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Impact of Perturbation Schemes on the Ensemble Prediction in a Coupled Lorenz Model

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Advances in Atmospheric Sciences 2023年第3期40卷 501-513页

作者： Qian ZOU Quanjia ZHONG Jiangyu MAO ruiqiang ding Deyu LU Jianping LI Xuan LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth Science University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing 100875China Frontiers Science Center for Deep Ocean Multispheres and Earth System(FDOMES)/Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies Ocean University of ChinaQingdao 266100China Laboratory for Ocean Dynamics and Climate Pilot Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China

Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics.Four initial perturbation approaches are used in the ensemble forecasting experiments:the random perturbation(RP),the bred vector(BV),the ensemble transform Kalman filter(ETKF),and the nonlinear local Lyapunov vector(NLLV)methods.Results show that,regardless of the method used,the ensemble averages behave indistinguishably from the control forecasts during the first few time steps.Due to different error growth in different time-scale systems,the ensemble averages perform better than the control forecast after very short lead times in a fast subsystem but after a relatively long period of time in a slow subsystem.Due to the coupled dynamic processes,the addition of perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow variables.Regarding the initial perturbation approaches,the NLLVs show higher forecasting skill than the BVs or RPs overall.The NLLVs and ETKFs had nearly equivalent prediction skill,but NLLVs performed best by a narrow margin.In particular,when adding perturbations to slow variables,the independent perturbations(NLLVs and ETKFs)perform much better in ensemble prediction.These results are simply implied in a real coupled air–sea model.For the prediction of oceanic variables,using independent perturbations(NLLVs)and adding perturbations to oceanic variables are expected to result in better performance in the ensemble prediction.

关键词： ensemble prediction nonlinear local Lyapunov vector(NLLV) ensemble transform Kalman filter(ETKF) coupled air-sea models

Influence of the NAO on Wintertime Surface Air Temperature over East Asia:Multidecadal Variability and Decadal Prediction

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Advances in Atmospheric Sciences 2022年第4期39卷 625-642页

作者： Jianping LI Tiejun XIE Xinxin TANG Hao WANG Cheng SUN Juan FENG Fei ZHENG ruiqiang ding Frontiers Science Center for Deep Ocean Multispheres and Earth System-Key Laboratory of Physical Oceanography-Institute for Advanced Ocean Studies-Academy of the Future Ocean Ocean University of ChinaQingdao 266100China Laboratory for Ocean Dynamics and Climate Pilot Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Global Change and Earth System Sciences Beijing Normal UniversityBeijing 100875China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing 100875China

In this paper,we investigate the influence of the winter NAO on the multidecadal variability of winter East Asian surface air temperature(EASAT)and EASAT decadal prediction.The observational analysis shows that the winter EASAT and East Asian minimum SAT(EAmSAT)display strong in-phase fluctuations and a significant 60-80-year multidecadal variability,apart from a long-term warming trend.The winter EASAT experienced a decreasing trend in the last two decades,which is consistent with the occurrence of extremely cold events in East Asia winters in recent years.The winter NAO leads the detrended winter EASAT by 12-18 years with the greatest significant positive correlation at the lead time of 15 years.Further analysis shows that ENSO may affect winter EASAT interannual variability,but does not affect the robust lead relationship between the winter NAO and EASAT.We present the coupled oceanic-atmospheric bridge(COAB)mechanism of the NAO influences on winter EASAT multidecadal variability through its accumulated delayed effect of~15 years on the Atlantic Multidecadal Oscillation(AMO)and Africa-Asia multidecadal teleconnection(AAMT)pattern.An NAO-based linear model for predicting winter decadal EASAT is constructed on the principle of the COAB mechanism,with good hindcast performance.The winter EASAT for 2020-34 is predicted to keep on fluctuating downward until~2025,implying a high probability of occurrence of extremely cold events in coming winters in East Asia,followed by a sudden turn towards sharp warming.The predicted 2020/21 winter EASAT is almost the same as the 2019/20 winter.

