A debris flow forecast model based on a water-soil coupling mechanism that takes the debrisflow watershed as a basic forecast unit was established here for the prediction of disasters at the watershed scale.This was a...A debris flow forecast model based on a water-soil coupling mechanism that takes the debrisflow watershed as a basic forecast unit was established here for the prediction of disasters at the watershed scale.This was achieved through advances in our understanding of the formation mechanism of debris flow.To expand the applicable spatial scale of this forecasting model,a method of identifying potential debris flow watersheds was used to locate areas vulnerable to debris flow within a forecast region.Using these watersheds as forecasting units and a prediction method based on the water-soil coupling mechanism,a new forecasting method of debris flow at the regional scale was established.In order to test the prediction ability of this new forecasting method,the Sichuan province,China was selected as a study zone and the large-scale debris flow disasters attributable to heavy rainfall in this region on July 9,2013 were taken as the study case.According to debris flow disaster data on July 9,2013 which were provided by the geo-environmental monitoring station of Sichuan province,there were 252 watersheds in which debris flow events actually occurred.The current model predicted that 265 watersheds were likely to experience a debris flow event.Among these,43 towns including 204 debrisflow watersheds were successfully forecasted and 24 towns including 48 watersheds failed.The false prediction rate and failure prediction rate of thisforecast model were 23% and 19%,respectively.The results show that this method is more accurate and more applicable than traditional methods.展开更多
The accurate simulation of the equatorial sea surlhce temperature （SST） variability is crucial for a proper representation or prediction of the El Nino-Southern Os- cillation （ENSO）. This paper describes the trop...The accurate simulation of the equatorial sea surlhce temperature （SST） variability is crucial for a proper representation or prediction of the El Nino-Southern Os- cillation （ENSO）. This paper describes the tropical variability simulated by the Max Planck Institute （MPI） forr meteorology coupled atmosphere-ocean general circulation model （CGCM）. A control simulation with pre-industrial greenhouse gases is analyzed, and the simulation of key oceanic features, such as SST, is compared with observa- tions. Results from the 400-yr control simulation show that the model＇s ENSO variability is quite realistic in terms of structure, strength, and period. Also, two related features （the annual cycle of SST and the-phase locking of ENSO events）, which are significant in determining the model＇s performance of realistic ENSO prediction, are further validated to be well reproduced by the MPI cli mate model, which is an atmospheric model ECHAM5 （which fuses the EC tbr European Center and HAM for Hamburg） coupled to an MPI ocean model （MPI-OM）, ECHAMS/MPI-OM.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 tropical Pacific is currently experiencing an El Nifio event. Various coupled models with different degrees of complexity have been used to make real-time E1 Nifio predictions, but large uncertainties exist in the...The tropical Pacific is currently experiencing an El Nifio event. Various coupled models with different degrees of complexity have been used to make real-time E1 Nifio predictions, but large uncertainties exist in the inten- sity forecast and are strongly model dependent. An intermediate coupled model （ICM） is used at the Institute of Oceanology, Chinese Academy of Sciences （IOCAS）, named the IOCAS ICM, to predict the sea surface temper- ature （SST） evolution in the tropical Pacific during the 2015-2016 E! Nifio event. One unique feature of the IOCAS ICM is the way in which the temperature of subsurface water entrained in the mixed layer （Te） is parameterized. Observed SST anomalies are only field that is utilized to initialize the coupled prediction using the IOCAS ICM. Examples are given of the model＇s ability to predict the SST conditions in a real-time manner. As is commonly evident in E1 Nifio- Southern Oscillation predictions using coupled models, large discrepancies occur between the observed and pre- dicted SST anomalies in spring 2015. Starting from early summer 2015, the model can realistically predict warming conditions. Thereafter, good predictions can be made through the summer and fall seasons of 2015. A transition to normal and cold conditions is predictecl to occur in rote spring 2016. Comparisons with other model predictions are made and factors influencing the prediction performance of the IOCAS ICM are also discussed.展开更多
