(2015). The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets. Remote Sensing of Environment: an interdisciplinary journal, 162 344-362. The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets Abstract The GHG-CCI project is one of several projects of the European Space Agency's (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The "ECV Greenhouse Gases" (ECV GHG) is the global distribution of important climate relevant gases-atmospheric CO2 and CH4-with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. Two satellite instruments deliver the main input data for GHG-CCI: SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The first order priority goal of GHG-CCI is the further development of retrieval algorithms for near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 and XCH4, to meet the demanding user requirements. GHG-CCI focuses on four core data products: XCO2 from SCIAMACHY and TANSO and XCH4 from the same two sensors. For each of the four core data products at least two candidate retrieval algorithms have been independently further developed and the corresponding data products have been quality-assessed and inter-compared. This activity is referred to as "Round Robin" (RR) activity within the CCI. The main goal of the RR was to identify for each of the four core products which algorithms should be used to generate the Climate Research Data Package (CRDP). The CRDP will essentially be the first version of the ECV GHG. This manuscript gives an overview of the GHG-CCI RR and related activities. This comprises the establishment of the user requirements, the improvement of the candidate retrieval algorithms and comparisons with ground-based observations and models. The manuscript summarizes the final RR algorithm selection decision and its justification. Comparison with ground-based Total Carbon Column Observing Network (TCCON) data indicates that the "breakthrough" single measurement precision requirement has been met for SCIAMACHY and TANSO XCO2 (< 3 ppm) and TANSO XCH4 (< 17 ppb). The achieved relative accuracy for XCH4 is 3-15 ppb for SCIAMACHY and 2-8 ppb for TANSO depending on algorithm and time period. Meeting the 0.5 ppm systematic error requirement for XCO2 remains a challenge: approximately 1 ppm has been achieved at the validation sites but also larger differences have been found in regions remote from TCCON. More research is needed to identify the causes for the observed differences. In this context GHG-CCI suggests taking advantage of the ensemble of existing data products, for example, via the EnseMble Median Algorithm (EMMA). Abstract 41 The GHG-CCI pr...

Abstract. The three carbon gases carbon monoxide (CO), carbon dioxide (CO 2 ), and methane (CH 4 ) are important atmospheric constituents affecting air quality and climate. The near-infrared nadir spectra measured by SCIAMACHY on ENVISAT contain information on the vertical columns of these gases which we retrieve using a modified DOAS algorithm (WFM-DOAS or WFMD). Our main data products are CO vertical columns and dry-air column averaged mixing ratios of methane (CH 4 ) and CO 2 (denoted XCH 4 and XCO 2 ). For CO and CH 4 we present new results for the year 2003 obtained with an improved version of WFM-DOAS (WFMDv0.5) retrieved from Level 1 version 4 (Lv1v4) spectra. This data set has recently been compared with a network of ground based FTIR stations. Here we describe the WFMDv0.5 algorithm, present global and regional maps, and comparisons with global reference data. We show that major problems of the previous versions (v0.4 and v0.41) related to the varying ice-layer on the SCIAMACHY channel 8 detector have been solved. Compared to MOPITT the SCIAMACHY CO columns are on average higher by about 10-20%. Regionally, however, especially over central South America, differences can be much larger. For methane we present global and regional maps which are compared to TM5 model simulations performed using standard methane emission inventories. We show that methane source regions can be clearly detected with SCIAMACHY. We also show that the methane data product can be significantly further improved using Lv1v5 spectra with improved calibration. For CO 2 we present three years of SCIAMACHY CO 2 measurements over Park Falls, Wisconsin, USA, retrieved from Lv1v5. We show that the quality of CO 2 retrieved from Correspondence to: M. Buchwitz (michael.buchwitz@iup.physik.uni-bremen.de) these spectra is significantly higher compared to WFMDv0.4 XCO 2 retrieved from Lv1v4.

