Siberian-Arctic heatwaves(SAHs)disrupt ecosystems by increasing wildfires,thawing permafrost,and threatening Arctic communities.As SAHs become more frequent and intense,accurate prediction is crucial for preparedness ...Siberian-Arctic heatwaves(SAHs)disrupt ecosystems by increasing wildfires,thawing permafrost,and threatening Arctic communities.As SAHs become more frequent and intense,accurate prediction is crucial for preparedness and mitigating their impacts.We demonstrate that April surface temperatures in the Siberian Arctic can be predicted one month in advance with a skill of 0.75(1979-2022)using a regression model based on Arctic stratospheric ozone,the Arctic Oscillation,and sea ice in the Kara Sea.This model successfully predicts six of seven SAHs,identifying three driven by extreme ozone depletion and three by significant sea-ice loss.Additionally,from 1979 to 1997,warming was primarily caused by ozone depletion,while from 1998 to 2022,sea-ice loss became the main factor.Our findings indicate that SAHs are predictable and recommend this model for real-time monitoring and forecasting,highlighting its potential to enhance preparedness and reduce adverse effects.展开更多
The frequency of marine heatwaves(MHWs)in the South China Sea(SCS)has increased recently.However,the relative roles of thermal and dynamic processes regulating the changes of sCs MHWs remain an open question.This stud...The frequency of marine heatwaves(MHWs)in the South China Sea(SCS)has increased recently.However,the relative roles of thermal and dynamic processes regulating the changes of sCs MHWs remain an open question.This study examines all long-lived MHWs(>10 days)in the SCS from 1982 to 2021,categorizing them into intensified and attenuated MHWs based on the overall trend of sea surface temperature during an MHW event.A mixed-layer heat budget analysis reveals that the thermal processes primarily driven by the latent heat flux are crucial in modulating the SCS MHWs,particularly for attenuated MHWs.However,under intensified conditions,the proportions of dynamically dominated MHWs(40%)is approximately comparable to that of thermally dominated ones(47%).This study highlights the significance of dynamic processes in shaping SCS MHWs and discusses the potential impacts induced by tropical cyclones on these MHWs.展开更多
Marine heatwaves(MHWs)in the South China Sea(SCS)significantly impact marine ecosystems and socioeconomic development,yet accurately forecasting MHWs remains a challenge.This study developed an upper-ocean temperature...Marine heatwaves(MHWs)in the South China Sea(SCS)significantly impact marine ecosystems and socioeconomic development,yet accurately forecasting MHWs remains a challenge.This study developed an upper-ocean temperature forecasting model based on ConvLSTM for the northern SCS and,in conjunction with the ocean forecasting system LICOM Forecast System(LFS),constructed a hybrid Fusion model using Wasserstein-Distance optimization.The ability of these three models to forecast key MHW metrics with a 10-day lead was assessed during the summer of 2022 in the SCS.Overall,the Fusion model takes advantage of LFS and ConvLSTM,providing superior forecasts for both the duration and intensity of MHWs in the southern SCS.LFS(ConvLSTM)overestimates(underestimates)the duration of MHWs and all models exhibit limitations in forecasting the intensity of MHWs in part of the SCS.The Fusion model's superior forecast skill for MHWs may be attributable to its more realistic representation of the upper-ocean thermal structure with shallower mixed-layer depths during MHWs.This study highlights that combining the deep learning technique with a dynamical model can improve MHW forecasting and has certain physical interpretability.展开更多
Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on th...Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on the Nanjing University of Information Science&Technology Climate Forecast System version 1.1(NUIST CFS1.1)and analyze the related physical processes.Our results show that the model can accurately forecast the occurrence of MHWs on a global scale out to a lead time of 25 days.Notably,even at lead times of 51–55 days,the forecast skill in most tropical regions,as well as in the northeastern and southeastern Pacific,is superior to both random forecasts and persistence forecasts.Accurate predictions of sea level pressure,zonal currents,and mixed-layer depth are important for MHW forecasting.Furthermore,we also conduct forecast skill assessments for two well-documented MHW events.Due to its ability to correctly forecast the changes in heat flux anomalies at a lead time of 25 days,the model can accurately forecast the strong MHW event that occurred in the South China Sea in May–October 2020.However,the forecasting results were less than optimal for the strong MHW event that occurred along the Australian west coast in January–April 2011.Although the model accurately forecasts its occurrence,the forecast of its intensity is poor.Additionally,when the lead time exceeds 10 days,forecasts of the relevant physical processes of this MHW event are also inaccurate.展开更多
