印度洋偶极子预报技巧在多模式中的对比研究

本文采用北美多模式集合产品数据，分析了印度洋偶极子指数在不同模式中实际预报技巧和潜在可预报性的差异，并进一步探究其可能的原因。结果表明，印度洋偶极子的有效预报时效在不同模式中差别较大，从2个月到4个月不等。其中东极子海温异常在不同模式中预报技巧的差别较西极子海表面温度异常更明显，表明模式误差和初始误差对东极子海表面温度异常演变的影响更为显著。另外，印度洋偶极子的实际预报技巧和潜在预报技巧存在明显的线性关系，潜在预报技巧高的模式，其实际预报技巧也高。最后，本文诊断、分析了厄尔尼诺对印度洋偶极子预报技巧的影响，发现在厄尔尼诺和印度洋偶极子相关性较高的气候模式中，印度洋偶极子实际预报技巧也较高。     

Assimilation of historical SST data for long-term ENSO retrospective forecasts

In this study, the assimilation of historic SST (sea surface temperature) data was performed for long-term ENSO hindcasts. The emphasis was placed on the design of background error covariance (BEC) that dominates the transfer of SST information to the subsurface. Four different data-assimilation schemes, based on Optimal Interpolation (OI) algorithm, were proposed, and compared in terms of ENSO simulation and prediction skills for the period from 1876 to 2000.It was found that the data-assimilation scheme that has a three-dimensional BEC constructed from model simulations forced by observed wind stress can effectively correct the second-layer temperature in the SST assimilation and lead to the best ENSO prediction skill. Further analysis for the long-term hindcasts shows that the prediction skills have a striking decadal/interdecadal variability similar to that found in other models. These results provide a fundamental basis for the further study of ENSO predictability.     

东亚夏季风降雨的可预报性研究

本文根据CMAP(The Climate Prediction Center Merged Analysis of Precipitation)观测资料,使用相关系数和均方根误差,对CHFP2(Coupled Historical Forecast Project, phase 2 )的2个模式对东亚夏季降雨的季节预报技巧作出评价。在完美模式的理论框架下,分别使用基于信噪比的潜在相关系数和基于信息熵的潜在可预报性指标,对该区域主要针对夏季降雨的可预报性作出评价。通过最可预报分量分析(PrCA),得到季节降雨的最可预报型。将最可预报型投影到海温场,得到了降水最可预报型对应的海温分布。研究发现:相关系数所反映的预报和观测的线性相关程度总体上是低纬度海洋区域比高纬度陆地区域高,而均方根误差反映的则是在海洋区域降雨预报偏离实际值的程度较陆地区域大,预报水平与目前降雨的季节预报水平相符。潜在可预报性估计表明,潜在可预报率存在空间上的变化,从低纬度向高纬度、从海洋到内陆,呈衰减趋势。同时,信号和噪音的分析表明,信号成分占主导作用,形成了潜在可预报率的空间分布格局,暗示了海洋外强迫的重要作用;中国大陆缺少像海洋区域那样明显的外强迫,因此降水季节预报技巧相比热带海洋区域非常有限。海温投影的分析表明海洋的外强迫是东亚降雨季节预报的重要来源。尽管厄尔尼诺本身的复杂性,它对东亚夏季风的重要影响及其与东亚降雨预报之间的遥相关揭示了它们内在的联系。     

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。     

Sea surface temperature predictability in the North Pacific from multi-model seasonal forecast

