ALBERT

Description: Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases contributing to global climate change. SCIAMACHY onboard ENVISAT (launch 2002) was the first and is now with TANSO onboard GOSAT (launch 2009) one of only two satellite instruments currently in space whose measurements are sensitive to CO2 and CH4 concentration changes in the lowest atmospheric layers where the variability due to sources and sinks is largest. We present long-term SCIAMACHY retrievals (2003–2009) of column-averaged dry air mole fractions of both gases (denoted XCO2 and XCH4) derived from absorption bands in the near-infrared/shortwave-infrared (NIR/SWIR) spectral region focusing on large-scale features. The results are obtained using an upgraded version (v2) of the retrieval algorithm WFM-DOAS including several improvements, while simultaneously maintaining its high processing speed. The retrieved mole fractions are compared to global model simulations (CarbonTracker XCO2 and TM5 XCH4) being optimised by assimilating highly accurate surface measurements from the NOAA/ESRL network and taking the SCIAMACHY averaging kernels into account. The comparisons address seasonal variations and long-term characteristics. The steady increase of atmospheric carbon dioxide primarily caused by the burning of fossil fuels can be clearly observed with SCIAMACHY globally. The retrieved global annual mean XCO2 increase agrees with CarbonTracker within the error bars (1.80±0.13 ppm yr−1 compared to 1.81±0.09 ppm yr−1). The amplitude of the XCO2 seasonal cycle as retrieved by SCIAMACHY, which is 4.3±0.2 ppm for the Northern Hemisphere and 1.4±0.2 ppm for the Southern Hemisphere, is on average about 1 ppm larger than for CarbonTracker. An investigation of the boreal forest carbon uptake during the growing season via the analysis of longitudinal gradients shows good agreement between SCIAMACHY and CarbonTracker concerning the overall magnitude of the gradients and their annual variations. The analysis includes a discussion of the relative uptake strengths of the Russian and North American boreal forest regions. The retrieved XCH4 results show that after years of stability, atmospheric methane has started to rise again in recent years which is consistent with surface measurements. The largest increase is observed for the tropics and northern mid- and high-latitudes amounting to about 7.5±1.5 ppb yr−1 since 2007. Due care has been exercised to minimise the influence of detector degradation on the quantitative estimate of this anomaly.

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: At present, global CO2 emission inventories are mainly based on bottom-up estimates that rely, for example, on reported fossil fuel consumptions and fuel types1,2. The associated uncertainties propagate into the CO2-to-NOx emission ratios that are used in pollution prediction and monitoring3, as well as into biospheric carbon fluxes derived by inverse models4. Here we analyse simultaneous and co-located satellite retrievals from SCIAMACHY (ref. 5; SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY) of the column-average dry-air mole fraction of CO2 (refs 6,7) and NO2 (refs 8-10) for the years 2003-2011 to provide a top-down estimate of trends in emissions and in the ratio between CO2 and NOx emissions. Our analysis shows that the CO2-to-NOcx emission ratio has increased by 4.2 ± 1.7%yr-1 in East Asia. In this region, we find a large positive trend of CO2 emissions (9.8 ± 1.7%yr-1), which we largely attribute to the growing Chinese economy. This trend exceeds the positive trend of NOx emissions (5.8±0.9%yr-1). Our findings suggest that the recently installed and renewed technology in East Asia, such as power plants, transportation and so on, is cleaner in terms of NOx emissions than the old infrastructure, and roughly matches relative emission levels in North America and Europe. © 2014 Macmillan Publishers Limited. All rights reserved.

Description: This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-average dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free run simulation and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation DOAS (BESD) algorithm during the year 2013 provides XCO2 fields with an improved station-to-station bias deviation of 0.7 parts per million (ppm) compared to the free run (1.4 ppm) and an improved estimated precision of ~ 1 ppm compared to the used GOSAT data (3.4 ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10 day forecast from each analysis at 00:00 UTC. Compared to its own analysis the CO2 forecast shows synoptic skill for the largest scale weather patterns even up to day 5 according to the anomaly correlation coefficient.

