Arctic sea ice cover has decreased dramatically over the last three decades. This study quanti?es the sea ice concentration(SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and ...Arctic sea ice cover has decreased dramatically over the last three decades. This study quanti?es the sea ice concentration(SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest(smallest) rate of decline found in September(March) is-0.48%/a(-0.10%/a).The summer(-0.42%/a) and autumn(-0.31%/a) seasons show faster decrease rates than those of winter(-0.12%/a) and spring(-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara(-0.60%/a) and Barents Seas(-0.54%/a), followed by the Chukchi Sea(-0.48%/a), East Siberian Sea(-0.43%/a), Laptev Sea(-0.38%/a), and Beaufort Sea(-0.36%/a). The annual SIC trend for the whole Arctic Ocean is-0.26%/a over the same period. Furthermore, the in?uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), Dipole anomaly(DA), sea surface temperature(SST), surface air temperature(SAT), and surface wind(SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices(AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice(i.e., loss of the insulating layer) likely contributes to a colder sea surface(i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged in?uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas(typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passage展开更多
Pollen grains deposited in marine sediments are transported from land to sea by wind or surface water flows.We analyzed pollen collected from the air and seawater from the coast of the Yellow Sea near China and into t...Pollen grains deposited in marine sediments are transported from land to sea by wind or surface water flows.We analyzed pollen collected from the air and seawater from the coast of the Yellow Sea near China and into the western Pacific Ocean between December 2008 and January 2009 during the cruise "KX08-973".Results showed that abundant pollen grains of Artemisia and Chenopodiaceae were probably transported to the continental shelf of the East China Sea,the East Philippine Sea and the equatorial regions of the Pacific Ocean by the winter monsoon.Some pollen may have even traveled over 2000 km from the East Asia continent to the tropical Pacific Ocean.However,a gradual decline of temperate components and an increase in tropical components was observed towards the tropical regions.Fern spores were rare in the air samples,but much more abundant in seawater samples,even though they were collected in nearly the same areas,which indicates that most fern spores were carried to the ocean by flowing water.These results suggest that the winter monsoon may be the major pollen carrier and transporter in the study area during winter.展开更多
星载微波散射计是获取全球海面风场信息的主要手段,HY-2B卫星散射计的成功发射为全球海面风场数据获取的持续性提供了重要保障。本文利用欧洲中期天气预报中心(EuropeanCenter forMedium-RangeWeatherForecasts,ECMWF)再分析风场数据、...星载微波散射计是获取全球海面风场信息的主要手段,HY-2B卫星散射计的成功发射为全球海面风场数据获取的持续性提供了重要保障。本文利用欧洲中期天气预报中心(EuropeanCenter forMedium-RangeWeatherForecasts,ECMWF)再分析风场数据、热带大气海洋观测计划(TropicalAtmosphereOceanArray,TAO)和美国国家数据浮标中心(National Data Buoy Center,NDBC)浮标获取的海面风矢量实测数据,对HY-2B散射计海面风场数据产品的质量进行统计分析。分析表明,HY-2B风场与ECMWF再分析风场对比,在4~24m·s^-1风速区间内,风速和风向均方根误差(root mean squareerror,RMSE)分别为1.58m·s^-1和15.34°;与位于开阔海域的TAO浮标数据对比,风速、风向RMSE分别为1.03m·s^-1和14.98°,可见HY-2B风场能较好地满足业务化应用的精度要求(风速优于2m·s^-1,风向优于20°)。与主要位于近海海域的NDBC浮标对比,HY-2B风场的风速、风向RMSE分别为1.60m·s^-1和19.14°,说明HY-2B散射计同时具备了对近海海域风场的良好观测能力。本文还发现HY-2B风场质量会随风速、地面交轨位置等变化,为用户更好地使用HY-2B风场产品提供参考。展开更多
Existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System （EOS）...Existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System （EOS） Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer （AMSRoE） onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSRoE measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square （RMS） errors for SST and SSW are about 1.8 K and 1.9 m s^- 1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds （e.g., its liquid water path is larger than 0.5 mm）, while they are 1.1 K for SST and 2.0 m s^-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.展开更多
