基于IAP AGCM4的欧亚大陆春季雪水当量可预报性评估

欧亚大陆积雪是重要的气候预测因子,评估其在气候模式中的预测潜力可为季节气候预测和模式发展提供重要参考。本文利用IAP AGCM4的多年集合后报结果,分析了欧亚大陆春季雪水当量的可预报性。结果表明该模式对提前1月后报的欧亚大陆春季雪水当量的空间分布,主要模态及变化趋势具有较好的可预报能力。此外模式对欧亚中高纬积雪的年际异常也具有较高的预报技巧,特别是高纬度区域。可预报性来源分析则表明,大气初始异常对欧亚中高纬积雪可预报性的影响与海温异常相比显得更为重要。     

基于IAP AGCM4的欧亚大陆春季雪水当量可预报性评估（英文）

欧亚大陆积雪是重要的气候预测因子,评估其在气候模式中的预测潜力可为季节气候预测和模式发展提供重要参考。本文利用IAP AGCM4的多年集合后报结果,分析了欧亚大陆春季雪水当量的可预报性。结果表明该模式对提前1月后报的欧亚大陆春季雪水当量的空间分布,主要模态及变化趋势具有较好的可预报能力。此外模式对欧亚中高纬积雪的年际异常也具有较高的预报技巧,特别是高纬度区域。可预报性来源分析则表明,大气初始异常对欧亚中高纬积雪可预报性的影响与海温异常相比显得更为重要。     

欧亚中高纬陆面感热异常影响我国夏季气候的数值模拟

使用区域气候模式RegCM4.4.5.7,通过改变春季欧亚大陆中高纬地区的陆面感热通量,对欧亚中高纬感热异常影响中国夏季气候进行模拟分析,并探讨其影响机制。试验结果表明:当春季欧亚中高纬陆面感热通量加强时,我国长江流域和东北东部夏季气温降低,降水偏多;华北地区气温升高,降水偏少。春季陆面感热增强引起近地面和对流层低层大气热力状况异常,进而导致高度场和环流场的异常,长江流域和东北地区有气旋环流,对流运动旺盛,结合充足的水汽条件,对应降水偏多,而华北地区则相反,有反气旋环流和微弱的气流辐合,对应降水偏少。研究表明欧亚中高纬陆面感热异常是影响我国夏季气候的一个不可忽视的因子。     

春季欧亚大陆积雪对春夏季南北半球大气质量交换的可能影响

本文基于春季欧亚雪盖资料与大气再分析资料的奇异值分解(SVD)分析结果,结合数值试验,研究了春季欧亚大陆积雪变化与春、夏季南北半球大气质量交换的联系。研究表明,当春季欧亚积雪异常偏多时,同期欧亚大陆中高纬大范围地区的地面气温异常偏低,这种冷却效应可能持续至夏季,同时,冷空气的堆积造成了欧亚大陆地表气压(气柱大气质量)的增加,并且对应了夏季北半球大气总质量的异常上升,而南半球大气质量却明显下降。分析发现,春季欧亚积雪异常与夏季南北半球际大气质量涛动存在显著的滞后相关,而且前者还与同期及后期包括索马里急流和对流层上部80°E~120°E区域高空急流在内的多处越赤道气流变化联系密切。从数值模拟结果分析发现,以改变春季初始积雪状况作为驱动,欧亚大陆中高纬地区的低层大气环流出现了显著响应,即当积雪增加时,同期及其后夏季地面气温显著降低,并且冷异常区域对应着气柱质量的异常升高。     

中国冬季积雪特征及欧亚大陆积雪对中国气候影响

该文首先回顾了有关中国冬季积雪的研究进展,包括中国冬季积雪的空间分布气候特征以及季节、年际和年代际变化,中国冬季降雪特征,气象因子对中国冬季积雪水量平衡的影响,外强迫和大气环流系统在积雪形成中的作用等。冬春季欧亚大陆积雪对同期和后期中国气候影响的相关研究说明与欧亚大陆积雪异常相关联的中国气候异常以及积雪通过改变土壤湿度、表面温度和辐射分布,引起大气环流异常,进而对中国气候产生影响的物理过程。应用美国环境预测中心 (NCEP) 第2版气候预测系统 (CFSv2) 的回报试验结果,对CFSv2在欧亚大陆积雪变化及其与中国气候关系的可预报性方面的分析表明,CFSv2能够较好地回报出春季欧亚积雪的年际和年代际变异及其与中国夏季降水之间的联系。文章最后提出了在积雪及其气候效应研究方面一些有待解决的问题。     