The thermal state of seawater is a fundamental property of the ocean.Extreme changes in the ocean's thermal conditions can significantly impact the marine environment,climate system,ecosystems,and economic activit...The thermal state of seawater is a fundamental property of the ocean.Extreme changes in the ocean's thermal conditions can significantly impact the marine environment,climate system,ecosystems,and economic activities.Marine heatwaves(MHWs)are extreme high-temperature events occurring in the ocean at weather or short-to-medium-term climate scales,representing extreme variations in oceanic conditions(Pearce et al.,2011;Feng et al.,2013;Hobday et al.,2016).展开更多
Under global warming,understanding the impact of urbanization on the characteristics of different heatwaves is important for sustainable development.In this study,we investigated the changes of heatwaves characteristi...Under global warming,understanding the impact of urbanization on the characteristics of different heatwaves is important for sustainable development.In this study,we investigated the changes of heatwaves characteristics in the Yangtze River Delta urban agglomeration(YRDUG)and analyzed the influencing mechanisms of urbanization.Results showed that:(1)the duration,frequency,and intensity of NHWs(Nighttime Heatwaves)and CHWs(Daytime-nighttime compound Heatwaves)had shown a significant increase and the CHWs showed the greatest increasing trend.Furthermore,the NHWs exhibited higher durations,frequencies,and intensities compared to DHWs(Daytime Heatwaves);(2)Since 1990,the DHWs and CHWs were greater in urban areas than in rural areas,NHWs had been more pronounced in rural areas than in urban centers;and(3)Cloud cover,solar radiation,etc.affected heatwaves.Furthermore,in the process of urbanization,the increase in impervious area and the decrease in green land exacerbated heatwaves.Considering the combined effect of DHWs and NHWs,CHWs continued to increase.展开更多
India is highly vulnerable to climate change and is going to increase its average annual temperature over the next few decades.The impact of heatwaves and related mortality is a concern for the country.In this paper,w...India is highly vulnerable to climate change and is going to increase its average annual temperature over the next few decades.The impact of heatwaves and related mortality is a concern for the country.In this paper,we aim to study the heatwaves and heat stress-related Heat Index vulnerability using heat index temperature.In this analysis,a heat in-dex temperature is calculated based on temperature and relative humidity for six different states(Delhi,West Bengal,Punjab,Uttar Pradesh,Andhra Pradesh,and Madhya Pradesh)of India to determine the heat stress vulnerability for which heat cramps and heat strokes are possible.Our analysis shows that most of the heatwaves and severe heatwaves occurred during 2010 for all the states.The heatwaves are observed only in the summer months.All the states of our study reached the Extreme Caution category of the Heat Index showing the Danger to Extreme Danger category dur-ing April to June.Future projection scenarios show an increase in heat stress-related vulnerability.SSP2-4.5 scenario showed that Delhi,Punjab,and West Bengal reached an Extreme Danger state during June for which death due to heat strokes is possible under continued exposure to heatwaves.The HI related vulnerability of SSP5-8.5 is like SSP2-4.5 except for Andhra Pradesh which shows an Extreme Danger state in May and June during which heat strokes are possi-ble under continued exposure to heatwaves.This study provides spatial variability of heat stress and Heat Index vulner-ability which may help adopt future strategies for heat-related policy implication.展开更多
