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| 1 | Predicting the spread of nuclear radiation from the damaged Fukushima Nuclear Power Plant显示文摘Japan suffered a M9.0 earthquake and massive tsunami on March 11, 2011, which seriously damaged the Fukushima Nuclear Power Plant and caused a nuclear crisis. The spread of nuclear radiation from the power plant through the atmosphere and ocean was predicted with a short-term climate forecasting model and an ocean circulation model under some idealized assumptions. If nuclear matter were leaked in the near-ground layer of 992 hPa, the climate model results show that the nuclear radiation would cover North America 10 days after the initial leakage, with the concentration at the forefront dramatically reduced to 10 millionths of the initial model concentration at the source. The radiation would span Europe in 15 days and cover much of the Northern Hemisphere in 30 days. If the initial leakage was assumed to occur in the layer 5000-m above the ground, the radiation would cover Europe in 10 days and cover much of the Northern Hemisphere in 15 days. Moreover, under the assumption that the nuclear matter leaked in the 10000-m layer, the radiation would affect much of China after 10 days. The ocean circulation model indicates that the nuclear material would be slowly transported northeast of Fukushima and reach 150°E in 50 days, and the nuclear debris in the ocean would be confined to a narrow band. Compared with the spread in the ocean, the area affected by leaked nuclear radiation in the atmosphere would be very large. Atmospheric monitors in North America and Europe will be helpful for estimating the effect in China of any leaked nuclear material. | QIAO FangLi WANG GuanSuo ZHAO Wei ZHAO JieChen DAI DeJun SONG YaJuan SONG ZhenYa | 2011 | Chinese Science Bulletin2011，56，18： | 18 |
| 2 | Albedo of Coastal Landfast Sea Ice in Prydz Bay,Antarctica:Observations and Parameterization显示文摘The snow/sea-ice albedo was measured over coastal landfast sea ice in Prydz Bay, East Antarctica(off Zhongshan Station)during the austral spring and summer of 2010 and 2011. The variation of the observed albedo was a combination of a gradual seasonal transition from spring to summer and abrupt changes resulting from synoptic events, including snowfall, blowing snow, and overcast skies. The measured albedo ranged from 0.94 over thick fresh snow to 0.36 over melting sea ice. It was found that snow thickness was the most important factor influencing the albedo variation, while synoptic events and overcast skies could increase the albedo by about 0.18 and 0.06, respectively. The in-situ measured albedo and related physical parameters(e.g., snow thickness, ice thickness, surface temperature, and air temperature) were then used to evaluate four different snow/ice albedo parameterizations used in a variety of climate models. The parameterized albedos showed substantial discrepancies compared to the observed albedo, particularly during the summer melt period, even though more complex parameterizations yielded more realistic variations than simple ones. A modified parameterization was developed,which further considered synoptic events, cloud cover, and the local landfast sea-ice surface characteristics. The resulting parameterized albedo showed very good agreement with the observed albedo. | Qinghua YANG Jiping LIU Matti LEPPRANTA Qizhen SUN Rongbin LI Lin ZHANG Thomas JUNG Ruibo LEI Zhanhai ZHANG Ming LI Jiechen ZHAO Jingjing CHENG | 2016 | Advances in Atmospheric Sciences2016，33，5： | 7 |
| 3 | Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing: a case study显示文摘A regional Arctic configuration of the Massachusetts Institute of Technology general circulation model(MITgcm) is used as the coupled ice-ocean model for forecasting sea ice conditions in the Arctic Ocean at the National Marine Environmental Forecasting Center of China(NMEFC), and the numerical weather prediction from the National Center for Environmental Prediction Global Forecast System(NCEP GFS) is used as the atmospheric forcing. To improve the sea ice forecasting, a recently developed Polar Weather Research and Forecasting model(Polar WRF) model prediction is also tested as the atmospheric forcing. Their forecasting performances are evaluated with two different satellite-derived sea ice concentration products as initializations:(1) the Special Sensor Microwave Imager/Sounder(SSMIS) and(2) the Advanced Microwave Scanning Radiometer for EOS(AMSR-E). Three synoptic cases, which represent the typical atmospheric circulations over the Arctic Ocean in summer 2010, are selected to carry out the Arctic sea ice numerical forecasting experiments. The evaluations suggest that the forecasts of sea ice concentrations using the Polar WRF atmospheric forcing show some improvements as compared with that of the NCEP GFS. | YANG Qinghua LIU Jiping ZHANG Zhanhai SUI Cuijuan XING Jianyong LI Ming LI Chunhua ZHAO Jiechen ZHANG Lin | 2014 | Acta Oceanologica Sinica2014，33，12： | 5 |