关键词： winter East Asian surface air temperature North Atlantic Oscillation Atlantic Multidecadal Oscillation Africa-Asia multidecadal teleconnection pattern coupled oceanic-atmospheric bridge multidecadal variability

Quantitative Comparison of Predictabilities of Warm and Cold Events Using the Backward Nonlinear Local Lyapunov Exponent Method

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Advances in Atmospheric Sciences 2020年第9期37卷 951-958页

作者： Xuan LI ruiqiang ding Jianping LI Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China Key Laboratory of Physical Oceanography Institute for Advanced Ocean StudiesOcean University of China and Qingdao National Laboratory for Marine Science and TechnologyQingdao 266100China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing 100875China Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies Ocean University of China and Qingdao National Laboratory for Marine Science and TechnologyQingdao 266100China

The backward nonlinear local Lyapunov exponent method(BNLLE)is applied to quantify the predictability of warm and cold events in the Lorenz model.Results show that the maximum prediction lead times of warm and cold events present obvious layered structures in phase space.The maximum prediction lead times of each warm(cold)event on individual circles concentric with the distribution of warm(cold)regime events are roughly the same,whereas the maximum prediction lead time of events on other circles are different.Statistical results show that warm events are more predictable than cold events.

关键词： backward nonlinear local Lyapunov exponent maximum prediction lead time layered structure statistical result

Determining the Spectrum of the Nonlinear Local Lyapunov Exponents in a Multidimensional Chaotic System

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Advances in Atmospheric Sciences 2017年第9期34卷 1027-1034页

作者： ruiqiang ding Jianping LI Baosheng LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology College of Global Change and Earth System Sciences Beijing Normal University University of Chinese Academy of Sciences

For an n-dimensional chaotic system, we extend the definition of the nonlinear local Lyapunov exponent （NLLE） from one- to n-dimensional spectra, and present a method for computing the NLLE spectrum. The method is tested on three chaotic systems with different complexity. The results indicate that the NLLE spectrum realistically characterizes the growth rates of initial error vectors along different directions from the linear to nonlinear phases of error growth. This represents an improvement over the traditional Lyapunov exponent spectrum, which only characterizes the error growth rates during the linear phase of error growth. In addition, because the NLLE spectrum can effectively separate the slowly and rapidly growing perturbations, it is shown to be more suitable for estimating the predictability of chaotic systems, as compared to the traditional Lyapunov exponent spectrum.

关键词： Lyapunov exponent nonlinear local Lyapunov exponent predictability

Determination of the Backward Predictability Limit and Its Relationship with the Forward Predictability Limit

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Advances in Atmospheric Sciences 2019年第6期36卷 669-677页

作者： Xuan LI ruiqiang ding Jianping LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric Physics Chinese Academy of Sciences College of Earth Science University of Chinese Academy of Sciences Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology College of Global Change and Earth System Sciences (GCESS) Beijing Normal University

In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states.Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.

关键词： nonlinear local Lyapunov exponent forward and backward predictability limit negative correlation average value

Application of Backward Nonlinear Local Lyapunov Exponent Method to Assessing the Relative Impacts of Initial Condition and Model Errors on Local Backward Predictability

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Advances in Atmospheric Sciences 2021年第9期38卷 1486-1496页

作者： Xuan LI Jie FENG ruiqiang ding Jianping LI Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing 100875China Frontiers Science Center for Deep Ocean Multispheres and Earth System(FDOMES)/Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies Ocean University of ChinaQingdao 266100China Laboratory for Ocean Dynamics and Climate Pilot Qingdao National Laboratory for Marine Science and Technology(QNLM)Qingdao 266237China

Initial condition and model errors both contribute to the loss of atmospheric predictability.However,it remains debatable which type of error has the larger impact on the prediction lead time of specific states.In this study,we perform a theoretical study to investigate the relative effects of initial condition and model errors on local prediction lead time of given states in the Lorenz model.Using the backward nonlinear local Lyapunov exponent method,the prediction lead time,also called local backward predictability limit(LBPL),of given states induced by the two types of errors can be quantitatively estimated.Results show that the structure of the Lorenz attractor leads to a layered distribution of LBPLs of states.On an individual circular orbit,the LBPLs are roughly the same,whereas they are different on different orbits.The spatial distributions of LBPLs show that the relative effects of initial condition and model errors on local backward predictability depend on the locations of given states on the dynamical trajectory and the error magnitudes.When the error magnitude is fixed,the differences between the LBPLs vary with the locations of given states.The larger differences are mainly located on the inner trajectories of regimes.When the error magnitudes are different,the dissimilarities in LBPLs are diverse for the same given state.

