Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemic...Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemical transport model in simulating PM_(2.5).Based on the NASM joint chemical data assimilation system,the authors quantified the impacts of different meteorological fields on the pollutant simulations as well as revealed the role of meteorological conditions in the accumulation,maintenance,and dissipation of heavy haze pollution.During the two heavy pollution processes from 10 to 24 November 2018,the meteorological fields were obtained using NCEP FNL and ERA5 reanalysis data,each used to drive the WRF model,to analyze the differences in the simulated PM_(2.5) concentration.The results show that the meteorological field has a strong influence on the concentration levels and spatial distribution of the pollution simulations.The ERA5 group had relatively small simulation errors,and more accurate PM_(2.5) simulation results could be obtained.The RMSE was 11.86𝜇g m^(-3)lower than that of the FNL group before assimilation,and 5.77𝜇g m^(-3)lower after joint assimilation.The authors used the PM_(2.5) simulation results obtained by ERA5 data to discuss the role of the wind field and circulation situation on the pollution process,to analyze the correlation between wind speed,temperature,relative humidity,and boundary layer height and pollutant concentrations,and to further clarify the key formation mechanism of this pollution process.展开更多
This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and...This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification;it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method.展开更多
We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Fore...We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Forecasting(WRF)model.Observation data included radial velocity(Vr)and reflectivity(Z)data from a single Doppler radar,quality controlled prior to assimilation.Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods.Compared with a forecast that began with NCEP analysis data,our radar data assimilation results were clearly improved in terms of structure,intensity,track,and precipitation prediction for Typhoon Haikui(2012).The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more efficient.The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity,track,and precipitation;however,the impacts of Vr data were significantly greater that those of Z data.Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.展开更多
A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid N...A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.展开更多
Terrestrial ecosystems play an important role in the global carbon cycle,offsetting nearly one-third of annual anthropogenic carbon emissions[1].Thisterrestrial carbon sink doubled in the past five decades and is proj...Terrestrial ecosystems play an important role in the global carbon cycle,offsetting nearly one-third of annual anthropogenic carbon emissions[1].Thisterrestrial carbon sink doubled in the past five decades and is projected to persist,primarily due to the fertilization of increasing CO_(2)on photosynthesis,particularly in tropical forest ecosystems and warming-induced productivity enhancement in arctic and boreal ecosystems[2].Climate extremes,which amplify interannual variations in land carbon sinks[3],have emerged as the major driver increasing the risk of destabilization in global land carbon sinks[4].展开更多
Given the interpretability,accuracy,and stability of numerical weather prediction(NWP)models,current operational weather forecasting relies heavily on the NWP approach[1].In the past two years,the rapid development of...Given the interpretability,accuracy,and stability of numerical weather prediction(NWP)models,current operational weather forecasting relies heavily on the NWP approach[1].In the past two years,the rapid development of Artificial Intelligence(AI)has provided an alternative solution for medium-range(1-10 d)weather forecasting.展开更多
Current ground-based observation networks for atmospheric greenhouse gases(GHG)volume mixing ratios are sparse,with only two stations-namely Waliguan and Shangri-La-located on the eastern border of the Tibetan Plateau...Current ground-based observation networks for atmospheric greenhouse gases(GHG)volume mixing ratios are sparse,with only two stations-namely Waliguan and Shangri-La-located on the eastern border of the Tibetan Plateau(TP)providing publicly available data(Masarie et al.,2014).展开更多
