivity experiment assuming a 100% urban world showed spatially averaged northern hemisphere 2‐m temperatures increased by 0.54 K (January) and 0.42 K (July) at night caused by changes in the albedo, emissivity, roughness, and thermal and hydrological properties. Global ∼1‐km resolution simulations using ancillary urban mapping information produce an urban heat island effect over major and minor conurbations. Only major conurbations were well represented at ∼9‐km resolution. Results from SCM simulations show a heightening of the planetary boundary layer over city sites, with the largest enhancements occurring at night in July (84 ± 48 m) caused by an increased sensible heat flux. These initial developments show the importance of a high‐resolution urban representation within NWP models. Improved parameterization and mapping will enable an online representation of energy, water, and trace gas fluxes over residential areas.

In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

Measurements of atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4), and carbon monoxide (XCO) have been collected across the Pacific Ocean during the Measuring Ocean REferences 2 (MORE-2) campaign in June 2019. We deployed a shipborne variant of the EM27/SUN Fourier transform spectrometer (FTS) on board the German R/V Sonne which, during MORE-2, crossed the Pacific Ocean from Vancouver, Canada, to Singapore. Equipped with a specially manufactured fast solar tracker, the FTS operated in direct-sun viewing geometry during the ship cruise reliably delivering solar absorption spectra in the shortwave infrared spectral range (4000 to 11000 cm−1). After filtering and bias correcting the dataset, we report on XCO2, XCH4, and XCO measurements for 22 d along a trajectory that largely aligns with 30∘ N of latitude between 140∘ W and 120∘ E of longitude. The dataset has been scaled to the Total Carbon Column Observing Network (TCCON) station in Karlsruhe, Germany, before and after the MORE-2 campaign through side-by-side measurements. The 1σ repeatability of hourly means of XCO2, XCH4, and XCO is found to be 0.24 ppm, 1.1 ppb, and 0.75 ppb, respectively. The Copernicus Atmosphere Monitoring Service (CAMS) models gridded concentration fields of the atmospheric composition using assimilated satellite observations, which show excellent agreement of 0.52±0.31 ppm for XCO2, 0.9±4.1 ppb for XCH4, and 3.2±3.4 ppb for XCO (mean difference ± SD, standard deviation, of differences for entire record) with our observations. Likewise, we find excellent agreement to within 2.2±6.6 ppb with the XCO observations of the TROPOspheric MOnitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite (S5P). The shipborne measurements are accessible at https://doi.org/10.1594/PANGAEA.917240 (Knapp et al., 2020).