ALBERT

Description: Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

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: Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

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 GtC a−1 for 2000–2005 (Schulze et al., 2009), 0.17±0.44 GtC a−1 for 2001–2007 (Peters et al., 2010), 0.45±0.40 GtC a−1 for 2010 (Chevallier et al., 2014), 0.40±0.42 GtC 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 (1.0–1.3 GtC a−1 for 09/2009–08/2010 (Basu et al., 2013), 1.2–1.8 GtC a−1 in 2010 (Chevallier et al., 2014)). However, these results have been considered unrealistic due to potential retrieval biases and/or transport errors (Chevallier et al., 2014) or have not been discussed at all (Basu et al., 2013; Takagi et al., 2014). 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. We show that the satellite-derived European terrestrial carbon sink is indeed much larger (1.02±0.30 GtC a−1 in 2010) than previously expected. This is qualitatively consistent 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 (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)). The difference to in situ based inversions (Peylin et al., 2013), whilst large with respect to the mean reported European carbon sink (0.4 GtC a−1 for 2001–2004), is similar in magnitude to the reported uncertainty (0.42 GtC a−1). The highest gain in information is obtained during the growing season when satellite observation conditions are advantageous, a priori uncertainties are largest, and the surface sink maximises; during the dormant season, the results are dominated by the a priori. Our results provide evidence that the current understanding of the European carbon sink has to be revisited.

Description: Stratospheric profiles of methane (CH4) and carbon dioxide (CO2) have been derived from solar occultation measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The retrieval is performed using a method called "Onion Peeling DOAS" (ONPD) which combines an onion peeling approach with a weighting function DOAS (Differential Optical Absorption Spectroscopy) fit. By use of updated pointing information and optimisation of the data selection and of the retrieval approach the altitude range for reasonable CH4 could be extended to about 17 to 45 km. Furthermore, the quality of the derived CO2 has been assessed such that now the first stratospheric profiles of CO2 from SCIAMACHY are available. Comparisons with independent data sets yield an estimated accuracy of the new SCIAMACHY stratospheric profiles of about 5–10 % for CH4 and 2–3 % for CO2. The accuracy of the products is currently mainly restricted by the appearance of unexpected vertical oscillations in the derived profiles which need further investigation. Using the improved ONPD retrieval, CH4 and CO2 stratospheric data sets covering the whole SCIAMACHY time series (August 2002–April 2012) and the latitudinal range between about 50 and 70° N have been derived. Based on these time series, CH4 and CO2 trends have been estimated, which are in reasonable agreement with total column trends for these gases. This shows that the new SCIAMACHY data sets can provide valuable information about the stratosphere.