FGOALS-g模式关于ENSO事件的春季预报障碍现象

Using the sea surface temperature (SST) predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g), an analysis of the prediction errors was performed for the seasonally dependent predictability of SST anomalies both for neutral years and for the growth/decay phase of El Niño/La Niña events. The study results indicated that for the SST predictions relating to the growth phase and the decay phase of El Niño events, the prediction errors have a seasonally dependent evolution. The largest increase in errors occurred in the spring season, which indicates that a prominent spring predictability barrier (SPB) occurs during an El Niño-Southern Oscillation (ENSO) warming episode. Furthermore, the SPB associated with the growth-phase prediction is higher than that associated with the decay-phase prediction. However, for the neutral years and for the growth and decay phases of La Niña events, the SPB phenomenon was less prominent. These results indicate that the SPB phenomenon depends extensively on the ENSO events themselves. In particular, the SPB depends on the phases of the ENSO events. These results may provide useful knowledge for improving ENSO forecasting.     

ENSO机理及其预测研究

资料分析研究表明ENSO(El Ni?o和La Ni?a)实际上是热带太平洋次表层海温距平的循环,而次表层海温距平的循环是赤道西太平洋异常纬向风所驱动的,赤道西太平洋的异常纬向风又主要由异常东亚冬季风所激发。因此可以将ENSO的机理视为主要是由东亚季风异常造成的赤道西太平洋异常纬向风所驱动的热带太平洋次表层海温距平的循环。同时分析还表明,热带西太平洋大气季节内振荡(ISO)的明显年际变化,作为一种外部强迫,对ENSO循环起着十分重要的作用;El Ni?o的发生同大气ISO的明显系统性东传有关。资料分析也表明,El Ni?o持续时间的长短与大气环流异常有密切关系。 用非线性最优化方法研究El Ni?o－南方涛动(ENSO)事件的可预报性问题,揭示了最容易发展成ENSO事件的初始距平模态,即条件非线性最优扰动(CNOP)型初始距平;找出能够导致显著春季可预报性障碍(SPB),且对ENSO预报结果有最大影响的一类初始误差——CNOP型初始误差,进而探讨耦合过程的非线性在SPB研究中的重要作用,提出了关于ENSO事件发生SPB的一种可能机制;用CNOP方法揭示了ENSO强度的不对称现象,探讨ENSO不对称性的年代际变化问题,提出ENSO不对称性年代际变化的一种机制;建立了关于ENSO可预报性的最大可预报时间下界、最大预报误差上界和最大允许初始误差下界的三类可预报性问题,分别从三个方面揭示ENSO事件的春季可预报性障碍现象,比较有效地量化了模式ENSO事件的可预报性。 利用中国科学院大气物理研究所地球流体力学数值模拟国家重点实验室的ENSO预测系统,研究了海洋资料同化在ENSO预测中的应用,该系统可以同时对温、盐剖面资料和卫星高度计资料进行同化。并且在模式中采用次表层上卷海温的非局地参数化方法,可有效地改进ENSO模拟水平。采用集合卡曼滤波(Ensemble Kalman Filter,EnKF)同化方法以及在集合资料同化中“平衡的”多变量模式误差扰动方法为集合预报提供更加精确和协调的初始场,ENSO预报技巧得到提高。     

What Kind of Initial Errors Cause the Severest Prediction Uncertainty of EI Nino in Zebiak-Cane Model

With the Zebiak-Cane (ZC) model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation (CNOP). The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOP- type errors, we find that for the normal states and the relatively weak EI Nino events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong EI Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of EI Nino in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector (LSV) approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.     

What Kind of Initial Errors Cause the Severest Prediction Uncertainty of El Nino in Zebiak-Cane Model

With the Zebiak-Cane(ZC)model,the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation(CNOP).The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model.By analyzing the behavior of CNOP- type errors,we find that for the normal states and the relatively weak El Nino events in the ZC model,the predictions tend to yield false alarms due to the uncertainties caused by CNOP.For the relatively strong El Nino events,the ZC model largely underestimates their intensities.Also,our results suggest that the error growth of El Nino in the ZC model depends on the phases of both the annual cycle and ENSO.The condition during northern spring and summer is most favorable for the error growth.The ENSO prediction bestriding these two seasons may be the most diffcult.A linear singular vector(LSV)approach is also used to estimate the error growth of ENSO,but it underestimates the prediction uncertainties of ENSO in the ZC model.This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes.CNOP yields the severest prediction uncertainty.That is to say,the prediction skill of ENSO is closely related to the types of initial error.This finding illustrates a theoretical basis of data assimilation.It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.     

