MJO potential predictability and predictive skill in IAP AGCM 4.1

A 30-year hindcast was performed using version 4.1 of the IAP AGCM（IAP AGCM4.1）, and its potential predictability of the MJO was then evaluated. The results showed that the potential predictability of the MJO is 13 and 24 days, evaluated using the signal-to-error ratio method based on a single member and the ensemble mean, respectively. However, the MJO prediction skill is only9 and 10 days using the two methods mentioned above. It was further found that the potential predictability and prediction skill depend on the MJO amplitude in the initial conditions. Prediction initiated from conditions with a strong MJO amplitude tends to be more skillful. Together with the results of other measures, the current MJO prediction ability of IAP AGCM4.1 is around 10 days, which is much lower than other climate prediction systems. Furthermore, the smaller difference between the MJO predictability and prediction skill evaluated by a single member and the ensemble mean methods could be ascribed to the relatively smaller size of the ensemble member of the model.Therefore, considerable effort should be made to improve MJO prediction in IAP AGCM4.1 through application of a reasonable model initialization and ensemble forecast strategy.

Seasonal Prediction Skill and Biases in GloSea5 Relating to the East Asia Winter Monsoon

The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.

Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system

Upper ocean heat content(OHC)has been widely recognized as a crucial precursor to high-impact climate variability,especially for that being indispensable to the long-term memory of the ocean.Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts.Recently developed retrospective forecast experiments,based on a Community Earth System Model ensemble prediction system,offer a great opportunity to comprehensively explore OHC predictability.Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends.The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill,indicating that the persistence of OHC serves as the primary predictive signal for its predictability.The decline in actual prediction skill is more pronounced in the Indian and Atlantic oceans than in the Pacific Ocean,particularly within tropical regions.Additionally,notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability.The potential predictability of OHC generally surpasses the actual prediction skill at all lead times,highlighting significant room for improvement in current OHC predictions,especially for the North Indian Ocean and the Atlantic Ocean.Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations,develop effective data assimilation methods,and reduce model bias.

Contrasting the Skills and Biases of Deterministic Predictions for the Two Types of El Nio

The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Nio and how much is common to both this type and the conventional Eastern Pacific(EP)-type El Nio. In this study, the deterministic performance of an El Nio–Southern Oscillation(ENSO) ensemble prediction system is examined for the two types of El Nio. Ensemble hindcasts are run for the nine EP El Nio events and twelve CP El Nio events that have occurred since 1950. The results show that(1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times;(2) the systematic forecast biases come mostly from the prediction of the CP events; and(3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Nio. Further improvements to coupled atmosphere–ocean models in terms of CP El Nio prediction should be recognized as a key and high-priority task for the climate prediction community.

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 February 2008,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 S2S 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).

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.

Prediction Skills and Its Influence on Learners’ Achievement in Reading Comprehension: A Case of Vihiga County, Kenya

Prediction is one of the comprehension processing skills encapsulated by the interactive approach instruction.Prediction skills enable learners to decode the meaning of comprehension passages by making guesses about the contents of texts to be read.Learners in Vihiga County perform poorer in English language examinations than their peers in neighbouring counties.The performance is weaker in comprehension than in grammar sections.Despite this,no study has assessed the nexus between the use of prediction skills and learners’achievement in reading comprehension.This study applied the Solomon Four-Group Design to obtain primary data from 279 primary school learners and eight teachers in 2017.Multiple Linear Regression was used to generate two models,one for the experimental group(Model 1)and one for the control group(Model 2).Results show that the influence of prediction skills on learners’achievement in reading comprehension was statistically significant the experimental group,but insignificant in the control group.However,the influence seemed to be stronger in the experimental than in the control group,which suggests that training English language teachers on how to correctly apply prediction skills is likely to improve learners’achievement in reading comprehension.The study recommends the need to:sensitise teachers to use textbooks cautiously,while supplementing with relevant resource materials;sensitise teachers on the need to guide learners through titles;as well as update the teacher training curriculum by integrating inter alia,new instructional methods based on information and communication technology and entrenching innovation to enable teachers diversify instructional resources.

A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms

Forecasting convective storms using NWP models is an important goal and a highly active area of ongoing research. Skillful and reliable NWP of convective storms could allow for severe weather warnings with longer lead times, as opera- tional forecasters begin to incorporate convective-scale fore- casts into severe weather forecast operations （Stensrud et al., 2009, 2013）. This would then provide vulnerable individuals and industries with more time to seek shelter and/or mitigate the impact of severe weather hazards.

