大渡河安谷水电站过鱼设施改造效果研究

大渡河安谷水电站建成后运行水位变更,竖缝式鱼道和仿自然通道下凿闸底板,降低高程,以保证水流条件。为了解改造后鱼道水力条件和过鱼效果,测量过鱼设施内的水位和流速,使用网捕法调查生态河道和过鱼设施内的鱼类特征,分析改造前后过鱼设施运行状况和水生生态调查结果的变化,得到以下主要结论:改造后鱼道和通道的运行保证率大幅提高,水动力条件接近设计效果;改造后调查区域内鱼类种类更丰富,部分洄游鱼类的种群数量明显增加;安谷水电站生态河道放水闸竖缝式鱼道和仿自然通道的渔获物物种组成有明显差异,鱼道和通道适用于不同鱼类,有效互补。

巴拉水电站过鱼方案比选研究

为缓解水电站建设对鱼类生存环境的阻隔,建设过鱼设施是最有效的方法之一,过鱼设施的集鱼和转运方案选择直接影响过鱼效果,但尚无通用的固定方法,需综合考虑过鱼目标、建设条件和经济等因素,成为巴拉水电站过鱼设施建设的一个技术问题。本文结合巴拉水电站过鱼要求和工程特点,初拟集鱼和转运方案,并提出比选建议,为巴拉水电站过鱼设施建设提供技术支撑,可供同类工程参考。

深度学习在印度洋偶极子预报中的应用研究

印度洋偶极子(IOD)是热带印度洋秋季最强的年际变率,它会通过大气遥相关来影响世界许多地区的气候。目前耦合气候模式对IOD预报技巧仍非常有限,远低于热带太平洋的厄尔尼诺事件的预报技巧。鉴于深度学习具备高效的数据处理能力,本文使用深度学习中的卷积神经网络(CNN)与人工神经网络中的多层感知机(MLP)处理再分析观测资料,从而进行IOD预报。由于当预报初始时刻为北半球冬春季时,对IOD事件的预报技巧较低。因此,为探索CNN的预报能力,本文仅使用三种(1~3月、2~4月、3~5月)初始时刻的海表温度异常(SSTA)作为CNN的输入数据,来预报其后续七个月的印度洋偶极子指数(DMI)、东极子指数(EIOI)和西极子指数(WIOI)。结果表明:CNN对DMI、EIOI和WIOI的有效预测时效均超过了6个月。与现在耦合动力模式相比,CNN模型能够显著提升DMI和EIOI的预报技巧,但对WIOI的预报技巧提升有限。当预报提前时间为7个月时,CNN模型能够比较准确地预报1994、1997与2019年的IOD事件。由于CNN模型能够更好地抓住印度洋海温的空间结构特征,它对IOD事件的预报技巧比传统神经网络MLP高。

A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance.Meanwhile,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO modeling.We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made recently.As ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and improvements.Now,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and applications.Here,model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models,pinpointing to the areas where they need to be further improved for ENSO studies.These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.

台风期间浮标和潜标上ADCP的空间变化、数据误差及校正

基于南海北部浮标和潜标的声学多普勒流速剖面仪(ADCP)数据,通过一套几何算法计算了台风海鸥(1415)期间ADCP的空间变化和流速误差,并进行数据校正。浮标上,台风过后ADCP的水平位移最大可达2.61 km,水平流速误差最大可达0.27 m/s,垂向流速误差最大仅为5×10^(-4) m/s;温跃层流速校正值在台风过后显著大于流速测值,这表明水平校正对于温跃层流速的质量控制很重要。潜标上,ADCP最大垂向位移增量为179 m,最大绳子倾角为35°,最大水平位移为1.5 km;ADCP水平流速误差和倾角误差都很小,在数据校正中可忽略不计,但对台风过后中层流速的垂向校正不能忽略。

Comparison and combination of EAKF and SIR-PF in the Bayesian filter framework

Bayesian estimation theory provides a general approach for the state estimate of linear or nonlinear and Gaussian or non-Gaussian systems. In this study, we first explore two Bayesian-based methods： ensemble adjustment Kalman filter（EAKF） and sequential importance resampling particle filter（SIR-PF）, using a well-known nonlinear and non-Gaussian model（Lorenz ＇63 model）. The EAKF, which is a deterministic scheme of the ensemble Kalman filter（En KF）, performs better than the classical（stochastic） En KF in a general framework. Comparison between the SIR-PF and the EAKF reveals that the former outperforms the latter if ensemble size is so large that can avoid the filter degeneracy, and vice versa. The impact of the probability density functions and effective ensemble sizes on assimilation performances are also explored. On the basis of comparisons between the SIR-PF and the EAKF, a mixture filter, called ensemble adjustment Kalman particle filter（EAKPF）, is proposed to combine their both merits. Similar to the ensemble Kalman particle filter, which combines the stochastic En KF and SIR-PF analysis schemes with a tuning parameter, the new mixture filter essentially provides a continuous interpolation between the EAKF and SIR-PF. The same Lorenz ＇63 model is used as a testbed, showing that the EAKPF is able to overcome filter degeneracy while maintaining the non-Gaussian nature, and performs better than the EAKF given limited ensemble size.

