In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been rich...In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been richly described in many species,comparatively little is known about how those patterns contribute to song achieving its important functions.For each of song’s main functions,territorial defense and mate attraction,evidence of a role for syntax is limited.One species for which syntax has been thoroughly described is the Hermit Thrush(Catharus guttatus),which presents song types from their repertoires in a semi-predictable order and,in doing so,rapidly cycle up and down the frequency spectrum.The objective of the present study was to explore the importance of song syntax in the Hermit Thrush through a within-subject examination of how measures of syntax,such as the predictability of song type order within song sequences,shift over the breeding season.We hypothesized that,if such syntactical characteristics are important to breeding behaviour,they would be most prominent at the start of the breeding season when activity associated with territory establishment and mate attraction is most intense.Analysis revealed that,as predicted,the rigidness of song type ordering within se-quences was highest at the start of the season and declined thereafter.That song type sequences were most predictable at the vitally important early part of the breeding season fit our hypothesis that this aspect of song syntax is important to song’s functions related to territory establishment and/or mate attraction.Future work will clarify whether that role relates to one of song’s two main functions or serves song transmission in some broader way.展开更多
The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the refo...The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.展开更多
Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive thes...Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive these plasticity patterns.Turtles have a hearing range that appears to be limited to under 1.5 kHz and are often thought to be non-vocal;thus,they are commonly neglected in vocal communication research.In this study,we aimed to determine whether the auditory phenotype exhibits seasonal variation in sensitivity and to analyze the potential factors driving such variation patterns in turtles.We measured hearing sensitivity and sex hormone levels in female(estradiol)and male(testosterone and dihydrotestosterone)Red-eared sliders(Trachemys scripta elegans)during spring and winter.The results showed that auditory brainstem response(ABR)thresholds were significantly lower in spring than in winter at a frequency range of 0.5-0.9 kHz.The hearing-sensitivity bandwidth was wider,and the ABR latency was significantly shorter in spring than in winter.No significant differences were found in estradiol,testosterone,and dihydrotestosterone levels in T.scripta elegans between spring and winter.This study is the first to reveal the seasonal variation of peripheral hearing sensitivity in turtles,a special animal group with limited hearing range and less vocalization.Temperature variations may be used to explain these seasonal effects,but further research is required to confirm our findings.展开更多
Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental pro...Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.展开更多
Litterfall is the largest source of nutrients to for-est soils of tropical rainforests.However,variability in lit-terfall production,nutrient remobilization,and changes in leaf nutrient concentration with climate seas...Litterfall is the largest source of nutrients to for-est soils of tropical rainforests.However,variability in lit-terfall production,nutrient remobilization,and changes in leaf nutrient concentration with climate seasonality remain largely unknown for the central Amazon.This study meas-ured litterfall production,leaf nutrient remobilization,and leaf area index on a forest plateau in the central Amazon.Litterfall was measured at monthly intervals during 2014,while nitrogen,phosphorus,potassium,calcium and mag-nesium concentrations of leaf litter and canopy leaves were measured in the dry and rainy seasons,and remobilization rates determined.Leaf area index was also recorded in the dry and rainy seasons.Monthly litterfall varied from 33.2(in the rainy season)to 87.6 g m^(-2) in the dry season,while leaf area index increased slightly in the rainy season.Climatic seasonality had no effect on concentrations of nitrogen,calcium,and magnesium,whereas phosphorous and potassium responded to rainfall seasonality oppositely.While phosphorous increased,potassium decreased during the dry season.Over seasons,nitrogen,potassium,and phosphorous decreased in leaf litter;calcium increased in leaf litter,while magnesium remained unaffected with leaf aging.Regardless,the five nutrients had similar remobilization rates over the year.The absence of climate seasonality on nutrient remobilization suggests that the current length of the dry season does not alter nutrient remobilization rates but this may change as dry periods become more prolonged in the future due to climate change.展开更多
This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that co...This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.展开更多
