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| 1 | A new approach to studying ENSO predictability:Conditional nonlinear optimal perturbation显示文摘A new approach, the conditional nonlinear optimal perturbation (CNOP) is introduced to study the predictability of El Ni駉-Southern Oscillation (ENSO) using a theoretical coupled ocean-atmosphere model. The differences between CNOP and linear singular vector (LSV) are demonstrated. The results suggest that the nonlinear model and CNOP are superior in determining error growth for studying predictability of the ENSO. In particular, the CNOP approach is used to explore the nature of the 憇pring predictability barrier?in ENSO prediction. | MU Mu & DUAN Wansuo LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China | 2003 | Chinese Science Bulletin2003，48，10： | 74 |
| 2 | Conditional nonlinear optimal perturbation: Applications to stability, sensitivity, and predictability显示文摘Conditional nonlinear optimal perturbation (CNOP) is a nonlinear generalization of linear singular vector (LSV) and features the largest nonlinear evolution at prediction time for the initial perturbations in a given constraint. It was proposed initially for predicting the limitation of predictability of weather or climate. Then CNOP has been applied to the studies of the problems related to predictability for weather and climate. In this paper, we focus on reviewing the recent advances of CNOP's applications, which involves the ones of CNOP in problems of ENSO amplitude asymmetry, block onset, and the sensitivity analysis of ecosystem and ocean's circulations, etc. Especially, CNOP has been primarily used to construct the initial perturbation fields of ensemble forecasting, and to determine the sensitive area of target observation for precipitations. These works extend CNOP's applications to investigating the nonlinear dynamical behaviors of atmospheric or oceanic systems, even a coupled system, and studying the problem of the transition between the equilibrium states. These contributions not only attack the particular physical problems, but also show the superiority of CNOP to LSV in revealing the effect of nonlinear physical processes. Consequently, CNOP represents the optimal precursors for a weather or climate event; in predictability studies, CNOP stands for the initial error that has the largest negative effect on prediction; and in sensitivity analysis, CNOP is the most unstable (sensitive) mode. In multi-equilibrium state regime, CNOP is the initial perturbation that induces the transition between equilibriums most probably. Furthermore, CNOP has been used to construct ensemble perturbation fields in ensemble forecast studies and to identify sensitive area of target observation. CNOP theory has become more and more substantial. It is expected that CNOP also serves to improve the predictability of the realistic predictions for weather and climate events plays an increasingly important role in exploring the nonlinear dynamics of atmospheric, oceanic and coupled atmosphere-ocean system. | DUAN WanSuo MU Mu | 2009 | Science China Earth Sciences2009，52，7： | 32 |
| 3 | Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting显示文摘In this paper, we emphasize the importance of accurate initial conditions in predicting high-impact ocean-atmospheric environmental events, such as El Nin?o-Southern Oscillation(ENSO), Indian Ocean Dipole(IOD), tropical cyclone(TC), and Kuroshio large meander(KLM), by reviewing recent progresses toward target observations for improving the initialization of these events forecasting. Since ield observations are costly and will never be dense enough to fully cover the vast space of these events, it is necessary to develop methodologies that guide the design of eicient and efective observation strategy. Of particular interest is a method called conditional non-linear optimal perturbation(CNOP), which has been shown to be very useful in determining the sensitive areas for target observations applicable to the predictions of ENSO, IOD, TC, and KLM. Further studies are needed to understand the predictability of these events under the inluence of climate change, and to explore the possibility of implementing ield programs of target observations. hese studies are challenging but are crucially important for improving our forecast skill of the high-impact ocean-atmospheric environmental events, and thus for disaster prevention,climate change mitigation, and sustainable socio-economic development. | Mu Mu Wansuo Duan Dake Chen Weidong Yu | 2015 | National Science Review2015，2，2： | 21 |