Marine heatwaves(MHWs)in the East China Sea(ECS),especially those occurring on the ocean bottom(referred to as bottom marine heatwaves,BMHWs),can significantly affect regional ecosystems.However,our understanding of t...Marine heatwaves(MHWs)in the East China Sea(ECS),especially those occurring on the ocean bottom(referred to as bottom marine heatwaves,BMHWs),can significantly affect regional ecosystems.However,our understanding of the seasonal variations in the MHWs in the ECS remains limited.This study investigates the characteristics of MHWs in the ECS in summer and winter using high-resolution oceanic reanalysis.Our analyses reveal distinct spatial patterns of BMHWs in these seasons.During summer,the Taiwan Warm Current plays a crucial role in transporting warm water northward,potentially leading to intense BMHWs on the central ECS shelf.These BMHW events usually occur independently of surface warming due to strong stratification in summer.Conversely,winter BMHWs are more prevalent in coastal regions under the influence of coastal currents and typically feature consistent warming from surface to bottom with a deepened mixed layer.These findings inform the coherent vertical structure and driving mechanisms of MHWs in the ECS,which are essential for predicting and managing these extreme events in the future.展开更多
With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of arti...With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.展开更多
Marine heatwaves(MHWs)are extreme ocean events characterized by anomalously warm upper-ocean temperatures,posing significant threats to marine ecosystems.While various factors driving MHWs have been extensively studie...Marine heatwaves(MHWs)are extreme ocean events characterized by anomalously warm upper-ocean temperatures,posing significant threats to marine ecosystems.While various factors driving MHWs have been extensively studied,the role of ocean salinity remains poorly understood.This study investigates the influence of salinity on the major 2013-2014 MHW event in the Northeast Pacific using reanalysis data and climate model outputs.Our results show that salinity variabilities are crucial for the development of the MHW event.Notably,a significant negative correlation exists between sea surface temperature anomalies(SSTAs)and sea surface salinity anomalies(SSSAs)during the MHW,with the SSSAs emerging simultaneously with SSTAs in the same area.Negative salinity anomalies(SAs)result in a shallower mixed layer,which suppresses vertical mixing and thus sustains the upper-ocean warming.Moreover,salinity has a greater impact on mixed layer depth anomalies than temperature.Model sensitivity experiments further demonstrate that negative SAs during MHWs amplify positive SSTAs by enhancing upper-ocean stratification,intensifying the MHW.Additionally,our analysis indicates that the SAs are predominantly driven by local freshwater flux anomalies,which are mainly induced by positive precipitation anomalies during the MHW event.展开更多
Using complex network methods,we construct undirected and directed heatwave networks to systematically analyze heatwave events over China from 1961 to 2023,exploring their spatiotemporal evolution patterns in differen...Using complex network methods,we construct undirected and directed heatwave networks to systematically analyze heatwave events over China from 1961 to 2023,exploring their spatiotemporal evolution patterns in different regions.The findings reveal a significant increase in heatwaves since the 2000s,with the average occurrence rising from approximately 3 to 5 times,and their duration increasing from 15 to around 30 days,nearly doubling.An increasing trend of“early onset and late withdrawal”of heatwaves has become more pronounced each year.In particular,eastern regions experience heatwaves that typically start earlier and tend to persist into September,exhibiting greater interannual variability compared to western areas.The middle and lower reaches of the Yangtze River and Xinjiang are identified as high-frequency heatwave areas.Complex network analysis reveals the dynamics of heatwave propagation,with degree centrality and synchronization distance indicating that the middle and lower reaches of the Yangtze River,Northeast China,and Xinjiang are key nodes in heatwave spread.Additionally,network divergence analysis shows that Xinjiang acts as a“source”area for heatwaves,exporting heat to surrounding regions,while the central region functions as a major“sink,”receiving more heatwave events.Further analysis from 1994 to 2023 indicates that heatwave events exhibit stronger network centrality and more complex synchronization patterns.These results suggest that complex networks provide a refined framework for depicting the spatiotemporal dynamics of heatwave propagation,offering new avenues for studying their occurrence and development patterns.展开更多
