Intraseasonal oscillation of the southwest monsoon over Sri Lanka and evaluation of its subseasonal forecast skill

斯里兰卡的雨季发生于5-9月间,主要受西南季风的控制.本文发现该地区的西南季风降水存在很强的次季节变率,主导周期为10-35天.降水的季节内变化与西传的异常气旋有关.进一步,利用S2S比较计划中欧洲中心的数值预报模式(ECMWF)提供的回报试验数据,评估了当今动力模式对斯里兰卡西南季风次季节变化的预报技巧.结果显示,对季风指数的预测技巧超过30天,而对降水指数的预测技巧大约两周,且模式的预报技巧具有明显的年际差异.分析表明,能否正确模拟出大尺度环流对热带对流的响应是影响斯里兰卡降水预测的重要因子.     

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.     

时空投影模型(STPM)的次季节至季节(S2S)预测应用进展

随着数值天气预报技术和季节动力预报系统的发展,短期天气预报及长期气候预测的能力持续提高,然而介于两者之间的次季节至季节(S2S,两周至三个月)预测技巧偏低,成为当今气象学界和业务服务的难题。南京信息工程大学国家特聘专家李天明教授团队于2012年研发了基于时空投影技术的统计预报模型(STPM),成功地对中国大陆降水和气温距平,以及区域极端降水、夏季高温、冬季低温和西太平洋台风群发事件等高影响天气进行提前10~30 d的预报,并在国家气候中心及多个省份开展了业务应用。STPM也成功应用于台湾春雨预报、南海季风爆发和ENSO预测等季节至年际变化的预测。本文对S2S预测的理论基础、STPM的发展和应用进行了完整的介绍,并讨论了S2S预测业务中所面临的挑战和未来展望。     

两个集合预报系统对秦岭及周边降水预报性能对比

利用欧洲中期天气预报中心 (ECMWF)、美国大气环境预报中心 (NCEP) 集合预报系统 (EPS) 降水量预报资料,CMORPH (NOAA Climate Prediction Center Morphing Method) 卫星与全国3万个自动气象站降水量融合资料,基于技巧评分、ROC (relative operating characteristic) 分析等方法,对比两个集合预报系统对秦岭及周边地区的降水预报性能。结果表明:两个系统均能较好表现降水量的空间形态,对于不同量级降水,ECMWF集合预报系统0~240 h控制及扰动预报优于NCEP集合预报系统,但NCEP集合预报系统264~360 h预报时效整体表现更好; ECMWF集合预报系统0~120 h大雨集合平均优于NCEP集合预报系统,两个系统集合平均的预报技巧整体低于其控制及扰动成员预报,这种现象ECMWF集合预报系统表现更为显著; ECMWF集合预报系统降水预报概率优于NCEP集合预报系统。ROC分析显示,随着预报概率的增大,ECMWF集合预报系统在命中率略微下降的情况下,显著减小了空报率,NCEP集合预报系统则表现出高空报、高命中率。     

GRAPES区域集合预报对2019年中国汛期降水预报评估

2019年,数值预报中心开发了以GRAPES全球模式为驱动场,集合变换卡尔曼滤波为初值扰动方法,随机物理过程倾向项为模式扰动方法的10 km水平分辨率GRAPES-REPS V3.0区域集合预报模式,并投入业务运行。基于该模式,作者开展了2019年7～9月夏季降水不确定性的集合预报实时试验,并从统计检验和个例分析角度,与GRAPES-REPS V2.0和ECMWF全球集合预报模式进行对比,由此对GRAPES-REPS V3.0区域集合预报模式的降水预报能力给予客观评价,并分析了引起中尺度强降水预报不确定性的物理机制,研究结论可为诊断集合预报模式及改进集合预报方法提供依据。结果表明:（1）GRAPES-REPS V3.0区域集合预报系统的降水ETS评分在所有预报时效和量级内均优于GRAPES-REPS V2.0区域集合预报模式,降水成员具有明显等同性,且概率预报技巧FSS评分较高,GRAPES-REPS V3.0区域集合预报模式降水预报效果全面优于GRAPES-REPS V2.0区域集合预报模式。（2）GRAPES-REPS V3.0区域集合预报的集合平均降水BIAS评分及小雨和暴雨ETS评分均明显优于ECMWF全球集合预报系统,降水概率预报与ECMWF降水概率具有一定可比性。（3）个例分析结果表明,不同集合预报模式通过刻画中尺度特征物理量不确定性来捕捉降水预报不确定性,初始时刻,GRAPES-REPS V3.0区域集合预报模式和ECMWF全球集合预报模式环流形势分布较为相似,随预报时效演变,GRAPES-REPS V3.0区域集合预报模式对中尺度动力、热力场捕捉更为准确,相应地对降水落区与量级预报较好,概率预报技巧较优。（4）与ECMWF全球集合预报模式相比,GRAPES区域集合预报模式集合成员能很好地预报降水发生、发展、消亡整个过程,故GRAPES-REPS V3.0区域集合预报系统对中国汛期降水具有较强的预报能力。     

