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. High-resolution atmospheric transport simulations were used to investigate the potential for detecting carbon dioxide (CO2) plumes of the city of Berlin and neighboring power stations with the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, which is a proposed constellation of CO2 satellites with imaging capabilities. The potential for detecting plumes was studied for satellite images of CO2 alone or in combination with images of nitrogen dioxide (NO2) and carbon monoxide (CO) to investigate the added value of measurements of other gases coemitted with CO2 that have better signal-to-noise ratios. The additional NO2 and CO images were either generated for instruments on the same CO2M satellites (2 km× 2 km resolution) or for the Sentinel-5 instrument (7.5 km× 7.5 km) assumed to fly 2 h earlier than CO2M. Realistic CO2, CO and NOX(=NO+NO2) fields were simulated at 1 km× 1 km horizontal resolution with the Consortium for Small-scale Modeling model extended with a module for the simulation of greenhouse gases (COSMO-GHG) for the year 2015, and they were used as input for an orbit simulator to generate synthetic observations of columns of CO2, CO and NO2 for constellations of up to six satellites. A simple plume detection algorithm was applied to detect coherent structures in the images of CO2, NO2 or CO against instrument noise and variability in background levels. Although six satellites with an assumed swath of 250 km were sufficient to overpass Berlin on a daily basis, only about 50 out of 365 plumes per year could be observed in conditions suitable for emission estimation due to frequent cloud cover. With the CO2 instrument only 6 and 16 of these 50 plumes could be detected assuming a high-noise (σVEG50=1.0 ppm) and low-noise (σVEG50=0.5 ppm) scenario, respectively, because the CO2 signals were often too weak. A CO instrument with specifications similar to the Sentinel-5 mission performed worse than the CO2 instrument, while the number of detectable plumes could be significantly increased to about 35 plumes with an NO2 instrument. CO2 and NO2 plumes were found to overlap to a large extent, although NOX had a limited lifetime (assumed to be 4 h) and although CO2 and NOX were emitted with different NOX:CO2 emission ratios by different source types with different temporal and vertical emission profiles. Using NO2 observations from the Sentinel-5 platform instead resulted in a significant spatial mismatch between NO2 and CO2 plumes due to the 2 h time difference between Sentinel-5 and CO2M. The plumes of the coal-fired power plant Jänschwalde were easier to detect with the CO2 instrument (about 40–45 plumes per year), but, again, an NO2 instrument could detect significantly more plumes (about 70). Auxiliary measurements of NO2 were thus found to greatly enhance the capability of detecting the location of CO2 plumes, which will be invaluable for the quantification of CO2 emissions from large point sources.
Under the Paris Agreement progress of emission reduction efforts is tracked on the basis of regular updates to national Greenhouse Gas (GHG) inventories, referred to as bottom-up estimates. However, only top-down atmospheric measurements can provide observation-based evidence of emission trends. Today there is no internationally agreed, operational capacity to monitor anthropogenic GHG emission trends using atmospheric measurements to complement national bottom-up inventories. The European Commission (EC), the European Space Agency, the European Centre for Medium-Range Weather Forecasts, the European Organisation for the Exploitation of Meteorological Satellites and international experts, are joining forces to develop such an operational capacity for monitoring anthropogenic CO2 emissions as a new CO2 service under EC's Copernicus Programme. Design studies have been used to translate identified needs into defined requirements and functionalities of this anthropogenic CO2 emissions Monitoring and Verification Support (CO2MVS) capacity. It adopts a holistic view and includes components such as atmospheric space-borne and in-situ measurements, bottom-up CO2 emission maps, improved modeling of the carbon cycle, an operational data-assimilation system integrating top-down and bottom-up information, and a policy-relevant decision support tool. The CO2MVS capacity with operational capabilities by 2026, is expected to visualize regular updates of global CO2 emissions, likely at 0.05°x0.05°. This will complement the PA’s enhanced transparency framework, providing actionable information on anthropogenic CO2 emissions that are the main driver of climate change. This information will be available to all stakeholders, including governments and citizens, allowing them to reflect on trends and effectiveness of reduction measures. The new EC gave green light to pass the CO2MVS from exploratory to implementing phase.