El Ni?o–Southern Oscillation(ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and de...El Ni?o–Southern Oscillation(ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and decaying over the next spring. Several studies have demonstrated that this feature arises as a result of seasonal variation in the growth rate of ENSO as expressed by the sea surface temperature(SST). The bias towards simulating the phase locking of ENSO by many state-of-the-art climate models is also attributed to the unrealistic depiction of the growth rate. In this study, the seasonal variation of SST growth rate in the Ni?o-3.4 region(5°S–5°N, 120°–170°W) is estimated in detail based on the mixed layer heat budget equation and recharge oscillator model during 1981–2020. It is suggested that the consideration of a variable mixed layer depth is essential to its diagnostic process. The estimated growth rate has a remarkable seasonal cycle with minimum rates occurring in spring and maximum rates evident in autumn. More specifically, the growth rate derived from the meridional advection(surface heat flux) is positive(negative) throughout the year. Vertical diffusion generally makes a negative contribution to the evolution of growth rate and the magnitude of vertical entrainment represents the smallest contributor. Analysis indicates that the zonal advective feedback is regulated by the meridional immigration of the intertropical convergence zone, which approaches its southernmost extent in February and progresses to its northernmost location in September, and dominates the seasonal variation of the SST growth rate.展开更多
Bituminous materials are heat-sensitive, and their mechanical properties vary with temperature. This variation in properties is not without consequences on the performance of flexible road structures under the repeate...Bituminous materials are heat-sensitive, and their mechanical properties vary with temperature. This variation in properties is not without consequences on the performance of flexible road structures under the repeated passage of multi-axles. This study determines the influence of seasonal variations on the rate of permanent deformation, the rut depth of flexible pavements and the effect of alternating loading of heavy goods vehicles following the temperature variations on the durability of roads. Thus, an ambient and pavement surface temperature measurement was carried out in 2022. The temperature profile at different layers of the modelled pavement, the evaluation of deformation rates and rutting depth were determined using several models. The results show that the permanent deformation and rutting rates are higher at the level of the bituminous concrete layer than at the level of the asphalt gravel layer because the stresses decrease from the surface to the depth of the pavement. On the other hand, the variations in these rates, permanent deformations and ruts between the hot and so-called cold periods are more pronounced in the bitumen gravel than in bituminous concrete, showing that gravel bitumen is more sensitive to temperature variations than bituminous concrete despite its higher rigidity. Of these results, we suggested a periodic and alternating loading of the different types of heavy goods vehicles. These loads consist of fully applying the WAEMU standards with a tolerance of 15% during periods of high and low temperatures. This regulation has increased 2 to 3 times in the durability of roadways depending on the type of heavy goods vehicle.展开更多
The main objective of this study is to evaluate the seasonal performance of 20 MW solar power plants in Senegal. The analysis revealed notable seasonal variations in the performance of all stations. The most significa...The main objective of this study is to evaluate the seasonal performance of 20 MW solar power plants in Senegal. The analysis revealed notable seasonal variations in the performance of all stations. The most significant yields are recorded in spring, autumn and winter, with values ranging from 5 to 7.51 kWh/kWp/day for the reference yield and 4.02 to 7.58 kWh/kWp/day for the final yield. These fluctuations are associated with intense solar activity during the dry season and clear skies, indicating peak production. Conversely, minimum values are recorded during the rainy season from June to September, with a final yield of 3.86 kWh/kW/day due to dust, clouds and high temperatures. The performance ratio analysis shows seasonal dynamics throughout the year with rates ranging from 77.40% to 95.79%, reinforcing reliability and optimal utilization of installed capacity. The results of the capacity factor vary significantly, with March, April, May, and sometimes October standing out as periods of optimal performance, with 16% for Kahone, 16% for Bokhol, 18% for Malicounda and 23% for Sakal. Total losses from solar power plants show similar seasonal trends standing out for high loss levels from June to July, reaching up to 3.35 kWh/kWp/day in June. However, using solar trackers at Sakal has increased production by up to 25%, demonstrating the operational stability of this innovative technology compared with the plants fixed panel. Finally, comparing these results with international studies confirms the outstanding efficiency of Senegalese solar power plants, other installations around the world.展开更多
Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water r...Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.展开更多
