ENSO与南海台风活动的统计分析

本文对厄尔尼诺年和反厄尔尼诺年的当年和来年,弱南方涛动和强南方涛动的当年和来年的南海台风(包括进入南海的西太平洋台风和南海生成的台风)活动进行了统计分析。结果表明,厄尔尼诺年当年南海台风偏少,来年南海台风偏多,来年9—11月最显著。此外,厄尔尼诺年在广东登陆的台风偏少,在广西登陆的偏多;反厄尔尼诺年相反。厄尔尼诺年来年7—9月登陆两广的台风、强台风偏多。统计资料还表明,厄尔尼诺年登陆我国的台风纬度偏南,反厄尔尼诺年偏北。反厄尔尼诺年来年登陆我国的台风纬度偏南。     

大气季节内振荡：其全球同步性及其与ENSO的关系

利用美国国家环境预报中心和大气研究中心的大气再分析资料，分析研究了大气季节内振荡的年际变化及其与ＥＮＳＯ的关系。揭示了全球不同纬度带之间存在着的大气季节内振荡年际变化的同步性，以及大气季节内振荡与海温和大气向外长波辐射之关系的复杂性。我们还发现大气季节内振荡与Ｎｉｎｏ３指数的关系存在年代际尺度的变化，即，这种关系有时强时弱的现象。     

石羊河流域干旱气候对ENSO事件的响应及预测分析

根据石羊河流域5个气象站1961—2018年的降水、气温、干旱实况资料,利用气候统计学方法分析ENSO事件对该区气候变化及干旱的影响。结果表明:厄尔尼诺事件会造成流域春季降水偏多,春、秋、冬季气温偏高,易出现暖冬;拉尼娜事件则春季降水偏少,秋季降水偏多,冬季气温偏低,易出现冷冬,中下游发生中度以上春旱、春末夏初旱和伏旱的概率较高。应用1968—2010年旬、月气象要素和大气环流特征量,采用最优子集回归方法,建立降水和干旱统计预测模式,然后结合ENSO事件,通过加权平均法构建集成预测概念模型。对模型进行检验,拟合率与准确率较高,已投入业务使用。     

ENSO事件与区域气候异常的统计事实

利用实测气象资料,统计分析了山东省80年代以来气象要素异常百分比的年季变化,发现气象要素异常发生的站次有逐渐增加的趋势,ENSO事件是气象要素发生异常的主要原因。     

平流层极涡振荡与ENSO热带海温异常的时空联系

利用一个在等熵位涡坐标下建立的平流层极涡振荡(Polar Vortex Oscillation)指数,以及NCEP/NCAR再分析资料和NOAA_OISST_V2逐周海温资料,研究了月尺度以上热带海温异常与平流层极涡振荡的时间和空间联系,通过相关、回归以及合成分析发现,两者的显著联系主要发生在3—5年的年际时间尺度上,表现为ENSO型海温异常与极涡振荡年际趋势之间的时空关联。首先,两者的关系并不表现在同期相关上,而是当ENSO海温异常超前极涡振荡年际异常9—11个月时,两者具有最大的负相关关系。当前期热带海温偏暖(冷)时,9—11个月后极涡振荡的年际趋势为负(正)的异常,即极涡偏弱(强)或负(正)振荡事件为主。统计合成结果进一步证实,前期热带ENSO海温暖事件背景下,后期极涡振荡负事件的发生比正事件更多,强度更强,持续时间也更长,反之亦然;另外,前期热带ENSO暖海温背景下,极涡正、负振荡事件的发生均更频繁。研究连接热带ENSO海温异常与平流层极涡振荡的过程发现,热带海温异常不仅可在热带地区引起大气温度的异常响应,而且可以引发从平流层热带到极区的一系列温度异常同时向下和向极区的传播过程,并与9—11个月以后发生在极区的极涡振荡异常相联系。表明与热带海温异常相联系的极涡振荡年际异常,可能存在与季节尺度极涡振荡中类似的全球质量环流的异常变化。     

Sensitivity of ENSO characteristics to a new interactive flux correction scheme in a coupled GCM