关键词： Initial condition model errors error magnitude error location LBPL

Influence of the North American Dipole on ENSO onset as simulated by a coupled ocean-Atmosphere model

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Atmospheric and Oceanic Science Letters 2021年第6期14卷 1-6页

作者： Jinghua Chao Guangzhou Fan ruiqiang ding School of Atmospheric Sciences Chengdu University of Information TechnologyChengduChina State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijingChina

北美偶极子(NAD)是热带北大西洋西部和北美东北部的南北向海平面气压异常偶极型模态.以往的观测研究表明,NAD可以有效地影响ENSO事件的爆发.本文利用全球耦合模式FGOALS-g2,评估了NAD与ENSO的关系.结果表明,该模式能较好地重现NAD模态.... 详细信息

北美偶极子(NAD)是热带北大西洋西部和北美东北部的南北向海平面气压异常偶极型模态.以往的观测研究表明,NAD可以有效地影响ENSO事件的爆发.本文利用全球耦合模式FGOALS-g2,评估了NAD与ENSO的关系.结果表明,该模式能较好地重现NAD模态.进一步的分析验证了冬季NAD可以通过强迫冬末春初副热带东北太平洋上空的反气旋和暖海温的出现,在随后的冬季触发El Nino事件.此外,在同化NAD实验中,发生El Niño事件的概率增加了将近一倍.相比之下,NAO未能在副热带东北太平洋上空引起表面风和海温的异常,因而不能有效地激发次年冬季ENSO事件.

关键词：北美偶极子 ENSO 海气耦合模式

Influence of the Preceding Austral Summer Southern Hemisphere Annular Mode on the Amplitude of ENSO Decay

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Advances in Atmospheric Sciences 2017年第11期34卷 1358-1379页

作者： Fei ZHENG Jianping LI ruiqiang ding State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technologyqingdao 266237China Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technologychengdu 610103China

There is increasing evidence of the possible role of extratropical forcing in the evolution of ENSO. The Southern Hemi- sphere Annular Mode （SAM） is the dominant mode of atmospheric circulation in the Southern Hemisphere extratropics. This study shows that the austral summer （December-January-February; DJF） SAM may also influence the amplitude of ENSO decay during austral autumn （March-April-May; MAM）. The mechanisms associated with this SAM-ENSO relationship can be briefly summarized as follows： The SAM is positively （negatively） correlated with SST in the Southern Hemisphere middle （high） latitudes. This dipole-like SST anomaly pattern is referred to as the Southern Ocean Dipole （SOD）. The DJF SOD, caused by the DJF SAM, could persist until MAM and then influence atmospheric circulation, including trade winds, over the Nifio3.4 area. Anomalous trade winds and SST anomalies over the Nifio3.4 area related to the DJF SAM are further developed through the Bjerkness feedback, which eventually results in a cooling （warming） over the Nifio3.4 area followed by the positive （negative） DJF SAM.

关键词： Southern Hemisphere Annular Mode ENSO Southern Ocean Dipole

Comparison of Nonlinear Local Lyapunov Vectors with Bred Vectors, Random Perturbations and Ensemble Transform Kalman Filter Strategies in a Barotropic Model

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Advances in Atmospheric Sciences 2016年第9期33卷 1036-1046页

作者： Jie FENG ruiqiang ding Jianping LI Deqiang LIU State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 College of Earth Science University of Chinese Academy of SciencesBeijing 100049 Global Systems Division Earth System Research Laboratory/Oceanic and Atmospheric Research/National Oceanic and Atmospheric AdministrationBoulderCO80305USA Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information TechnologyChengdu 610225 College of Global Change and Earth System Sciences Beijing Normal UniversityBeijing 100875 Joint Center for Global Change Studies Beijing 100875 Fujian Meteorological Observatory Fuzhou 350001

The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors （BVs） are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector （NLLV） scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more com- ponents in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random pertur- bation （RP） technique, and the BV method, as well as its improved version--the ensemble transform Kalman filter （ETKF） method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.

关键词： ensemble forecasting bred vector nonlinear local Lyapunov vector ensemble transform Kalman filter