Empirical Orthogonal Function (EOF) analysis and the related Principal Components (PC) analysis are used to extract valuable vegetation cover derived information from the National Oceanic and Atmos- pheric Administration (NOAA-AVHRR)'s Leaf Area Index (LAI) satellite images. Results suggest that from 1982 to 2000 global climate change has contributed to an increase in vegetation cover in the Qinghai-Tibet Plateau. The correlation between rainfall and LAI EOF PC1 and PC2 indicates that rainfall is the major climatic factor influencing interannual variations of average vegetation cover throughout the entire Plateau. However, annual mean vegetation cover trends in the Qinghai-Tibet Plateau are mainly out of phase with air temperature increasing, which is primarily responsible for nonsynchro- nous changes of vegetation cover. In the southern ridge of the Qinghai-Tibet Plateau, recent warming trends contribute to humid weather and favorable conditions for vegetation growth. By contrast, higher temperatures have led to arid conditions and insufficient rainfall in the northern part of the Plateau, leading to drought and other climatic conditions which are not conducive to increased vegetation cover.
The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.
