We used the global atmospheric chemical transport model,GEOS-Chem,to simulate the spatial distribution and seasonal variation of surface-layer methane （CH4） in 2004,and quantify the impacts of individual domestic so...We used the global atmospheric chemical transport model,GEOS-Chem,to simulate the spatial distribution and seasonal variation of surface-layer methane （CH4） in 2004,and quantify the impacts of individual domestic sources and foreign transport on CH4 concentrations over China.Simulated surface-layer CH4 concentrations over China exhibit maximum concentrations in summer and minimum concentrations in spring.The annual mean CH4 concentrations range from 1800 ppb over western China to 2300 ppb over the more populated eastern China.Foreign emissions were found to have large impacts on CH4 concentrations over China,contributing to about 85％ of the CH4 concentrations over western China and about 80％ of those over eastern China.The tagged simulation results showed that coal mining,livestock,and waste are the dominant domestic contributors to CH4 concentrations over China,accounting for 36％,18％,and 16％,respectively,of the annual and national mean increase in CH4 concentration from all domestic emissions.Emissions from rice cultivation were found to make the largest contributions to CH4 concentrations over China in the summer,which is the key factor that leads to the maximum seasonal mean CH4 concentrations in summer.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
The influences of strong El Nino events(1997/98 and 2015/16)on summertime near-surface ozone(O_(3))concentrations over China are investigated using the GEOS-Chem model.The results show that near-surface O_(3) concentr...The influences of strong El Nino events(1997/98 and 2015/16)on summertime near-surface ozone(O_(3))concentrations over China are investigated using the GEOS-Chem model.The results show that near-surface O_(3) concentrations increased by a maximum of 6 ppb(parts per billion)during the summer of the developing phase of the 1997/98 El Nino in northeastern China,mainly due to the increased chemical production related to the hot and dry conditions.Besides,the O_(3) concentration increased by 3 ppb during the developing summer of both the 1997/98 and 2015/16 El Nino in southern China.It was linked to the weakened prevailing monsoon winds,which led to the accumulation of O_(3) in southern China.In contrast,in the summer of the decaying phase of the two El Nino events,O_(3) concentrations decreased over many regions of China when the El Nino reversed to the cooling phase.This highlights that El Nino plays an important role in modulating near-surface O_(3) concentrations over China.展开更多
The rise in atmospheric carbon dioxide(C02)concentrations caused by human activities is leading to global climate change,which poses a threat to human development and survival.This study analyzed the distribution of t...The rise in atmospheric carbon dioxide(C02)concentrations caused by human activities is leading to global climate change,which poses a threat to human development and survival.This study analyzed the distribution of the ocean carbon flux with interannual changes and compared it with the climatological ocean carbon flux to deepen our understanding of carbon sources and sinks.To simulate global CO2 concentrations for the years2008-2010,the ocean carbon flux with interannual changes and the climatological ocean carbon flux were used to drive the GEOS-Chem model,an atmospheric chemical transport model.The simulated values were compared with the CO2 concentrations at nine observation stations to explore the influence of interannual changes in the ocean carbon fluxes on the simulated CO2 concentrations.The authors found that the difference between the two simulation results was greater in the Southern Hemisphere all year,and the difference in autumn was the largest.Compared with the observations,the simulated CO2 concentration of the ocean carbon flux with interannual changes is closer to the observations,indicating that this simulation is more accurate.展开更多
Ambient SO2 concentration at a high rain fall site, Shillong (25.67oN, 91.91oE, 1064 m ASL), located in North-East India, was measured during March 2009 and January 2010 with the aim to understand the effect of long r...Ambient SO2 concentration at a high rain fall site, Shillong (25.67oN, 91.91oE, 1064 m ASL), located in North-East India, was measured during March 2009 and January 2010 with the aim to understand the effect of long range transport of pollutants from North-East Asia on the ambient SO2 levels at this relatively clean site. The concentrations recorded during the former sampling period were very high (Max: 262.3 ppb)—which decayed down gradually towards the end the sampling period—whereas those during the latter sampling period were well within the acceptable limits (Max: 29.7 ppb). This elevated SO2 concentrations during March 2009 is proposed to have association with a major cold air outbreak and an associated cyclone preceding one of the dust storm events reported in China, and a resultant sudden change in wind trajectory leading to the long range transport of pollutants to the sampling site. The argument is formulated on the basis of the back trajectory analysis performed using HYSPLIT for the month of March 2009—the plots clearly showed a drastic change in wind trajectories between 8th and 15th of March 2009 wherein the winds traveled over some of the highly polluted regions such as the Perm region of Russia—and on the results from model runs performed using the global 3-D model of tropospheric chemistry, GEOS-Chem (v8-03-01)—it clearly showed the tropospheric SO2 over Perm region in Russia peaking during Nov, Dec, Jan, Feb and Mar every year, possibly due to central heating. The observation of long range transport of SO2 from the highly industrialized areas of Perm in Russia to North-East India during dust storm events has important implications to the present understanding on its relative contribution to the Asian pollutant outflow to the Pacific during spring as the GEOS-Chem model runs also showed regions in and around Russia with relatively high concentrations of atmospheric NOx, Peroxyacetyl Nitrate, Lumped Peroxypropionyl Nitrate, HNO3, HNO4,C3H8, C2H6, SO4, NH4, Inorganic Sulphur Nitrates and 展开更多
