By characterizing the patterns of temperature extremes over nine integrated agricultural regions (IARs) in China from 1961 to 2011, this study performed trend analyses on 16 extreme temperature indices using a high-resolution (0.5° × 0.5°) daily gridded dataset and the Mann-Kendall method. The results show that annually, at both daytime and nighttime, cold extremes significantly decreased but warm extremes significantly increased across all IARs. Overall, nighttimes tended to warm faster than daytimes. Diurnal temperature ranges (DTR) diminished, apart from the mid-northern Southwest China Region and the mid-Loess Plateau Region. Seasonally, DTR widely diminished across all IARs during the four seasons except for spring. Higher minimum daily minimum temperature (TNn) and maximum daily maximum temperature (TXx), in both summer and winter, were recorded for most IARs except for the Huang-Huai-Hai Region; in autumn, all IARs generally encountered higher TNn and TXx. In all seasons, warming was observed at daytime and nighttime but, again, nighttimes warmed faster than daytimes. The results also indicate a more rapid warming trend in Northern and Western China than in Southern and Eastern China, with accelerated warming at high elevations. The increases in TNn and TXx might cause a reduction in agriculture yield in spring over Northern China, while such negative impact might occur in Southern China during summer. In autumn and winter, however, the negative impact possibly occurred in most of the IARs. Moreover, increased TXx in the Pearl River Delta and Yangtze River Delta is possibly related to rapid local urbanization. Climatically, the general increase in temperature extremes across Chinese IARs may be induced by strengthened Northern Hemisphere Subtropical High or weakened Northern Hemisphere Polar Vortex.
A nonlinear wavelet neural network (WNN) model with natural orthogonal expansion (NOE) and combined weights is constructed to predict the annual frequency of tropical cyclones (TCF) occurring over the coastal regions of Southern China. Combined weights are obtained by calculating categorical weights, based on the particle swarm projection pursuit, and ranking weights, based on fuzzy mathematics, followed by optimization. The global monthly mean heights at 500?hPa and sea-surface temperature fields are used as two predictors. The linear and nonlinear information of the predictors with reduced dimensions is gathered through the NOE and combined weights, respectively, and treated as the input into the WNN model. This model is first trained with the 55-year (i.e., 1950?C2004) TCF data and then used to predict annual TCFs for the subsequent 5?years (i.e., 2005?C2009). Results show that the mean absolute and relative errors are 0.6175 and 9.34?%, respectively. The impacts of the combined weights, NOE and WNN as well as the traditional multi-regression approach on the TCF prediction are examined. Results show superior performance of the WNN-based model in the annual TCF prediction. 相似文献