On the sensitive areas for targeted observations in ENSO forecasting

本研究分别从初始误差增长和粒子滤波同化的角度识别了 ENSO预测的目标观测敏感区.结果表明:与粒子滤波同化方法确定的敏感区相比,基于初始误差增长确定的敏感区位置稍微偏东,且在东南太平洋也有分布,但整体而言,这两种方法确定的敏感区存在大范围重合区域,是互为印证的.在实际目标观测中,如果考虑使用确定敏感区方法的不确定性,那么选择上述两种方法确定的敏感区的重合区域作为ENSO预测的目标观测敏感区将更为合理.     

Errors in Current Velocity in the Low-latitude North Pacific:Results from the Regional Ocean Modeling System

Using the Regional Ocean Modeling System, this study investigates the simulation uncertainties in the current velocity in the low-latitude North Pacific where the Kuroshio originates [i.e., the beginning of the Kuroshio(BK)]. The results show that the simulation uncertainties largely reflect the contributions of wind stress forcing errors, especially zonal wind stress errors,rather than initial or boundary errors. Using the idea of a nonlinear forcing singular vector, two types of zonal wind stress errors(but sharing one EOF mode) are identified from error samples derived from reanalysis data as having the potential to yield large simulation uncertainties. The type-1 error possesses a pattern with positive anomalies covering the two zonal bands of 0?–15?N and 25?–40?N in the Pacific Ocean, with negative anomalies appearing between these two bands; while the type-2 error is almost opposite to the type-1 error. The simulation uncertainties induced by the type-1 and-2 errors consist of both large-scale circulation errors controlled by a mechanism similar to the Sverdrup relation and mesoscale eddy-like errors generated by baroclinic instability. The type-1 and-2 errors suggest two areas: one is located between the western boundary and the meridional 130?E along 15?–20?N, and the other is located between 140?–150?E and along 15?–20?N. The reduction of errors over these two areas can greatly improve the simulation accuracy of the current velocity at BK. These two areas represent sensitive areas for targeted observations associated with the simulation of the current velocity at BK.     

初始扰动振幅和集合样本数对CNOPs集合预报的影响

初始扰动振幅的大小和集合样本数对于集合预报取得更高预报技巧具有重要意义。本文将正交条件非线性最优扰动方法（orthogonal conditional nonlinear optimal perturbations,简称CNOPs）应用于概念模型Lorenz-96模式探讨了初始扰动振幅和集合样本数对集合预报技巧的影响,从而为使用更复杂模式进行集合预报提供指导。结果表明,由于CNOPs扮演了非线性系统中的最优初始扰动,从而使得当初始扰动振幅小于初始分析误差的大小时,CNOPs集合预报获得更高的预报技巧,并且CNOPs集合预报的最高预报技巧总是高于奇异向量法（singular vectors,简称SVs）集合预报的最高预报技巧。结果还表明,CNOPs集合预报倾向于具有一个合适的样本数时,达到最高技巧。更好的集合离散度——预报误差关系和更为平坦的Talagrand图（Talagrand diagram）进一步证明了CNOPs集合预报系统的可靠性,从而夯实了上述结果的合理性。因此,针对CNOPs集合预报,本文认为采用一个适当小于初始分析误差的初始扰动振幅和一个合适的集合样本数,有利于CNOPs集合预报达到最高预报技巧。     

气候系统可预报性理论研究

介绍了作者近年来关于气候系统可预报性理论研究的一些工作,包括:非线性最快增长扰动理论以及在气候预测的可预报性研究中的应用;从一个新的角度研究了2类可预报性问题,并提出可预报性的3类子问题;根据计算不确定性原理,讨论了模式可预报性与机器精度的关系;探讨了可预报性与时空尺度的关系,建立了可预报性的相对观.     

