Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air poll...Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.展开更多
Based on the C-Coupler platform,the semi-unstructured Climate System Model,Synthesis Community Integrated Model version 2(SYCIM2.0),has been developed at the School of Atmospheric Sciences,Sun Yat-sen University.SYCIM...Based on the C-Coupler platform,the semi-unstructured Climate System Model,Synthesis Community Integrated Model version 2(SYCIM2.0),has been developed at the School of Atmospheric Sciences,Sun Yat-sen University.SYCIM2.0 aims to meet the demand for seamless climate prediction through accurate climate simulations and projections.This paper provides an overview of SYCIM2.0 and highlights its key features,especially the coupling of an unstructured ocean model and the tuning process.An extensive evaluation of its performance,focusing on the East Asian Summer Monsoon(EASM),is presented based on long-term simulations with fixed external forcing.The results suggest that after nearly 240 years of integration,SYCIM2.0 achieves a quasi-equilibrium state,albeit with small trends in the net radiation flux at the top-of-atmosphere(TOA)and Earth’s surface,as well as with global mean near-surface temperatures.Compared to observational and reanalysis data,the model realistically simulates spatial patterns of sea surface temperature(SST)and precipitation centers to include their annual cycles,in addition to the lower-level wind fields in the EASM region.However,it exhibits a weakened and eastward-shifted Western Pacific Subtropical High(WPSH),resulting in an associated precipitation bias.SYCIM2.0 robustly captures the dominant mode of the EASM and its close relationship with the El Niño-Southern Oscillation(ENSO)but exhibits relatively poor performance in simulating the second leading mode and the associated air–sea interaction processes.Further comprehensive evaluations of SYCIM2.0 will be conducted in future studies.展开更多
We propose that the level at which the conodont species Idiognathodus simulator （Ellison 1941） （sensu stricto） first appears be selected to mark the base of the Gzhelian Stage, because we believe that this is the ...We propose that the level at which the conodont species Idiognathodus simulator （Ellison 1941） （sensu stricto） first appears be selected to mark the base of the Gzhelian Stage, because we believe that this is the optimal level by which this boundary can be correlated. This taxon has a short range and a wide distribution, as shown by correlation of glacial-eustatic cyclothems across the Kasimovian-Gzhelian boundary interval among Midcontinent North America and the Moscow and Donets basins of eastern Europe, based on scale of the cyclothems along with several aspects of biostrati- graphy. Outside of these areas, I. simulator （sensu stricto） is known also from other parts of the U.S., and is reported from the southern Urals and south-central China in its expected position between other widespread taxa. Its first appearance is consistent with the current ammonoid placement of the boundary （first appearance of Shumardites cuyleri）, and it is also compatible with certain aspects of the distribution of Eurasian fusulinid faunas （e.g., lectotype ofRauserites rossicus）.展开更多
An ensemble optimal interpolation(EnOI)data assimilation method is applied in the BCCCSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework.Pseudoo...An ensemble optimal interpolation(EnOI)data assimilation method is applied in the BCCCSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework.Pseudoobservations of sea surface temperature(SST),sea surface height(SSH),sea surface salinity(SSS),temperature and salinity(T/S)profiles were first generated in a free model run.Then,a series of sensitivity tests initialized with predefined bias were conducted for a one-year period;this involved a free run(CTR)and seven assimilation runs.These tests allowed us to check the analysis field accuracy against the"truth".As expected,data assimilation improved all investigated quantities;the joint assimilation of all variables gave more improved results than assimilating them separately.One-year predictions initialized from the seven runs and CTR were then conducted and compared.The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles,but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies.The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles,while surface data assimilation became more important at higher latitudes,particularly near the western boundary currents.The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables.Finally,a central Pacific El Ni?o was well predicted from the joint assimilation of surface data,indicating the importance of joint assimilation of SST,SSH,and SSS for ENSO predictions.展开更多
