Objective: The paper aims to analyze the dynamic characteristics of litter production and nutrient return of the forest ecosystems in subtropical areas, and provide a theoretical basis for the nutrient cycling study i...Objective: The paper aims to analyze the dynamic characteristics of litter production and nutrient return of the forest ecosystems in subtropical areas, and provide a theoretical basis for the nutrient cycling study in southwest Hubei Province and carbon sink function of the whole forest ecosystem. Methods: Three typical forest stands (Chinese fir plantation, Cryptomeria fortunei plantation and evergreen and deciduous broad-leaved mixed forest) in Golden Mountain Forest Farm in southwest Hubei Province were investigated and monitored continuously for the litter types and productivity and nutrient return. Results: The annual litter productivity of the three forest stands ranged from 161.77 to 396.26 kg·hm<sup>-2</sup>;Litters of branches, leaves and reproductive organs accounted for 14.14% - 20.85%, 33.26% - 78.33%, 7.52% - 42.18% of the total, respectively;The litter productivity and total litter productivity of each composition in the three forest stands show unimodal or bimodal changes over months, and the total litter productivity reached the highest value in January, April and October respectively. For different nutrient contents of the three forest stands, the common feature is C > N. The order of nutrient return amount from greatest to least is evergreen and deciduous broad-leaved mixed forest, Cryptomeria fortunei plantation and Chinese fir plantation. For different nutrient return amounts, the common feature is C > N, and the nutrient return amounts are 76.51-180.69 kg·hm<sup>-2</sup> and 2.3 - 5.71 kg·hm<sup>-2</sup> respectively. Conclusion: The annual litter productivity and nutrient return amount of the evergreen and deciduous broad-leaved mixed forest are the highest among the three forest stands. Therefore, protecting the evergreen and deciduous broad-leaved mixed forest and studying the litter changes of Chinese fir plantation and Cryptomeria fortunei plantation are of far-reaching significance for the development of sustainable forest management in this region and the further improvement of the carbon sequestration function of the whole forest ecosystem.展开更多
In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming ...In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.展开更多
In the boreal summer and autumn of 2023,the globe experienced an extremely hot period across both oceans and continents.The consecutive record-breaking mean surface temperature has caused many to speculate upon how th...In the boreal summer and autumn of 2023,the globe experienced an extremely hot period across both oceans and continents.The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter.In this report,as shown in the multi-model ensemble mean(MME)prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences,a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2−3 months,which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection,thus serving to modulate the winter climate in East Asia and North America.Despite some uncertainty due to unpredictable internal atmospheric variability,the global mean surface temperature(GMST)in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend.Specifically,the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter,and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991.Moreover,the necessary early warnings are still reliable in the timely updated mediumterm numerical weather forecasts and sub-seasonal-to-seasonal prediction.展开更多
The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m oc...The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.展开更多
In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,ma...In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.展开更多
Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves...Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.展开更多
It provides a theoretical basis for the artificial promotion of the reproduction and regeneration of Liriodendron chinense through exploring the effects of light intensity and seeding density on the seed germination a...It provides a theoretical basis for the artificial promotion of the reproduction and regeneration of Liriodendron chinense through exploring the effects of light intensity and seeding density on the seed germination and seedling growth of this rare plant. Controlled experiments were conducted by setting different seeding densities and shading intensities to observe and detect the seed germination and seedling growth under different experimental conditions. The data were analyzed by SPSS and the model was fitted to calculate the seed germination rate, seedling emergence rate and seedling growth parameters of Liriodendron chinense, which were used to analyze the effects of seeding density and light intensity on the seed germination and seedling growth of Liriodendron chinense. Light has a significant impact on the seed germination and seedling growth of Liriodendron chinense (P P > 0.05). High density promotes the seedling height (the seedling height showed a rising trend with the increase of seeding density, reaching the highest at 500 seeds/m<sup>2</sup>. “3-stitch + 300 seeds/m<sup>2</sup>” combination is an appropriate condition for Liriodendron chinense reproduction since it can realize higher germination rate and better seedling growth potential. The seedling height and basal diameter growth curve of Liriodendron chinense fits well to the Logistic equation (R<sup>2</sup> ≥ 0.977). Light has a significant impact on the seed germination and seedling growth of Liriodendron chinense, and appropriate shading treatment can increase the seed germination rate and seedling emergence rate of Liriodendron chinense. Seeding density has little influence on the seed germination rate of Liriodendron chinense, but high density will promote seedling height.展开更多
文摘Objective: The paper aims to analyze the dynamic characteristics of litter production and nutrient return of the forest ecosystems in subtropical areas, and provide a theoretical basis for the nutrient cycling study in southwest Hubei Province and carbon sink function of the whole forest ecosystem. Methods: Three typical forest stands (Chinese fir plantation, Cryptomeria fortunei plantation and evergreen and deciduous broad-leaved mixed forest) in Golden Mountain Forest Farm in southwest Hubei Province were investigated and monitored continuously for the litter types and productivity and nutrient return. Results: The annual litter productivity of the three forest stands ranged from 161.77 to 396.26 kg·hm<sup>-2</sup>;Litters of branches, leaves and reproductive organs accounted for 14.14% - 20.85%, 33.26% - 78.33%, 7.52% - 42.18% of the total, respectively;The litter productivity and total litter productivity of each composition in the three forest stands show unimodal or bimodal changes over months, and the total litter productivity reached the highest value in January, April and October respectively. For different nutrient contents of the three forest stands, the common feature is C > N. The order of nutrient return amount from greatest to least is evergreen and deciduous broad-leaved mixed forest, Cryptomeria fortunei plantation and Chinese fir plantation. For different nutrient return amounts, the common feature is C > N, and the nutrient return amounts are 76.51-180.69 kg·hm<sup>-2</sup> and 2.3 - 5.71 kg·hm<sup>-2</sup> respectively. Conclusion: The annual litter productivity and nutrient return amount of the evergreen and deciduous broad-leaved mixed forest are the highest among the three forest stands. Therefore, protecting the evergreen and deciduous broad-leaved mixed forest and studying the litter changes of Chinese fir plantation and Cryptomeria fortunei plantation are of far-reaching significance for the development of sustainable forest management in this region and the further improvement of the carbon sequestration function of the whole forest ecosystem.