The unprecedented Zhengzhou heavy rainfall in July 2021 occurred under the background of a northward shift of the western Pacific subtropical high(WPSH).Although the occurrence of this extreme event could not be captu...The unprecedented Zhengzhou heavy rainfall in July 2021 occurred under the background of a northward shift of the western Pacific subtropical high(WPSH).Although the occurrence of this extreme event could not be captured by seasonal predictions,a skillful prediction of the WPSH variation might have warned us of the increased probability of extreme weather events in Central and Northern China.However,the mechanism for the WPSH variation in July 2021 and its seasonal predictability are still unknown.Here,the observed northward shift of the WPSH in July 2021 is shown to correspond to a meridional dipole pattern of the 850-hPa geopotential height to the east of China,the amplitude of which became the strongest since 1979.The meridional dipole pattern is two nodes of the Pacific–Japan pattern.To investigate the predictability of the WPSH variation,a 21-member ensemble of seasonal predictions initiated from the end of June 2021 was conducted.The predictable and unpredictable components of the meridional dipole pattern were identified from the ensemble simulations.Its predictable component is driven by positive precipitation anomalies over the tropical western Pacific.The positive precipitation anomalies are caused by positive horizonal advection of the mean moist enthalpy by southwesterly anomalies to the northwestern flank of anticyclonic anomalies excited by the existing La Niña,which is skillfully predicted by the model.The leading mode of the unpredictable component is associated with the atmospheric internal intraseasonal oscillations,which are not initialized in the simulations.The relative contributions of the predictable and unpredictable components to the observed northward shift of the WPSH at 850 hPa are 28.0%and 72.0%,respectively.展开更多
Extreme high temperatures frequently occur in southwestern China,significantly impacting the local ecological system and economic development.However,accurate prediction of extreme high-temperature days(EHDs)in this r...Extreme high temperatures frequently occur in southwestern China,significantly impacting the local ecological system and economic development.However,accurate prediction of extreme high-temperature days(EHDs)in this region is still an unresolved challenge.Based on the spatiotemporal characteristics of EHDs over China,a domain-averaged EHD index over southwestern China(SWC-EHDs)during April-May is defined.The simultaneous dynamic and thermodynamic fields associated with the increased SWC-EHDs are a local upper-level anticyclonic(high-pressure)anomaly and wavy geopotential height anomaly patterns over Eurasia.In tracing the origins of the lower boundary anomalies,two physically meaningful precursors are detected for SWC-EHDs.They are the tripolar SST change tendency from December-January to February-March in the northern Atlantic and the February-March mean snow depth in central Asia.Using these two selected predictors,a physics-based empirical model prediction was applied to the training period of 1961–2005 to obtain a skillful prediction of the EHDs index,attaining a correlation coefficient of 0.76 in the independent prediction period(2006–19),suggesting that 58%of the total SWC-EHDs variability is predictable.This study provides an estimate for the lower bound of the seasonal predictability of EHDs as well as for the hydrological drought over southwestern China.展开更多
A critical function of animal movement is to maximize access to essential resources in temporally fluctuating and spatially heterogeneous environments.Seasonally mediated resource fluctuations may influence animal mov...A critical function of animal movement is to maximize access to essential resources in temporally fluctuating and spatially heterogeneous environments.Seasonally mediated resource fluctuations may influence animal movements,enabling them to track changing resource distributions,resulting in annual migration patterns.The conservation-dependent giant panda(Ailuropoda melanoleuca) displays seasonal movement patterns;however,the key factor driving these seasonal migration patterns remains poorly understood.Here,we used GPS tracking collars to monitor the movements of six giant pandas over a 12-year period across different elevations,and performed statistical analysis of seasonal migration directions,routes,habitat revisitation,home range overlap,first arrival events,and stability.Our results revealed a compelling pattern of seasonal migrations that facilitated the ability of the pandas to forage at the appropriate time and place to maximize nutritional intake.Our results indicated that pandas utilize spatial memory to locate reliable food resources,as evidenced by their annual return to the same or similar winter and summer home ranges and the consistently maintained percentage of home range overlap.These novel insights into giant panda foraging and movement ecology not only enhance our understanding of its ability to adapt to nutritionally poor dietary resources but also provide important information for the development of resource utilization-based protection and management strategies.展开更多
To explore how to respond to seasonal freeze–thaw cycles on forest ecosystems in the context of climate change through thinning,we assessed the potential impact of thinning intensity on carbon cycle dynamics.By varyi...To explore how to respond to seasonal freeze–thaw cycles on forest ecosystems in the context of climate change through thinning,we assessed the potential impact of thinning intensity on carbon cycle dynamics.By varying the number of temperature cycles,the eff ects of various thinning intensities in four seasons.The rate of mass,litter organic carbon,and soil organic carbon(SOC)loss in response to temperature variations was examined in two degrees of decomposition.The unfrozen season had the highest decomposition rate of litter,followed by the frozen season.Semi-decomposed litter had a higher decomposition rate than undecomposed litter.The decomposition rate of litter was the highest when the thinning intensity was 10%,while the litter and SOC were low.Forest litter had a good carbon sequestration impact in the unfrozen and freeze–thaw seasons,while the converse was confi rmed in the frozen and thaw seasons.The best carbon sequestration impact was identifi ed in litter,and soil layers under a 20–25%thinning intensity,and the infl uence of undecomposed litter on SOC was more noticeable than that of semi-decomposed litter.Both litter and soil can store carbon:however,carbon is transported from undecomposed litter to semi-decomposed litter and to the soil over time.In summary,the best thinning intensity being 20–25%.展开更多
