A Hybrid Coupled Ocean–Atmosphere Model and Its Simulation of ENSO and Atmospheric Responses

A new hybrid coupled model(HCM) is presented in this study, which consists of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model. The ocean component is the intermediate ocean model(IOM)of the intermediate coupled model(ICM) used at the Institute of Oceanology, Chinese Academy of Sciences(IOCAS). The atmospheric component is ECHAM5, the fifth version of the Max Planck Institute for Meteorology atmospheric general circulation model. The HCM integrates its atmospheric and oceanic components by using an anomaly coupling strategy. A100-year simulation has been made with the HCM and its simulation skills are evaluated, including the interannual variability of SST over the tropical Pacific and the ENSO-related responses of the global atmosphere. The model shows irregular occurrence of ENSO events with a spectral range between two and five years. The amplitude and lifetime of ENSO events and the annual phase-locking of SST anomalies are also reproduced realistically. Despite the slightly stronger variance of SST anomalies over the central Pacific than observed in the HCM, the patterns of atmospheric anomalies related to ENSO,such as sea level pressure, temperature and precipitation, are in broad agreement with observations. Therefore, this model can not only simulate the ENSO variability, but also reproduce the global atmospheric variability associated with ENSO, thereby providing a useful modeling tool for ENSO studies. Further model applications of ENSO modulations by ocean–atmosphere processes, and of ENSO-related climate prediction, are also discussed.     

Impact of Subsurface Entrainment on ENSO Prediction: 1997-98 El Nino

Twenty-one-year hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific were performed to validate the influence of ocean subsurface entrainment on SST prediction.A new hybrid coupled model was used that considered the entrainment of subsurface temperature anomalies into the sea surface.The results showed that predictions were improved significantly in the new coupled model.The predictive correlation skill increased by about 0.2 at a lead time of 9 months,and the root-mean-square (RMS) errors were decreased by nearly 0.2°C in general.A detailed analysis of the 1997-98 El Nio hindcast showed that the new model was able to predict the onset,peak (both time and amplitude),and decay of the 1997-98 strong El Nio event up to a lead time of one year,factors that are not represented well by many other forecast systems.This implies,in terms of prediction,that subsurface anomalies and their impact on the SST are one of the controlling factors in ENSO cycles.Improving the presentation of such effects in models would increase the forecast skill.     

Simulation of the Effects of the Preceding SST Anomalies over the Tropical Eastern Pacific on Precipitation to the South of the Yangtze River in June

Numerical experiments are performed to simulate the response of the atmospheric circulation and pre-cipitation over East China in June to the sea surface temperature（SST）anomalies over the tropical eastern Pacific（TEP）from preceding September to June by using an atmospheric general circulation model （AGCM）.We constructed composite positive/negative SST anomalies（P-SSTAs/N-SSTAs）based on the observational SST anomalies over the TEP from September 1997 to June 1998.The results show that：（1） the response of the precipitation in the Yangtze River basin and its southern area（YRBS）to El Nino with different durations varies with the maximum amplitude of the precipitation anomalies appearing when the imposed duration is from November to next June,and the minimum appearing when the SST anomalies is only imposed in June.The anomalies of the precipitation are reduced when the duration of the forcing SST anomalies over the TEP is shortened and the positive SST anomalies in the preceding autumn tend to cause significantly more rainfall in the YRBS.This is in agreement with previous diagnostic analysis results.（2）The simulated precipitation anomalies over the YRBS are always obviously positive under strong or weak positive SST anomalies over the TEP.The intensity of the precipitation anomalies increases with increasing intensity of the SST anomalies in the experiments.The simulation results are consistent with the observations during the warm SST events,suggesting reasonable modeling results.（3）When negative SST anomalies in the TEP are put into the model,the results are different from those of the diagnostic analysis of La Nina events.Negative precipitation anomalies in YRBS could be reproduced only when the negative SST anomalies are strong enough.     

Evaluation of ocean data assimilation in CAS-ESM-C: Constraining the SST field

A weakly coupled assimilation system, in which SST observations are assimilated into a coupled climate model(CASESM-C) through an ensemble optimal interpolation scheme, was established. This system is a useful tool for historical climate simulation, showing substantial advantages, including maintaining the atmospheric feedback, and keeping the oceanic fields from drifting far away from the observation, among others. During the coupled model integration, the bias of both surface and subsurface oceanic fields in the analysis can be reduced compared to unassimilated fields. Based on 30 model years of output from the system, the climatology and interannual variability of the climate system were evaluated. The results showed that the system can reasonably reproduce the climatological global precipitation and SLP, but it still suffers from the double ITCZ problem. Besides, the ENSO footprint, which is revealed by ENSO-related surface air temperature, geopotential height and precipitation during El Ni ?no evolution, is basically reproduced by the system. The system can also simulate the observed SST–rainfall relationships well on both interannual and intraseasonal timescales in the western North Pacific region, in which atmospheric feedback is crucial for climate simulation.     