Forecast skill of the tropical intraseasonal oscillation in the NCEP GFS dynamical extended range forecasts

This study examines the forecast performance of tropical intraseasonal oscillation (ISO) in recent dynamical extended range forecast (DERF) experiments conducted with the National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) model. The present study extends earlier work by comparing prediction skill of the northern winter ISO (Madden-Julian Oscillation) between the current and earlier experiments. Prediction skill for the northern summer ISO is also investigated. Since the boreal summer ISO exhibits northward propagation as well as eastward propagation along the equator, forecast skill for both components is computed. For the 5-year period from 1 January, 1998 through 31 December, 2002, 30-day forecasts were made once a day. Compared to the previous DERF experiment, the current model has shown some improvements in forecasting the ISO during winter season so that the skillful forecasts (anomaly correlation>0.6) for upper-level zonal wind anomaly extend from the previous shorter-than 5 days out to 7 days lead-time. A similar level of skill is seen for both northward and eastward propagation components during the summer season as in the winter case. Results also show that forecasts from extreme initial states are more skillful than those from null phases for both seasons, extending the skillful range by 3–6 days. For strong ISO convection phases, the GFS model performs better during the summer season than during the winter season. In summer forecasts, large-scale circulation and convection anomalies exhibit northward propagation during the peak phase. In contrast, the GFS model still has difficulties in sustaining ISO variability during the northern winter as in the previous DERF run. That is, the forecast does not maintain the observed eastward propagating signals associated with large-scale circulation; rather the forecast anomalies appear to be stationary at their initial location and decay with time. The NCEP Coupled Forecast System produces daily operational forecasts and its predication skill of the MJO will be reported in the future.     

西北太平洋热带气旋频次的延伸期动力-统计预报方法和评估

介绍了西北太平洋热带气旋(TC)频次的延伸期预报方法,比较了新构建的动力-统计和统计预报模型的预测技巧,并探讨了预报误差来源及改进方向。动力-统计预报模型是基于动力模式预测的热带季节内振荡(ISO)信号及ISO-TC生成的同期统计关系来进行预报;统计预报模型则是基于TC生成的前兆ISO信号建模预报。预报评估结果显示,动力-统计混合预报模型的预报技巧高于统计预报模型,原因在于影响TC次季节变化的前兆信号并不稳定,且随着预报超前时间迅速消散,无法提供有效且稳定的可预报源;相反地,TC生成与同期的ISO背景场显著相关,动力模式对ISO(预报因子)有较好的预报能力,因此动力-统计相结合的预报方法为TC延伸期预报提供了有效途径。虽然目前动力-统计预报模型的预报技巧可达5～6周,但仍有进一步改进和提高的空间。通过对不同类型TC预报技巧检验和误差分析,研究认为年际和年代际背景场对ISO调控TC活动的影响不可忽略,且热带外ISO信号(如罗斯贝波破碎和西风急流强度等)对TC频次和轨迹也有显著影响,这些因子为TC延伸期预报提供了潜在可预报源。     

Deficiencies and possibilities for long-lead coupled climate prediction of the Western North Pacific-East Asian summer monsoon

Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981–2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean–Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models’ multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Niño and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead–lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and WNPSMI as predictors. It is shown that the statistical postprocessing is able to improve forecast skill for the summer mean precipitation over most of the WNP-EASM region at all forecast leads. It is noteworthy that the MME prediction, after applying statistical postprocessing, shows the best anomaly pattern correlation skill for the EASM precipitation at a 4-month lead (February initial condition) and for the WNPSM precipitation at a 5-month lead (January initial condition), indicating its potential for improving long-lead prediction of the monsoon precipitation.     