| 4 | Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations显示文摘This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error. | Zhenhua HUO Wansuo DUAN Feifan ZHOU | 2019 | Advances in Atmospheric Sciences2019，36，2： | 11 |
| 5 | Conditional nonlinear optimal perturbation and its applications to the studies of weather and climate predictability显示文摘Conditional nonlinear optimal perturbation (CNOP) is the initial perturbation that has the largest nonlinear evolution at prediction time for initial perturba-tions satisfying certain physical constraint condition. It does not only represent the optimal precursor of certain weather or climate event, but also stand for the initial error which has largest effect on the prediction uncertainties at the prediction time. In sensitivity and stability analyses of fluid motion, CNOP also describes the most unstable (or most sensitive) mode. CNOP has been used to estimate the upper bound of the prediction error. These physical characteristics of CNOP are examined by applying respectively them to ENSO pre-dictability studies and ocean’s thermohaline circulation (THC) sensitivity analysis. In ENSO predictability studies, CNOP, rather than linear singular vector (LSV), represents the initial patterns that evolve into ENSO events most poten-tially, i.e. the optimal precursors for ENSO events. When initial perturbation is considered to be the initial error of ENSO, CNOP plays the role of the initial error that has larg-est effect on the prediction of ENSO. CNOP also derives the upper bound of prediction error of ENSO events. In the THC sensitivity and stability studies, by calculating the CNOP (most unstable perturbation) of THC, it is found that there is an asymmetric nonlinear response of ocean’s THC to the finite amplitude perturbations. Finally, attention is paid to the feasibility of CNOP in more complicated model. It is shown that in a model with higher dimensions, CNOP can be computed successfully. The corresponding optimization algo-rithm is also shown to be efficient. | MU Mu DUAN Wansuo | 2005 | Chinese Science Bulletin2005，50，21： | 11 |
| 6 | Applications of nonlinear optimization methods to quantifying the predictability of a numerical model for El Nino-Southern Oscillation显示文摘The nonlinear optimization methods are applied to quantify the predictability of a numerical model for El Nino-Southern Oscillation (ENSO). We establish a lower bound of maximum predictability time for the model ENSO events (i. e. ENSO events in the numerical model), an upper bound of maximum prediction error, and a lower bound of maximum allowable initial error, all of which poENSO event and support the previous views on SPB. Additionally, we also explore the differences between the linear evolution of prediction error and its nonlinear counterpart. The results demonstrate the limitation of linear estimation of prediction error. All these above resuits suggest that the nonlinear optimization method is one of the useful tools of quantifying the predictability of the numerical model for ENSO. | DUAN Wansuo MU Mu | 2005 | Progress in Natural Science:Materials International2005，15，10： | 10 |
| 7 | Progress in ENSO prediction and predictability study显示文摘ENSO is the strongest interannual signal in the global climate system with worldwide climatic, ecological and societal impacts. Over the past decades, the research about ENSO prediction and predictability has attracted broad attention. With the development of coupled models, the improvement in initialization schemes and the progress in theoretical studies, ENSO has become the most predictable climate mode at the time scales from months to seasons. This paper reviews in detail the progress in ENSO predictions and predictability studies achieved in recent years. An emphasis is placed on two fundamental issues: the improvement in practical prediction skills and progress in the theoretical study of the intrinsic predictability limit. The former includes progress in the couple models, data assimilations, ensemble predictions and so on, and the latter focuses on efforts in the study of the optimal error growth and in the estimate of the intrinsic predictability limit. | Youmin Tang Rong-Hua Zhang Ting Liu Wansuo Duan Dejian Yang Fei Zheng Hongli Ren Tao Lian Chuan Gao Dake Chen Mu Mu | 2018 | National Science Review2018，5，6： | 8 |