基金supported by the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.ZDBS-LY-DQC010)the National Natural Science Foundation of China(Grant No.42175045).
文摘In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.
基金the Key Research Program of Frontier Sciences,CAS(Grant No.ZDBS-LYDQC010)the National Natural Science Foundation of China(Grant No.42175045).
文摘In the boreal summer and autumn of 2023,the globe experienced an extremely hot period across both oceans and continents.The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter.In this report,as shown in the multi-model ensemble mean(MME)prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences,a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2−3 months,which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection,thus serving to modulate the winter climate in East Asia and North America.Despite some uncertainty due to unpredictable internal atmospheric variability,the global mean surface temperature(GMST)in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend.Specifically,the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter,and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991.Moreover,the necessary early warnings are still reliable in the timely updated mediumterm numerical weather forecasts and sub-seasonal-to-seasonal prediction.
基金supported by the National Natural Science Foundation of China (Grant Nos. 42076202, 42122046, 42206208 and 42261134536)the Open Research Cruise NORC2022-10+NORC2022-303 supported by NSFC shiptime Sharing Projects 42149910+7 种基金the new Cornerstone Science Foundation through the XPLORER PRIZE, DAMO Academy Young Fellow, Youth Innovation Promotion Association, Chinese Academy of SciencesNational Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab)sponsored by the US National Science Foundationsupported by NASA Awards 80NSSC17K0565, 80NSSC21K1191, and 80NSSC22K0046by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy’s Office of Biological & Environmental Research (BER) via National Science Foundation IA 1947282supported by NOAA (Grant No. NA19NES4320002 to CISESS-MD at the University of Maryland)supported by the Young Talent Support Project of Guangzhou Association for Science and Technologyfunded by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) in agreement between INGV, ENEA, and GNV SpA shipping company that provides hospitality on its commercial vessels
文摘The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.
基金supported by the National Natural Science Foundation of China(Grant Nos.41976193 and 42176243).
文摘In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.
基金support from the National Science Foundation of China(Grant Nos.62075078 and 62135004)the Knowledge Innovation Program of Wuhan-Shuguang Project(Grant No.2022010801020095).
文摘Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.
文摘It provides a theoretical basis for the artificial promotion of the reproduction and regeneration of Liriodendron chinense through exploring the effects of light intensity and seeding density on the seed germination and seedling growth of this rare plant. Controlled experiments were conducted by setting different seeding densities and shading intensities to observe and detect the seed germination and seedling growth under different experimental conditions. The data were analyzed by SPSS and the model was fitted to calculate the seed germination rate, seedling emergence rate and seedling growth parameters of Liriodendron chinense, which were used to analyze the effects of seeding density and light intensity on the seed germination and seedling growth of Liriodendron chinense. Light has a significant impact on the seed germination and seedling growth of Liriodendron chinense (P P > 0.05). High density promotes the seedling height (the seedling height showed a rising trend with the increase of seeding density, reaching the highest at 500 seeds/m<sup>2</sup>. “3-stitch + 300 seeds/m<sup>2</sup>” combination is an appropriate condition for Liriodendron chinense reproduction since it can realize higher germination rate and better seedling growth potential. The seedling height and basal diameter growth curve of Liriodendron chinense fits well to the Logistic equation (R<sup>2</sup> ≥ 0.977). Light has a significant impact on the seed germination and seedling growth of Liriodendron chinense, and appropriate shading treatment can increase the seed germination rate and seedling emergence rate of Liriodendron chinense. Seeding density has little influence on the seed germination rate of Liriodendron chinense, but high density will promote seedling height.