Ambient sulphur dioxide (SO2) measurements have been performed at a high altitude site in the semi arid region of western India, Gurushikhar, Mt. Abu (24.6°N, 72.7°E, 1680 m ASL), during different sampling p...Ambient sulphur dioxide (SO2) measurements have been performed at a high altitude site in the semi arid region of western India, Gurushikhar, Mt. Abu (24.6°N, 72.7°E, 1680 m ASL), during different sampling periods span over Sep-Dec 2009 and Feb-Mar 2010. A global three dimensional chemical transport Model, GEOS-Chem, (v8-03-01) is employed to generate the SO2 profile for the entire region for the different sampling months which in turn is used to explain the major features in the measured SO2 spectra via correlating with HYSPLIT generated wind back trajectories. The mean SO2 concentrations recorded at the sampling site varied for the different sampling periods (4.3 ppbv in Sep-Oct 2009, 3.4 ppbv in Nov 2009, 3.5 ppbv in Dec 2009, 7.7 ppbv in Feb 2010 and 9.2 ppbv in Mar 2010) which were found to be strongly influenced by long range transport from a source region surrounding 30°N, 75°E—the one projected with the highest SO2 concentration in the GEOS-Chem generated profiles for the region—lying only a few co-ordinates away. A diurnal cycle of SO2 concentration exists throughout the sampling periods, with the greatest day-night changes observed during Feb and Mar 2010, barely detectable during Sep-Oct 2009, and intermediate values for Nov and Dec 2009 which are systematically studied using the time series PBL height and OH radical values from the GEOS-Chem model. During the sampling period in Nov 2009, a plume transport to the sampling site also was detected when a major fire erupted at an oil depot in Jaipur (26.92°N, 75.82°E), located few co-ordinates away. Separate runs of the model, performed to study the long range transport effects, show a drop in the SO2 levels over the sampling region in the absence of transport, throughout the year with Jan to Apr seen to be influenced the lowest by long range transport while Jul and Dec influenced the highest.展开更多
Systematic monitoring of the fluctuations in atmospheric SO2 oxidation efficiency—measured as a molar ratio of SO42- to total SOx (SOx=SO2+SO42-), referred as S-ratio—have been performed during a major long range pl...Systematic monitoring of the fluctuations in atmospheric SO2 oxidation efficiency—measured as a molar ratio of SO42- to total SOx (SOx=SO2+SO42-), referred as S-ratio—have been performed during a major long range plume transport to northeast India (Shillong: 25.67°N, 91.91°E, 1064 m ASL) in March 2009. Anomalously low S-ratios (median, 0.03) were observed during the episode—associated with a cyclonic circulation—and the SO42- and SO2 exhibited unusual features in the ‘relative phase’ of their peaks. During initial days, when SO2 levels were dictated by the long range influx, the SO42- and SO2 variabilities were in anti-phase—for the differing mobility/loss mechanisms. When SO2 levels were governed by the boundary layer diurnality in the latter days, the anti-phase is explained by a ‘depleted OH level’—major portion being consumed in the initial period by the elevated SO2 and other pollutants. Simulations with a global 3D chemical transport model, GEOS-Chem (v8-03-01), also indicated ‘suppressed oxidation conditions’—with characteristic low S-ratios and poor phase agreements. The modelled OH decreased steadily from the initial days, and OH normalized to SO2—referred as OHspecific—was consistently low during the ‘suppressed S-ratio period’. Further, the geographical distribution of modelled OH showed a pronounced minimum over the region surrounding (20°N, 95°E) spanning parts of northeast India and the adjacent regions to the southeast of it—prevalent throughout the year, though the magnitude and the area of influence have a seasonality to it—with significant implications for reducing the oxidizing power of the regional atmosphere. A second set of measurements during January 2010—when prominent long range transports were absent—exhibited no anomalies, and the S-ratios were well within the acceptable limits (median, 0.32). This work highlights the GEOS-Chem model skill in simulating/detecting the ‘transient fluctuations’ in the oxidation efficiency, down to a regiona展开更多
The nested-grid capability of the global chemical transport model GEOS-Chem, with a horizontal resolution of 1/4°× 5/16° （latitude x longitude）, was used to identify the chemical species whose reducti...The nested-grid capability of the global chemical transport model GEOS-Chem, with a horizontal resolution of 1/4°× 5/16° （latitude x longitude）, was used to identify the chemical species whose reductions made the largest contributions to decreases in PM2.s concentrations （fine particulate matter, diameter 〈 2.5μm, defined in this study as the sum of sulfate, nitrate, ammonium, black carbon, and organic carbon aerosols） in Beijing during the 2014 Asia-Pacific Economic Cooperation （APEC） summit. A number of numerical experiments were carried out for the period 15 October-29 November 2014. The model reproduced the observed daily variations of concentrations of PM2.s and gas-phase species （carbon monoxide, nitrogen dioxide, and sulfur dioxide）. Simulated PM2.s concentrations decreased by 55.9%-58.5% during the APEC period, compared to other periods in October and November 2014, which agreed closely with measurements. Sensitivity results showed that emissions control measures regarding nitrogen oxides and organic carbon over North China led to the largest reductions in PM2.s concentrations in Beijing during the APEC summit, which led to overall reductions in the PM2.5 concentration of Beijing by 5.7% and 4.6%, respectively. The control of ammonia emissions was found to be able to greatly reduce PM2.5 concentrations in the whole of North China during the APEC meeting.展开更多