Behaviors of nonlinearities modulating the El Niño events induced by optimal precursory disturbances

In the present paper, we explore the manner in which nonlinearities modulate El Niño events by investigating the optimal precursory disturbance for El Niño events in the Zebiak-Cane model. The initial anomalies of conditional nonlinear optimal perturbations (CNOPs) and linear singular vectors (LSVs) are investigated. The CNOPs evolve into stronger El Niño events than the LSVs and act as the optimal precursor for El Niño events. By examining the role of nonlinearities in El Niño events induced by CNOPs and LSVs, we determined that, when the initial anomalies of the CNOP and LSV structures are large, the nonlinearities enhance CNOP-El Niño events but suppress LSV-El Niño events. Nonlinearities in the Zebiak-Cane model arise from nonlinear temperature advection (NTA), sub-surface temperature parameterization (STP), and wind stress anomalies (WSA). Using these types of nonlinearities, we trace the approach of the nonlinearities modulating the CNOP- and LSV-El Niño events. The results demonstrate that nonlinearities that originate from NTA enhance both CNOP-El Niño events and LSV-El Niño events, while nonlinearities originating from STP and WSA suppress these events. For the CNOP-El Niño events, the enhancement effect of NTA is larger than the suppression effect of STP and WSA, resulting in the combined effect of the nonlinearities in the Zebiak-Cane model being an enhancement of the CNOP-El Niño events. However, for the LSV-El Niño events, the enhancement effect of NTA is smaller than the suppression effect of WSA and STP. Consequently, the combined effect of the nonlinearities in the Zebiak-Cane model suppresses the LSV-El Niño events.     

Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011)

Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007-2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types:(1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following:(1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Nin o-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.     

Can the uncertainties of Madden-Jullian Oscillation cause a significant “Spring Predictability Barrier” for ENSO events?

With the Zebiak-Cane model and a parameterized stochastic representation of intraseasonal forcing, the impact of the uncertainties of Madden-Jullian Oscillation (MJO) on the ??Spring Predictability Barrier (SPB)?? for El Ni?o-Southern Oscillation (ENSO) prediction is studied. The parameterized form of MJO forcing is added physically to the Zebiak-Cane model to obtain the so-called Zebiak-Cane-MJO model and then the effects of initial error, stochastic model error, and their joint error mode on the SPB associated with El Ni?o prediction are estimated. The results show that the model errors caused by stochastic MJO forcing could hardly lead to a significant SPB while initial errors can do; furthermore, the joint error mode of initial error and model error associated with the stochastic MJO forcing can also lead to a significant SPB. These demonstrate that the initial error is probably the main error source of the SPB, which may provide a theoretical foundation of data assimilation for ENSO forecasts.     

数值天气预报和气候预测的可预报性问题

考察由初始状态误差和模式中参数误差所引起的预报结果的不确定性。提出了数值天气预报与气候预测中三类可预报性问题，即，最大可预报时间，最大预报误差，初值与参数的最大允许误差。然后将这三类问题化成了对应的非线性优化问题，给出了处理此类非线性优化问题的思路，并且有数值方法对Lorenz模型研究了这三类问题。     

Seasonal Dependence of the North Pacific and North Atlantic SST Predictability and Forecast Skill

In this paper, the decadal predictability and forecast skill of the Sea Surface Temperature Anomalies (SSTA) in the North Pacific and North Atlantic Ocean were investigated by conducting three sets of perfect model forecast experiments using a global coupled general circulation model. The results show that the annual mean SSTA in the North Pacific is less predictable on decadal time scale, with the forecast skill notably weaker than that of the North Atlantic. By analyzing the predictability and forecast skill of seasonal mean SSTA, it is found that the decadal predictability and forecast skill of the winter mean (JFM) SSTA in the central and western North Pacific are significantly higher than those of other seasons, and the magnitude is comparable with that of the North Atlantic. The predictability and forecast skill of the North Atlantic SSTA also show seasonal variations. Further analysis indicates that the seasonal dependence of the SSTA decadal predictability and forecast skill in the North Pacific is due to the winter-to-winter reemergence mechanism of SSTA in the North Pacific, which results from the seasonal variation of the mixed layer depth of the North Pacific Ocean. While the seasonal dependence of the North Atlantic SSTA predictability and forecast skill might be related to seasonal variations of other processes, such as the Atlantic Decadal Oscillation. The results of this paper suggest that for decadal climate prediction, if the forecast skill of the seasonal mean is taken into account, we might obtain higher than annual mean forecast skill for some seasons.