| 8 | Influence of Positive and Negative Indian Ocean Dipoles on ENSO via the Indonesian Throughflow: Results from Sensitivity Experiments显示文摘The role of the Indonesian Throughflow(ITF) in the influence of the Indian Ocean Dipole(IOD) on ENSO is investigated using version 2 of the Parallel Ocean Program(POP2) ocean general circulation model. We demonstrate the results through sensitivity experiments on both positive and negative IOD events from observations and coupled general circulation model simulations. By shutting down the atmospheric bridge while maintaining the tropical oceanic channel, the IOD forcing is shown to influence the ENSO event in the following year, and the role of the ITF is emphasized. During positive IOD events,negative sea surface height anomalies(SSHAs) occur in the eastern Indian Ocean, indicating the existence of upwelling.These upwelling anomalies pass through the Indonesian seas and enter the western tropical Pacific, resulting in cold anomalies there. These cold temperature anomalies further propagate to the eastern equatorial Pacific, and ultimately induce a La Nia-like mode in the following year. In contrast, during negative IOD events, positive SSHAs are established in the eastern Indian Ocean, leading to downwelling anomalies that can also propagate into the subsurface of the western Pacific Ocean and travel further eastward. These downwelling anomalies induce negative ITF transport anomalies, and an El Nio-like mode in the tropical eastern Pacific Ocean that persists into the following year. The effects of negative and positive IOD events on ENSO via the ITF are symmetric. Finally, we also estimate the contribution of IOD forcing in explaining the Pacific variability associated with ENSO via ITF. | ZHOU Qian DUAN Wansuo MU Mu FENG Rong | 2015 | Advances in Atmospheric Sciences2015，32，6： | 6 |
| 9 | The application of the orthogonal conditional nonlinear optimal perturbations method to typhoon track ensemble forecasts显示文摘The orthogonal conditional nonlinear optimal perturbations (CNOPs) method, orthogonal singular vectors (SVs)method and CNOP+SVs method, which is similar to the orthogonal SVs method but replaces the leading SV (LSV) with the first CNOP, are adopted in both the Lorenz-96 model and Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Fifth-Generation Mesoscale Model (MM5) for ensemble forecasts. Using the MM5, typhoon track ensemble forecasting experiments are conducted for strong Typhoon Matsa in 2005. The results of the Lorenz-96 model show that the CNOP+SVs method has a higher ensemble forecast skill than the orthogonal SVs method, but ensemble forecasts using the orthogonal CNOPs method have the highest forecast skill. The results from the MM5 show that orthogonal CNOPs have a wider horizontal distribution and better describe the forecast uncertainties compared with SVs. When generating the ensemble mean forecast, equally averaging the ensemble members in addition to the anomalously perturbed forecast members may contribute to a higher forecast skill than equally averaging all of the ensemble members. Furthermore, for given initial perturbation amplitudes, the CNOP+SVs method may not have an ensemble forecast skill greater than that of the orthogonal SVs method, but the orthogonal CNOPs method is likely to have the highest forecast skill. Compared with SVs, orthogonal CNOPs fully consider the influence of nonlinear physical processes on the forecast results; therefore, considering the influence of nonlinearity may be important when generating fast-growing initial ensemble perturbations. All of the results show that the orthogonal CNOP method may be a potential new approach for ensemble forecasting. | Zhenhua HUO Wansuo DUAN | 2019 | Science China Earth Sciences2019，62，2： | 6 |
| 10 | Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF显示文摘The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution. | Xiaohao QIN Wansuo DUAN Hui XU | 2020 | Advances in Atmospheric Sciences2020，37，3： | 6 |
| 11 | Using CMIP5 model outputs to investigate the initial errors that cause the “spring predictability barrier” for El Nio events显示文摘Most ocean-atmosphere coupled models have difficulty in predicting the El Nio-Southern Oscillation(ENSO) when starting from the boreal spring season. However, the cause of this spring predictability barrier(SPB) phenomenon remains elusive. We investigated the spatial characteristics of optimal initial errors that cause a significant SPB for El Nio events by using the monthly mean data of the pre-industrial(PI) control runs from several models in CMIP5 experiments. The results indicated that the SPB-related optimal initial errors often present an SST pattern with positive errors in the central-eastern equatorial Pacific, and a subsurface temperature pattern