Abstract. Carbon Monitoring Satellite (CarbonSat) is one of two candidate missions for ESA's Earth Explorer 8 (EE8) satellite to be launched around the end of this decade. The overarching objective of the CarbonSat mission is to improve our understanding of natural and anthropogenic sources and sinks of the two most important anthropogenic greenhouse gases (GHGs) carbon dioxide (CO 2 ) and methane (CH 4 ). The unique feature of CarbonSat is its "GHG imaging capability", which is achieved via a combination of high spatial resolution (2 km × 2 km) and good spatial coverage (wide swath and gap-free across-and along-track ground sampling). This capability enables global imaging of localized strong emission source, such as cities, power plants, methane seeps, landfills and volcanos, and likely enables better disentangling of natural and anthropogenic GHG sources and sinks. Source-sink information can be derived from the retrieved atmospheric column-averaged mole fractions of CO 2 and CH 4 , i.e. XCO 2 and XCH 4 , by inverse modelling. Using the most recent instrument and mission specification, an error analysis has been performed using the Bremen optimal EStimation DOAS (BESD/C) retrieval algorithm. We assess the retrieval performance for atmospheres containing aerosols and thin cirrus clouds, assuming that the retrieval forward model is able to describe adequately all relevant scattering properties of the atmosphere. To compute the errors for each single CarbonSat observation in a one-year period, we have developed an error parameterization scheme comprising six relevant input parameters: solar zenith angle, surface albedo in two bands, aerosol and cirrus optical depth, and cirrus altitude variations. Other errors, e.g. errors resulting from aerosol type variations, are partially quantified but not yet accounted for in the error parameterization. Using this approach, we have generated and analysed one year of simulated CarbonSat observations. Using this data set we estimate that systematic errors are for the overwhelming majority of cases (≈ 85 %) below 0.3 ppm for XCO 2 (below 0.5 ppm for 99.5 %) and below 2 ppb for XCH 4 (below 4 ppb for 99.3 %). We also show that the single-measurement precision is typically around 1.2 ppm for XCO 2 and 7 ppb for XCH 4 (1σ ). The number of quality-filtered observations over cloud-and ice-free land surfaces is in the range of 33 to 47 million per month depending on season. Recently it has been shown that terrestrial vegetation chlorophyll fluorescence (VCF) emission needs to be considered for accurate XCO 2 retrieval. We therefore retrieve VCF from clear Fraunhofer lines located around 755 nm and show that CarbonSat will provide valuable information on VCF. We estimate that the VCF single-measurement precision is approximately 0.3 mW m −2 nm −1 sr −1 (1σ ).
Abstract. Current knowledge about the European terrestrial biospheric carbon sink, from the Atlantic to the Urals, relies upon bottom-up inventory and surface flux inverse model
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 ...
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.
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