The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories added the method of assimilating atmospheric CO_(2) concentrations to invert carbon sources and sinks;however,many global carbon ...The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories added the method of assimilating atmospheric CO_(2) concentrations to invert carbon sources and sinks;however,many global carbon inversion models are not publicly available.In addition,our regional assimilation inversion system,CCMVS-R(China Carbon Monitoring,Verification and Supporting for Regional),needs a global carbon inversion model with higher assimilation efficiency to provide boundary conditions.Here,an inversion model based on the global atmospheric chemistry model GEOS-Chem and a more accurate and easier-to-implement ensemble square root Kalman filter(EnSRF)algorithm is constructed and used to infer global and China's carbon fluxes in 2019.Atmospheric CO_(2) concentrations from ObsPack sites and five additional CO_(2) observational sites from China's Greenhouse Gas Observation Network(CGHGNET)were used for data assimilation to improve the estimate.The inverted annual global terrestrial and oceanic carbon uptake is 2.12 and 2.53 Pg C per year,respectively,accounting for 21.1%and 25.1%of global fossil fuel CO_(2) emissions.The remaining 5.41 Pg C per year in the atmosphere is consistent with the global atmospheric CO_(2) growth rates of 5.44 Pg C per year reported by the National Oceanic and Atmospheric Administration(NOAA),showing that the inversion model can provide a reasonable estimate of global-scale natural carbon sinks.The inverted terrestrial carbon sink of China is 0.37 Pg C per year,accounting for approximately 13%of China's fossil CO_(2) emissions.展开更多
In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net-...In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net- works based on the monthly average grid emission data （1979-2008） from different geographical regions. A three-dimensional global chemical transport model, God- dard Earth Observing System （GEOS）-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from -1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, - 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and tem- poral distribution of the atmospheric C02 concentration and ft.展开更多
The performance of a joint data assimilation system（Tan-Tracker）,which is based on the PODEn4 Dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was eva...The performance of a joint data assimilation system（Tan-Tracker）,which is based on the PODEn4 Dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was evaluated.Atmospheric 3D CO2 concentrations and CO2 surface fluxes（CFs） from2010 were simulated using a global chemistry transport model（GEOS-Chem）.TheTan-Tracker system used the simulated CO2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO2（X（CO2）） data to optimize CO2 concentrations and CFs in the same assimilation window.Monthly simulated X（CO2）（X（CO2）Sim）） and assimilated X（CO2）（X（CO2）,TT） data retrieved at different satellite scan positions were compared with GOSAT-observed X（CO2）（X（CO2）,obs）data.The average RMSE between the monthly X（CO2）,TT and X（CO2）,Obs data was significantly（30%） lower than the average RMSE between X（CO2）,Sim and X（CO2）,Obs）.Specifically,reductions in error were found for the positions of northern Africa（the Sahara）,the Indian peninsula,southern Africa,southern North America,and western Australia.The difference between the correlation coefficients of the X（CO2）,Sim）and X（CO2）,Obs and those of the X（CO2）Π）,TT and X（CO2）,Obs was only small.In general,the Tan-Tracker system performed very well after assimilating the GOSAT data.展开更多
Paved road dust is one of the most important aerosols in China. The authors estimated road dust emissions using an empirical model （AP-42 model） developed by the U.S. Environmental Protection Agency, and simulated r...Paved road dust is one of the most important aerosols in China. The authors estimated road dust emissions using an empirical model （AP-42 model） developed by the U.S. Environmental Protection Agency, and simulated road dust concentrations over China for the years 2006-2011 using the GEOS-Chem model.The annual road dust emissions amount averaged over 2006-2011 is estimated to be 2331.4 kt, with much higher emissions in eastern China than in western China. Because of heavy traffic and a dense road network, emissions are high over Beijing-Tianjin-Tanggu （BTT）, Henan Province, and Shandong Province. Meanwhile, emissions are calculated to be 459.1, 112.0, and 102.7 kt, respectively, over BTT, the Pearl River Delta （PRD） region, and the Yangtze River Delta （YRD）. Due to the monthly variation of precipitation, road dust emissions over China are simulated to be highest in December and lowest in June. The highest annual mean road dust concentration is simulated to be 14.5 tJg m-3 in Beijing. Over 2006-2011, because of the increases in road length and number of vehicles, annual road dust emissions for China as a whole, Bl-r, the PRD, and the YRD, are simulated to increase by 260%, 239%, 266%, and 59%, respectively, leading to 233%, 243%, 273%, and 100% increases in road dust concentrations in these regions, respectively. Our results have important implications for air pollution control in China.展开更多
Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite...Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite(GOSAT) and ground-based the Total Carbon Column Observing Network(TCCON) data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42(Darwin) and 0.92(Izana). Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation's inputs, as well as the mismatch of satellite retrieval model input parameters.展开更多
The gaseous or particulate forms of divalent mercury(HgⅡ) significantly impact the spatial distribution of atmospheric mercury concentration and deposition flux(FLX). In the new nested-grid GEOS-Chem model, we try to...The gaseous or particulate forms of divalent mercury(HgⅡ) significantly impact the spatial distribution of atmospheric mercury concentration and deposition flux(FLX). In the new nested-grid GEOS-Chem model, we try to modify the HgⅡ gas-particle partitioning relationship with synchronous and hourly observations at four sites in China. Observations of gaseous oxidized Hg(GOM), particulate-bound Hg(PBM), and PM 2.5 were used to derive an empirical gas-particle partitioning coefficient as a function of temperature( T) and organic aerosol(OA) concentrations under different relative humidity(RH). Results showed that with increasing RH, the dominant process of HgⅡ gas-particle partitioning changed from physical adsorption to chemical desorption. And the dominant factor of HgⅡ gas-particle partitioning changed from T to OA concentrations. We thus improved the simulated OA concentration field by introducing intermediate-volatility and semi-volatile organic compounds(I/SVOCs) emission inventory into the model framework and refining the volatile distributions of I/SVOCs according to new filed tests in the recent literatures. Finally, normalized mean biases(NMBs) of monthly gaseous element mercury(GEM), GOM, PBM, WFLX were reduced from-33%–29%, 95%–300%, 64%–261%, 117%–122% to-13%–0%,-20%–80%,-31%–50%,-17%–23%. The improved model explains 69%–98% of the observed atmospheric Hg decrease during 2013–2020 and can serve as a useful tool to evaluate the effectiveness of the Minamata Convention on Mercury.展开更多
The seasonal cycle of atmospheric CO2 at surface observation stations in the northern hemisphere is driven primarily by net ecosystem production (NEP) fluxes from terrestrial ecosystems. In addition to NEP from terres...The seasonal cycle of atmospheric CO2 at surface observation stations in the northern hemisphere is driven primarily by net ecosystem production (NEP) fluxes from terrestrial ecosystems. In addition to NEP from terrestrial ecosystems, surface fluxes from fossil fuel combustion and ocean exchange also contribute to the seasonal cycle of atmospheric CO2. Here the authors use the Goddard Earth Observing System-Chemistry (GEOS-Chem) model (version 8-02-01), with modifications, to assess the impact of these fluxes on the seasonal cycle of atmospheric CO2 in 2005. Modifications include monthly fossil and ocean emission inventories. CO2 simulations with monthly varying and annual emission inventories were carried out separately. The sources and sinks of monthly averaged net surface flux are different from those of annual emission inventories for every month. Results indicate that changes in monthly averaged net surface flux have a greater impact on the average concentration of atmospheric CO2 in the northern hemisphere than on the average concentration for latitudes 30-90°S in July. The concentration values differ little between both emission inventories over the latitudinal range from the equator to 30°S in January and July. The accumulated impacts of the monthly averaged fossil and ocean emissions contribute to an increase of the total global monthly average of CO2 from May to December.An apparent discrepancy for global average CO2 concentration between model results and observation was because the observation stations were not sufficiently representative. More accurate values for monthly varying net surface flux will be necessary in future to run the CO2 simulation.展开更多
基金supported by the Chinese Academy of Sciences Strategic Priority Research Program (Grant No. XDA05100503)the National Natural Science Foundation of China (Grant Nos. 40825016 and 41021004)
文摘We used the global atmospheric chemical transport model,GEOS-Chem,to simulate the spatial distribution and seasonal variation of surface-layer methane （CH4） in 2004,and quantify the impacts of individual domestic sources and foreign transport on CH4 concentrations over China.Simulated surface-layer CH4 concentrations over China exhibit maximum concentrations in summer and minimum concentrations in spring.The annual mean CH4 concentrations range from 1800 ppb over western China to 2300 ppb over the more populated eastern China.Foreign emissions were found to have large impacts on CH4 concentrations over China,contributing to about 85％ of the CH4 concentrations over western China and about 80％ of those over eastern China.The tagged simulation results showed that coal mining,livestock,and waste are the dominant domestic contributors to CH4 concentrations over China,accounting for 36％,18％,and 16％,respectively,of the annual and national mean increase in CH4 concentration from all domestic emissions.Emissions from rice cultivation were found to make the largest contributions to CH4 concentrations over China in the summer,which is the key factor that leads to the maximum seasonal mean CH4 concentrations in summer.