A robust phenomenon termed the Arctic Amplification(AA)refers to the stronger warming taking place over the Arctic compared to the global mean.The AA can be confirmed through observations and reproduced in climate mod...A robust phenomenon termed the Arctic Amplification(AA)refers to the stronger warming taking place over the Arctic compared to the global mean.The AA can be confirmed through observations and reproduced in climate model simulations and shows significant seasonality and inter-model spread.This study focuses on the influence of surface type on the seasonality of AA and its inter-model spread by dividing the Arctic region into four surface types:ice-covered,ice-retreat,ice-free,and land.The magnitude and inter-model spread of Arctic surface warming are calculated from the difference between the abrupt-4×CO_(2)and pre-industrial experiments of 17 CMIP6 models.The change of effective thermal inertia(ETI)in response to the quadrupling of CO_(2) forcing is the leading mechanism for the seasonal energy transfer mechanism,which acts to store heat temporarily in summer and then release it in winter.The ETI change is strongest over the ice-retreat region,which is also responsible for the strongest AA among the four surface types.The lack of ETI change explains the nearly uniform warming pattern across seasons over the ice-free(ocean)region.Compared to other regions,the ice-covered region shows the maximum inter-model spread in JFM,resulting from a stronger inter-model spread in the oceanic heat storage term.However,the weaker upward surface turbulent sensible and latent heat fluxes tend to suppress the inter-model spread.The relatively small inter-model spread during summer is caused by the cancellation of the inter-model spread in ice-albedo feedback with that in the oceanic heat storage term.展开更多
Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mo...Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.展开更多
As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates th...As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates the models’capability to simulate and predict the Madden-Julian Oscillation(MJO).Three versions of the Beijing Climate Center Climate System Model(BCC-CSM)are used to conduct historical simulations and re-forecast experiments(referred to as EXP1,EXP1-M,and EXP2,respectively).In simulating MJO characteristics,the newly-developed high-resolution BCC-CSM outperforms its predecessors.In terms of MJO prediction,the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M,and further to 24 days in EXP2.Within the first forecast week,the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill.However,during forecast weeks 2–3,EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3.Particularly at initial phases 2–3,EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection,leading to the highest prediction skill of the MJO.Our results reveal that,during the participation of the CMA models in the S2S Project,both the improved model initialization and updated model physics played positive roles in improving MJO prediction.Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.展开更多
基金partly funded by an NSERC Discovery Grant received by LS Phillmorea UNB University Research Fund grant received by SP Roach
文摘In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been richly described in many species,comparatively little is known about how those patterns contribute to song achieving its important functions.For each of song’s main functions,territorial defense and mate attraction,evidence of a role for syntax is limited.One species for which syntax has been thoroughly described is the Hermit Thrush(Catharus guttatus),which presents song types from their repertoires in a semi-predictable order and,in doing so,rapidly cycle up and down the frequency spectrum.The objective of the present study was to explore the importance of song syntax in the Hermit Thrush through a within-subject examination of how measures of syntax,such as the predictability of song type order within song sequences,shift over the breeding season.We hypothesized that,if such syntactical characteristics are important to breeding behaviour,they would be most prominent at the start of the breeding season when activity associated with territory establishment and mate attraction is most intense.Analysis revealed that,as predicted,the rigidness of song type ordering within se-quences was highest at the start of the season and declined thereafter.That song type sequences were most predictable at the vitally important early part of the breeding season fit our hypothesis that this aspect of song syntax is important to song’s functions related to territory establishment and/or mate attraction.Future work will clarify whether that role relates to one of song’s two main functions or serves song transmission in some broader way.