The development of passive NO_(x)adsorbers with cost-benefit and high NO_(x)storage capacity remains an on-going challenge to after-treatment technologies at lower temperatures associated with cold-start NO_(x)emissio...The development of passive NO_(x)adsorbers with cost-benefit and high NO_(x)storage capacity remains an on-going challenge to after-treatment technologies at lower temperatures associated with cold-start NO_(x)emissions.Herein,Cs_(1)Mg_(3)Al catalyst prepared by sol-gel method was cyclic tested in NO_(x)storage under 5 vol%water.At 100°C,the NO_(x)storage capacity(1219 μmol g^(-1))was much higher than that of Pt/BaO/Al_(2)O_(3)(610 μmol g^(-1)).This provided new insights for non-noble metal catalysts in low-temperature passive NO_(x)adsorption.The addition of Cs improved the mobility of oxygen species and thus improved the NO_(x)storage capacity.The XRD,XPS,IR spectra and in situ DRIFTs with NH3 probe showed an interaction between CsO_(x)and AlO_(x)sites via oxygen species formed on Cs_(1)Mg_(3)Al catalyst.The improved mobility of oxygen species inferred from O2-TPD was consistent with high NO_(x)storage capacity related to enhanced formation of nitrate and additional nitrite species by NO_(x)oxidation.Moreover,the addition of Mg might improve the stability of Cs_(1)Mg_(3)Al by stabilizing surface active oxygen species in cyclic experiments.展开更多
The Yangtze River Economic Belt(YREB)is a pivotal contributor to China's economic growth,particularly as the nation undergoes a green transformation.Achieving synergistic reductions on pollution and carbon emissio...The Yangtze River Economic Belt(YREB)is a pivotal contributor to China's economic growth,particularly as the nation undergoes a green transformation.Achieving synergistic reductions on pollution and carbon emissions is deemed crucial for this transition.This paper examines the spatial and temporal changes in the synergy of pollution and carbon reduction in the YREB and delves into the underlying mechanisms.Our findings indicate that while the synergy in the YREB is increasing,it manifests disparities across regions,with the lower reaches outperforming the middle and upper ones.Enterprise behavior,government guidance,and regional endowments influence this synergy.Cities in the YREB must strategically plan their urban scale,curb population overgrowth,recalibrate their industrial structures,curtail energy consumption,and enhance policy efficacy.Distinct regions should prioritize various objectives:the lower reaches should hasten scientific advancements and technological innovations;the middle reaches should foster innovation and industrial upgrades;and the upper reaches should prioritize rural and urban land intensification.展开更多
Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been...Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.展开更多
The escalating degradation of urban eco-environments has underscored the significance of ecological security in sustainable urban development.Green infrastructure bridges green spaces in cities and increases ecosystem...The escalating degradation of urban eco-environments has underscored the significance of ecological security in sustainable urban development.Green infrastructure bridges green spaces in cities and increases ecosystem connectivity,thereby optimizing urban ecological security patterns.This study uses Nanjing as a case study and adopts a research paradigm that involves identifying ecological sources,constructing resistance surfaces,and subsequently extracting corridors within the ecological security pattern.This method amalgamates the evaluation of green infrastructure supply and demand,leading to the identification of both ecological corridors and nodes.The findings reveal that while the supply of green infrastructure in Nanjing is low in the city center and high in the suburbs,demand is high in the central area and low in the periphery,indicating a spatial mismatch between supply and demand.Ecological corridors and nodes are categorized into the core,important,and general levels based on their centrality and areas of supply–demand optimization.The connectivity,supply capacity,and supply–demand relationship of green infrastructure in Nanjing have been enhanced to varying degrees through the ecological security pattern optimization.The results of this study can serve as a decision-making reference for optimizing green infrastructure network patterns and enhancing urban ecological security.展开更多
Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of ...Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of China’s Major Research Plan entitled“Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China”(or the Plan)has funded 76 research projects to explore the causes of air pollution in China,and the key processes of air pollution in atmospheric physics and atmospheric chemistry.In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally,an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan.This project has classified and integrated these data,forming eight categories containing 258 datasets and 15 technical reports in total.The integration project has led to the successful establishment of the China Air Pollution Data Center(CAPDC)platform,providing storage,retrieval,and download services for the eight categories.This platform has distinct features including data visualization,related project information querying,and bilingual services in both English and Chinese,which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research.Air pollution control in China,especially in the past decade,is undeniably a global exemplar,and this data center is the first in China to focus on research into the country’s air pollution complex.展开更多
Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy....Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy.Although deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty.This article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling.Such a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling.This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process.Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and quantitatively.Specifically,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation coefficient.In summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.展开更多
Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climat...Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.展开更多
The terrestrial hydrological process is an essential but weak link in global/regional climate models. In this paper, the development status, research hotspots and trends in coupled atmosphere-hydrology simulations are...The terrestrial hydrological process is an essential but weak link in global/regional climate models. In this paper, the development status, research hotspots and trends in coupled atmosphere-hydrology simulations are identified through a bibliometric analysis, and the challenges and opportunities in this field are reviewed and summarized. Most climate models adopt the one-dimensional (vertical) land surface parameterization, which does not include a detailed description of basin-scale hydrological processes, particularly the effects of human activities on the underlying surfaces. To understand the interaction mechanism between hydrological processes and climate change, a large number of studies focused on the climate feedback effects of hydrological processes at different spatio-temporal scales, mainly through the coupling of hydrological and climate models. The improvement of the parameterization of hydrological process and the development of large-scale hydrological model in land surface process model lay a foundation for terrestrial hydrological-climate coupling simulation, based on which, the study of terrestrial hydrological-climate coupling is evolving from the traditional unidirectional coupling research to the two-way coupling study of "climate-hydrology" feedback. However, studies of fully coupled atmosphere-hydrology simulations (also called atmosphere-hydrology two-way coupling) are far from mature. The main challenges associated with these studies are: improving the potential mismatch in hydrological models and climate models; improving the stability of coupled systems; developing an effective scale conversion scheme; perfecting the parameterization scheme; evaluating parameter uncertainties; developing effective methodology for model parameter transplanting; and improving the applicability of models and high/super-resolution simulation. Solving these problems and improving simulation accuracy are directions for future hydro-climate coupling simulation research.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
The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with differen...The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Nio and how much is common to both this type and the conventional Eastern Pacific(EP)-type El Nio. In this study, the deterministic performance of an El Nio–Southern Oscillation(ENSO) ensemble prediction system is examined for the two types of El Nio. Ensemble hindcasts are run for the nine EP El Nio events and twelve CP El Nio events that have occurred since 1950. The results show that(1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times;(2) the systematic forecast biases come mostly from the prediction of the CP events; and(3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Nio. Further improvements to coupled atmosphere–ocean models in terms of CP El Nio prediction should be recognized as a key and high-priority task for the climate prediction community.展开更多
A nested circulation model system based on the Princeton ocean model （POM） is set up to simulate the currentmeter data from a bottom-mounted Acoustic Doppler Profiler （ADP） deployed at the 30 m depth in the Lunan...A nested circulation model system based on the Princeton ocean model （POM） is set up to simulate the currentmeter data from a bottom-mounted Acoustic Doppler Profiler （ADP） deployed at the 30 m depth in the Lunan（South Shandong Province, China） Trough south of the Shandong Peninsula in the summer of 2008, and to study the dynamics of the circulation in the southwestern Huanghai Sea （Yellow Sea）. The model has reproduced well the observed subtidal current at the mooring site. The results of the model simulation suggest that the bottom topography has strong steering effects on the regional circulation in summer. The model simulation shows that the Subei （North Jiangsu Province, China）coastal current flows north- ward in summer, in contrast to the southeastward current in the center of the Lunan Trough measured by the moored currentmeter. The analyses of the model results suggest that the southeastward current at the mooring site in the Lunan Trough is forced by the westward wind-driven current along the Lunan coast, which meets the northward Subei coastal current at the head of the Haizhou Bay to flow along an offshore path in the southeastward direction in the Lunan Trough. Analysis suggests that the Subei coastal current, the Lunan coastal current, and the circulation in the Lunan Trough are independent current systems con- trolled by different dynamics. Therefore, the current measurements in the Lunan Trough cannot be used to represent the Subei coastal current in general.展开更多