Bottom marine heatwaves(BMHWs),i.e.,anomalous ocean warming at the seafloor,can happen without concurrent surface marine heatwaves(SMHWs),which pose a serious threat to marine ecosystems and present a challenge to det...Bottom marine heatwaves(BMHWs),i.e.,anomalous ocean warming at the seafloor,can happen without concurrent surface marine heatwaves(SMHWs),which pose a serious threat to marine ecosystems and present a challenge to detect and study them adequately.This type of event is called independent BMHWs.This study examines the summertime BMHWs on the continental shelf of the East China Sea(ECS)using oceanic reanalysis data from 1993 to 2020.Our results show that summertime BMHWs in the ECS are generally more intense than SMHWs,with some BMHW events occurring without surface expression.Through heat budget analyses of the 2016 SMHW event and the 2019 BMHW event,we investigated the drivers of independent summertime BMHWs.It is indicated that the occurrences of bottom temperature anomalies in summer are predominantly attributed to oceanic horizontal advection.Specifically,the summertime BMHWs on the central ECS shelf are closely related to the strengthening of the inshore branch of the Taiwan Warm Current(TWC)and the weakening of the offshore TWC branch.These findings provide important insights into the underlying physical processes and diagnostic tools for monitoring and managing independent BMHWs in the ECS.展开更多
受全球气候变暖的影响,青藏高原湖表温度、湖泊热浪的总天数和平均强度呈现显著增加,使得热力分层期间的湖表温度更易被加热,导致夏季湖表温度升高更快,湖表可能出现缺氧。现有研究在分析湖泊热浪的变化特征时是将较大区域内的多个湖泊...受全球气候变暖的影响,青藏高原湖表温度、湖泊热浪的总天数和平均强度呈现显著增加,使得热力分层期间的湖表温度更易被加热,导致夏季湖表温度升高更快,湖表可能出现缺氧。现有研究在分析湖泊热浪的变化特征时是将较大区域内的多个湖泊的热浪特征进行平均,而青海湖的热浪特征尚不清楚。因此,本研究使用青海湖水温和湖表温度的原位观测数据、刚察气象站观测数据、MODIS地表温度观测数据、第三极地区长时间序列高分辨率地面气象要素驱动数据集(A high-resolution near-surface meteorological forcing dataset for the Third Pole region,TPMFD)和一维湖泊模式(Freshwater Lake Model,FLake)研究了青海湖1980-2022年湖表温度的变化和热浪特征,利用相关性分析和去趋势分析法揭示了湖表温度和湖泊热浪变化的原因。研究表明:(1)TPMFD再分析数据的气温、比湿和风速与刚察气象站观测的气温、比湿和风速相关性较好且偏差和均方根误差较小,两者的相关系数分别为0.96、0.84、0.74,偏差分别为0.55℃、0.00068 g·g^(-1)、-0.31 m·s^(-1),均方根误差分别为0.59℃、0.00069 g·g^(-1)、0.38 m·s^(-1),TPMFD气温的变化速率[0.48℃·(10a)^(-1)]与观测气温的变化速率[0.44℃·(10a)^(-1)]接近,TPMFD比湿的变率[0.0001 g·g^(-1)·(10a)^(-1)]与观测值一致,TPMFD风速的变率[-0.1 m·s^(-1)·(10a)^(-1)]较观测[-0.25 m·s^(-1)·(10a)^(-1)]略小,并且TPMFD和刚察气象站的气温、比湿和风速的变化速率均通过了95%的显著性检验。模拟的青海湖湖水、湖表温度与青海湖原位观测的湖水和湖表温度相关性很好且偏差及均方根误差较小,长时间序列的模拟湖表温度与MODIS地表温度的相关性也较好且偏差和均方根误差均在合理范围,模拟结果与三种观测的相关系数分别为0.99、0.96、0.98,偏差分别为0.25℃、-0.1℃、0.87℃,均方根误差分别为0.58℃、2.65℃、2.20℃。(2)1980-2022年的青海湖湖表温度和湖泊热浪特征均呈现出显著的升高趋势(p<0.05),湖泊热浪的频次在0~6次之间波动,每年发生湖泊热浪的总天数明显增多,2022年的总天数达到150天,多数年份的平均持续时间都超过了10 d·time^(-1),2022年的热浪最长持续时间甚至达到76天,平均强度也显著增强,其中2016年和2022年的青海湖热浪强度等级已处于比多年平均强度等级(“中等”等级)强两个量级的“严重”等级状态。(3)气温、比湿、向下长波辐射、向下短波辐射及气压与模拟湖表温度、湖泊热浪总天数和平均强度呈现正相关关系,而风速则与之呈负相关,与湖泊热浪总天数的增加和平均强度的增强呈正相关。对湖表温度的升高呈正贡献的气象要素从大到小依次为气温(23.83%)、比湿(20.52%)、风速(16.05%)、向下长波辐射(14.79%)和向下短波辐射(10.68%);对湖泊热浪总天数的增加呈正贡献的气象要素分别为气温(37.54%)、风速(35.86%)、比湿(30.03%)、向下长波辐射(28.27%)、向下短波辐射(27.72%);对湖泊热浪强度的增强呈正贡献的气象要素分别为气温(13.25%)、风速(13.07%)、比湿(12.35%)、向下长波辐射(11.05%)、向下短波辐射(10.98%),气压则对湖表温度、湖泊热浪总天数和平均强度的升高呈现抑制作用。展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF0805104)the National Natural Science Foundation of China(NSFC)under Grant Nos.41925022,42105016 and 42375070+1 种基金supported by the NSFC under Grant No.41888101the Natural Sciences and Engineering Research Council of Canada(Grant No.RGPIN-2019-04511)。
文摘Siberian-Arctic heatwaves(SAHs)disrupt ecosystems by increasing wildfires,thawing permafrost,and threatening Arctic communities.As SAHs become more frequent and intense,accurate prediction is crucial for preparedness and mitigating their impacts.We demonstrate that April surface temperatures in the Siberian Arctic can be predicted one month in advance with a skill of 0.75(1979-2022)using a regression model based on Arctic stratospheric ozone,the Arctic Oscillation,and sea ice in the Kara Sea.This model successfully predicts six of seven SAHs,identifying three driven by extreme ozone depletion and three by significant sea-ice loss.Additionally,from 1979 to 1997,warming was primarily caused by ozone depletion,while from 1998 to 2022,sea-ice loss became the main factor.Our findings indicate that SAHs are predictable and recommend this model for real-time monitoring and forecasting,highlighting its potential to enhance preparedness and reduce adverse effects.
