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.     

Improved ENSO Forecasts by Assimilating Sea Surface Temperature Observations into an Intermediate Coupled Model

A simple method for initializing intermediate coupled models (ICMs) using only sea surface temperature (SST) anomaly data is comprehensively tested in two sets of hindcasts with a new ICM. In the initialization scheme, both the magnitude of the nudging parameter and the duration of the assimilation are considered, and initial conditions for both atmosphere and ocean are generated by running the coupled model with SST anomalies nudged to the observations. A comparison with the observations indicates that the scheme can generate realistic thermal fields and surface dynamic fields in the equatorial Pacific through hindcast experiments. An ideal experiment is performed to get the optimal nudging parameters which include the nudging intensity and nudging time length. Twelve-month-long hindcast experiments are performed with the model over the period 1984–2003 and the period 1997–2003. Compared with the original prediction results, the model prediction skills are significantly improved by the nudging method especially beyond a 6-month lead time during the two different periods. Potential problems and further improvements are discussed regarding the new coupled assimilation system.     

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     

How are seasonal prediction skills related to models’ performance on mean state and annual cycle?

Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

Roles of Indian and Pacific Ocean air–sea coupling in tropical atmospheric variability

Sea surface temperature (SST) variations include negative feedbacks from the atmosphere, whereas SST anomalies are specified in stand-alone atmospheric general circulation simulations. Is the SST forced response the same as the coupled response? In this study, the importance of air–sea coupling in the Indian and Pacific Oceans for tropical atmospheric variability is investigated through numerical experiments with a coupled atmosphere-ocean general circulation model. The local and remote impacts of the Indian and Pacific Ocean coupling are obtained by comparing a coupled simulation with an experiment in which the SST forcing from the coupled simulation is specified in either the Indian or the Pacific Ocean. It is found that the Indian Ocean coupling is critical for atmospheric variability over the Pacific Ocean. Without the Indian Ocean coupling, the rainfall and SST variations are completely different throughout most of the Pacific Ocean basin. Without the Pacific Ocean coupling, part of the rainfall and SST variations in the Indian Ocean are reproduced in the forced run. In regions of large mean rainfall where the atmospheric negative feedback is strong, such as the North Indian Ocean and the western North Pacific in boreal summer, the atmospheric variability is significantly enhanced when air–sea coupling is replaced by specified SST forcing. This enhancement is due to the lack of the negative feedback in the forced SST simulation. In these regions, erroneous atmospheric anomalies could be induced by specified SST anomalies derived from the coupled model. The ENSO variability is reduced by about 20% when the Indian Ocean air–sea coupling is replaced by specified SST forcing. This change is attributed to the interfering roles of the Indian Ocean SST and Indian monsoon in western and central equatorial Pacific surface wind variations.     

Performance of GCMs for seasonal prediction over India��a case study for 2009 monsoon

The 2009 drought in India was one of the major droughts that the country faced in the last 100?years. This study describes the anomalous features of 2009 summer monsoon and examines real-time seasonal predictions made using six general circulation models (GCMs). El Ni?o conditions evolved in the Pacific Ocean, and sea surface temperatures (SSTs) over the Indian Ocean were warmer than normal during monsoon 2009. The observed circulation patterns indicate a weaker monsoon in that year over India with weaker than normal convection over the Bay of Bengal and Indian landmass. Skill of the GCMs during hindcast period shows that neither these models simulate the observed interannual variability nor their multi-model ensemble (MME) significantly improves the skill of monsoon rainfall predictions. Except for one model used in this study, the real-time predictions with longer lead (2- and 1-month lead) made for the 2009 monsoon season did not provide any indication of a highly anomalous monsoon. However, with less lead time (zero lead), most of the models as well as the MME had provided predictions of below normal rainfall for that monsoon season. This study indicates that the models could not predict the 2009 drought over India due to the use of less warm SST anomalies over the Pacific in the longer lead runs. Hence, it is proposed that the uncertainties in SST predictions (the lower boundary condition) have to be represented in the model predictions of summer monsoon rainfall over India.     

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.     

The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST

The impact of realistic atmospheric initialisation on the seasonal prediction of tropical Pacific sea surface temperatures is explored with the Predictive Ocean–Atmosphere Model for Australia (POAMA) dynamical seasonal forecast system. Previous versions of POAMA used data from an Atmospheric Model Intercomparison Project (AMIP)-style simulation to initialise the atmosphere for the hindcast simulations. The initial conditions for the hindcasts did not, therefore, capture the true intra-seasonal atmospheric state. The most recent version of POAMA has a new Atmosphere and Land Initialisation scheme (ALI), which captures the observed intra-seasonal atmospheric state. We present the ALI scheme and then compare the forecast skill of two hindcast datasets, one with AMIP-type initialisation and one with realistic initial conditions from ALI, focussing on the prediction of El Niño. For eastern Pacific (Niño3) sea surface temperature anomalies (SSTAs), both experiments beat persistence and have useful SSTA prediction skill (anomaly correlations above 0.6) at all lead times (forecasts are 9 months duration). However, the experiment with realistic atmospheric initial conditions from ALI is an improvement over the AMIP-type initialisation experiment out to about 6 months lead time. The improvements in skill are related to improved initial atmospheric anomalies rather than an improved initial mean state (the forecast drift is worse in the ALI hindcast dataset). Since we are dealing with a coupled system, initial atmospheric errors (or differences between experiments) are amplified though coupled processes which can then lead to long lasting errors (or differences).     

Potential Predictability of Sea Surface Temperature in a Coupled Ocean--Atmosphere GCM

Using the Flexible Global Ocean--Atmosphere--Land System model (FGOALS) version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface temperature (SST), singular value decomposition (SVD) analyses were applied to extract dominant coupled modes between observed and predicated SST from the hindcasting experiments in this study. The fields discussed are sea surface temperature anomalies over the tropical Pacific basin (20^oS--20^oN, 120^oE--80^oW), respectively starting in four seasons from 1982 to 2005. On the basis of SVD analysis, the simulated pattern was replaced with the corresponding observed pattern to reconstruct SST anomaly fields to improve the ability of the simulation. The predictive skill, anomaly correlation coefficients (ACC), after systematic error correction using the first five modes was regarded as potential predictability. Results showed that: 1) the statistical postprocessing approach was effective for systematic error correction; 2) model error sources mainly arose from mode 2 extracted from the SVD analysis---that is, during the transition phase of ENSO, the model encountered the spring predictability barrier; and 3) potential predictability (upper limits of predictability) could be high over most of the tropical Pacific basin, including the tropical western Pacific and an extra 10-degrees region of the mid and eastern Pacific.