Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO<sub>2</sub> and CH<sub>4</sub> retrieval errors by error parameterization

Abstract

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 ).

Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

“…We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other current or future missions, e.g. OCO-2 (Crisp et al, 2004), GOSAT-2 and CarbonSat Buchwitz et al, 2013a).…”

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).…”

Application of a PCA‐Based Fast Radiative Transfer Model to XCO2 Retrievals in the Shortwave Infrared

“…Satellites are generating a quickly growing volume of data (from ≈8,000 clear-sky scenes per day for the SCIAMACHY instrument, up to ≈200,000 for NASA's OCO-2, Taylor et al, 2016, or a CarbonSat-like mission, Buchwitz et al, 2013) which necessitates acceleration methods that reduce the computational effort for RT calculations while at the same time retaining a high level of accuracy of the forward model. As pointed out by Chevallier et al (2007), regional biases in the low sub-ppm (parts per million, 0.1 ppm ≈ 0.03%) range can significantly alter the surface fluxes obtained from flux inversions.…”