基金supported by the National Key R&D Program of China[grant number 2022YFF0801400]the National Natural Science Foundation of China [grant numbers 42376027 and W2441014]+2 种基金the Development Fund of the South China Sea Institute of Oceanology of the Chinese Academy of Sciences [grant number SCSIO202208]the Special Fund of the South China Sea Institute of Oceanology of the Chinese Academy of Sciences [grant number SCSIO2023QY01]the Science and Technology Projects in Guangzhou [grant number 202201010367]。
文摘The frequency of marine heatwaves(MHWs)in the South China Sea(SCS)has increased recently.However,the relative roles of thermal and dynamic processes regulating the changes of sCs MHWs remain an open question.This study examines all long-lived MHWs(>10 days)in the SCS from 1982 to 2021,categorizing them into intensified and attenuated MHWs based on the overall trend of sea surface temperature during an MHW event.A mixed-layer heat budget analysis reveals that the thermal processes primarily driven by the latent heat flux are crucial in modulating the SCS MHWs,particularly for attenuated MHWs.However,under intensified conditions,the proportions of dynamically dominated MHWs(40%)is approximately comparable to that of thermally dominated ones(47%).This study highlights the significance of dynamic processes in shaping SCS MHWs and discusses the potential impacts induced by tropical cyclones on these MHWs.
基金supported by the National Natural Science Foundation of China [grant numbers 42375168 and 42205035]a Shanghai Science and Technology Commission Project [grant number 23DZ1204704]。
文摘Marine heatwaves(MHWs)in the South China Sea(SCS)significantly impact marine ecosystems and socioeconomic development,yet accurately forecasting MHWs remains a challenge.This study developed an upper-ocean temperature forecasting model based on ConvLSTM for the northern SCS and,in conjunction with the ocean forecasting system LICOM Forecast System(LFS),constructed a hybrid Fusion model using Wasserstein-Distance optimization.The ability of these three models to forecast key MHW metrics with a 10-day lead was assessed during the summer of 2022 in the SCS.Overall,the Fusion model takes advantage of LFS and ConvLSTM,providing superior forecasts for both the duration and intensity of MHWs in the southern SCS.LFS(ConvLSTM)overestimates(underestimates)the duration of MHWs and all models exhibit limitations in forecasting the intensity of MHWs in part of the SCS.The Fusion model's superior forecast skill for MHWs may be attributable to its more realistic representation of the upper-ocean thermal structure with shallower mixed-layer depths during MHWs.This study highlights that combining the deep learning technique with a dynamical model can improve MHW forecasting and has certain physical interpretability.
基金supported by National Natural Science Foundation of China(Grant Nos.42030605 and 42088101)National Key R&D Program of China(Grant No.2020YFA0608004)High Performance Computing of Nanjing University of Information Science&Technology for their support of this work。
文摘Marine heatwaves(MHWs),which can exert devastating socioeconomic and ecological impacts,have attracted much public interest in recent years.In this study,we evaluate the sub-seasonal forecast skill of MHWs based on the Nanjing University of Information Science&Technology Climate Forecast System version 1.1(NUIST CFS1.1)and analyze the related physical processes.Our results show that the model can accurately forecast the occurrence of MHWs on a global scale out to a lead time of 25 days.Notably,even at lead times of 51–55 days,the forecast skill in most tropical regions,as well as in the northeastern and southeastern Pacific,is superior to both random forecasts and persistence forecasts.Accurate predictions of sea level pressure,zonal currents,and mixed-layer depth are important for MHW forecasting.Furthermore,we also conduct forecast skill assessments for two well-documented MHW events.Due to its ability to correctly forecast the changes in heat flux anomalies at a lead time of 25 days,the model can accurately forecast the strong MHW event that occurred in the South China Sea in May–October 2020.However,the forecasting results were less than optimal for the strong MHW event that occurred along the Australian west coast in January–April 2011.Although the model accurately forecasts its occurrence,the forecast of its intensity is poor.Additionally,when the lead time exceeds 10 days,forecasts of the relevant physical processes of this MHW event are also inaccurate.