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 together with TANSO onboard GOSAT (launch 2009) the only satellite instrument 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. <br></br> We present long-term SCIAMACHY retrievals (2003–2009) of column-averaged 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. <br></br> The steady increase of atmospheric carbon dioxide primarily caused by the burning of fossil fuels can be clearly observed with SCIAMACHY globally. The retrieved annual mean XCO₂ increase over both hemispheres agrees with CarbonTracker within the error bars but is on average somewhat smaller (1.8 ppm yr⁻¹ compared to 1.9 ppm yr⁻¹). The amplitude of the XCO₂ seasonal cycle as retrieved by SCIAMACHY, which is 4.3 ppm for the Northern Hemisphere and 1.4 ppm for the Southern Hemisphere, is on average about 1 ppm larger than for CarbonTracker. <br></br> 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. <br></br> 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 8 ppb yr⁻¹ since 2007. Due care has been exercised to minimise the influence of detector degradation on the quantitative estimate of this anomaly

Abstract. Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. SCIAMACHY on ENVISAT is the first satellite instrument whose measurements are sensitive to concentration changes of the two gases at all altitude levels down to the Earth's surface where the source/sink signals are largest. We have processed three years (2003–2005) of SCIAMACHY near-infrared nadir measurements to simultaneously retrieve vertical columns of CO2 (from the 1.58 μm absorption band), CH4 (1.66 μm) and oxygen (O2 A-band at 0.76 μm) using the scientific retrieval algorithm WFM-DOAS. We show that the latest version of WFM-DOAS, version 1.0, which is used for this study, has been significantly improved with respect to its accuracy compared to the previous versions while essentially maintaining its high processing speed (~1 min per orbit, corresponding to ~6000 single measurements, and per gas on a standard PC). The greenhouse gas columns are converted to dry air column-averaged mole fractions, denoted XCO2 (in ppm) and XCH4 (in ppb), by dividing the greenhouse gas columns by simultaneously retrieved dry air columns. For XCO2 dry air columns are obtained from the retrieved O2 columns. For XCH4 dry air columns are obtained from the retrieved CO2 columns because of better cancellation of light path related errors compared to using O2 columns retrieved from the spectrally distant O2 A-band. Here we focus on a discussion of the XCH4 data set. The XCO2 data set is discussed in a separate paper (Part 1). For 2003 we present detailed comparisons with the TM5 model which has been optimally matched to highly accurate but sparse methane surface observations. After accounting for a systematic low bias of ~2% agreement with TM5 is typically within 1–2%. We investigated to what extent the SCIAMACHY XCH4 is influenced by the variability of atmospheric CO2 using global CO2 fields from NOAA's CO2 assimilation system CarbonTracker. We show that the CO2 corrected and uncorrected XCH4 spatio-temporal pattern are very similar but that agreement with TM5 is better for the CarbonTracker CO2 corrected XCH4. In line with previous studies (e.g., Frankenberg et al., 2005) we find significantly higher methane over the tropics compared to the model. We show that tropical methane is also higher when normalizing the CH4 columns with retrieved O2 columns instead of CO2. Concerning inter-annual variability we find similar methane spatio-temporal pattern for 2003 and 2004. For 2005 the retrieved methane shows significantly higher variability compared to the two previous years, most likely due to somewhat larger noise of the spectral measurements.

Abstract. An optimal estimation based retrieval scheme for satellite based measurements of XCO2 (the column averaged mixing ratio of atmospheric CO2) is presented enabling accurate retrievals also in the presence of thin clouds. The proposed method is designed to analyze near-infrared nadir measurements of the SCIAMACHY instrument in the CO2 absorption band at 1580 nm and in the O2-A absorption band at around 760 nm. The algorithm accounts for scattering in an optically thin cirrus cloud layer and at aerosols of a default profile. The scattering information is mainly obtained from the O2-A band and a merged fit windows approach enables the transfer of information between the O2-A and the CO2 band. Via the optimal estimation technique, the algorithm is able to account for a priori information to further constrain the inversion. Test scenarios of simulated SCIAMACHY sun-normalized radiance measurements are analyzed in order to specify the quality of the proposed method. In contrast to existing algorithms, the systematic errors due to cirrus clouds with optical thicknesses up to 1.0 are reduced to values typically below 4 ppm. This shows that the proposed method has the potential to reduce uncertainties of SCIAMACHY retrieved XCO2 making this data product useful for surface flux inverse modeling.