渤海表层悬沙的时空分布特征及其动力成因

针对目前渤海整个海域悬浮泥沙分布全貌的研究不充分。根据2000—2004年渤海表层悬浮泥沙分布特征选取7个典型海区,通过利用长时间序列悬沙质量浓度和风场遥感反演资料,在分析悬沙质量浓度与局地风速、物质来源等关系的基础上,定量研究风浪和潮流共同作用下、随季节显著变化的沉积物再悬浮过程,从而揭示整个渤海海域代表性海区悬沙质量浓度时空分布的动力成因。渤海不同海区表层悬沙质量浓度绝对值差别很大,多年平均最高质量浓度在20~450mg/L变化,高质量浓度集中在近岸河口区及其邻近海域,如黄河口和辽河口地区,低质量浓度区位于渤海中部、渤海海峡以及秦皇岛外海(属于近岸海域却质量浓度常年偏低的特殊海区)。渤海表层悬沙质量浓度具有明显的季节变化特征,风场的季节变化是主要影响因子,各代表性海区悬沙质量浓度与风速之间具有显著正相关关系。悬沙质量浓度与风速之间存在一定时间段的滞后相关。沉积物再悬浮的定量研究表明,除渤海海峡外,渤海其它典型海区表层悬沙质量浓度及其季节变化,均与各自海区风浪和潮流共同作用产生的最大底流速及其季节变化相对应。在渤海,底层沉积物再悬浮的季节变化是影响悬沙质量浓度季节变化最关键的动力过程。     

The seasonal foot printing mechanism of spring Arctic sea ice in the Bergen climate models

The influence of spring Arctic sea ice variability on the Pacific Decadal Oscillation(PDO) like sea surface temperature(SST) variability is established and investigated using an Atmosphere Ocean General Circulation Model(AOGCM) of the Bergen Climate Model version 2(BCM2). The spring Arctic sea ice variability affects the mid-latitudes and tropics through the propagation of the anomalous Eliassen-Palm(E-P) flux from the polar region to mid- and low-latitudes during boreal spring. The pathway includes anomalous upward wave activity, which propagates to the high troposphere from near the surface of the polar region, turns southward between 500 h Pa and 200 h Pa and extends downward between 50°N and 70°N, influencing the near surface atmospheric circulation. The alteration of the near surface atmospheric circulation then causes anomalous surface ocean circulation. These circulation changes consequently leads to the SST anomalies in the North Pacific which may persist until the following summer, named seasonal "foot printing" mechanism(SFPM).     

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Seasonal Variations in Phytoplankton Community Structure in the Sanggou,Ailian, and Lidao Bays

The seasonal variations in phytoplankton community structure were investigated for the Sanggou Bay （SGB） and the adjacent Ailian Bay （ALB） and Lidao Bay （LDB） in Shandong Peninsula,eastern China.The species composition and cell abundance of phytoplankton in the bay waters in spring （April 2011）,summer （August 2011）,autumn （October 2011）,and winter （January 2012） were examined using the Uterm6hl method.A total of 80 taxa of phytoplankton that belong to 39 genera of 3 phyla were identified.These included 64 species of 30 genera in the Phylum Bacillariophyta,13 species of 8 genera in the Phylum Dinophyta,and 3 species of 1 genus in the Phylum Chrysophyta.During the four seasons,the number of phytoplankton species （43） was the highest in spring,followed by summer and autumn （40）,and the lowest number ofphytoplankton species （35） was found in winter.Diatoms,especially Paralia sulcata （Ehrenberg） Cleve and Coscinodiscus oculus-iridis Ehrenberg,were predominant in the phytoplankton community throughout the study period,whereas the dominance of dinoflagellate appeared in summer only.The maximum cell abundance of phytoplankton was detected in summer （average 8.08 × 103 cells L-1） whereas their minimum abundance was found in autumn （average 2.60 x 103 cellsL-1）.The phytoplankton abundance was generally higher in the outer bay than in the inner bay in spring and autumn.In summer,the phytoplankton cells were mainly concentrated in the south of inner SGB,with peak abundance observed along the western coast.In winter,the distribution of phytoplankton cells showed 3 patches,with peak abundance along the western coast as well.On seasonal average,the Shannon-Wiener diversity indices of phytoplankton community ranged from 1.17 to 1.78 （autumn 〉 summer 〉 spring 〉 winter）,and the Pielou＇s evenness indices of phytoplankton ranged from 0.45 to 0.65 （autumn 〉 spring 〉 summer〉 winter）.According to the results of canonical correspondence analysis,phosphate level w     

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Modeling seasonal variations of subsurface chlorophyll maximum in South China Sea

In the South China Sea(SCS), the subsurface chlorophyll maximum(SCM) is frequently observed while the mechanisms of SCM occurrence have not been well understood. In this study, a 1-D physical-biochemical coupled model was used to study the seasonal variations of vertical profiles of chlorophyll-a(Chl-a) in the SCS. Three parameters(i.e., SCM layer(SCML) depth, thickness, and intensity) were defined to characterize the vertical distribution of Chl-a in SCML and were obtained by fitting the vertical profile of Chl-a in the subsurface layer using a Gaussian function. The seasonal variations of SCMs are reproduced reasonably well compared to the observations. The annual averages of SCML depth, thickness, and intensity are 75 ± 10 m, 31 ± 6.7 m, and 0.37 ± 0.11 mg m-3, respectively. A thick, close to surface SCML together with a higher intensity occurs during the northeastern monsoon. Both the SCML thickness and intensity are sensitive to the changes of surface wind speed in winter and summer, but the surface wind speed exerts a minor influence on the SCML depth; for example, double strengthening of the southwestern monsoon in summer can lead to the thickening of SCML by 46%, the intensity decreasing by 30%, and the shoaling by 6%. This is because part of nutrients are pumped from the upper nutricline to the surface mixed layer by strong vertical mixing. Increasing initial nutrient concentrations by two times will increase the intensity of SCML by over 80% in winter and spring. The sensitivity analysis indicates that light attenuation is critical to the three parameters of SCM. Decreasing background light attenuation by 20% extends the euphotic zone, makes SCML deeper(~20%) and thicker(12% – 41%), and increases the intensity by over 16%. Overall, the depth of SCML is mainly controlled by light attenuation, and the SCML thickness and intensity are closely associated with wind and initial nitrate concentration in the SCS.     