Sea surface temperature (SST) prediction based on the multi-model seasonal forecast with numerous ensemble members have more useful skills to estimate the possibility of climate events than individual models. Hence, we assessed SST predictability in the North Pacific (NP) from multi-model seasonal forecasts. We used 23 years of hindcast data from three seasonal forecasting systems in the Copernicus Climate Change Service to estimate the prediction skill based on temporal correlation. We evaluated the predictability of the SST from the ensemble members' width spread, and co-variability between the ensemble mean and observation. Our analysis revealed that areas with low prediction skills were related to either the large spread of ensemble members or the ensemble members not capturing the observation within their spread. The large spread of ensemble members reflected the high forecast uncertainty, as exemplified in the Kuroshio–Oyashio Extension region in July. The ensemble members not capturing the observation indicates the model bias; thus, there is room for improvements in model prediction. On the other hand, the high prediction skills of the multi-model were related to the small spread of ensemble members that captures the observation, as in the central NP in January. Such high predictability is linked to El Niño Southern Oscillation (ENSO) via teleconnection.

     

FIO-ESM气候模式北太平洋海表温度和降水的季节性预报技巧评估

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.     

几种统计模型对热带印度洋海温异常的预报

本文利用神经网络模型、多元线性回归模型和马尔科夫模型分别建立了统计预报模型,对热带印度洋海表温度异常(SSTA)和印度洋偶极子(IOD)指数进行了63 a的长时间回报实验,并详细比较了线性和非线性统计预报模型的差异。结果表明:统计模型对IOD指数的预报技巧和现有动力模式预报技巧相差不大,对偶极子指数(DMI)有效预报时效为3个月,东极子指数(EIO)为5～6个月,西极子指数(WIO)达到8～9个月。IOD事件强烈的季节锁相特性使得对秋季的DMI指数可以提前4个月做出有效预报。加入同期的ENSO指数来预报IOD指数,能有效地提高IOD预报技巧,特别是对IOD峰值的预报。复杂的神经网络模型和简单的多元线性回归模型在对SSTA和IOD指数的预报具有同等的效果。     

在Zebiak-Cane模式中观测MJO强迫对ENSO可预报性的影响, 第1部分:对最大预报误差的影响

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Julian Oscillation (MJO) as external forcing on El Ni(n)o–Southern Oscillation (ENSO) predictability is studied. The obs...     

Zebiak-Cane模式中条件非线性最优扰动对ENSO春季预报障碍的影响

使用Zebiak-Cane模式和条件非线性最优扰动(CNOP)方法,研究初始误差和参数误差共同作用对ENSO春季预报障碍现象的影响。选取模式中的8个El Ni?o事件,包括4次强事件和4次弱事件,每个El Ni?o事件又分别从8个不同的起始时间做1 a的预报,这样一共64个预报实验。对每个实验分别计算CNOP误差(初始误差和参数误差同时存在时的最优误差),通过分析误差增长,发现CNOP误差引起的1 a后的预报误差随着初始预报时间的不同有较大差异,并且不同强度的El Ni?o事件也会影响CNOP误差的发展,增长位相中强事件的预报误差要比弱事件的预报误差大一些;而衰减位相中恰恰相反,弱事件的预报误差要比强事件的预报误差要大一些;同时也发现高频El Ni?o事件对误差增长率的影响较大。本结论有助于提高Zebiak-Cane模式预报ENSO的技巧。     

Marine spatio-temporal process semantics and its applications-taking the El Niño Southern Oscilation process and Chinese rainfall anomaly as an example

Spatio-temporal semantics based on "object views" or "event views" has few abilities to represent and model the continuity and gradual oceanic phenomena or objects, which seriously limits the specific marine applications and knowledge discovery and data mining, so this paper proposes a hi- erarchical abstraction semantics with "marine spatio-temporal process→life span phases→evolution sequences→state units" and process objects included by level with "marine process objects→phase objects→sequence object→state objects" with the oceanic process characteristics into the marine process semantics. In addition, this paper designs the storage and representation of marine process objects using the backus normal forms (BNF) and abstract data type (ADT). Base on El Niño Southern Oscilation (ENSO) index and Chinese rain gauging station data, this paper also gives a case of study. The spatio-temporal analysis between ENSO process and Chinese rainfall anomalies shows that the marine spatio-temporal semantics not only can illustrate the spatial distribution of Chinese rainfall anomalies in different time scales at ENSO process, life span phases and state units, but also analyze the dynamic chan     

