Multidecadal Trends in Large-Scale Annual Mean SATa Based on CMIP5 Historical Simulations and Future Projections

Based on observations and Coupled Model lntercomparison Project Phase 5 （CMIP5） results, multidecadal variations and trends in annual mean surface air temperature anomalies （SATa） at global, hemispheric, and hemispheric land and ocean scales in the past and under the future scenarios of two representative concentration pathways （RCPs） are analyzed. Fifteen models are selected based on their performances in capturing the temporal variability, long-term trend, multidecadal variations, and trends in global annual mean SATa. Observational data analysis shows that the multidecadal variations in annual mean SATa of the land and ocean in the northern hemisphere （NH） and of the ocean in the southern hemisphere （SH） are similar to those of the global mean, showing an increase during the 1900-1944 and 1971-2000 periods, and flattening or even cooling during the 1945-1970 and 2001-2013 periods. These observed characteristics are basically reproduced by the models. However, SATa over SH land show an increase during the 1945-1970 period, which differs from the other hemispheric scales, and this feature is not captured well by the models. For the recent hiatus period （2001-2013）, the projected trends of BCC-CSM1-1-m, CMCC-CM, GFDL-ESM2M, and NorESM1-ME at the global and hemispheric scales are closest to the observations based on RCP4.5 and RCP8.5 scenarios, suggesting that these four models have better projection capability in SATa. Because these four models are better at simulating and projecting the multidecadal trends of SATa, they are selected to analyze future SATa variations at the global and hemispheric scales during the 2006-2099 period. The selected multi-model ensemble （MME） projected trends in annual mean SATa for the globe, NH, and SH under RCP4.5 （RCP8.5） are 0.17 （0.29） ℃, 0.22 （0.36） ℃, and 0.11 （0.23） ℃-decade-1 in the 21st century, respectively. These values are significantly lower than the projections of CMIP5 MME without model selection.

The Application of Wavelet Transform in Analysis of Digital Precursory Observational Data

Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and to discriminate between the trend anomalies and the short-term anomalies. This paper presents a method to separate the high frequency information from the low ones by using the wavelet transform to analyze the digital data of precursors, and illustrates with examples the train of thoughts of discriminating the short-term anomalies from trend anomalies by using the wavelet transform, thus provide a new effective approach for extracting the short-term and trend anomalies from the digital data of precursors.

ON THE PREDICTION OF TROPICAL CYCLONE LANDFALL AND INFLUENCE ON SOUTHERN CHINA USING MONTHLY OLR ANOMALIES FOR PRIME SUMMER

With the OLR data, the landfall and activity of tropical cyclones (TC) in southern China over a 20-year period (1975～1994) are studied. The result shows that the variation of the monthly anomalous OLR is somewhat teleconnected with the TC activity in southern China. The former is used to predict short-term climate for the latter over months with frequent or no TC influence. To some extent, the relationship between the TC activity in southern China and the monthly mean OLR anomalies is dependent on the climatological location of the subtropical high in northwestern Pacific region.