基金Supported by the National Natural Science Foundation of China(No.42476016)the Laoshan Laboratory(No.LSKJ202202702)the Indo-Pacific Ocean and Climate Laboratory Project(No.424530)from Hohai University。
文摘The thermal state of seawater is a fundamental property of the ocean.Extreme changes in the ocean's thermal conditions can significantly impact the marine environment,climate system,ecosystems,and economic activities.Marine heatwaves(MHWs)are extreme high-temperature events occurring in the ocean at weather or short-to-medium-term climate scales,representing extreme variations in oceanic conditions(Pearce et al.,2011;Feng et al.,2013;Hobday et al.,2016).
基金National Natural Science Foundation of China,No.42271037Natural Science Foundation of Anhui Province,No.2408085MD095+2 种基金Key Research and Development Program Project of Anhui Province,No.2022m07020011University Synergy Innovation Program of Anhui Province,No.GXXT-2021-048Science Foundation for Excellent Young Scholars of Anhui,No.2108085Y13。
文摘Under global warming,understanding the impact of urbanization on the characteristics of different heatwaves is important for sustainable development.In this study,we investigated the changes of heatwaves characteristics in the Yangtze River Delta urban agglomeration(YRDUG)and analyzed the influencing mechanisms of urbanization.Results showed that:(1)the duration,frequency,and intensity of NHWs(Nighttime Heatwaves)and CHWs(Daytime-nighttime compound Heatwaves)had shown a significant increase and the CHWs showed the greatest increasing trend.Furthermore,the NHWs exhibited higher durations,frequencies,and intensities compared to DHWs(Daytime Heatwaves);(2)Since 1990,the DHWs and CHWs were greater in urban areas than in rural areas,NHWs had been more pronounced in rural areas than in urban centers;and(3)Cloud cover,solar radiation,etc.affected heatwaves.Furthermore,in the process of urbanization,the increase in impervious area and the decrease in green land exacerbated heatwaves.Considering the combined effect of DHWs and NHWs,CHWs continued to increase.
文摘India is highly vulnerable to climate change and is going to increase its average annual temperature over the next few decades.The impact of heatwaves and related mortality is a concern for the country.In this paper,we aim to study the heatwaves and heat stress-related Heat Index vulnerability using heat index temperature.In this analysis,a heat in-dex temperature is calculated based on temperature and relative humidity for six different states(Delhi,West Bengal,Punjab,Uttar Pradesh,Andhra Pradesh,and Madhya Pradesh)of India to determine the heat stress vulnerability for which heat cramps and heat strokes are possible.Our analysis shows that most of the heatwaves and severe heatwaves occurred during 2010 for all the states.The heatwaves are observed only in the summer months.All the states of our study reached the Extreme Caution category of the Heat Index showing the Danger to Extreme Danger category dur-ing April to June.Future projection scenarios show an increase in heat stress-related vulnerability.SSP2-4.5 scenario showed that Delhi,Punjab,and West Bengal reached an Extreme Danger state during June for which death due to heat strokes is possible under continued exposure to heatwaves.The HI related vulnerability of SSP5-8.5 is like SSP2-4.5 except for Andhra Pradesh which shows an Extreme Danger state in May and June during which heat strokes are possi-ble under continued exposure to heatwaves.This study provides spatial variability of heat stress and Heat Index vulner-ability which may help adopt future strategies for heat-related policy implication.
基金The National Natural Science Foundation of China under contract No.42030410the Laoshan Laboratory under contract Nos LSKJ202202404 and LSKJ202202403+2 种基金the Startup Foundation for Introducing Talent of Nanjing University of Information Science and TechnologyJiangsu Innovation Research Group under contract No.JSSCTD202346Jiangsu Funding Program for Excellent Postdoctoral Talent under contract No.2023ZB690。
文摘Marine heatwaves(MHWs)in the East China Sea(ECS),especially those occurring on the ocean bottom(referred to as bottom marine heatwaves,BMHWs),can significantly affect regional ecosystems.However,our understanding of the seasonal variations in the MHWs in the ECS remains limited.This study investigates the characteristics of MHWs in the ECS in summer and winter using high-resolution oceanic reanalysis.Our analyses reveal distinct spatial patterns of BMHWs in these seasons.During summer,the Taiwan Warm Current plays a crucial role in transporting warm water northward,potentially leading to intense BMHWs on the central ECS shelf.These BMHW events usually occur independently of surface warming due to strong stratification in summer.Conversely,winter BMHWs are more prevalent in coastal regions under the influence of coastal currents and typically feature consistent warming from surface to bottom with a deepened mixed layer.These findings inform the coherent vertical structure and driving mechanisms of MHWs in the ECS,which are essential for predicting and managing these extreme events in the future.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)。
文摘With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.