Sea ice concentration anomalies as long range predictors of anomalous conditions in the North Atlantic basin

Long-range empirical forecasts of North Atlantic anomalous conditions are issued, using sea ice concentration anomalies in the same region as predictors. Conditions in the North Atlantic are characterized by anomalies of sea surface temperature, of 850 hPa air temperature and of sea level pressure. Using the Singular Value Decomposition of the cross-covariance matrix between the sea ice field (the predictor) and each of the predictand variables, empirical models are built, and forecasts at lead times from 3 to 18 months are presented. The forecasts of the air temperature anomalies score the highest levels of the skill, while forecasts of the sea level pressure anomalies are the less sucessful ones.
To investigate the sources of the forecast skill, we analyze their spatial patterns. In addition, we investigate the influence of major climatic signals on the forecast skill. In the case of the air temperature anomalies, the spatial pattern of the skill may be connected to El Niño Southern Oscillation (ENSO) influences. The ENSO signature is present in the predictor field, as shown in the composite analysis. The composite pattern indicates a higher (lower) sea ice concentration in the Labrador Sea and the opposite situation in the Greenland–Barents Seas during the warm (cold) phase of ENSO. The forecasts issued under the El Niño conditions show improved skill in the Labrador region, the Iberian Peninsula and south of Greenland for the lead times considered in this paper. For the Great Lakes region the skill increases when the predictor is under the influence of a cold phase. Some features in the spatial structure of the skill of the forecasts issued in the period of the Great Salinity Anomaly present similarities with those found for forecasts made during the cold phase of ENSO. The strength of the dependence on the Great Salinity Anomaly makes it very difficult to determine the influence of the North Atlantic Oscillation.     

A Dynamical Statistical Model for Prediction of a Tropical Cyclone

A dynamical statistical method is applied for operational forecasting of the Bay of Bengal tropical cyclone “Nargis” of April–May 2008. The method consists of three forecast components, namely (a) analysis of Genesis Potential Parameter (GPP) and maximum potential intensity, (b) track prediction, and (c) 12 hourly intensity prediction for forecasts up to 72 hours. The results of the study showed that GPP could provide necessary predictive signal at early stages of development on the further intensification of the low pressure system into a tropical cyclone. The landfall forecast position errors by different operational numerical models (NWP) showed landfall position errors ranging from 10 km to 150 km and landfall time error ranges from 6 hours early to 6 hours delay. The dynamical statistical model is capable to provide 12 hourly nearly realistic intensity forecasts up to 60 hours of forecast.     

气候模式FIO-ESM对2015/16年厄尔尼诺的预测

Recently atmospheric and oceanic observations indicate the tropical Pacific is at the El Ni?o condition. However,it's not clear whether this El Ni?o event of this year is comparable to the very strong one of 1997/98 which brought huge influence on the whole world. In this study, based on the Ensemble Adjusted Kalman Filter(EAKF)assimilation scheme and First Institute of Oceanography-Earth System Model(FIO-ESM), the assimilation system is setup, which can provide reasonable initial conditions for prediction. And the hindcast results suggest the skill of El Ni?o-Southern Oscillation(ENSO) prediction is comparable to other dynamical coupled models. Then the prediction for 2015/16 El Ni?o by using FIO-ESM is started from 1 November 2015. The ensemble results indicate that the 2015/16 El Ni?o will continue to be strong. By the end of 2015, the strongest strength is very like more than 2.0°C and the ensemble mean strength is 2.34°C, which indicates 2015/16 El Ni?o event will be very strong but slightly less than that of 1997/98 El Ni?o event(2.40°C) calculated relative a climatology based on the years1992–2014. The prediction results also suggest 2015/16 El Ni?o event will be a transition to ENSO-neutral level in the early spring(FMA) 2016, and then may transfer to La Ni?a in summer 2016.     

