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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.

Incremental 4D-Var is a data assimilation algorithm used routinely at operational numerical weather predictions centers worldwide.This paper implements a new method for parallelizing incremental 4D-Var, the Randomized Incremental Optimal Technique (RIOT), which replaces the traditional sequential conjugate gradient (CG) iterations in the inner-loop of the minimization with fully parallel randomized singular value decomposition (RSVD) of the preconditioned Hessian of the cost function. RIOT is tested using the standard Lorenz-96 model (L-96) as well as two realistic high-dimensional atmospheric source inversion problems based on aircraft observations of black carbon concentrations. A new outer-loop preconditioning technique tailored to RSVD was introduced to improve convergence stability and performance. Results obtained with the L-96 system show that the performance improvement from RIOT compared to standard CG algorithms increases significantly with non-linearities. Overall, in the realistic black carbon source inversion experiments, RIOT reduces the wall-time of the 4D-Var minimization by a factor 2-3, at the cost of a factor 4-10 increase in energy cost due to the large number of parallel cores used. Furthermore, RIOT enables reduction of the wall-time computation of the analysis error covariance matrix by a factor 40 compared to a standard iterative Lanczos approach. Finally, as evidenced in this study, implementation of RIOT in an operational numerical weather prediction system will require a better understanding of its convergence properties as a function of the Hessian characteristics and, in particular, the degree of freedom for signal (DOFs) of the inverse problem.

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.

Atmospheric inversions inform us about the magnitude and variations of greenhouse gas (GHG) sources and sinks from global to local scales. Deployment of observing systems such as spaceborne sensors and ground-based instruments distributed around the globe has started to offer an unprecedented amount of information to estimate surface exchanges of GHG at finer spatial and temporal scales. However, all inversion methods still rely on imperfect atmospheric transport models whose error structures directly affect the inverse estimates of GHG fluxes. The impact of spatial error structures on the retrieved fluxes increase concurrently with the density of the available measurements. In this study, we diagnose the spatial structures due to transport model errors affecting modeled in situ carbon dioxide (CO2) mole fractions and total-column dry air mole fractions of CO2 (XCO2). We implement a cost-effective filtering technique recently developed in the meteorological data assimilation community to describe spatial error structures using a small-size ensemble. This technique can enable ensemble-based error analysis for multiyear inversions of sources and sinks. The removal of noisy structures due to sampling errors in our small-size ensembles is evaluated by comparison with larger-size ensembles. A second filtering approach for error covariances is proposed (Wiener filter), producing similar results over the 1-month simulation period compared to a Schur filter. Based on a comparison to a reference 25-member calibrated ensemble, we demonstrate that error variances and spatial error correlation structures are recoverable from small-size ensembles of about 8 to 10 members, improving the representation of transport errors in mesoscale inversions of CO2 fluxes. Moreover, error variances of in situ near-surface and free-tropospheric CO2 mole fractions differ significantly from total-column XCO2 error variances. We conclude that error variances for remote-sensing observations need to be quantified independently of in situ CO2 mole fractions due to the complexity of spatial error structures at different altitudes. However, we show the potential use of meteorological error structures such as the mean horizontal wind speed, directly available from ensemble prediction systems, to approximate spatial error correlations of in situ CO2 mole fractions, with similarities in seasonal variations and characteristic error length scales.

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M).

In this study we present a novel monitoring methodology to detect local CH4 concentration anomalies worldwide that are related to rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget. The method uses high resolution (7 km × 7 km) retrievals of total column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) onboard 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) close to the native satellite resolution at appropriate time. Investigating the 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 large-scale biases on both forecasts and satellite observations. We then use a simple classification on the filtered departures to detect anomalies and plumes coming from CAMS emissions that are missing (e.g. pipeline or facility leaks), under-reported or over-reported (e.g. depleted drilling fields). Additionally, the classification helps to detect local satellite retrieval errors due to land surface albedo issues.

Anthropogenic carbon dioxide (CO2) emissions and their observed growing trends raise awareness in scientific, political and public sectors of the society as the major driver of climate-change. For an increased understanding of the CO2 emission sources, patterns and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. In this study anthropogenic CO2 emission inventories are processed into gridded maps to provide an estimate of prior CO2 emissions for 7 main emissions groups: 1) power generation super-emitters and 2) energy production average-emitters, 3) manufacturing, 4) settlements, 5) aviation, 6) transport and 7) others, with estimation of their uncertainty and covariance to be included in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The emission inventories are sourced from the Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines for National Greenhouse Gas Inventories and revised information from its 2019 Refinements, and the global grid-maps of Emissions Database for Global Atmospheric Research (EDGAR) inventory. The anthropogenic CO2 emissions for 2012 and 2015, (EDGAR versions 4.3.2 and 4.3.2_FT2015 respectively) are considered, updated with improved apportionment of the energy sector, energy usage for manufacturing and diffusive CO2 emissions from coal mines. These emissions aggregated into 7 ECMWF groups with their emission uncertainties are calculated per country considering its statistical infrastructure development level and sector considering the most typical fuel type and use the IPCC recommended error propagation method assuming fully uncorrelated emissions to generate covariance matrices of parsimonious dimension (7×7). While the uncertainty of most groups remains relatively small, the largest contribution to the total uncertainty is determined by the group with usually the smallest budget, consisting of oil refineries and transformation industry, fuel exploitation, coal production, agricultural soils and solvents and products use emissions. Several sensitivity studies are performed: for country type (with well-/less well-developed statistical infrastructure), for fuel type specification, and for national emission source distribution (highlights the importance of 30 accurate point source mapping). Uncertainties are compared with United Nations Framework Convention on Climate Change (UNFCCC) and the Netherlands Organisation for Applied Scientific Research (TNO) data. Upgraded anthropogenic CO2 emission maps with their yearly and monthly uncertainties are combined into the CHE_EDGAR-ECMWF_2015 dataset (Choulga et al., 2020) available from https://doi.org/10.5281/zenodo.3712339.