基金supported by the National Key R&D Program of China (Grant No. 2022YFE0106300)the National Natural Science Foundation of China (Grant Nos. 41941009 and 42006191)+2 种基金the China Postdoctoral Science Foundation (Grant No. 2023M741526)the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grant Nos. SML2022SP401 and SML2023SP207)the Program of Marine Economy Development Special Fund under Department of Natural Resources of Guangdong Province (Grant No. GDNRC [2022]18)。
文摘The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.
基金funded by the Natural Science Foundation of Hainan Province(320QN256 to TW)the High-level Talent Project of the Hainan Natural Science Foundation(322RC661 to TW)+1 种基金the National Natural Science Foundation of China(31860608 to JW)the Specific Research Fund of the Innovation Platform for Academicians of Hainan Province.
文摘Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive these plasticity patterns.Turtles have a hearing range that appears to be limited to under 1.5 kHz and are often thought to be non-vocal;thus,they are commonly neglected in vocal communication research.In this study,we aimed to determine whether the auditory phenotype exhibits seasonal variation in sensitivity and to analyze the potential factors driving such variation patterns in turtles.We measured hearing sensitivity and sex hormone levels in female(estradiol)and male(testosterone and dihydrotestosterone)Red-eared sliders(Trachemys scripta elegans)during spring and winter.The results showed that auditory brainstem response(ABR)thresholds were significantly lower in spring than in winter at a frequency range of 0.5-0.9 kHz.The hearing-sensitivity bandwidth was wider,and the ABR latency was significantly shorter in spring than in winter.No significant differences were found in estradiol,testosterone,and dihydrotestosterone levels in T.scripta elegans between spring and winter.This study is the first to reveal the seasonal variation of peripheral hearing sensitivity in turtles,a special animal group with limited hearing range and less vocalization.Temperature variations may be used to explain these seasonal effects,but further research is required to confirm our findings.
基金The National Key R&D Program of China under contract No.2022YFC2807604the Basic Scientific Fund for National Public Research Institutes of China under contract Nos 2022S02,2022Q03 and 2018S02+3 种基金the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)under contract No.2018SDKJ0105-3the National Natural Science Foundation of China under contract Nos 41876030,41976021,41876231,4190060432 and 41706220the program Impact and Response of Antarctic Seas to Climate Change under contract No.IRASCC 01-01-01Athe Taishan Scholars Project Fund under contract No.ts20190963。
文摘Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.
基金supported by the Ministerio da Ciencia,Tecnologia e Inovacoes (MCTI-INPA),Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq,grant number:303913/2021-5)Fundagao de Amparo a Pesquisa do Estado do Amazonas (FAPEAM)Coordenagao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES code 0001).
文摘Litterfall is the largest source of nutrients to for-est soils of tropical rainforests.However,variability in lit-terfall production,nutrient remobilization,and changes in leaf nutrient concentration with climate seasonality remain largely unknown for the central Amazon.This study meas-ured litterfall production,leaf nutrient remobilization,and leaf area index on a forest plateau in the central Amazon.Litterfall was measured at monthly intervals during 2014,while nitrogen,phosphorus,potassium,calcium and mag-nesium concentrations of leaf litter and canopy leaves were measured in the dry and rainy seasons,and remobilization rates determined.Leaf area index was also recorded in the dry and rainy seasons.Monthly litterfall varied from 33.2(in the rainy season)to 87.6 g m^(-2) in the dry season,while leaf area index increased slightly in the rainy season.Climatic seasonality had no effect on concentrations of nitrogen,calcium,and magnesium,whereas phosphorous and potassium responded to rainfall seasonality oppositely.While phosphorous increased,potassium decreased during the dry season.Over seasons,nitrogen,potassium,and phosphorous decreased in leaf litter;calcium increased in leaf litter,while magnesium remained unaffected with leaf aging.Regardless,the five nutrients had similar remobilization rates over the year.The absence of climate seasonality on nutrient remobilization suggests that the current length of the dry season does not alter nutrient remobilization rates but this may change as dry periods become more prolonged in the future due to climate change.
基金supported by the National Key Research and Development Program of China (Grant No.2020YFA0608000)the National Natural Science Foundation of China (Grant No. 42030605)the High-Performance Computing of Nanjing University of Information Science&Technology for their support of this work。
文摘This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.
