Study on the “winter persistence barrier” of Indian Ocean dipole events using observation data and CMIP5 model outputs

This study investigates the persistence barrier phenomenon associated with positive Indian Ocean dipole (IOD) events during the various phases of its development. The results derived from three observational datasets (the Simple Ocean Data Assimilation, International Comprehensive Ocean–Atmosphere Data Set, and Extended Reconstructed Sea Surface Temperature) indicate that significant winter persistence barriers (WPBs) occur during IOD events, both in its growing and decaying phases. The simulation skill of the 14 models within the Coupled Model Intercomparison Project Phase 5 with respect to persistence barriers was also evaluated and compared with observational data. The results show that although most models were able to simulate the WPB reasonably well during the growing phase, only five models could capture the appropriate WPB during the decaying phase. Further analysis demonstrates that the zonal equatorial gradient of climatological sea surface temperature (SST) and zonal sea surface winds at the equator in the Indian Ocean are very weak in winter, which indicates that the coupling between ocean and atmosphere is weakest in winter and encourages a rapid variation of IOD events and a swift reduction of persistence, favoring the occurrence of WPBs; furthermore, a deep climatological thermocline in winter implies that the subsurface water temperature cannot influence SST readily, and the memory of the subsurface temperature cannot help SST to recover from the loss of persistence during this period, leading to the occurrence of WPBs. In addition, an analysis of the climatological conditions in the outputs from the 14 models shows that those models that can (cannot) capture the winter climatological conditions frequently simulate the WPBs appropriately (poorly). This confirms that the occurrence of the WPB for IOD events may be closely related to particular winter climatological conditions, indicating that the WPB is an inherent property of IOD events.     

北京地区一次空气重污染过程的目标观测分析

针对北京市2016年12月16~21日的空气重污染过程进行了回报试验,探讨了该次事件预报的目标观测敏感区。使用新一代高分辨率中尺度气象模式（Weather Research Forecasting,WRF）和嵌套网格空气质量模式（Nested Air Quality Prediction Model System,NAQPMS）,针对初始气象场的不确定性,通过4套初始场资料识别了影响北京地区细颗粒物（PM2.5）预报水平的目标观测敏感变量及其敏感区。结果表明:当综合考虑初始气象场的风场、温度、比湿不确定性的影响时,发现改善黑龙江区域上述气象要素的初始场精度,对北京地区PM2.5预报不确定的减小最显著;当分别考察风场、温度、比湿的不确定性的影响时,发现初始风场精度的改善,尤其是黑龙江区域风场精度的改善,能够更大程度地减小北京地区PM2.5的预报误差,对北京东南地区的PM2.5预报误差的减小甚至可达到40%以上。因此,优先对黑龙江区域的气象场,尤其是该区域的风场进行目标观测,并将其同化到预报模式的初始场中,将会有效提高初始气象场的质量,进而大大减小北京地区PM2.5浓度的预报误差,提高北京地区空气质量的预报技巧。初始风场代表了北京地区该次空气重污染事件预报的目标观测变量,而黑龙江地区则是该目标观测的敏感区域。     

非线性最优扰动方法在热带气旋目标观测研究和外场试验中的应用

综述了我国学者近年来用非线性最优扰动方法探索热带气旋目标观测及其外场试验的主要进展,具体包括：从提高数值模式初始场精度的角度,用条件非线性最优扰动方法确定了热带气旋路径和强度预报的目标观测敏感区,并成功应用于“风云四号”气象卫星和下投探空仪台风目标观测外场试验,助力业务部门获得了宝贵资料。从减小模式误差或外强迫不确定性的角度,将非线性强迫奇异向量方法应用于探讨热带气旋强度预报的敏感性,揭示了模式误差的敏感气象要素和敏感区,以及海表温度强迫的敏感区;用集合扰动的思路识别了热带气旋快速增强过程预报的行星边界层的模式误差敏感区。讨论了目前热带气旋目标观测研究存在的问题以及可能的解决方法,展望了未来热带气旋目标观测研究应努力的前沿方向,及其在实际预报中的应用前景。     

The role of initial signals in the tropical Pacific Ocean in predictions of negative Indian Ocean Dipole events

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 SST errors 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.     

What Kind of Initial Errors Cause the Severest Prediction Uncertainty of EI Nino in Zebiak-Cane Model

With the Zebiak-Cane (ZC) model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation (CNOP). The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOP- type errors, we find that for the normal states and the relatively weak EI Nino events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong EI Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of EI Nino in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector (LSV) approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.     

