Seasonal Prediction of June Rainfall over South China:Model Assessment and Statistical Downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation（APEC） Climate Center（APCC） multi-model ensemble（MME） in predicting station-scale rainfall in South China（SC） in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME＇s ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output（DMO） over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.

Modeling the Tropical Pacific Ocean Using a Regional Coupled Climate Model

A high-resolution tropical Pacific general circulation model （GCM） coupled to a global atmospheric GCM is described in this paper. The atmosphere component is the 5°× 4° global general circulation model of the Institute of Atmospheric Physics （IAP） with 9 levels in the vertical direction. The ocean component with a horizontal resolution of 0.5°, is based on a low-resolution model （2° × 1° in longitude-latitude）. Simulations of the ocean component are first compared with its previous version. Results show that the enhanced ocean horizontal resolution allows an improved ocean state to be simulated; this involves （1） an apparent decrease in errors in the tropical Pacific cold tongue region, which exists in many ocean models, （2） more realistic large-scale flows, and （3） an improved ability to simulate the interannual variability and a reduced root mean square error （RMSE） in a long time integration. In coupling these component models, a monthly ＂linear-regression＂ method is employed to correct the model＇s exchanged flux between the sea and the atmosphere. A 100-year integration conducted with the coupled GCM （CGCM） shows the effectiveness of such a method in reducing climate drift. Results from years 70 to 100 are described. The model produces a reasonably realistic annual cycle of equatorial SST. The large SSTA is confined to the eastern equatorial Pacific with little propagation. Irregular warm and cold events alternate with a broad spectrum of periods between 24 and 50 months, which is very realistic. But the simulated variability is weaker than the observed and is also asymmetric in the sense of the amplitude of the warm and cold events.