ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribu...ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.展开更多
Using the sea surface temperature （SST） predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil （FGOALS-g）, an analysis of the prediction errors was performed for...Using the sea surface temperature （SST） predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil （FGOALS-g）, an analysis of the prediction errors was performed for the seasonally dependent predictability of SST anomalies both for neutral years and for the growth/decay phase of El Nino/La Nina events. The study results indicated that for the SST predictions relating to the growth phase and the decay phase of El Nino events, the prediction errors have a seasonally dependent evolution. The largest increase in errors occurred in the spring season, which indicates that a prominent spring predictability barrier （SPB） occurs during an El Nino-Southern Oscillation （ENSO） warming episode. Furthermore, the SPB associated with the growth-phase prediction is more prominent than that associated with the decay-phase prediction. However, for the neutral years and for the growth and decay phases of La Nifia events, the SPB phenomenon was less prominent. These results indicate that the SPB phenomenon depends extensively on the ENSO events themselves. In particular, the SPB depends on the phases of the ENSO events. These results may provide useful knowledge for improving ENSO forecasting.展开更多
Most ocean-atmosphere coupled models have difficulty in predicting the E1 Nifio-Southern Oscillation （ENSO） when starting from the boreal spring season. However, the cause of this spring predictability barrier （SPB...Most ocean-atmosphere coupled models have difficulty in predicting the E1 Nifio-Southern Oscillation （ENSO） when starting from the boreal spring season. However, the cause of this spring predictability barrier （SPB） phenomenon remains elusive. We investigated the spatial characteristics of optimal initial errors that cause a significant SPB for E1 Nifio events by using the monthly mean data of the pre-industrial （PI） control runs from several models in CMIP5 experiments. The results indicated that the SPB-related optimal initial errors often present an SST pattern with positive errors in the central-eastern equatorial Pa- cific, and a subsurface temperature pattern with positive errors in the upper layers of the eastern equatorial Pacific, and nega- tive errors in the lower layers of the western equatorial Pacific. The SPB-related optimal initial errors exhibit a typical La Ni- fia-like evolving mode, ultimately causing a large but negative prediction error of the Nifio-3.4 SST anomalies for El Nifio events. The negative prediction errors were found to originate from the lower layers of the western equatorial Pacific and then grow to be large in the eastern equatorial Pacific. It is therefore reasonable to suggest that the E1 Nifio predictions may be most sensitive to the initial errors of temperature in the subsurface layers of the western equatorial Pacific and the Nifio-3.4 region, thus possibly representing sensitive areas for adaptive observation. That is, if additional observations were to be preferentially deployed in these two regions, it might be possible to avoid large prediction errors for E1 Nifio and generate a better forecast than one based on additional observations targeted elsewhere. Moreover, we also confirmed that the SPB-related optimal initial errors bear a strong resemblance to the optimal precursory disturbance for E1 Nifio and La Nifia events. This indicated that im- provement of the observation network by additional observations in the identified sensitive areas would also be helpful in de- tecting the signals provided by the precursory disturbance, which may greatly improve the ENSO prediction skill.展开更多
With the Zebiak-Cane model, the present study investigates the role of model errors represented by the nonlinear forcing singular vector （NFSV） in the ＂spring predictability barrier＂ （SPB） phenomenon in ENSO pre...With the Zebiak-Cane model, the present study investigates the role of model errors represented by the nonlinear forcing singular vector （NFSV） in the ＂spring predictability barrier＂ （SPB） phenomenon in ENSO prediction. The NFSV-related model errors are found to have the largest negative effect on the uncertainties of El Nino prediction and they can be classified into two types： the first is featured with a zonal dipolar pattern of SST anomalies （SSTA）, with the western poles centered in the equatorial central western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite to the first type. The first type of error tends to have the worst effects on El Nifio growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV- related errors exhibits prominent seasonality, with the fastest error growth in spring and/or summer; hence, these errors result in a significant SPB related to El Nifio events. The linear counterpart of NFSVs, the （linear） forcing singular vector （FSV）, induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate an SPB for El Nifio events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Nifio events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Nifio predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.展开更多
Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) probl...Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model.展开更多
The Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g) was used to study the spring prediction barrier (SPB) in an ensemble system. This coupled model was developed and maintained at the State Key Lab...The Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g) was used to study the spring prediction barrier (SPB) in an ensemble system. This coupled model was developed and maintained at the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). There are two steps in our hindcast experiments. The first is to integrate the coupled model continuously with sea surface temperature (SST) nudging, from 1971 to 2006. The second is to carry out a series of one-year hindcasts without SST nudging, by adopting initial values from the first step on January 1 st , April 1st , July 1st , and October 1st , from 1982 to 2005. We generate 10 ensemble members for a particular start date (1st ) by choosing different atmospheric and land conditions around the hindcast start date (1st through 10th ). To estimate the predicted SST, two methods are used: (1) Anomaly Correlation Coefficient and its rate of decrease; and (2) Talagrand distribution and its standard deviation. Results show that FGOALS-g offers a reliable ensemble system with realistic initial atmospheric and oceanic conditions, and high anomaly correlation (>0.5) within 6 month lead time. Further, the ensemble approach is effective, in that the anomaly correlation of ensemble mean is much higher than that of most individual ensemble members. The SPB exists in the FGOALS-g ensemble system, as shown by anomaly correlation and equal likelihood. Nevertheless, the role of the ensemble mean in reducing the SPB of ENSO prediction is significant. The rate of decrease of the ensemble mean is smaller than the largest deviations by 0.04-0.14. At the same time, the ensemble system "equal likelihood" declines during spring. An ensemble mean helps give a correct prediction direction, departing from largely-deviated ensemble members.展开更多
The ＂summer prediction barrier＂ （SPB） of SST anomalies （SSTA） over the Kuroshio--Oyashio Extension （KOE） refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summe...The ＂summer prediction barrier＂ （SPB） of SST anomalies （SSTA） over the Kuroshio--Oyashio Extension （KOE） refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October （ASO） season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transi- tion rates （Category-l） favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates （Category-2）. The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling （downwelling） in the ASO season also causes SSTA cooling （warming）, accelerating the transition rates of warm （cold） KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.展开更多
For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,t...For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.展开更多
The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （C...The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.展开更多
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 differen...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.展开更多
This study explored the spatial patterns of winter predictability barrier(WPB)-related optimal initial errors and optimal precursors for positive Indian Ocean dipole(IOD)events,and the associated physical mechanisms f...This study explored the spatial patterns of winter predictability barrier(WPB)-related optimal initial errors and optimal precursors for positive Indian Ocean dipole(IOD)events,and the associated physical mechanisms for their developments were analyzed using the Simple Ocean Data Assimilation dataset.Without consideration of the effects of model errors on"predictions,"it was assumed that different"predictions"are caused by different initial conditions.The two types of WPB-related optimal initial errors are almost opposite for the start months of July(-1)and July(0),although they both present a west-east dipole pattern in the tropical Indian Ocean,with the maximum errors located at the thermocline depth.Bjerknes feedback and ocean waves play important roles in the growth of prediction errors.These two physical mechanisms compete during July-December and ocean waves dominate during January-June.The spatial patterns of optimal precursors and the physical mechanisms for their developments are similar to those of WPB-related optimal initial errors.It is worth noting that large values of WPB-related optimal initial errors and optimal precursors are concentrated within a few locations,which probably represent the sensitive areas of targeted observations for positive IOD events.The great similarities between WPB-related optimal initial errors and optimal precursors suggest that were intensive observations performed over these areas,this would not only reduce initial errors and thus,prediction errors,but it would also permit the detection of the signal of IOD events in advance,greatly improving the forecast skill of positive IOD events.展开更多
