It is important to be able to characterize the thermal conditions over the equatorial Indian Ocean for both weather forecasting and climate prediction. This study compared the equatorial eastern Indian Ocean (EEIO) te...It is important to be able to characterize the thermal conditions over the equatorial Indian Ocean for both weather forecasting and climate prediction. This study compared the equatorial eastern Indian Ocean (EEIO) temperature and relative humidity profiles from three reanalysis products (JRA-55, MERRA2, and FGOALS-f2) with shipboard global positioning system (GPS) sounding measurements obtained during the Eastern Indian Ocean Open Cruise in spring 2018. The FGOALS-f2 reanalysis product is based on the initialization module of a sub-seasonal to seasonal prediction system with a nudging-based data assimilation method. The results indicated that:(1) both JRA-55 and MERRA2 were reliable in characterizing the temperature profile from 850 to 600 hPa, with a maximum deviation of about <0.5℃. Both datasets showed a large negative deviation below 825 hPa, with a maximum bias of about 2℃ at 1000 hPa and 1.5℃ at 900 hPa, respectively.(2) JRA-55 showed good performance in characterizing the relative humidity profile above 850 hPa, with a maximum deviation of < 8%, while it showed much wetter conditions below 850 hPa. MERRA2 overestimated the relative humidity in the middle to lower troposphere, with a maximum deviation of about 15% at 925 hPa.(3) The FGOALS-f2 reanalysis product more accurately reproduced the temperature profile in the marine atmospheric boundary layer over the EEIO than that in JRA-55 and MERRA2, but showed much wetter conditions than the GPS sounding observations, with a maximum deviation of up to 20% at 600 hPa. Future applications of GPS sounding datasets are discussed.展开更多
本文利用2010—2019年东印度洋海洋学综合科学考察基金委共享航次数据、Argo(array for real-time geostrophic oceanography)和简单海洋再分析数据(simple ocean data assimilation,SODA),研究了赤道东印度洋次表层高盐水(subsurface h...本文利用2010—2019年东印度洋海洋学综合科学考察基金委共享航次数据、Argo(array for real-time geostrophic oceanography)和简单海洋再分析数据(simple ocean data assimilation,SODA),研究了赤道东印度洋次表层高盐水(subsurface high salinity water,SHSW)的年际变化,并探讨了其形成机制。仅限于春季的观测资料显示,来自阿拉伯海的高盐水位于东印度洋赤道断面次表层70~130m深度处,且具有显著的年际变化。基于月平均SODA资料的研究结果表明,不同时期SHSW盐度异常的变化趋势存在显著差异,2010—2015年趋势比较稳定,而2016—2019年则呈现出显著的上升趋势。通过对SHSW的回归分析表明,风场和次表层纬向流是控制该高盐水年际变化的主要因子。进一步的分析表明,赤道印度洋的东风异常导致水体向西堆积,产生东向压强梯度力,进而激发出次表层异常东向流,最终引起SHSW盐度异常升高。此动力关联在印度洋偶极子事件中尤为显著,这进一步反映了赤道东印度洋SHSW的年际变化受到印度洋偶极子的调制。展开更多
基金supported by funds from the National Key Research and Development Program Global Change and Mitigation Project [grant number 2017YFA0604004]the National Natural Science Foundation of China [grant numbers41675100,91737306 and U1811464]provided by the SCSIO under the project ‘Scientific investigation of the Eastern Indian Ocean in 2018’,funded by the NSFC(NORC2018-10)
文摘It is important to be able to characterize the thermal conditions over the equatorial Indian Ocean for both weather forecasting and climate prediction. This study compared the equatorial eastern Indian Ocean (EEIO) temperature and relative humidity profiles from three reanalysis products (JRA-55, MERRA2, and FGOALS-f2) with shipboard global positioning system (GPS) sounding measurements obtained during the Eastern Indian Ocean Open Cruise in spring 2018. The FGOALS-f2 reanalysis product is based on the initialization module of a sub-seasonal to seasonal prediction system with a nudging-based data assimilation method. The results indicated that:(1) both JRA-55 and MERRA2 were reliable in characterizing the temperature profile from 850 to 600 hPa, with a maximum deviation of about <0.5℃. Both datasets showed a large negative deviation below 825 hPa, with a maximum bias of about 2℃ at 1000 hPa and 1.5℃ at 900 hPa, respectively.(2) JRA-55 showed good performance in characterizing the relative humidity profile above 850 hPa, with a maximum deviation of < 8%, while it showed much wetter conditions below 850 hPa. MERRA2 overestimated the relative humidity in the middle to lower troposphere, with a maximum deviation of about 15% at 925 hPa.(3) The FGOALS-f2 reanalysis product more accurately reproduced the temperature profile in the marine atmospheric boundary layer over the EEIO than that in JRA-55 and MERRA2, but showed much wetter conditions than the GPS sounding observations, with a maximum deviation of up to 20% at 600 hPa. Future applications of GPS sounding datasets are discussed.
文摘本文利用2010—2019年东印度洋海洋学综合科学考察基金委共享航次数据、Argo(array for real-time geostrophic oceanography)和简单海洋再分析数据(simple ocean data assimilation,SODA),研究了赤道东印度洋次表层高盐水(subsurface high salinity water,SHSW)的年际变化,并探讨了其形成机制。仅限于春季的观测资料显示,来自阿拉伯海的高盐水位于东印度洋赤道断面次表层70~130m深度处,且具有显著的年际变化。基于月平均SODA资料的研究结果表明,不同时期SHSW盐度异常的变化趋势存在显著差异,2010—2015年趋势比较稳定,而2016—2019年则呈现出显著的上升趋势。通过对SHSW的回归分析表明,风场和次表层纬向流是控制该高盐水年际变化的主要因子。进一步的分析表明,赤道印度洋的东风异常导致水体向西堆积,产生东向压强梯度力,进而激发出次表层异常东向流,最终引起SHSW盐度异常升高。此动力关联在印度洋偶极子事件中尤为显著,这进一步反映了赤道东印度洋SHSW的年际变化受到印度洋偶极子的调制。