欧亚大陆中高纬积雪消融异常对东北夏季低温的影响

利用美国冰雪资料中心提供的1979~2007年月平均积雪水当量资料、NCEP/NCAR的逐月再分析资料 以及中国743站的逐日气温资料,讨论了欧亚中高纬春季融雪异常分布与中国东北夏季温度的联系及其可能的影响机理。结果表明:欧亚大陆中高纬西部春季融雪偏多、东部春季融雪偏少时,我国东北夏季易出现低温。春季东部融雪量少,导致夏季剩余积雪偏多;夏季积雪融化吸热增多,加上后期的土壤湿度增加会导致该地区夏季温度异常偏低,高度场下降,500 hPa上欧亚中高纬东部的长波槽加深,槽后偏北气流加强;来自极地的冷空气容易入侵东亚中高纬地区,引起我国东北夏季低温。     

春季欧亚积雪异常影响中国夏季降水的数值试验

 利用NCAR的新一代GCM CAM3.1版本模式,研究了欧亚大陆春季积雪异常对北半球大气环流和中国夏季降水的影响。结果表明,春季积雪异常通过改变其后夏季的土壤湿度和温度分布,造成对流层厚度场的异常,激发一个从欧洲西部到东亚的500 hPa高度场异常波列。我国南、北方处于符号相反的高度场异常区,同时降水也呈现南北相异的态势,这表明春季欧亚积雪异常是影响我国夏季降水分布的一个重要因子。     

欧亚大陆积雪与2010年中国春末夏初降水的关系

利用欧亚大陆地区的积雪覆盖日数资料、中国160个台站逐月降水资料以及美国国家环境预报中心和国家大气研究中心(NCEP/NCAR)再分析资料,采用奇异值分解(SVD)方法和数值试验研究了欧亚大陆2-4月积雪与后期5-6月中国降水的关系。结合2010年积雪和降水的分布特征可以发现,SVD第二模态基本反映了2010年积雪异常与降水异常的时滞耦合关系。欧亚大陆积雪在欧洲、青藏高原东部和东亚地区异常偏多时,我国华南降水增多,长江中下游降水减少,欧亚大陆东、西部积雪异常偏多与后期中国华南降水存在正相关关系。根据统计分析得到的空间分布特征和积雪参数的观测数据,选用大气环流模式CAM3.1进行数值试验,模拟结果与统计分析结果比较一致。积雪异常通过反照率效应和水文效应引起地表及其上的大气热力状况异常,进而引起位势高度场、风场等各个大气环流要素场的调整,导致后期华南降水异常偏多。欧亚大陆积雪异常很可能是2010年春末夏初华南降水异常的一个重要诱发因子。     

NCEP CFSv2对中国2015年夏季月尺度降水和环流预报分析

利用美国环境预报中心的第二代气候预报系统(NCEP CFSv2)提供的1982~2010年历史回报资料和2015年6~8月预报产品、NCEP CFSR再分析资料及中国地面观测降水资料,评估了NCEP CFSv2对2015年(厄尔尼诺发展年)中国夏季月降水和环流形势的预报能力,并分析了影响模式预报技巧高低的可能因子。结果表明:1)模式对降水的预报技巧较低且表现出明显的月变化(7月最高,8月次之,6月最低),但总体水平都不高。预报技巧明显依赖于提前时间的长短。2)CFSv2对影响我国夏季降水的500h Pa关键区环流异常空间模态表现出较高的预报技巧。对全东亚区域,模式基本都可提前5~9天(7月9天,6月6天,8月5天)较为准确的预报出未来一个月高度异常空间模态。3)通过对比分析发现,CFSv2环流预报中选取12个集合成员(滑动3天)可以得到较稳定的预报结果。4)在2015年夏季月尺度环流异常模态预报中,东亚全区的环流预报水平很大程度上取决于中高纬地区的预报。CFSv2对中高纬环流月预报技巧(6~8月都能从提前4天开始就基本稳定维持在较高水平)比热带地区更高更稳定。      