基金supported by the foundation of the Research Fund for Commonweal Trades (Meteorology) (Grant No. GYHY201006039)the International Cooperation Project of the Department of Science and Technology of Sichuan Province (Grant No. 2009HH0005)
文摘A debris flow forecast model based on a water-soil coupling mechanism that takes the debrisflow watershed as a basic forecast unit was established here for the prediction of disasters at the watershed scale.This was achieved through advances in our understanding of the formation mechanism of debris flow.To expand the applicable spatial scale of this forecasting model,a method of identifying potential debris flow watersheds was used to locate areas vulnerable to debris flow within a forecast region.Using these watersheds as forecasting units and a prediction method based on the water-soil coupling mechanism,a new forecasting method of debris flow at the regional scale was established.In order to test the prediction ability of this new forecasting method,the Sichuan province,China was selected as a study zone and the large-scale debris flow disasters attributable to heavy rainfall in this region on July 9,2013 were taken as the study case.According to debris flow disaster data on July 9,2013 which were provided by the geo-environmental monitoring station of Sichuan province,there were 252 watersheds in which debris flow events actually occurred.The current model predicted that 265 watersheds were likely to experience a debris flow event.Among these,43 towns including 204 debrisflow watersheds were successfully forecasted and 24 towns including 48 watersheds failed.The false prediction rate and failure prediction rate of thisforecast model were 23% and 19%,respectively.The results show that this method is more accurate and more applicable than traditional methods.
基金supported by the National Program for Support of Top-notch Young Professionals, the National Basic Research Program of China (Grant Nos. 2012CB955202 and 2012CB417404)"Western Pacific Ocean System: Structure, Dynamics, and Consequences" of the Chinese Academy Sciences (WPOS+1 种基金 Grant No. XDA10010405)the National Natural Science Foundation of China (Grant No. 41176014)
文摘The accurate simulation of the equatorial sea surlhce temperature （SST） variability is crucial for a proper representation or prediction of the El Nino-Southern Os- cillation （ENSO）. This paper describes the tropical variability simulated by the Max Planck Institute （MPI） forr meteorology coupled atmosphere-ocean general circulation model （CGCM）. A control simulation with pre-industrial greenhouse gases is analyzed, and the simulation of key oceanic features, such as SST, is compared with observa- tions. Results from the 400-yr control simulation show that the model＇s ENSO variability is quite realistic in terms of structure, strength, and period. Also, two related features （the annual cycle of SST and the-phase locking of ENSO events）, which are significant in determining the model＇s performance of realistic ENSO prediction, are further validated to be well reproduced by the MPI cli mate model, which is an atmospheric model ECHAM5 （which fuses the EC tbr European Center and HAM for Hamburg） coupled to an MPI ocean model （MPI-OM）, ECHAMS/MPI-OM.
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the National Natural Science Foundation of China(41490644,41475101 and41421005)the CAS Strategic Priority Project+1 种基金the Western Pacific Ocean System(XDA11010105,XDA11020306 and XDA11010301)the NSFC-Shandong Joint Fund for Marine Science Research Centers(U1406401)
文摘The tropical Pacific is currently experiencing an El Nifio event. Various coupled models with different degrees of complexity have been used to make real-time E1 Nifio predictions, but large uncertainties exist in the inten- sity forecast and are strongly model dependent. An intermediate coupled model （ICM） is used at the Institute of Oceanology, Chinese Academy of Sciences （IOCAS）, named the IOCAS ICM, to predict the sea surface temper- ature （SST） evolution in the tropical Pacific during the 2015-2016 E! Nifio event. One unique feature of the IOCAS ICM is the way in which the temperature of subsurface water entrained in the mixed layer （Te） is parameterized. Observed SST anomalies are only field that is utilized to initialize the coupled prediction using the IOCAS ICM. Examples are given of the model＇s ability to predict the SST conditions in a real-time manner. As is commonly evident in E1 Nifio- Southern Oscillation predictions using coupled models, large discrepancies occur between the observed and pre- dicted SST anomalies in spring 2015. Starting from early summer 2015, the model can realistically predict warming conditions. Thereafter, good predictions can be made through the summer and fall seasons of 2015. A transition to normal and cold conditions is predictecl to occur in rote spring 2016. Comparisons with other model predictions are made and factors influencing the prediction performance of the IOCAS ICM are also discussed.
