A Note on Reviving the Goddard Satellite-Based Surface Turbulent Fluxes （GSSTF） Dataset

Accurate sea surface flux measurements are crucial for understanding the global water and energy cycles. The oceanic evaporation, which is a major component of the global oceanic fresh water flux, is useful for predicting oceanic circulation and transport. The global Goddard Satellite-based Surface Turbulent Fluxes Version-2 (GSSTF2; July 1987--December 2000) dateset that was officially released in 2001 has been widely used by scientific community for global energy and water cycle research, and regional and short period data analyses. We have recently been funded by NASA to resume processing the GSSTF dataset with an objective of continually producing a uniform dataset of sea surface turbulent fluxes, derived from remote sensing data. The dataset is to be reprocessed and brought up-to-date (GSSTF2b) using improved input datasets such as a recently upgraded NCEP/DOE sea surface temperature reanalysis, and an upgraded surface wind and microwave brightness temperature V6 dataset (Version 6) from the Special Sensor Microwave Imager (SSM/I) produced by Remote Sensing Systems (RSS). A second new product (GSSTF3) is further proposed with a finer temporal (12-h) and spatial (0.25^ox0.25^o) resolution. GSSTF2b (July 1987--December 2008) and GSSTF3 (July 1999--December 2009) will be released for the research community to use by late 2009 and early 2011, respectively.     

Modeling Methane Emissions from Paddy Rice Fields under Elevated Atmospheric Carbon Dioxide Conditions

Methane(CH4 ) emissions from paddy rice fields substantially contribute to the dramatic increase of this greenhouse gas in the atmosphere.Due to great concern about climate change,it is necessary to predict the effects of the dramatic increase in atmospheric carbon dioxide(CO2 ) on CH4 emissions from paddy rice fields.CH4MOD 1.0 is the most widely validated model for simulating CH4 emissions from paddy rice fields exposed to ambient CO2(hereinafter referred to as aCO2 ).We upgraded the model to CH4MOD 2.0 b...     

Development Processes of the Tropical Pacific Meridional Mode

Mechanisms for the spatio-temporal development of the Tropical Pacific Meridional Mode(TPMM) are investigated using a coupled ocean-atmosphere model and observations.In both observations and the model,this meridional mode displays decadal variations and is most pronounced in spring and early summer.The model simulation suggests that once SST anomalies in the subtropical northeastern Pacific are initiated,say by northeasterly trade wind variability,perturbations evolve into a merdional dipole in 2-3 months.A...     

Aerosols and their influence on radiation partitioning and savanna productivity in northern Australia

Aerosols have been shown to affect the quantity and quality of solar radiation on the Earth’s surface. Savanna regions are subject to frequent burning and release of aerosols that may impact on radiation components and possibly vegetation productivity in this region. Therefore, in this study, we have analyzed the optical properties of aerosols (aerosol optical depth (AOD) and Angstrom coefficient) from the Atmospheric Radiation Measurement site in Darwin for the periods from April 2002 to June 2005 as measured by a multifilter rotating shadowband radiometer. The influence of aerosols and their effect on surface shortwave incoming solar radiation and savanna productivity were examined for the dry season using sky radiation collection of radiometers and eddy covariance measurements from the Howard Springs flux site. Results indicated that aerosol concentrations in the region were relatively low compared to other savanna regions with the maximum monthly average AOD over the period being the greatest in October (0.29?±?0.003 standard error at 500 nm). The highest monthly average Angstrom exponent was also found in October (1.38?±?0.008). The relatively low aerosol concentration in this region can be attributed to the mixture of smoke aerosols with humidity haze and local circulations. Over a range of AODs from 0.1 to 0.4, we found a modest increase in the fraction of diffuse radiation to total radiation from 11% to 21%. This small increase in diffuse fraction did not affect the carbon flux significantly. However, because the current range of AOD in the region is relatively low, the region could be sensitive to increases in aerosols and diffuse fraction in the future.     

中国地区高空气象探测气球空间漂移分析

利用中国120个探空站2004年08时所有探空气球漂移信息,结合NCEP再分析场,分析规定等压面上的时空漂移规律,重点选择100、500与925 hPa等压面,采用1、4、7、10月分别代表冬季、春季、夏季、秋季4个季节。初步分析表明;气球的漂移方向主要是受大气环流影响,不同季节中国受不同的系统影响,导致漂移的方向差异明显,夏季以西南漂移为主,冬季、春季与秋季以西北漂移为主,但是在100 hPa以上4个季节均以西南漂移为主,以冬季的漂移距离最大,漂移距离超过120km,夏季最小,约为40 km,春、秋季次之,接近70 km。漂移距离的差异东部比西部显著;沿经圈漂移以向南为主,冬、春与秋季沿经圈漂移以向南为主,春季漂移最大,超过5 km;冬、秋季节漂移距离分别超过3与2 km,夏季沿经圈漂移受大尺度环流影响,200 hPa以下向北漂移,最远漂移超过1 km,200 hPa以上转向南漂移,最远漂移达3 km;沿纬圈以向东为主,以冬季最大,达77 km,夏季最小,接近13 km,春、秋季次之,分别达49和46 km;沿纬圈漂移量要大于沿经圈漂移量,沿纬圈漂移全年平均接近60 km,沿经圈漂移量全年平均接近3.5 km,有量级上的差别;从低层到高层漂移量呈增大趋势,漂移大的范围随高度往西扩张;探空漂移的整体分布范围较大,并且比较均匀。同时还选取了个例分析低空急流条件下探空漂移,结果显示对于中小尺度天气系统不可忽略。     

2008-07-22襄樊特大暴雨FY-3A微波资料分析

搭载在中国新一代极轨卫星FY-3A上的微波湿度计对于大气湿度以及云雨分布特征具有较好的探测能力。利用其150GHｚ极化波段亮温和183-31GHｚ附近的水汽吸收波段亮温及其反演产品,对2008年7月22日襄樊特大暴雨中10：00-11:00强降水成因进行了分析。结果表明：襄樊10：00-11:00强降水产生时,微波湿度计5个波段亮温均处于低值区的大梯度带;极化比、极化差出现明显的大、小值分裂;微波湿度计的高频通道资料表明,襄樊处于深对流中心地带,正是这种深厚的对流系统导致了襄樊的强降水。     

Variations of Terrestrial Water Storage in the Yangtze River Basin under Climate Change Scenarios

In this study, the water balance-based Precipitation-Evapotranspiration-Runoff (PER) method combined with the land surface model Variable Infiltration Capacity (VIC) was used to estimate the spatiotemporal variations of terrestrial water storage (TWS) for two periods, 1982–2005 (baseline) and 2071–2100, under future climate scenarios A2 and B2 in the Yangtze River basin. The results show that the estimated TWS during the baseline period and under the two future climate scenarios have similar seasonal amplitudes of 60–70 mm. The higher values of TWS appear in June during the baseline period and under the B2 scenario, whereas the TWS under A2 shows two peaks in response to the related precipitation pattern. It also shows that the TWS is recharged from February to June during the baseline period, but it is replenished from March to June under the A2 and B2 scenarios. An analysis of the standard derivation of seasonal and interannual TWS time series under the three scenarios demonstrates that the seasonal TWS of the southeastern part of the Yangtze River basin varies remarkably and that the southeastern and central parts of the basin have higher variations in interannual TWS. With respect to the first mode of the Empirical Orthogonal Function (EOF), the inverse-phase change in seasonal TWS mainly appears across the Guizhou-Sichuan-Shaanxi belt, and the entire basin generally represents a synchronous change in interannual TWS. As a whole, the TWS under A2 presents a larger seasonal variation whereas that under B2 displays a greater interannual variation. These results imply that climate change could trigger severe disasters in the southeastern and central parts of the basin.     

基于CMIP5模拟的中国区域陆气耦合强度评估及未来情景预估

基于第五次国际耦合模式比较计划(CMIP5)11个模式历史情景的模拟结果计算得到了中国区域夏季的陆气耦合强度并进行集合平均,结果表明,位于半干旱区的华北和内蒙古地区陆气耦合强度相对较强,西部干旱区的陆气耦合强度相对较弱,位于湿润区的中国东北地区东部、长江中下游和西南地区陆气耦合强度最弱。利用上述模式集合平均结果与由NCEP再分析资料和欧洲中心的中期气象预报40年再分析资料(ERA40)计算得到的陆气耦合强度相比较,结果显示这些模式的集合平均与再分析资料NCEP和ERA40的计算结果有较好的一致性。利用历史情景模拟和不同的典型排放路径(RCP),即低排放情景RCP2.6、中排放情景RCP4.5和高排放情景RCP8.5下的模拟结果预估陆气耦合强度未来变化。结果显示:与历史情景相比较,位于湿润区的中国南方地区蒸散发的主要控制因子是温度,在3种排放情景下随着温度上升引起蒸散发增加所导致陆气耦合强度升高;位于青藏高原以及半干旱区的内蒙古大部分地区蒸散发在未来的年际变化幅度减弱导致陆气耦合指数降低;位于西北干旱区陆气耦合强度在RCP2.6和RCP4.5情景上升,然而在RCP8.5情景下陆气耦合强度下降,其原因是在高排放情景下,水汽平流输送明显增强,局地蒸散发异常对空气湿度变化的贡献减弱,导致了陆气耦合强度降低。未来预估结果在中国南方可信度相对较高,从全国来看,在RCP4.5情景下可信度相对较高。     

铅对不同土壤中青菜生长的影响

采用盆栽试验,研究了南京和北京土壤中Pb污染对青菜生长的影响。结果表明：土壤质地对青菜生长有明显的影响;在土壤Pb含量低的处理水平下,对青菜的生长有一定影响;低含量范围内,青菜中Pb含量随土壤Pb处理含量的增加出现先升后降的变化趋势;在100mg／kg时,青菜的生长受到抑制,植株中Pb的含量高于对照组和200mg／kgPb处理。     

基于MATLAB的主成分RBF神经网络降水预报模型

以前期500 hPa高度场、海温场为预报因子,采用径向基函数(RBF)神经网络与主成分分析相结合的方法,建立了广西中部5月平均降水预报模型。在5年独立样本的预测检验中,预测的平均相对误差、均方误差及平均绝对误差分别为18.12%、50.52和34.23。对比分析RBF神经网络与BP(Back Propagation)神经网络的预测结果,表明RBF神经网络预测结果更准确、精度更高。