升级版中国多模式集合预报系统(CMMEv2.0)的季节预测检验

基于我国自主的6个气候模式和3个国际业务模式,中国气象局国家气候中心（NCC）将中国多模式集合预测系统（CMME）升级为至2.0版本（CMMEv2.0）,增加了新的模式成员,迭代了模式版本,扩充了预报产品。本研究对升级后的CMME在历史回报（1993–2016）和实时预测（2021–2024）期间的性能进行了综合评估。结果表明,CMMEv2.0在捕获更真实的赤道海表温度（SST）变化方面优于所有单个模式。CMMEv2.0对降水和2 m温度异常的预测能力较好,其中对东亚地区的预测能力提高显著。CMMEv2.0的优势可归因于其对ENSO（Niño 3.4指数的时间相关系数提前6个月达到0.87）和ENSO相关遥相关的较好预测。近3年的实时预测检验表明,CMMEv2.0也具备相对稳定的技能：如成功预测了2021至2023年夏季中国北方主要雨带和2022/2023年的暖冬状况。除此之外,集合抽样实验表明,当模式集合成员数量增加到5–6个时,CMMEv2.0理论预测能力上限趋于饱和,这表明适当选择一个最优的集合子集就有利于进一步提升预测性能,特别是对温带地区,其潜在原因有待进一步研究。

     

集合敏感性在预报误差及可预报性研究中的应用进展综述

研究大气的可预报性和预报误差产生的原因,对于改进数值预报,提升业务预报技巧具有重要意义。集合敏感性基于具有流依赖特性的集合预报,通过建立预报与初始场或前期预报大气状态之间的统计关系,为揭示与预报对象可预报性相关的动力学特征及理解预报误差来源和传播机制提供了一种新方法。同时,介绍了集合敏感性的定义和度量,并综述了其针对典型天气系统和高影响天气事件研究的进展,并讨论了该方法的优势和局限性。     

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.     

热带太平洋-印度洋相互关系的年代际改变

用Nino3指数、印度洋单极指数、偶极子指数表示热带太平洋．印度洋SST的年际异常,发现1978年以后ENSO在热带印度洋的信号减弱了,可以解释如下：1978年之前,印度洋和太平洋纬向垂直环流的异常在海洋性大陆附近作齿轮式耦合;1978年以后,由于热带太平洋的上升支的东移,导致两洋的纬向垂直环流异常的耦合减弱。     

大气初始异常在跨季度短期气候预测中作用的研究

针对大气初始异常在跨季度短期气候预测中的可能作用,利用中国科学院大气物理研究所9层大气环流格点模式(简称IAP 9L-AGCM)对全球夏季气候进行了30年(1970～1999年)集合回报试验,系统地考察了基于大气初始异常条件下模式对跨季度短期气候异常的可预测性.结果表明,在热带、副热带及高纬都存在气候异常的可预测性区域,说明大气初始场异常本身对跨季度短期气候预测有很重要的影响.相对于全球来说,大气初始异常对跨季度短期气候预测的影响在东亚地区更为显著,其作用不可忽略.通过对典型年份的个例分析发现,在某些气候异常剧烈的特殊年份,春季大气初始异常在我国夏季气候形成中扮演着尤为重要的角色.     

ENSO对云南地区降水影响的年代际变化

通过对云南地区近50年的降水与尼诺3区的海面温度(以下简称SST)的相关性研究发现,同期或是前期的SST均与该地区的降水存在一定的相关关系,如云南地区初夏降水与前期SST呈负相关关系,而秋季降水与前期SST呈正相关关系,即在ElNino(LaNina)年,该地区初夏降水容易偏少(多),而秋季降水容易偏多(少),整个地区雨季有后(前)移的可能性。因此我们认为,ENS0对云南降水的影响主要表现为云南雨季起讫的早晚。同时发现,这种影响存在明显的年代际变化特征,即云南地区初夏和秋季降水与前期SST的相关关系在1970年代中期到1980年代末期这段时期表现得尤为显著,之前或之后这种相关关系都没有通过显著性检验。     

国家气象中心中期集合预报系统概况

随着对大气初值及数值模式存在不确定性的认识以及计算机技术的飞速发展，集合预报已经成为数值预报发展的重要方向之一。本文介绍集合预报的基本概念及初值扰动的特点和国家气象中心集合预报系统的概况。     

ENSO与北半球冬季大气环流异常的年代际关系

利用NCEP/NCAR再分析资料和CPC(气候预测中心)Nino3-4区海表温度序列,研究了1950/1951-2002/2003年冬季ENSO事件与北半球大气环流的相互关系及其年代际变化.结果表明:北半球大气环流对ENSO事件的响应在1978/1979年有一个明显的跃变.跃变后,低纬中高层大气环流对ENSO事件的响应明显减弱,其中东南亚的减弱最为明显,而低层大气环流对ENSO事件的响应则有所增强;东半球中高纬大气环流异常与ENSO事件关系明显减弱;西半球中高纬大气环流与ENSO事件的关系加强.     

