Abstract
Abstract. Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO2, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.
Highlights
According to the IPCC 2007 report (Solomon et al, 2007), based on estimates of radiative forcing between 1750 and 2005, carbon dioxide and methane combined account for over 80 % of the anthropogenic greenhouse gas warming effect
The scope of the round-robin algorithm–Total Carbon Column Observing Network (TCCON) comparisons was to identify any remaining shortcomings in the data products generated with the competing algorithms and determine any inter-algorithm quality differences
Shown in each section are overview figures (Figs. 4, 7, 10 and 13) and tables (Tables 3 through 12) that list the statistical parameters obtained at each station, and for all station data combined (ALL)
Summary
According to the IPCC 2007 report (Solomon et al, 2007), based on estimates of radiative forcing between 1750 and 2005, carbon dioxide and methane combined account for over 80 % of the anthropogenic greenhouse gas warming effect. It is important to understand the magnitude and distribution of the CO2 and CH4 sources and sinks Despite their importance, our knowledge of the sources and sinks still has significant gaps (e.g. Stephens et al, 2007; Canadell et al, 2010). Our knowledge of the sources and sinks still has significant gaps (e.g. Stephens et al, 2007; Canadell et al, 2010) For instance it is still unclear why between ∼ 2000 and 2006 methane levels in the atmosphere were rather stable (Simpson et al, 2012), while before and after this period they were rising Sensitive to the nearsurface CO2 and CH4 variations, are important data sets to improve these flux estimations (Chevallier et al, 2007; Bergamaschi et al, 2009). The satellite accuracy requirements are very demanding, since small errors in the retrieved total column concentrations may result in significant errors in the derived fluxes (e.g. Meirink et al, 2006; Chevallier et al, 2007)
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