The East Asian summer monsoon(EASM) is a distinctive component of the Asian climate system and critically influences the economy and society of the region. To understand the ability of AGCMs in capturing the major features of EASM, 10 models that participated in Coupled Model Intercomparison Project/Atmospheric Model Intercomparison Project(CMIP5/AMIP), which used observational SST and sea ice to drive AGCMs during the period 1979–2008, were evaluated by comparing with observations and AMIP II simulations. The results indicated that the multi-model ensemble(MME) of CMIP5/AMIP captures the main characteristics of precipitation and monsoon circulation, and shows the best skill in EASM simulation, better than the AMIP II MME. As for the Meiyu/Changma/Baiyu rainbelt, the intensity of rainfall is underestimated in all the models. The biases are caused by a weak western Pacific subtropical high(WPSH) and accompanying eastward southwesterly winds in group I models, and by a too strong and west-extended WPSH as well as westerly winds in group II models. Considerable systematic errors exist in the simulated seasonal migration of rainfall, and the notable northward jumps and rainfall persistence remain a challenge for all the models. However, the CMIP5/AMIP MME is skillful in simulating the western North Pacific monsoon index(WNPMI).
利用政府间气候变化委员会第四次评估报告(the Fourth Assessment Report of the Intergov-ernmental Panel on Climate Change,IPCC AR4)的14个全球气候耦合模式对中国淮河流域气温和降水的模拟能力进行了评估,预估了该地区21世纪的降水和气温变化。同时,还分析了14个气候模式对1961-1999年气温和降水的模拟能力,并且根据Taylor方法选取具有较好模拟能力的模式做集合分析。结果表明,不同的气候模式对淮河流域的气温和降水都具有一定的模拟能力,但大多数模式模拟的气温偏低、降水偏多;选取的模式集合可以明显改善模式的模拟能力,但是没有表现出明显的优势。对淮河流域降水和气温未来情景的预估表明,各模式给出的情景结果尽管存在一定的差异,但模拟的21世纪气候变化的趋势基本一致,即气温持续增加,降水出现区域性增加;还重点分析了14个模式集合的结果在2010-2039年、2040-2069年和2070-2099年3个时段的年平均、季节平均降水和气温变化及其时空变化特征,结果表明,3个时段的气温和降水在不同情景下都是逐渐增加的,A2情景下增幅最显著,B1情景下增幅最小。
Based on observations and 12 simulations from Coupled Model Intercomparison Project Phase 5(CMIP5) models, climatic extremes and their changes over China in the past and under the future scenarios of three Representative Concentration Pathways(RCPs) are analyzed. In observations, frost days(FD) and low-temperature threshold days(TN10P) show a decreasing trend, and summer days(SU), high-temperature threshold days(TX90P), heavy precipitation days(R20), and the contribution of heavy precipitation days(P95T) show an increasing trend. Most models are able to simulate the main characteristics of most extreme indices. In particular, the mean FD and TX90 P are reproduced the best, and the basic trends of FD, TN10 P, SU and TX90 P are represented. For the FD and SU indexes, most models show good ability in capturing the spatial differences between the mean state of the periods 1986–2005 and 1961–80; however, for other indices, the simulation abilities for spatial disparity are less satisfactory and need to be improved. Under the high emissions scenario of RCP8.5,the century-scale linear changes of the multi-model ensemble(MME) for FD, SU, TN10 P, TX90 P, R20 and P95 T are-46.9,46.0,-27.1, 175.4, and 2.9 days, and 9.9%, respectively; and the spatial change scope for each index is consistent with the emissions intensity. Due to the complexities of physical process parameterizations and the limitation of forcing data, great uncertainty still exists with respect to the simulation of climatic extremes.
This paper describes the model speed and model In/Out (I/O) efficiency of the high-resolution atmospheric general circulation model FAMIL (Finite-volume Atmospheric Model of IAP/LASG) at the National Supercomputer Center in Tianjin, China, on its Tianhe-1A supercomputer platform. A series of three-model-day simulations were carried out with standard Aqua Planet Experiment (APE) designed within FAMIL to obtain the time stamp for the calculation of model speed, simulation cost, and model I/O efficiency. The results of the simulation demonstrate that FAMIL has remarkable scalability below 3456 and 6144 cores, and the lowest simulation costs are 1536 and 3456 cores for 12.5 km and 6.25 km resolutions, respectively. Furthermore, FAMIL has excellent I/O scalability and an efficiency of more than 80% on 6 I/Os and more than 99% on 1536 I/Os.
When one applies the wavelet transform to analyze finite-length time series, discontinuities at the data boundaries will distort its wavelet power spectrum in some regions which are defined as a wavelength-dependent cone of influence (COI). In the COI, significance tests are unreliable. At the same time, as many time series are short and noisy, the COI is a serious limitation in wavelet analysis of time series. In this paper, we will give a method to reduce boundary effects and discover significant frequencies in the COI. After that, we will apply our method to analyze Greenland winter temperature and Baltic sea ice. The new method makes use of line removal and odd extension of the time series. This causes the derivative of the series to be continuous (unlike the case for other padding methods). This will give the most reasonable padding methodology if the time series being analyzed has red noise characteristics.