In recent decades,a greening tendency due to increased vegetation has been noted around the Taklimakan Desert(TD),but the impact of such a change on the local hydrological cycle remains uncertain.Here,we investigate t...In recent decades,a greening tendency due to increased vegetation has been noted around the Taklimakan Desert(TD),but the impact of such a change on the local hydrological cycle remains uncertain.Here,we investigate the response of the local hydrological cycle and atmospheric circulation to a green TD in summer using a pair of global climate model(Community Earth System Model version 1.2.1)simulations.With enough irrigation to support vegetation growth in the TD,the modeling suggests first,that significant increases in local precipitation are attributed to enhanced local recycling of water,and second,that there is a corresponding decrease of local surface temperatures.On the other hand,irrigation and vegetation growth in this low-lying desert have negligible impacts on the large-scale circulation and thus the moisture convergence for enhanced precipitation.It is also found that the green TD can only be sustained by a large amount of irrigation water supply since only about one-third of the deployed water can be“recycled”locally.Considering this,devising a way to encapsulate the irrigated water within the desert to ensure more efficient water recycling is key for maintaining a sustainable,greening TD.展开更多
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
Achieving carbon neutrality is crucial in dealing with climate change and containing the increase in global temperature at below 1.5℃compared with preindustrial levels.During the general debate at the 75th session of...Achieving carbon neutrality is crucial in dealing with climate change and containing the increase in global temperature at below 1.5℃compared with preindustrial levels.During the general debate at the 75th session of the United Nations General Assembly in September 2020,President Xi Jinping announced that China would adopt more vigorous policies and measures against climate change.展开更多
基金supported by the National Natural Science Foundation of China(42277087,42130708,42471021,42277482,and 42361144876)the Natural Science Foundation of Guangdong Province(2024A1515012550)+3 种基金the Hainan Institute of National Park grant(KY-23ZK01)the Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan(JC2022011)the Shenzhen Science and Technology Program(JCYJ20240813112106009 and ZDSYS20220606100806014)the Scientific Research Start-up Funds(QD2021030C)from Tsinghua Shenzhen International Graduate School。
文摘Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.
基金funded by the National Natural Science Foundation of China(Grant Nos.U21A6001,42261144687,42175173)the Project supported by Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.SML2023SP208)the GuangDong Basic and Applied Basic Research Foundation(2023A1515240036).
文摘Based on the C-Coupler platform,the semi-unstructured Climate System Model,Synthesis Community Integrated Model version 2(SYCIM2.0),has been developed at the School of Atmospheric Sciences,Sun Yat-sen University.SYCIM2.0 aims to meet the demand for seamless climate prediction through accurate climate simulations and projections.This paper provides an overview of SYCIM2.0 and highlights its key features,especially the coupling of an unstructured ocean model and the tuning process.An extensive evaluation of its performance,focusing on the East Asian Summer Monsoon(EASM),is presented based on long-term simulations with fixed external forcing.The results suggest that after nearly 240 years of integration,SYCIM2.0 achieves a quasi-equilibrium state,albeit with small trends in the net radiation flux at the top-of-atmosphere(TOA)and Earth’s surface,as well as with global mean near-surface temperatures.Compared to observational and reanalysis data,the model realistically simulates spatial patterns of sea surface temperature(SST)and precipitation centers to include their annual cycles,in addition to the lower-level wind fields in the EASM region.However,it exhibits a weakened and eastward-shifted Western Pacific Subtropical High(WPSH),resulting in an associated precipitation bias.SYCIM2.0 robustly captures the dominant mode of the EASM and its close relationship with the El Niño-Southern Oscillation(ENSO)but exhibits relatively poor performance in simulating the second leading mode and the associated air–sea interaction processes.Further comprehensive evaluations of SYCIM2.0 will be conducted in future studies.
