我国短期气候预测技术进展

经过近60年的发展,我国短期气候预测技术和方法也有了长足进步。近年来,一些新的预报技术和机理认识不断应用于短期气候预测业务。ARGO海洋观测资料的使用大大提高了业务模式的预测技巧,新一代气候预测模式系统已经投入准业务化运行,研发了多种模式降尺度释用技术,多模式气候预测产品解释应用集成系统(MODES)和动力-统计结合的季节预测系统(FODAS)逐渐应用于业务中,大气季节内振荡(MJO)逐步在延伸期预报中得到应用。近年来,对全球海洋、北极海冰、欧亚积雪、南半球环流系统对东亚季风影响的新认识也不断引入到短期气候预测业务中。这些新技术和新认识的应用极大提高了我国短期气候预测的业务能力。     

A Regression-based Assessment of the Predictability of New Zealand Climate Anomalies

Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed. Received July 18, 1997 Revised April 2, 1998     

Progress in Predictability Studies in China （2003-2006）

Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast.     

多模式对东北地区月气温的预测性能对比评估

基于BCC的DERF 2.0(中国)、CFSv2(美国)、EC(欧洲)、TCC(日本)4种模式开展的1983—2010年对东北地区的回报试验结果、2011—2014年业务应用结果和国家气象信息中心提供的东北地区172个台站气象观测资料,利用距平相关系数(ACC)、趋势异常综合检验(PS)评估和距平符号一致率(PC)3种定量方法对比评估了4种模式对东北地区月气温的预测性能。结果表明,EC模式和CFSv2模式与BCC模式和TCC模式相比,对月气温的总体预测效果较好,有一定的预测技巧。从空间上来看,CFSv2的PC在前半年表现的比后半年的略好,PC超过80%的范围比较大。CFSv2和EC模式对东北地区夏季典型低温年有一定的预测能力。     

An analysis of prediction skill of monthly mean climate variability

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30?C40?days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability.     

成都区域气象中心短期气候预测业务评估

采用预报评分（P）、技巧评分（S）、距平相关系数（Acc）三种参数，对10年来成都中心气象台月降水距平百分率和月平均气温距平预测、汛期（6－8月）降水距平百分率预测、以及年度降水距平百分率和年平均气温距平预测进行了评估，并对评估结果作了分析。     

短期气候预测的评估问题

该文系统地介绍了国内外评估月、季尺度短期气候预测结果的方法 ,比较了相关系数(R)、预报技巧分 (S)和准确率 (P)的特点 ,并对当前国内外气候预测业务水平进行了分析 ,着重对大气环流、气温、降水及 ENSO的预测水平进行了评估 ,指出国内外月、季尺度的降水预报的水平目前在 55%～ 60 %左右 ,对 ENSO的发生、结束和强度的预报水平有限 .文中探讨了短期气候预测的可预报性问题 ,提出月、季尺度气候预测的可预报性的理论上限可能为 6～ 1 2月 ,准确率在 80 %～ 85%之间 .     

季度预测研究回顾

对月平均大气环流预报试验、季度预测和中国汛期降水预测进行了总结。结果表明气候预测的对象必须是要素的时间平均场。利用数值模拟进行气候预测是今后的主要发展方向,而季度预测技巧的提高依赖于对物理参数化和物理机制的研究。最后,讨论了季平均气温和季总降水的可预报性问题,即时效性和准确率。     

国家气候中心短期气候预测模式系统业务化进展

该文简要介绍了国家气候中心短期气候预测模式系统的研发成果,并侧重于从海洋资料同化系统、陆面资料同化系统、月动力延伸预测模式系统、季节气候预测模式系统4个方面介绍了第2代短期气候预测模式系统的业务化进展。第2代海洋资料同化系统已初步建成,其对温盐的同化效果总体上优于第1代同化系统;陆面资料同化系统正在研发中,目前已完成其中的多源降水融合子系统的业务建设工作,可为陆面分量提供实时的大气降水强迫分析场;第2代月动力延伸预测系统基于国家气候中心大气环流模式BCC_AGCM2.2建立,已于2012年8月进入准业务运行阶段;第2代季节预测模式系统基于国家气候中心气候系统模式BCC_CSM1.1(m) 建立,将于2013年底投入准业务运行。初步评估表明:第2代月动力延伸预测模式系统和季节气候预测模式系统分别对候、旬、月和季节、年际时间尺度的气候变率体现出了一定的预测能力,其对降水、气温、环流等要素的预测技巧总体上要高于第1代预测系统。     

近年来我国短期气候预测中动力相似预测方法研究与应用进展

鉴于当前短期气候预测中,纯粹使用动力学方法尚不能完全满足气候预测业务的需要,因此有必要在策略上采取动力和统计相结合的办法.在具体实现手段上,简要介绍了从历史资料中提炼预测信息,发展了一种基于历史相似误差订正的相似—动力短期气候预测新方法.分别在月、季节以及ENSO等短期气候预测领域相继开展了试验和应用,取得了—些令人鼓舞的初步结果,为下一步深入开展有关短期气候预测科学的研究提供了些许借鉴.     