印度洋海表盐度预报的线性马尔可夫模型及其改进办法

海洋盐度在水循环、海洋环流、海洋生态系统、全球天气和气候变化等方面起着至关重要的作用。然而, 受观测的限制, 以往对海洋盐度的研究相对匮乏, 对其进行预报的工作更为少见。本文采用线性马尔可夫模型对印度洋海表面盐度(sea surface salinity, SSS)开展初步的预报工作。根据混合层盐度收支方程, 选择海表面高度(sea surface height, SSH)、海表面温度 (sea surface temperature, SST)、SSS等物理量的异常值作为模型的组成部分, 对印度洋SSS开展预报工作。结果表明, 马尔可夫模型可提前9个月对印度洋SSS进行较好的预报。此外, 南太平洋海表面温度异常(sea surface temperature anomaly, SSTA), 海表面高度异常(sea surface height anomaly, SSHA)和印度洋偶极子(Indian Ocean dipole, IOD)系数等遥相关因素的加入可将线性马尔可夫预报对印度洋SSS的预报效果(相关系数)平均提高10%。利用改进的模型对印度洋SSS进行提前1~11个月的“实时”预测, 得出预报的SSS时空变化特征与观测场相吻合。综上所述, 改进的线性马尔可夫模型对印度洋SSS具有一定的预测能力, 未来可进一步完善。     

Covariances and linear predictability of the Atlantic Ocean

The problem of understanding linear predictability of elements of the ocean circulation is explored in the Atlantic Ocean for two disparate elements: (1) sea surface temperature (SST) under the storm track in a small region east of the Grand Banks and, (2) the meridional overturning circulation north of 30.5°S. To be worthwhile, any nonlinear method would need to exhibit greater skill, and so a rough baseline from which to judge more complex methods is the goal. A 16-year ocean state estimate is used, under the assumption that internal oceanic variability is dominating externally imposed changes. No evidence exists of significant nonlinearity in the bulk of the system over this time span. Linear predictability is the story of time and space correlations, and some predictive skill exists for a few months in SST, with some minor capability extending to a few years. Sixteen years is, however, far too short for an evaluation for interannual, much less decadal, variability, although orders of magnitude are likely stably estimated. The meridional structure of the meridional overturning circulation (MOC), defined as the time-varying vertical integral to the maximum meridional volume transport at each latitude, shows nearly complete decorrelation in the variability across about 35°N—the Gulf Stream system. If a time-scale exists displaying coherence of the MOC between subpolar and subtropical gyres, it lies beyond the existing observation duration, and that has consequences for observing system strategies and the more general problem of detectability of change.     

2个耦合模式中MJO的预报技巧

本文使用加拿大气候模拟与分析中心(Canadian Center for Climate Modeling and Analysis,CCCma)的耦合模式预报产品,应用以信息论为基础的可预报性理论框架,诊断、分析了耦合模式中Madden-Julian Oscillation(MJO)的预报率,包括实际预报技巧和潜在预报率,以及热带季节内尺度变率(Intraseasonal Variability,ISV)最可预报模态的空间分布。并在此基础上讨论了不同时间尺度平均对MJO预报技巧的影响。结果表明:本文使用的2个耦合模式中,MJO的预报技巧与目前全球主要使用的预报模式相近,约为10 d。潜在可预报技巧可以达到30 d以上。随着时间尺度从日平均增加到10 d平均,MJO的实际预报技巧与潜在可预报技巧都相应提高,尤其是潜在可预报技巧的提高更加显著。进一步分析发现,影响实际预报技巧的一个重要因素是初始条件MJO信号的强弱,当MJO信号很强时,预报技巧较高,反之则较低。本文最后分析了模式中ISV最可预报模态的空间分布,并讨论了如何利用这种最可预报空间分布提高ISV的实际预报技巧。     

