Monitoring atmospheric carbon dioxide（CO_2） 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 cli...Monitoring atmospheric carbon dioxide（CO_2） 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 Carbon Tracker-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 XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 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.展开更多
An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Obser...An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Observing SATellite （GOSAT） LIB data and validated using the Total Column Carbon Observing Network （TCCON） measurements. The retrieved XCO2 agrees well with TCCON measurements in a low bias of 0.15 ppmv and RMSE of 1.48 ppmv, and captured the seasonal vari- ation and increasing of XCO2 in Northern and Southern Hemisphere, respectively, as other measurements.展开更多
Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmo...Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2con展开更多
Spatiotemporal patterns of column-averaged dry air mole fraction of CO2(XCO2)have not been well characterized on a regional scale due to limitations in data availability and precision.This paper addresses these issues...Spatiotemporal patterns of column-averaged dry air mole fraction of CO2(XCO2)have not been well characterized on a regional scale due to limitations in data availability and precision.This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset(2009-2016)derived from the Greenhouse gases Observing SATellite(GOSAT).XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model.The following results are found:Firstly,the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn.Secondly,on average,XCO2 increases by 2.08 ppm every year from2010 to 2015,with a sharp increase of 2.6 ppm in 2013.Lastly,in the analysis of three typical regions,the GOSAT XCO2 time series is inbetter agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region(the least difference with bias 0.65±0.78 ppm),compared with the northern urban agglomerationregion(-1.3±1.2 ppm)and the northeastern forest region(-1.4±1.4 ppm).The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data.展开更多
基金funded by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues(Grant No.XDA05040200)the National Key Research and Development Program of China(Grant No.2016YFA0600203)+1 种基金the National Natural Science Foundation of China(Grant Nos.41375035 and 31500402)the Chinese Academy of Sciences Strategic Priority Program on Space Science(Grant No.XDA04077300)
文摘Monitoring atmospheric carbon dioxide（CO_2） 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 Carbon Tracker-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 XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 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.
基金supported by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues(XDA05040200)the National High-tech Research and Development Program(2011AA12A104)
文摘An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Observing SATellite （GOSAT） LIB data and validated using the Total Column Carbon Observing Network （TCCON） measurements. The retrieved XCO2 agrees well with TCCON measurements in a low bias of 0.15 ppmv and RMSE of 1.48 ppmv, and captured the seasonal vari- ation and increasing of XCO2 in Northern and Southern Hemisphere, respectively, as other measurements.
基金supported by the National Natural Science Foundation of China(Grant No.41071234)"Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues"of the Chinese Academy of Sciences(Grant No.XDA05040401)the National High Techondogy Research and Development Program of China(Grant No.2012AA12A301)
文摘Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2con
基金supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600303)the Key Deployment Projects of the Chinese Academy of Sciences (Grant No. ZDRWZS-2019-1-3)
文摘Spatiotemporal patterns of column-averaged dry air mole fraction of CO2(XCO2)have not been well characterized on a regional scale due to limitations in data availability and precision.This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset(2009-2016)derived from the Greenhouse gases Observing SATellite(GOSAT).XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model.The following results are found:Firstly,the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn.Secondly,on average,XCO2 increases by 2.08 ppm every year from2010 to 2015,with a sharp increase of 2.6 ppm in 2013.Lastly,in the analysis of three typical regions,the GOSAT XCO2 time series is inbetter agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region(the least difference with bias 0.65±0.78 ppm),compared with the northern urban agglomerationregion(-1.3±1.2 ppm)and the northeastern forest region(-1.4±1.4 ppm).The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data.