东海西部陆架海域水团的季节特征分析

On the basis of the CTD data and the modeling results in the winter and summer of 2009, the seasonal characteristics of the water masses in the western East China Sea shelf area were analyzed using a cluster analysis method. The results show that the distributions and temperature-salinity characteristics of the water masses in the study area are of distinct seasonal difference. In the western East China Sea shelf area, there are three water masses during winter, i.e., continental coastal water（CCW）, Taiwan Warm Current surface water（TWCSW） and Yellow Sea mixing water（YSMW）, but four ones during summer, i.e., the CCW, the TWCSW, Taiwan Warm Current deep water（TWCDW） and the YSMW. Of all, the CCW, the TWCSW and the TWCDW are all dominant water masses. The CCW, primarily characterized by a low salinity, has lower temperature, higher salinity and smaller spatial extent in winter than in summer. The TWCSW is warmer, fresher and smaller in summer than in winter, and it originates mostly from the Kuroshio surface water（KSW） northeast of Taiwan, China and less from the Taiwan Strait water during winter, but it consists of the strait water and the KSW during summer. The TWCDW is characterized by a low temperature and a high salinity, and originates completely in the Kuroshio subsurface water northeast of Taiwan.     

山东荣成天鹅湖大叶藻形态和生长的季节性变化研究

2012年8月至2013年7月,作者逐月对天鹅湖大叶藻(Zostera marina L.)的形态特征、植株密度、生物量和生产力进行了监测。结果表明,大叶藻周年株高最高值和最低值分别出现在7月和1月;叶鞘高度、叶鞘宽度和叶宽的最高值均出现在7月,叶鞘高度最低值出现在1月,叶鞘宽度和叶宽最低值均出现在2月;大叶藻顶枝、侧枝和花枝的周年密度最大值分别出现在6月、4月和5月,最小值分别出现在1月、8月和7月;单株生物量和地上部分生物量最大值均出现在7月,地下部分出现在10月,而单株生物量和地上部分生物量最小值均出现在1月,地下部分生物量最小值出现在3月;单株地上和地下生产力最大值均出现在6月,最小值则分别出现在1月和2月。分析显示,大叶藻在冬季由于水温较低导致生长缓慢,且植株较小,在春季随水温上升,生长开始加快。水温在夏初达到大叶藻的最适生长水温,大叶藻的生物量和初级生产力达到最高值,而夏末和初秋由于水温过高导致大叶藻个体生物量、密度和初级生产力开始降低。这种季节性变化与水温的季节性变化密切相关。     

Heat budget of the south-central equatorial Pacific in CMIP3 models

ABSTRACT Using data from 17 coupled models and nine sets of corresponding Atmospheric Model Intercomparison Project （AMIP） results, we investigated annual and seasonal variation biases in the upper 50 m of the south-central equatorial Pacific, with a focus on the double-ITCZ bias, and examined the causes for the amplitude biases by using heat budget analysis. The results showed that, in the research region, most of the models simulate SSTs that are higher than or similar to observed. The simulated seasonal phase is close to that observed, but the amplitudes of more than half of the model results are larger than or equal to observations. Heat budget analysis demonstrated that strong shortwave radiation in individual atmospheric models is the main factor that leads to high SST values and that weak southward cold advection is an important mechanism for maintaining a high SST. For seasonal circulation, large surface shortwave radiation amplitudes cause large SST amplitudes.     

Chinese Contribution to CMIP5：An Overview of Five Chinese Models’Performances

An overview of Chinese contribution to Coupled Model Intercomparison Project-Phase 5 （CMIP5） is presented. The performances of five Chinese Climate/Earth System Models that participated in the CMIP5 pro ject are assessed in the context of climate mean states, seasonal cycle, intraseasonal oscillation, interan-nual variability, interdecadal variability, global monsoon, Asian-Australian monsoon, 20th-century historical climate simulation, climate change pro jection, and climate sensitivity. Both the strengths and weaknesses of the models are evaluated. The models generally show reasonable performances in simulating sea surface tem-perature （SST） mean state, seasonal cycle, spatial patterns of Madden-Julian oscillation （MJO） amplitude and tropical cyclone Genesis Potential Index （GPI）, global monsoon precipitation pattern, El Ni-no-Southern Oscillation （ENSO）, and Pacific Decadal Oscillation （PDO） related SST anomalies. However, the perfor-mances of the models in simulating the time periods, amplitude, and phase locking of ENSO, PDO time periods, GPI magnitude, MJO propagation, magnitude of SST seasonal cycle, northwestern Pacific mon-soon and North American monsoon domains, as well as the skill of large-scale Asian monsoon precipitation need to be improved. The model performances in simulating the time evolution and spatial pattern of the 20th-century global warming and the future change under representative concentration pathways pro jection are compared to the multimodel ensemble of CMIP5 models. The model discrepancies in terms of climate sensitivity are also discussed.