The predictability of El Ni-o-Southern Oscillation (ENSO) has been an important area of study for years. Searching for the optimal precursor (OPR) of ENSO occurrence is an effective way to understand its predictabilit...The predictability of El Ni-o-Southern Oscillation (ENSO) has been an important area of study for years. Searching for the optimal precursor (OPR) of ENSO occurrence is an effective way to understand its predictability. The CNOP (conditional nonlinear optimal perturbation), one of the most effective ways to depict the predictability of ENSO, is adopted to study the optimal sea surface temperature (SST) precursors (SST-OPRs) of ENSO in the IOCAS ICM (intermediate coupled model developed at the Institute of Oceanology, Chinese Academy of Sciences). To seek the SST-OPRs of ENSO in the ICM, non-ENSO events simulated by the ICM are chosen as the basic state. Then, the gradient-definition-based method (GD method) is employed to solve the CNOP for different initial months of the basic years to obtain the SSTOPRs. The experimental results show that the obtained SST-OPRs present a positive anomaly signal in the western-central equatorial Pacific, and obvious differences exist in the patterns between the different seasonal SST-OPRs along the equatorial western-central Pacific, showing seasonal dependence to some extent. Furthermore, the non-El Ni-o events can eventually evolve into El Ni-o events when the SST-OPRs are superimposed on the corresponding seasons;the peaks of the Ni-o3.4 index occur at the ends of the years, which is consistent with the evolution of the real El Ni-o. These results show that the GD method is an effective way to obtain SST-OPRs for ENSO events in the ICM. Moreover, the OPRs for ENSO depicted using the GD method provide useful information for finding the early signal of ENSO in the ICM.展开更多
基金supported by the Fundamental Research Funds for the Central Universities (Grant No. 22120190 207)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060102)+4 种基金the National Key Research and Development Program of China (Grant No. 2017YFC1404102(2017YFC1404100))the National Programme on Global Change and Air-Sea Interaction (Grant No. GASIIPOVAI-06)National Natural Science Foundation of China (Grant Nos. 41690122(41690120), 41490644(41490640), 414210 05)the Taishan Scholarshipthe Institute of Oceanology, Chinese Academy of Sciences, for providing technical support for IOCAS ICM
文摘The predictability of El Ni-o-Southern Oscillation (ENSO) has been an important area of study for years. Searching for the optimal precursor (OPR) of ENSO occurrence is an effective way to understand its predictability. The CNOP (conditional nonlinear optimal perturbation), one of the most effective ways to depict the predictability of ENSO, is adopted to study the optimal sea surface temperature (SST) precursors (SST-OPRs) of ENSO in the IOCAS ICM (intermediate coupled model developed at the Institute of Oceanology, Chinese Academy of Sciences). To seek the SST-OPRs of ENSO in the ICM, non-ENSO events simulated by the ICM are chosen as the basic state. Then, the gradient-definition-based method (GD method) is employed to solve the CNOP for different initial months of the basic years to obtain the SSTOPRs. The experimental results show that the obtained SST-OPRs present a positive anomaly signal in the western-central equatorial Pacific, and obvious differences exist in the patterns between the different seasonal SST-OPRs along the equatorial western-central Pacific, showing seasonal dependence to some extent. Furthermore, the non-El Ni-o events can eventually evolve into El Ni-o events when the SST-OPRs are superimposed on the corresponding seasons;the peaks of the Ni-o3.4 index occur at the ends of the years, which is consistent with the evolution of the real El Ni-o. These results show that the GD method is an effective way to obtain SST-OPRs for ENSO events in the ICM. Moreover, the OPRs for ENSO depicted using the GD method provide useful information for finding the early signal of ENSO in the ICM.
