A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.

Nonlinear Measurement Function in the Ensemble Kalman Filter

ABSTRACT The optimal Kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement function is nonlinear. It is argued that when the measurement function is nonlinear, the current ensemble Kalman Filter algorithm seems to contain implicit assumptions： the forecast of the measurement function is unbiased or the nonlinear measurement function is linearized. While the forecast of the model state is assumed to be unbiased, the two assumptions are actually equivalent. On the above basis, we present two modified Kalman gain algorithms. Compared to the current Kalman gain algorithm, the modified ones remove the above assumptions, thereby leading to smaller estimated errors. This outcome was confirmed experimentally, in which we used the simple Lorenz 3-component model as the test-bed. It was found that in such a simple nonlinear dynamical system, the modified Kalman gain can perform better than the current one. However, the application of the modified schemes to realistic models involving nonlinear measurement functions needs to be further investigated.

A two-stage inflation method in parameter estimation to compensate for constant parameter evolution in Community Earth System Model

Parameter estimation is defined as the process to adjust or optimize the model parameter using observations.A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be constant during model integration.This assumption will cause underestimation of parameter ensemble spread,such that the parameter ensemble tends to collapse before an optimal solution is found.In this work,a two-stage inflation method is developed for parameter estimation,which can address the collapse of parameter ensemble due to the constant evolution of parameters.In the first stage,adaptive inflation is applied to the augmented states,in which the global scalar parameter is transformed to fields with spatial dependence.In the second stage,extra multiplicative inflation is used to inflate the scalar parameter ensemble to compensate for constant parameter evolution,where the inflation factor is determined according to the spread growth ratio of model states.The observation system simulation experiment with Community Earth System Model(CESM)shows that the second stage of the inflation scheme plays a crucial role in successful parameter estimation.With proper multiplicative inflation factors,the parameter estimation can effectively reduce the parameter biases,providing more accurate analyses.

Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2

The El Niño-Southern Oscillation(ENSO)ensemble prediction skills of the Beijing Climate Center(BCC)climate prediction system version 2(BCC-CPS2)are examined for the period from 1991 to 2018.The upper-limit ENSO predictability of this system is quantified by measuring its“potential”predictability using information-based metrics,whereas the actual prediction skill is evaluated using deterministic and probabilistic skill measures.Results show that:(1)In general,the current operational BCC model achieves an effective 10-month lead predictability for ENSO.Moreover,prediction skills are up to 10–11 months for the warm and cold ENSO phases,while the normal phase has a prediction skill of just 6 months.(2)Similar to previous results of the intermediate coupled models,the relative entropy(RE)with a dominating ENSO signal component can more effectively quantify correlation-based prediction skills compared to the predictive information(PI)and the predictive power(PP).(3)An evaluation of the signal-dependent feature of the prediction skill scores suggests the relationship between the“Spring predictability barrier(SPB)”of ENSO prediction and the weak ENSO signal phase during boreal spring and early summer.

A multi-model prediction system for ENSO

The El Niño and Southern Oscillation(ENSO)is the primary source of predictability for seasonal climate prediction.To improve the ENSO prediction skill,we established a multi-model ensemble(MME)prediction system,which consists of 5 dynamical coupled models with various complexities,parameterizations,resolutions,initializations and ensemble strategies,to account for the uncertainties as sufficiently as possible.Our results demonstrated the superiority of the MME over individual models,with dramatically reduced the root mean square error and improved the anomaly correlation skill,which can compete with,or even exceed the skill of the North American Multi-Model Ensemble.In addition,the MME suffered less from the spring predictability barrier and offered more reliable probabilistic prediction.The real-time MME prediction adequately captured the latest successive La Niña events and the secondary cooling trend six months ahead.Our MME prediction has,since April 2022,forecasted the possible occurrence of a third-year La Niña event.Overall,our MME prediction system offers better skill for both deterministic and probabilistic ENSO prediction than all participating models.These improvements are probably due to the complementary contributions of multiple models to provide additive predictive information,as well as the large ensemble size that covers a more reasonable uncertainty distribution.

热带印度洋-太平洋三极模态的理论探讨

热带太平洋的厄尔尼诺-南方涛动(El Ni o-Southern Oscillation,ENSO)现象是过去几十年里海洋与气候研究的重点.随着近年来印度洋偶极子模态(Indian Ocean Dipole,IOD)的提出,热带印度洋中的短期气候变化也逐步被重视.然而,人们对这些现象的研究更多的是局限在单个的海盆之内,而不是将其作为一个整体来思考.观测表明,在年际间尺度上,热带印度洋和热带太平洋的海表温度异常(Sea Surface Temperature Anomaly,SSTA)和海表高度异常(Sea Surface Height Anomaly,SSHA)等物理量的有着很明显的反向变化趋势.对这种反向变化可以给出一个简单的解释:由于双圈沃克环流在暖池区幅聚上升,海表风场在热带印度洋为西风,在热带太平洋为东风;它们通过驱动海水的上翻使得热带西印度洋与东太平洋SSTA变冷,SSHA变低,同时也通过暖水的堆积使得暖池区SSTA升高,SSHA增加.这样就在整个热带印度洋-太平洋地区形成了一个SSTA和SSHA的三极子结构.随着热带印度洋-太平洋上空沃克环流圈的增强或减弱,两个海洋之间的反向梯度关系也会随之做相应的调整,并通过梯度与沃克环流之间的正反馈作用得以维持.这一振荡模态被称为印-太三极子(Indo-Pacific Tripole,IPT).本文将通过资料分析和一个简单的概念模型来讨论IPT模态的发展和变化机制,并着重考虑ENSO与IOD对IPT模态的影响.该模型包含了最基本的海洋与大气的物理变量和他们之间的相互作用,可以为更深层次地理解和研究热带地区短期气候变化提供重要参考.

An intermediate coupled model for the tropical ocean-atmosphere system

An intermediate ocean-atmosphere coupled model is developed to simulate and predict the tropical interannual variability. Originating from the basic physical framework of the Zebiak-Cane(ZC) model, this tropical intermediate couple model(TICM) extends to the entire global tropics, with a surface heat flux parameterization and a surface wind bias correction added to improve model performance and inter-basin connections. The model well reproduces the variabilities in the tropical Pacific and Indian basins. The simulated El Ni?o-Southern Oscillation(ENSO) shows a period of 3–4 years and an amplitude of about 2°C, similar to those observed. The variabilities in the Indian Ocean, including the Indian Ocean basin mode(IOBM) and the Indian Ocean Dipole(IOD), are also reasonably captured with a realistic relationship to the Pacific. However, the tropical Atlantic variability in the TICM has a westward bias and is overly influenced by the tropical Pacific. A 47-year hindcast experiment using the TICM for the period of 1970–2016 indicates that ENSO is the most predictable mode in the tropics. Skillful predictions of ENSO can be made one year ahead, similar to the skill of the latest version of the ZC model, while a "spring predictability barrier" still exists as in other models. In the tropical Indian Ocean, the predictability seems much higher in the west than in the east. The correlation skill of IOD prediction reaches 0.5 at a 5-month lead, which is comparable to that of the state-of-the-art coupled general circulation models. The prediction of IOD shows a significant "winter-spring predictability barrier", implying combined influences from the tropical Pacific and the local sea-air interaction in the eastern Indian Ocean. The TICM has little predictive skill in the equatorial Atlantic for lead times longer than 3 months, which is a common problem of current climate models badly in need of further investigation.