Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,so...Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.展开更多
With the completion of Chinese BeiDou Navigation Satellite System(BDS),the world has begun to enjoy the Positioning,Navigation,and Timing(PNT)services of four Global Navigation Satellite Systems(GNSS).In order to impr...With the completion of Chinese BeiDou Navigation Satellite System(BDS),the world has begun to enjoy the Positioning,Navigation,and Timing(PNT)services of four Global Navigation Satellite Systems(GNSS).In order to improve the GNSS performance and expand its applications,Low Earth Orbit(LEO)Enhanced Global Navigation Satellite System(LeGNSS)is being vigorously advocated.Combined with high-,medium-,and low-earth orbit satellites,it can improve GNSS performance in terms of orbit determination,Precise Point Positioning(PPP)convergence time,etc.This paper comprehensively reviews the current status of LeGNSS,focusing on analyzing its advantages and challenges for precise orbit and clock determination,PPP convergence,earth rotation parameter estimation,and global ionosphere modeling.Thanks to the fast geometric change brought by LEO satellites,LeGNSS is expected to fundamentally solve the problem of the long convergence time of PPP without any augmentation.The convergence time can be shortened within 1 minute if appropriate LEO constellations are deployed.However,there are still some issues to overcome,such as the optimization of LEO constellation as well as the real time LEO precise orbit and clock determination.展开更多
The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-s...The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.展开更多
The role of the Sun in climate change is hotly debated.Some studies suggest its impact is significant,while others suggest it is minimal.The Intergovernmental Panel on Climate Change(IPCC)supports the latter view and ...The role of the Sun in climate change is hotly debated.Some studies suggest its impact is significant,while others suggest it is minimal.The Intergovernmental Panel on Climate Change(IPCC)supports the latter view and suggests that nearly 100%of the observed surface warming from 1850–1900 to 2020 is due to anthropogenic emissions.However,the IPCC’s conclusions are based solely on computer simulations made with global climate models(GCMs)forced with a total solar irradiance(TSI)record showing a low multi-decadal and secular variability.The same models also assume that the Sun affects the climate system only through radiative forcing–such as TSI–even though the climate could also be affected by other solar processes.In this paper I propose three“balanced”multi-proxy models of total solar activity(TSA)that consider all main solar proxies proposed in scientific literature.Their optimal signature on global and sea surface temperature records is assessed together with those produced by the anthropogenic and volcanic radiative forcing functions adopted by the CMIP6 GCMs.This is done by using a basic energy balance model calibrated with a differential multi-linear regression methodology,which allows the climate system to respond to the solar input differently than to radiative forcings alone,and to evaluate the climate’s characteristic time-response as well.The proposed methodology reproduces the results of the CMIP6 GCMs when their original forcing functions are applied under similar physical conditions,indicating that,in such a scenario,the likely range of the equilibrium climate sensitivity(ECS)could be 1.4℃to 2.8℃,with a mean of 2.1℃(using the HadCRUT5 temperature record),which is compatible with the low-ECS CMIP6 GCM group.However,if the proposed solar records are used as TSA proxies and the climatic sensitivity to them is allowed to differ from the climatic sensitivity to radiative forcings,a much greater solar impact on climate change is found,along with a significantly reduced radiative effect.展开更多
基金supported by NASA Kentucky NNX15AR69H,NSF grant nos.1940696,1903722,and 1243232Andrew Carnegie Fellowship Award no.G-F-19-56910.
文摘Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.
基金the National Natural Science Funds of China[grant numbers 41874030,42074026]Natural Science Funds of Shanghai[grant number 21ZR1465600]+3 种基金the Program of Shanghai Academic Research Leader[grant number 20XD1423800]the Innovation Program of Shanghai Municipal Education Commission[grant number 2021-01-07-00-07-E00095]the“Shuguang Program”supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission[grant number 20SG18]the Scientific and Technological Innovation Plan from Shanghai Science and Technology Committee[grant numbers 20511103302,20511103402 and 20511103702].
文摘With the completion of Chinese BeiDou Navigation Satellite System(BDS),the world has begun to enjoy the Positioning,Navigation,and Timing(PNT)services of four Global Navigation Satellite Systems(GNSS).In order to improve the GNSS performance and expand its applications,Low Earth Orbit(LEO)Enhanced Global Navigation Satellite System(LeGNSS)is being vigorously advocated.Combined with high-,medium-,and low-earth orbit satellites,it can improve GNSS performance in terms of orbit determination,Precise Point Positioning(PPP)convergence time,etc.This paper comprehensively reviews the current status of LeGNSS,focusing on analyzing its advantages and challenges for precise orbit and clock determination,PPP convergence,earth rotation parameter estimation,and global ionosphere modeling.Thanks to the fast geometric change brought by LEO satellites,LeGNSS is expected to fundamentally solve the problem of the long convergence time of PPP without any augmentation.The convergence time can be shortened within 1 minute if appropriate LEO constellations are deployed.However,there are still some issues to overcome,such as the optimization of LEO constellation as well as the real time LEO precise orbit and clock determination.
基金This work was supported in part by the New Generation Artificial Intelligence Development Plan of China(2015-2030)(Grant No.2021ZD0111205)the National Natural Science Foundation of China(Grant Nos.72025404,72293575 and 72074209).
文摘The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.
文摘The role of the Sun in climate change is hotly debated.Some studies suggest its impact is significant,while others suggest it is minimal.The Intergovernmental Panel on Climate Change(IPCC)supports the latter view and suggests that nearly 100%of the observed surface warming from 1850–1900 to 2020 is due to anthropogenic emissions.However,the IPCC’s conclusions are based solely on computer simulations made with global climate models(GCMs)forced with a total solar irradiance(TSI)record showing a low multi-decadal and secular variability.The same models also assume that the Sun affects the climate system only through radiative forcing–such as TSI–even though the climate could also be affected by other solar processes.In this paper I propose three“balanced”multi-proxy models of total solar activity(TSA)that consider all main solar proxies proposed in scientific literature.Their optimal signature on global and sea surface temperature records is assessed together with those produced by the anthropogenic and volcanic radiative forcing functions adopted by the CMIP6 GCMs.This is done by using a basic energy balance model calibrated with a differential multi-linear regression methodology,which allows the climate system to respond to the solar input differently than to radiative forcings alone,and to evaluate the climate’s characteristic time-response as well.The proposed methodology reproduces the results of the CMIP6 GCMs when their original forcing functions are applied under similar physical conditions,indicating that,in such a scenario,the likely range of the equilibrium climate sensitivity(ECS)could be 1.4℃to 2.8℃,with a mean of 2.1℃(using the HadCRUT5 temperature record),which is compatible with the low-ECS CMIP6 GCM group.However,if the proposed solar records are used as TSA proxies and the climatic sensitivity to them is allowed to differ from the climatic sensitivity to radiative forcings,a much greater solar impact on climate change is found,along with a significantly reduced radiative effect.
