Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(...Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(XCO_(2))since 2014.In this study,the OCO-2 XCO_(2)products were compared between in-situ data from the Total Carbon Column Network(TCCON)and Global Monitoring Division(GMD),and modeling data from CarbonTracker2019 over global ocean and land.Results showed that the OCO-2 XCO_(2)data are consistent with the TCCON and GMD in situ XCO_(2)data,with mean absolute biases of 0.25×10^(-6)and 0.67×10^(-6),respectively.Moreover,the OCO-2 XCO_(2)data are also consistent with the CarbonTracker2019 modeling XCO_(2)data,with mean absolute biases of 0.78×10^(-6)over ocean and 1.02×10^(-6)over land.The results indicated the high accuracy of the OCO-2 XCO_(2)product over global ocean which could be applied to estimate the air-sea CO_(2)flux.展开更多
A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2(OCO-2)cloud screening algorithms.Cloud screening strategies are ...A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2(OCO-2)cloud screening algorithms.Cloud screening strategies are essential to remove scenes with significant cloud and/or aerosol contamination from OCO-2 observations,which helps to save on the data processing cost and ensure high quality retrievals of the column-averaged CO2 dry air mole fraction(XCO2).Based on the radiance measurements in the 0.76μm O2A band,1.61μm(weak),and 2.06μm(strong)CO2 bands,the current combination of the A-Band Preprocessor(ABP)algorithm and Iterative Maximum A Posteriori(IMAP)Differential Optical Absorption Spectroscopy(DOAS)Preprocessor(IDP)algorithm passes around 20%-25%of all soundings,which means that some contaminated scenes also pass the screening process.In this work,three independent pairs of threshold parameters used in the ABP and IDP algorithms are sufficiently tuned until the overall pass rate is close to the monthly clear-sky fraction from the MODIS cloud mask.The tightened thresholds are applied to observations over land surfaces in Europe and Japan in 2016.The results show improvement of agreement and positive predictive value compared to the collocated MODIS cloud mask,especially in summer and fall.In addition,analysis indicates that XCO2 retrievals with more stringent thresholds are in closer agreement with measurements from collocated Total Carbon Column Observing Network(TCCON)sites.展开更多
In order to correctly use the column-averaged atmospheric COdry-air mole fraction(XCO) data in the COflux studies, XCOmeasurements retrieved from the Orbiting Carbon Observatory-2(OCO-2) in 2015 were compared with tho...In order to correctly use the column-averaged atmospheric COdry-air mole fraction(XCO) data in the COflux studies, XCOmeasurements 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 XCOretrieved 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 COconcentration 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 XCOretrieved from target and nadir modes within the observation period are similar, and larger than those from glint mode. We also used the FTS site in Beijing, China, to assess the OCO-2 XCOin 2016. This site is located in a typical urban area, which has been absent in previous studies. Overall, OCO-2 XCOagrees well with that from FTS at this site. Such a study will benefit the validation of the newly launched TanSat products in China.展开更多
A global mapping data of atmospheric carbon dioxide(CO_(2))concen-trations can help us to better understand the spatiotemporal varia-tions of CO_(2) and the driving factors of the variations to support the actions for...A global mapping data of atmospheric carbon dioxide(CO_(2))concen-trations can help us to better understand the spatiotemporal varia-tions of CO_(2) and the driving factors of the variations to support the actions for emissions reduction and control.Greenhouse gases satel-lites that measure atmospheric CO_(2),such as the Greenhouse Gases Observing Satellite(GOSAT)and Orbiting Carbon Observatory(OCO-2),have been providing global observations of the column averaged dry-air mole fractions of CO_(2)(XCO_(2))since 2009.However,these XCO_(2) retrievals are irregular in space and time with many gaps.In this paper,we mapped a global spatiotemporally continuous XCO_(2) data-set(Mapping-XCO_(2))using the XCO_(2) retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps.The dataset covers a geographic range from 56°S to 65°N and 169°W to 180°E for a 1°grid interval in space and 3-day time interval.The uncer-tainties of the mapped XCO_(2) values are generally less than 1.5 parts per million(ppm).The spatiotemporal characteristics of global XCO_(2) that are revealed by the Mapping-XCO_(2) are similar to the model data obtained from CarbonTracker.Compared to the ground observa-tions,the overall standard bias is 1.13 ppm.The results indicate that this long-term Mapping-XCO_(2) dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO_(2) and can support studies related to the carbon cycle and anthropogenic CO_(2) emissions.The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.展开更多
基金The National Key Research and Development Programme of China under contract No.2017YFA0603004the Fund of Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)(Zhanjiang Bay Laboratory)under contract No.ZJW-2019-08+1 种基金the National Natural Science Foundation of China under contract Nos 41825014,41676172 and 41676170the Global Change and Air-Sea Interaction Project of China under contract Nos GASI-02-SCS-YGST2-01,GASI-02-PACYGST2-01 and GASI-02-IND-YGST2-01。
文摘Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(XCO_(2))since 2014.In this study,the OCO-2 XCO_(2)products were compared between in-situ data from the Total Carbon Column Network(TCCON)and Global Monitoring Division(GMD),and modeling data from CarbonTracker2019 over global ocean and land.Results showed that the OCO-2 XCO_(2)data are consistent with the TCCON and GMD in situ XCO_(2)data,with mean absolute biases of 0.25×10^(-6)and 0.67×10^(-6),respectively.Moreover,the OCO-2 XCO_(2)data are also consistent with the CarbonTracker2019 modeling XCO_(2)data,with mean absolute biases of 0.78×10^(-6)over ocean and 1.02×10^(-6)over land.The results indicated the high accuracy of the OCO-2 XCO_(2)product over global ocean which could be applied to estimate the air-sea CO_(2)flux.