A fast coupled global climate model (CGCM) is used to study the sensitivity of El Ni?o Southern Oscillation (ENSO) characteristics to a new interactive flux correction scheme. With no flux correction applied our CGCM reveals typical bias in the background state: for instance, the cold tongue in the tropical east Pacific becomes too cold, thus degrading atmospheric sensitivity to variations of sea surface temperature (SST). Sufficient atmospheric sensitivity is essential to ENSO. Our adjustment scheme aims to sustain atmospheric sensitivity by counteracting the SST drift in the model. With reduced bias in the forcing of the atmosphere, the CGCM displays ENSO-type variability that otherwise is absent. The adjustment approach employs a one-way anomaly coupling from the ocean to the atmosphere: heat fluxes seen by the ocean are based on full SST, while heat fluxes seen by the atmosphere are based on anomalies of SST. The latter requires knowledge of the model??s climatological SST field, which is accumulated interactively in the spin-up phase (??training??). Applying the flux correction already during the training period (by utilizing the evolving SST climatology) is necessary for efficiently reducing the bias. The combination of corrected fluxes seen by the atmosphere and uncorrected fluxes seen by the ocean implies a restoring mechanism that counteracts the bias and allows for long stable integrations in our CGCM. A suite of sensitivity runs with varying training periods is utilized to study the effect of different levels of bias in the background state on important ENSO properties. Increased duration of training amplifies the coupled sensitivity in our model and leads to stronger amplitudes and longer periods of the Nino3.4 index, increased emphasis of warm events that is reflected in enhanced skewness, and more pronounced teleconnections in the Pacific. Furthermore, with longer training durations we observe a mode switch of ENSO in our model that closely resembles the observed mode switch related to the mid-1970s ??climate shift??.     

Kinetic energy exchanges between the time scales of ENSO and the Pacific decadal oscillation

This paper examines the non-linear kinetic energy interactions of the atmospheric ENSO and decadal oscillations over the Pacific. The calculations are based on a 54-year dataset of tropospheric winds from NCEP reanalysis. We verify that the decadal oscillations have two dominant modes, corresponding well to the pentadecadal and bidecadal modes reported in the literature. Energy interactions involving the range of decadal oscillations and the range of ENSO oscillations are considered in the context of kinetic energy exchanges in the frequency domain. We quantify the relative amplitudes and spatial structures of the quadratic and triplet terms of the kinetic energy exchanges over the Pacific and conclude that quadratic interactions with the mean flow are the dominant term in the kinetic-to-kinetic energy exchanges. Additionally, we show that triplet interactions provide a non-trivial contribution to the total. The interactions between the range of decadal oscillations and the range of ENSO oscillations are found to be the strongest near the regions of maximum oscillation amplitude and of the maximum oscillation amplitude gradient. Due to their similar spatial structures, the two dominant ENSO modes and the bidecadal mode are found to interact in a resonant way. The interactions among the range of ENSO modes and the range of decadal modes are found to strengthen the ENSO modes in the equatorial, subtropical and midlatitude belts, and to weaken the decadal modes in all but the equatorial belt.     

四个耦合模式ENSO后报试验的“春季预报障碍”

用CliPAS计划中3个气候模式和中国科学院大气物理研究所耦合模式FGOALS-g短期气候异常回报试验结果,将动力和统计方法相结合,考察了1982—2003年厄尔尼诺/拉尼娜事件发展期和衰减期海表温度春季可预报性障碍现象。结果表明,所考察的耦合模式对ENSO事件预报的误差发展存在明显的季节依赖性,最大误差增长通常发生在春季,发生显著的可预报性障碍现象。进一步分析发现厄尔尼诺事件和拉尼娜事件在发展期的季节预报障碍现象比衰减期明显,以厄尔尼诺事件发展期春季可预报性障碍现象最为显著,拉尼娜事件衰减期季节预报障碍现象不显著。研究还发现,预报误差的增长在ENSO事件冷暖位相具有显著的非对称性,发展期暖位相预报误差强于冷位相,而衰减期冷位相的预报误差比暖位相大。通过回归分析,诊断了海-气相互作用的强度,发现耦合系统在春季最不稳定,使预报误差最易在春季发展,从而导致可预报性障碍。     

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The “observation” of the SST anomaly, which is sampled from a “truth” model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.     