| 4 | The spatiotemporal patterns of sea ice in the Bohai Sea during the winter seasons of 2000–2016显示文摘In this study,sea ice thickness(SIT)and sea ice extent(SIE)in the Bohai Sea from 2000 to 2016 were investigated.A surface heat balance equation was applied to calculate SIT using ice surface temperatures estimated from the Moderate Resolution Imaging Spectroradiometer(MODIS)data with input from air temperature and wind speed from reanalyzing weather data.No trend was found in SIT during 2000–2016.The mean SIT and SIE during this period were 5.58±0.86 cm and 23×10^(3)±8×10^(3)km^(2),respectively.The largest SIT and SIE periods were observed during the second half of January and the first half of February,respectively.The Spearman correlation coefficient between mean ice thickness and average air temperature from 21 automatic weather stations around the Bohai Sea was–0.94(P<.005),and the coefficient between median ice extent and negative accumulated temperature was–0.503(P<.001).The rate of increase in air temperature around the Bohai Sea is 0.271℃per decade in winter for 1979–2016(P<.05),which is much lower than that in northern polar area(0.648℃per decade).This rate has not resulted in a decreasing trend in SIT and SIE for the past 16 years in the Bohai Sea. | Lunxi Ouyang Fengming Hui Lixian Zhu Xiao Cheng Bin Cheng Mohammed Shokr Jiechen Zhao Minghu Ding Tao Zeng | 2019 | International Journal of Digital Earth2019，12，8： | 3 |
| 5 | Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions显示文摘Sea surface winds from reanalysis(NCEP-2 and ERA-40 datasets)and satellite-based products(QuikSCAT and NCDC blended sea winds)are evaluated using in situ ship measurements from the Chinese National Antarctic Research Expeditions(CHINAREs)from 1989 through 2006,with emphasis on the Southern Ocean(south of 45°S).Compared with ship observations,the reanalysis winds have a positive mean bias(0.32 m·s^(-1)for NCEP-2 and 0.13 m·s^(-1)for ERA-40),and this bias is more pronounced in the Southern Ocean(0.57 m·s^(-1)and 0.45 m·s^(-1),respectively).However,mean biases are negative in the tropics and subtropics.The satellite-based winds also show positive mean biases,larger than those of the reanalysis data.All four wind products overestimate ship wind speed for weak winds(<4 m·s^(-1))but underestimate for strong winds(>10 m·s^(-1)).Differences between the reanalysis and satellite winds are examined to identify regions with large discrepancies. | LI Ming YANG Qinghua ZHAO Jiechen ZHANG Lin LI Chunhua MENG Shang | 2013 | Advances in Polar Science2013，24，3： | 2 |
| 6 | Spatial and temporal evolution of landfast ice near Zhongshan Station, East Antarctica, over an annual cycle in 2011/2012显示文摘Annual observations of first-year ice(FYI) and second-year ice(SYI) near Zhongshan Station, East Antarctica,were conducted for the first time from December 2011 to December 2012. Melt ponds appeared from early December 2011. Landfast ice partly broke in late January, 2012 after a strong cyclone. Open water was refrozen to form new ice cover in mid-February, and then FYI and SYI co-existed in March with a growth rate of 0.8 cm/d for FYI and a melting rate of 2.7 cm/d for SYI. This difference was due to the oceanic heat flux and the thickness of ice,with weaker heat flux through thicker ice. From May onward, FYI and SYI showed a similar growth by 0.5 cm/d.Their maximum thickness reached 160.5 cm and 167.0 cm, respectively, in late October. Drillings showed variations of FYI thickness to be generally less than 1.0 cm, but variations were up to 33.0 cm for SYI in March,suggesting that the SYI bottom was particularly uneven. Snow distribution was strongly affected by wind and surface roughness, leading to large thickness differences in the different sites. Snow and ice thickness in Nella Fjord had a similar 'east thicker, west thinner' spatial distribution. Easterly prevailing wind and local topography led to this snow pattern. Superimposed ice induced by snow cover melting in summer thickened multi-year ice,causing it to be thicker than the snow-free SYI. The estimated monthly oceanic heat flux was ~30.0 W/m2 in March–May, reducing to ~10.0 W/m2 during July–October, and increasing to ~15.0 W/m2 in November. The seasonal change and mean value of 15.6 W/m2 was similar to the findings of previous research. The results can be used to further our understanding of landfast ice for climate change study and Chinese Antarctic Expedition services. | Jiechen Zhao Qinghua Yang Bin Cheng Matti Lepp?ranta Fengming Hui Surui Xie Meng Chen Yining Yu Zhongxiang Tian Ming Li Lin Zhang | 2019 | Acta Oceanologica Sinica2019，38，5： | 2 |