The greenhouse gas budget on the Tibetan Plateau remains unknown and the potential for methane(CH_(4))and nitrous oxide(N_(2)O)emissions from an intensifying livestock system and expanding surface water in offsetting ...The greenhouse gas budget on the Tibetan Plateau remains unknown and the potential for methane(CH_(4))and nitrous oxide(N_(2)O)emissions from an intensifying livestock system and expanding surface water in offsetting terrestrial carbon dioxide(CO_(2))sinks are both of great concerns and uncertainties,which compromise an accurate assessment of Tibetan Plateau contribution to China’s ambitious climate goals by 2060s.Here we integrated greenhouse gas flux measurements at∼500 sites in empirical modeling approaches,emissions from the livestock sector with process-based biogeochemistry modeling to estimate CH_(4)and N_(2)O fluxes across terrestrial ecosystems and inland waters in 2000s and 2010s.We found that emissions from livestock and inland waters,predominantly contributed by CH_(4),compensated∼21%and∼13%of carbon sinks provided by forests and grasslands after adjusting carbon burial in sediments and riverine carbon export,respectively.The Tibetan Plateau then acted as an appreciable greenhouse gas sink that almost compensated for its contemporary anthropogenic emissions,making it nearly climate-neutral.The enhancement of terrestrial CO_(2)sinks in the 2060s under medium warming scenario would be counterbalanced by livestock CH_(4)emissions when the current overgrazing status continues.By transitioning to a livestock-forage balance and implementing mitigation initiatives to reduce livestock emission intensity,the greenhouse gas sink is projected to increase by more than 1.5 times.We suggested that a transition towards sustainable pastoralism illuminates the path to minimizing ecosystem greenhouse gas emissions and amplifying the role of the Tibetan Plateau in fulfilling China’s climate ambition.展开更多
In this study,the Chinese carbon cyle dataassimilation system Tan-Tracker is developed based on the atmospheric chemical transport model(GEOS-Chem)platform.Tan-Tracker is a dual-pass data-assimilation system in which ...In this study,the Chinese carbon cyle dataassimilation system Tan-Tracker is developed based on the atmospheric chemical transport model(GEOS-Chem)platform.Tan-Tracker is a dual-pass data-assimilation system in which both CO2concentrations and CO2fluxes are simultaneously assimilated from atmospheric observations.It has several advantages,including its advanced data-assimilation method,its highly efficient computing performance,and its simultaneous assimilation of CO2concentrations and CO2fluxes.Preliminary observing system simulation experiments demonstrate its robust performance with high assimilation precision,making full use of observations.The Tan-Tracker system can only assimilate in situ observations for the moment.In the future,we hope to extend Tan-Tracker with functions for using satellite measurements,which will form the quasioperational Chinese carbon cycle data-assimilation system.展开更多
Accurate estimate of the size of land carbon sink is essential for guiding climate mitigation actions to fulfill China's net-zero ambitions before 2060.The atmospheric inversion is an effective approach to provide...Accurate estimate of the size of land carbon sink is essential for guiding climate mitigation actions to fulfill China's net-zero ambitions before 2060.The atmospheric inversion is an effective approach to provide spatially explicit estimate of surface CO_(2)fluxes that are optimally consistent with atmospheric CO_(2)measurements.But atmospheric inversion of China's land carbon sink has enormous uncertainties,with one major source arising from the poor coverage of CO_(2)observation stations.Here we use a regional atmospheric inversion framework to design an observation network that could minimize uncertainties in inverted estimate of China's land carbon sink.Compared with the large spread of inverted sink(~1 Pg C a~(-1))from state-of-the-art inversions using existing CO_(2)observations,the uncertainty is constrained within 0.3 Pg C a~(-1)when a total of 30 stations were deployed,and is further reduced to approximately 0.2 Pg C a~(-1)when 60 stations were deployed.The proposed stations are mostly distributed over areas with high biosphere productivity during the growing season,such as Southeast China,Northeast China,North China,and the Tibetan Plateau.Moreover,the proposed stations can cover areas where existing satellites have limited coverage due to cloud shadowing in the monsoon season or over complex topography.Such ground-based observation network will be a critical component in the future integrated observing system for monitoring China's land carbon fluxes.展开更多
A regional surface carbon dioxide （C02） flux inversion system, the Tan-Tracker-Region, was developed by incor- porating an assimilation scheme into the Community Multiscale Air Quality （CMAQ） regional chemical tra...A regional surface carbon dioxide （C02） flux inversion system, the Tan-Tracker-Region, was developed by incor- porating an assimilation scheme into the Community Multiscale Air Quality （CMAQ） regional chemical transport model to resolve fine-scale CO2 variability over East Asia. The proper orthogonal decomposition-based ensemble four-dimensional variational data assimilation approach （POD-4DVar） is the core algorithm for the joint assimilation framework, and simultaneous assimilations of CO2 concentrations and surface CO2 fluxes are applied to help reduce the uncertainty in initial CO2 concentrations. A persistence dynamical model was developed