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
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 1980-2010 winter GODAS oceanic assimilations, study is conducted of the winter heat content(HC) established in the subsurface layer(5 to 366 m in depth) over the western Pacific warm pool(WP), followed by in...Using the 1980-2010 winter GODAS oceanic assimilations, study is conducted of the winter heat content(HC) established in the subsurface layer(5 to 366 m in depth) over the western Pacific warm pool(WP), followed by investigating the HC spatiotemporal characteristics, persistence and the impacts on the climate anomalies of neighboring regions. Results are as follows: 1) the pattern of integral consistency is uncovered by the leading EOF1(PC1) mode of HC interannual variability, the year-to-year fluctuation of the time coefficients being well indicative of the interannual anomaly of the WP winter subsurface-layer thermal regime. The HC variation is bound up with El Ni觡o-Southern Oscillation, keeping pronounced autocorrelation during the following two seasons and more, with the persistence being more stable in comparison to sea surface temperature anomaly in the equatorial middle eastern Pacific; 2) the winter HC anomalies produce lasting effect on the WP thermal state in the following spring and summer and corresponding changes in the warm water volume lead to the meridional transport and vertical exchange of warm water, which exerts greater impacts upon the sea surface temperature/heat flux over the warm pool per se and neighboring regions, especially in the Philippine Sea during the posterior spring and summer; 3) the increase in the winter HC corresponds to the spring outgoing longwave radiation(OLR) decrease and richer precipitation over the waters east to the Philippine Sea and the resultant convective heating anomalies are responsible for the rise of geopotential isobaric surfaces over tropical and subtropical western North Pacific, thereby producing effect on the western Pacific subtropical high(anomaly). Subsequently, the sea-surface heat flux exchange is intensified in the warm pool, a robust anomalous cyclone shows up at lower levels, air-sea interactions are enhanced and abnormal convective heating occurs, together making the winter HC anomalies even more closely associated with the variation in the su展开更多
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
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supported by the National Natural Science Foundation of China[grant Nos.42125503 and 42075137]the National Key Research and Development Program of China[grant Nos.2020YFA0608000 and 2020YFA0607900].
基金National Key Basic Research/Development Project(2012CB417403)Public Sector(Meteorology)Special Research Foundation(GYHY201306022,GYHY201406024)+1 种基金Foundation of National Natural Sciences(41205065)Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions
文摘Using the 1980-2010 winter GODAS oceanic assimilations, study is conducted of the winter heat content(HC) established in the subsurface layer(5 to 366 m in depth) over the western Pacific warm pool(WP), followed by investigating the HC spatiotemporal characteristics, persistence and the impacts on the climate anomalies of neighboring regions. Results are as follows: 1) the pattern of integral consistency is uncovered by the leading EOF1(PC1) mode of HC interannual variability, the year-to-year fluctuation of the time coefficients being well indicative of the interannual anomaly of the WP winter subsurface-layer thermal regime. The HC variation is bound up with El Ni觡o-Southern Oscillation, keeping pronounced autocorrelation during the following two seasons and more, with the persistence being more stable in comparison to sea surface temperature anomaly in the equatorial middle eastern Pacific; 2) the winter HC anomalies produce lasting effect on the WP thermal state in the following spring and summer and corresponding changes in the warm water volume lead to the meridional transport and vertical exchange of warm water, which exerts greater impacts upon the sea surface temperature/heat flux over the warm pool per se and neighboring regions, especially in the Philippine Sea during the posterior spring and summer; 3) the increase in the winter HC corresponds to the spring outgoing longwave radiation(OLR) decrease and richer precipitation over the waters east to the Philippine Sea and the resultant convective heating anomalies are responsible for the rise of geopotential isobaric surfaces over tropical and subtropical western North Pacific, thereby producing effect on the western Pacific subtropical high(anomaly). Subsequently, the sea-surface heat flux exchange is intensified in the warm pool, a robust anomalous cyclone shows up at lower levels, air-sea interactions are enhanced and abnormal convective heating occurs, together making the winter HC anomalies even more closely associated with the variation in the su