| 7 | Snow and ice thicknesses derived from Fast Ice Prediction System Version 2.0(FIPS V2.0)in Prydz Bay,East Antarctica:comparison with in-situ observations显示文摘In this paper,snow and ice thickness products derived from an updated Fast Ice Prediction System Version 2.0(FIPS V2.0)in Prydz Bay,East Antarctica,are introduced and compared with in-situ obser-vations.FIPS V2.0 is comprised of a newly-developed snowdrift parameterization compared to the original FIPS V1.0.The simulation domain covers the entire fast ice region in Prydz Bay and is config-ured to 720 grid cells,with a spatial resolution of 0.125°.The ERAInterim reanalysis from the European Centre for Medium-Range Weather Forecasting(ECMWF)were used as the atmospheric forcing.The in-situ observations were obtained near Zhongshan Station by the wintering team,and the measurement frequency of the snow and ice thicknesses was around one week.Both the FIPS V2.0 pro-ducts and in-situ observations introduced in this paper cover the time periods from 2012 to 2016.The primary assessments based on the in-situ observations show that FIPS V2.0 has mean biases of 0.01±0.07 m and 0.23±0.09 m for snow and ice thickness simula-tions,respectively.The results indicate that the updated FIPS V2.0 produces a reasonable snow thickness due to the newly-developed snowdrift parameterization,but it overestimates the ice thickness due to the cold bias in the air temperature forcing.These 2-D snow and ice thickness distributions provide important references for sea ice thermodynamic studies,remote sensing validations,and ice-breaker navigation assessments in this region.The dataset is avail-able at http://gffzza4c772bdab62464fhxb0b0960906q6kxo.ffgz.tsg.suse.edu.cn/10.11922/sciencedb.j00076.00066. | Jiechen Zhao Jingjing Cheng Zhongxiang Tian Xiaopeng Han Hui Shen Guanghua Hao Honglin Guo Qi Shu | 2022 | Big Earth Data2022，6，4： | 2 |
| 8 | Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system显示文摘To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction. | Qi Shu Fangli Qiao Jiping Liu Zhenya Song Zhiqiang Chen Jiechen Zhao Xunqiang Yin Yajuan Song | 2021 | Acta Oceanologica Sinica2021，40，10： | 1 |
| 9 | The variability of surface radiation fluxes over landfast sea ice near Zhongshan station,east Antarctica during austral spring显示文摘Surface radiative fluxes over landfast sea ice off Zhongshan station have been measured in austral spring for five springs between 2010 and 2015.Downward and upward solar radiation vary diurnally with maximum amplitudes of 473 and 290 W m^(−2),respectively.The maximum and minimum long-wave radiation values of the mean diurnal cycle are 218 and 210 W m^(−2)for downward radiation,277 and 259 W m^(−2)for upward radiation and 125 and−52 W m^(−2)for net radiation.The albedo has a U-shaped mean diurnal cycle with a minimum of 0.64 at noon.Sea ice thickness is in the growth phase for most spring days,but can be disturbed by synoptic processes.The surface temperature largely determines the occurrence of ice melting.Surface downward and upward long-wave radiation show synoptic oscillations with a 5–8 day period and intraseasonal variability with a 12–45 day period.The amplitudes of the diurnal,synoptic and intraseasonal variability show some differences during the five austral springs considered here.The intraseasonal and synoptic variability of downward and upward long-wave radiation are associated with the variability of cloud cover and surface temperature induced by the atmospheric circulation. | Lejiang Yu Qinghua Yang Mingyu Zhou Donald H.Lenschow Xianqiao Wang Jiechen Zhao Qizhen Sun Zhongxiang Tian Hui Shen Lin Zhang | 2019 | International Journal of Digital Earth2019，12，8： | 1 |
| 10 | The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018显示文摘Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships,while limited by the capability of climate models.A bias correction methodology in this study was proposed and performed on raw products from two climate models,the First Institute Oceanography Earth System Model(FIOESM)and the National Centers for Environmental Prediction(NCEP)Climate Forecast System(CFS),to improve 60 days predictions for Arctic sea ice.Both models were initialized on July 1,August 1,and September 1 in 2018.A 60-day forecast was conducted as a part of the official sea ice service,especially for the ninth Chinese National Arctic Research Expedition(CHINARE)and the China Ocean Shipping(Group)Company(COSCO)Northeast Passage voyages during the summer of 2018.The results indicated that raw products from FIOESM underestimated sea ice concentration(SIC)overall,with a mean bias of SIC up to 30%.Bias correction resulted in a 27%improvement in the Root Mean Square Error(RMSE)of SIC and a 10%improvement in the Integrated Ice Edge Error(IIEE)of sea ice edge(SIE).For the CFS,the SIE overestimation in the marginal ice zone was the dominant features of raw products.Bias correction provided a 7%reduction in the RMSE of SIC and a 17%reduction in the IIEE of SIE.In terms of sea ice extent,FIOESM projected a reasonable minimum time and amount in mid-September;however,CFS failed to project both.Additional comparison with subseasonal to seasonal(S2S)models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases. | Jiechen Zhao Qi Shu Chunhua Li Xingren Wu Zhenya Song Fangli Qiao | 2020 | Acta Oceanologica Sinica2020，39，9： | 1 |