to describe the evolu- tion of the surface CO2 fluxes and help avoid the ＂signal-to-noise＂ problem; thus, CO2 fluxes could be estimated as a whole at the model grid scale, with better use of observation information. The performance of the regional inversion system was evaluated through a group of single-observation-based observing system simulation experiments （OSSEs）. The results of the experiments suggest that a reliable performance of Tan-Tracker-Region is dependent on certain assimilation parameter choices, for example, an optimized window length of approximately 3 h, an ensemble size of approximately 100, and a covariance localization radius of approximately 320 km. This is probably due to the strong diurnal variation and spatial heterogeneity in the fine-scale CMAQ simulation, which could affect the perform- ance of the regional inversion system. In addition, because all observations can be artificially obtained in OSSEs, the performance of Tan-Tracker-Region was further evaluated through different densities of the artificial observation net- work in different CO2 flux situations. The results indicate that more observation sites would be useful to systematic- ally improve the estimation of CO2 concentration and flux in large areas over the model domain. The work presented here forms a foundation for future research in which a thorough estimation of CO2 flux variability over East Asia could be performed with the regional inversion system.展开更多
Satellite carbon dioxide(CO_(2))retrievals provide important constraints on surface carbon fluxes in regions that are undersampled by global in situ networks.In this study,we developed an atmospheric inversion system ...Satellite carbon dioxide(CO_(2))retrievals provide important constraints on surface carbon fluxes in regions that are undersampled by global in situ networks.In this study,we developed an atmospheric inversion system to infer CO_(2)sources and sinks from Orbiting Carbon Observatory-2(OCO-2)column CO_(2)retrievals during 2015–2019,and compared our estimates to five other state-of-the-art inversions.By assimilating satellite CO_(2)retrievals in the inversion,the global net terrestrial carbon sink(net biome productivity,NBP)was found to be 1.03±0.39 petagrams of carbon per year(Pg C yr^(-1));this estimate is lower than the sink estimate of 1.46–2.52 Pg C yr^(-1),obtained using surface-based inversions.We estimated a weak northern uptake of 1.30 Pg C yr-1and weak tropical release of-0.26 Pg C yr^(-1),consistent with previous reports.By contrast,the other inversions showed a strong northern uptake(1.44–2.78 Pg C yr-1),but diverging tropical carbon fluxes,from a sink of 0.77 Pg C yr^(-1) to a source of-1.26 Pg C yr^(-1).During the 2015–2016 El Ni?o event,the tropical land biosphere was mainly responsible for a higher global CO_(2)growth rate.Anomalously high carbon uptake in the northern extratropics,consistent with concurrent extreme Northern Hemisphere greening,partially offset the tropical carbon losses.This anomalously high carbon uptake was not always found in surface-based inversions,resulting in a larger global carbon release in the other inversions.Thus,our satellite constraint refines the current understanding of flux partitioning between northern and tropical terrestrial regions,and suggests that the northern extratropics acted as anomalous high CO_(2)sinks in response to the 2015–2016 El Nino event.展开更多
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program of Ministry of Science and Technology of the People's Republic of China[grant number 2022QZKK0101]the Science and Technology Department of the Tibet Program[grant number XZ202301ZY0035G]。
文摘Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemical transport model in simulating PM_(2.5).Based on the NASM joint chemical data assimilation system,the authors quantified the impacts of different meteorological fields on the pollutant simulations as well as revealed the role of meteorological conditions in the accumulation,maintenance,and dissipation of heavy haze pollution.During the two heavy pollution processes from 10 to 24 November 2018,the meteorological fields were obtained using NCEP FNL and ERA5 reanalysis data,each used to drive the WRF model,to analyze the differences in the simulated PM_(2.5) concentration.The results show that the meteorological field has a strong influence on the concentration levels and spatial distribution of the pollution simulations.The ERA5 group had relatively small simulation errors,and more accurate PM_(2.5) simulation results could be obtained.The RMSE was 11.86𝜇g m^(-3)lower than that of the FNL group before assimilation,and 5.77𝜇g m^(-3)lower after joint assimilation.The authors used the PM_(2.5) simulation results obtained by ERA5 data to discuss the role of the wind field and circulation situation on the pollution process,to analyze the correlation between wind speed,temperature,relative humidity,and boundary layer height and pollutant concentrations,and to further clarify the key formation mechanism of this pollution process.
