Tracking city CO<sub>2</sub> emissions from space using a high-resolution inverse modelling approach: a case study for Berlin, Germany

Abstract

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 ...

Satellite-derived methane hotspot emission estimates using a fast data-driven method

“…The longer-term objective of releasing an observing system comprising instruments with the performance of CarbonSat within a CarbonSat constellation (Bovensmann et al, 2010;Velazco et al, 2011;Buchwitz et al, 2013;Pillai et al, 2016;ESA, 2015) is currently being discussed by the ESA and European Union (EU) representatives within the Copernicus program focusing on CO 2 (e.g. Ciais et al, 2015).…”

“…Ciais et al, 2014). The latter aspect was studied in the development of the CarbonSat mission (Bovensmann et al, 2010;Velazco et al, 2011;Buchwitz et al, 2013;Pillai et al, 2016) for CO 2 using performance assessments based on simulated satellite observations (ESA, 2015) but so far only few studies have been published using real satellite data (e.g. Wecht et al, 2014a;Turner et al, 2015Turner et al, , 2016, for USA methane emissions).…”

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

“…Other future satellites have a wide swath (CarbonSat, proposed) or are geostationary (GeoCARB and GEO-CAPE; selected and proposed, respectively). They would generate higher density observations across the US relative to OCO-2 and GOSAT (Fishman et al, 2012;Polonsky et al, 2014;Bovensmann et al, 2015;Buchwitz et al, 2013;Bousserez et al, 2016;Pillai et al, 2016). Lidar-based missions (e.g., MERLIN and ASCENDS; selected and proposed, respectively) measure in the absence of sunlight and through thin or scattered clouds (Kiemle et al, 2011;ASCENDS Ad Hoc Science Definition Team, 2015).…”

The potential of satellite spectro-imagery for monitoring CO<sub>2</sub> emissions from large cities

“…Figure S2 gives an illustration of the systematics errors in the Paris area, which are characterized by patterns of negative or positive errors whose amplitude exceeds 0.1 ppm over 50 to 100 km spatial scales. These simulations of random and systematic errors have also been used by Pillai et al (2016) to assess the potential of CarbonSat for monitoring city-scale CO 2 emissions. Assessing their level of realism is out of the scope of this study and we refer the reader to Buchwitz et al (2013) for further details and interpretation.…”

“…They are extracted from simulations of CarbonSat sampling over the whole globe and for a full year by Bucwitz et al (2013). Pillai et al (2016) also used these simulations to model CarbonSat observations with 240 and 500 km swaths. From these simulations, 69 CarbonSat passes in our modelling domain over the year provide at least 1 XCO 2 data in the 100 km radius circle centred on Paris and are considered to be "over the Paris area".…”