Implications from Subseasonal Prediction Skills of the Prolonged Heavy Snow Event over Southern China in Early 2008

An exceptionally prolonged heavy snow event(PHSE) occurred in southern China from 10 January to 3 February2008, which caused considerable economic losses and many casualties. To what extent any dynamical model can predict such an extreme event is crucial for disaster prevention and mitigation. Here, we found the three S2 S models(ECMWF,CMA1.0 and CMA2.0) can predict the distribution and intensity of precipitation and surface air temperature(SAT)associated with the PHSE at 10-day lead and 10-15-day lead, respectively. The success is attributed to the models' capability in forecasting the evolution of two important low-frequency systems in the tropics and mid-latitudes [the persistent Siberian High and the suppressed phase of the Madden-Julian Oscillation(MJO)], especially in the ECMWF model. However, beyond the 15-day lead, the three models show almost no skill in forecasting this PHSE.The bias in capturing the two critical circulation systems is responsible for the low skill in forecasting the 2008 PHSE beyond the 15-day lead. On one hand, the models cannot reproduce the persistence of the Siberian High, which results in the underestimation of negative SAT anomalies over southern China. On the other hand, the models cannot accurately capture the suppressed convection of the MJO, leading to weak anomalous southerly and moisture transport, and therefore the underestimation of precipitation over southern China.The Singular Value Decomposition(SVD) analyses between the critical circulation systems and SAT/precipitation over southern China shows a robust historical relation, indicating the fidelity of the predictability sources for both regular events and extreme events(e.g., the 2008 PHSE).     

1998年夏季长江流域大气季节内振荡的结构演变及其对降水的影响

利用中国逐日降水格点资料和NCAR/NCEP再分析资料,对1998年发生在我国东部长江中下游流域的夏季持续性强降水过程中显著的大气季节内振荡(ISO)的三维结构演变等活动特征进行了分析。1998年夏季长江及江南地区的异常强降水对应着该地区强的ISO活动。利用位相合成方法,对长江流域两个典型的季节内循环周期的ISO降水、850 hPa水平风场以及水汽和垂直速度等循环过程的时空分布特征进行了诊断分析。在低频环流场上,对流层低层的低频气旋和反气旋环流表现出交替在热带西北太平洋增强并向西偏北方向移动发展的特征,当异常气旋环流移动到长江流域上空时,长江流域正好位于气旋环流西南侧的东北风异常和西北太平洋上向西移动的反气旋环流西北侧的西南风异常环流汇合处的下方,引起该地区强降水的发生。在强降水阶段的ISO的垂直结构上,上升运动和水汽表现出从华南到长江流域自南向北移动的特征,强烈的垂直上升运动以及来自南方充足的水汽为增强长江流域地区的降水起到了重要作用。     

Prediction skill of the Madden and Julian Oscillation in dynamical extended range forecasts

The Madden and Julian Oscillation (MJO) is the most prominent mode of intraseasonal variations in the tropical region. It plays an important role in climate variability and has a significant influence on medium-to-extended ranges weather forecasting in the tropics. This study examines the forecast skill of the oscillation in a set of recent dynamical extended range forecasts (DERF) experiments performed by the National Centers for Environmental Prediction (NCEP). The present DERF experiments were done with the reanalysis version of the medium range forecast (MRF) model and include 50-day forecasts, initialized once-a-day (0Z) with reanalyses fields, for the period between 1 January, 1985, and 31 December, 1989. The MRF model shows large mean errors in representing intraseasonal variations of the large-scale circulation, especially over the equatorial eastern Pacific Ocean. A diagnostic analysis has considered the different phases of the MJO and the associated forecast skill of the MRF model. Anomaly correlations on the order of 0.3 to 0.4 indicate that skillful forecasts extend out to 5 to 7 days lead-time. Furthermore, the results show a slight increase in the forecast skill for periods when convective anomalies associated with the MJO are intense. By removing the mean errors, the analysis shows systematic errors in the representation of the MJO with weaker than observed upper level zonal circulations. The examination of the climate run of the MRF model shows the existence of an intraseasonal oscillation, although less intense (50–70%) and with faster (nearly twice as fast) eastward propagation than the observed MJO. The results indicate that the MRF model likely has difficulty maintaining the MJO, which impacts its forecast. A discussion of future work to improve the representation of the MJO in dynamical models and assess its prediction is presented. Received: 28 December 1998 / Accepted: 27 September 1999     

Factors Limiting the Forecast Skill of the Boreal Summer Intraseasonal Oscillation in a Subseasonal-to-Seasonal Model

