Analysis of Sampling Error Uncertainties and Trends in Maximum and Minimum Temperatures in China

In this paper we report an analysis of sampling error uncertainties in mean maximum and minimum temperatures （Tmax and Tmin） carried out on monthly,seasonal and annual scales,including an examination of homogenized and original data collected at 731 meteorological stations across China for the period 1951-2004.Uncertainties of the gridded data and national average,linear trends and their uncertainties,as well as the homogenization effect on uncertainties are assessed.It is shown that the sampling error variances of homogenized Tmax and Tmin,which are larger in winter than in summer,have a marked northwest-southeast gradient distribution,while the sampling error variances of the original data are found to be larger and irregular.Tmax and Tmin increase in all months of the year in the study period 1951-2004,with the largest warming and uncertainties being 0.400℃ （10 yr）-1 ＋ 0.269℃ （10 yr）-1 and 0.578℃ （10 yr）-1 ＋ 0.211℃ （10 yr）-1 in February,and the least being 0.022℃ （10 yr）-1 ＋ 0.085℃ （10 yr）-1 and 0.104℃ （10 yr）-1 ＋0.070℃ （10 yr）-1 in August.Homogenization can remove large uncertainties in the original records resulting from various non-natural changes in China.

Effects of meteorological conditions and topography on the bioaccumulation of PAHs and metal elements by native lichen(Xanthoria parietina)

The bioaccumulation of PAHs and metal elements in the indigenous lichens Xanthoria parietina was monitored during two years at a quarterly frequency,in 3 sites of contrasted anthropic influence.The impact of the meteorological factors(temperature,relative humidity,rainfall,wind speed)was first estimated through principal component analysis,and then by stepwise multilinear regressions to include wind directions.The pollutants levels reflected the proximity of atmospheric emissions,in particular from a large industrial harbor.High humidity and mild temperatures,and in a lower extent low wind speed and rainfall,also favored higher concentration levels.The contributions of these meteorological aspects became minor when including wind direction,especially when approaching major emission sources.The bioaccumulation integration time towards meteorological variations was on a seasonal basis(1–2 months)but the wind direction and thus local emissions also relied on a longer time scale(12 months).This showed that the contribution of meteorological conditions may be prevalent in remote places,while secondary in polluted areas,and should be definitely taken into account regarding long-term lichen biomonitoring and inter-annual comparisons.In the same time,a quadruple sampling in each site revealed a high homogeneity among supporting tree species and topography.The resulting uncertainty,including sampling,preparation and analysis was below 30%when comfortable analytical conditions were achieved.Finally,the occurrence of unexpected events such as a major forest fire,permitted to evaluate that this type of short,although intense,events did not have a strong influence on PAH and metals bioaccumulation by lichen.

Static Frame Model Validation with Small Samples Solution Using Improved Kernel Density Estimation and Confidence Level Method

An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.