with positive errors in the upper layers of the eastern equatorial Pacific, and negative errors in the lower layers of the western equatorial Pacific. The SPB-related optimal initial errors exhibit a typical La Ni-a-like evolving mode, ultimately causing a large but negative prediction error of the Nio-3.4 SST anomalies for El Nio events. The negative prediction errors were found to originate from the lower layers of the western equatorial Pacific and then grow to be large in the eastern equatorial Pacific. It is therefore reasonable to suggest that the El Nio predictions may be most sensitive to the initial errors of temperature in the subsurface layers of the western equatorial Pacific and the Nio-3.4 region, thus possibly representing sensitive areas for adaptive observation. That is, if additional observations were to be preferentially deployed in these two regions, it might be possible to avoid large prediction errors for El Nio and generate a better forecast than one based on additional observations targeted elsewhere. Moreover, we also confirmed that the SPB-related optimal initial errors bear a strong resemblance to the optimal precursory disturbance for El Nio and La Nia events. This indicated that improvement of the observation network by additional observations in the identified sensitive areas would also be helpful in detecting the signals provided by the precursory disturbance, which may greatly improve the ENSO prediction skill. | ZHANG Jing DUAN WanSuo ZHI XieFei | 2015 | Science China Earth Sciences2015，58，5： | 6 |
| 12 | On the “spring predictability barrier” for strong El Nio events as derived from an intermediate coupled model ensemble prediction system显示文摘Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the 'spring predictability barrier'(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model. | QI QianQian DUAN WanSuo ZHENG Fei TANG YouMin | 2017 | Science China Earth Sciences2017，60，9： | 5 |
| 13 | A SVD-based ensemble projection algorithm for calculating the conditional nonlinear optimal perturbation显示文摘Conditional nonlinear optimal perturbation(CNOP) is an extension of the linear singular vector technique in the nonlinear regime.It represents the initial perturbation that is subjected to a given physical constraint,and results in the largest nonlinear evolution at the prediction time.CNOP-type errors play an important role in the predictability of weather and climate.Generally,when calculating CNOP in a complicated numerical model,we need the gradient of the objective function with respect to the initial perturbations to provide the descent direction for searching the phase space.The adjoint technique is widely used to calculate the gradient of the objective function.However,it is difficult and cumbersome to construct the adjoint model of a complicated numerical model,which imposes a limitation on the application of CNOP.Based on previous research,this study proposes a new ensemble projection algorithm based on singular vector decomposition(SVD).The new algorithm avoids the localization procedure of previous ensemble projection algorithms,and overcomes the uncertainty caused by choosing the localization radius empirically.The new algorithm is applied to calculate the CNOP in an intermediate forecasting model.The results show that the CNOP obtained by the new ensemble-based algorithm can effectively approximate that calculated by the adjoint algorithm,and retains the general spatial characteristics of the latter.Hence,the new SVD-based ensemble projection algorithm proposed in this study is an effective method of approximating the CNOP. | CHEN Lei DUAN WanSuo XU Hui | 2015 | Science China Earth Sciences2015，58，3： | 5 |
| 14 | The predictability of atmospheric and oceanic motions:Retrospect and prospects显示文摘This paper reviews the historic understanding of the predictability of atmospheric and oceanic motions, and interprets it in a general framework. On this basis, the existing challenges and unsolved problems in the study of the intrinsic predictability limit(IPL) of weather and climate events of different spatio-temporal scales are summarized. Emphasis is also placed on the structure of the initial error and model parameter errors as well as the associated targeting observation issue. Finally, the predictability of atmospheric and oceanic motion in the ensemble-probabilistic methods widely used in current operational forecasts are discussed.The necessity of considering IPLs in the framework of stochastic dynamic systems is also addressed. | MU Mu DUAN WanSuo TANG YouMin | 2017 | Science China Earth Sciences2017，60，11： | 4 |