The ocean surface wind(OSW)data retrieved from microwave scatterometers have high spatial accuracy and represent the only wind data assimilated by global numerical models on the ocean surface,thus playing an important...The ocean surface wind(OSW)data retrieved from microwave scatterometers have high spatial accuracy and represent the only wind data assimilated by global numerical models on the ocean surface,thus playing an important role in improving the forecast skills of global medium-range weather prediction models.To improve the forecast skills of the Global/Regional Assimilation and Prediction System Global Forecast System(GRAPES_GFS),the HY-2B OSW data is assimilated into the GRAPES_GFS four-dimensional variational assimilation(4DVAR)system.Then,the impacts of the HY-2B OSW data assimilation on the analyses and forecasts of GRAPES_GFS are analyzed based on one-month assimilation cycle experiments.The results show that after assimilating the HY-2B OSW data,the analysis errors of the wind fields in the lower-middle troposphere(1000-600 hPa)of the tropics and the southern hemisphere(SH)are significantly reduced by an average rate of about 5%.The impacts of the HY-2B OSW data assimilation on the analysis fields of wind,geopotential height,and temperature are not solely limited to the boundary layer but also extend throughout the entire troposphere after about two days of cycling assimilation.Furthermore,assimilating the HY-2B OSW data can significantly improve the forecast skill of wind,geopotential height,and temperature in the troposphere of the tropics and SH.展开更多
Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s off...Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s offshore area in 2019 conducted jointly by Chinese and Kenyan scientists were used to evaluate the performance of the major high-frequency atmospheric reanalysis products in the western Indian Ocean region.Compared with observations,the sea level pressure field could be accurately simulated using the atmospheric reanalysis data.However,significant discrepancies existed between the surface wind reanalysis data,especially between meridional wind and the observational data.Most of the data provide a complete understanding of sea level pressure,except for the Japanese 55-year Reanalysis data,which hold a significant system bias.The Modern-Era Reanalysis for Research and Applications,Version-2,provides an improved description of all datasets.All the reanalysis datasets for zonal wind underestimate the strength during the study period.Among reanalysis data,NCEP-DOE Atmospheric Model Intercomparison Project reanalysis data presents an inaccurate description due to the worst correlation with the observations.For meridional wind,most reanalysis datasets underestimate the variance,while the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 has a larger variance than the observations.In addition to the original data comparison,the diurnal variability of sea level pressure and surface wind are also assessed,and the result indicates that the diurnal variations have a significant gap between observation and reanalysis data.This study indicates that the current high-frequency reanalysis data still have disadvantages when describing the atmospheric parameters in the Western Indian Ocean region.展开更多
By taking into consideration the effects of ocean surface wave-induced Stokes drift velocity Un, and current velocity Uc on the drag coefficient, the spatial distributions of drag coefficient and wind stress in 2004 a...By taking into consideration the effects of ocean surface wave-induced Stokes drift velocity Un, and current velocity Uc on the drag coefficient, the spatial distributions of drag coefficient and wind stress in 2004 are computed over the tropical and northern Pacific using an empirical drag coefficient parameterization formula based on wave steepness and wind speed. The global ocean current field is generated from the Hybrid Coordinate Ocean Model （HYCOM） and the wave data are generated from Wavewatch Ill （WW3）. The spatial variability of the drag coefficient and wind stress is analyzed. Preliminary results indicate that the ocean surface Stokes drift velocity and current velocity exert an important influence on the wind stress. The results also show that consideration of the effects of the ocean surface Stokes drift velocity and current velocity on the wind stress can significantly improve the modeling of ocean circulation and air-sea interaction processes.展开更多
