Responses of the Southern Ocean mixed layer depth to the eastern and central Pacific El Niño events during austral winter

Based on the Ocean Reanalysis System version 5(ORAS5)and the fifth-generation reanalysis datasets derived from European Centre for Medium-Range Weather Forecasts(ERA5),we investigate the different impacts of the central Pacific(CP)El Niño and the eastern Pacific(EP)El Niño on the Southern Ocean(SO)mixed layer depth(MLD)during austral winter.The MLD response to the EP El Niño shows a dipole pattern in the South Pacific,namely the MLD dipole,which is the leading El Niño-induced MLD variability in the SO.The tropical Pacific warm sea surface temperature anomaly(SSTA)signal associated with the EP El Niño excites a Rossby wave train propagating southeastward and then enhances the Amundsen Sea low(ASL).This results in an anomalous cyclone over the Amundsen Sea.As a result,the anomalous southerly wind to the west of this anomalous cyclone advects colder and drier air into the southeast of New Zealand,leading to surface cooling through less total surface heat flux,especially surface sensible heat(SH)flux and latent heat(LH)flux,and thus contributing to the mix layer(ML)deepening.The east of the anomalous cyclone brings warmer and wetter air to the southwest of Chile,but the total heat flux anomaly shows no significant change.The warm air promotes the sea ice melting and maintains fresh water,which strengthens stratification.This results in a shallower MLD.During the CP El Niño,the response of MLD shows a separate negative MLD anomaly center in the central South Pacific.The Rossby wave train triggered by the warm SSTA in the central Pacific Ocean spreads to the Amundsen Sea,which weakens the ASL.Therefore,the anomalous anticyclone dominates the Amundsen Sea.Consequently,the anomalous northerly wind to the west of anomalous anticyclone advects warmer and wetter air into the central and southern Pacific,causing surface warming through increased SH,LH,and longwave radiation flux,and thus contributing to the ML shoaling.However,to the east of the anomalous anticyclone,there is no statistically significant impact on the MLD.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Impacts of central-Pacific El Niño and physical drivers on eastern Pacific bigeye tuna

Bigeye tuna Thunnus obesus is an important migratory species that forages deeply,and El Niño events highly influence its distribution in the eastern Pacific Ocean.While sea surface temperature is widely recognized as the main factor affecting bigeye tuna(BET)distribution during El Niño events,the roles of different types of El Niño and subsurface oceanic signals,such as ocean heat content and mixed layer depth,remain unclear.We conducted A spatial-temporal analysis to investigate the relationship among BET distribution,El Niño events,and the underlying oceanic signals to address this knowledge gap.We used monthly purse seine fisheries data of BET in the eastern tropical Pacific Ocean(ETPO)from 1994 to 2012 and extracted the central-Pacific El Niño(CPEN)indices based on Niño 3 and Niño 4indexes.Furthermore,we employed Explainable Artificial Intelligence(XAI)models to identify the main patterns and feature importance of the six environmental variables and used information flow analysis to determine the causality between the selected factors and BET distribution.Finally,we analyzed Argo datasets to calculate the vertical,horizontal,and zonal mean temperature differences during CPEN and normal years to clarify the oceanic thermodynamic structure differences between the two types of years.Our findings reveal that BET distribution during the CPEN years is mainly driven by advection feedback of subsurface warmer thermal signals and vertically warmer habitats in the CPEN domain area,especially in high-yield fishing areas.The high frequency of CPEN events will likely lead to the westward shift of fisheries centers.

Different El Niño Flavors and Associated Atmospheric Teleconnections as Simulated in a Hybrid Coupled Model

A previously developed hybrid coupled model(HCM)is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model(AGCM),denoted as HCMAGCM.In this study,different El Niño flavors,namely the Eastern-Pacific(EP)and Central-Pacific(CP)types,and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCMAGCM.The HCMAGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific,including the amplitude and spatial patterns of sea surface temperature(SST),zonal wind stress,and precipitation anomalies.An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events,respectively.Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events,the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter.In particular,the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere,while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American(PNA)pattern.As a result,different climatic impacts exist in North American regions,with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño,respectively.This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCMAGCM.