基金the National Key Research Program of China(Grant No.2016YFC0200900)the National Natural Science Foundation of China(NSFC)(Grant No.41775023)+1 种基金the Excellent Young Scientists Program of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LR19D050001)the Fundamental Research Funds for the Central Universities,and the State Key Laboratory of Modern Optical Instrumentation Innovation Program.
文摘A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2(OCO-2)cloud screening algorithms.Cloud screening strategies are essential to remove scenes with significant cloud and/or aerosol contamination from OCO-2 observations,which helps to save on the data processing cost and ensure high quality retrievals of the column-averaged CO2 dry air mole fraction(XCO2).Based on the radiance measurements in the 0.76μm O2A band,1.61μm(weak),and 2.06μm(strong)CO2 bands,the current combination of the A-Band Preprocessor(ABP)algorithm and Iterative Maximum A Posteriori(IMAP)Differential Optical Absorption Spectroscopy(DOAS)Preprocessor(IDP)algorithm passes around 20%-25%of all soundings,which means that some contaminated scenes also pass the screening process.In this work,three independent pairs of threshold parameters used in the ABP and IDP algorithms are sufficiently tuned until the overall pass rate is close to the monthly clear-sky fraction from the MODIS cloud mask.The tightened thresholds are applied to observations over land surfaces in Europe and Japan in 2016.The results show improvement of agreement and positive predictive value compared to the collocated MODIS cloud mask,especially in summer and fall.In addition,analysis indicates that XCO2 retrievals with more stringent thresholds are in closer agreement with measurements from collocated Total Carbon Column Observing Network(TCCON)sites.
基金Supported by the TanSat Project(2011AA12A104)under a contract with the National Science and Technology Support Program of China
文摘In order to correctly use the column-averaged atmospheric COdry-air mole fraction(XCO) data in the COflux studies, XCOmeasurements 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 XCOretrieved 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 COconcentration 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 XCOretrieved from target and nadir modes within the observation period are similar, and larger than those from glint mode. We also used the FTS site in Beijing, China, to assess the OCO-2 XCOin 2016. This site is located in a typical urban area, which has been absent in previous studies. Overall, OCO-2 XCOagrees well with that from FTS at this site. Such a study will benefit the validation of the newly launched TanSat products in China.
基金This work was supported by the National Key Research and Development Program of China(Grant No.2020YFA0607503)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19080303)the Key Program of the Chinese Academy of Sciences(Grant No.ZDRW-ZS-2019-1-3).
文摘A global mapping data of atmospheric carbon dioxide(CO_(2))concen-trations can help us to better understand the spatiotemporal varia-tions of CO_(2) and the driving factors of the variations to support the actions for emissions reduction and control.Greenhouse gases satel-lites that measure atmospheric CO_(2),such as the Greenhouse Gases Observing Satellite(GOSAT)and Orbiting Carbon Observatory(OCO-2),have been providing global observations of the column averaged dry-air mole fractions of CO_(2)(XCO_(2))since 2009.However,these XCO_(2) retrievals are irregular in space and time with many gaps.In this paper,we mapped a global spatiotemporally continuous XCO_(2) data-set(Mapping-XCO_(2))using the XCO_(2) retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps.The dataset covers a geographic range from 56°S to 65°N and 169°W to 180°E for a 1°grid interval in space and 3-day time interval.The uncer-tainties of the mapped XCO_(2) values are generally less than 1.5 parts per million(ppm).The spatiotemporal characteristics of global XCO_(2) that are revealed by the Mapping-XCO_(2) are similar to the model data obtained from CarbonTracker.Compared to the ground observa-tions,the overall standard bias is 1.13 ppm.The results indicate that this long-term Mapping-XCO_(2) dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO_(2) and can support studies related to the carbon cycle and anthropogenic CO_(2) emissions.The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.