| 15 | The role of constant optimal forcing in correcting forecast models显示文摘In this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model.The results show that when we only consider model error caused by parameter error,which also changes with the development of state variables in a numerical model,the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model.The COF can also offset the impact of model error caused by stochastic processes.In reality,the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions.Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results.In summary,although the variation in the model error due to physical process is time-dependent,the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results.Therefore,the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models. | FENG Fan DUAN WanSuo | 2013 | Science China Earth Sciences2013，56，3： | 3 |
| 16 | Nonlinearity Modulating Intensities and Spatial Structures of Central Pacific and Eastern Pacific El Nio Events显示文摘This paper compares data from linearized and nonlinear Zebiak–Cane model, as constrained by observed sea surface temperature anomaly(SSTA), in simulating central Pacific(CP) and eastern Pacific(EP) El Nio. The difference between the temperature advections(determined by subtracting those of the linearized model from those of the nonlinear model),referred to here as the nonlinearly induced temperature advection change(NTA), is analyzed. The results demonstrate that the NTA records warming in the central equatorial Pacific during CP El Nio and makes fewer contributions to the structural distinctions of the CP El Nio, whereas it records warming in the eastern equatorial Pacific during EP El Nio, and thus significantly promotes EP El Nio during El Nio–type selection. The NTA for CP and EP El Nio varies in its amplitude,and is smaller in CP El Nio than it is in EP El Nio. These results demonstrate that CP El Nio are weakly modulated by small intensities of NTA, and may be controlled by weak nonlinearity; whereas, EP El Nio are significantly enhanced by large amplitudes of NTA, and are therefore likely to be modulated by relatively strong nonlinearity. These data could explain why CP El Nio are weaker than EP El Nio. Because the NTA for CP and EP El Nio differs in spatial structures and intensities, as well as their roles within different El Nio modes, the diversity of El Nio may be closely related to changes in the nonlinear characteristics of the tropical Pacific. | Wansuo DUAN Chaoming HUANG Hui XU | 2017 | Advances in Atmospheric Sciences2017，34，6： | 2 |
| 17 | Which Features of the SST Forcing Error Most Likely Disturb the Simulated Intensity of Tropical Cyclones?显示文摘Among all of the sources of tropical cyclone(TC) intensity forecast errors, the uncertainty of sea surface temperature(SST) has been shown to play a significant role. In the present study, we determine the SST forcing error that causes the largest simulation error of TC intensity during the entire simulation period by using the WRF model with time-dependent SST forcing. The SST forcing error is represented through the application of a nonlinear forcing singular vector(NFSV)structure. For the selected 12 TC cases, the NFSV-type SST forcing errors have a nearly coherent structure with positive(or negative) SST anomalies located along the track of TCs but are especially concentrated in a particular region. This particular region tends to occur during the specific period of the TCs life cycle when the TCs present relatively strong intensity, but are still intensifying just prior to the mature phase, especially within a TC state exhibiting a strong secondary circulation and very high inertial stability. The SST forcing errors located along the TC track during this time period are verified to have the strongest disturbing effect on TC intensity simulation. Physically, the strong inertial stability of TCs during this time period induces a strong response of the secondary circulation from diabatic heating errors induced by the SST forcing error. Consequently, this significantly influences the subsidence within the warm core in the eye region, which,in turn, leads to significant errors in TC intensity. This physical mechanism explains the formation of NSFV-type SST forcing errors. According to the sensitivity of the NFSV-type SST forcing errors, if one increases the density of SST observations along the TC track and assimilates them to the SST forcing field, the skill of TC intensity simulation generated by the WRF model could be greatly improved. However, this adjustment is most advantageous in improving simulation skill during the time period when TCs become strong but are still intensifying just prior to reaching full maturity. In light of this, the region along the TC track but in the time period of TC movement when the NFSV-type SST forcing errors occur may represent the sensitive area for targeting observation for SST forcing field associated with TC intensity simulation. | Jiawei YAO Wansuo DUAN Xiaohao QIN | 2021 | Advances in Atmospheric Sciences2021，38，4： | 2 |