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.
基金This study was supported by the National Key Research and Development Program of China[grant numbers 2020YFA0607803 and 2019YFA0606800]the National Natural Science Foundation of China[grant number 41975159].
文摘The influences of strong El Nino events(1997/98 and 2015/16)on summertime near-surface ozone(O_(3))concentrations over China are investigated using the GEOS-Chem model.The results show that near-surface O_(3) concentrations increased by a maximum of 6 ppb(parts per billion)during the summer of the developing phase of the 1997/98 El Nino in northeastern China,mainly due to the increased chemical production related to the hot and dry conditions.Besides,the O_(3) concentration increased by 3 ppb during the developing summer of both the 1997/98 and 2015/16 El Nino in southern China.It was linked to the weakened prevailing monsoon winds,which led to the accumulation of O_(3) in southern China.In contrast,in the summer of the decaying phase of the two El Nino events,O_(3) concentrations decreased over many regions of China when the El Nino reversed to the cooling phase.This highlights that El Nino plays an important role in modulating near-surface O_(3) concentrations over China.
基金partially supported by the National Key Research and Development Program of China [grant number 2016YFA0600203]the National Natural Science Foundation of China [grant number 41575100]
文摘The rise in atmospheric carbon dioxide(C02)concentrations caused by human activities is leading to global climate change,which poses a threat to human development and survival.This study analyzed the distribution of the ocean carbon flux with interannual changes and compared it with the climatological ocean carbon flux to deepen our understanding of carbon sources and sinks.To simulate global CO2 concentrations for the years2008-2010,the ocean carbon flux with interannual changes and the climatological ocean carbon flux were used to drive the GEOS-Chem model,an atmospheric chemical transport model.The simulated values were compared with the CO2 concentrations at nine observation stations to explore the influence of interannual changes in the ocean carbon fluxes on the simulated CO2 concentrations.The authors found that the difference between the two simulation results was greater in the Southern Hemisphere all year,and the difference in autumn was the largest.Compared with the observations,the simulated CO2 concentration of the ocean carbon flux with interannual changes is closer to the observations,indicating that this simulation is more accurate.
文摘Ambient SO2 concentration at a high rain fall site, Shillong (25.67oN, 91.91oE, 1064 m ASL), located in North-East India, was measured during March 2009 and January 2010 with the aim to understand the effect of long range transport of pollutants from North-East Asia on the ambient SO2 levels at this relatively clean site. The concentrations recorded during the former sampling period were very high (Max: 262.3 ppb)—which decayed down gradually towards the end the sampling period—whereas those during the latter sampling period were well within the acceptable limits (Max: 29.7 ppb). This elevated SO2 concentrations during March 2009 is proposed to have association with a major cold air outbreak and an associated cyclone preceding one of the dust storm events reported in China, and a resultant sudden change in wind trajectory leading to the long range transport of pollutants to the sampling site. The argument is formulated on the basis of the back trajectory analysis performed using HYSPLIT for the month of March 2009—the plots clearly showed a drastic change in wind trajectories between 8th and 15th of March 2009 wherein the winds traveled over some of the highly polluted regions such as the Perm region of Russia—and on the results from model runs performed using the global 3-D model of tropospheric chemistry, GEOS-Chem (v8-03-01)—it clearly showed the tropospheric SO2 over Perm region in Russia peaking during Nov, Dec, Jan, Feb and Mar every year, possibly due to central heating. The observation of long range transport of SO2 from the highly industrialized areas of Perm in Russia to North-East India during dust storm events has important implications to the present understanding on its relative contribution to the Asian pollutant outflow to the Pacific during spring as the GEOS-Chem model runs also showed regions in and around Russia with relatively high concentrations of atmospheric NOx, Peroxyacetyl Nitrate, Lumped Peroxypropionyl Nitrate, HNO3, HNO4,C3H8, C2H6, SO4, NH4, Inorganic Sulphur Nitrates and