Understanding the Low Predictability of the 2015/16 El Niño Event Based on a Deep Learning Model

The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity,but most dynamical models had a relatively low prediction skill for this event before the summer months.Therefore,the attribution of this particular event can help us to understand the cause of super El Niño–Southern Oscillation events and how to forecast them skillfully.The present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event.A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Niño-3.4 index.The integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Niño-3.4 index.These crucial signals are then masked in the initial conditions to verify their roles in the prediction.In addition to confirming the key signals inducing the super El Niño event revealed in previous attribution studies,we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event,emphasizing the crucial role of the interactions among them and the North Pacific.This approach is also applied to other El Niño events,revealing several new precursor signals.This study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.

El Niño事件发展期对中国东部夏季极端降水的影响

利用1961−2020年的再分析资料和中国台站观测降水数据集,研究了东部型El Niño事件发展期夏季对中国夏季极端降水的影响.结果表明,东部型El Niño在发展期夏季对中国极端降水的影响主要表现在中国东部地区,造成华北和江南地区极端降水减少,江淮地区极端降水显著增多.进一步分析其中的物理过程发现,当东部型El Niño事件处于发展期夏季时,赤道东太平洋出现显著的海表面温度(sea surface temperature,SST)暖异常,西太平洋区域表现为冷异常,导致反气旋性环流异常.同时,西北太平洋区域存在SST暖异常,对应气旋性环流异常.异常的SST分布激发了“正-负-正(+−+)”的东亚-太平洋型(East Asia-Pacific,EAP)波列异常,对应着“负-正-负(−+−)”的降水配置.在2个异常环流的交汇处有显著的辐合上升运动,为江淮地区带去了充足的水汽.而华北地区主要受到反气旋性环流和蒙古高压的共同控制,并受到来自高纬度地区的异常西北风影响,存在显著的辐散下沉运动,降水的动力条件不足.并且,在东部型El Niño事件发展期夏季,西太平洋副热带高压位置异常偏东,不利于江南地区降水的发生及水汽的输送,进一步造成江南地区极端降水减少.以上结果显示东部型El Niño事件在其发展期夏季对中国极端降水存在重要的影响,为区域极端气候预测提供了理论依据.

Estimating the Yield Loss of Winter Wheat from Drought in the United States Southern Plains Region as Influenced by El Niño-Southern Oscillation (ENSO)

Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.

Wavelet Multiview-Based Hybrid Deep Learning Model for Forecasting El Niño-Southern Oscillation Cycles

The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.

海气相互作用在模式FGOALS-g3模拟东亚夏季风及其对前冬El Niño响应中的贡献

本文基于观测、再分析资料和中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG)最新版本气候系统模式FGOALS-g3,探究了海气相互作用在模拟东亚夏季风及其对前冬El Niño响应中的贡献。大气环流模式(AGCM)模拟的气候态夏季风雨带偏东,东亚季风区表现为干偏差,耦合模式(CGCM)虽模拟出了夏季风雨带的位置,但降水仍偏弱。AGCM由于缺乏海气耦合过程,夏季西北太平洋地区对流模拟过强,使得副热带高压(简称副高)偏东、南中国海季风槽偏东,造成东亚夏季风雨带偏东;东亚陆地区域水汽偏少,也是降水干偏差的一个重要原因,此两项可以解释70%以上的干偏差。在考虑海气相互作用后,西北太平洋的降水正异常减弱了局地海表温度,因此CGCM显著改进了副高以及南中国海季风槽偏东等偏差,使得夏季风雨带位置得到改进,季风区降水干偏差减小了36%,但由于水汽偏少,水汽纬向输送偏少,东亚季风区仍维持着显著的干偏差。另一方面,对前冬El Niño的响应,CGCM能够再现El Niño衰减年夏季印度—西太平洋电容器效应(IPOC机制)对西北太平洋异常反气旋(WNPAC)的维持作用及偶极型分布的降水异常。而AGCM中夏季西北太平洋以及孟加拉湾、印度半岛周围海域对流对于海温的响应过于敏感,一方面西北太平洋局地暖异常造成的对流质量输送一定程度上抑制了WNPAC的建立,另一方面孟加拉湾、印度半岛周围海域过强的上升异常,通过局地环流,抑制了其南侧印度洋的对流异常,导致无法模拟出IPOC机制对衰减年夏季WNPAC的维持作用。因此,缺乏海气耦合过程是AGCM不能模拟出东亚夏季风对前冬El Niño滞后响应的关键原因。

