Variations in Column Concentration of Greenhouse Gases in China and Their Response to the 2015-2016 El Niño Event

Since the industrial revolution,enhancement of atmospheric greenhouse gas concentrations as a result of human activities has been the primary cause of global warming.The monitoring and evaluation of greenhouse gases are significant prerequisites for carbon emission control.Using monthly data of global atmospheric carbon dioxide(CO_(2))and methane(CH4)column concentrations(hereinafter XCO_(2) and XCH_(4),respectively)retrieved by the Greenhouse Gas Observation Satellite(GOSAT),we analyzed the variations in XCO_(2)and XCH_(4)in China during 2010-2022 after confirming the reliability of the data.Then,the influence of a strong El Niño event in 2015-2016 on XCO_(2) and XCH_(4) variations in China was further studied.The results show that the retrieved XCO_(2) and XCH_(4) from GOSAT have similar temporal variation trends and significant correlations with the ground observation and emission inventory data of an atmospheric background station,which could be used to assess the variations in XCO_(2) and XCH_(4) in China.XCO_(2) is high in spring and winter while XCH_(4) is high in autumn.Both XCO_(2) and XCH_(4) gradually declined from Southeast China to Northwest and Northeast China,with variation ranges of 401-406 and 1.81-1.88 ppmv,respectively;and the high value areas are located in the middle-lower Yangtze River basin.XCO_(2) and XCH_(4) in China increased as a whole during 2010-2022,with rapid enhancement and high levels of XCO_(2) and XCH_(4) in several areas.The significant increases in XCO_(2) and XCH_(4) over China in 2016 might be closely related to the strong El Niño-Southern Oscillation(ENSO)event during 2015-2016.Under a global warming background in 2015,XCO_(2) and XCH_(4) increased by 0.768%and 0.657%in 2016 in China.Data analysis reveals that both the XCO_(2) and XCH_(4) variations might reflect the significant impact of the ENSO event on glacier melting in the Tibetan Plateau.

Quantification of Central and Eastern China's atmospheric CH_(4) enhancement changes and its contributions based on machine learning approach

Methane is the second largest anthropogenic greenhouse gas,and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks.Therefore,the monitoring of CH_(4) concentration changes and the assessment of underlying driving factors can provide scientific basis for the government’s policy making and evaluation.China is the world’s largest emitter of anthropogenic methane.However,due to the lack of ground-based observation sites,little work has been done on the spatial-temporal variations for the past decades and influencing factors in China,especially for areas with high anthropogenic emissions as Central and Eastern China.Here to quantify atmospheric CH_(4) enhancements trends and its driving factors in Central and Eastern China,we combined the most up-to-date TROPOMI satellite-based column CH_(4)(xCH_(4))concentration from 2018 to 2022,anthropogenic and natural emissions,and a random forest-based machine learning approach,to simulate atmospheric xCH_(4) enhancements from 2001 to 2018.The results showed that(1)the random forest model was able to accurately establish the relationship between emission sources and xCH_(4) enhancement with a correlation coefficient(R^(2))of 0.89 and a root mean-square error(RMSE)of 11.98 ppb;(2)The xCH_(4) enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018,with a relative change of 3.27%±0.13%;(3)The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH_(4) enhancement,contributing 68.00% and 31.21%,respectively,and the decrease of animal ruminants contributed-6.70% of its enhancement trend.

VERTICAL DISTRIBUTION OF ATMOSPHERIC AEROSOL CONCENTRATION AT XIANGHE

This paper summarizes atmospheric aerosol concentrations of 5 stratospheric balloon soundings during the period from 1984 to 1994. Aerosol-rich layers in the troposphere were detected and the causes were analyzed. The main results are as follows: (1) the vertical distribution of the atmospheric aerosol is affected by atmospheric dynamic processes, humidity, etc.; (2) the tropospheric column concentrations of aerosol were 72.2×105, 20.2×105, 20.7×105 and 34.4×105 cm-2 and occupying 81%, 61% and 60% of the 0-to-30 km aerosol column, on Aug. 23, 1984, Aug. 22, 1993, Sept. 12, 1993 and Sept. 15, 1994, respectively; (3) the effect of volcano eruption was still evident in the aerosol profiles, 28 and 27 months after the El Chichon and Pinatubo eruption; (4) the aerosol concentration in the troposphere did not decrease at all heights as atmospheric aerosol model.

Global land 1° mapping dataset of XCO_(2) from satellite observations of GOSAT and OCO-2 from 2009 to 2020

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.