An Equatorial Oscillation Index(EOI) is defined, based on the zonal gradient of sea surface pressure between the western Pacific and eastern Pacific along the equator, to describe the distribution of wind and pressure...An Equatorial Oscillation Index(EOI) is defined, based on the zonal gradient of sea surface pressure between the western Pacific and eastern Pacific along the equator, to describe the distribution of wind and pressure within the equatorial Pacific. The EOI has a stronger correlation with the Ni?o3.4 sea surface temperature anomaly(SSTA), as well as with westerly/easterly wind bursts(WWBs/EWBs), showing a superiority over the Southern Oscillation Index(SOI). In general, the EOI is consistent with the SOI, both of which reflect large-scale sea level pressure oscillations. However, when there are inconsistent SSTAs between the equator and subtropical regions, the SOI may contrast with the EOI due to the reverse changes in sea level pressure in the subtropical regions. As a result, the SOI fails to match the pattern of El Ni?o, while the EOI can still match it well. Hence, the EOI can better describe the variability of the Ni?o3.4 SSTA and WWBs/EWBs. The correlation between the SOI and Ni?o3.4 SSTA falls to its minimum in May, due to the large one-month changes of sea level pressure from April to May in the subtropical southern Pacific, which may be related to the spring predictability barrier(SPB). The newly defined EOI may be helpful for monitoring El Ni?o–Southern Oscillation(ENSO) and predicting ENSO.展开更多
基金jointly sponsored by the National Nature Scientific Foundation of China (Grant Nos.41230420 and 41006015)the National Basic Research Program of China (Grant No.2012CB417404)the Basic Research Program of Science and Technology Projects of Qingdao (Grant No11-1-4-95-jch)
文摘ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.
基金sponsored by the Knowledge Innovation Programof the Chinese Academy of Sciences (Grant No. KZCX2-YW-QN203)the National Basic Research Program of China (GrantNos. 2010CB950400 and 2007CB411800)
文摘Using the sea surface temperature （SST） predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil （FGOALS-g）, an analysis of the prediction errors was performed for the seasonally dependent predictability of SST anomalies both for neutral years and for the growth/decay phase of El Nino/La Nina events. The study results indicated that for the SST predictions relating to the growth phase and the decay phase of El Nino events, the prediction errors have a seasonally dependent evolution. The largest increase in errors occurred in the spring season, which indicates that a prominent spring predictability barrier （SPB） occurs during an El Nino-Southern Oscillation （ENSO） warming episode. Furthermore, the SPB associated with the growth-phase prediction is more prominent than that associated with the decay-phase prediction. However, for the neutral years and for the growth and decay phases of La Nifia events, the SPB phenomenon was less prominent. These results indicate that the SPB phenomenon depends extensively on the ENSO events themselves. In particular, the SPB depends on the phases of the ENSO events. These results may provide useful knowledge for improving ENSO forecasting.
基金sponsored by the National Basic Research Program of China(Grant No.2012CB955200)the National Public Benefit(Meteorology)Research Foundation of China(Grant No.GYHY201306018)+2 种基金the National Natural Science Foundation of China(Grant Nos.41230420,41176013)Zhang Jing was supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Jiangsu Innovation Cultivation Project for Graduate Student(Grant No.CXZZ13_0502)
文摘Most ocean-atmosphere coupled models have difficulty in predicting the E1 Nifio-Southern Oscillation （ENSO） when starting from the boreal spring season. However, the cause of this spring predictability barrier （SPB） phenomenon remains elusive. We investigated the spatial characteristics of optimal initial errors that cause a significant SPB for E1 Nifio events by using the monthly mean data of the pre-industrial （PI） control runs from several models in CMIP5 experiments. The results indicated that the SPB-related optimal initial errors often present an SST pattern with positive errors in the central-eastern equatorial Pa- cific, and a subsurface temperature pattern with positive errors in the upper layers of the eastern equatorial Pacific, and nega- tive errors in the lower layers of the western equatorial Pacific. The SPB-related optimal initial errors exhibit a typical La Ni- fia-like evolving mode, ultimately causing a large but negative prediction error of the Nifio-3.4 SST anomalies for El Nifio events. The negative prediction errors were found to originate from the lower layers of the western equatorial Pacific and then grow to be large in the eastern equatorial Pacific. It is therefore reasonable to suggest that the E1 Nifio predictions may be most sensitive to the initial errors of temperature in the subsurface layers of the western equatorial Pacific and the Nifio-3.4 region, thus possibly representing sensitive areas for adaptive observation. That is, if additional observations were to be preferentially deployed in these two regions, it might be possible to avoid large prediction errors for E1 Nifio and generate a better forecast than one based on additional observations targeted elsewhere. Moreover, we also confirmed that the SPB-related optimal initial errors bear a strong resemblance to the optimal precursory disturbance for E1 Nifio and La Nifia events. This indicated that im- provement of the observation network by additional observations in the identified sensitive areas would also be helpful in de- tecting the signals provided by the precursory disturbance, which may greatly improve the ENSO prediction skill.
