The South China Sea is a hotspot for regional climate research.Over the past 40 years,considerable improvement has been made in the development and utilization of the islands in the South China Sea,leading to a substa...The South China Sea is a hotspot for regional climate research.Over the past 40 years,considerable improvement has been made in the development and utilization of the islands in the South China Sea,leading to a substantial change in the land-use of the islands.However,research on the impact of human development on the local climate of these islands is lacking.This study analyzed the characteristics of local climate changes on the islands in the South China Sea based on data from the Yongxing Island Observation Station and ERA5 re-analysis.Furthermore,the influence of urbanization on the local climate of the South China Sea islands was explored in this study.The findings revealed that the 10-year average temperature in Yongxing Island increased by approximately 1.11℃from 1961 to 2020,and the contribution of island development and urbanization to the local warming rate over 60 years was approximately 36.2%.The linear increasing trend of the annual hot days from 1961–2020 was approximately 14.84 days per decade.The diurnal temperature range exhibited an increasing trend of 0.05℃per decade,whereas the number of cold days decreased by 1.06days per decade.The rapid increase in construction on Yongxing Island from 2005 to 2021 led to a decrease in observed surface wind speed by 0.32 m s^(-1)per decade.Consequently,the number of days with strong winds decreased,whereas the number of days with weak winds increased.Additionally,relative humidity exhibited a rapid decline from 2001 to 2016 and then rebounded.The study also found substantial differences between the ERA5 re-analysis and observation data,particularly in wind speed and relative humidity,indicating that the use of re-analysis data for climate resource assessment and climate change evaluation on island areas may not be feasible.展开更多
This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5(CMIP5)and 33 models from the CMIP phase 6(CMIP6)to determine the climatological and seasonal variation...This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5(CMIP5)and 33 models from the CMIP phase 6(CMIP6)to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m.The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth.However,this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean,with a global average of−0.44 g kg^(−1) for the sea surface salinity(SSS)and−0.26 g kg^(−1) for the 0-1000-m averaged salinity(S1000)compared with the CMIP5 multimodel mean(−0.25 g kg^(−1) for the SSS and−0.07 g kg^(−1) for the S1000).In terms of the seasonal variation,both CMIP6 and CMIP5 models show positive(negative)anomalies in the first(second)half of the year in the global average SSS and S1000.The model-simulated variation in SSS is consistent with the observations,but not for S1000,suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity.The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S-20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans.These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.展开更多
With the continuous development of various types of fixed marine observation equipment,satellite remote sensing technology and computer simulation technology,modern marine scientific research has entered the era of bi...With the continuous development of various types of fixed marine observation equipment,satellite remote sensing technology and computer simulation technology,modern marine scientific research has entered the era of big data.Interactive ocean visuali-zation has become ubiquitous owing to the use of ocean data in studies of marine disasters,global climate change and fisheries.However,the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves.Therefore,an interactive multi-scale,multivariate visualization sys-tem with dynamic expansion potential is needed for analyzing larger volumes of ocean data.In this study,a unified visual data service was constructed,and a component-based interactive visua-lization structure for multi-dimensional,spatiotemporal ocean data is presented in this paper.Based on this structure,users can easily customize the system to visualize other types of scientific data.展开更多
基金National Natural Science Foundation of China(U21A6001,42075059)Specific Research Fund of The Innovation Platform for Academicians of Hainan Province(YSPTZX202143)+1 种基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Science and Technology Project of Guangdong Meteorological Service(GRMC2020M29)。
文摘The South China Sea is a hotspot for regional climate research.Over the past 40 years,considerable improvement has been made in the development and utilization of the islands in the South China Sea,leading to a substantial change in the land-use of the islands.However,research on the impact of human development on the local climate of these islands is lacking.This study analyzed the characteristics of local climate changes on the islands in the South China Sea based on data from the Yongxing Island Observation Station and ERA5 re-analysis.Furthermore,the influence of urbanization on the local climate of the South China Sea islands was explored in this study.The findings revealed that the 10-year average temperature in Yongxing Island increased by approximately 1.11℃from 1961 to 2020,and the contribution of island development and urbanization to the local warming rate over 60 years was approximately 36.2%.The linear increasing trend of the annual hot days from 1961–2020 was approximately 14.84 days per decade.The diurnal temperature range exhibited an increasing trend of 0.05℃per decade,whereas the number of cold days decreased by 1.06days per decade.The rapid increase in construction on Yongxing Island from 2005 to 2021 led to a decrease in observed surface wind speed by 0.32 m s^(-1)per decade.Consequently,the number of days with strong winds decreased,whereas the number of days with weak winds increased.Additionally,relative humidity exhibited a rapid decline from 2001 to 2016 and then rebounded.The study also found substantial differences between the ERA5 re-analysis and observation data,particularly in wind speed and relative humidity,indicating that the use of re-analysis data for climate resource assessment and climate change evaluation on island areas may not be feasible.
基金supported by the National Natural Science Foundation of China(Grant No.42076202)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB42040402).
文摘This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5(CMIP5)and 33 models from the CMIP phase 6(CMIP6)to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m.The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth.However,this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean,with a global average of−0.44 g kg^(−1) for the sea surface salinity(SSS)and−0.26 g kg^(−1) for the 0-1000-m averaged salinity(S1000)compared with the CMIP5 multimodel mean(−0.25 g kg^(−1) for the SSS and−0.07 g kg^(−1) for the S1000).In terms of the seasonal variation,both CMIP6 and CMIP5 models show positive(negative)anomalies in the first(second)half of the year in the global average SSS and S1000.The model-simulated variation in SSS is consistent with the observations,but not for S1000,suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity.The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S-20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans.These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.
基金the Key R&D project of Shandong Province(2019JZZY010102)the Big Earth Data Science Engineering Project(XDA19060104)the 13th Five-year Informatization Plan of the Chinese Academy of Sciences,the Construction of Scientific Data Center System(XXH-13514).
文摘With the continuous development of various types of fixed marine observation equipment,satellite remote sensing technology and computer simulation technology,modern marine scientific research has entered the era of big data.Interactive ocean visuali-zation has become ubiquitous owing to the use of ocean data in studies of marine disasters,global climate change and fisheries.However,the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves.Therefore,an interactive multi-scale,multivariate visualization sys-tem with dynamic expansion potential is needed for analyzing larger volumes of ocean data.In this study,a unified visual data service was constructed,and a component-based interactive visua-lization structure for multi-dimensional,spatiotemporal ocean data is presented in this paper.Based on this structure,users can easily customize the system to visualize other types of scientific data.