基金Supported by the National Natural Science Foundation of China(No.41406215)the NSFC-Shandong Joint Fund for Marine Science Research Centers(No.U1606401)+2 种基金the Qingdao National Laboratory for Marine Science and Technology,the Postdoctoral Science Foundation of China(No.2014M561971)the Open Funds for the Key Laboratory of Marine Geology and Environment,Institute of Oceanology,Chinese Academy of Sciences(No.MGE2013KG07)the Natural Science Foundation of Jiangsu Province of China(No.BK20140186)
文摘Arctic sea ice cover has decreased dramatically over the last three decades. This study quanti?es the sea ice concentration(SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest(smallest) rate of decline found in September(March) is-0.48%/a(-0.10%/a).The summer(-0.42%/a) and autumn(-0.31%/a) seasons show faster decrease rates than those of winter(-0.12%/a) and spring(-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara(-0.60%/a) and Barents Seas(-0.54%/a), followed by the Chukchi Sea(-0.48%/a), East Siberian Sea(-0.43%/a), Laptev Sea(-0.38%/a), and Beaufort Sea(-0.36%/a). The annual SIC trend for the whole Arctic Ocean is-0.26%/a over the same period. Furthermore, the in?uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), Dipole anomaly(DA), sea surface temperature(SST), surface air temperature(SAT), and surface wind(SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices(AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice(i.e., loss of the insulating layer) likely contributes to a colder sea surface(i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged in?uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas(typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passage
基金supported by National Basic Research Program of China (Grant No. 2007CB815900)National Natural Science Foundation of China (Grant No. 40771072)the Discretionary Foundation of State Key Laboratory of Marine Geology,Tongji University (Grant No. MG20080207)
文摘Pollen grains deposited in marine sediments are transported from land to sea by wind or surface water flows.We analyzed pollen collected from the air and seawater from the coast of the Yellow Sea near China and into the western Pacific Ocean between December 2008 and January 2009 during the cruise "KX08-973".Results showed that abundant pollen grains of Artemisia and Chenopodiaceae were probably transported to the continental shelf of the East China Sea,the East Philippine Sea and the equatorial regions of the Pacific Ocean by the winter monsoon.Some pollen may have even traveled over 2000 km from the East Asia continent to the tropical Pacific Ocean.However,a gradual decline of temperate components and an increase in tropical components was observed towards the tropical regions.Fern spores were rare in the air samples,but much more abundant in seawater samples,even though they were collected in nearly the same areas,which indicates that most fern spores were carried to the ocean by flowing water.These results suggest that the winter monsoon may be the major pollen carrier and transporter in the study area during winter.
文摘星载微波散射计是获取全球海面风场信息的主要手段,HY-2B卫星散射计的成功发射为全球海面风场数据获取的持续性提供了重要保障。本文利用欧洲中期天气预报中心(EuropeanCenter forMedium-RangeWeatherForecasts,ECMWF)再分析风场数据、热带大气海洋观测计划(TropicalAtmosphereOceanArray,TAO)和美国国家数据浮标中心(National Data Buoy Center,NDBC)浮标获取的海面风矢量实测数据,对HY-2B散射计海面风场数据产品的质量进行统计分析。分析表明,HY-2B风场与ECMWF再分析风场对比,在4~24m·s^-1风速区间内,风速和风向均方根误差(root mean squareerror,RMSE)分别为1.58m·s^-1和15.34°;与位于开阔海域的TAO浮标数据对比,风速、风向RMSE分别为1.03m·s^-1和14.98°,可见HY-2B风场能较好地满足业务化应用的精度要求(风速优于2m·s^-1,风向优于20°)。与主要位于近海海域的NDBC浮标对比,HY-2B风场的风速、风向RMSE分别为1.60m·s^-1和19.14°,说明HY-2B散射计同时具备了对近海海域风场的良好观测能力。本文还发现HY-2B风场质量会随风速、地面交轨位置等变化,为用户更好地使用HY-2B风场产品提供参考。
文摘Existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System （EOS） Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer （AMSRoE） onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSRoE measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square （RMS） errors for SST and SSW are about 1.8 K and 1.9 m s^- 1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds （e.g., its liquid water path is larger than 0.5 mm）, while they are 1.1 K for SST and 2.0 m s^-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.
