Dry-air Mole Fractions Of CO2 Research Articles

Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research

The global carbon cycle research requires precise and sufficient observations of the column-averaged dry-air mole fraction of CO 2 (XCO 2 ) in addition to conventional surface mole fraction observations. In addition, assessing the consistency of multi-satellite data are crucial for joint utilization to better infer information about CO 2 sources and sinks. In this work, we evaluate the consistency of long-term XCO 2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2) in comparison with Total Carbon Column Observing Network (TCCON) and the 3D model of CO 2 mole fractions data from CarbonTracker 2017 (CT2017). We create a consistent joint dataset and compare it with the long-term model data to assess their abilities to characterize the carbon cycle climate. The results show that, although slight increasing differences are found between the GOSAT and TCCON XCO 2 in the northern temperate latitudes, the GOSAT and OCO-2 XCO 2 retrievals agree well in general, with a mean bias ± standard deviation of differences of 0.21 ± 1.3 ppm. The differences are almost within ±2 ppm and are independent of time, indicating that they are well calibrated. The differences between OCO-2 and CT2017 XCO 2 are much larger than those between GOSAT and CT XCO 2 , which can be attributed to the significantly different spatial representatives of OCO-2 and the CT-transport model 5 (TM5). The time series of the combined OCO-2/GOSAT dataset and the modeled XCO 2 agree well, and both can characterize significantly increasing atmospheric CO 2 under the impact of a large El Niño during 2015 and 2016. The trend calculated from the dataset using the seasonal Kendall (S-K) method indicates that atmospheric CO 2 is increasing by 2–2.6 ppm per year.

A global map of emission clumps for future monitoring of fossil fuel CO<sub>2</sub> emissions from space

Abstract. A large fraction of fossil fuel CO2 emissions emanate from “hotspots”, such as cities (where direct CO2 emissions related to fossil fuel combustion in transport, residential, commercial sectors, etc., excluding emissions from electricity-producing power plants, occur), isolated power plants, and manufacturing facilities, which cover a small fraction of the land surface. The coverage of all high-emitting cities and point sources across the globe by bottom-up inventories is far from complete, and for most of those covered, the uncertainties in CO2 emission estimates in bottom-up inventories are too large to allow continuous and rigorous assessment of emission changes (Gurney et al., 2019). Space-borne imagery of atmospheric CO2 has the potential to provide independent estimates of CO2 emissions from hotspots. But first, what a hotspot is needs to be defined for the purpose of satellite observations. The proposed space-borne imagers with global coverage planned for the coming decade have a pixel size on the order of a few square kilometers and a XCO2 accuracy and precision of <1 ppm for individual measurements of vertically integrated columns of dry-air mole fractions of CO2 (XCO2). This resolution and precision is insufficient to provide a cartography of emissions for each individual pixel. Rather, the integrated emission of diffuse emitting areas and intense point sources is sought. In this study, we characterize area and point fossil fuel CO2 emitting sources which generate coherent XCO2 plumes that may be observed from space. We characterize these emitting sources around the globe and they are referred to as “emission clumps” hereafter. An algorithm is proposed to identify emission clumps worldwide, based on the ODIAC global high-resolution 1 km fossil fuel emission data product. The clump algorithm selects the major urban areas from a GIS (geographic information system) file and two emission thresholds. The selected urban areas and a high emission threshold are used to identify clump cores such as inner city areas or large power plants. A low threshold and a random walker (RW) scheme are then used to aggregate all grid cells contiguous to cores in order to define a single clump. With our definition of the thresholds, which are appropriate for a space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11 314 individual clumps, with 5088 area clumps, and 6226 point-source clumps (power plants) are identified. These clumps contribute 72 % of the global fossil fuel CO2 emissions according to the ODIAC inventory. The emission clumps is a new tool for comparing fossil fuel CO2 emissions from different inventories and objectively identifying emitting areas that have a potential to be detected by future global satellite imagery of XCO2. The emission clump data product is distributed from https://doi.org/10.6084/m9.figshare.7217726.v1.

Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO<sub>2</sub> monitoring in urban areas

Abstract. CO2 emission estimates from urban areas can be obtained with a network of in situ instruments measuring atmospheric CO2 combined with high-resolution (inverse) transport modelling. Because the distribution of CO2 emissions is highly heterogeneous in space and variable in time in urban areas, gradients of atmospheric CO2 (here, dry air mole fractions) need to be measured by numerous instruments placed at multiple locations around and possibly within these urban areas. This calls for the development of lower-cost medium-precision sensors to allow a deployment at required densities. Medium precision is here set to be a random error (uncertainty) on hourly measurements of ±1 ppm or less, a precision requirement based on previous studies of network design in urban areas. Here we present tests of newly developed non-dispersive infrared (NDIR) sensors manufactured by Senseair AB performed in the laboratory and at actual field stations, the latter for CO2 dry air mole fractions in the Paris area. The lower-cost medium-precision sensors are shown to be sensitive to atmospheric pressure and temperature conditions. The sensors respond linearly to CO2 when measuring calibration tanks, but the regression slope between measured and assigned CO2 differs between individual sensors and changes with time. In addition to pressure and temperature variations, humidity impacts the measurement of CO2, with all of these factors resulting in systematic errors. In the field, an empirical calibration strategy is proposed based on parallel measurements with the lower-cost medium-precision sensors and a high-precision instrument cavity ring-down instrument for 6 months. The empirical calibration method consists of using a multivariable regression approach, based on predictors of air temperature, pressure and humidity. This error model shows good performances to explain the observed drifts of the lower-cost medium-precision sensors on timescales of up to 1–2 months when trained against 1–2 weeks of high-precision instrument time series. Residual errors are contained within the ±1 ppm target, showing the feasibility of using networks of HPP3 instruments for urban CO2 networks. Provided that they could be regularly calibrated against one anchor reference high-precision instrument these sensors could thus collect the CO2 (dry air) mole fraction data required as for top-down CO2 flux estimates.

Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

NASA’s carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015–2018) of global (60°S–60°N) XCO2 anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO2 dry air mole fraction, XCO2, are robust and consistent from year-to-year. We evaluate the method by comparing the anomalies to fluxes from anthropogenic, biospheric, and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO2 (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Highveld region in South Africa), shown as large positive XCO2 enhancements in the model simulations. We also find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO2 enhancements from simulations and high solar-induced chlorophyll fluorescence (SIF) values (indicating the occurrence of photosynthesis). The largest discrepancies between the anomaly patterns and the model-based results are observed in the tropical regions, where OCO-2 shows persistent positive anomalies over every season of every year included in this study. Finally, we demonstrate how XCO2 anomalies enable the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. In particular, we analyze the XCO2 anomalies collocated with the recent TROPOspheric Monitoring Instrument NO2 observations (used as indicator of anthropogenic fossil fuel combustion) over the Highveld region in South Africa. The results highlight the capability of satellite-based observations to monitor natural and man-made CO2 signatures on global scale.

Using a Speed-Dependent Voigt Line Shape to Retrieve O2 from Total Carbon Column Observing Network Solar Spectra to Improve Measurements of XCO2.

High-resolution, laboratory, absorption spectra of the oxygen (O2) band measured using cavity ring-down spectroscopy were fitted using the Voigt and speed-dependent Voigt line shapes. We found that the speed-dependent Voigt line shape was better able to model the measured absorption coefficients than the Voigt line shape. We used these line shape models to calculate absorption coefficients to retrieve atmospheric total columns abundances of O2 from ground-based spectra from four Fourier transform spectrometers that are apart of the Total Carbon Column Observing Network (TCCON) Lower O2 total columns were retrieved with the speed-dependent Voigt line shape, and the difference between the total columns retrieved using the Voigt and speed-dependent Voigt line shapes increased as a function of solar zenith angle. Previous work has shown that carbon dioxide (CO2) total columns are better retrieved using a speed-dependent Voigt line shape with line mixing. The column-averaged dry-air mole fraction of CO2 (XCO2) was calculated using the ratio between the columns of CO2 and O2 retrieved (from the same spectra) with both line shapes from measurements made over a one-year period at the four sites. The inclusion of speed dependence in the O2 retrievals significantly reduces the airmass dependence of XCO2 and the bias between the TCCON measurements and calibrated integrated aircraft profile measurements was reduced from 1% to 0.4%. These results suggest that speed dependence should be included in the forward model when fitting near-infrared CO2 and O2 spectra to improve the accuracy of XCO2 measurements.

