Abstract. Currently, 52 % of the world's population resides in urban areas and as a consequence, approximately 70 % of fossil fuel emissions of CO 2 arise from cities. This fact, in combination with large uncertainties associated with quantifying urban emissions due to lack of appropriate measurements, makes it crucial to obtain new measurements useful to identify and quantify urban emissions. This is required, for example, for the assessment of emission mitigation strategies and their effectiveness. Here, we investigate the potential of a satellite mission like Carbon Monitoring Satellite (CarbonSat) which was proposed to the European Space Agency (ESA) to retrieve the city emissions globally, taking into account a realistic description of the expected retrieval errors, the spatiotemporal distribution of CO 2 fluxes, and atmospheric transport. To achieve this, we use (i) a high-resolution modelling framework consisting of the Weather Research Forecasting model with a greenhouse gas module (WRF-GHG), which is used to simulate the atmospheric observations of column-averaged CO 2 dry air mole fractions (XCO 2 ), and (ii) a Bayesian inversion method to derive anthropogenic CO 2 emissions and their errors from the CarbonSat XCO 2 observations. We focus our analysis on Berlin, Germany using CarbonSat's cloud-free overpasses for 1 reference year. The dense (wide swath) CarbonSat simulated observations with high spatial resolution (approximately 2 km × 2 km) permits one to map the city CO 2 emission plume with a peak enhancement of typically 0.8-1.35 ppm relative to the background. By performing a Bayesian inversion, it is shown that the random error (RE) of the retrieved Berlin CO 2 emission for a single overpass is typically less than 8-10 Mt CO 2 yr −1 (about 15-20 % of the total city emission). The range of systematic errors (SEs) of the retrieved fluxes due to various sources of error (measurement, modelling, and inventories) is also quantified. Depending on the assumptions made, the SE is less than about 6-10 Mt CO 2 yr −1 for most cases. We find that in particular systematic modelling-related errors can be quite high during the summer months due to substantial XCO 2 variations caused by biogenic CO 2 fluxes at and around the target region. When making the extreme worst-case assumption that biospheric XCO 2 variations cannot be modelled at all (which is overly pessimistic), the SE of the retrieved emission is found to be larger than 10 Mt CO 2 yr −1 for about half of the sufficiently cloud-free overpasses, and for some of the overpasses we found that SE may even be on the order of magnitude of the anthropogenic emission. This indicates that biogenic XCO 2 variations cannot be neglected but must be considered during forward and/or inverse modelling. Overall, we conclude that a satellite mission such as CarbonSat has high potential to obtain city-scale CO 2 emissions as needed to enhance our current understanding of anthropogenic carbon fluxes, and that CarbonSat-like satellites should be an important component of ...

[1] Tropical regions, especially the Amazon region, account for large emissions of methane (CH 4 )

Abstract. Accurate simulation of the spatial and temporal variability of tracer mixing ratios over complex terrain is challenging, but essential in order to utilize measurements made in complex orography (e.g. mountain and coastal sites) in an atmospheric inverse framework to better estimate regional fluxes of these trace gases. This study investigates the ability of high-resolution modeling tools to simulate meteorological and CO 2 fields around Ochsenkopf tall tower, situated in Fichtelgebirge mountain range-Germany (1022 m a.s.l.; 50 • 1 48" N, 11 • 48 30" E). We used tower measurements made at different heights for different seasons together with the measurements from an aircraft campaign. Two tracer transport models -WRF (Eulerian based) and STILT (Lagrangian based), both with a 2 km horizontal resolution -are used together with the satellite-based biospheric model VPRM to simulate the distribution of atmospheric CO 2 concentration over Ochsenkopf. The results suggest that the high-resolution models can capture diurnal, seasonal and synoptic variability of observed mixing ratios much better than coarse global models. The effects of mesoscale transports such as mountain-valley circulations and mountain-wave activities on atmospheric CO 2 distributions are reproduced remarkably well in the high-resolution models. With this study, we emphasize the potential of using high-resolution models in the context of inverse modeling frameworks to utilize measurements provided from mountain or complex terrain sites.

Abstract. Satellite retrievals for column CO 2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO 2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO 2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO 2 from the high resolution modeling framework WRF-VPRM, which links CO 2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km 2 horizontal resolution. Sub-grid variability of column averaged CO 2 , i.e. the variability not resolved by global models, reached up to 1.2 ppm with a median value of 0.4 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO 2 fluxes as well as resolved mixing ratio of CO 2 , a linear model can be formulated that could explain about 50% of the spatial patterns in the systematic (bias or correlated error) component of representation error in column and near-surface CO 2 during day-and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

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