Atmospheric flux inversions use observations of atmospheric CO2 to provide anthropogenic and biogenic CO2 flux estimates at a range of spatio-temporal scales. Inversions require prior flux, a forward model and observation errors to estimate posterior fluxes and uncertainties. Here, we investigate the forward transport error and the associated biogenic feedback in an Earth system model (ESM) context. These errors can occur from uncertainty in the initial meteorology, the analysis fields used, or the advection schemes and physical parameterisation of the model. We also explore the spatio-temporal variability and flow-dependent error covariances. We then compare the error with the atmospheric response to uncertainty in the prior anthropogenic emissions. Although transport errors are variable, average total-column CO2 (XCO2) transport errors over anthropogenic emission hotspots (0.1–0.8 ppm) are comparable to, and often exceed, prior monthly anthropogenic flux uncertainties projected onto the same space (0.1–1.4 ppm). Average near-surface transport errors at three sites (Paris, Caltech and Tsukuba) range from 1.7 to 7.2 ppm. The global average XCO2 transport error standard deviation plateaus at ∼0.1 ppm after 2–3 d, after which atmospheric mixing significantly dampens the concentration gradients. Error correlations are found to be highly flow dependent, with XCO2 spatio-temporal correlation length scales ranging from 0 to 700 km and 0 to 260 min. Globally, the average model error caused by the biogenic response to atmospheric meteorological uncertainties is small (

We present top-down constraints on global monthly N2O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N2O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4° × 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 Tg N yr−1 (SVD-based inversion) to 17.5–17.7 Tg N yr−1 (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropical N2O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N2O flux than is predicted by the prior bottom-up inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here. We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N2O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2O observations: by defining the optimal resolution of the solution based on the information content of the inversion, it provides spatial information that is lost when aggregating to political or geographic regions, while also providing more temporal information than a standard 4D-Var inversion.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

International audience

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

International audience; We quantify methane (CH4) emissions in California's San Joaquin Valley (SJV) by using 4 days of aircraft measurements from a field campaign during May–June 2010 together with a Bayesian inversion method and a mass balance approach. For the inversion estimates, we use the FLEXible PARTicle dispersion model (FLEXPART) to establish the source‐receptor relationship between sampled atmospheric concentrations and surface fluxes. Our prior CH4 emission estimates are from the California Greenhouse Gas Emissions Measurements (CALGEM) inventory. We use three meteorological configurations to drive FLEXPART and subsequently construct three inversions to analyze the final optimized estimates and their uncertainty (one standard deviation). We conduct May and June inversions independently and derive similar total CH4 emission estimates for the SJV: 135 ± 28 Mg/h in May and 135 ± 19 Mg/h in June. The inversion result is 1.7 times higher than the prior estimate from CALGEM. We also use an independent mass balance approach to estimate CH4 emissions in the northern SJV for one flight when meteorological conditions allowed. The mass balance estimate provides a confirmation of our inversion results, and these two independent estimates of the total CH4 emissions in the SJV are consistent with previous studies. In this study, we provide optimized CH4 emissions estimates at 0.1° horizontal resolution. Using independent spatial information on major CH4 sources, we estimate that livestock contribute 75–77% and oil/gas production contributes 15–18% of the total CH4 emissions in the SJV. Livestock explain most of the discrepancies between the prior and the optimized emissions from our inversion.

We have examined the utility of retrieved column-averaged, dry-air mole fractions of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT) for quantifying monthly, regional flux estimates of CO2, using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system. We focused on assessing the potential impact of biases in the GOSAT CO2 data on the regional flux estimates. Using different screening and bias correction approaches, we selected three different subsets of the GOSAT XCO2 data for the 4D-Var inversion analyses, and found that the inferred global fluxes were consistent across the three XCO2 inversions. However, the GOSAT observational coverage was a challenge for the regional flux estimates. In the northern extratropics, the inversions were more sensitive to North American fluxes than to European and Asian fluxes due to the lack of observations over Eurasia in winter and over eastern and southern Asia in summer. The regional flux estimates were also sensitive to the treatment of the residual bias in the GOSAT XCO2 data. The largest differences obtained were for temperate North America and temperate South America, for which the largest spread between the inversions was 1.02 and 0.96 Pg C, respectively. In the case of temperate North America, one inversion suggested a strong source, whereas the second and third XCO2 inversions produced a weak and strong sink, respectively. Despite the discrepancies in the regional flux estimates between the three XCO2 inversions, the a posteriori CO2 distributions were in good agreement (with a mean difference between the three inversions of typically less than 0.5 ppm) with independent data from the Total Carbon Column Observing Network (TCCON), the surface flask network, and from the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign. The discrepancy in the regional flux estimates from the different inversions, despite the agreement of the global flux estimates suggests the need for additional work to determine the minimum spatial scales at which we can reliably quantify the fluxes using GOSAT XCO2. The fact that the a posteriori CO2 from the different inversions were in good agreement with the independent data although the regional flux estimates differed significantly, suggests that innovative ways of exploiting existing data sets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.