| 18 | The role of initial signals in the tropical Pacific Ocean in predictions of negative Indian Ocean Dipole events显示文摘Using reanalysis data, the role of initial signals in the tropical Pacific Ocean in predictions of negative Indian Ocean Dipole(IOD) events were analyzed. It was found that the summer predictability barrier(SPB) phenomenon exists in predictions,which is closely related to initial sea temperature errors in the tropical Pacific Ocean, with type-1 initial errors presenting a significant west-east dipole pattern in the tropical Pacific Ocean, and type-2 initial errors showing the opposite spatial pattern. In contrast, SPB-related initial sea temperature errors in the tropical Indian Ocean are relatively small. The initial errors in the tropical Pacific Ocean induce anomalous winds in the tropical Indian Ocean by modulating the Walker circulation in the tropical oceans. In the first half of the prediction year, the anomalous winds, combined with the climatological winds in the tropical Indian Ocean, induce a basin-wide mode of sea surface temperature(SST) errors in the tropical Indian Ocean. With the reversal of the climatological wind in the second half of the prediction year, a west-east dipole pattern of SSTerrors appears in the tropical Indian Ocean, which is further strengthened under the Bjerknes feedback, yielding a significant SPB. Moreover, two types of precursors were also identified: a significant west-east dipole pattern in the tropical Pacific Ocean and relatively small temperature anomalies in the tropical Indian Ocean. Under the combined effects of temperature anomalies in the tropical Indian and Pacific oceans, northwest wind anomalies appear in the tropical Indian Ocean, which induce a significant west-east dipole pattern of SST anomalies, and yield a negative IOD event. | Rong FENG Wansuo DUAN | 2018 | Science China Earth Sciences2018，61，12： | 2 |
| 19 | Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅱ: Model Uncertainty显示文摘This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II.Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60?S and 60?N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved. | Feifan ZHOU Wansuo DUAN He ZHANG Munehiko YAMAGUCHI | 2018 | Advances in Atmospheric Sciences2018，35，10： | 2 |
| 20 | Investigating the Initial Errors that Cause Predictability Barriers for Indian Ocean Dipole Events Using CMIP5 Model Outputs显示文摘By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Ocean Dipole events were identified. The initial errors cause a significant winter predictability barrier(WPB) or summer predictability barrier(SPB).The WPB is closely related with the initial errors in the tropical Indian Ocean, where two types of WPB-related initial errors display opposite patterns and a west–east dipole. In contrast, the occurrence of the SPB is mainly caused by initial errors in the tropical Pacific Ocean, where two types of SPB-related initial errors exhibit opposite patterns, with one pole in the subsurface western Pacific Ocean and the other in the upper eastern Pacific Ocean. Both of the WPB-related initial errors grow the fastest in winter, because the coupled system is at its weakest, and finally cause a significant WPB. The SPB-related initial errors develop into a La Ni ?na–like mode in the Pacific Ocean. The negative SST errors in the Pacific Ocean induce westerly wind anomalies in the Indian Ocean by modulating the Walker circulation in the tropical oceans. The westerly wind anomalies first cool the sea surface water in the eastern Indian Ocean. When the climatological wind direction reverses in summer, the wind anomalies in turn warm the sea surface water, finally causing a significant SPB. Therefore, in addition to the spatial patterns of the initial errors, the climatological conditions also play an important role in causing a significant predictability barrier. | Rong FENG Wansuo DUAN | 2018 | Advances in Atmospheric Sciences2018，35，10： | 2 |
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