Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability  

作　　者：Ming ZHANG Ruiqiang DING Quanjia ZHONG Jianping LI Deyu LU 

机构地区：[1]State Key Laboratory of Earth Surface Processes and Resource Ecology(ESPRE)/Key Laboratory of Environmental Change and Natural Disasters of Chinese,Ministry of Education,Beijing Normal University,Beijing 100875,China [2]Department of Ocean Science,Hong Kong University of Science and Technology,Hong Kong,China [3]College of Oceanic and Atmospheric Sciences/Frontiers Science Center for Deep Ocean Multispheres and Earth System(DOMES)/Key Laboratory of Physical Oceanography,Ministry of Education/Academy of the Future Ocean/Center for Ocean Carbon Neutrality,Ocean University of China,Qingdao 266100,China [4]Laoshan Laboratory,Qingdao 266237,China [5]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [6]College of Earth Science,University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Advances in Atmospheric Sciences》2024年第9期1769-1786,共18页大气科学进展（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.42225501 and 42105059);the National Key Scientific and Tech-nological Infrastructure project“Earth System Numerical Simula-tion Facility”(EarthLab).

摘　　要：In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

关 键 词：conditional nonlinear local Lyapunov exponent second-kind predictability coupled Lorenz model ENSO 

分 类 号：P732.4[天文地球—海洋科学] P714.2