Seasonal modulations of different impacts of two types of ENSO events on tropical cyclone activity in the western North Pacific

The paper examines different impacts of eastern Pacific warm/cold (EPW/EPC) and central Pacific warm/cold (CPW/CPC) events on tropical cyclones (TCs) in the western North Pacific (WNP) by considering the early season of April–June (AMJ), the peak season of July–September (JAS) and the late season of October–December (OND). During AMJ, EPW (EPC) is associated with a significant increase of the TC genesis number in the southeastern (southwestern) sub-region of the WNP, but no class of El Niño-Southern Oscillation (ENSO) events shows a significant change in the TC lifetime and intensity. During JAS, EPW corresponds to an increase (decrease) of the TC genesis number in the southeastern (northwestern) sub-region, but CPW shows no significant change. EPC increases the TC genesis in the northwestern and northeastern sub-regions and decreases the genesis in the southwestern sub-region, whereas CPC suppresses the genesis in the southeastern sub-region. Both the lifetime and intensity of TCs are increased in EPW, but only a shortened lifetime is seen for CPC. During OND, EPW reduces the TC genesis in the southwestern and northwestern sub-regions, whereas CPW enhances the genesis in the southeastern sub-region. Over the South China Sea, CPW and CPC show a significant decrease and increase of the TC genesis, respectively. The TC lifetime is significantly longer in both EPW and CPW and shorter in EPC, and TCs tend to be more (less) intense in EPW (CPC). All of these variations are consistent with the development of ENSO-related SST anomalies during different seasons and are supported by distributions of the genesis potential index—a combination of large-scale oceanic and atmospheric factors that affect TC activity. TCs in the WNP mainly take the straight westward, northwestward and recurving tracks. During AMJ of EPW years, the TC steering flow patterns favor the recurving track and suppress the straight westward and northwestward tracks. During JAS, EPW is associated with the steering flows that are unfavorable for TCs to move northwestward or westward, whereas CPW favors the northwestward track and suppresses the straight westward track. The steering flow patterns during OND are similar to those during JAS, except that EPC may increase the possibility of the northwestward track.     

关于线性奇异向量和条件非线性最优扰动差别的一个注记

奇异向量(singular vectors,SVs)和条件非线性最优扰动(conditional nonlinear optimal perturbation,CNOP)已广泛应用于研究大气—海洋系统的不稳定性以及与其相关的可预报性、集合预报和目标观测问题研究。本文首先回顾了SVs和CNOP的发展历史,并简单描述了它们的基本原理;然后针对二维正压准地转模式,使用不同的范数组合,分析了第一线性奇异向量(first singular vector,FSV)和CNOP之间的异同。结果表明,当优化时间较短时,度量SVs和CNOP大小的范数不同也将导致FSV和CNOP相差很大,而当度量SVs和CNOP大小的范数相同时,FSV和CNOP之间的差别则主要是由非线性物理过程作用的结果。因此,针对不同的物理问题,应该选取合适的度量范数研究FSV和CNOP以及其所引起的大气或海洋动力学的异同,从而揭示非线性物理过程的影响机理。     

Simulations of two types of El Niño events by an optimal forcing vector approach

In this paper, an optimal forcing vector (OFV) approach is proposed. The OFV offsets tendency errors and optimizes the agreement of the model simulation with observation. We apply the OFV approach to the well-known Zebiak–Cane model and simulate several observed eastern Pacific (EP) El Niño and central Pacific (CP) El Niño events during 1980–2004. It is found that the Zebiak–Cane model with a proper initial condition often reproduces the EP-El Niño events; however, the Zebiak–Cane model fails to reproduce the CP-El Niño events. The model may be much more influenced by model errors when simulating the CP-El Nino events. As expected, when we use the OFV to correct the Zebiak–Cane model, the model reproduces the three CP-El Niño events well. Furthermore, the simulations of the corresponding winds and thermocline depths are also acceptable. In particular, the thermocline depth simulations for the three CP-El Niño events lead us to believe that the discharge process of the equatorial heat content associated with the CP-El Niño is not efficient and emphasizes the role of the zonal advection in the development of the CP-El Nino events. The OFVs associated with the three CP-El Niño events often exhibit a sea surface temperature anomaly (SSTA) tendency with positive anomalies in the equatorial eastern Pacific; therefore, the SST tendency errors occurring in the equatorial eastern Pacific may dominate the uncertainties of the Zebiak–Cane model while simulating CP-El Nino events. A further investigation demonstrates that one of the model errors offset by the OFVs is of a pattern similar to the SST cold-tongue cooling mode, which may then provide one of the climatological conditions for the frequent occurrence of CP-El Nino events. The OFV may therefore be a useful tool for correcting forecast models and then for helping improve the forecast skill of the models.     

Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio events？ Results from a Theoretical Model

Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ``spring predictability barrier' (SPB) for El Nino events. First, sensitivity experiments were respectively performed to the air--sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation (CNOP) approach was used to study the effect of the optimal mode (CNOP-P) of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nino events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode (i.e. CNOP error) of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nino events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.