文摘We propose that the level at which the conodont species Idiognathodus simulator （Ellison 1941） （sensu stricto） first appears be selected to mark the base of the Gzhelian Stage, because we believe that this is the optimal level by which this boundary can be correlated. This taxon has a short range and a wide distribution, as shown by correlation of glacial-eustatic cyclothems across the Kasimovian-Gzhelian boundary interval among Midcontinent North America and the Moscow and Donets basins of eastern Europe, based on scale of the cyclothems along with several aspects of biostrati- graphy. Outside of these areas, I. simulator （sensu stricto） is known also from other parts of the U.S., and is reported from the southern Urals and south-central China in its expected position between other widespread taxa. Its first appearance is consistent with the current ammonoid placement of the boundary （first appearance of Shumardites cuyleri）, and it is also compatible with certain aspects of the distribution of Eurasian fusulinid faunas （e.g., lectotype ofRauserites rossicus）.
基金The National Key Research and Development Program of China under contract Nos 2016YFA0602102 and2016YFC1401702the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)under contract No.GML2019ZD0306+1 种基金the National Natural Science Foundation of China under contract No.41306005CAS Pioneer Hundred Talents Program Startup Fund by South China Sea Institute of Oceanology under contract No.Y9SL011001。
文摘An ensemble optimal interpolation(EnOI)data assimilation method is applied in the BCCCSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework.Pseudoobservations of sea surface temperature(SST),sea surface height(SSH),sea surface salinity(SSS),temperature and salinity(T/S)profiles were first generated in a free model run.Then,a series of sensitivity tests initialized with predefined bias were conducted for a one-year period;this involved a free run(CTR)and seven assimilation runs.These tests allowed us to check the analysis field accuracy against the"truth".As expected,data assimilation improved all investigated quantities;the joint assimilation of all variables gave more improved results than assimilating them separately.One-year predictions initialized from the seven runs and CTR were then conducted and compared.The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles,but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies.The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles,while surface data assimilation became more important at higher latitudes,particularly near the western boundary currents.The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables.Finally,a central Pacific El Ni?o was well predicted from the joint assimilation of surface data,indicating the importance of joint assimilation of SST,SSH,and SSS for ENSO predictions.
基金supported by the National Natural Science Foundation of China(Grant No.51938014,Grant No.22176217,Grant No.22276215)the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China(No.22XNKJ28).
文摘The development of passive NO_(x)adsorbers with cost-benefit and high NO_(x)storage capacity remains an on-going challenge to after-treatment technologies at lower temperatures associated with cold-start NO_(x)emissions.Herein,Cs_(1)Mg_(3)Al catalyst prepared by sol-gel method was cyclic tested in NO_(x)storage under 5 vol%water.At 100°C,the NO_(x)storage capacity(1219 μmol g^(-1))was much higher than that of Pt/BaO/Al_(2)O_(3)(610 μmol g^(-1)).This provided new insights for non-noble metal catalysts in low-temperature passive NO_(x)adsorption.The addition of Cs improved the mobility of oxygen species and thus improved the NO_(x)storage capacity.The XRD,XPS,IR spectra and in situ DRIFTs with NH3 probe showed an interaction between CsO_(x)and AlO_(x)sites via oxygen species formed on Cs_(1)Mg_(3)Al catalyst.The improved mobility of oxygen species inferred from O2-TPD was consistent with high NO_(x)storage capacity related to enhanced formation of nitrate and additional nitrite species by NO_(x)oxidation.Moreover,the addition of Mg might improve the stability of Cs_(1)Mg_(3)Al by stabilizing surface active oxygen species in cyclic experiments.
基金National Natural Science Foundation of China,No.42371318。
文摘The Yangtze River Economic Belt(YREB)is a pivotal contributor to China's economic growth,particularly as the nation undergoes a green transformation.Achieving synergistic reductions on pollution and carbon emissions is deemed crucial for this transition.This paper examines the spatial and temporal changes in the synergy of pollution and carbon reduction in the YREB and delves into the underlying mechanisms.Our findings indicate that while the synergy in the YREB is increasing,it manifests disparities across regions,with the lower reaches outperforming the middle and upper ones.Enterprise behavior,government guidance,and regional endowments influence this synergy.Cities in the YREB must strategically plan their urban scale,curb population overgrowth,recalibrate their industrial structures,curtail energy consumption,and enhance policy efficacy.Distinct regions should prioritize various objectives:the lower reaches should hasten scientific advancements and technological innovations;the middle reaches should foster innovation and industrial upgrades;and the upper reaches should prioritize rural and urban land intensification.
