Indonesia experienced an exceptional number of fires in 2015 as a result of droughts related to the recent El Niño event and human activities. These fires released large amounts of carbon dioxide (CO2) into the atmosphere. Emission databases such as the Global Fire Assimilation System version 1.2 and the Global Fire Emission Database version 4s estimated the CO2 emission to be approximately 1100 MtCO2 in the time period from July to November 2015. This emission was indirectly estimated by using parameters like burned area, fire radiative power, and emission factors. In the study presented in this paper, we estimate the Indonesian fire CO2 emission by using the column‐averaged dry air mole fraction of CO2, XCO2, derived from measurements of the Orbiting Carbon Observatory‐2 satellite mission. The estimated CO2 emission is 748 ± 209 MtCO2, which is about 30% lower than provided by the emission databases.

Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e. XCH4, can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH4 show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1σ uncertainties directly from maps of annually averaged satellite XCH4. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite XCH4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003–2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5–8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR.

Carbon dioxide (CO2) and methane (CH4) are the two most important greenhouse gases emitted by mankind. Better knowledge of the surface sources and sinks of these Essential Climate Variables (ECVs) and related carbon uptake and release processes is needed for important climate change related applications such as improved climate modelling and prediction. Some satellites provide near-surface-sensitive atmospheric CO2 and CH4 observations that can be used to obtain information on CO2 and CH4 surface fluxes. The goal of the GHG-CCI project of the European Space Agency's (ESA) Climate Change Initiative (CCI) is to use satellite data to generate atmospheric CO2 and CH4 data products meeting demanding GCOS (Global Climate Observing System) greenhouse gas (GHG) ECV requirements. To achieve this, retrieval algorithms are regularly being improved followed by annual data reprocessing and analysis cycles to generate better products in terms of extended time series and continuously improved data quality. Here we present an overview about the latest GHG-CCI data set called Climate Research Data Package No. 3 (CRDP3) focusing on the GHG-CCI core data products, which are column-averaged dry-air mole fractions of CO2 and CH4, i.e., XCO2 and XCH4, as retrieved from SCIAMACHY/ENVISAT and TANSO/GOSAT satellite radiances covering the time period end of 2002 to end of 2014. We present global maps and time series including initial validation results obtained by comparisons with Total Carbon Column Observing Network (TCCON) ground-based observations. We show that the GCOS requirements for systematic error (<1ppm for XCO2, <10ppb for XCH4) and long-term stability (<0.2ppm/year for XCO2, <2ppb/year for XCH4) are met for nearly all products (an exception is SCIAMACHY methane especially since 2010). For XCO2 we present comparisons with global models using the output of two CO2 assimilation systems (MACC version 14r2 and CarbonTracker version CT2013B). We show that overall there is reasonable consistency and agreement between all data sets (within ~1–2ppm) but we also found significant differences depending on region and time period.

29 Carbon dioxide (CO2) and methane (CH4) are the two most important greenhouse gases emitted by 30 mankind. Better knowledge of the surface sources and sinks of these Essential Climate Variables 31 (ECVs) and related carbon uptake and release processes is needed for important climate change related 32 applications such as improved climate modelling and prediction. Some satellites provide near-surface33 sensitive atmospheric CO2 and CH4 observations that can be used to obtain information on CO2 and 34 CH4 surface fluxes. The goal of the GHG-CCI project of the European Space Agency’s (ESA) Climate 35 Change Initiative (CCI) is to use satellite data to generate atmospheric CO2 and CH4 data products 36 meeting demanding GCOS (Global Climate Observing System) greenhouse gas (GHG) ECV 37 requirements. To achieve this, retrieval algorithms are regularly being improved followed by annual 38 data reprocessing and analysis cycles to generate better products in terms of extended time series and 39 continuously improved data quality. Here we present an overview about the latest GHG-CCI data set 40 called Climate Research Data Package No. 3 (CRDP3) focusing on the GHG-CCI core data products, 41 which are column-averaged dry-air mole fractions of CO2 and CH4, i.e., XCO2 and XCH4, as retrieved 42 from SCIAMACHY/ENVISAT and TANSO/GOSAT satellite radiances covering the time period end 43 of 2002 to end of 2014. We present global maps and time series including initial validation results 44 obtained by comparisons with Total Carbon Column Observing Network (TCCON) ground-based 45 observations. We show that the GCOS requirements for systematic error (< 1 ppm for XCO2, < 10 ppb 46 for XCH4) and long-term stability (< 0.2 ppm/year for XCO2, < 2 ppb/year for XCH4) are met for 47 nearly all products (an exception is SCIAMACHY methane especially since 2010). For XCO2 we 48 present comparisons with global models using the output of two CO2 assimilation systems (MACC 49 version 14r2 and CarbonTracker version CT2013B). We show that overall there is reasonable 50 consistency and agreement between all data sets (within ~1-2 ppm) but we also found significant 51 differences depending on region and time period. 52