Genesis of the 2014–2016 El Nio events

The 2015/2016 El Nio was one of the strongest El Nio events in history, and this strong event was preceded by a weak El Nio in 2014. This study systematically analyzed the dynamical processes responsible for the genesis of these events. It was found that the weak 2014 El Nio had two warming phases, the spring-summer warming was produced by zonal advection and downwelling Kelvin waves driven by westerly wind bursts(WWBs), and the autumn-winter warming was produced by meridional advection, surface heating as well as downwelling Kelvin waves. The 2015/2016 extreme El Nio, on the other hand, was primarily a result of sustained zonal advection and downwelling Kelvin waves driven by a series of WWBs, with enhancement from the Bjerknes positive feedback. The vast difference between these two El Nio events mainly came from the different amount of WWBs in 2014 and 2015. As compared to the 1982/1983 and 1997/1998 extreme El Nio events, the 2015/2016 El Nio exhibited some distinctive characteristics in its genesis and spatial pattern. We need to include the effects of WWBs to the theoretical framework of El Nio to explain these characteristics, and to improve our understanding and prediction of El Nio.

A theoretical investigation of the tropical Indo-Pacific tripole mode

The El Ni o-Southern Oscillation(ENSO)phenomenon in the tropical Pacific has been a focus of ocean and climate studies in the last few decades.Recently,the short-term climate variability in the tropical Indian Ocean has attracted increasingly more attention,especially with the proposition of the Indian Ocean Dipole(IOD)mode.However,these phenomena are often studied separately without much consideration of their interaction.Observations reveal a striking out-of-phase relationship between zonal gradients of sea surface height anomaly(SSHA)and sea surface temperature anomaly(SSTA)in the tropical Indian and Pacific Oceans.Since the two oceans share the ascending branch of the Walker cells over the warm pool,the variation within one of them will affect the other.The accompanied zonal surface wind anomalies are always opposite over the two basins,thus producing a tripole structure with opposite zonal gradients of SSHA/SSTA in the two oceans.This mode of variability has been referred to as Indo-Pacific Tripole(IPT).Based on observational data analyses and a simple ocean-atmosphere coupled model,this study tries to identify the characteristics and physical mechanism of IPT with a particular emphasis on the relationships among ENSO,IOD,and IPT.The model includes the basic oceanic and atmospheric variables and the feedbacks between them,and takes into account the inter-basin connection through an atmospheric bridge,thus providing a valuable framework for further research on the short-term tropical climate variability.

On the “spring predictability barrier” for strong El Nio events as derived from an intermediate coupled model ensemble prediction system

Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model.

The predictability of atmospheric and oceanic motions:Retrospect and prospects

This paper reviews the historic understanding of the predictability of atmospheric and oceanic motions, and interprets it in a general framework. On this basis, the existing challenges and unsolved problems in the study of the intrinsic predictability limit(IPL) of weather and climate events of different spatio-temporal scales are summarized. Emphasis is also placed on the structure of the initial error and model parameter errors as well as the associated targeting observation issue. Finally, the predictability of atmospheric and oceanic motion in the ensemble-probabilistic methods widely used in current operational forecasts are discussed.The necessity of considering IPLs in the framework of stochastic dynamic systems is also addressed.

Frequency-specified EOF analysis and its application to Pacific decadal oscillation

A frequency-specified empirical orthogonal function(FSEOF)analysis is proposed in this study.The aim of FSEOF is to specify a prescribed-band of frequency in leading principal components with less information losing at the ends of the data,thus well characterizing the signals of interest.The FSEOF can well capture prescribed variability in leading modes,and has intrinsic merits in resolving frequency-related modes,especially those associated with low frequency oscillations.An application of the FSEOF to the tropical and northern Pacific sea surface temperature shows that this new method can successfully separate Pacific decadal oscillation(PDO)mode from the El Nino-Southern oscillation mode,and clearly detect all regime shifts of PDO in the past centurv.