基于动力统计方法对中国夏季温度模式误差订正的研究

Study of Model Error Correction on Summer Temperature in China Based on the Method of Combine with Dynamic and Statistical

利用气候趋势系数分析1983-2012年NCEP/NCAR温度逐日再分析资料和国家气候中心全球海气耦合模式(CGCM)逐月温度资料,得出近30年中国夏季观测温度和模式预报温度趋势变化特征。通过经验模态分解方法拟合并去除观测温度整体趋势得到无趋势温度资料,结合系统误差订正和最优多因子组合动力统计相结合两种不同的模式误差订正预测方法,分析了全球变暖对中国夏季温度预测的影响。结果表明:近30年来中国夏季大部分地区观测温度呈上升趋势,且通过了0.01显著性水平检验,而模式预报温度几乎无趋势变化,对全球变暖存在明显的模拟不足。为避免这种模式对观测气温整体趋势的模拟不足对预测效果的影响并得到最优的预测结果,利用经验模态分解方法拟合并去除观测温度的整体趋势,对观测温度的拟合趋势预测时采取先去除预测后再回加的方式在预测过程中避开模式结果模拟不足的问题。近10年独立样本回报预测结果表明,上述方法可以有效地提高预测效果,并解决直接预测出现的预测结果偏低的问题,说明了在对模式预报结果后处理过程中去除增温趋势的重要性。

By using climate trend coefficient analysis the variation of observed and model predicted China summer temperature in the past 30 years.The dates come from NCEP/NCAR daily reanalysis date and National Climatic Center monthly models temperature date.Through empirical mode decomposition method fitting the raise trend of observed temperature and remove the trend in the context of global warming.Combined with systematic error correction and seasonal prediction method- statistical method of combining power to analysis the trend on the impact of summer temperatures forecast.The results show that:most of the area in China has a significant increase trend in summer temperature in last 30 years.In most areas the climate trend coefficient get through 0.01 significance level.But mode temperature almost have no change in trend in last 30 years,there is obviously inadequate for global warming.In order to avoid this kind of model simulation of observed temperature the overall trend is insufficient to predict effect and the effect of the optimal predicted results.Using the empirical mode decomposition method can effectively fitting and remove the trend of observed temperature.When forecasting the temperature we remove the trend first and add the trend in the last.Trough tests find that the method can significantly improve the result compare with direct forecast result in most area.And solve the problem of forecast result is lower than observed result.All the results show that it is necessary to remove the observed temperature increase trend in numerical prediction model results in post-processing.