基金supported by the National Natural Science Foundation of China (Grant No. 42192564)Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2020B0301030004)the Ministry of Science and Technology of the People's Republic of China (Grant No.2020YFA0608802)。
文摘El Ni?o–Southern Oscillation(ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and decaying over the next spring. Several studies have demonstrated that this feature arises as a result of seasonal variation in the growth rate of ENSO as expressed by the sea surface temperature(SST). The bias towards simulating the phase locking of ENSO by many state-of-the-art climate models is also attributed to the unrealistic depiction of the growth rate. In this study, the seasonal variation of SST growth rate in the Ni?o-3.4 region(5°S–5°N, 120°–170°W) is estimated in detail based on the mixed layer heat budget equation and recharge oscillator model during 1981–2020. It is suggested that the consideration of a variable mixed layer depth is essential to its diagnostic process. The estimated growth rate has a remarkable seasonal cycle with minimum rates occurring in spring and maximum rates evident in autumn. More specifically, the growth rate derived from the meridional advection(surface heat flux) is positive(negative) throughout the year. Vertical diffusion generally makes a negative contribution to the evolution of growth rate and the magnitude of vertical entrainment represents the smallest contributor. Analysis indicates that the zonal advective feedback is regulated by the meridional immigration of the intertropical convergence zone, which approaches its southernmost extent in February and progresses to its northernmost location in September, and dominates the seasonal variation of the SST growth rate.
文摘Bituminous materials are heat-sensitive, and their mechanical properties vary with temperature. This variation in properties is not without consequences on the performance of flexible road structures under the repeated passage of multi-axles. This study determines the influence of seasonal variations on the rate of permanent deformation, the rut depth of flexible pavements and the effect of alternating loading of heavy goods vehicles following the temperature variations on the durability of roads. Thus, an ambient and pavement surface temperature measurement was carried out in 2022. The temperature profile at different layers of the modelled pavement, the evaluation of deformation rates and rutting depth were determined using several models. The results show that the permanent deformation and rutting rates are higher at the level of the bituminous concrete layer than at the level of the asphalt gravel layer because the stresses decrease from the surface to the depth of the pavement. On the other hand, the variations in these rates, permanent deformations and ruts between the hot and so-called cold periods are more pronounced in the bitumen gravel than in bituminous concrete, showing that gravel bitumen is more sensitive to temperature variations than bituminous concrete despite its higher rigidity. Of these results, we suggested a periodic and alternating loading of the different types of heavy goods vehicles. These loads consist of fully applying the WAEMU standards with a tolerance of 15% during periods of high and low temperatures. This regulation has increased 2 to 3 times in the durability of roadways depending on the type of heavy goods vehicle.
文摘The main objective of this study is to evaluate the seasonal performance of 20 MW solar power plants in Senegal. The analysis revealed notable seasonal variations in the performance of all stations. The most significant yields are recorded in spring, autumn and winter, with values ranging from 5 to 7.51 kWh/kWp/day for the reference yield and 4.02 to 7.58 kWh/kWp/day for the final yield. These fluctuations are associated with intense solar activity during the dry season and clear skies, indicating peak production. Conversely, minimum values are recorded during the rainy season from June to September, with a final yield of 3.86 kWh/kW/day due to dust, clouds and high temperatures. The performance ratio analysis shows seasonal dynamics throughout the year with rates ranging from 77.40% to 95.79%, reinforcing reliability and optimal utilization of installed capacity. The results of the capacity factor vary significantly, with March, April, May, and sometimes October standing out as periods of optimal performance, with 16% for Kahone, 16% for Bokhol, 18% for Malicounda and 23% for Sakal. Total losses from solar power plants show similar seasonal trends standing out for high loss levels from June to July, reaching up to 3.35 kWh/kWp/day in June. However, using solar trackers at Sakal has increased production by up to 25%, demonstrating the operational stability of this innovative technology compared with the plants fixed panel. Finally, comparing these results with international studies confirms the outstanding efficiency of Senegalese solar power plants, other installations around the world.
文摘Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106500]the Youth Innovation Promotion Association of the Chinese Academy of Sciences[grant number 2022076]the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab)[grant number 2023-EL-ZD-00012].
