Impact of the Intraseasonal Indo-west Pacific Convective Oscillation on Subseasonal-seasonal Atmospheric Predictability
This work quantifies the contribution of seasonal IPCO to S2S atmospheric predictability by using the nonlinear local Lyapunov exponent and conditional nonlinear local Lyapunov exponent to estimate the forecastable period of intraseasonal IPCO and RMM index,and investigates the variation pattern of the spatial distribution of S2S predictability limit under the evolution of intraseasonal IPCO.The results show that:(1)Compared with the RMM index,the intraseasonal IPCO index is more predictable,with predictability limit of about 31 days,which is more than 2 weeks higher than the RMM index.(2)The S2S atmospheric predictability is the strongest in the Indo-West Pacific,with predictability limit of more than 30 days,in which the intraseasonal IPCO is one of the main predictability sources in this region,with its contribution reaching more than 6 days.(3)With the evolution of intraseasonal IPCO,the S2S atmospheric predictability of the Indian Ocean western Pacific region has a spatial structure change,manifesting as the propagation and oscillation of predictability period anomalies.The S2S atmospheric predictability anomalies propagate along the intraseasonal IPCO paths,one starting from the equatorial western and central Indian Ocean northward to the Indian Peninsula,and one propagating eastward through the oceanic continents to the equatorial western Pacific Ocean and then northward to southern Japan.Meanwhile,the propagation of the predictability anomaly shows reverse variability in the eastern Indian Ocean and the western Pacific Ocean,forming an east-west polar oscillation.When the intraseasonal IPCO develops toward the positive phase,the eastern Indian Ocean has stronger predictability and the western Pacific Ocean has weaker predictability,and vice versa.The development(decline)of intraseasonal IPCO can make the S2S atmosphere in the East Indian Ocean(West Pacific)more predictable,and the model forecast skill has more room for improvement.
国家科技期刊平台
NSTL
万方数据
