A new approach to forecasting the year-to-year increment of rainfall in North China in July-August (JA) is proposed. DY is defined as the difference of a variable between the current year and the preceding year (year-to-year increment). NR denotes the seasonal mean precipitation rate over North China in JA. After analyzing the atmospheric circulation anomalies associated with the DY of NR, five key predictors for the DY of NR have been identified. The prediction model for the DY of NR is established by using multi-linear regression method and the NR is obtained (the current forecasted DY of NR added to the preceding observed NR). The prediction model shows a high correlation coefficient (0.8) between the simulated and the observed DY of NR throughout period 1965-1999, with an average relative root mean square error of 19% for the percentage of precipitation rate anomaly over North China. The prediction model makes a hindcast for 2000-2007, with an average relative root mean square error of 21% for the percentage of precipitation rate anomaly over North China. The model reproduces the downward trend of the percentage of precipitation rate anomaly over North China during 1965-2006. Because the current operational prediction models of the summer precipitation have average forecast scores of 60%-70%, it has been more difficult to forecast the summer rainfall over North China. Thus this new approach for predicting the year-to-year increment of the summer precipitation (and hence the summer precipitation itself) has the potential to significantly improve operational forecasting skill for summer precipitation.
FAN Ke1, LIN MeiJing1,2 & GAO YuZhong3 1 Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
The Tropical Cyclone Genesis Potential Index （GPI） was employed to investigate possible impacts of global warming on tropical cyclone genesis over the western North Pacific （WNP）. The outputs of 20th century climate simulation by eighteen GCMs were used to evaluate the models＇ ability to reproduce tropical cyclone genesis via the GPI. The GCMs were found in general to reasonably reproduce the observed spatial distribution of genesis. Some of the models also showed ability in capturing observed temporal variation. Based on the evaluation, the models （CGCM3.1-T47 and IPSL-CM4） found to perform best when reproducing both spatial and temporal features were chosen to project future GPI. Results show that both of these models project an upward trend of the GPI under the SRES A2 scenario, however the rate of increase differs between them.
A new seasonal prediction model for annual tropical storm numbers （ATSNs） over the western North Pacific was developed using the preceding January-February （JF） and April-May （AM） grid-point data at a resolution of 2.5° × 2.5°. The JF and AM mean precipitation and the AM mean 500-hPa geopotential height in the Northern Hemisphere, together with the JF mean 500-hPa geopotential height in the Southern Hemisphere, were employed to compose the ATSN forecast model via the stepwise multiple linear regression technique. All JF and AM mean data were confined to the Eastern ttemisphere. We established two empirical prediction models for ATSN using the ERA40 reanalysis and NCEP reanalysis datasets, respectively, together with the observed precipitation. The performance of the models was verified by cross-validation. Anomaly correlation coefficients （ACC） at 0.78 and 0.74 were obtained via comparison of the retrospective predictions of the two models and the observed ATSNs from 1979 to 2002. The multi-year mean absolute prediction errors were 3.0 and 3.2 for the two models respectively, or roughly 10% of the average ATSN. In practice, the final prediction was made by averaging the ATSN predictions of the two models. This resulted in a higher score, with ACC being further increased to 0.88, and the mean absolute error reduced to 1.92, or 6.13% of the average ATSN.
Ke Fan1,2 1 Nansen-Zhu International Research Center,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China 2 Key Laboratory of Regional Climate-Environment Research for Temperate East Asia Chinese Academy of Science,Beijing 100029,China
The year-to-year increment prediction approach proposed by was applied to forecast the annual number of tropical cyclones (TCs) making landfall over China.The year-to-year increase or decrease in the number of land-falling TCs (LTCs) was first predicted to yield a net number of LTCs between successive years.The statistical prediction scheme for the year-to-year increment of annual LTCs was developed based on data collected from 1977 to 2007,which includes five predictors associated with high latitude circulations in both Hemispheres and the circulation over the local,tropical western North Pacific Ocean.The model shows reasonably high predictive ability,with an average root mean square error (RMSE) of 1.09,a mean absolute error (MAE) of 0.9,and a correlation coefficient between the predicted and observed annual number of LTCs of 0.86,accounting for 74% of the total variance.The cross-validation test further demonstrated the high predictive ability of the model,with an RMSE value of 1.4,an MAE value of 1.2,and a correlation coefficient of 0.74 during this period.
