ALBERT

Description: Urban areas, which are home to the majority of today's world population, are responsible for more than two-thirds of the global energy-related carbon dioxide emissions. Given the ongoing demographic growth and rising energy consumption in metropolitan regions particularly in the developing world, urban-based emissions are expected to further increase in the future. As a consequence, monitoring and independent verification of reported anthropogenic emissions is becoming more and more important. It is demonstrated using SCIAMACHY nadir measurements that anthropogenic CO2 emissions can be detected from space and that emission trends might be tracked using satellite observations. This is promising with regard to future satellite missions with high spatial resolution and wide swath imaging capability aiming at constraining anthropogenic emissions down to the point-source scale. By subtracting retrieved background values from those retrieved over urban areas we find significant CO2 enhancements for several anthropogenic source regions, namely 1.3 ± 0.7 ppm for the Rhine-Ruhr metropolitan region and the Benelux, 1.1 ± 0.5 ppm for the East Coast of the United States, and 2.4 ± 0.9 ppm for the Yangtze River Delta. The order of magnitude of the enhancements is in agreement with what is expected for anthropogenic CO2 signals. The larger standard deviation of the retrieved Yangtze River Delta enhancement is due to a distinct positive trend of 0.3 ± 0.2 ppm yr−1, which is quantitatively consistent with anthropogenic emissions from the Emission Database for Global Atmospheric Research (EDGAR) in terms of percentual increase per year. Potential contributions to the retrieved CO2 enhancement by several error sources, e.g. aerosols, albedo, and residual biospheric signals due to heterogeneous seasonal sampling, are discussed and can be ruled out to a large extent.

Description: We analyze an ensemble of seven XCO2 retrieval algorithms for SCIAMACHY (scanning imaging absorption spectrometer of atmospheric chartography) and GOSAT (greenhouse gases observing satellite). The ensemble spread can be interpreted as regional uncertainty and can help to identify locations for new TCCON (total carbon column observing network) validation sites. Additionally, we introduce the ensemble median algorithm EMMA combining individual soundings of the seven algorithms into one new data set. The ensemble takes advantage of the algorithms' independent developments. We find ensemble spreads being often 〈 1 ppm but rising up to 2 ppm especially in the tropics and East Asia. On the basis of gridded monthly averages, we compare EMMA and all individual algorithms with TCCON and CarbonTracker model results (potential outliers, north/south gradient, seasonal (peak-to-peak) amplitude, standard deviation of the difference). Our findings show that EMMA is a promising candidate for inverse modeling studies. Compared to CarbonTracker, the satellite retrievals find consistently larger north/south gradients (by 0.3–0.9 ppm) and seasonal amplitudes (by 1.5–2.0 ppm).

Description: The terrestrial biosphere is currently acting as a net carbon sink on the global scale exhibiting significant interannual variability in strength. To reliably predict the future strength of the land sink and its role in atmospheric CO2 growth the underlying processes and their response to a changing climate need to be well understood. In particular, better knowledge of the impact of key climate variables like temperature or precipitation on the biospheric carbon reservoir is essential. It is demonstrated using nearly a decade of SCIAMACHY nadir measurements that years with higher temperatures during the growing season can be robustly associated with larger growth rates in atmospheric CO2 and smaller seasonal cycle amplitudes for northern mid-latitudes. We find linear relationships between warming and CO2 growth as well as seasonal cycle amplitude at the 98% significance level. This suggests that the terrestrial carbon sink is less efficient at higher temperatures, which might lead to future sink saturation via a positive carbon-climate feedback. Quantitatively, the covariation between the annual CO2 growth rates derived from SCIAMACHY data and warm season surface temperature anomaly amounts to 1.25±0.32 ppm yr−1 K−1 for the Northern Hemisphere where the bulk of the terrestrial carbon sink is located. In comparison, the relation is less pronounced in the Southern Hemisphere. The covariation of the seasonal cycle amplitudes derived from satellite and temperature anomaly is −1.30±0.31 ppm K−1 for the north temperate zone. These estimates are consistent with those from the CarbonTracker data assimilated CO2 data product indicating that the temperature dependence of the model surface fluxes is realistic.

Description: 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 (CO2) and methane (CH4). 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 CO2 and CH4, i.e. XCO2 and XCH4, 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 XCO2 (below 0.5 ppm for 99.5%) and below 2 ppb for XCH4 (below 4 ppb for 99.3%). We also show that the single-measurement precision is typically around 1.2 ppm for XCO2 and 7 ppb for XCH4 (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 XCO2 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).

Description: Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS and SCIAMACHY instruments on board GOSAT and ENVISAT using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier Transform Spectrometers (FTS) 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" (GHG). For CO2, all algorithms reach the precision requirements for inverse modelling (〈 8 ppb), 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 CH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fail to meet the 〈 34 ppb threshold, 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 ppb and 10.5 ppb respectively.

Description: Carbon Monitoring Satellite (CarbonSat) is one of two candidate missions for ESA's Earth Explorer 8 (EE8) satellite – the selected one to be launched around the end of this decade. The 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 (GHG) carbon dioxide (CO2) and methane (CH4). 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 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 CO2 and CH4, i.e. XCO2 and XCH4, via inverse modeling. Using the most recent instrument and mission specification, an error analysis has been performed using the BESD/C retrieval algorithm. We focus on systematic errors due to aerosols and thin cirrus clouds, as this is the dominating error source especially with respect to XCO2 systematic errors. To compute the errors for each single CarbonSat observation in a one year time period, we have developed an error parameterization scheme based on six relevant input parameters: we consider solar zenith angle, surface albedo in two bands, aerosol and cirrus optical depth, and cirrus altitude variations but neglect, for example, aerosol type variations. Using this method we have generated and analyzed one year of simulated CarbonSat observations. Using this data set we estimate that scattering related systematic errors are mostly (approx. 85%) below 0.3 ppm for XCO2 (