文摘Ambient sulphur dioxide (SO2) measurements have been performed at a high altitude site in the semi arid region of western India, Gurushikhar, Mt. Abu (24.6°N, 72.7°E, 1680 m ASL), during different sampling periods span over Sep-Dec 2009 and Feb-Mar 2010. A global three dimensional chemical transport Model, GEOS-Chem, (v8-03-01) is employed to generate the SO2 profile for the entire region for the different sampling months which in turn is used to explain the major features in the measured SO2 spectra via correlating with HYSPLIT generated wind back trajectories. The mean SO2 concentrations recorded at the sampling site varied for the different sampling periods (4.3 ppbv in Sep-Oct 2009, 3.4 ppbv in Nov 2009, 3.5 ppbv in Dec 2009, 7.7 ppbv in Feb 2010 and 9.2 ppbv in Mar 2010) which were found to be strongly influenced by long range transport from a source region surrounding 30°N, 75°E—the one projected with the highest SO2 concentration in the GEOS-Chem generated profiles for the region—lying only a few co-ordinates away. A diurnal cycle of SO2 concentration exists throughout the sampling periods, with the greatest day-night changes observed during Feb and Mar 2010, barely detectable during Sep-Oct 2009, and intermediate values for Nov and Dec 2009 which are systematically studied using the time series PBL height and OH radical values from the GEOS-Chem model. During the sampling period in Nov 2009, a plume transport to the sampling site also was detected when a major fire erupted at an oil depot in Jaipur (26.92°N, 75.82°E), located few co-ordinates away. Separate runs of the model, performed to study the long range transport effects, show a drop in the SO2 levels over the sampling region in the absence of transport, throughout the year with Jan to Apr seen to be influenced the lowest by long range transport while Jul and Dec influenced the highest.
文摘Systematic monitoring of the fluctuations in atmospheric SO2 oxidation efficiency—measured as a molar ratio of SO42- to total SOx (SOx=SO2+SO42-), referred as S-ratio—have been performed during a major long range plume transport to northeast India (Shillong: 25.67°N, 91.91°E, 1064 m ASL) in March 2009. Anomalously low S-ratios (median, 0.03) were observed during the episode—associated with a cyclonic circulation—and the SO42- and SO2 exhibited unusual features in the ‘relative phase’ of their peaks. During initial days, when SO2 levels were dictated by the long range influx, the SO42- and SO2 variabilities were in anti-phase—for the differing mobility/loss mechanisms. When SO2 levels were governed by the boundary layer diurnality in the latter days, the anti-phase is explained by a ‘depleted OH level’—major portion being consumed in the initial period by the elevated SO2 and other pollutants. Simulations with a global 3D chemical transport model, GEOS-Chem (v8-03-01), also indicated ‘suppressed oxidation conditions’—with characteristic low S-ratios and poor phase agreements. The modelled OH decreased steadily from the initial days, and OH normalized to SO2—referred as OHspecific—was consistently low during the ‘suppressed S-ratio period’. Further, the geographical distribution of modelled OH showed a pronounced minimum over the region surrounding (20°N, 95°E) spanning parts of northeast India and the adjacent regions to the southeast of it—prevalent throughout the year, though the magnitude and the area of influence have a seasonality to it—with significant implications for reducing the oxidizing power of the regional atmosphere. A second set of measurements during January 2010—when prominent long range transports were absent—exhibited no anomalies, and the S-ratios were well within the acceptable limits (median, 0.32). This work highlights the GEOS-Chem model skill in simulating/detecting the ‘transient fluctuations’ in the oxidation efficiency, down to a regiona
基金supported by the National Basic Research Program of China[973 program,grant number 2014CB441202]the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA05100503]the National Natural Science Foundation of China[grant numbers 41021004,41475137,and 91544219]
文摘The nested-grid capability of the global chemical transport model GEOS-Chem, with a horizontal resolution of 1/4°× 5/16° （latitude x longitude）, was used to identify the chemical species whose reductions made the largest contributions to decreases in PM2.s concentrations （fine particulate matter, diameter 〈 2.5μm, defined in this study as the sum of sulfate, nitrate, ammonium, black carbon, and organic carbon aerosols） in Beijing during the 2014 Asia-Pacific Economic Cooperation （APEC） summit. A number of numerical experiments were carried out for the period 15 October-29 November 2014. The model reproduced the observed daily variations of concentrations of PM2.s and gas-phase species （carbon monoxide, nitrogen dioxide, and sulfur dioxide）. Simulated PM2.s concentrations decreased by 55.9%-58.5% during the APEC period, compared to other periods in October and November 2014, which agreed closely with measurements. Sensitivity results showed that emissions control measures regarding nitrogen oxides and organic carbon over North China led to the largest reductions in PM2.s concentrations in Beijing during the APEC summit, which led to overall reductions in the PM2.5 concentration of Beijing by 5.7% and 4.6%, respectively. The control of ammonia emissions was found to be able to greatly reduce PM2.5 concentrations in the whole of North China during the APEC meeting.