基金partially supported by the National Key R&D Program of China (Grant No. 2016YFA0600203)the National Natural Science Foundation of China (Grant No. 41575100)
文摘This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification;it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method.
基金partially supported by the National Key Research and Development Program of China(Grant No.2016YFA0600203)the National Natural Science Foundation of China(Grant No.41575100)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDY-SSW-DQC012)。
文摘We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Forecasting(WRF)model.Observation data included radial velocity(Vr)and reflectivity(Z)data from a single Doppler radar,quality controlled prior to assimilation.Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods.Compared with a forecast that began with NCEP analysis data,our radar data assimilation results were clearly improved in terms of structure,intensity,track,and precipitation prediction for Typhoon Haikui(2012).The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more efficient.The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity,track,and precipitation;however,the impacts of Vr data were significantly greater that those of Z data.Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.
基金the National Key Research and Development Program of China(Grant No.2016YFA0600203)the National Natural Science Foundation of China(Grant No.41575100)+1 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDY-SSW-DQC012)the CMA Special Public Welfare Research Fund(Grant No.GYHY201506002).
文摘A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.
基金supported by the National Natural Science Foundation of China(42425106 and 42501110)the National Key Research and Development Program of China(2024YFF0809104)the Postdoctoral Innovation Talents Support Program ofChina(Bx20240018).
文摘Terrestrial ecosystems play an important role in the global carbon cycle,offsetting nearly one-third of annual anthropogenic carbon emissions[1].Thisterrestrial carbon sink doubled in the past five decades and is projected to persist,primarily due to the fertilization of increasing CO_(2)on photosynthesis,particularly in tropical forest ecosystems and warming-induced productivity enhancement in arctic and boreal ecosystems[2].Climate extremes,which amplify interannual variations in land carbon sinks[3],have emerged as the major driver increasing the risk of destabilization in global land carbon sinks[4].
基金supported by the National University of Defense Technology(NUDT)Research Initiation Funding for High-Level Scientific and Technological Innovative Talents(202402-YJRC-LJ-001)the Independent Innovation Science Fund of National University of Defense Technology(22-ZZCX-081)the Guangdong Province Introduction of Innovative R&D Team Project China(2019ZT08G669)。
文摘Given the interpretability,accuracy,and stability of numerical weather prediction(NWP)models,current operational weather forecasting relies heavily on the NWP approach[1].In the past two years,the rapid development of Artificial Intelligence(AI)has provided an alternative solution for medium-range(1-10 d)weather forecasting.
基金supported by the National Key Research and Development Program of China(Grant No.2024YFF0809103)the National Natural Science Foundation of China(Grant Nos.41988101&42205140)the Innovation Program for Young Scholars of TPESER(Grant No.TPESERQNCX2022ZD-01)。
文摘Current ground-based observation networks for atmospheric greenhouse gases(GHG)volume mixing ratios are sparse,with only two stations-namely Waliguan and Shangri-La-located on the eastern border of the Tibetan Plateau(TP)providing publicly available data(Masarie et al.,2014).
基金supported by grants from the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2024QZKK0301)the National Key Research and Development Program of China(2024YFF0809104)the National Natural Science Foundation of China(42425106).