一个全球耦合模式的ENSO后报试验

A group of seasonal hindcast experiments are conducted using a coupled model known as the Flexible Global Ocean-Atmosphere-Land System Modelgamil1.11 (FGOALS-g1.11) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG).Two steps are included in our ElNi o-Southern Oscillation (ENSO) hindcast experiments.The first step is to integrate the coupled GCM with the Sea Surface Temperature (SST) strongly nudged towards the observation from 1971 to 2006.The second step is to remove the SST nudging term.The authors carried out a one-year hindcast by adopting the initial values from SST nudging experiments from the first step on January 1st,April 1st,July 1st,and October 1st from 1982 to 2005.In the SST nudging experiment,the model can reproduce the observed equatorial thermocline anomalies and zonal wind stress anomalies in the Pacific,which demonstrates that the SST nudging approach can provide realistic atmospheric and oceanic initial conditions for seasonal prediction experiments.The model also demonstrates a high Anomaly Correlation Coefficient (ACC) score for SST in most of the tropical Pacific,Atlantic Ocean,and some Indian Ocean regions with a 3-month lead.Compared with the persistence ACC score,this model shows much higher ACC scores for the Ni o-3.4 index for a 9-month lead.     

集合预报业务使用现状和趋势

依据 1999年 11月 14～ 19日在欧洲中期天气预报中心举行的第 7届天气预报业务系统研讨会的情况 ,对集合预报的业务使用现状、集合预报产品以及未来趋势进行了介绍     

近百年东亚冬季风与ENSO循环的相互关系及其年代际异常

运用相关及滑动相关的计算技术,讨论了近百年东亚冬季风与ENSO循环的相互关系及其年代际异常。研究指出,东亚冬季风与赤道东太平洋海温的年际关系具有年代际的变化特征;季风与ENSO循环的关系受到季风的QBO以及季风-海洋的年代际背景场配置关系的共同作用;当季风与海洋的背景场处于同样状态时,强冬季风有利于第二年冬季赤道东太平洋的升温,产生El Ni?o事件;当两者的背景场处于反位相状态时,强冬季风对应于第二年冬季的La Ni?a位相。     

使用ECMWF集合预报数据集估计全球季节平均气候异常的潜在可预报性

利用ECMWF PROVOST项目产生的在给定海表温度强迫下的150(1979～1993)季节集合预报数据集,分析揭示了季节平均气候异常潜在可预报性的全球分布。首先,利用可再现的强迫模态重建集合资料场,在Kolmogorov-Smirnov(K-S)检验的基础上定义潜在可预报性指数PU^k,然后,将重建场的PU^k与重建场贡献于集合平均的方差比结合,提出了定量估计局地潜在可预报性的指数PI。以全球850hPa温度季节平均异常场为例,对PI进行定量计算表明：不仅大部分热带地区,而且热带外一些地区的季节平均气候异常具有潜在可预报性,主要分布在北美、南非和亚洲部分季风区;全球大部分潜在可预报地区主要受ENSO型强迫控制,而部分温带地区如中国华北、中亚、北美南部主要受非ENSO型强迫控制;局地潜在可预报性具有季节性,夏季可预报性较强,冬季较弱。通过与其他几种估计季节潜在可预报性的方法进行比较表明,本文提出的PI方法能更好地把热带外地区受外强迫控制的可预报信号提取出来。     

一个全球耦合模式的ENSO后报试验

A group of seasonal hindcast experiments are conducted using a coupled model known as the Flexible Global Ocean-Atmosphere-Land System Model-gamil1.11 (FGOALS-g1.11) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). Two steps are included in our El Niño-Southern Oscillation (ENSO) hindcast experiments. The first step is to integrate the coupled GCM with the Sea Surface Temperature (SST) strongly nudged towards the observation from 1971 to 2006. The second step is to remove the SST nudging term. We carried out a one-year hindcast by adopting the initial values from SST nudging experiments from the first step on January 1st, April 1st, July 1st, and October 1st from 1982 to 2005. In the SST nudging experiment, the model can reproduce the observed equatorial thermocline anomalies and zonal wind stress anomalies in the Pacific, which demonstrates that the SST nudging approach can provide realistic atmospheric and oceanic initial conditions for seasonal prediction experiments. The model also demonstrates a high Anomaly Correlation Coefficient (ACC) score for SST in most of the tropical Pacific, Atlantic Ocean, and some Indian Ocean regions with a 3-month lead. Compared with the persistence ACC score, this model shows much higher ACC scores for the Nino3.4 index for a 9-month lead.