基金This work was jointly supported by the National Natural Science Foundation of China projects[grant numbers 42305178 and U2344224]the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
基金supported by the Chinese-Norwegian Collaboration Projects within Climate Systems jointly funded by the National Key Research and Development Program of China[grant number 2022YFE0106800]the National Natural Science Foundation of China[grant number 42088101]+1 种基金a Research Council of Norway funded project(MAPARC)[grant number 328943]the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311020001].
基金supported by the National Natural Science Foundation of China under Grant No.41988101the Chinese Academy of Sciences under Grant XDA20060102the China Postdoctoral Science Foundation under Grant No.2022T150638 and K.C.Wong Education Foundation.
文摘The unprecedented Zhengzhou heavy rainfall in July 2021 occurred under the background of a northward shift of the western Pacific subtropical high(WPSH).Although the occurrence of this extreme event could not be captured by seasonal predictions,a skillful prediction of the WPSH variation might have warned us of the increased probability of extreme weather events in Central and Northern China.However,the mechanism for the WPSH variation in July 2021 and its seasonal predictability are still unknown.Here,the observed northward shift of the WPSH in July 2021 is shown to correspond to a meridional dipole pattern of the 850-hPa geopotential height to the east of China,the amplitude of which became the strongest since 1979.The meridional dipole pattern is two nodes of the Pacific–Japan pattern.To investigate the predictability of the WPSH variation,a 21-member ensemble of seasonal predictions initiated from the end of June 2021 was conducted.The predictable and unpredictable components of the meridional dipole pattern were identified from the ensemble simulations.Its predictable component is driven by positive precipitation anomalies over the tropical western Pacific.The positive precipitation anomalies are caused by positive horizonal advection of the mean moist enthalpy by southwesterly anomalies to the northwestern flank of anticyclonic anomalies excited by the existing La Niña,which is skillfully predicted by the model.The leading mode of the unpredictable component is associated with the atmospheric internal intraseasonal oscillations,which are not initialized in the simulations.The relative contributions of the predictable and unpredictable components to the observed northward shift of the WPSH at 850 hPa are 28.0%and 72.0%,respectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.42088101 and 42175033)the High-Performance Computing Center of Nanjing University of Information Science&Technology。
文摘Extreme high temperatures frequently occur in southwestern China,significantly impacting the local ecological system and economic development.However,accurate prediction of extreme high-temperature days(EHDs)in this region is still an unresolved challenge.Based on the spatiotemporal characteristics of EHDs over China,a domain-averaged EHD index over southwestern China(SWC-EHDs)during April-May is defined.The simultaneous dynamic and thermodynamic fields associated with the increased SWC-EHDs are a local upper-level anticyclonic(high-pressure)anomaly and wavy geopotential height anomaly patterns over Eurasia.In tracing the origins of the lower boundary anomalies,two physically meaningful precursors are detected for SWC-EHDs.They are the tripolar SST change tendency from December-January to February-March in the northern Atlantic and the February-March mean snow depth in central Asia.Using these two selected predictors,a physics-based empirical model prediction was applied to the training period of 1961–2005 to obtain a skillful prediction of the EHDs index,attaining a correlation coefficient of 0.76 in the independent prediction period(2006–19),suggesting that 58%of the total SWC-EHDs variability is predictable.This study provides an estimate for the lower bound of the seasonal predictability of EHDs as well as for the hydrological drought over southwestern China.
基金supported by the National Natural Science Foundation of China (31821001)Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3100000)。
文摘A critical function of animal movement is to maximize access to essential resources in temporally fluctuating and spatially heterogeneous environments.Seasonally mediated resource fluctuations may influence animal movements,enabling them to track changing resource distributions,resulting in annual migration patterns.The conservation-dependent giant panda(Ailuropoda melanoleuca) displays seasonal movement patterns;however,the key factor driving these seasonal migration patterns remains poorly understood.Here,we used GPS tracking collars to monitor the movements of six giant pandas over a 12-year period across different elevations,and performed statistical analysis of seasonal migration directions,routes,habitat revisitation,home range overlap,first arrival events,and stability.Our results revealed a compelling pattern of seasonal migrations that facilitated the ability of the pandas to forage at the appropriate time and place to maximize nutritional intake.Our results indicated that pandas utilize spatial memory to locate reliable food resources,as evidenced by their annual return to the same or similar winter and summer home ranges and the consistently maintained percentage of home range overlap.These novel insights into giant panda foraging and movement ecology not only enhance our understanding of its ability to adapt to nutritionally poor dietary resources but also provide important information for the development of resource utilization-based protection and management strategies.