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.     

用复数自回归模式预报月平均气温

在复数域最小二乘法的基础上, 建立了复数自回归模式。数学推导和实例应用表明:这一复数自回归模式不同于将复数序列中的实部和虚部分开来计算的结果, 将实部和虚部分开来计算的方法不是真正意义的最小二乘法。应用包括一个任意给定的复数序列和全国160个基本气象台站上历年7月月平均气温。采用距平相关系数和均方根误差两种检验标准, 对独立预报结果进行检验, 并与其他3种常用统计模型作比较。结果显示:该复数自回归模式确实具有较好的预报效果。     

基于集合预报产品的降尺度降水预报试验

利用降水距平百分率的降尺度预报方法和1951-2008 NCEP资料及我国降水资料,建立了降水距平百分率的预报模型,基于T106L19模式的月动力延伸集合预报结果,进行了2007-2009年3 a的预报试验和效果检验.结果表明,基于集合预报产品的统计降尺度方法对降水距平百分率的预报技巧高于模式降水的预报技巧;500 hPa月平均高度场的预报技巧直接影响到降水距平百分率的预报技巧,平均环流的预报技巧越高,降水距平百分率的预报技巧越高;无论集合成员数为多少,集合预报的结果都明显优于控制预报,随着集合成员数的增多,预报技巧呈增大的趋势;我国降水具有显著的季节性和区域性,以江淮地区的降水距平百分率预报技巧最高,华南地区的预报技巧其次.     

中国区域月平均温度和降水的模式可预报性分析

基于中国台站降水和温度观测资料、中国气象局国家气候中心月动力延伸预报的回算和预测结果讨论了中国区域月平均温度和降水模式可预报性的时空变化特征。文中以持续性预报来表征中国区域月平均温度和降水受外强迫影响下的可预报性,持续性预报技巧存在明显的年际和年代际变化特征;春末夏初和秋季预报评分相对偏低;在中国区域气候变暖和平均降水强度极值增加的背景下,温度的持续性预报评分有明显提高,降水的持续性预报略有下降。月动力延伸预报对月降水和温度的预报能力也存在明显的年际和年代际变化特征;与持续性预报相比,月动力延伸温度预报总体优于持续性预报,降水预报在初春略差,温度预报在8月相对最低。近20余年,月动力延伸预报相对于持续性预报的温度和降水的均方根误差技巧均大于零,其年际变化表现为模式对降水的预测略有提高。两种预报评估结果的空间分布分析表明月动力延伸预报达到显著性水平的正相关区域总体上比持续性预报的范围大,并基本涵盖了持续性预报的高相关区。原因是可预测信息部分来源于外强迫异常的影响,部分来源于对大气内部动力过程的模拟。     

不同海温强迫的月动力延伸集合预报试验

利用全球谱模式T106L19和增长模繁殖法（BGM）建立了月动力延伸集合预报系统,基于气候海表面温度（SST）和预测海表面温度,设计了三组集合预报试验,一组为气候SST作为模式下边界条件的集合预报试验（CSST试验）,另一组为预测SST作为模式的下边界条件的集合预报试验（FSST试验）,第三组为前两组试验的集合预报结果之和（AVE30试验）,对两种海温强迫分别进行了48个月的试验,并对预报结果进行了检验和分析。结果表明：相对于单一的控制预报,不管是CSST试验还是FSST试验,利用BGM方法制作的初值集合预报能显著提高月平均环流的预报技巧,集合预报对PNA区域的预报技巧改进显著,特别是预测SST强迫有正的贡献;同时考虑初值和边值不确定性影响的集合预报试验（AVE30试验）,其全球预报技巧不仅高于控制预报,也分别高于FSST试验和CSST试验,这说明要提高月延伸预报技巧,必须同时考虑初值和边值的影响;大气对SST强迫的响应在模式积分10天开始显著,SST对第二旬和第三旬的作用直接影响月平均环流的预报效果,而SST对第二旬和第三旬预报的影响不仅与SST本身变化有关,还与初值有关,不同的初值其作用不同;集合预报对我国夏季月平均温度分布具有较强预报能力,采用预报海温强迫的预报结果,总体上优于气候海温强迫的结果。     