Description: Current knowledge about the European terrestrial biospheric carbon sink, from the Atlantic to the Urals, relies upon bottom-up inventory and surface flux inverse model estimates (e.g., 0.27 ± 0.16 Gt C a−1 for 2000–2005 5 (Schulze et al., 2009), 0.17 ± 0.44 Gt C a−1 for 2001–2007 (Peters et al., 2010), 0.45 ± 0.40 Gt C a−1 for 2010 (Chevallier et al., 2014), 0.40 ± 0.42 Gt C a−1 for 2001–2004 (Peylin et al., 2013). Inverse models assimilate in situ CO2 atmospheric concentrations measured by surface-based air sampling networks. The intrinsic sparseness of these networks is one reason for the relatively large flux uncertainties (Peters et al., 2010; Bruhwiler et al., 2011). Satellite-based CO2 measurements have the potential to reduce these uncertainties (Miller et al., 2007; Chevallier et al., 2007). Global inversion experiments using independent models and independent GOSAT satellite data products consistently derived a considerably larger European sink (0.9–1.2 Gt C a−1 for September 2009–August 2010 (Basu et al., 2013), 1.2–1.8 Gt C a−1 in 2010, Chevallier et al., 2014). However, these results have been considered unrealistic due to potential large scale retrieval biases and/or long-range transport errors (Chevallier et al., 2014) or have not been discussed at all (Basu et al., 2013; Takagi et al., 2014). Here we show that the satellite-derived European terrestrial carbon sink is indeed much larger (1.02 ± 0.30 Gt C a−1 in 2010) than previously expected. Our analysis comprises a regional inversion approach using STILT (Gerbig et al., 2003; Lin et al., 2003) short range (days) particle dispersion modelling, rendering it insensitive to large scale retrieval biases and less sensitive to long-range transport errors. The highest gain in information is obtained during the growing season when satellite observation conditions are advantageous and a priori uncertainties are largest. The consistency among an ensemble of five different inversion set-ups and five independent satellite retrievals (BESD (Reuter et al., 2011) 2003–2010, ACOS (O’Dell et al., 2012) 2010, UoL-FP (Cogan et al., 2012) 2010, RemoTeC C (Butz et al., 2011) 2010, and NIES (Yoshida et al., 2013) 2010) using data from two different instruments (SCIAMACHY, Bovensmann et al., 1999 and GOSAT, Kuze et al., 2009) provides evidence that our current understanding of the European carbon sink has to be revisited.

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 dioxide (CO2) is the most important greenhouse gas whose atmospheric loading has been significantly increased by anthropogenic activity leading to global warming. Accurate measurements and models are needed in order to reliably predict our future climate. This, however, has challenging requirements. Errors in measurements and models need to be identified and minimised. In this context, we present a comparison between satellite-derived column-averaged dry air mole fractions of CO2, denoted XCO2, retrieved from SCIAMACHY/ENVISAT using the WFM-DOAS algorithm, and output from NOAA's global CO2 modelling and assimilation system CarbonTracker. We investigate to what extent differences between these two data sets are influenced by systematic retrieval errors due to aerosols and unaccounted clouds. We analyse seven years of SCIAMACHY WFM-DOAS version 2.1 retrievals (WFMDv2.1) using the latest version of CarbonTracker (version 2010). We investigate to what extent the difference between SCIAMACHY and CarbonTracker XCO2 are temporally and spatially correlated with global aerosol and cloud data sets. For this purpose, we use a global aerosol data set generated within the European GEMS project, which is based on assimilated MODIS satellite data. For clouds, we use a data set derived from CALIOP/CALIPSO. We find significant correlations of the SCIAMACHY minus CarbonTracker XCO2 difference with thin clouds over the Southern Hemisphere. The maximum temporal correlation we find for Darwin, Australia (r2 = 54%). Large temporal correlations with thin clouds are also observed over other regions of the Southern Hemisphere (e.g. 43% for South America and 31% for South Africa). Over the Northern Hemisphere the temporal correlations are typically much lower. An exception is India, where large temporal correlations with clouds and aerosols have also been found. For all other regions the temporal correlations with aerosol are typically low. For the spatial correlations the picture is less clear. They are typically low for both aerosols and clouds, but dependent on region and season, they may exceed 30% (the maximum value of 46% has been found for Darwin during September to November). Overall we find that the presence of thin clouds can potentially explain a significant fraction of the difference between SCIAMACHY WFMDv2.1 XCO2 and CarbonTracker over the Southern Hemisphere. Aerosols appear to be less of a problem. Our study indicates that the quality of the satellite derived XCO2 will significantly benefit from a reduction of scattering related retrieval errors at least for the Southern Hemisphere.

Description: We analyze an ensemble of seven XCO2 retrieval algorithms for SCIAMACHY and GOSAT. The ensemble spread can be interpreted as regional uncertainty and can help to identify locations for new TCCON validation sites. Additionally, we introduce the ensemble median algorithm EMMA combining individual soundings of the seven algorithms into one new dataset. The ensemble takes advantage of the algorithms' independent developments. We find ensemble spreads being often

Description: Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now as the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m.) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat can verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1) with a systematic error of less than ~4.9 % for 50 % of all the power plants. For 90 % of all the power plants, the systematic error was less than ~12.4 %. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Finally, we recommend the CarbonSat constellation configuration that achieves daily global coverage.