In this study,we evaluate the forecast skill of the subseasonal-to-seasonal(S2S)prediction model of the Beijing Climate Center(BCC)for the boreal summer intraseasonal oscillation(BSISO).We also discuss the key factors that inhibit the BSISO forecast skill in this model.Based on the bivariate anomaly correlation coefficient(ACC)of the BSISO index,defined by the first two EOF modes of outgoing longwave radiation and 850-hPa zonal wind anomalies over the Asian monsoon region,we found that the hindcast skill degraded as the lead time increased.The ACC dropped to below 0.5for lead times of 11 days and longer when the predicted BSISO showed weakened strength and insignificant northward propagation.To identify what causes the weakened forecast skill of BSISO at the forecast lead time of 11 days,we diagnosed the main mechanisms responsible for the BSISO northward propagation.The same analysis was also carried out using the observations and the outputs of the four-day forecast lead that successfully predicted the observed northward-propagating BSISO.We found that the lack of northward propagation at the 11-day forecast lead was due to insufficient increases in low-level cyclonic vorticity,moistening and warm temperature anomalies to the north of the convection,which were induced by the interaction between background mean flows and BSISO-related anomalous fields.The BCC S2S model can predict the background monsoon circulations,such as the low-level southerly and the northerly and easterly vertical shears,but has limited capability in forecasting the distributions of circulation and moisture anomalies.     

西北太平洋台风在S2S时间尺度预报效果评估

使用世界气象组织季节内至季节尺度（Subseasonal to Seasonal, S2S）预测项目数据库评估了多个集合预报系统在S2S时间尺度对台风的预报能力。评估的时间段为1999—2010年期间每年5月1日—10月31日。为评估S2S时间尺度台风的预报技巧,使用了台风密集度来描述台风的生成及移动状况。台风密集度定义为一段时间内500 km范围内台风出现的概率。台风密集度由6个S2S集合预报系统后报结果计算得出,它们分别由BoM、CMA、ECMWF、JMA、CNRM和NCEP开发使用。这6个预报系统台风密集度的预报技巧评分表明,当预报时效为11~30天时,ECMWF预报系统的评分为正值,比基于气候状态的参考预报能略好地预报台风。      

Application of a serial extended forecast experiment using the ECMWF model to interpret the predictive skill of tropical intraseasonal variability

The extended-range forecast skill of the ECMWF operational forecast model is evaluated during tropical intraseasonal oscillation (ISO) events in the Indo-West Pacific warm pool. The experiment consists of ensemble extended serial forecasts including winter and summer ISO cases. The forecasts are compared with the ERA-40 analyses. The analysis focuses on understanding the origin of forecast errors by studying the vertical structure of relevant dynamical and moist convective features associated with the ISO. The useful forecast time scale for circulation anomalies is in average 13 days during winter compared to 7–8 days during summer. The forecast skill is not stationary and presents evidence of a flow-dependent nature, with states of the coupled system corresponding to long-lived convective envelopes associated with the ISO for which the skill is always low regardless of the starting date of the forecast. The model is not able to forecast skillfully the generation of specific humidity anomalies and results indicate that the convective processes in the model are associated with the erosion of the ISO forecast skill in the model. Circulation-associated anomalies are forecast better than moist convective associated anomalies. The model tends to generate a more stable atmosphere, limiting the model’s capability to reproduce deep convective events, resulting in smaller humidity and circulation anomalies in the forecasts compared to those in ERA-40.     

Active and break phases of the South American summer monsoon: MJO influence and subseasonal prediction

The role of the Madden–Julian Oscillation (MJO) in producing active and break periods of the South American (SA) monsoon and the performance of the ECMWF and NCEP models in predicting these periods at multiweek lead times are assessed. Two monsoon indices, based on precipitation and wind, are proposed to characterize these periods. The models represent well the observed association of active and break monsoon days with large scale convection and circulation anomalies. Although reproducing approximately the distribution of active and break days proportions in each phase of the MJO cycle, models produce a phase shift between observed and simulated distributions because they establish the teleconnection between Central Pacific and South America, as well as its impacts, sooner than in observations. The predictive skill of both rainfall and wind anomalies is limited to about 2 weeks, with the monsoon wind index displaying higher correlation score till week 3. The forecast performance is apparently not affected by initialization on active or break monsoon days. However, it is higher for prediction of lower precipitation in break days than heavier rainfall in active days. Wind is much better predicted than rainfall for active days, which could be used for extreme rainfall events forecast. Although relatively small at shorter lead times, the MJO contribution is the major source of rainfall predictability after week 3. To improve the multiweek prediction of SA monsoon, models need not only to predict correctly the MJO phase, but also to reproduce in the right phase the MJO-related SA rainfall anomalies.

