This paper deals with a problem proposed by H. Brezis on the existence of positive solutionsto the equation An + u(rt+2)/(n--2) + f(x,u) = 0 under the Neumann boundaly collditionD.u = un/(rt--z), where f(x, u) is a lo...This paper deals with a problem proposed by H. Brezis on the existence of positive solutionsto the equation An + u(rt+2)/(n--2) + f(x,u) = 0 under the Neumann boundaly collditionD.u = un/(rt--z), where f(x, u) is a lower order perturbation of u(n+2)/(n--2) at infinity.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
文摘This paper deals with a problem proposed by H. Brezis on the existence of positive solutionsto the equation An + u(rt+2)/(n--2) + f(x,u) = 0 under the Neumann boundaly collditionD.u = un/(rt--z), where f(x, u) is a lower order perturbation of u(n+2)/(n--2) at infinity.
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.