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201306018)National Natural Science Foundation of China(41230420 and 41525017)National Program on Global Change and Air–Sea Interactions(GASI-IPOVAI-06)
文摘With the Zebiak-Cane model, the present study investigates the role of model errors represented by the nonlinear forcing singular vector （NFSV） in the ＂spring predictability barrier＂ （SPB） phenomenon in ENSO prediction. The NFSV-related model errors are found to have the largest negative effect on the uncertainties of El Nino prediction and they can be classified into two types： the first is featured with a zonal dipolar pattern of SST anomalies （SSTA）, with the western poles centered in the equatorial central western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite to the first type. The first type of error tends to have the worst effects on El Nifio growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV- related errors exhibits prominent seasonality, with the fastest error growth in spring and/or summer; hence, these errors result in a significant SPB related to El Nifio events. The linear counterpart of NFSVs, the （linear） forcing singular vector （FSV）, induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate an SPB for El Nifio events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Nifio events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Nifio predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41230420 & 41525017)the National Public Benefit (Meteorology) Research Foundation of China (Grant No. GYHY201306018)
文摘Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model.
基金Supported by the National Basic Research Program of China (973 Program)(No. 2007CB411806)the Knowledge Innovation Program of Chinese Academy of Sciences (Nos. KZCX2-YW-Q11-02, XDA05090404)+1 种基金the National Natural Science Foundation of China (No. 40975065)the National High Technology Research and Development Program of China(863 Program) (No. 2010AA012304)
文摘The Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g) was used to study the spring prediction barrier (SPB) in an ensemble system. This coupled model was developed and maintained at the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). There are two steps in our hindcast experiments. The first is to integrate the coupled model continuously with sea surface temperature (SST) nudging, from 1971 to 2006. The second is to carry out a series of one-year hindcasts without SST nudging, by adopting initial values from the first step on January 1 st , April 1st , July 1st , and October 1st , from 1982 to 2005. We generate 10 ensemble members for a particular start date (1st ) by choosing different atmospheric and land conditions around the hindcast start date (1st through 10th ). To estimate the predicted SST, two methods are used: (1) Anomaly Correlation Coefficient and its rate of decrease; and (2) Talagrand distribution and its standard deviation. Results show that FGOALS-g offers a reliable ensemble system with realistic initial atmospheric and oceanic conditions, and high anomaly correlation (>0.5) within 6 month lead time. Further, the ensemble approach is effective, in that the anomaly correlation of ensemble mean is much higher than that of most individual ensemble members. The SPB exists in the FGOALS-g ensemble system, as shown by anomaly correlation and equal likelihood. Nevertheless, the role of the ensemble mean in reducing the SPB of ENSO prediction is significant. The rate of decrease of the ensemble mean is smaller than the largest deviations by 0.04-0.14. At the same time, the ensemble system "equal likelihood" declines during spring. An ensemble mean helps give a correct prediction direction, departing from largely-deviated ensemble members.
基金jointly sponsored by the National Natural Science Foundation of China (Grant No. 41376018)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA11010303)+2 种基金the China Meteorological Administration Special Public Welfare Research Fund (GYHY201506013)the Project for Development of Key Techniques in Meteorological Forecasting Operation (YBGJXM201705)the Open Foundation of the LASG/IAP/CAS
文摘The ＂summer prediction barrier＂ （SPB） of SST anomalies （SSTA） over the Kuroshio--Oyashio Extension （KOE） refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October （ASO） season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transi- tion rates （Category-l） favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates （Category-2）. The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling （downwelling） in the ASO season also causes SSTA cooling （warming）, accelerating the transition rates of warm （cold） KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.
