Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and ...Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.展开更多
Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworth...Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.展开更多
Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-compar...Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer （MODIS） land cover product, global land cover map of 2009 （GlobCover2009）, finer resolution observation and monitoring of global cropland （FROM-GC） and 30-m global land cover dataset （GlobeLand30）. The temporal coverage of these four datasets are circa 2010. One of the typical agricultur- al regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects： accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy （OA） of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement （mainly located in the plain ecotope of Shaanxi） and the non-cropland spatial agreement （sparsely distributed in the south and middle of Shaanxi） of the four datasets only makes up 33.96% of the whole province. FIROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on each ecotope region. The approaches taken in this study could be used to derive a fused cropland classification dataset.展开更多
Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change...Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.展开更多
ABSTRACT The optimal Kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement fun...ABSTRACT The optimal Kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement function is nonlinear. It is argued that when the measurement function is nonlinear, the current ensemble Kalman Filter algorithm seems to contain implicit assumptions： the forecast of the measurement function is unbiased or the nonlinear measurement function is linearized. While the forecast of the model state is assumed to be unbiased, the two assumptions are actually equivalent. On the above basis, we present two modified Kalman gain algorithms. Compared to the current Kalman gain algorithm, the modified ones remove the above assumptions, thereby leading to smaller estimated errors. This outcome was confirmed experimentally, in which we used the simple Lorenz 3-component model as the test-bed. It was found that in such a simple nonlinear dynamical system, the modified Kalman gain can perform better than the current one. However, the application of the modified schemes to realistic models involving nonlinear measurement functions needs to be further investigated.展开更多
Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone(TC).However,it is difficult to be fully represented in regional models due to dom...Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone(TC).However,it is difficult to be fully represented in regional models due to domain size and a lack of observation data,particularly at sea used in regional data assimilation.Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis.Research of the impact of this large-scale blending scheme for the Global/Regional Assimilation and PrEdiction System(CMA-MESO)regional model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform(2D-DCT)filter on the model forecast of Typhoon Haima over Shenzhen,China in 2016 and considering various cut-off wavelengths.Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme,indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields.The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors(RMSEs)by comparing the experiments with and without blending analysis.Furthermore,the higher equitable threat score(ETS)provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the CMA-MESO analysis.Furthermore,significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast.It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast.In this paper,the methods and data applied in this study will be firstly introduced,before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly,followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section.展开更多
Information on species composition of an urban forest is essential for its management.However,to obtain this information becomes increasingly difficult due to limited taxonomic expertise.In this study,we tested the po...Information on species composition of an urban forest is essential for its management.However,to obtain this information becomes increasingly difficult due to limited taxonomic expertise.In this study,we tested the possibility of using plant identification applications running on mobile platforms to fill this vacuum.Five plant identification apps were compared for their potential in identifying urban tree species in China.An online survey was conducted to determine the features of apps that contributed to users’satisfaction.The results show that identification accuracy varied significantly among the apps.The best performer achieved an accuracy of 74.6%at the species level,which is comparable to the accuracy by professionals in field surveys.Among the features of apps,accuracy of identification was the most important factor that contributed to users’satisfaction.However,plant identification apps did not perform well when used on rare species or outside of the regions where they have been developed.Results indicate that plant identification apps have great potential in urban forest studies and management,but users need to be cautious when deciding which one to use.展开更多
Background:An increasing number of ecological processes have been incorporated into Earth system models.However,model evaluations usually lag behind the fast development of models,leading to a pervasive simulation unc...Background:An increasing number of ecological processes have been incorporated into Earth system models.However,model evaluations usually lag behind the fast development of models,leading to a pervasive simulation uncertainty in key ecological processes,especially the terrestrial carbon(C)cycle.Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models.Thus,a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.Methods:A new cloud-based model evaluation platform,i.e.,the online traceability analysis system for model evaluation(TraceME v1.0),was established.The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project(CMIP6).Results:The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models.For example,the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models.Among all models,IPSL-CM6A-LR simulated the lowest land C storage,which mainly resulted from its shortest baseline C residence time.Over the historical period of 1850–2014,gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.Conclusion:TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.展开更多
The diurnal temperature range(DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multitemporal DTR varia...The diurnal temperature range(DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multitemporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic pathway(SSP) scenarios for the near,middle, and long term of future projection are analyzed using 11 selected robustly performing models. Key findings reveal:(1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR,FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatiotemporal variability of DTR over the Tibetan Plateau.(2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. In certain areas, such as the southeastern edge of the Tibetan Plateau, western hinterland of the Tibetan Plateau, southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large.(3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future.展开更多
Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related disasters.The utilisation of multiscale methods is effec...Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related disasters.The utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological drought.Nevertheless,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time scales.To address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple scales.We formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis dataset.These indicators span time scales of approximately 30 d and one season.Short-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future trends.We focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal prediction.The model's performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought events.Experimental results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of drought.Each of the four drought indicators accurately delineates specific facets of the meteorological drought trend.Moreover,three representative drought events,namely flash drought,sustained drought and severe drought,underscore the significance of selecting appropriate prediction indicators to effectively denote different types of drought events.This study provides methodological and technological support for using a deep learning approach in meteorological drought prediction.Such findings also demonstrate prediction issues related to natural hazards in regions with scarce observational data,complex topography and diverse microclimate systems.展开更多
China is now confronting the intertwined challenges of air pollution and climate change.Given the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoti...China is now confronting the intertwined challenges of air pollution and climate change.Given the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoting synergetic control of these two issues.The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators.The Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmospheric composition,and health impacts and benefits of coordinated control.Compared to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones.These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric vehicles.Additionally,in 2022,China issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for China's pollution-carbon co-control.These changes highlight China's efforts to reshape its energy,economic,and transportation structures to meet the demand for synergetic control and sustainable development.Consequently,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.展开更多
Carbon mitigation technologies lead to air quality improvement and health co-benefits,while the practical effects of the technologies are dependent on the energy composition,technological advancements,and economic dev...Carbon mitigation technologies lead to air quality improvement and health co-benefits,while the practical effects of the technologies are dependent on the energy composition,technological advancements,and economic development.In China,mitigation technologies such as end-of-pipe treatment,renewable energy adoption,carbon capture and storage(CCS),and sector electrification demonstrate significant promise in meeting carbon reduction targets.However,the optimization of these technologies for maximum co-benefits remains unclear.Here,we employ an integrated assessment model(AIM/enduse,CAM-chem,IMED|HEL)to analyze air quality shifts and their corresponding health and economic impacts at the provincial level in China within the two-degree target.Our findings reveal that a combination of end-of-pipe technology,renewable energy utilization,and electrification yields the most promising results in air quality improvement,with a reduction of fine particulate matter(PM2.5)by−34.6μg m^(−3) and ozone by−18.3 ppb in 2050 compared to the reference scenario.In contrast,CCS technology demonstrates comparatively modest improvements in air quality(−9.4μg m^(−3) for PM_(2.5) and−2.4 ppb for ozone)and cumulative premature deaths reduction(−3.4 million from 2010 to 2050)compared to the end-of-pipe scenario.Notably,densely populated regions such as Henan,Hebei,Shandong,and Sichuan experience the most health and economic benefits.This study aims to project effective future mitigation technologies and climate policies on air quality improvement and carbon mitigation.Furthermore,it seeks to delineate detailed provincial-level air pollution control strategies,offering valuable guidance for policymakers and stakeholders in pursuing sustainable and health-conscious environmental management.展开更多
In China,following national economic reform and the opendoor policy in 1978,there has been a rapid industrialization and urbanization of coastal regions which has dramatically changed the environment and ecosystems in...In China,following national economic reform and the opendoor policy in 1978,there has been a rapid industrialization and urbanization of coastal regions which has dramatically changed the environment and ecosystems in these areas[1].Regions with rapidly growing economies,such as Shanghai and Shenzhen,face the double pressure of limited land area and population growth,展开更多
Urban ecology is experiencing the third paradigm shift.To understand the interactions between the social system and the natural system in the city across time and space,and to provide theories and solutions to sustain...Urban ecology is experiencing the third paradigm shift.To understand the interactions between the social system and the natural system in the city across time and space,and to provide theories and solutions to sustainable urban development are essential tasks for urban ecology in the next decade.Big data can play a crucial role in future urban ecology studies due to the interdisciplinary nature of urban ecology,the fact that cities are factories of big data,and the new insights gained by using big data in studies.Nevertheless,to translate big data from a concept to research results that can guide planning,policymaking,and management of cities,we need to overcome multiple challenges existing in the theoretical framework,data acquisition,and analytic methods.Urban ecologists should enhance the collaboration with the data scientists to increase the application of big data in studies of urban biodiversity,urban ecosystem services and human wellbeing,and processes of urban ecosystems.展开更多
It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on gree...It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction,air quality improvement,and improved health.In the context of carbon peak,carbon neutrality,and clean air policies,this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators.The 18 indicators cover the following five aspects:air pollution and associated weather-climate conditions,progress in structural transition,sources,inks,and mitigation pathway of atmospheric composition,health impacts and benefits of coordinated control,and synergetic governance system and practices.By tracking the progress in each indicator,this perspective presents the major accomplishment of coordinated control,identifies the emerging challenges toward the synergetic governance,and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.展开更多
Product trade plays an increasing role in relocating production and the associated air pollution impact among sectors and regions.While a comprehensive depiction of atmospheric pollution redistribution through trade c...Product trade plays an increasing role in relocating production and the associated air pollution impact among sectors and regions.While a comprehensive depiction of atmospheric pollution redistribution through trade chains is missing,which may hinder targeted clean air cooperation among sectors and regions.Here,we combined five state-of-the-art models from physics,economy,and epidemiology to track the anthropogenic fine particle matters(PM_(2.5))related premature mortality along the supply chains within China in 2017.Our results highlight the key sectors that affect PM_(2.5)-related mortality from both production and consumption perspectives.The consumption-based effects from food,light industry,equipment,construction,and services sectors,caused 2e22 times higher deaths than those from a production perspective and totally contributed 63%of the national total.From a cross-boundary perspective,25.7%of China's PM_(2.5)-related deaths were caused by interprovincial trade,with the largest transfer occurring from the central and northern regions to well-developed east coast provinces.Capital investment dominated the cross-boundary effect(56%of the total)by involving substantial equipment and construction products,which greatly rely on product exports from regions with specific resources.This supply chain-based analysis provides a comprehensive quantification and may inform more effective joint-control efforts among associated regions and sectors from a health risk perspective.展开更多
基金supported by grants from the National Aeronautics and Space Administration Applied Science Program,USA (NNX12AQ31G,NNX14AP91G,PI:Dr.Liping Di)
文摘Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.
基金supported by the National Natural Science Foundation of China(Grant Nos.42141019 and 42261144687)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0102)+4 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB42010404)the National Natural Science Foundation of China(Grant No.42175049)the Guangdong Meteorological Service Science and Technology Research Project(Grant No.GRMC2021M01)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)for computational support and Prof.Shiming XIANG for many useful discussionsNiklas BOERS acknowledges funding from the Volkswagen foundation.
文摘Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.
基金supported by the National High-Tech R&D Program of China (2012AA12A408)the Independent Scientific Research of Tsinghua University,China (20131089277,553302001)
文摘Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer （MODIS） land cover product, global land cover map of 2009 （GlobCover2009）, finer resolution observation and monitoring of global cropland （FROM-GC） and 30-m global land cover dataset （GlobeLand30）. The temporal coverage of these four datasets are circa 2010. One of the typical agricultur- al regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects： accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy （OA） of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement （mainly located in the plain ecotope of Shaanxi） and the non-cropland spatial agreement （sparsely distributed in the south and middle of Shaanxi） of the four datasets only makes up 33.96% of the whole province. FIROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on each ecotope region. The approaches taken in this study could be used to derive a fused cropland classification dataset.
基金supported by grants from the National Aeronautics and Space Administration (NASA) of the United States (NNX12AQ31G and NNX12AQ31G NNX14AP91G,PI:Dr.Liping Di)
文摘Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.
基金supported by research grants from the NSERC (Natural Sciences and Engineering Research Council of Canada) Discovery Programthe National Natural Science Foundation of China (Grant Nos.41276029 and 40730843)the National Basic Research Program (Grant No.2007CB816005)
文摘ABSTRACT The optimal Kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement function is nonlinear. It is argued that when the measurement function is nonlinear, the current ensemble Kalman Filter algorithm seems to contain implicit assumptions： the forecast of the measurement function is unbiased or the nonlinear measurement function is linearized. While the forecast of the model state is assumed to be unbiased, the two assumptions are actually equivalent. On the above basis, we present two modified Kalman gain algorithms. Compared to the current Kalman gain algorithm, the modified ones remove the above assumptions, thereby leading to smaller estimated errors. This outcome was confirmed experimentally, in which we used the simple Lorenz 3-component model as the test-bed. It was found that in such a simple nonlinear dynamical system, the modified Kalman gain can perform better than the current one. However, the application of the modified schemes to realistic models involving nonlinear measurement functions needs to be further investigated.
基金Project of Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173610028)。
文摘Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone(TC).However,it is difficult to be fully represented in regional models due to domain size and a lack of observation data,particularly at sea used in regional data assimilation.Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis.Research of the impact of this large-scale blending scheme for the Global/Regional Assimilation and PrEdiction System(CMA-MESO)regional model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform(2D-DCT)filter on the model forecast of Typhoon Haima over Shenzhen,China in 2016 and considering various cut-off wavelengths.Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme,indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields.The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors(RMSEs)by comparing the experiments with and without blending analysis.Furthermore,the higher equitable threat score(ETS)provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the CMA-MESO analysis.Furthermore,significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast.It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast.In this paper,the methods and data applied in this study will be firstly introduced,before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly,followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section.
基金supported financially by China National Natural Science Foundation(grant number 31570458)Microsoft Research Lab-Asia(grant number 041902008).
文摘Information on species composition of an urban forest is essential for its management.However,to obtain this information becomes increasingly difficult due to limited taxonomic expertise.In this study,we tested the possibility of using plant identification applications running on mobile platforms to fill this vacuum.Five plant identification apps were compared for their potential in identifying urban tree species in China.An online survey was conducted to determine the features of apps that contributed to users’satisfaction.The results show that identification accuracy varied significantly among the apps.The best performer achieved an accuracy of 74.6%at the species level,which is comparable to the accuracy by professionals in field surveys.Among the features of apps,accuracy of identification was the most important factor that contributed to users’satisfaction.However,plant identification apps did not perform well when used on rare species or outside of the regions where they have been developed.Results indicate that plant identification apps have great potential in urban forest studies and management,but users need to be cautious when deciding which one to use.
基金supported by the National Key R&D Program of China(2017YFA0604600)National Natural Science Foundation of China(31722009).
文摘Background:An increasing number of ecological processes have been incorporated into Earth system models.However,model evaluations usually lag behind the fast development of models,leading to a pervasive simulation uncertainty in key ecological processes,especially the terrestrial carbon(C)cycle.Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models.Thus,a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.Methods:A new cloud-based model evaluation platform,i.e.,the online traceability analysis system for model evaluation(TraceME v1.0),was established.The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project(CMIP6).Results:The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models.For example,the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models.Among all models,IPSL-CM6A-LR simulated the lowest land C storage,which mainly resulted from its shortest baseline C residence time.Over the historical period of 1850–2014,gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.Conclusion:TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.
基金supported by The Second Tibetan Plateau Scientific Expedition and Research (STEP) program(Grant No. 2019QZKK0102)the National Natural Science Foundation of China (Grant No. 41975135)+1 种基金the Natural Science Foundation of Sichuan,China (Grant No. 2022NSFSC1092)funded by the China Scholarship Council。
文摘The diurnal temperature range(DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multitemporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic pathway(SSP) scenarios for the near,middle, and long term of future projection are analyzed using 11 selected robustly performing models. Key findings reveal:(1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR,FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatiotemporal variability of DTR over the Tibetan Plateau.(2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. In certain areas, such as the southeastern edge of the Tibetan Plateau, western hinterland of the Tibetan Plateau, southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large.(3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future.
基金This work is supported by the National Key Research and Development Program of China(2022YFE0195900,2021YFC3101600,2020YFA0607900,and 2020YFA0608000)the National Natural Science Foundation of China(42125503 and 42075137).
文摘Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related disasters.The utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological drought.Nevertheless,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time scales.To address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple scales.We formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis dataset.These indicators span time scales of approximately 30 d and one season.Short-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future trends.We focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal prediction.The model's performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought events.Experimental results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of drought.Each of the four drought indicators accurately delineates specific facets of the meteorological drought trend.Moreover,three representative drought events,namely flash drought,sustained drought and severe drought,underscore the significance of selecting appropriate prediction indicators to effectively denote different types of drought events.This study provides methodological and technological support for using a deep learning approach in meteorological drought prediction.Such findings also demonstrate prediction issues related to natural hazards in regions with scarce observational data,complex topography and diverse microclimate systems.
基金supported by the National Natural Science Foundation of China,China(72243008,41921005,and 72140003)the Energy Foundation,China.
文摘China is now confronting the intertwined challenges of air pollution and climate change.Given the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoting synergetic control of these two issues.The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators.The Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmospheric composition,and health impacts and benefits of coordinated control.Compared to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones.These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric vehicles.Additionally,in 2022,China issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for China's pollution-carbon co-control.These changes highlight China's efforts to reshape its energy,economic,and transportation structures to meet the demand for synergetic control and sustainable development.Consequently,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.