基金The Laoshan Laboratory under contract Nos LSKJ202202403 and LSKJ202202402the National Natural Science Foundation of China under contract Nos 42030410 and 42406202+3 种基金the Natural Science Foundation of Jiangsu Province under contract No.BK20240718the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technologythe Jiangsu Innovation Research Group under contract No.JSSCTD202346the Jiangsu Funding Program for Excellent Postdoctoral Talent under contract No.2023ZB690.
文摘Marine heatwaves(MHWs)are extreme ocean events characterized by anomalously warm upper-ocean temperatures,posing significant threats to marine ecosystems.While various factors driving MHWs have been extensively studied,the role of ocean salinity remains poorly understood.This study investigates the influence of salinity on the major 2013-2014 MHW event in the Northeast Pacific using reanalysis data and climate model outputs.Our results show that salinity variabilities are crucial for the development of the MHW event.Notably,a significant negative correlation exists between sea surface temperature anomalies(SSTAs)and sea surface salinity anomalies(SSSAs)during the MHW,with the SSSAs emerging simultaneously with SSTAs in the same area.Negative salinity anomalies(SAs)result in a shallower mixed layer,which suppresses vertical mixing and thus sustains the upper-ocean warming.Moreover,salinity has a greater impact on mixed layer depth anomalies than temperature.Model sensitivity experiments further demonstrate that negative SAs during MHWs amplify positive SSTAs by enhancing upper-ocean stratification,intensifying the MHW.Additionally,our analysis indicates that the SAs are predominantly driven by local freshwater flux anomalies,which are mainly induced by positive precipitation anomalies during the MHW event.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2022YFE0136000 and 2024YFC3013100)the Joint Meteorological Fund(Grant No.U2342211)+1 种基金the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSZ004)the National Meteorological Information Center(Grant No.NMICJY202301)。
文摘Using complex network methods,we construct undirected and directed heatwave networks to systematically analyze heatwave events over China from 1961 to 2023,exploring their spatiotemporal evolution patterns in different regions.The findings reveal a significant increase in heatwaves since the 2000s,with the average occurrence rising from approximately 3 to 5 times,and their duration increasing from 15 to around 30 days,nearly doubling.An increasing trend of“early onset and late withdrawal”of heatwaves has become more pronounced each year.In particular,eastern regions experience heatwaves that typically start earlier and tend to persist into September,exhibiting greater interannual variability compared to western areas.The middle and lower reaches of the Yangtze River and Xinjiang are identified as high-frequency heatwave areas.Complex network analysis reveals the dynamics of heatwave propagation,with degree centrality and synchronization distance indicating that the middle and lower reaches of the Yangtze River,Northeast China,and Xinjiang are key nodes in heatwave spread.Additionally,network divergence analysis shows that Xinjiang acts as a“source”area for heatwaves,exporting heat to surrounding regions,while the central region functions as a major“sink,”receiving more heatwave events.Further analysis from 1994 to 2023 indicates that heatwave events exhibit stronger network centrality and more complex synchronization patterns.These results suggest that complex networks provide a refined framework for depicting the spatiotemporal dynamics of heatwave propagation,offering new avenues for studying their occurrence and development patterns.
基金Supported by the National Natural Science Foundation of China(No.42030410)the Laoshan Laboratory(Nos.LSKJ202202404,LSKJ202202403)+1 种基金the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST),Jiangsu Innovation Research Group(No.JSSCTD202346)the Jiangsu Funding Program for Excellent Postdoctoral Talent(No.2023ZB690)。
文摘Bottom marine heatwaves(BMHWs),i.e.,anomalous ocean warming at the seafloor,can happen without concurrent surface marine heatwaves(SMHWs),which pose a serious threat to marine ecosystems and present a challenge to detect and study them adequately.This type of event is called independent BMHWs.This study examines the summertime BMHWs on the continental shelf of the East China Sea(ECS)using oceanic reanalysis data from 1993 to 2020.Our results show that summertime BMHWs in the ECS are generally more intense than SMHWs,with some BMHW events occurring without surface expression.Through heat budget analyses of the 2016 SMHW event and the 2019 BMHW event,we investigated the drivers of independent summertime BMHWs.It is indicated that the occurrences of bottom temperature anomalies in summer are predominantly attributed to oceanic horizontal advection.Specifically,the summertime BMHWs on the central ECS shelf are closely related to the strengthening of the inshore branch of the Taiwan Warm Current(TWC)and the weakening of the offshore TWC branch.These findings provide important insights into the underlying physical processes and diagnostic tools for monitoring and managing independent BMHWs in the ECS.