基金funded by the National Key R&D Program of China(2017YFC0504103)Project for Applied Technology Research and Development in Heilongjiang Province(GA19C006).
文摘To explore how to respond to seasonal freeze–thaw cycles on forest ecosystems in the context of climate change through thinning,we assessed the potential impact of thinning intensity on carbon cycle dynamics.By varying the number of temperature cycles,the eff ects of various thinning intensities in four seasons.The rate of mass,litter organic carbon,and soil organic carbon(SOC)loss in response to temperature variations was examined in two degrees of decomposition.The unfrozen season had the highest decomposition rate of litter,followed by the frozen season.Semi-decomposed litter had a higher decomposition rate than undecomposed litter.The decomposition rate of litter was the highest when the thinning intensity was 10%,while the litter and SOC were low.Forest litter had a good carbon sequestration impact in the unfrozen and freeze–thaw seasons,while the converse was confi rmed in the frozen and thaw seasons.The best carbon sequestration impact was identifi ed in litter,and soil layers under a 20–25%thinning intensity,and the infl uence of undecomposed litter on SOC was more noticeable than that of semi-decomposed litter.Both litter and soil can store carbon:however,carbon is transported from undecomposed litter to semi-decomposed litter and to the soil over time.In summary,the best thinning intensity being 20–25%.
基金the National Natural Science Foundation of China(Grant No.41922044)the National Key Research and Development Program of China(Grants Nos.2019YFA0607000,2022YFE0106300)+2 种基金the National Natural Sci-ence Foundation of China(Grants Nos.42075028 and 42222502)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.SML2021SP302)the fundamental research funds for the Norges Forskningsråd(Grant No.328886).
文摘A robust phenomenon termed the Arctic Amplification(AA)refers to the stronger warming taking place over the Arctic compared to the global mean.The AA can be confirmed through observations and reproduced in climate model simulations and shows significant seasonality and inter-model spread.This study focuses on the influence of surface type on the seasonality of AA and its inter-model spread by dividing the Arctic region into four surface types:ice-covered,ice-retreat,ice-free,and land.The magnitude and inter-model spread of Arctic surface warming are calculated from the difference between the abrupt-4×CO_(2)and pre-industrial experiments of 17 CMIP6 models.The change of effective thermal inertia(ETI)in response to the quadrupling of CO_(2) forcing is the leading mechanism for the seasonal energy transfer mechanism,which acts to store heat temporarily in summer and then release it in winter.The ETI change is strongest over the ice-retreat region,which is also responsible for the strongest AA among the four surface types.The lack of ETI change explains the nearly uniform warming pattern across seasons over the ice-free(ocean)region.Compared to other regions,the ice-covered region shows the maximum inter-model spread in JFM,resulting from a stronger inter-model spread in the oceanic heat storage term.However,the weaker upward surface turbulent sensible and latent heat fluxes tend to suppress the inter-model spread.The relatively small inter-model spread during summer is caused by the cancellation of the inter-model spread in ice-albedo feedback with that in the oceanic heat storage term.
基金supported by Science and Technology Project of State Grid Shandong Electric Power Company(52062622000R,Research on Aggregation and Regulation Technology of Regional Integrated Energy System).
文摘Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.
基金supported by the National Natural Science Foundation of China(Grant No.42075161).
文摘As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates the models’capability to simulate and predict the Madden-Julian Oscillation(MJO).Three versions of the Beijing Climate Center Climate System Model(BCC-CSM)are used to conduct historical simulations and re-forecast experiments(referred to as EXP1,EXP1-M,and EXP2,respectively).In simulating MJO characteristics,the newly-developed high-resolution BCC-CSM outperforms its predecessors.In terms of MJO prediction,the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M,and further to 24 days in EXP2.Within the first forecast week,the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill.However,during forecast weeks 2–3,EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3.Particularly at initial phases 2–3,EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection,leading to the highest prediction skill of the MJO.Our results reveal that,during the participation of the CMA models in the S2S Project,both the improved model initialization and updated model physics played positive roles in improving MJO prediction.Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.