逐步回归周期分析的改进方案及其在气候预测中的应用

考虑均值生成函数随机性的强弱,本文引进了筛选周期因子的新标准,提出了逐步回归周期分析的改进方案,运用东太平洋海温、长江流域和华南地区汛期降水量、太阳黑子和年轮指数序列进行实例计算,结果表明,本方案不但有较高的长期预测效果,且有一定的分析时间序列隐含周期的能力。     

Global warming,low-frequency variability,and biennial oscillation: an attempt to understand the physical mechanisms driving major ENSO events

Three primary global modes of sea surface temperature (SST) variability during the period of 1871–2010 are identified through cyclostationary empirical orthogonal function analysis. The first mode exhibits a clear trend and represents global SST warming with an ‘El Niño-like’ SST pattern in the tropical Pacific. The second mode is characterized by considerable low-frequency variability in both the tropical Pacific and the North Pacific regions, indicating that there is a close connection between the two regions on interannual and decadal time scales. The third mode shows a seesaw pattern between El Niño and La Niña within a two-year period; this mode is derived by the oscillatory tendency of the tropical Pacific ocean–atmosphere coupled system. A SST reconstruction based on these three modes captures a significant portion of the SST variability in the raw data, which is primarily associated with El Niño-Southern Oscillation (ENSO) events in the tropical Pacific. Additionally, this study attempts to interpret the major ENSO events that have occurred since the 1970s in terms of the interplay originating from these three modes of variability. In particular, two key points are derived from this analysis: (1) the most extreme El Niño events occurred in 1982/1983 and 1997/1998 are attributed to the positive contributions of all three modes; and (2) the central Pacific (CP) El Niño events in the 1990s and 2000s have different physical mechanisms, that is, the CP El Niño events in the early 1990s originated mainly from the low-frequency mode, while those in the early 2000s derived mainly from the global warming mode.     

Principal oscillation pattern analysis of the tropical 30- to 60-day oscillation

In Part I (Storch and Xu 1990) the principal oscillation pattern (POP) analysis of 200 mb equatorial velocity potential leads to the definition of a bivariate (POP-) index of the tropical 30- to 60-day oscillation. Using the POP prediction scheme this index is predictable for a few days in advance. In Part 11, the prediction of the equatorial velocity potential field, made by the POP method and made by two GCMs, is investigated. The POP index forecast can incorporate skillful forecasts of the equatorial velocity potential () field. Its ensemble correlation skill score passes the 0.50 level at 7 days, whereas persistence passes after 3 days. If there is a strong 30- to 60-day oscillation signal in the initial state, useful forecasts of more than 20 days are sometimes possible; if the initial signal is weak, the POP forecast fails. Also, the forecast skill of two GCMs is considered. The NCAR T31 GCM appears to be quite skillful in predicting the equatorial -field, and in particular the 30- to 60-day oscillation. Its skill, however, is less than that of the POP scheme. The CNRM T42 GCM seems not to be able to predict the regular development associated with the tropical 30- to 60-day oscillation. The power of the POP index in explaining the equatorial x-field is a measure of the strength and dominance of the 30- to 60-day oscillation. This measure at day 0 is an a priori indicator of the NCAR T31 GCM's skill in predicting the equatorial velocity potential field.The National Center for Atmospheric Research is sponsored by the National Science Foundation     

Principal oscillation pattern analysis of the 30- to 60-day oscillation in the tropical troposphere