文摘For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.
基金supported by the National Natural Science Foundation of China (NFSC Grant Nos. 41690122, 41690120, 41490644, 41490640 and 41475101)+5 种基金the Ao Shan Talents Program supported by Qingdao National Laboratory for Marine Science and Technology (Grant No. 2015ASTP)a Chinese Academy of Sciences Strategic Priority Projectthe Western Pacific Ocean System (Grant Nos. XDA11010105, XDA11020306)the NSFC–Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401)the National Natural Science Foundation of China Innovative Group Grant (Grant No. 41421005)the Taishan Scholarship and Qingdao Innovative Program (Grant No. 2014GJJS0101)
文摘The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.
基金supported by the National Program for Support of Top-notch Young Professionalsthe National Natural Science Foundation of China (Grant No. 41576019)J.-Y. YU was supported by the US National Science Foundation (Grant No. AGS-150514)
文摘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.
基金sponsored jointly by the National Programme on Global Change and Air-Sea Interaction(Grant No.GASIIPOVAI-06)the National Natural Science Foundation of China(Grant Nos.41506032 & 41530961)
文摘This study explored the spatial patterns of winter predictability barrier(WPB)-related optimal initial errors and optimal precursors for positive Indian Ocean dipole(IOD)events,and the associated physical mechanisms for their developments were analyzed using the Simple Ocean Data Assimilation dataset.Without consideration of the effects of model errors on"predictions,"it was assumed that different"predictions"are caused by different initial conditions.The two types of WPB-related optimal initial errors are almost opposite for the start months of July(-1)and July(0),although they both present a west-east dipole pattern in the tropical Indian Ocean,with the maximum errors located at the thermocline depth.Bjerknes feedback and ocean waves play important roles in the growth of prediction errors.These two physical mechanisms compete during July-December and ocean waves dominate during January-June.The spatial patterns of optimal precursors and the physical mechanisms for their developments are similar to those of WPB-related optimal initial errors.It is worth noting that large values of WPB-related optimal initial errors and optimal precursors are concentrated within a few locations,which probably represent the sensitive areas of targeted observations for positive IOD events.The great similarities between WPB-related optimal initial errors and optimal precursors suggest that were intensive observations performed over these areas,this would not only reduce initial errors and thus,prediction errors,but it would also permit the detection of the signal of IOD events in advance,greatly improving the forecast skill of positive IOD events.
基金Supported by the National Key Research and Development Program of China(2016YFA0600602)National Natural Science Foundation of China(41776039)。
文摘An Equatorial Oscillation Index(EOI) is defined, based on the zonal gradient of sea surface pressure between the western Pacific and eastern Pacific along the equator, to describe the distribution of wind and pressure within the equatorial Pacific. The EOI has a stronger correlation with the Ni?o3.4 sea surface temperature anomaly(SSTA), as well as with westerly/easterly wind bursts(WWBs/EWBs), showing a superiority over the Southern Oscillation Index(SOI). In general, the EOI is consistent with the SOI, both of which reflect large-scale sea level pressure oscillations. However, when there are inconsistent SSTAs between the equator and subtropical regions, the SOI may contrast with the EOI due to the reverse changes in sea level pressure in the subtropical regions. As a result, the SOI fails to match the pattern of El Ni?o, while the EOI can still match it well. Hence, the EOI can better describe the variability of the Ni?o3.4 SSTA and WWBs/EWBs. The correlation between the SOI and Ni?o3.4 SSTA falls to its minimum in May, due to the large one-month changes of sea level pressure from April to May in the subtropical southern Pacific, which may be related to the spring predictability barrier(SPB). The newly defined EOI may be helpful for monitoring El Ni?o–Southern Oscillation(ENSO) and predicting ENSO.
