The variation of the atmospheric Carbon Dioxide （CO2） concentration plays an important role in global cli- mate and agriculture. We analyzed the spatial-temporal characteristics of CO2 in the China region and around...The variation of the atmospheric Carbon Dioxide （CO2） concentration plays an important role in global cli- mate and agriculture. We analyzed the spatial-temporal characteristics of CO2 in the China region and around the globe with the CO2 column mixing ratios observed by the Japanese GOSAT satellite （Greenhouse Gases Observing Satellite）. In order to make sure that the accuracy of the CO2 data retrieved by the satellite meets the needs of the climate charac- teristics analyses, we ran a validation on the CO2 column mixing ratios retrieved by the satellite against the ground-based TCCON （Total Carbon Column Observing Network） observation data. The result shows that the two sets of data have a correlation coefficient of higher than 0.7, and a bias of within 2.2 ppmv. Therefore, the GOSAT CO2 da- ta can be used for the climate characteristics analysis of global CO2. Our analysis on the spatial-temporal characteristics of the CO2 column mixing ratios observed during the period of June 2009 through January 2014 proved that, with the impact of the natural emission of near ground CO2 and human activities, the global CO2 concentration has a significant latitudinal characteristics with its highest level averaging 390 oomv in the 0-40？N latitudinal zone in the Northern Hemisphere, and 387 ppmv in the Southern Hemisphere. China has a relatively higher CO2 concentration with the highest level exceeding 398 ppmv, and the eastern area higher than the western area. The variation of global CO2 concentration shows a seasonal pattern, i.e. the CO2 concen- tration reaches its highest in spring in the Northern Hemisphere averaging more than 392 ppmv, second highest in win- ter, and lowest in summer averaging less than 387 ppmv. It fluctuates the most in the Northern Hemisphere with an av- erage concentration of 392.5 ppmv in April, and 385.5 ppmv in July. While in the Southern Hemisphere, the seasonal fluctuation is smaller with the highest concentration occurring in July. Over the recent years, the global CO2 concentra- tion has shown an elevating trend with an average annual increase rate of 1.58 ppmv per year. It is a challenge that the human kind has to face to slow down the increase of the CO2 concentration.展开更多
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
基金National Natural Science Foundation of China(41375025)863 Program(2012AA120903,2011AA12A104-3)+2 种基金Public Welfare Research Foundation of China Meteorological Administration(GYHY201106044,GYHY201106045)Meteorological Application Demonstration Project(E310/1112)4th and 5th GOSAT/TANSO joint research Project 2013-2015
文摘The variation of the atmospheric Carbon Dioxide （CO2） concentration plays an important role in global cli- mate and agriculture. We analyzed the spatial-temporal characteristics of CO2 in the China region and around the globe with the CO2 column mixing ratios observed by the Japanese GOSAT satellite （Greenhouse Gases Observing Satellite）. In order to make sure that the accuracy of the CO2 data retrieved by the satellite meets the needs of the climate charac- teristics analyses, we ran a validation on the CO2 column mixing ratios retrieved by the satellite against the ground-based TCCON （Total Carbon Column Observing Network） observation data. The result shows that the two sets of data have a correlation coefficient of higher than 0.7, and a bias of within 2.2 ppmv. Therefore, the GOSAT CO2 da- ta can be used for the climate characteristics analysis of global CO2. Our analysis on the spatial-temporal characteristics of the CO2 column mixing ratios observed during the period of June 2009 through January 2014 proved that, with the impact of the natural emission of near ground CO2 and human activities, the global CO2 concentration has a significant latitudinal characteristics with its highest level averaging 390 oomv in the 0-40？N latitudinal zone in the Northern Hemisphere, and 387 ppmv in the Southern Hemisphere. China has a relatively higher CO2 concentration with the highest level exceeding 398 ppmv, and the eastern area higher than the western area. The variation of global CO2 concentration shows a seasonal pattern, i.e. the CO2 concen- tration reaches its highest in spring in the Northern Hemisphere averaging more than 392 ppmv, second highest in win- ter, and lowest in summer averaging less than 387 ppmv. It fluctuates the most in the Northern Hemisphere with an av- erage concentration of 392.5 ppmv in April, and 385.5 ppmv in July. While in the Southern Hemisphere, the seasonal fluctuation is smaller with the highest concentration occurring in July. Over the recent years, the global CO2 concentra- tion has shown an elevating trend with an average annual increase rate of 1.58 ppmv per year. It is a challenge that the human kind has to face to slow down the increase of the CO2 concentration.
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.