The Principal Oscillation Pattern technique is used to derive an index of the 30- to 60-day oscillation in the tropical troposphere. In the 200-mb equatorial velocity potential field, one dominant pair of POPS is found. Its properties compare very well with the properties of the oscillation identified in previous studies. In particular, a good correlation between the time evolution of the POP coefficients and area-averaged outgoing long-wave radiation (ORL) is found. The POPS are derived from a 2-year subinterval of the whole 5-year data set. This leaves independent data for subsequent verification. The patterns and their characteristic numbers are almost unchanged if the whole data set is analysed. Also, the analysis is insensitive to changes of the analysis area: if the analysis is limited to 90°-longitude equatorial sectors, the signal is also identified and its patterns are consistent with the patterns derived from the full data set. Interestingly, the signal is best defined in the eastern hemisphere. The POPS may be used to derive associated correlation patterns of other quantities in winter and summer separately. The path of the oscillation has a marked annual cycle: in northern winter it migrates from the Indian Ocean across northern Australia into the region of the South Pacific Convergence Zone (SPCZ) and in northern summer it moves from the Indian Ocean across South Asia along the intertropical Convergence Zone (ITCZ) to South America. The POP coefficient may be seen as a bivariate index of the state (phase and strength) of the 30- to 60-day oscillation. Since the POP technique incorporates a prediction equation for the phase of the POP coefficients, the POP model allows for the prediction of the complex amplitude of the oscillation. In a sequence of forecast experiments, of which about two-thirds used independent data, the POP forecasts were found to be useful in about half of all cases for lead times of several days. The correlation and RMS skills were calculated for the POP forecast and for persistence. The POP forecast appears to be considerably better with respect to both measures. The correlation skill scores 60% after 7 days. The POP forecast is most skillful in northern winter and if strong signals are present with minima of velocity potential in the eastern hemisphere.     

Application of a cloud-texture analysis scheme to the cloud cluster structure recognition and rainfall estimation in a mesoscale rainstorm process

It is thought that satellite infrared (IR) images can aid the recognition of the structure of the cloud and aid the rainfall estimation. In this article, the authors explore the application of a classification method relevant to four texture features, viz. energy, entropy, inertial-quadrature and local calm, to the study of the structure of a cloud cluster displaying a typical meso-scale structure on infrared satellite images. The classification using the IR satellite images taken during 4–5 July 2003, a time when a meso-scale torrential rainstorm was occurring over the Yangtze River basin, illustrates that the detailed structure of the cloud cluster can be obviously seen by means of the neural network classification method relevant to textural features, and the relationship between the textural energy and rainfall indicates that the structural variation of a cloud cluster can be viewed as an exhibition of the convection intensity evolvement. These facts suggest that the scheme of following a classification method relevant to textural features applied to cloud structure studies is helpful for weather analysis and forecasting.     

基于偏最小二乘回归理论的西北太平洋热带气旋强度统计预报方法

热带气旋(TC)的强度预报是TC研究中的前沿性问题和实际业务中的难点.当前具有参考价值的预报方法主要是统计类或模式释用类方法,例如气候持续性(CLIPER)模型等.CLIPER模型的核心技术为多元线性回归,这种回归算法在预报因子之间存在多重相关性时会丧失建模的稳健性,进而影响CLIPER模型的预报精度.为了提高CLIP...     

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system:comparison of full-field and anomaly initialization

本文使用中国科学院大气物理研究所近期气候预测系统IAP-DecPreS,评估了全场初始化和异常场初始化对ENSO的预测技巧。结果表明:采用异常场初始化方法对典型ENSO和El NioModoki的预报技巧均优于采用全场初始化方法的预报技巧。采用异常场初始化方法的回报结果能超前10个月回报强ENSO事件,超前4-7个月回报相对较弱的ENSO事件。采用异常场初始化方法对El Nio Modoki和典型ENSO的预报技巧相当。此外,采用异常场初始化方法的回报结果能超前1-4个月模拟出典型ENSO和El NioModoki的冬季海表面温度、降水以及大气环流异常的主要空间分布特征。     

Seasonal extreme rainfall over Indian monsoon region: a moisture budget analysis to distinguish the role of ENSO and non-ENSO forcing

Theoretical and Applied Climatology - Indian summer monsoon rainfall (ISMR) variability of ± 10% of its long-term mean leads to flood and drought, affecting the life and economic...