基金supported by the Key Special Project for the Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (Grant No. GML2019ZD0302)the National Key R&D Program of China (Grant No. 2018YFC1506205)
文摘The ocean surface wind(OSW)data retrieved from microwave scatterometers have high spatial accuracy and represent the only wind data assimilated by global numerical models on the ocean surface,thus playing an important role in improving the forecast skills of global medium-range weather prediction models.To improve the forecast skills of the Global/Regional Assimilation and Prediction System Global Forecast System(GRAPES_GFS),the HY-2B OSW data is assimilated into the GRAPES_GFS four-dimensional variational assimilation(4DVAR)system.Then,the impacts of the HY-2B OSW data assimilation on the analyses and forecasts of GRAPES_GFS are analyzed based on one-month assimilation cycle experiments.The results show that after assimilating the HY-2B OSW data,the analysis errors of the wind fields in the lower-middle troposphere(1000-600 hPa)of the tropics and the southern hemisphere(SH)are significantly reduced by an average rate of about 5%.The impacts of the HY-2B OSW data assimilation on the analysis fields of wind,geopotential height,and temperature are not solely limited to the boundary layer but also extend throughout the entire troposphere after about two days of cycling assimilation.Furthermore,assimilating the HY-2B OSW data can significantly improve the forecast skill of wind,geopotential height,and temperature in the troposphere of the tropics and SH.
基金supported by the Global Change and Air-Sea Interaction Program(No.GASI-04-QYQH-03)the Taishan Scholars Program of Shandong Province(No.tsqn 201909165)+3 种基金the National Natural Science Foundation of China(No.41876028)the Global Change and Air-Sea Interaction Program(No.GASI-01-WIND-STwin)the Shandong Science and Technology Foundation(No.2013GRC 31504)the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2022QNLM010103-3).
文摘Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s offshore area in 2019 conducted jointly by Chinese and Kenyan scientists were used to evaluate the performance of the major high-frequency atmospheric reanalysis products in the western Indian Ocean region.Compared with observations,the sea level pressure field could be accurately simulated using the atmospheric reanalysis data.However,significant discrepancies existed between the surface wind reanalysis data,especially between meridional wind and the observational data.Most of the data provide a complete understanding of sea level pressure,except for the Japanese 55-year Reanalysis data,which hold a significant system bias.The Modern-Era Reanalysis for Research and Applications,Version-2,provides an improved description of all datasets.All the reanalysis datasets for zonal wind underestimate the strength during the study period.Among reanalysis data,NCEP-DOE Atmospheric Model Intercomparison Project reanalysis data presents an inaccurate description due to the worst correlation with the observations.For meridional wind,most reanalysis datasets underestimate the variance,while the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 has a larger variance than the observations.In addition to the original data comparison,the diurnal variability of sea level pressure and surface wind are also assessed,and the result indicates that the diurnal variations have a significant gap between observation and reanalysis data.This study indicates that the current high-frequency reanalysis data still have disadvantages when describing the atmospheric parameters in the Western Indian Ocean region.
基金the National Basic Research Program of China (grant Nos2005CB422302, 2005CB422307 and 2007CB411806)Great Project of National Natural Science Foundation of China (No 40490263)the NOAA/NECP data server are appreciated
文摘By taking into consideration the effects of ocean surface wave-induced Stokes drift velocity Un, and current velocity Uc on the drag coefficient, the spatial distributions of drag coefficient and wind stress in 2004 are computed over the tropical and northern Pacific using an empirical drag coefficient parameterization formula based on wave steepness and wind speed. The global ocean current field is generated from the Hybrid Coordinate Ocean Model （HYCOM） and the wave data are generated from Wavewatch Ill （WW3）. The spatial variability of the drag coefficient and wind stress is analyzed. Preliminary results indicate that the ocean surface Stokes drift velocity and current velocity exert an important influence on the wind stress. The results also show that consideration of the effects of the ocean surface Stokes drift velocity and current velocity on the wind stress can significantly improve the modeling of ocean circulation and air-sea interaction processes.