基金financially supported by the General Project of Top-Design of Mlti-Scale Nature-Social Models,Data Support and Decision Support System for NSFC Carbon Neutrality Major Project and the Basic Scientific Research Fund of the Chinese Academy of Meteorological Sciences(2021Z014)。
文摘The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories added the method of assimilating atmospheric CO_(2) concentrations to invert carbon sources and sinks;however,many global carbon inversion models are not publicly available.In addition,our regional assimilation inversion system,CCMVS-R(China Carbon Monitoring,Verification and Supporting for Regional),needs a global carbon inversion model with higher assimilation efficiency to provide boundary conditions.Here,an inversion model based on the global atmospheric chemistry model GEOS-Chem and a more accurate and easier-to-implement ensemble square root Kalman filter(EnSRF)algorithm is constructed and used to infer global and China's carbon fluxes in 2019.Atmospheric CO_(2) concentrations from ObsPack sites and five additional CO_(2) observational sites from China's Greenhouse Gas Observation Network(CGHGNET)were used for data assimilation to improve the estimate.The inverted annual global terrestrial and oceanic carbon uptake is 2.12 and 2.53 Pg C per year,respectively,accounting for 21.1%and 25.1%of global fossil fuel CO_(2) emissions.The remaining 5.41 Pg C per year in the atmosphere is consistent with the global atmospheric CO_(2) growth rates of 5.44 Pg C per year reported by the National Oceanic and Atmospheric Administration(NOAA),showing that the inversion model can provide a reasonable estimate of global-scale natural carbon sinks.The inverted terrestrial carbon sink of China is 0.37 Pg C per year,accounting for approximately 13%of China's fossil CO_(2) emissions.
基金supported by the Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Sciences (Grant No. XDA05040000)the National Natural Science Foundation of China (Grant Nos. 41005023 and 41275046)
文摘In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net- works based on the monthly average grid emission data （1979-2008） from different geographical regions. A three-dimensional global chemical transport model, God- dard Earth Observing System （GEOS）-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from -1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, - 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and tem- poral distribution of the atmospheric C02 concentration and ft.
基金partially supported by the National High Technology Research and Development Program of China[grant number 2013AA122002]the National Natural Science Foundation of China[grant numbers 41575100 and 91437220]+1 种基金the Knowledge Innovation Program of the Chinese Academy of Sciences[grant number KZCX2-EW-QN207]the Special Fund for Meteorological Scientific Research in Public Interest[grant number GYHY201506002]
文摘The performance of a joint data assimilation system（Tan-Tracker）,which is based on the PODEn4 Dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was evaluated.Atmospheric 3D CO2 concentrations and CO2 surface fluxes（CFs） from2010 were simulated using a global chemistry transport model（GEOS-Chem）.TheTan-Tracker system used the simulated CO2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO2（X（CO2）） data to optimize CO2 concentrations and CFs in the same assimilation window.Monthly simulated X（CO2）（X（CO2）Sim）） and assimilated X（CO2）（X（CO2）,TT） data retrieved at different satellite scan positions were compared with GOSAT-observed X（CO2）（X（CO2）,obs）data.The average RMSE between the monthly X（CO2）,TT and X（CO2）,Obs data was significantly（30%） lower than the average RMSE between X（CO2）,Sim and X（CO2）,Obs）.Specifically,reductions in error were found for the positions of northern Africa（the Sahara）,the Indian peninsula,southern Africa,southern North America,and western Australia.The difference between the correlation coefficients of the X（CO2）,Sim）and X（CO2）,Obs and those of the X（CO2）Π）,TT and X（CO2）,Obs was only small.In general,the Tan-Tracker system performed very well after assimilating the GOSAT data.