文摘The greenhouse gas budget on the Tibetan Plateau remains unknown and the potential for methane(CH_(4))and nitrous oxide(N_(2)O)emissions from an intensifying livestock system and expanding surface water in offsetting terrestrial carbon dioxide(CO_(2))sinks are both of great concerns and uncertainties,which compromise an accurate assessment of Tibetan Plateau contribution to China’s ambitious climate goals by 2060s.Here we integrated greenhouse gas flux measurements at∼500 sites in empirical modeling approaches,emissions from the livestock sector with process-based biogeochemistry modeling to estimate CH_(4)and N_(2)O fluxes across terrestrial ecosystems and inland waters in 2000s and 2010s.We found that emissions from livestock and inland waters,predominantly contributed by CH_(4),compensated∼21%and∼13%of carbon sinks provided by forests and grasslands after adjusting carbon burial in sediments and riverine carbon export,respectively.The Tibetan Plateau then acted as an appreciable greenhouse gas sink that almost compensated for its contemporary anthropogenic emissions,making it nearly climate-neutral.The enhancement of terrestrial CO_(2)sinks in the 2060s under medium warming scenario would be counterbalanced by livestock CH_(4)emissions when the current overgrazing status continues.By transitioning to a livestock-forage balance and implementing mitigation initiatives to reduce livestock emission intensity,the greenhouse gas sink is projected to increase by more than 1.5 times.We suggested that a transition towards sustainable pastoralism illuminates the path to minimizing ecosystem greenhouse gas emissions and amplifying the role of the Tibetan Plateau in fulfilling China’s climate ambition.
基金supported by the Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues (XDA05040200)the National High Technology Research and Development Program of China (Grant No. 2013AA122002)+1 种基金the National Natural Science Foundation of China (41075076)the Knowledge Innovation Program of the Chinese Academy of Sciences (KZCX2-EW-QN207)
文摘In this study,the Chinese carbon cyle dataassimilation system Tan-Tracker is developed based on the atmospheric chemical transport model(GEOS-Chem)platform.Tan-Tracker is a dual-pass data-assimilation system in which both CO2concentrations and CO2fluxes are simultaneously assimilated from atmospheric observations.It has several advantages,including its advanced data-assimilation method,its highly efficient computing performance,and its simultaneous assimilation of CO2concentrations and CO2fluxes.Preliminary observing system simulation experiments demonstrate its robust performance with high assimilation precision,making full use of observations.The Tan-Tracker system can only assimilate in situ observations for the moment.In the future,we hope to extend Tan-Tracker with functions for using satellite measurements,which will form the quasioperational Chinese carbon cycle data-assimilation system.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(2022QZKK0101)the National Natural Science Foundation of China(41988101,42001104,and 41975140)+1 种基金the National Key Scientific and Technological Infrastructure Project“Earth System Science Numerical Simulator Facility”(Earth Lab,201715003471104355)the Innovation Program for Young Scholars of TPESER(TPESER-QNCX2022ZD-01)。
文摘Accurate estimate of the size of land carbon sink is essential for guiding climate mitigation actions to fulfill China's net-zero ambitions before 2060.The atmospheric inversion is an effective approach to provide spatially explicit estimate of surface CO_(2)fluxes that are optimally consistent with atmospheric CO_(2)measurements.But atmospheric inversion of China's land carbon sink has enormous uncertainties,with one major source arising from the poor coverage of CO_(2)observation stations.Here we use a regional atmospheric inversion framework to design an observation network that could minimize uncertainties in inverted estimate of China's land carbon sink.Compared with the large spread of inverted sink(~1 Pg C a~(-1))from state-of-the-art inversions using existing CO_(2)observations,the uncertainty is constrained within 0.3 Pg C a~(-1)when a total of 30 stations were deployed,and is further reduced to approximately 0.2 Pg C a~(-1)when 60 stations were deployed.The proposed stations are mostly distributed over areas with high biosphere productivity during the growing season,such as Southeast China,Northeast China,North China,and the Tibetan Plateau.Moreover,the proposed stations can cover areas where existing satellites have limited coverage due to cloud shadowing in the monsoon season or over complex topography.Such ground-based observation network will be a critical component in the future integrated observing system for monitoring China's land carbon fluxes.