ENSO Variability Simulated by a Coupled General Circulation Model:ECHAM5/MPI-OM

The accurate simulation of the equatorial sea surlhce temperature （SST） variability is crucial for a proper representation or prediction of the El Nino-Southern Os- cillation （ENSO）. This paper describes the tropical variability simulated by the Max Planck Institute （MPI） forr meteorology coupled atmosphere-ocean general circulation model （CGCM）. A control simulation with pre-industrial greenhouse gases is analyzed, and the simulation of key oceanic features, such as SST, is compared with observa- tions. Results from the 400-yr control simulation show that the model＇s ENSO variability is quite realistic in terms of structure, strength, and period. Also, two related features （the annual cycle of SST and the-phase locking of ENSO events）, which are significant in determining the model＇s performance of realistic ENSO prediction, are further validated to be well reproduced by the MPI cli mate model, which is an atmospheric model ECHAM5 （which fuses the EC tbr European Center and HAM for Hamburg） coupled to an MPI ocean model （MPI-OM）, ECHAMS/MPI-OM.     

INFLUENCE OF TIBETAN PLATEAU ON AIR-SEA INTERACTION OVER TROPICAL PACIFIC IN OCEAN-ATMOSPHERIC COUPLED SYSTEM

A hybrid coupled ocean-atmosphere model is designed,which consists of a global atmosphericgeneral circulation model(L9R15 AGCM)and a simple ocean model(ZC ocean model over tropicalPacific).Using the model,experimental predictions are performed for the 1986/87 El Nino eventand the 1988/89 La Nina event with and without the Tibetan Plateau respectively(called TPFORC and NTP FORC hereinafter).It is found as follows:(1)The coupled system cansuccessfully predict the El Nino or La Nina event even if the Tibetan Plateau orography is notincluded in the model.The patterns of SSTA and wind anomalies in the model without the TibetanPlateau are similar to those with the Tibetan Plateau,which further verifies the fact that ENSOprocess is mainly caused by the air-sea interaction in tropical Pacific.(2)However.the existenceof the Tibetan Plateau exerts its influences on the intensity and duration of El Nino(or La Nina).It is unfavorable to the development and maintenance of westerly anomalies,so to some extent,restrains the development of El Nino,but favors the development of La Nina.(3)Effects of theTibetan Plateau orography on the wind anomalies in the coupled system are different from those inuncoupled AGCM simulation.     

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.     

MONTHLY AND SEASONAL NUMERICAL FORECASTS BY USING THE ANOMALY OCEAN-ATMOSPHERE COUPLED FILTERED MODEL

Case experiments of monthly predictions of eight winter months during 1976-1977 and 1982-1983 El Nino events are performed by using a three-layer anomalous filtered model (AFM), in which transient Rossby waves are filtered. The results show that this model predicts successfully the large-scale patterns of the monthly mean surface temperature anomalies. The correlation coefficients between observations and predictions are basically higher than those of persistence predictions. By comparison with the anomalous general circulation model (AGCM) the AFM gives almost the identical results, but the computation time required for running the AFM is nearly 100 times less than that required for running the AGCM. It is also shown that the results of the three-layer model are better than those of the one-layer model. In the meanwhile, four seasonal forecasts are also carried out by using the same model. It seems that the AFM possesses potential ability in predicting large-scale circulation anomalies.On the basis of     

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4 D variational(4 D-Var) data assimilation system was developed for an intermediate coupled model(ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer(T_e), which is empirically and explicitly related to sea level(SL) variation.The strength of the thermocline effect on SST(referred to simply as "the thermocline effect") is represented by an introduced parameter, αT_e. A numerical procedure is developed to optimize this model parameter through the 4 D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only,and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling.The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4 D-Var method provides a modeling platform for ENSO studies. Further applications of the 4 D-Var data assimilation system implemented in the ICM are also discussed.     

ENSO phase-locking in an ocean-atmosphere coupled model FGCM-1.0

The mean climatology and the basic characteristics of the ENSO cycle simulated by a coupled model FGCM-1.0 are investigated in this study. Although with some common model biases as in other directly coupled models, FGCM-1.0 is capable of producing the interannual variability of the tropical Pacific, such as the ENSO phenomenon. The mechanism of the ENSO events in the coupled model can be explained by “delayed oscillator” and “recharge-discharge” hypotheses. Compared to the observations, the simulated ENSO events show larger amplitude with two distinctive types of phase-locking： one with its peak phase-locked to boreal winter and the other to boreal summer. These two types of events have a similar frequency of occurrence, but since the second type of event is seldom observed, it may be related to the biases of the coupled model. Analysis show that the heat content anomalies originate from the central south Pacific in the type of events peaking in boreal summer, which can be attributed to a different background climatology from the normal events. The mechanisms of their evolutions are also discussed.     