Subseasonal Prediction of Early-summer Northeast Asian Cut-off Lows by BCC-CSM2-HR and GloSea5

Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas.Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance.Using two dynamical forecasting systems,one from the Beijing Climate Center(BCC-CSM2-HR)and the other from the Met Office(GloSea5),this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows.Both models are shown to have good ability in representing the spatial structure of cut-off lows,but they underestimate the intensity.The skillful prediction time scales for cut-off low intensity are about 10.2 days for BCC-CSM2-HR and 11.4 days for GloSea5 in advance.Further examination shows that both models can essentially capture the initial Rossby wave train,rapid growth and decay processes responsible for the evolution of cut-off lows,but the models show weaker amplitudes for the three-stage processes.The underestimated simulated strength of both the Eurasian midlatitude and East Asian subtropical jets may lead to the weaker local eddy-mean flow interaction responsible for the cut-off low evolution.

Progress of MJO Prediction at CMA from Phase I to Phase II of the Sub-Seasonal to Seasonal Prediction Project

As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates the models’capability to simulate and predict the Madden-Julian Oscillation(MJO).Three versions of the Beijing Climate Center Climate System Model(BCC-CSM)are used to conduct historical simulations and re-forecast experiments(referred to as EXP1,EXP1-M,and EXP2,respectively).In simulating MJO characteristics,the newly-developed high-resolution BCC-CSM outperforms its predecessors.In terms of MJO prediction,the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M,and further to 24 days in EXP2.Within the first forecast week,the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill.However,during forecast weeks 2–3,EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3.Particularly at initial phases 2–3,EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection,leading to the highest prediction skill of the MJO.Our results reveal that,during the participation of the CMA models in the S2S Project,both the improved model initialization and updated model physics played positive roles in improving MJO prediction.Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.

Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models

Seasonal rainfall predictability over the Huaihe River basin is evaluated in this paper on the basis of 23-year(1981-2003) retrospective forecasts by 10 climate models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) prediction system.It is found that the summer rainfall variance in this basin is largely internal,which leads to lower rainfall predictability for most individual climate models.By dividing the 10 models into three categories according to their sea surface temperature(SST) boundary conditions including observed,predicted,and persistent SSTs,the MME deterministic predictive skill of summer rainfall over Huaihe River basin is investigated.It is shown that the MME is effective for increasing the current seasonal forecast skill.Further analysis shows that the MME averaged over predicted SST models has the highest rainfall prediction skill,which is closely related to model's capability in reproducing the observed dominant modes of the summer rainfall anomalies in Huaihe River basin.This result can be further ascribed to the fact that the predicted SST MME is the most effective model ensemble for capturing the relationship between the summer rainfall anomalies over Huaihe River basin and the SST anomalies(SSTAs) in equatorial oceans.

Assessing the Impacts of Initial Snow Conditions over the Tibetan Plateau on China Precipitation Prediction Using a Global Climate Model

Two ensemble experiments were conducted using a general atmospheric circulation model. These experiments were used to investigate the impacts of initial snow anomalies over the Tibetan Plateau(TP) on China precipitation prediction. In one of the experiments, the initial snow conditions over the TP were climatological values; while in the other experiment, the initial snow anomalies were snow depth estimates derived from the passive microwave remote-sensing data. In the current study, the difference between these two experiments was assessed to evaluate the impact of initial snow anomalies over the TP on simulated precipitation. The results indicated that the model simulation for precipitation over eastern China had certain improvements while applying a more realistic initial snow anomaly, especially for spring precipitation over Northeast China and North China and for summer precipitation over North China and Southeast China. The results suggest that seasonal prediction could be enhanced by using more realistic initial snow conditions over TP, and microwave remote-sensing snow data could be used to initialize climate models and improve the simulation of eastern China precipitation during spring and summer. Further analyses showed that higher snow anomalies over TP cooled the surface, resulting in lower near- surface air temperature over the TP in spring and summer. The surface cooling over TP weakened the Asian summer monsoon and brought more precipitation in South China in spring and more precipitation to Southeast China during summer.

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.

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.

Improved EOF-based bias correction method for seasonal forecasts and its application in IAP AGCM4.1

An effective improvement on the empirical orthogonal function(EOF)–based bias correctionmethod for seasonal forecasts is proposed in this paper,by introducing a stepwise regression method into the process of EOF time series correction.Using 30-year(1981–2010)hindcast results from IAP AGCM4.1(the latest version of this model),the improved method is validated for the prediction of summer(June–July–August)rainfall anomalies in Southeast China.The results in terms of the pattern correction coefficient(PCC)of rainfall anomalies shows that the 30-year-averaged prediction skill improves from 0.01 to 0.06 with the original correction method,and to 0.29 using the improved method.The applicability in real-time prediction is also investigated,using 2016 summer rainfall prediction as a test case.With a PCC of 0.59,the authors find that the new correction method significantly improves the prediction skill;the PCC using the direct prediction of the model is?0.04,and using the old bias correction method it is 0.37.

Why Are Arctic Sea Ice Concentration in September and Its Interannual Variability Well Predicted over the Barents–East Siberian Seas by CFSv2?

To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.