全球大气环流模式BCC_AGCM2.0.1对1998年夏季江淮流域强降水过程的回报试验研究

利用中国气象局北京气候中心全球大气环流模式(BCC_AGCM2.0.1) 对1998年6月24日～7月3日发生在我国江淮流域的强降水天气过程进行了回报试验。模式起报时间为1998年6月24日00时, 使用前10天NCEP-II再分析逐时温度、涡度和散度场进行预报前初始协调 (spin-up) 积分, 产生模式初值, 预报时段为1998年6月24日～7月10日, 回报试验结果表明: 模式对全球500 hPa位势高度的天气尺度演变过程具有4～7天的可预报性; BCC_AGCM2.0.1模式对中国区域的降水以及大气环流场具有3～4天的可预报性, 6月24日起报后3天内的预报降水区域位置与实况一致, 但中心强度有所差异。对起报后未来2天的5 mm和10 mm以上的降水预报能力相对较强, ETS评分值达到了0.25以上, HK评分超过了0.4, 降水区域范围预报较为准确, BIA评分趋于1.0。模式对20 mm以上的降水也具有一定的可预报性, 但模式对大于30 mm以上强降水的预报能力较差。     

Assessing the Impacts of Eurasian Snow Conditions on Climate Predictability with a Global Climate Model

On the basis of two ensemble experiments conducted by a general atmospheric circulation model (Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model, hereinafter referred to as IAP9L_CoLM), the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated. The predictive skill of sea level pressures (SLP) and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model. Furthermore, the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature (SAT) anomalies over China was improved by applying realistic (prescribed) Eurasian snow conditions. The predictive skill of the summer precipitation in China was low; however, when realistic snow conditions were employed, the predictability increased, illustrating the effectiveness of the application of realistic Eurasian snow conditions. Overall, the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China. When Eurasian snow conditions in the global climate model (GCM) can be more realistically represented, the predictability of summer climate over China increases.     

Potential predictability of Eurasian snow cover

Potential predictability and skill of simulated Eurasian snow cover are explored using a suite of seasonal ensemble hindcasts (i.e. retrospective forecasts), an ensemble climate simulation (spanning the years 1982–1998) and observations. Using remotely sensed observations of snow cover, we find significant point-wise correlation over the North Atlantic and North Pacific between winter and spring averaged sea-surface temperatures and Eurasian snow cover area. The observed correlation shows no discernible pattern related to the El Niño-Southern Oscillation (ENSO). The hindcasts show correlation patterns similar to the observations. However, the climate simulation shows an exaggerated ENSO pattern. The results underscore the importance of initialization in seasonal climate forecasts, and that the observed potential predictability of Eurasian snowcover cannot be solely attributed to ENSO.     

Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study

Land surface hydrology (LSH) is a potential source of long-range atmospheric predictability that has received less attention than sea surface temperature (SST). In this study, we carry out ensemble atmospheric simulations driven by observed or climatological SST in which the LSH is either interactive or nudged towards a global monthly re-analysis. The main objective is to evaluate the impact of soil moisture or snow mass anomalies on seasonal climate variability and predictability over the 1986–1995 period. We first analyse the annual cycle of zonal mean potential (perfect model approach) and effective (simulated vs. observed climate) predictability in order to identify the seasons and latitudes where land surface initialization is potentially relevant. Results highlight the influence of soil moisture boundary conditions in the summer mid-latitudes and the role of snow boundary conditions in the northern high latitudes. Then, we focus on the Eurasian continent and we contrast seasons with opposite land surface anomalies. In addition to the nudged experiments, we conduct ensembles of seasonal hindcasts in which the relaxation is switched off at the end of spring or winter in order to evaluate the impact of soil moisture or snow mass initialization. LSH appears as an effective source of surface air temperature and precipitation predictability over Eurasia (as well as North America), at least as important as SST in spring and summer. Cloud feedbacks and large-scale dynamics contribute to amplify the regional temperature response, which is however, mainly found at the lowest model levels and only represents a small fraction of the observed variability in the upper troposphere.     