     

中国南方降水及其极端事件的动力-统计相结合延伸期预报

延伸期预报是无缝隙预测系统中的薄弱环节,如何提高灾害天气过程的延伸期预报技巧是国际热点及前沿问题。本研究基于2005年12月—2014年8月的观测/再分析资料,通过奇异值分解方法,揭示了与中国南方低频降水变化高度耦合的热带对流和中纬度波列信号。利用中国气象局参加国际次季节至季节预报计划模式（BCC-CPS-S2Sv2模式,简称BCC S2S模式）的回报数据,对中国南方低频降水异常场进行统计降尺度,构建了一套动力-统计相结合的延伸期降水预测模型。独立预测时段（2014年12月—2019年8月）的结果表明,BCC S2S模式可以提前10—15 d预报中国南方大部分区域的异常降水;提前15—20 d以上预报时,动力-统计结合预报模型对冬季（夏季）华南沿海地区（长江以北地区）的降水时间演变、降水空间分布及极端强降水事件的预报技巧均优于BCC S2S模式。文中提出的思路和方法可广泛应用于其他区域气象要素和极端天气事件的延伸期预报。     

Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982–2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere–ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.     

Dynamical linkage of tropical and subtropical weather systems to the intraseasonal oscillations of the Indian summer monsoon rainfall. Part II: Simulations in the ENSEMBLES project

We assess the ability of individual models (single-model ensembles) and the multi-model ensemble (MME) in the European Union-funded ENSEMBLES project to simulate the intraseasonal oscillations (ISOs; specifically in 10–20-day and 30–50-day frequency bands) of the Indian summer monsoon rainfall (ISMR) over the Western Ghats (WG) and the Bay of Bengal (BoB), respectively. This assessment is made on the basis of the dynamical linkages identified from the analysis of observations in a companion study to this work. In general, all models show reasonable skill in simulating the active and break cycles of the 30–50-day ISOs over the Indian summer monsoon region. This skill is closely associated with the proper reproduction of both the northward propagation of the intertropical convergence zone (ITCZ) and the variations of monsoon circulation in this band. However, the models do not manage to correctly simulate the eastward propagation of the 30–50-day ISOs in the western/central tropical Pacific and the eastward extension of the ITCZ in a northwest to southeast tilt. This limitation is closely associated with a limited capacity of models to accurately reproduce the magnitudes of intraseasonal anomalies of both the ITCZ in the Asian tropical summer monsoon regions and trade winds in the tropical Pacific. Poor reproduction of the activity of the western Pacific subtropical high on intraseasonal time scales also amplify this limitation. Conversely, the models make good reproduction of the WG 10–20-day ISOs. This success is closely related to good performance of the models in the representation of the northward propagation of the ITCZ, which is partially promoted by local air–sea interactions in the Indian Ocean in this higher-frequency band. Although the feature of westward propagation is generally represented in the simulated BoB 10–20-day ISOs, the air–sea interactions in the Indian Ocean are spuriously active in the models. This leads to active WG rainfall, which is not present in the observed BoB 10–20-day ISOs. Further analysis indicates that the intraseasonal variability of the ISMR is generally underrepresented in the simulations. Skill of the MME in seasonal ISMR forecasting is strongly dependent on individual model performance. Therefore, in order to improve the model skill with respect to seasonal ISMR forecasting, we suggest it is necessary to better represent the robust dynamical links between the ISOs and the relevant circulation variations, as well as the proportion of intraseasonal variability in the individual models.     

Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part I. The leading forced mode of atmospheric circulation

Using observations and 1-month lead hindcast data from six coupled atmosphere–ocean climate models, this study investigates the interdecadal change in the leading maximum covariance analysis mode (MCA1) of atmospheric circulation in response to the changes in the El Niño and Southern Oscillation (ENSO) occurred around late 1970s. We focus on boreal winter climate variability and predictability over the North Pacific–North American (NPNA) region using December–January–February prediction initiated from November 1st in the period of 1960–1980 (P1) and 1981–2001 (P2). Observed analysis reveals that ENSO variability, the related tropical convective activity, and thus the MCA1 are considerably enhanced from P1 to P2. As a result, surface climate anomalies over the NPNA are more significantly correlated with the MCA1 in P2 than P1, particularly over North America. The six coupled models and their multi-model ensemble not only are capable of capturing the interdecadal change of the MCA1 and its relationship with surface air temperature and precipitation over the NPNA regions but also have significantly higher forecast skills for the MCA1 and the surface climate anomalies in P2 than P1. However, models have systematic biases in the spatial distribution of the MCA1. It is demonstrated that the interdecadal change in the MCA1 should contribute to the improved forecast skill of the NPNA climate during recent epoch.     

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed.