In this paper, seasonal prediction of spring dust weather frequency (DWF) in Beijing during 1982-2008 has been performed. First, correlation analyses are conducted to identify antecedent climate signals during last winter that are statistically significantly related to spring DWF in Beijing. Then, a seasonal prediction model of spring DWF in Beijing is established through multivariate linear regression analysis, in which the systematic error between the result of original prediction model and the observation, averaged over the last 10 years, is corrected. In addition, it is found that climate signals occurring synchronously with spring dust weather, particularly meridional wind at 850 hPa over western Mongolian Plateau, are also linked closely to spring DWF in Beijing. As such, statistical and dynamic prediction approaches should be combined to include these synchronous predictors into the prediction model in the real-time operational prediction, so as to further improve the prediction accuracy of spring DWF in Beijing, even over North China. However, realizing such a prediction idea in practice depends essentially on the ability of climate models in predicting key climate signals associated with spring DWF in Beijing.
LIU Ying 1,2,FAN Ke1,and WANG Hui-Jun1 1 Nansen-Zhu International Research Centre,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China 2 Graduate University of the Chinese Academy of Sciences,Beijing 100049,China
The capability of an improved Dynamic Global Vegetation Model (DGVM) in reproducing the impact of climate on the terrestrial ecosystem is evaluated. The new model incorporates the Community Land ModelDGVM (CLM3.0-DGVM) with a submodel for temperate and boreal shrubs, as well as other revisions such as the "two-leaf" scheme for photosynthesis and the definition of fractional coverage of plant functional types (PFTs). Results show that the revised model may correctly reproduce the global distribution of temperate and boreal shrubs, and improves the model performance with more realistic distribution of different vegetation types. The revised model also correctly reproduces the zonal distributions of vegetation types. In reproducing the dependence of the vegetation distribution on climate conditions, the model shows that the dominant regions for trees, grasses, shrubs, and bare soil are clearly separated by a climate index derived from mean annual precipitation and temperature, in good agreement with the CLM4 surface data. The dominant plant functional type mapping to a two dimensional parameter space of mean annual temperature and precipitation also qualitatively agrees with the results from observations and theoretical ecology studies.
检验了区域气候模式RegCM3(Regional Climate Model version3)对中国淮河流域(30°55′—36°36′N,111°55′—121°25′E)1982—2001年夏季降水及大尺度环流场的模拟能力,并选取降水明显偏多的2003年夏季为个例,评估了RegCM3模式对该年淮河流域夏季降水的集合模拟能力。模拟的20a降水的空间分布与实测资料对比表明,RegCM3成功地模拟出了淮河流域夏季降水的空间分布和年际变化;通过分析对比RegCM3模拟出的低层850hPa流场结构和水汽输送状况与实测情况,可知RegCM3能模拟出低层流场结构的大致分布和水汽输送特点,但模拟所得风速和湿度均比实况偏大。对2003年淮河流域夏季降水的集合模拟结果表明,RegCM3对中小尺度极端强降水的降水量和降水中心的模拟能力尚有待进一步提高。