基金This work was supported by the National Natural Science Foundation of China(72221002,42271375)the Strategic Priority Research Program(XDA28060100)the Informatization Plan Project(CAS-WX2021PY-0109)of the Chinese Academy of Sciences.
文摘Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.
基金supported by the National Natural Science Foundation of China(grant number 42371318)
文摘The escalating degradation of urban eco-environments has underscored the significance of ecological security in sustainable urban development.Green infrastructure bridges green spaces in cities and increases ecosystem connectivity,thereby optimizing urban ecological security patterns.This study uses Nanjing as a case study and adopts a research paradigm that involves identifying ecological sources,constructing resistance surfaces,and subsequently extracting corridors within the ecological security pattern.This method amalgamates the evaluation of green infrastructure supply and demand,leading to the identification of both ecological corridors and nodes.The findings reveal that while the supply of green infrastructure in Nanjing is low in the city center and high in the suburbs,demand is high in the central area and low in the periphery,indicating a spatial mismatch between supply and demand.Ecological corridors and nodes are categorized into the core,important,and general levels based on their centrality and areas of supply–demand optimization.The connectivity,supply capacity,and supply–demand relationship of green infrastructure in Nanjing have been enhanced to varying degrees through the ecological security pattern optimization.The results of this study can serve as a decision-making reference for optimizing green infrastructure network patterns and enhancing urban ecological security.
基金supported by the National Natural Science Foundation of China(Grant No.92044303)。
文摘Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of China’s Major Research Plan entitled“Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China”(or the Plan)has funded 76 research projects to explore the causes of air pollution in China,and the key processes of air pollution in atmospheric physics and atmospheric chemistry.In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally,an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan.This project has classified and integrated these data,forming eight categories containing 258 datasets and 15 technical reports in total.The integration project has led to the successful establishment of the China Air Pollution Data Center(CAPDC)platform,providing storage,retrieval,and download services for the eight categories.This platform has distinct features including data visualization,related project information querying,and bilingual services in both English and Chinese,which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research.Air pollution control in China,especially in the past decade,is undeniably a global exemplar,and this data center is the first in China to focus on research into the country’s air pollution complex.
基金supported by the following funding bodies:the National Key Research and Development Program of China(Grant No.2020YFA0608000)National Science Foundation of China(Grant Nos.42075142,42375148,42125503+2 种基金42130608)FY-APP-2022.0609,Sichuan Province Key Tech nology Research and Development project(Grant Nos.2024ZHCG0168,2024ZHCG0176,2023YFG0305,2023YFG-0124,and 23ZDYF0091)the CUIT Science and Technology Innovation Capacity Enhancement Program project(Grant No.KYQN202305)。
文摘Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy.Although deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty.This article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling.Such a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling.This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process.Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and quantitatively.Specifically,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation coefficient.In summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.
基金National Key R&D Program of China,No.2017YFA0603702National Natural Science Foundation of China,No.41571019,No.41701023,No.41571028China Postdoctoral Science Foundation,No.2017M610867
文摘The terrestrial hydrological process is an essential but weak link in global/regional climate models. In this paper, the development status, research hotspots and trends in coupled atmosphere-hydrology simulations are identified through a bibliometric analysis, and the challenges and opportunities in this field are reviewed and summarized. Most climate models adopt the one-dimensional (vertical) land surface parameterization, which does not include a detailed description of basin-scale hydrological processes, particularly the effects of human activities on the underlying surfaces. To understand the interaction mechanism between hydrological processes and climate change, a large number of studies focused on the climate feedback effects of hydrological processes at different spatio-temporal scales, mainly through the coupling of hydrological and climate models. The improvement of the parameterization of hydrological process and the development of large-scale hydrological model in land surface process model lay a foundation for terrestrial hydrological-climate coupling simulation, based on which, the study of terrestrial hydrological-climate coupling is evolving from the traditional unidirectional coupling research to the two-way coupling study of "climate-hydrology" feedback. However, studies of fully coupled atmosphere-hydrology simulations (also called atmosphere-hydrology two-way coupling) are far from mature. The main challenges associated with these studies are: improving the potential mismatch in hydrological models and climate models; improving the stability of coupled systems; developing an effective scale conversion scheme; perfecting the parameterization scheme; evaluating parameter uncertainties; developing effective methodology for model parameter transplanting; and improving the applicability of models and high/super-resolution simulation. Solving these problems and improving simulation accuracy are directions for future hydro-climate coupling simulation research.
