大气中CO2含量的增加速率已经超过了自然界所能吸收的速度,并逐步影响到全球气候变暖。利用模型模拟分析已经成为一个重要的工具用以深入对碳循环的理解。本文使用2008~2010年的生物模型SiB3(Simple Biosphere version 3)与优化后的CT20...大气中CO2含量的增加速率已经超过了自然界所能吸收的速度,并逐步影响到全球气候变暖。利用模型模拟分析已经成为一个重要的工具用以深入对碳循环的理解。本文使用2008~2010年的生物模型SiB3(Simple Biosphere version 3)与优化后的CT2016(Carbon Tracker 2016)陆地生态系统碳通量驱动GEOS-Chem大气化学传输模型模拟全球CO2浓度。通过分析模拟CO2浓度的空间分布与季节变化,加深对全球碳源汇分布特点的理解,探究陆地生态系统碳通量不确定性对模拟结果的影响,进而认识陆地生态系统碳通量反演精度提升的重要性。SiB3与优化后的CT2016陆地生态系统碳通量都具有明显的季节变化,但在欧洲地区碳源汇的表现相反,其全球总量与空间分布也存在极大的不确定性。模拟CO2浓度结果表明:在人为活动较少地区,陆地生态系统碳通量对近地面CO2浓度空间分布起主导作用,尤其在南半球和欧洲地区模拟浓度有明显差异,且两种模拟结果的季节差异依赖于陆地生态系统碳通量的季节变化。将模拟结果与9个观测站点资料进行对比,以期选用合适的陆地生态系统碳通量来提升GEOS-Chem模拟CO2浓度的精度。实验结果表明:两种模拟结果均能较好的模拟CO2浓度的季节变化及其峰谷值,但CT2016模拟的CO2浓度在多数站点处更接近观测资料,模拟准确性更高。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
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 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
Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to...Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to curb the rising CO_(2) concentrations. To investigate the impact of China's carbon neutrality goal on atmospheric CO_(2) concentrations, we conducted a series of ideal simulations from 2015 to 2019 using a global 3D chemistry transport model, Goddard Earth Observing System Chemistry(GEOS-Chem). Compared with the column-averaged dry-air mole fraction of atmospheric CO_(2) (XCO_(2) ) from Orbiting Carbon Observatory-2(OCO-2) and surface CO_(2) measurements in Obs Pack, we find that GEOS-Chem effectively reproduces the spatiotemporal variability of CO_(2) . The model exhibits a root mean square error(RMSE) of 1.51 ppm(R^(2)=0.89) for OCO-2 XCO_(2) in China and 2.65 ppm(R^(2)=0.75) for surface CO_(2) concentrations at the WLG station. Further, compared to 2.83 ppm yr^(-1)in the control experiment, we suggest that net-zero CO_(2) emissions in China decelerate the increasing trends of XCO_(2) to 1.81 ppm yr^(-1),making a decrease of approximately 35.89%. Meanwhile, the seasonal cycle amplitude(SCA) of XCO_(2) is moderately reduced from 7.39±0.81 to 6.75±0.70 ppm, representing a relative reduction of 9.91%. Spatially, net-zero CO_(2) emissions induce a more significant decrease in XCO_(2) trends over northern and southern China, while their impact on SCA is more evident in northern and northeastern China. Moreover, ideal experiments demonstrate that zero fossil CO_(2) emissions lead to a greater attenuation of the linear trends of XCO_(2) by 40.81%, while the absence of terrestrial CO_(2) sinks largely diminishes the SCA by 16.61%. Additionally,trends and SCA in surface CO_(2) concentrations exhibit almost identical decreasing responses to net-zero CO_(2) emissions but display greater sensitivities compared to XCO_(2) . Overall, our study underscores the potential of China's carbon neutrality goal in mitigating global warming, underscoring the need for concerted and collaborative efforts from nations worldwide.展开更多
The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels...The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels and background variability,particularly owing to the influence of terrestrial biospheric fluxes.This necessitates the development of a regional inversion methodology based on atmospheric CO_(2)observations to verify bottom-up estimations independently.This study presents a promising approach for estimating China's FFCO_(2)emissions by incorporating the model residual errors(MREs)of the column-averaged dry-air mole fractions of CO_(2)(XCO_(2))from FFCO_(2)emissions(MREff)retained in the analysis of natural flux optimization.China's FFCO_(2)emissions during the COVID-19 lockdown in 2020 are estimated using the GEOS-Chem adjoint model.The relationship between the MREff and FFCO_(2)is determined using the model based on a regional FFCO_(2)anomaly suggested by posterior NOx emissions from air-quality data assimilation.The MREff is typically one-tenth in magnitude,but some positively skewed outliers exceed 1 ppm because the prior emissions lack lockdown impacts,thereby exerting considerable observation forcing given the satellite retrieval uncertainties.We initialize the FFCO_(2)with posterior NOx emissions and optimize the colinear emission ratio.Synthetic data experiments demonstrate that this approach reduces the FFCO_(2)bias to less than 10%.The real-data experiments estimate 19%lower FFCO_(2)with GOSAT XCO_(2)and 26%lower with OCO-2 XCO_(2)than the bottom-up estimations.This study proves the feasibility of our regional FFCO_(2)inversion,highlighting the importance of addressing the outlier behaviors observed in satellite XCO_(2)retrievals.展开更多