Abstract. Inverse modeling of anthropogenic and biospheric CO2 fluxes from ground-based and satellite observations critically depends on the accuracy of atmospheric transport simulations. Previous studies emphasized the impact of errors in simulated winds and vertical mixing in the planetary boundary layer, whereas the potential importance of releasing emissions not only at the surface but distributing them in the vertical was largely neglected. Accounting for elevated emissions may be critical, since more than 50 % of CO2 in Europe is emitted by large point sources such as power plants and industrial facilities. In this study, we conduct high-resolution atmospheric simulations of CO2 with the mesoscale Consortium for Small-scale Modeling model extended with a module for the simulation of greenhouse gases (COSMO-GHG) over a domain covering the city of Berlin and several coal-fired power plants in eastern Germany, Poland and Czech Republic. By including separate tracers for anthropogenic CO2 emitted only at the surface or according to realistic, source-dependent profiles, we find that releasing CO2 only at the surface overestimates near-surface CO2 concentrations in the afternoon on average by 14 % in summer and 43 % in winter over the selected model domain. Differences in column-averaged dry air mole XCO2 fractions are smaller, between 5 % in winter and 8 % in summer, suggesting smaller yet non-negligible sensitivities for inversion modeling studies assimilating satellite rather than surface observations. The results suggest that the traditional approach of emitting CO2 only at the surface is problematic and that a proper allocation of emissions in the vertical deserves as much attention as an accurate simulation of atmospheric transport.
Responding to plans of the European Commission for extending the observation capabilities of the Copernicus programme, the European Space Agency (ESA) has initiated Phase A industrial (technical feasibility) studies for several new space-borne Earth Observation missions. High priority is given to a constellation of LEO satellites in Sunsynchronous orbit with the purpose of observing anthropogenic carbon dioxide (CO2) emissions [European Commission, 2017]. The observing system shall acquire images of CO2 concentration in terms of dry air column-averaged mole fractions (XCO2), providing complete global land coverage at high spatial resolution (4 km 2 ) within five days. The demanding requirements call for a payload comprising a combination of multiple instruments, which perform simultaneous measurements. The XCO2 is inferred from reflectance measurements in the Near-Infrared (NIR) and Short-Wave Infrared spectral regions (SWIR). This requires at least three spatially co-registered push-broom imaging spectrometers, measuring spectral radiance and solar irradiance in the NIR (747-773 nm), SWIR-1 (1595-1675 nm) and SWIR-2 (1990SWIR-2 ( -2095 at moderate spectral resolving power (R~5000-7000). In addition, the observations for CO2 concentration will be complemented by Differential Optical Absorption Spectroscopy (DOAS) measurements of nitrogen dioxide (NO2) over the same area. The NO2 measurements in the visible region (400-500 nm) are expected to serve as a tracer for plumes of high CO2 concentration resulting from high temperature combustion, which will facilitate plume identification and mapping. The third component of the payload is a multiple-angle polarimeter (MAP), performing high-precision measurements of aerosol (and cloud) properties. Its measurements of polarized radiance under various observation angles are expected to reduce XCO2 bias error and significantly increase the yield of useful retrievals from the NIR and SWIR spectra. The complex observation architecture, involving multiple instruments and platforms, call for optimized observational requirements, driven by the primary goal of detecting and quantifying point-sources of greenhouse gas emissions. In particular, high single-sounding precision is essential for identifying plumes of elevated CO2 concentration from instantaneous image acquisitions without regional and temporal averaging. This translates into stringent requirements for Signal-to-noise ratio (SNR), as well as spatial co-registration and spectral stability, which drive the instrument design. The presentation will introduce the different elements of the candidate Copernicus mission, in view of the ambitious mission goals. The payload components and observation requirements are addressed with special emphasis on the derivation of the SNR and spectral resolution requirements, which determine the instrument sizing.
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