tions concerning the performance of our method. These investigations are based on a simulated high-resolution methane data set. Furthermore, we now better highlight already in the abstract limitations of our method. We show that our method typically provides a conservative estimate of the emissions, i.e., our emissions are typically underestimated. We better explain why our method tends to underestimate emissions. The large uncertainty of our method is reflected in quite large uncertainty estimates which are typically on the order of 100% for the source regions discussed in our manuscript. Our fast and simple method has been developed to obtain a reasonable estimate of the annual methane emission of a region which shows elevated methane relative to its surrounding region in maps of annually averaged satellite-derived XCH4. When applying our method to multiple years of satellite data, the results will show, if elevated atmospheric methane is present in all years or not. If the methane is elevated in all years than this is very likely due to an “underlying” methane emission source (assuming that the satellite data do not have a “local bias”). We would not apply our method to situations, where this is not the case (although our method can be applied also to methane fields which are spatially constant/flat but in this case our method will deliver an estimated emission of zero together with a large error bar). There are no limitations w.r.t. the size of the region (as size is (approximately) considered by parameter L) or the size of the XCH4 signal (as explained above) or the magnitude of the emission (which is unknown as the satellite only provides XCH4). As shown in our manuscript we have not identified any conditions, where the method is shown to fail entirely but we recommend to be careful if the targeted source region is known to exhibit “pooling overnight” (more details on this aspect are given below) and/or for regions with complex topography (where, for example, methane can accumulate in valleys; these are however situations where all inversion methods will likely have severe difficulty). Our method assumes that the emission sources are homogeneously distributed in the targeted source region. We show in our manuscript that the estimated emissions are underestimated if this is not the case. Underestimation also results from sources located in the surrounding region. From all this we conclude that our method can be

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 (in ppm) and XCH4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products ("EMMA products"). Here we also present a first GHG-CCI Level 3 product, namely XCO2 and XCH4 in Obs4MIPs format (monthly, 5°×5°).

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-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TC-CON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively) and an improved estimated precision of 1 ppm compared to the GOSAT BESD data (3.3 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, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000 km) even up to day 5 compared to its own analysis.

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 (in ppm) and XCH4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products (EMMA products). Here we also present a first GHG-CCI Level 3 product, namely XCO2 and XCH4 in Obs4MIPs format (monthly, 5°×5°).

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).CORRESPONDING AUTHOR E-MAIL: M. Reuter, mail@maxreuter.org

The GHG-CCI project (http://www.esa-ghg-cci.org/) 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 – namely atmospheric CO2 and CH4 - with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. The main goal of GHG-CCI is to generate long-term highly accurate and precise time series of global near-surface-sensitive satellite observations of CO2 and CH4, i.e., XCO2 and XCH4, starting with the launch of ESA’s ENVISAT satellite. These products are currently retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode observations in the near-infrared/shortwave-infrared spectral region. In addition, other sensors (e.g., IASI and MIPAS) and viewing modes (e.g., SCIAMACHY solar occultation) are also considered and in the future also data from other satellites. The GHG-CCI data products and related documentation are freely available via the GHG-CCI website and yearly updates are foreseen. Here we present an overview about the latest data set (Climate Research Data Package No. 2 (CRDP#2)) and summarize key findings from using satellite CO2 and CH4 retrievals to improve our understanding of the natural and anthropogenic sources and sinks of these important atmospheric greenhouse gases. We also shortly mention ongoing activities related to validation and initial user assessment of CRDP#2 and future plans.

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.

CarbonSat is one of two candidate missions for ESA’s Earth Explorer 8 (EE8) satellite; one of them will be selected for implementation in November 2015 for a targeted launch date around 2023. The main goal of CarbonSat is to advance our knowledge of the sources and sinks, both natural and man-made, of the two most important anthropogenic greenhouse gases; carbon dioxide (CO2) and methane (CH4) from the global via the sub-continental to the local scale. CarbonSat will be the first satellite mission to image local scale emission hot spots of CO2 (e.g., cities, volcanoes, industrial areas) and CH4 (e.g., fossil fuel production, landfills, seeps) and to quantify their emissions and discriminate them from surrounding biospheric fluxes. The primary geophysical data products of CarbonSat are atmospheric columnaveraged dry air mole fractions of CO2 and CH4, i.e., XCO2 (in ppm) and XCH4 (in ppb), respectively. In addition, CarbonSat will deliver a number of secondary data products, which will also be of good quality, such as vegetation chlorophyll Sun-Induced Fluorescence (SIF) as retrieved from clear solar Fraunhofer lines located at 755 nm; SIF will be retrieved simultaneously with the primary products. Here we present an updated error budget using the latest retrieval algorithm and instrument/mission specification focusing on nadir observations over land.

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 (<3ppm) and TANSO XCH4 (<17ppb). The achieved relative accuracy for XCH4 is 3–15ppb for SCIAMACHY and 2–8ppb for TANSO depending on algorithm and time period. Meeting the 0.5ppm systematic error requirement for XCO2 remains a challenge: approximately 1ppm 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).

Introduction Conclusions References