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 the National Program for Support of Top-notch Young Professionalsthe National Natural Science Foundation of China (Grant No. 41576019)J.-Y. YU was supported by the US National Science Foundation (Grant No. AGS-150514)
文摘The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Nio and how much is common to both this type and the conventional Eastern Pacific(EP)-type El Nio. In this study, the deterministic performance of an El Nio–Southern Oscillation(ENSO) ensemble prediction system is examined for the two types of El Nio. Ensemble hindcasts are run for the nine EP El Nio events and twelve CP El Nio events that have occurred since 1950. The results show that(1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times;(2) the systematic forecast biases come mostly from the prediction of the CP events; and(3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Nio. Further improvements to coupled atmosphere–ocean models in terms of CP El Nio prediction should be recognized as a key and high-priority task for the climate prediction community.
基金The 973 Project of China under contract No.2012CB95600the National Natural Science Foundation of China under contract Nos 40888001 and 41176019+1 种基金the Chinese Academy of Sciences under contract No. KZCX2-YW-JS204Qingdao Municipal under contract No.10-3-3-38jh
文摘A nested circulation model system based on the Princeton ocean model （POM） is set up to simulate the currentmeter data from a bottom-mounted Acoustic Doppler Profiler （ADP） deployed at the 30 m depth in the Lunan（South Shandong Province, China） Trough south of the Shandong Peninsula in the summer of 2008, and to study the dynamics of the circulation in the southwestern Huanghai Sea （Yellow Sea）. The model has reproduced well the observed subtidal current at the mooring site. The results of the model simulation suggest that the bottom topography has strong steering effects on the regional circulation in summer. The model simulation shows that the Subei （North Jiangsu Province, China）coastal current flows north- ward in summer, in contrast to the southeastward current in the center of the Lunan Trough measured by the moored currentmeter. The analyses of the model results suggest that the southeastward current at the mooring site in the Lunan Trough is forced by the westward wind-driven current along the Lunan coast, which meets the northward Subei coastal current at the head of the Haizhou Bay to flow along an offshore path in the southeastward direction in the Lunan Trough. Analysis suggests that the Subei coastal current, the Lunan coastal current, and the circulation in the Lunan Trough are independent current systems con- trolled by different dynamics. Therefore, the current measurements in the Lunan Trough cannot be used to represent the Subei coastal current in general.
基金This work was supported by the National Key Research Project of China(Grant No.2018YFC 1507001).
文摘In recent decades,a greening tendency due to increased vegetation has been noted around the Taklimakan Desert(TD),but the impact of such a change on the local hydrological cycle remains uncertain.Here,we investigate the response of the local hydrological cycle and atmospheric circulation to a green TD in summer using a pair of global climate model(Community Earth System Model version 1.2.1)simulations.With enough irrigation to support vegetation growth in the TD,the modeling suggests first,that significant increases in local precipitation are attributed to enhanced local recycling of water,and second,that there is a corresponding decrease of local surface temperatures.On the other hand,irrigation and vegetation growth in this low-lying desert have negligible impacts on the large-scale circulation and thus the moisture convergence for enhanced precipitation.It is also found that the green TD can only be sustained by a large amount of irrigation water supply since only about one-third of the deployed water can be“recycled”locally.Considering this,devising a way to encapsulate the irrigated water within the desert to ensure more efficient water recycling is key for maintaining a sustainable,greening TD.
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(72140004).
文摘Achieving carbon neutrality is crucial in dealing with climate change and containing the increase in global temperature at below 1.5℃compared with preindustrial levels.During the general debate at the 75th session of the United Nations General Assembly in September 2020,President Xi Jinping announced that China would adopt more vigorous policies and measures against climate change.