基于CESM气候模式的ENSO后报试验

基于美国国家大气研究中心(NCAR)开发的通用地球系统模式(CESM),本文设计了nudging次表层海温的同化方案,进行了后报实验。对1982-2011年后报结果的分析表明,通过nudging同化,模式对ENSO现象有一定的模拟和预报能力,对赤道太平洋SST、纬向风、降水等海洋、大气要素等的后报结果与GODAS和NCEP再分析资料较接近,可以较好地重现历次厄尔尼诺和拉尼娜事件中异常东传的过程,超前1、3、6个月时,模式预报的Nio3指数与CPC指数的相关性分别达到0.88、0.81、0.70。但模式同时也表现出一定的春季预报障碍,秋季起报的后报效果最好,春季最差。对1982/1983和1997/1999两个厄尔尼诺事件的分析表明,模式后报的纬向风、热通量、风应力等大气变量的响应较实际滞后,而海洋的变化与实际情况相似,这与我们的同化方案设置有关,即模式只同化了次表层海温,进而强迫大气的响应,从而导致大气的变化较滞后。     

An analysis of regional climate model performance over the tropical Americas. Part II: simulating subseasonal variability of precipitation associated with ENSO forcing

The El Niño/Southern Oscillation (ENSO) constitutes a major source of potential predictability in the tropics. The majority of past seasonal prediction studies have concentrated on precipitation anomalies at the seasonal mean timescale. However, fields such as agriculture and water resource management require higher time frequency forecasts of precipitation variability. Regional climate models (RCMs), with their increased resolution, may offer one means of improving general circulation model forecasts of higher time frequency precipitation variability.
Part I of this study evaluated the ability of the Rossby Centre regional atmospheric model (RCA), forced by analysed boundary conditions, to simulate seasonal mean precipitation anomalies over the tropical Americas associated with ENSO variability. In this paper the same integrations are analysed, with the focus now on precipitation anomalies at subseasonal (pentad) timescales.
RCA simulates the climatological annual cycle of pentad-mean precipitation intensity quite accurately. The timing of the rainy season (onset, demise and length) is well simulated, with biases generally of less than 2 weeks. Changes in the timing and duration of the rainy season, associated with ENSO forcing, are also well captured. Finally, pentad-mean rainfall intensity distributions are simulated quite accurately, as are shifts in these distributions associated with ENSO forcing.     

Impact of tropospheric and stratospheric data assimilation on mesospheric prediction

Numerical experiments are used to assess the potential benefit of the assimilation of tropospheric and stratospheric observations on mesospheric prediction. A simulated atmosphere, taken as truth, is created using the Canadian Middle Atmosphere Model (CMAM). The truth is sampled at the locations of the measurements from the actual observing system to produce observations, which are then assimilated with the CMAM-DAS (Data Assimilation System). Obtained forecasts are compared with the truth, and error statistics are calculated. An assessment based on predictability shows that upward propagation of information resulting from the assimilation of tropospheric and stratospheric observations improves the mesosphere in the largest scales (with horizontal wavenumbers less than approximately 10). At the same time, the principal inability of the system to predict mesospheric small scales is demonstrated.     

ENSO related modulation of coastal upwelling in the eastern Atlantic

An index of ENSO in the Pacific during early boreal winter is shown to account for a significant part of the variability of coastal SST anomalies measured a few months later within the wind driven West African coastal upwelling region from 10°N to 26°N. This teleconnection is thought to result from an atmospheric bridge between the Pacific and Atlantic oceans, leading to warm (cold) ENSO events being associated with a relaxation (intensification) of the Atlantic trade winds and of the wind-induced coastal upwelling. This ENSO related modulation of the wind-driven coastal upwelling appears to contribute to the connection observed at the basin-scale between ENSO and SST in the north Atlantic. The ability to use this teleconnection to give warnings of large changes in the West African upwelling several months in advance is successfully tested using data from the 1998 and 1999 ENSO events.