基金Supported by the National Natural Science Foundation of China(41130528)National High Technology Research and Development Program of China(2013AA122002)+1 种基金Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues(XDA05040404)National Key Technology Research and Development Program of China(2016YFC0202103)
文摘A regional surface carbon dioxide （C02） flux inversion system, the Tan-Tracker-Region, was developed by incor- porating an assimilation scheme into the Community Multiscale Air Quality （CMAQ） regional chemical transport model to resolve fine-scale CO2 variability over East Asia. The proper orthogonal decomposition-based ensemble four-dimensional variational data assimilation approach （POD-4DVar） is the core algorithm for the joint assimilation framework, and simultaneous assimilations of CO2 concentrations and surface CO2 fluxes are applied to help reduce the uncertainty in initial CO2 concentrations. A persistence dynamical model was developed to describe the evolu- tion of the surface CO2 fluxes and help avoid the ＂signal-to-noise＂ problem; thus, CO2 fluxes could be estimated as a whole at the model grid scale, with better use of observation information. The performance of the regional inversion system was evaluated through a group of single-observation-based observing system simulation experiments （OSSEs）. The results of the experiments suggest that a reliable performance of Tan-Tracker-Region is dependent on certain assimilation parameter choices, for example, an optimized window length of approximately 3 h, an ensemble size of approximately 100, and a covariance localization radius of approximately 320 km. This is probably due to the strong diurnal variation and spatial heterogeneity in the fine-scale CMAQ simulation, which could affect the perform- ance of the regional inversion system. In addition, because all observations can be artificially obtained in OSSEs, the performance of Tan-Tracker-Region was further evaluated through different densities of the artificial observation net- work in different CO2 flux situations. The results indicate that more observation sites would be useful to systematic- ally improve the estimation of CO2 concentration and flux in large areas over the model domain. The work presented here forms a foundation for future research in which a thorough estimation of CO2 flux variability over East Asia could be performed with the regional inversion system.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(2022QZKK0101)the National Natural Science Foundation of China(Grant Nos.41975140&42105150)。
文摘Satellite carbon dioxide(CO_(2))retrievals provide important constraints on surface carbon fluxes in regions that are undersampled by global in situ networks.In this study,we developed an atmospheric inversion system to infer CO_(2)sources and sinks from Orbiting Carbon Observatory-2(OCO-2)column CO_(2)retrievals during 2015–2019,and compared our estimates to five other state-of-the-art inversions.By assimilating satellite CO_(2)retrievals in the inversion,the global net terrestrial carbon sink(net biome productivity,NBP)was found to be 1.03±0.39 petagrams of carbon per year(Pg C yr^(-1));this estimate is lower than the sink estimate of 1.46–2.52 Pg C yr^(-1),obtained using surface-based inversions.We estimated a weak northern uptake of 1.30 Pg C yr-1and weak tropical release of-0.26 Pg C yr^(-1),consistent with previous reports.By contrast,the other inversions showed a strong northern uptake(1.44–2.78 Pg C yr-1),but diverging tropical carbon fluxes,from a sink of 0.77 Pg C yr^(-1) to a source of-1.26 Pg C yr^(-1).During the 2015–2016 El Ni?o event,the tropical land biosphere was mainly responsible for a higher global CO_(2)growth rate.Anomalously high carbon uptake in the northern extratropics,consistent with concurrent extreme Northern Hemisphere greening,partially offset the tropical carbon losses.This anomalously high carbon uptake was not always found in surface-based inversions,resulting in a larger global carbon release in the other inversions.Thus,our satellite constraint refines the current understanding of flux partitioning between northern and tropical terrestrial regions,and suggests that the northern extratropics acted as anomalous high CO_(2)sinks in response to the 2015–2016 El Nino event.