基金National Key R&D Program of China(2020YFA0607804)National Natural Science Foundation of China(42375172 and 71903010)。
文摘Carbon mitigation technologies lead to air quality improvement and health co-benefits,while the practical effects of the technologies are dependent on the energy composition,technological advancements,and economic development.In China,mitigation technologies such as end-of-pipe treatment,renewable energy adoption,carbon capture and storage(CCS),and sector electrification demonstrate significant promise in meeting carbon reduction targets.However,the optimization of these technologies for maximum co-benefits remains unclear.Here,we employ an integrated assessment model(AIM/enduse,CAM-chem,IMED|HEL)to analyze air quality shifts and their corresponding health and economic impacts at the provincial level in China within the two-degree target.Our findings reveal that a combination of end-of-pipe technology,renewable energy utilization,and electrification yields the most promising results in air quality improvement,with a reduction of fine particulate matter(PM2.5)by−34.6μg m^(−3) and ozone by−18.3 ppb in 2050 compared to the reference scenario.In contrast,CCS technology demonstrates comparatively modest improvements in air quality(−9.4μg m^(−3) for PM_(2.5) and−2.4 ppb for ozone)and cumulative premature deaths reduction(−3.4 million from 2010 to 2050)compared to the end-of-pipe scenario.Notably,densely populated regions such as Henan,Hebei,Shandong,and Sichuan experience the most health and economic benefits.This study aims to project effective future mitigation technologies and climate policies on air quality improvement and carbon mitigation.Furthermore,it seeks to delineate detailed provincial-level air pollution control strategies,offering valuable guidance for policymakers and stakeholders in pursuing sustainable and health-conscious environmental management.
基金supported by the Special Fund for Meteorology Scientific Research in the Public Welfare (GYHY201506023) of Chinareviewed by the National Administration of Surveying, Mapping and Geoinformation (GS(2018)2347)
文摘In China,following national economic reform and the opendoor policy in 1978,there has been a rapid industrialization and urbanization of coastal regions which has dramatically changed the environment and ecosystems in these areas[1].Regions with rapidly growing economies,such as Shanghai and Shenzhen,face the double pressure of limited land area and population growth,
基金supported by the National Key Research and Development Program of China(Grant No.2019YFA0607201)。
文摘Urban ecology is experiencing the third paradigm shift.To understand the interactions between the social system and the natural system in the city across time and space,and to provide theories and solutions to sustainable urban development are essential tasks for urban ecology in the next decade.Big data can play a crucial role in future urban ecology studies due to the interdisciplinary nature of urban ecology,the fact that cities are factories of big data,and the new insights gained by using big data in studies.Nevertheless,to translate big data from a concept to research results that can guide planning,policymaking,and management of cities,we need to overcome multiple challenges existing in the theoretical framework,data acquisition,and analytic methods.Urban ecologists should enhance the collaboration with the data scientists to increase the application of big data in studies of urban biodiversity,urban ecosystem services and human wellbeing,and processes of urban ecosystems.
基金This work was supported by the National Natural Science Foundation of China(41921005,42130708,and 72140003)and the Energy Foundation.
文摘It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction,air quality improvement,and improved health.In the context of carbon peak,carbon neutrality,and clean air policies,this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators.The 18 indicators cover the following five aspects:air pollution and associated weather-climate conditions,progress in structural transition,sources,inks,and mitigation pathway of atmospheric composition,health impacts and benefits of coordinated control,and synergetic governance system and practices.By tracking the progress in each indicator,this perspective presents the major accomplishment of coordinated control,identifies the emerging challenges toward the synergetic governance,and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.
基金This work is supported by the National Natural Science Foundation of China(71904097,41921005,91744310 and 42205183)the Fundamental Research Funds for the Central Universities(2021NTST21).
文摘Product trade plays an increasing role in relocating production and the associated air pollution impact among sectors and regions.While a comprehensive depiction of atmospheric pollution redistribution through trade chains is missing,which may hinder targeted clean air cooperation among sectors and regions.Here,we combined five state-of-the-art models from physics,economy,and epidemiology to track the anthropogenic fine particle matters(PM_(2.5))related premature mortality along the supply chains within China in 2017.Our results highlight the key sectors that affect PM_(2.5)-related mortality from both production and consumption perspectives.The consumption-based effects from food,light industry,equipment,construction,and services sectors,caused 2e22 times higher deaths than those from a production perspective and totally contributed 63%of the national total.From a cross-boundary perspective,25.7%of China's PM_(2.5)-related deaths were caused by interprovincial trade,with the largest transfer occurring from the central and northern regions to well-developed east coast provinces.Capital investment dominated the cross-boundary effect(56%of the total)by involving substantial equipment and construction products,which greatly rely on product exports from regions with specific resources.This supply chain-based analysis provides a comprehensive quantification and may inform more effective joint-control efforts among associated regions and sectors from a health risk perspective.
基金supported by the National Natural Science Foundation of China(72242105)the National Key Research and Development Program of China(2022YFE0208700 and2022YFE0208500)the Norwegian Research Council(287690/F20)。