本研究基于GEOS-Chem三维大气化学传输模式,模拟分析了2016~2018年东亚地区活性氮(Nr)沉降与排放的时空分布情况,以及其在季风降水的影响下的季节变化特征。沉降排放比(D/E)分析表明,中国与印度D/E较低,是东亚地区主要的活性氮排放源。...本研究基于GEOS-Chem三维大气化学传输模式,模拟分析了2016~2018年东亚地区活性氮(Nr)沉降与排放的时空分布情况,以及其在季风降水的影响下的季节变化特征。沉降排放比(D/E)分析表明,中国与印度D/E较低,是东亚地区主要的活性氮排放源。本研究进一步发现,不同国家和地区活性氮排放和季风降水不同位相的季节变化,导致其活性氮沉降的季节变化也不同。中国活性氮排放与东亚季风降水的季节变化基本同位相,各类活性氮沉降均呈“夏高冬低”的特征。印度地区则因活性氮排放和南亚季风降水的季节变化位相不同,导致除氧化氮干沉降外,其他各类氮沉降的季节变化主要受南亚季风强降水的影响,与对应排放季节变化存在较大差异。而东南亚地区降水受季风影响较弱,其沉降的季节变化主要取决于对应排放的季节变化。本研究解析了季风影响下活性氮排放对其沉降季节变化的影响,有助于制定更为科学有效的区域减排政策。This study uses the GEOS-Chem model to investigate the spatiotemporal distribution and seasonal mechanisms of reactive nitrogen (Nr) deposition and emissions across East Asia during 2016~2018. Analysis of deposition-to-emission ratios (D/E) shows that China and India have lower D/E ratios and are the main sources of active nitrogen emissions in East Asia. We find that the regional seasonal patterns of nitrogen deposition are shaped by phase relationships between emissions and monsoon precipitation. In China, synchronized peaks of the emissions and East Asian monsoon rainfall drive coherent “summer-high, winter-low” deposition patterns for all Nr deposition species. In contrast, India exhibits a phase mismatch: except for the dry deposition of nitrogen oxide, other Nr deposition species are mainly influenced by the heavy precipitation of the South Asian Monsoon, with a significant difference of the seasonal variation of emissions. While the Southeast Asia, with weaker monsoon influence to the precipitation, shows deposition seasonality primarily governed by local emission cycles. This study resolves the dual control of emissions and monsoons on Nr deposition, providing a framework for optimizing regional air quality policies.展开更多
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
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
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
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 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 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
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 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 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.
基金supported by the National Key Research and Development Program of China (Grant No. 2022YFB3904801)the National Natural Science Foundation of China (Grant No. 42475129)+2 种基金the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20221449)the Xizang Science and Technology Innovation Base Construction Project (Grant No. XZ202401YD0008)the National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility” (Grant No. 2023-EL-ZD-00022)。
文摘Elevated atmospheric carbon dioxide(CO_(2)) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to curb the rising CO_(2) concentrations. To investigate the impact of China's carbon neutrality goal on atmospheric CO_(2) concentrations, we conducted a series of ideal simulations from 2015 to 2019 using a global 3D chemistry transport model, Goddard Earth Observing System Chemistry(GEOS-Chem). Compared with the column-averaged dry-air mole fraction of atmospheric CO_(2) (XCO_(2) ) from Orbiting Carbon Observatory-2(OCO-2) and surface CO_(2) measurements in Obs Pack, we find that GEOS-Chem effectively reproduces the spatiotemporal variability of CO_(2) . The model exhibits a root mean square error(RMSE) of 1.51 ppm(R^(2)=0.89) for OCO-2 XCO_(2) in China and 2.65 ppm(R^(2)=0.75) for surface CO_(2) concentrations at the WLG station. Further, compared to 2.83 ppm yr^(-1)in the control experiment, we suggest that net-zero CO_(2) emissions in China decelerate the increasing trends of XCO_(2) to 1.81 ppm yr^(-1),making a decrease of approximately 35.89%. Meanwhile, the seasonal cycle amplitude(SCA) of XCO_(2) is moderately reduced from 7.39±0.81 to 6.75±0.70 ppm, representing a relative reduction of 9.91%. Spatially, net-zero CO_(2) emissions induce a more significant decrease in XCO_(2) trends over northern and southern China, while their impact on SCA is more evident in northern and northeastern China. Moreover, ideal experiments demonstrate that zero fossil CO_(2) emissions lead to a greater attenuation of the linear trends of XCO_(2) by 40.81%, while the absence of terrestrial CO_(2) sinks largely diminishes the SCA by 16.61%. Additionally,trends and SCA in surface CO_(2) concentrations exhibit almost identical decreasing responses to net-zero CO_(2) emissions but display greater sensitivities compared to XCO_(2) . Overall, our study underscores the potential of China's carbon neutrality goal in mitigating global warming, underscoring the need for concerted and collaborative efforts from nations worldwide.