中国业务动力季节预报的进展

利用动力模式开展季节到年际的短期气候预测 ,是目前国际上气候预测的发展方向。自 1996年以来 ,经过 8a多的研制和发展 ,国家气候中心已建立起第 1代动力气候模式预测业务系统 ,其中包括 1个全球大气 海洋耦合模式 (CGCM )、1个高分辨率东亚区域气候模式 (RegCM_NCC)和 5个简化的ENSO预测模式 (SAOMS) ,可用于季节—年际时间尺度的全球气候预测 ;全球海气耦合模式与区域气候模式嵌套 ,可以提供高分辨率的东亚区域气候模式制做季节预测。CGCM对 1982～ 2 0 0 0年夏季的历史回报试验表明 ,该模式对热带太平洋海表面温度和东亚区域的季节预测具有较好的预测能力。RegCM NCC的 5a模拟基本上能再现东亚地区主要雨带的季节进展。利用嵌套的区域气候模式RegCM NCC对 1991～ 2 0 0 0年的夏季回报表明 ,在预报主要季节雨带方面有一定技巧。 2 0 0 1～ 2 0 0 3年 ,CGCM和RegCM NCC的实时季节预报与观测相比基本合理。特别是 ,模式成功地预报了 2 0 0 3年梅雨季节长江和黄河之间比常年偏多的降水。SAOMS模式系统的回报试验表明 ,该系统对热带太平洋海表面温度距平有一定的预报能力 ,模式超前 6～ 12个月的回报与观测的相关系数明显高于持续预报。 1997～ 2 0 0 3年 ,SAOMS多模式集合实时预报与观测的相关系数达到     

The retrospective prediction of El Niño-southern oscillation from 1881 to 2000 by a hybrid coupled model: (I) Sea surface temperature assimilation with ensemble Kalman filter

In this study, we assimilated sea surface temperature (SST) data of the past 120 years into an oceanic general circulation model (OGCM) for El Niño-southern oscillation (ENSO) retrospective predictions using ensemble Kalman filter (EnKF). It was found that the ensemble covariance matrix in EnKF can act as a time-variant transfer operator to project the SST corrections onto the subsurface temperatures effectively when initial perturbations of ensemble were constructed using vertically coherent random fields. As such the increments of subsurface temperatures can be obtained via the transfer operator during assimilation cycles. The results show that the SST assimilation improves the model simulation skills significantly, not only for the SST anomalies over the whole assimilated domain, but also for the subsurface temperature anomalies of the upper 100 m over the tropical Pacific off the equator. Along the equator, the improvement of the assimilation is confined within the mixing layer because strong upwelling motions there prevent the downward transfer of SST information. The retrospective prediction skills of ENSO over the past 120 years from 1881 to 2000 were significantly improved by the SST assimilation at all leads of 1–12 months, especially for the 3–6 months leads, compared with those initialized by the control run without assimilation. The skilful predictions by the assimilation allow us to further study ENSO predictability using this coupled model.     

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.     

The seasonal footprinting mechanism in CFSv2: simulation and impact on ENSO prediction

The seasonal footprinting mechanism (SFM) is thought to be a pre-cursor to the El Nino Southern Oscillation (ENSO). Fluctuations in the North Pacific Oscillation (NPO) impact the ocean via surface heat fluxes during winter, leaving a sea-surface temperature (SST) “footprint” in the subtropics. This footprint persists through the spring, impacting the tropical Pacific atmosphere–ocean circulation throughout the following year. The simulation of the SFM in the National Centers for Environmental Prediction (NCEP)/Climate Forecast System, version 2 (CFSv2) is likely to have an impact on operational predictions of ENSO and potentially seasonal predictions in the United States associated with ENSO teleconnection patterns. The ability of the CFSv2 to simulate the SFM and the relationship between the SFM and ENSO prediction skill in the NCEP/CFSv2 are investigated. Results indicate that the CFSv2 is able to simulate the basic characteristics of the SFM and its relationship with ENSO, including extratropical sea level pressure anomalies associated with the NPO in the winter, corresponding wind and SST anomalies that impact the tropics, and the development of ENSO-related SST anomalies the following winter. Although the model is able to predict the correct sign of ENSO associated with the SFM in a composite sense, probabilistic predictions of ENSO following a positive or negative NPO event are generally less reliable than when the NPO is not active.     

