A strong （weak） East Asian summer monsoon （EASM） is usually concurrent with the tripole pattern of North Atlantic SST anomalies on the interannual timescale during summer, which has positive （negative） SST anomalies in the northwestern North Atlantic and negative （positive） SST anomalies in the subpolar and tropical ocean. The mechanisms responsible for this linkage are diagnosed in the present study. It is shown that a barotropie wave-train pattern occurring over the Atlantic-Eurasia region likely acts as a link between the EASM and the SST tripole during summer. This wave-train pattern is concurrent with geopotential height anomalies over the Ural Mountains, which has a substantial effect on the EASM. Diagnosis based on observations and linear dynamical model results reveals that the mechanism for maintaining the wave-train pattern involves both the anomalous diabatic heating and synoptic eddy-vorticity forcing. Since the North Atlantic SST tripole is closely coupled with the North Atlantic Oscillation （NAO）, the relationships between these two factors and the EASM are also examined. It is found that the connection of the EASM with the summer SST tripole is sensitive to the meridional location of the tripole, which is characterized by large seasonal variations due to the north-south movement of the activity centers of the NAO. The SST tripole that has a strong relationship with the EASM appears to be closely coupled with the NAO in the previous spring rather than in the simultaneous summer.
为了提高BCC_CSM气候系统模式运行效率,保障业务科研工作的顺利开展,进行BCC_CSM气候系统模式在IBM高性能计算系统的移植工作;通过性能优化使BCC_CSM模式运行效率显著提高,通过气候要素形势场分布和相对误差量化指标对BCC_CSM气候系统模式模拟性能进行验证。结果表明:移植优化后,BCC_CSM气候系统模式计算效率提高为原来的1.4倍;基于CMIP5 pi Control试验,完成531—540年10 a的气候模拟,全球年平均地表气温形势场分布合理,相对误差小于0.5%,BCC_CSM气候系统模式计算和模拟性能均能满足应用需求。
Historical simulations of annual mean surface air temperature over China with 25 CMIP5 models were assessed.The observational data from CRUT3v and CN05 were used and further compared with historical simulations of CMIP3.The results show that CMIP5 models were able to simulate the observed warming over China from 1906 to 2005(0.84 C per 100 years)with a warming rate of 0.77 C per 100 years based on the multi-model ensemble(MME).The simulations of surface air temperature in the late 20th century were much better than those in the early 20th century,when only two models could reproduce the extreme warming in the 1940s.The simulations for the spatial distribution of the 20-yearmean(1986–2005)surface air temperature over China fit relatively well with the observations.However,underestimations in surface air temperature climatology were still found almost all over China,and the largest cold bias and simulation uncertainty were found in western China.On sub-regional scale,northern China experienced stronger warming than southern China during 1961–1999,for which the CMIP5 MME provided better simulations.With CMIP5 the diference of warming trends in northern and southern China was underestimated.In general,the CMIP5 simulations are obviously improved in comparison with the CMIP3 simulations in terms of the variation in regional mean surface air temperature,the spatial distribution of surface air temperature climatology and the linear trends in surface air temperature all over China.
Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 （CMIP5） models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity （absolute indices and threshold indices）, duration （duration indices）, and frequency （percentile indices） of extreme temperature. The overall performance of each model is summarized by a ＂portrait＂ diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986-2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961-2005.
