摘要
We present a model for predicting summertime surface air temperature in Northeast China（NESSAT） using a year-to-year incremental approach.The predicted value for each year＇s increase or decrease of NESSAT is added to the observed value within a particular year to yield the net forecast NESSAT.The seasonal forecast model for the year-to-year increments of NESSAT is constructed based on data from 1975- 2007.Five predictors are used：an index for sea ice cover over the East Siberian Sea,an index for central Pacific tropical sea surface temperature,two high latitude circulation indices,as well as a North American pressure index.All predictors are available by no later than March,which allows for compilation of a seasonal forecast with a two-month lead time.The prediction model accurately captures the interannual variations of NESSAT during 1977-2007 with a correlation coefficient between the predicted and observed NESSAT of 0.87（accounting for 76%of total variance） and a mean absolute error（MAE） of 0.3℃.A cross-validation test during 1977-2008 demonstrates that the model has good predictive skill,with MAE of 0.4℃and a correlation coefficient between the predicted and observed NESSAT of 0.76.
We present a model for predicting summertime surface air temperature in Northeast China（NESSAT） using a year-to-year incremental approach.The predicted value for each year＇s increase or decrease of NESSAT is added to the observed value within a particular year to yield the net forecast NESSAT.The seasonal forecast model for the year-to-year increments of NESSAT is constructed based on data from 1975- 2007.Five predictors are used：an index for sea ice cover over the East Siberian Sea,an index for central Pacific tropical sea surface temperature,two high latitude circulation indices,as well as a North American pressure index.All predictors are available by no later than March,which allows for compilation of a seasonal forecast with a two-month lead time.The prediction model accurately captures the interannual variations of NESSAT during 1977-2007 with a correlation coefficient between the predicted and observed NESSAT of 0.87（accounting for 76%of total variance） and a mean absolute error（MAE） of 0.3℃.A cross-validation test during 1977-2008 demonstrates that the model has good predictive skill,with MAE of 0.4℃and a correlation coefficient between the predicted and observed NESSAT of 0.76.
基金
Supported by the Special Fund for Public Welfare(Meteorology)(GYHY200906018)
the Innovation Key Program of the Chinese Academy of Sciences(KZCX2-YW-BR-14)
the Basic Research Program of China(2009CB421406)
the National Excellent Ph.D.Dissertation Program of the Chinese Academy of Sciences
