In this paper, the monthly rainfall statistical data of Nanning City, Capital of </span><span style="font-family:Verdana;">Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were col<...In this paper, the monthly rainfall statistical data of Nanning City, Capital of </span><span style="font-family:Verdana;">Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were col</span><span style="font-family:Verdana;">lected. On the basis of qualitative analysis of the rainfall seasonal changing law, the non-linear seasonal rainfall forecast model on Nanning City with the method of Trend Comparison Ratio (TCR) was established by the statistical analysis </span><span style="font-family:Verdana;">software Office Excel 2013. The model was used to predict the rainfall in</span><span style="font-family:Verdana;"> spring, summer, autumn and winter in Nanning in 2019. The results were: 286.41 mm, 695.79 mm, 292.20 mm and 118.11</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">mm, respectively. It was also found that the predicted results were consistent with the seasonal distribution cha</span><span style="font-family:Verdana;">racteristics, annual distribution characteristics and the trend of historica</span><span style="font-family:Verdana;">l </span><span style="font-family:Verdana;">rainfall time series fluctuation, through the qualitative analysis of figures.</span><span style="font-family:Verdana;"> Compared with the actual measured rainfall data of Nanning City in 2019 in the China Statistical Yearbook (2020), the predicted values are </span></span><span style="font-family:Verdana;">basically </span><span style="font-family:Verdana;">consistent with the measured values.展开更多
Two reconstructed sea surface temperature(SST) datasets(HadISST1 and COBE SST2) with centennial-scale are compared on the SST climate change over the China Seas and their adjacent sea areas. Two independent datasets s...Two reconstructed sea surface temperature(SST) datasets(HadISST1 and COBE SST2) with centennial-scale are compared on the SST climate change over the China Seas and their adjacent sea areas. Two independent datasets show consistency in statistically significant trends, with a warming trend of 0.07—0.08 ℃ per decade from 1890 to2013. However, in shorter epochs(such as 1961—2013 and 1981—2013), HadISST1 exhibits stronger warming rates than those of COBE SST2. Both datasets experienced a sudden decrease in the global hiatus period(1998—2013), but the cooling rate of HadISST1 is lower than that of COBE SST2. These differences are possibly caused by the different observations sources which are incorporated to fill with data-sparse regions since 1982. Different data sources may lead to higher values in HadISST1 from 1981 to 2013 than that in COBE SST2. Meanwhile, the different data sources and bias adjustment before the World War II may also cause the large divergence between COBE SST2 and HadISST1,leading to lower SST from 1891 to 1930. These findings illustrate that the long-term linear trends are broadly similar in the centennial-scale in the China Seas using different datasets. However, there are large uncertainties in the estimate of warming or cooling tendency in the shorter epochs, because there are different data sources, different bias adjustment and interpolation method in different datasets.展开更多
文摘In this paper, the monthly rainfall statistical data of Nanning City, Capital of </span><span style="font-family:Verdana;">Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were col</span><span style="font-family:Verdana;">lected. On the basis of qualitative analysis of the rainfall seasonal changing law, the non-linear seasonal rainfall forecast model on Nanning City with the method of Trend Comparison Ratio (TCR) was established by the statistical analysis </span><span style="font-family:Verdana;">software Office Excel 2013. The model was used to predict the rainfall in</span><span style="font-family:Verdana;"> spring, summer, autumn and winter in Nanning in 2019. The results were: 286.41 mm, 695.79 mm, 292.20 mm and 118.11</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">mm, respectively. It was also found that the predicted results were consistent with the seasonal distribution cha</span><span style="font-family:Verdana;">racteristics, annual distribution characteristics and the trend of historica</span><span style="font-family:Verdana;">l </span><span style="font-family:Verdana;">rainfall time series fluctuation, through the qualitative analysis of figures.</span><span style="font-family:Verdana;"> Compared with the actual measured rainfall data of Nanning City in 2019 in the China Statistical Yearbook (2020), the predicted values are </span></span><span style="font-family:Verdana;">basically </span><span style="font-family:Verdana;">consistent with the measured values.
基金National Key Basic Research Program of China(2016YFA0602200,2012CB955203,2013CB430202)
文摘Two reconstructed sea surface temperature(SST) datasets(HadISST1 and COBE SST2) with centennial-scale are compared on the SST climate change over the China Seas and their adjacent sea areas. Two independent datasets show consistency in statistically significant trends, with a warming trend of 0.07—0.08 ℃ per decade from 1890 to2013. However, in shorter epochs(such as 1961—2013 and 1981—2013), HadISST1 exhibits stronger warming rates than those of COBE SST2. Both datasets experienced a sudden decrease in the global hiatus period(1998—2013), but the cooling rate of HadISST1 is lower than that of COBE SST2. These differences are possibly caused by the different observations sources which are incorporated to fill with data-sparse regions since 1982. Different data sources may lead to higher values in HadISST1 from 1981 to 2013 than that in COBE SST2. Meanwhile, the different data sources and bias adjustment before the World War II may also cause the large divergence between COBE SST2 and HadISST1,leading to lower SST from 1891 to 1930. These findings illustrate that the long-term linear trends are broadly similar in the centennial-scale in the China Seas using different datasets. However, there are large uncertainties in the estimate of warming or cooling tendency in the shorter epochs, because there are different data sources, different bias adjustment and interpolation method in different datasets.