基于DERF2.0的月平均温度概率订正预报

国家气候中心第二代月动力延伸模式回算资料的分析表明,二代模式月平均温度预报与观测实况仍然存在较大偏差,模式预报有较大改进空间。本文采用非参数百分位映射法对模式月平均温度预报进行概率订正,该方法基于模式集合平均给出的确定性预报,结合模式回算资料各集合成员计算得到的模式概率密度分布,给出确定性预报在模式概率密度分布中的百分位值,并将百分位值投影到观测资料的概率密度分布中,得到模式预报的概率订正值。对订正前后模式预报的检验评估显示,该订正方案不仅有效降低了模式预报与实况的均方根误差（RMSE）,对月平均温度距平分布的预报技巧也有所改善,不同超前时间模式预报的预测技巧评分（PS）和距平相关系数（ACC）均有提升,同时模式预报误差的大小对订正效果无明显影响。从分月的订正预报结果来看,对夏季各月的温度预测技巧的提升整体高于冬季各月。     

Skill of seasonal hindcasts as a function of the ensemble size

 Forecast skill as a function of the ensemble size is examined in a 24-member ensemble of northern winter (DJF) hindcasts produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. These integrations are initialized from the NCEP reanalyses at 6 h intervals prior to the forecast season. The sea surface temperatures that are applied as lower boundary conditions are predicted by persisting the monthly mean anomaly observed prior to the forecast period. The potential predictability that is attributed to lower boundary forced variability is estimated. In lagged-average forecasting, the forecast skill in the first two weeks, which originates predominately from the initial conditions, is greatest for relatively small ensemble sizes. The forecast skill increases monotonically with the ensemble size in the rest of the season. The skill of DJF 500 hPa geopotential height hindcasts in the Northern Hemisphere and in the Pacific/North America sector improves substantially when the ensemble size increases from 6 to 24. A statistical skill improvement technique based on the singular value decomposition method is also more successful for larger ensembles. Received: 22 February 2000 / Accepted: 6 December 2000     

Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components

A regression-based downscaling technique was applied to monthly mean surface wind observations from stations throughout western Canada as well as from buoys in the Northeast Pacific Ocean over the period 1979–2006. A predictor set was developed from principal component analysis of the three wind components at 500?hPa and mean sea-level pressure taken from the NCEP Reanalysis II. Building on the results of a companion paper, Curry et al. (Clim Dyn 2011, doi:10.1007/s00382-011-1173-3), the downscaling was applied to both wind speed and wind components, in an effort to evaluate the utility of each type of predictand. Cross-validated prediction skill varied strongly with season, with autumn and summer displaying the highest and lowest skill, respectively. In most cases wind components were predicted with better skill than wind speeds. The predictive ability of wind components was found to be strongly related to their orientation. Wind components with the best predictions were often oriented along topographically significant features such as constricted valleys, mountain ranges or ocean channels. This influence of directionality on predictive ability is most prominent during autumn and winter at inland sites with complex topography. Stations in regions with relatively flat terrain (where topographic steering is minimal) exhibit inter-station consistencies including region-wide seasonal shifts in the direction of the best predicted wind component. The conclusion that wind components can be skillfully predicted only over a limited range of directions at most stations limits the scope of statistically downscaled wind speed predictions. It seems likely that such limitations apply to other regions of complex terrain as well.     

Time-Series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making

A generalized,structural,time series modeling framework was developed to analyze the monthly records of absolute surface temperature,one of the most important environmental parameters,using a deterministicstochastic combined(DSC) approach.Although the development of the framework was based on the characterization of the variation patterns of a global dataset,the methodology could be applied to any monthly absolute temperature record.Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal,involving polynomial functions and the Fourier method,respectively,while stochastic processes were employed to account for any remaining patterns in the temperature signal,involving seasonal autoregressive integrated moving average(SARIMA) models.A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years.The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors,suggesting that DSC models,when coupled with other ecoenvironmental models,can be used as a supplemental tool for short-term(～10-year) environmental planning and decision making.     

欧亚大陆积雪对我国春季气候可预报性的影响

利用大气环流模式IAP9L_CoLM,通过两组集合后报试验,考察了欧亚大陆积雪对我国春季气候可预报性的影响。一组试验为常规后报试验,积雪是由模式陆面过程预报得到的,另一组试验为积雪试验,模式积分过程中欧亚大陆雪水当量由微波遥感积雪资料替代,一天替换一次。通过分析两组试验后报结果的差异,来考察欧亚大陆积雪对我国春季（3~5月）气候可预报性的影响。分析表明:欧亚大陆积雪模拟水平的改善能提高春季欧亚大陆中高纬环流场（海平面气压场和中、高层位势高度场）的可预报性,模式对我国春季气温异常的年际变化和空间分布的可预报能力也有显著增强。对我国春季降水,虽然预报技巧较低,但引入较真实的欧亚积雪作用后,由于中高纬环流场预报技巧的改进导致降水的预测能力也有所改进。个例分析也表明,欧亚中高纬春季积雪异常模拟水平的改善导致了欧亚中高纬贝加尔湖及以南区域环流场可预报性的提高,最终使中国东部区域春季气候异常模拟技巧得以改善。以上结果也证实,欧亚大陆积雪是影响东亚区域春季气候的一个重要因子,要提高模式对中国春季气候的预报技巧,积雪模拟水平的改进是非常必要的。