基金supported by the National Basic Research Program of China[973 program,grant number 2014CB441202]the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA05100503]the National Natural Science Foundation of China[grant number 41021004],[grant number 41475137],[grant number 91544219]
文摘Paved road dust is one of the most important aerosols in China. The authors estimated road dust emissions using an empirical model （AP-42 model） developed by the U.S. Environmental Protection Agency, and simulated road dust concentrations over China for the years 2006-2011 using the GEOS-Chem model.The annual road dust emissions amount averaged over 2006-2011 is estimated to be 2331.4 kt, with much higher emissions in eastern China than in western China. Because of heavy traffic and a dense road network, emissions are high over Beijing-Tianjin-Tanggu （BTT）, Henan Province, and Shandong Province. Meanwhile, emissions are calculated to be 459.1, 112.0, and 102.7 kt, respectively, over BTT, the Pearl River Delta （PRD） region, and the Yangtze River Delta （YRD）. Due to the monthly variation of precipitation, road dust emissions over China are simulated to be highest in December and lowest in June. The highest annual mean road dust concentration is simulated to be 14.5 tJg m-3 in Beijing. Over 2006-2011, because of the increases in road length and number of vehicles, annual road dust emissions for China as a whole, Bl-r, the PRD, and the YRD, are simulated to increase by 260%, 239%, 266%, and 59%, respectively, leading to 233%, 243%, 273%, and 100% increases in road dust concentrations in these regions, respectively. Our results have important implications for air pollution control in China.
基金supported by the National High Technology Research and Development Program of China (Grant No. 2013AA122002)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-EW-QN207)the National Basic Research Program of China (Grant Nos. 2010CB428403 and 2009CB421407)
文摘Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite(GOSAT) and ground-based the Total Carbon Column Observing Network(TCCON) data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42(Darwin) and 0.92(Izana). Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation's inputs, as well as the mismatch of satellite retrieval model input parameters.
基金supported by the National Natural Science Foundation of China (No. 21625701 )the Major State Basic Research Development Program of China ( 973 ) (No. 2013CB430001 )+1 种基金the Youth Project of National Natural Science Foundation of China (No. 21607090 )the Shuimu Tsinghua Scholar Program (No. 2021SM017)。
文摘The gaseous or particulate forms of divalent mercury(HgⅡ) significantly impact the spatial distribution of atmospheric mercury concentration and deposition flux(FLX). In the new nested-grid GEOS-Chem model, we try to modify the HgⅡ gas-particle partitioning relationship with synchronous and hourly observations at four sites in China. Observations of gaseous oxidized Hg(GOM), particulate-bound Hg(PBM), and PM 2.5 were used to derive an empirical gas-particle partitioning coefficient as a function of temperature( T) and organic aerosol(OA) concentrations under different relative humidity(RH). Results showed that with increasing RH, the dominant process of HgⅡ gas-particle partitioning changed from physical adsorption to chemical desorption. And the dominant factor of HgⅡ gas-particle partitioning changed from T to OA concentrations. We thus improved the simulated OA concentration field by introducing intermediate-volatility and semi-volatile organic compounds(I/SVOCs) emission inventory into the model framework and refining the volatile distributions of I/SVOCs according to new filed tests in the recent literatures. Finally, normalized mean biases(NMBs) of monthly gaseous element mercury(GEM), GOM, PBM, WFLX were reduced from-33%–29%, 95%–300%, 64%–261%, 117%–122% to-13%–0%,-20%–80%,-31%–50%,-17%–23%. The improved model explains 69%–98% of the observed atmospheric Hg decrease during 2013–2020 and can serve as a useful tool to evaluate the effectiveness of the Minamata Convention on Mercury.
基金supported by the National Basic Research Program of China(973 Program)(Grant No.2006CB403606)the Chinese Academy of Sciences(Grant Nos.KZCX2-YW-143 and KZCX2-YW-202)+1 种基金the National High Technology Research and Development Program of China(863 Program)(Grant No.2009AA12Z138)the National Natural Science Foundation of China(Grant Nos.40606008,40437017,and 40221503)
文摘The seasonal cycle of atmospheric CO2 at surface observation stations in the northern hemisphere is driven primarily by net ecosystem production (NEP) fluxes from terrestrial ecosystems. In addition to NEP from terrestrial ecosystems, surface fluxes from fossil fuel combustion and ocean exchange also contribute to the seasonal cycle of atmospheric CO2. Here the authors use the Goddard Earth Observing System-Chemistry (GEOS-Chem) model (version 8-02-01), with modifications, to assess the impact of these fluxes on the seasonal cycle of atmospheric CO2 in 2005. Modifications include monthly fossil and ocean emission inventories. CO2 simulations with monthly varying and annual emission inventories were carried out separately. The sources and sinks of monthly averaged net surface flux are different from those of annual emission inventories for every month. Results indicate that changes in monthly averaged net surface flux have a greater impact on the average concentration of atmospheric CO2 in the northern hemisphere than on the average concentration for latitudes 30-90°S in July. The concentration values differ little between both emission inventories over the latitudinal range from the equator to 30°S in January and July. The accumulated impacts of the monthly averaged fossil and ocean emissions contribute to an increase of the total global monthly average of CO2 from May to December.An apparent discrepancy for global average CO2 concentration between model results and observation was because the observation stations were not sufficiently representative. More accurate values for monthly varying net surface flux will be necessary in future to run the CO2 simulation.