| 11 | Snow depth and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm显示文摘An ice mass balance buoy(IMB)monitors the evolution of snow and ice cover on seas,ice caps and lakes through the measurement of various variables.The crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array(SIMBA)measures vertical temperature profiles through the air-snow-ice-water column using a thermistor string.The determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual process.We present an automated algorithm to perform this task.The algorithm is based on heat flux continuation,limit ratio between thermal heat conductivity of snow and ice,and minimum resolution(±0.0625°C)of the temperature sensors.The algorithm results are compared with manual analyses,in situ borehole measurements and numerical model simulation.The bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7%of the mean observed values.The algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced. | Zeliang Liao Bin Cheng JieChen Zhao Timo Vihma Keith Jackson Qinghua Yang Yu Yang Lin Zhang Zhijun Li Yubao Qiu Xiao Cheng | 2019 | International Journal of Digital Earth2019，12，8： | 0 |
| 12 | Studies of detector cells for a hadronic calorimeter based on plastic scintillators显示文摘In this paper,we study variations of a plastic scintillator tile coupled to silicon photomultiplier via a dome-shaped cavity originally developed in the CALICE collaboration.Four kinds of plastic scintillator detector cells with different sizes based on the structure were studied for applications in analog readout highly granular hadronic calorimeter(AHCAL)in future circular electron positron collider(CEPC)project.The responses to cosmic rays of the detector cells could reach 33.89 p.e./MIPs(minimum ionizing particles).The good results of both response uniformity and MIP detection efficiency(above 95%)show that the detector cells with larger sizes(40×40×3 and 50×50×3 mm^(3))could provide an option for AHCAL detector cells of CEPC detectors. | Zhe Wu Dongmei Xia Zhigang Wang Hang Zhao Peng Hu Wei Hu Tao Hu Jiechen Jiang Boxiang Yu | 2018 | Radiation Detection Technology and Methods2018，2，1： | 0 |
| 13 | Modelling the annual cycle of landfast ice near Zhongshan Station,East Antarctica显示文摘A high resolution one-dimensional thermodynamic snow and ice(HIGHTSI)model was used to model the annual cycle of landfast ice mass and heat balance near Zhongshan Station,East Antarctica.The model was forced and initialized by meteorological and sea ice in situ observations from April 2015 to April 2016.HIGHTSI produced a reasonable snow and ice evolution in the validation experiments,with a negligible mean ice thickness bias of(0.003±0.06)m compared to in situ observations.To further examine the impact of different snow conditions on annual evolution of first-year ice(FYI),four sensitivity experiments with different precipitation schemes(0,half,normal,and double)were performed.The results showed that compared to the snow-free case,the insulation effect of snow cover decreased bottom freezing in the winter,leading to 15%–26%reduction of maximum ice thickness.Thick snow cover caused negative freeboard and flooding,and then snow ice formation,which contributed 12%–49%to the maximum ice thickness.In early summer,snow cover delayed the onset of ice melting for about one month,while the melting of snow cover led to the formation of superimposed ice,accounting for 5%–10%of the ice thickness.Internal ice melting was a significant contributor in summer whether snow cover existed or not,accounting for 35%–56%of the total summer ice loss.The multi-year ice(MYI)simulations suggested that when snow-covered ice persisted from FYI to the 10th MYI,winter congelation ice percentage decreased from 80%to 44%(snow ice and superimposed ice increased),while the contribution of internal ice melting in the summer decreased from 45%to 5%(bottom ice melting dominated). | Jiechen Zhao Tao Yang Qi Shu Hui Shen Zhongxiang Tian Guanghua Hao Biao Zhao | 2021 | Acta Oceanologica Sinica2021，40，7： | 0 |
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