基于海温和环流特征量的江苏省小麦适播期预测

基于江苏省60个气象台站1961-2010年冬小麦冬前生长期间的气温资料，利用曼-肯德尔法（Mann-Kendall method）分析发现，小麦冬前生长期间日均气温及有效积温在全球气候变暖背景下均有明显的增暖趋势。同时根据江苏省各地区冬小麦有记录以来的农业气象资料分析可得，播期与冬前总积温、有效积温具有极显著的相关关系，尤以有效积温更为显著，均通过了0．001显著水平检验，故可利用冬前有效积温确定冬小麦适播期。在此基础上，根据海气相互作用原理以及500hPa大气环流特征量能表征天气形势和控制天气条件的这一特性，利用最优化因子相关技术对海温及环流特征量进行普查，从中挑选一批与冬前有效积温相关显著、稳定性强、因子相互独立、可靠的海温和环流特征量作为长期预报因子，建立冬前有效积温的海温模型和环流模型，其拟合效果和试报效果甚佳，可投入业务应用，提前1-2月预测冬前有效积温，从而确定冬小麦适播期。

Prediction of Optimum Sowing Date of Winter Wheat in Jiangsu Province Based on Sea Surface Temperature and Atmospheric Circulation Characteristics

Based on the analysis of temperature data from 60 meteorological stations in Jiangsu Province during the period 1961-2010 by using the Mann Kendall method, it was found out that both daily mean temperature and effective accumulative temperature during the growing period of winter wheat prior to winter have significant increasing trends in parallel with global warming. The statistical analysis on the agro meteorological records in different regions has shown that the sowing date of winter wheat is significantly correlated with the total and effective accumulative temperatures prior to the winter. The correlation between sowing date and effective accumulative temperature has passed the test at the 0.001 significance level. According to coupling between the atmosphere and oceans and the fact that the atmospheric circulation characteristics (ACC) of 500 hPa can indicate the weather patterns and control the weather conditions, a batch of effective sea surface temperature (SST) and ACC predictors of accumulative temperature prior to the winter were selected using the optimum correlation technique. These SST and ACC predictors are independent each other and have stable and significant correlations with the accumulative temperature. Then the models for predicting the accumulative temperature prior to the winter were developed. Calibrations and validations show that these developed models are able to predict the accumulative temperature prior to the winter with satisfied accuracy. They can be routinely operated to predict the accumulative temperature prior to the winter and to determine the optimum sowing date of winter wheat one to two months in advance.