Intraseasonal Variability of Summertime Surface Air Temperature over Mid-High-Latitude Eurasia and Its Prediction Skill in S2S Models

Features of the dominant modes of surface air temperature(SAT)on the intraseasonal timescale over the mid-highlatitude Eurasia(MHE)during boreal summer(June-September)are investigated based on the ERA5 reanalysis data from 1979 to 2016.The intraseasonal variability(ISV)of SAT over MHE is primarily characterized by an eastward propagation along 60°N,which is found to impact the regional weather in China,including summertime extreme hot and cool events.The forecast skill and potential predictability of the ISV of SAT over MHE are assessed for 5 dynamical models that have participated in the subseasonal-to-seasonal(S2 S)prediction project,by analyzing12 years’(1999-2010)model reforecast/hindcast data.By using the principal component(PC)index of the leading intraseasonal SAT modes as a predictand,we found that the forecast skill for ISV of SAT can reach out to 11-17 days,and the ECMWF model exhibits the best score.All the S2 S models tend to show 1)a relatively higher skill for strong intraseasonal oscillation(ISO)cases,2)a systematic underestimate of the amplitude of the SAT ISV signal,and 3)different skills during different phases of ISO cases.Analysis of potential predictability based on the perfectmodel assumption reveals a 4-6-day skill gap for most models,and the skill gap also varies among different phases of ISO events.The results imply the need for continued development of operational forecasting systems to improve the actual prediction skills for the ISV of SAT over MHE.

Prediction of Wintertime Northern Hemisphere Blocking by the NCEP Climate Forecast System

Daily output from the hindcasts by the NCEP Climate Forecast System version 2 （CFSv2） is analyzed to understand CFSv2＇s skill in forecasting wintertime atmospheric blocking in the Northern Hemisphere. Prediction skills of sector blocking, sector-blocking episodes, and blocking onset/decay are assessed with a focus on the Euro-Atlantic sector （20°W-45°E） and the Pacific sector （160°E 135°W）. Features of associated circulation and climate patterns are also examined. The CFSv2 well captures the observed features of longitudinal distribution of blocking activity, but underestimates blocking frequency and intensity and shows a decreasing trend in blocking frequency with increasing forecast lead time. Within 14-day lead time, the Euro-Atlantic sector blocking receives a higher skill than the Pacific sector blocking. Skillful forecast （taking the hit rate of 50~ as a criterion） can be obtained up to 9 days in the Euro-Atlantic sector, which is slightly longer than that in the Pacific sector （7 days）. The forecast skill of sector-blocking episodes is slightly lower than that of sector blocking in both sectors, and it is slightly higher in the Euro-Atlantic sector than in the Pacific sector. Compared to block onset, the skill for block decay is lower in the Euro-Atlantic sector, slightly higher in the Pacific sector during the early three days but lower after three days in lead time. In both the Euro-Atlantic and the Pacific sectors, a local dipole pattern in 500-hPa geopotential height associated with blocking is well presented in the CFSv2 prediction, but the wave-train like pattern that is far away from the blocking sector can only maintain in the forecast of relative short lead time. The CFSv2 well reproduces the observed characteristics of local temperature and precipitation anomalies associated with blocking.

Representation and Predictability of the East Asia-Pacific Teleconnection in the Beijing Climate Center and UK Met Office Subseasonal Prediction Systems

Based on the empirical orthogonal function(EOF) analysis, the East Asia–Pacific(EAP) teleconnection is extracted as the leading mode of the subseasonal variability over East Asia in summer, with a meridional tripole structure and significant periods of 10–30 and 50–70 days. A two-dimensional phase–space diagram is established for the EAP index and its time tendency so as to monitor the real-time state of EAP events. Based on the phase composite analysis, the general circulation anomalies first occur over the high-latitude area of Europe centered near Novaya Zemlya at the beginning of EAP events. These general circulation anomalies then influence rainfall over Northeast China,North China, and the region south of the Yangtze River valley(YRV) as the phases of EAP event progress. The representation, predictability, and prediction skill of the EAP teleconnection are examined in the two fully coupled subseasonal prediction systems of the Beijing Climate Center(BCC) and UK Met Office(UKMO GloSea5). Both models are able to simulate the EAP meridional tripole over East Asia as the leading mode and its characteristics of evolution as well, except for the weaker precursors over Novaya Zemlya and an inconspicuous influence on precipitation over Northeast China. The actual prediction skill of the EAP teleconnection during May–September(MJJAS) is about 10 days in the BCC model and 15 days in the UKMO model based on correlation measures, but is higher when initialized from the EAP peak phases or when targeted on strong EAP scenarios. However, both of the ensemble prediction systems are under-dispersive and the predictable signals extend to 18 and 30 days in BCC and UKMO models based on signal-to-error metrics, indicating that there may be further scope for enhancing the capability of these models for the EAP teleconnection prediction and the associated impacts studies.