Validation of Column-Averaged Dry-Air Mole Fraction of CO2 Retrieved from OCO-2 Using Ground-Based FTS Measurements

In order to correctly use the column-averaged atmospheric CO2 dry-air mole fraction (XCO2) data in the CO2 flux studies, XCO2 measurements retrieved from the Orbiting Carbon Observatory-2 (OCO-2) in 2015 were compared with those obtained from the global ground-based high-resolution Fourier Transform Spectrometer (FTS) participating in the Total Carbon Column Observing Network (TCCON). The XCO2 retrieved from three observing modes adopted by OCO-2, i.e., nadir, target, and glint, were separately validated by the FTS measurements at up to eight TCCON stations located in different areas. These comparisons show that OCO-2 glint mode yields the best qualitative estimations of CO2 concentration among the three operational approaches. The overall results regarding the glint mode show no obvious systematic biases. These facts may indicate that the glint concept is appropriate for not only oceans but also land regions. Negative systematic biases in nadir and target modes have been found at most TCCON sites. The standard deviations of XCO2 retrieved from target and nadir modes within the observation period are simi-lar, and larger than those from glint mode. We also used the FTS site in Beijing, China, to assess the OCO-2 XCO2 in 2016. This site is located in a typical urban area, which has been absent in previous studies. Overall, OCO-2 XCO2 agrees well with that from FTS at this site. Such a study will benefit the validation of the newly launched TanSat products in China.

Detection of Spatiotemporal Extreme Changes in Atmospheric CO2 Concentration Based on Satellite Observations

Atmospheric CO2 concentrations are sensitive to the effects of climate extremes on carbon sources and sinks of the land biosphere. Therefore, extreme changes of atmospheric CO2 can be used to identify anomalous sources and sinks of carbon. In this study, we develop a spatiotemporal extreme change detection method for atmospheric CO2 concentrations using column-averaged CO2 dry air mole fraction (XCO2) retrieved from the Greenhouse gases Observing SATellite (GOSAT) from 1 June 2009 to 31 May 2016. For extreme events identified, we attributed the main drivers using surface environmental parameters, including surface skin temperature, self-calibrating Palmer drought severity index, burned area, and gross primary production (GPP). We also tested the sensitivity of XCO2 response to changing surface CO2 fluxes using model simulations and Goddard Earth Observing System (GEOS)-Chem atmospheric transport. Several extreme high XCO2 events are detected around mid-2010 over Eurasia and in early 2016 in the tropics. The magnitudes of extreme XCO2 increases are around 1.5–1.8 ppm in the Northern Hemisphere and 1.2–1.4 ppm in Southern Hemisphere. The spatiotemporal pattern of detected high XCO2 events are similar to patterns of local surface environmental parameter extremes. The extreme high XCO2 events often occurred during periods of increased temperature, severe drought, increased wildfire or reduced GPP. Our sensitivity tests show that the magnitude of detectable anomalies varies with location, for example 25% or larger anomalies in local CO2 emission fluxes are detectable in tropical forest, whereas anomalies must be half again as large in mid-latitudes (~37.5%). In conclusion, we present a method for extreme high XCO2 detection, and large changes in land CO2 fluxes. This provides another tool to monitor large-scale changes in the land carbon sink and potential feedbacks on the climate system.

Analysis on the variations of atmospheric CO2 concentrations along the urban–rural gradients of Chinese cities based on the OCO-2 XCO2 data

ABSTRACTAnthropogenic CO2 emissions contribute the most to the growth of atmospheric CO2 concentrations. These emissions are largely concentrated in urban areas where human activities are intense. Studies have been conducted to explore the urban and rural difference in CO2 concentrations based on ground-based measurements. The launch of NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite provides a new opportunity to monitor CO2 concentrations and their spatial and temporal variations at city scale. The objective of this study is to analyse the spatial and temporal distributions of CO2 concentrations along the urban–rural gradients of Chinese cities, using the column averaged CO2 dry air mole fraction (XCO2) data derived from OCO-2. We used both conceptual and physical urban–rural gradients to analyse variations in CO2 concentrations over space. The results show that the urban and rural difference in CO2 concentrations of these cities can be monitored. And, the seasonal variations of CO2 concentrations in these cities can also be detected using the XCO2 data. Moreover, the variations in CO2 concentrations along the urban–rural gradients have four main types with significant enhancements of CO2 concentrations were observed in urban areas, urban–rural transitional areas, rural areas, and without regular patterns, respectively. The results are generally different from the common assumption that CO2 concentrations peak in central urban areas and decline in rural areas. In conclusion, the XCO2 data can be used to analyse the spatial-temporal variations of CO2 concentrations along the urban–rural gradients of Chinese cities, and the results have important policy implications for mitigating CO2 emissions.

The Total Carbon Column Observing Network site description for Lauder, New Zealand

Abstract. In this paper we describe the retrievals of atmospheric trace gases from near-infrared, high-resolution solar absorption spectroscopy measurements at the Lauder atmospheric research station in New Zealand and submitted to the Total Carbon Column Observing Network (TCCON) archive. The Lauder site (45.034° S, 169.68° E, 370 m a.s.l.) is located within a sparsely populated region of the South Island of New Zealand and is sheltered from the prevailing wind direction by the Southern Alps, which gives the site a high number of clear-sky days and an air mass that is largely unmodified by regional anthropogenic sources. The Lauder TCCON archive consists of data from two instruments: a Bruker IFS 120HR from June 2004 to February 2010 and a Bruker IFS 125HR from February 2010 to present. The bias between the two instruments is assessed to be 0.068 % for CO2. Since measurements using the IFS 125HR began, the SD about the hourly mean has been better than 0.1 % for 96.81 % of CO2 column retrievals. The retrievals have been calibrated against in situ airborne measurements to correct for biases and provide traceability to the World Meteorological Organization (WMO) scales with an accuracy of 0.1 % for CO2. The Lauder TCCON time series of retrieved dry-air mole fractions of CO2, CH4, N2O, HF, H2O, HDO and CO are available from the TCCON data archive. The DOIs arehttps://doi.org/10.14291/tccon.ggg2014.lauder01.R0/1149293 for the IFS 120HR datahttps://doi.org/10.14291/tccon.ggg2014.lauder02.R0/1149298 for the IFS 125HR data.

The Cross-Calibration of Spectral Radiances and Cross-Validation of CO2 Estimates from GOSAT and OCO-2

The Greenhouse gases Observing SATellite (GOSAT) launched in January 2009 has provided radiance spectra with a Fourier Transform Spectrometer for more than eight years. The Orbiting Carbon Observatory 2 (OCO-2) launched in July 2014, collects radiance spectra using an imaging grating spectrometer. Both sensors observe sunlight reflected from Earth’s surface and retrieve atmospheric carbon dioxide (CO2) concentrations, but use different spectrometer technologies, observing geometries, and ground track repeat cycles. To demonstrate the effectiveness of satellite remote sensing for CO2 monitoring, the GOSAT and OCO-2 teams have worked together pre- and post-launch to cross-calibrate the instruments and cross-validate their retrieval algorithms and products. In this work, we first compare observed radiance spectra within three narrow bands centered at 0.76, 1.60 and 2.06 µm, at temporally coincident and spatially collocated points from September 2014 to March 2017. We reconciled the differences in observation footprints size, viewing geometry and associated differences in surface bidirectional reflectance distribution function (BRDF). We conclude that the spectral radiances measured by the two instruments agree within 5% for all bands. Second, we estimated mean bias and standard deviation of column-averaged CO2 dry air mole fraction (XCO2) retrieved from GOSAT and OCO-2 from September 2014 to May 2016. GOSAT retrievals used Build 7.3 (V7.3) of the Atmospheric CO2 Observations from Space (ACOS) algorithm while OCO-2 retrievals used Version 7 of the OCO-2 retrieval algorithm. The mean biases and standard deviations are −0.57 ± 3.33 ppm over land with high gain, −0.17 ± 1.48 ppm over ocean with high gain and −0.19 ± 2.79 ppm over land with medium gain. Finally, our study is complemented with an analysis of error sources: retrieved surface pressure (Psurf), aerosol optical depth (AOD), BRDF and surface albedo inhomogeneity. We found no change in XCO2 bias or standard deviation with time, demonstrating that both instruments are well calibrated.

The Orbiting Carbon Observatory (OCO-2) tracks 2\u20133 peta-gram increase in carbon release to the atmosphere during the 2014\u20132016 El Ni\xf1o

The powerful El Niño event of 2015–2016 – the third most intense since the 1950s – has exerted a large impact on the Earth’s natural climate system. The column-averaged CO2 dry-air mole fraction (XCO2) observations from satellites and ground-based networks are analyzed together with in situ observations for the period of September 2014 to October 2016. From the differences between satellite (OCO-2) observations and simulations using an atmospheric chemistry-transport model, we estimate that, relative to the mean annual fluxes for 2014, the most recent El Niño has contributed to an excess CO2 emission from the Earth’s surface (land + ocean) to the atmosphere in the range of 2.4 ± 0.2 PgC (1 Pg = 1015 g) over the period of July 2015 to June 2016. The excess CO2 flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree from increased biomass burning. It is about the half of the CO2 flux anomaly (range: 4.4–6.7 PgC) estimated for the 1997/1998 El Niño. The annual total sink is estimated to be 3.9 ± 0.2 PgC for the assumed fossil fuel emission of 10.1 PgC. The major uncertainty in attribution arise from error in anthropogenic emission trends, satellite data and atmospheric transport.

The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes.

NASA's Orbiting Carbon Observatory-2 (OCO-2) mission was motivated by the need to diagnose how the increasing concentration of atmospheric carbon dioxide (CO2) is altering the productivity of the biosphere and the uptake of CO2 by the oceans. Launched on 2 July 2014, OCO-2 provides retrievals of the column-averaged CO2 dry-air mole fraction ([Formula: see text]) as well as the fluorescence from chlorophyll in terrestrial plants. The seasonal pattern of uptake by the terrestrial biosphere is recorded in fluorescence and the drawdown of [Formula: see text] during summer. Launched just before one of the most intense El Niños of the past century, OCO-2 measurements of [Formula: see text] and fluorescence record the impact of the large change in ocean temperature and rainfall on uptake and release of CO2 by the oceans and biosphere.

The arctic seasonal cycle of total column CO<sub>2</sub> and CH<sub>4</sub> from ground-based solar and lunar FTIR absorption spectrometry

Abstract. Solar absorption spectroscopy in the near infrared has been performed in Ny-Ålesund (78.9° N, 11.9° E) since 2002; however, due to the high latitude of the site, the sun is below the horizon from October to March (polar night) and no solar absorption measurements are possible. Here we present a novel method of retrieving the total column dry-air mole fractions (DMFs) of CO2 and CH4 using moonlight in winter. Measurements have been taken during the polar nights from 2012 to 2016 and are validated with TCCON (Total Carbon Column Observing Network) measurements by solar and lunar absorption measurements on consecutive days and nights during spring and autumn. The complete seasonal cycle of the DMFs of CO2 and CH4 is presented and a precision of up to 0.5 % is achieved. A comparison of solar and lunar measurements on consecutive days during day and night in March 2013 yields non-significant biases of 0. 66 ± 4. 56 ppm for xCO2 and −1. 94 ± 20. 63 ppb for xCH4. Additionally a model comparison has been performed with data from various reanalysis models.

Monitoring carbon dioxide from space: Retrieval algorithm and flux inversion based on GOSAT data and using CarbonTracker-China

Monitoring atmospheric carbon dioxide (CO2) from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of “The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change: Carbon Budget and Relevant Issues”. The carbon flux inversion system is composed of two separate parts: the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS), and CarbonTracker-China (CT-China), developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite (GOSAT) measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO2 (column-averaged CO2 dry-air mole fraction) is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO2 product is used in carbon flux estimation by CT-China. The net ecosystem CO2 exchange is −0.34 Pg C yr−1 (±0.08 Pg C yr−1), with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.

A comparison of satellite observations with the XCO2 surface obtained by fusing TCCON measurements and GEOS-Chem model outputs

Ground observations can capture CO2 concentrations accurately but the number of available TCCON (Total Carbon Column Observing Network) sites is too small to support a comprehensive analysis (i.e. validation) of satellite observations. Atmospheric transport models can provide continuous atmospheric CO2 concentrations in space and time, but some information is difficult to generate with model simulations. The HASM platform can model continuous column-averaged CO2 dry air mole fraction (XCO2) surface taking TCCON observations as its optimum control constraints and an atmospheric transport model as its driving field. This article presents a comparison of the satellite observations with a HASM XCO2 surface obtained by fusing TCCON measurements with GEOS-Chem model results. We first verified the accuracy of the HASM XCO2 surface using six years (2010–2015) of TCCON observations and the GEOS-Chem model XCO2 results. The validation results show that the largest MAE of bias between the HASM results and observations was 0.85ppm and the smallest MAE was only 0.39ppm. Next, we modeled the HASM XCO2 surface by fusing the TCCON measurements and GEOS-Chem XCO2 model results for the period 9/1/14 to 8/31/15. Finally, we compared the GOSAT and OCO-2 observations with the HASM XCO2 surface and found that the global OCO-2 XCO2 estimates more closely resembled the HASM XCO2 surface than the GOSAT XCO2 estimates.

A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations

Using measurements of the column-averaged CO2 dry air mole fraction (XCO2) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from MODIS, this study proposes a data-driven approach to assess the impacts of terrestrial biosphere activities on the seasonal cycle pattern of XCO2. A unique global land mapping dataset of XCO2 with a resolution of 1° by 1° in space, and three days in time, from June 2009 to May 2014, which facilitates the assessment at a fine scale, is first produced from GOSAT XCO2 retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO2 and NDVI, and implement correlation analyses of seasonal variation between XCO2 and the vegetation parameters. As a result, the spatial distribution of XCO2 SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO2 from CarbonTracker. The spatial pattern of XCO2 SCA corresponds well to the vegetation seasonal activity revealed by NDVI, with a strong correlation coefficient of 0.74 in the northern hemisphere (NH). Some hotspots in the subtropical areas, including Northern India (with SCA of 8.68 ± 0.49 ppm on average) and Central Africa (with SCA of 8.33 ± 0.25 ppm on average), shown by satellite measurements, but missed by model simulations, demonstrate the advantage of satellites in observing the biosphere–atmosphere interactions at local scales. Results from correlation analyses between XCO2 and NDVI, EVI, LAI, or GPP show a consistent spatial distribution, and NDVI and EVI have stronger negative correlations over all latitudes. This may suggest that NDVI and EVI can be better vegetation parameters in characterizing the seasonal variations of XCO2 and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO2 compared to NDVI in seasonal variation, which, to our knowledge, is the first such map derived from a completely data-driven approach using satellite observations. The impact of retrieval error of GOSAT data on the mapping data, especially over high-latitude areas, is further discussed. Results from this study provide reference for better understanding the distribution of the strength of carbon sink by terrestrial ecosystems and utilizing remote sensing data in assessing the impact of biosphere–atmosphere interactions on the seasonal cycle pattern of atmospheric CO2 columns.

The Orbiting Carbon Observatory-2: first 18 months of science data products

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set

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.

The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry-air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (&lt; 0.25 %) changes in the background XCO2 field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO2 distribution could be misinterpreted as evidence for CO2 fluxes. To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s−1 across a narrow (&lt; 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO2. Each of these soundings has an unprecedented combination of spatial resolution (&lt; 3 km2/sounding), spectral resolving power (λ∕Δλ &gt; 17 000), dynamic range (∼ 104), and sensitivity (continuum signal-to-noise ratio &gt; 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.

Quantification of uncertainties in OCO-2 measurements of XCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;: simulations and linear error analysis

Abstract. We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO2 (XCO2) by the NASA Orbiting Carbon Observatory-2 (OCO-2). The analysis is based on our best estimates for uncertainties in the OCO-2 operational algorithm and its inputs, and uses simulated spectra calculated for the actual flight and sounding geometry, with measured atmospheric analyses. The simulations are calculated for land nadir and ocean glint observations. We include errors in measurement, smoothing, interference, and forward model parameters. All types of error are combined to estimate the uncertainty in XCO2 from single soundings, before any attempt at bias correction has been made. From these results we also estimate the "variable error" which differs between soundings, to infer the error in the difference of XCO2 between any two soundings. The most important error sources are aerosol interference, spectroscopy, and instrument calibration. Aerosol is the largest source of variable error. Spectroscopy and calibration, although they are themselves fixed error sources, also produce important variable errors in XCO2. Net variable errors are usually &lt; 1 ppm over ocean and ∼ 0.5–2.0 ppm over land. The total error due to all sources is ∼ 1.5–3.5 ppm over land and ∼ 1.5–2.5 ppm over ocean.