Assessing the Impacts of Initial Snow Conditions over the Tibetan Plateau on China Precipitation Prediction Using a Global Climate Model

Two ensemble experiments were conducted using a general atmospheric circulation model. These experiments were used to investigate the impacts of initial snow anomalies over the Tibetan Plateau（TP） on China precipitation prediction. In one of the experiments, the initial snow conditions over the TP were climatological values; while in the other experiment, the initial snow anomalies were snow depth estimates derived from the passive microwave remote-sensing data. In the current study, the difference between these two experiments was assessed to evaluate the impact of initial snow anomalies over the TP on simulated precipitation. The results indicated that the model simulation for precipitation over eastern China had certain improvements while applying a more realistic initial snow anomaly, especially for spring precipitation over Northeast China and North China and for summer precipitation over North China and Southeast China. The results suggest that seasonal prediction could be enhanced by using more realistic initial snow conditions over TP, and microwave remote-sensing snow data could be used to initialize climate models and improve the simulation of eastern China precipitation during spring and summer. Further analyses showed that higher snow anomalies over TP cooled the surface, resulting in lower near- surface air temperature over the TP in spring and summer. The surface cooling over TP weakened the Asian summer monsoon and brought more precipitation in South China in spring and more precipitation to Southeast China during summer.     

Recent Advances in Dynamical Extra-Seasonal to Annual Climate Prediction at IAP／CAS

Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly,the second generation of the IAP dynamical climate prediction system (IAP DCP-II) has been described,and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980-1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998-2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature).Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques(e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of criticalre quirements needed for the further improvement of dynamical seasonal climate prediction.     

Impact of snow initialization on sub-seasonal forecasts

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004–2009, with either realistic initialization of snow variables based on re-analyses, or else with “scrambled” snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This “warm Arctic—cold continent” difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses.     

IAP短期气候距平预测系统的定量评估及订正技术的改进研究

采用一套多指标的评估方法, 对中国科学院大气物理所研制的短期气候距平数值预测系统（ＩＡＰＰＳＳＣＡ） 方案１ 的预测性能进行了定量的、系统的评估。评估表明： ＩＡＰＰＳＳＣＡ 对中国汛期大尺度的、重大的降水异常预报较好; 在不同区域,ＩＡＰＰＳＳＣＡ 的预测能力不同, 对中国东南区域显示出较高的预报准确度和可信度; 与单个预报相比, 集合预测水平稳定, 能提高总体预测水平。另外, 还对订正技术进行了改进研究, 距平百分率订正较距平订正效果为佳。     

欧亚北部冬季增雪“影响”我国夏季气候异常的机理研究——陆面季节演变异常的“纽带”作用

本文主要利用美国冰雪资料中心 (The National Snow and Ice Data Center) 提供的卫星反演积雪资料和ERA40土壤温度再分析资料, 采用相关分析, 对欧亚北部冬季新增雪盖面积 (冬季TFSE) 与我国夏季气候异常关系的可能物理途径进行了初步研究。结果表明, 春夏季陆面季节演变异常是上述“隔季相关” 的重要纽带: 当冬季TFSE偏大时, 欧亚北部大范围积雪—冻土自西向东、 由南向北的融化进程明显减慢, 受其影响, 至夏季, 东亚中高纬区积雪和地表冻土的融化异常强烈, 土壤温度明显偏低, 这种夏季陆面异常可能通过自身的冷却作用, 通过加强东亚中高纬异常北风对东亚中纬区夏季变冷产生直接影响, 进而与西太平洋副热带高压, 乃至与我国江南夏季降水异常产生关联; 冬季TFSE偏小时相反。分析表明, 冬季TFSE信号在东亚中高纬局地的春季积雪—冻土融化过程中被加强, 并在夏季达到显著。