基金jointly supported by the National Key Research and Development Plan(Grant No.2023YFB3907405)the National Natural Science Foundation of China(Grant No.42175132)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-037)。
文摘The challenge of establishing top-down constraints for regional emissions of fossil fuel CO_(2)(FFCO_(2))arises from the difficulty in distinguishing between atmospheric CO_(2)concentrations released from fossil fuels and background variability,particularly owing to the influence of terrestrial biospheric fluxes.This necessitates the development of a regional inversion methodology based on atmospheric CO_(2)observations to verify bottom-up estimations independently.This study presents a promising approach for estimating China's FFCO_(2)emissions by incorporating the model residual errors(MREs)of the column-averaged dry-air mole fractions of CO_(2)(XCO_(2))from FFCO_(2)emissions(MREff)retained in the analysis of natural flux optimization.China's FFCO_(2)emissions during the COVID-19 lockdown in 2020 are estimated using the GEOS-Chem adjoint model.The relationship between the MREff and FFCO_(2)is determined using the model based on a regional FFCO_(2)anomaly suggested by posterior NOx emissions from air-quality data assimilation.The MREff is typically one-tenth in magnitude,but some positively skewed outliers exceed 1 ppm because the prior emissions lack lockdown impacts,thereby exerting considerable observation forcing given the satellite retrieval uncertainties.We initialize the FFCO_(2)with posterior NOx emissions and optimize the colinear emission ratio.Synthetic data experiments demonstrate that this approach reduces the FFCO_(2)bias to less than 10%.The real-data experiments estimate 19%lower FFCO_(2)with GOSAT XCO_(2)and 26%lower with OCO-2 XCO_(2)than the bottom-up estimations.This study proves the feasibility of our regional FFCO_(2)inversion,highlighting the importance of addressing the outlier behaviors observed in satellite XCO_(2)retrievals.
文摘本研究基于GEOS-Chem三维大气化学传输模式,模拟分析了2016~2018年东亚地区活性氮(Nr)沉降与排放的时空分布情况,以及其在季风降水的影响下的季节变化特征。沉降排放比(D/E)分析表明,中国与印度D/E较低,是东亚地区主要的活性氮排放源。本研究进一步发现,不同国家和地区活性氮排放和季风降水不同位相的季节变化,导致其活性氮沉降的季节变化也不同。中国活性氮排放与东亚季风降水的季节变化基本同位相,各类活性氮沉降均呈“夏高冬低”的特征。印度地区则因活性氮排放和南亚季风降水的季节变化位相不同,导致除氧化氮干沉降外,其他各类氮沉降的季节变化主要受南亚季风强降水的影响,与对应排放季节变化存在较大差异。而东南亚地区降水受季风影响较弱,其沉降的季节变化主要取决于对应排放的季节变化。本研究解析了季风影响下活性氮排放对其沉降季节变化的影响,有助于制定更为科学有效的区域减排政策。This study uses the GEOS-Chem model to investigate the spatiotemporal distribution and seasonal mechanisms of reactive nitrogen (Nr) deposition and emissions across East Asia during 2016~2018. Analysis of deposition-to-emission ratios (D/E) shows that China and India have lower D/E ratios and are the main sources of active nitrogen emissions in East Asia. We find that the regional seasonal patterns of nitrogen deposition are shaped by phase relationships between emissions and monsoon precipitation. In China, synchronized peaks of the emissions and East Asian monsoon rainfall drive coherent “summer-high, winter-low” deposition patterns for all Nr deposition species. In contrast, India exhibits a phase mismatch: except for the dry deposition of nitrogen oxide, other Nr deposition species are mainly influenced by the heavy precipitation of the South Asian Monsoon, with a significant difference of the seasonal variation of emissions. While the Southeast Asia, with weaker monsoon influence to the precipitation, shows deposition seasonality primarily governed by local emission cycles. This study resolves the dual control of emissions and monsoons on Nr deposition, providing a framework for optimizing regional air quality policies.
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 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.