文摘受全球气候变暖的影响,青藏高原湖表温度、湖泊热浪的总天数和平均强度呈现显著增加,使得热力分层期间的湖表温度更易被加热,导致夏季湖表温度升高更快,湖表可能出现缺氧。现有研究在分析湖泊热浪的变化特征时是将较大区域内的多个湖泊的热浪特征进行平均,而青海湖的热浪特征尚不清楚。因此,本研究使用青海湖水温和湖表温度的原位观测数据、刚察气象站观测数据、MODIS地表温度观测数据、第三极地区长时间序列高分辨率地面气象要素驱动数据集(A high-resolution near-surface meteorological forcing dataset for the Third Pole region,TPMFD)和一维湖泊模式(Freshwater Lake Model,FLake)研究了青海湖1980-2022年湖表温度的变化和热浪特征,利用相关性分析和去趋势分析法揭示了湖表温度和湖泊热浪变化的原因。研究表明:(1)TPMFD再分析数据的气温、比湿和风速与刚察气象站观测的气温、比湿和风速相关性较好且偏差和均方根误差较小,两者的相关系数分别为0.96、0.84、0.74,偏差分别为0.55℃、0.00068 g·g^(-1)、-0.31 m·s^(-1),均方根误差分别为0.59℃、0.00069 g·g^(-1)、0.38 m·s^(-1),TPMFD气温的变化速率[0.48℃·(10a)^(-1)]与观测气温的变化速率[0.44℃·(10a)^(-1)]接近,TPMFD比湿的变率[0.0001 g·g^(-1)·(10a)^(-1)]与观测值一致,TPMFD风速的变率[-0.1 m·s^(-1)·(10a)^(-1)]较观测[-0.25 m·s^(-1)·(10a)^(-1)]略小,并且TPMFD和刚察气象站的气温、比湿和风速的变化速率均通过了95%的显著性检验。模拟的青海湖湖水、湖表温度与青海湖原位观测的湖水和湖表温度相关性很好且偏差及均方根误差较小,长时间序列的模拟湖表温度与MODIS地表温度的相关性也较好且偏差和均方根误差均在合理范围,模拟结果与三种观测的相关系数分别为0.99、0.96、0.98,偏差分别为0.25℃、-0.1℃、0.87℃,均方根误差分别为0.58℃、2.65℃、2.20℃。(2)1980-2022年的青海湖湖表温度和湖泊热浪特征均呈现出显著的升高趋势(p<0.05),湖泊热浪的频次在0~6次之间波动,每年发生湖泊热浪的总天数明显增多,2022年的总天数达到150天,多数年份的平均持续时间都超过了10 d·time^(-1),2022年的热浪最长持续时间甚至达到76天,平均强度也显著增强,其中2016年和2022年的青海湖热浪强度等级已处于比多年平均强度等级(“中等”等级)强两个量级的“严重”等级状态。(3)气温、比湿、向下长波辐射、向下短波辐射及气压与模拟湖表温度、湖泊热浪总天数和平均强度呈现正相关关系,而风速则与之呈负相关,与湖泊热浪总天数的增加和平均强度的增强呈正相关。对湖表温度的升高呈正贡献的气象要素从大到小依次为气温(23.83%)、比湿(20.52%)、风速(16.05%)、向下长波辐射(14.79%)和向下短波辐射(10.68%);对湖泊热浪总天数的增加呈正贡献的气象要素分别为气温(37.54%)、风速(35.86%)、比湿(30.03%)、向下长波辐射(28.27%)、向下短波辐射(27.72%);对湖泊热浪强度的增强呈正贡献的气象要素分别为气温(13.25%)、风速(13.07%)、比湿(12.35%)、向下长波辐射(11.05%)、向下短波辐射(10.98%),气压则对湖表温度、湖泊热浪总天数和平均强度的升高呈现抑制作用。