ENSO及其组合模态对中国东部各季节降水的影响

近期的研究发现,热带太平洋低层大气存在两种主要模态,即经向对称ENSO模态和ENSO与海表温度(SST)年循环相互作用产生的经向反对称组合模态。主要探讨了这两种不同ENSO模态对中国东部各季节降水的影响。结果表明,厄尔尼诺年秋季,中国西南、长江及华南大部分区域呈现显著正降水异常;冬季,正降水异常范围扩大,覆盖华南、华东及华北东南部地区。这两个季节的异常降水都主要受ENSO模态的影响。与ENSO模态相关的正异常海温局地强迫导致120°E以西出现反气旋性环流,其西北侧增强的西南暖湿气流使得中国东部地区降水增多。次年春季,从中国华南延伸到东北出现正的异常降水,主要是ENSO组合模态的贡献。因为次年春季热带太平洋地区ENSO模态信号只局限于赤道地区,并没有对中国东部降水有显著的影响,而ENSO与海温年循环相互作用的组合模态使得与ENSO相关的赤道大气异常可以扩展到赤道以外地区。ENSO组合模态对中国降水异常有重要影响,在今后的研究和短期预测中需引起重视。      

印度洋对ENSO事件的响应:观测与模拟

观测事实显示,在El Ni(n～)o期间,伴随着赤道中东太平洋表层海温(SST)的升高,热带印度洋SST出现正距平.作者利用海气耦合模式模拟了印度洋对ENSO事件的上述响应,并进而讨论了其物理机制.所用模式为法国国家科研中心Pierre-Simon-Laplace 全球环境科学联合实验室(IPSL)发展的全球海气耦合模式.该模式成功地控制了气候漂移,能够合理再现印度洋的基本气候态.观测中与ENSO相关的热带印度洋SST变化,表现为全海盆一致的正距平,并且这种变化要滞后赤道中东太平洋SST变化大约一个季度,意味着它主要是对东太平洋SST强迫的一种遥响应,模式结果也支持这一机制,尽管模式中的南方涛动现象被夸大了,使得模拟的与ENSO相关联的SST正距平的位置南移,阿拉伯海和孟加拉湾被负距平(而不是正距平)所控制.研究表明,东太平洋主要通过大气桥影响潜热释放来影响印度洋SST变化.赤道东太平洋El Ni(n～)o事件的发展,导致印度洋上空风场异常自东而西传播;伴随着风场的变化,潜热发生相应变化,并最终导致SST异常的发生.非洲东海岸受索马里急流控制的海域,其SST的变化不能简单地利用热通量的变化来解释.证据显示,印度洋的增暖是ENSO事件发生的结果而不是其前期信号.     

A deep learning–based U-Net model for ENSO-related precipitation responses to sea surface temperature anomalies over the tropical Pacific

SST–precipitation feedback plays an important role in ENSO evolution over the tropical Pacific and thus it is critically important to realistically represent precipitation-induced feedback for accurate simulations and predictions of ENSO. Typically, in hybrid coupled modeling for ENSO predictions, statistical atmospheric models are adopted to determine linear precipitation responses to interannual SST anomalies. However, in current coupled climate models, the observed precipitation–SST relationship is not well represented. In this study, a data-driven deep learning-based U-Net model was used to construct a nonlinear response model of interannual precipitation variability to SST anomalies. It was found that the U-Net model outperformed the traditional EOF-based method in calculating the precipitation variability. Particularly over the western-central tropical Pacific, the mean-square error (MSE) of the precipitation estimates in the U-Net model was smaller than that in the EOF model. The performance of the U-Net model was further improved when additional tendency information on SST and precipitation variability was also introduced as input variables, leading to a pronounced MSE reduction over the ITCZ.摘要SST–降水反馈过程在热带太平洋ENSO演变过程中起着重要作用, 能否真实地在数值模式中表征SST–降水年际异常之间的关系及相关反馈过程, 对于准确模拟和预测ENSO至关重要. 例如, 在一些模拟ENSO的混合型耦合模式中, 通常采用大气统计模型 (如经验正交函数; EOF) 来表征降水 (海气界面淡水通量的一个重要分量) 对SST年际异常的线性响应. 然而在当前的耦合模式中, 真实观测到的降水–SST统计关系还不能被很好地再现出来, 从而引起 ENSO模拟误差和不确定性. 在本研究中, 使用基于深度学习的U-Net模型来构建热带太平洋降水异常场对SST年际异常的非线性响应模型. 研究发现: U-Net模型的性能优于传统的基于EOF方法的模型. 特别是在热带西太平洋海区, U-Net模型估算的降水误差远小于EOF模型的模拟. 此外, 当SST和降水异常的趋势信息作为输入变量也被同时引入以进一步约束模式训练时, U-Net模型的性能可以进一步提高, 如能使热带辐合带区域的误差显著降低.