不同Niño指数对El Niño事件响应的差异性分析

ENSO事件是发生在热带太平洋地区、具有最强年际变化的全球尺度振荡信号。一方面直接使得赤道太平洋地区的天气出现异常,另一方面还会以遥相关的形式影响热带太平洋以外的地区,甚至影响了全球的天气变化,选取能够表明其特征的指标十分重要。不同类型El Niño事件对于各类Niño指数的响应也是不同的,东部型El Niño事件在Niño1 + 2指数、Niño3指数、Niño3.4指数的响应较为明显,在Niño4指数的响应较弱;而中部型El Niño事件相比较来说在Niño4指数和Niño3.4指数的响应较为明显,其中Niño3.4指数对于两类El Niño事件的响应表现较为突出。

东部型El Niño事件发展期秋季对中国极端降水的影响

利用1961-2020年中国地面降水逐日数据集和NCEP/NCAR逐月再分析资料,采用相关及合成分析等方法分析了东部型El Niño事件发展期秋季对同期我国极端降水的可能影响及物理过程。结果表明:东部型El Niño事件在发展期秋季对华中和华南地区的极端秋季降水有显著的影响,造成华南地区秋季极端降水增多而华中地区减少。进一步分析了造成这种影响的物理过程发现,当东部型El Niño事件处于发展期的秋季时,中东太平洋表现为显著的正海温异常,热带西太平洋及我国南海为负的海温异常。相应地,热带中东太平洋表现为异常的上升气流,西太平洋为异常的下沉气流,在华南地区表现为异常的辐合上升,为降水创造了有利的动力条件;同时,异常海温进一步对西北太平洋副热带高压(以下简称西太副高)位置产生影响,造成西太副高异常偏西,在南海到西太平洋地区表现为反气旋性环流异常,意味着西南气流的水汽输送增强,这促进了华南地区极端降水的发生。而在华中地区则表现为异常的偏北风和水汽辐散,不利于降水的生成。以上结果对认识我国东部地区的极端降水事件的物理机理提供了科学线索。

El Niño事件消亡期亚洲季风区对流层上层经向热力差异的演变

采用NOAA1979—2019年的南方涛动SOI指数、GPCC降水数据和ERA5气温数据探究El Niño事件消亡期亚洲季风区对流层上层海陆热力差异的演变特征。El Niño事件衰减期按所处季节可分为春季型、秋季型和冬季型,其中冬季型衰减对季风区对流层上层经向热力差(TIup)的贡献最大,且热力差异在冬季达到最大;春季型衰减会使伴随长江中下游夏季降水增加,秋季型衰减会使长江中下游夏季降水减少,冬季型衰减会使华北大部分地区夏季降水增多,只有华南、西南及东北部分地区降水略有减小。

Understanding the Development of the 2018/19 Central Pacific El Niño

A central Pacific(CP)El Niño event occurred in 2018/19.Previous studies have shown that different mechanisms are responsible for different subtypes of CP El Niño events(CP-I El Niño and CP-II El Niño).By comparing the evolutions of surface winds,ocean temperatures,and heat budgets of the CP-I El Niño,CP-II El Niño,and 2018/19 El Niño,it is illustrated that the subtropical westerly anomalies in the North Pacific,which led to anomalous convergence of Ekman flow and surface warming in the central equatorial Pacific,played an important role in the 2018/19 El Niño event as well as in the CP-II El Niño.Although the off-equatorial forcing played a vital role,it is found that the equatorial forcing acted as a driving(damping)term in boreal spring(summer)of the 2018/19 El Niño.The 2018/19 El Niño provides a timely and vivid example that helps illustrate the proposed mechanism of the CP El Niño,which could be leveraged to improve El Niño predictability.

Distinct Evolution of the SST Anomalies in the Far Eastern Pacific between the 1997/98 and 2015/16 Extreme El Niños

The 2015/16 El Niño displayed a distinct feature in the SST anomalies over the far eastern Pacific(FEP)compared to the 1997/98 extreme case.In contrast to the strong warm SST anomalies in the FEP in the 1997/98 event,the FEP warm SST anomalies in the 2015/16 El Niño were modest and accompanied by strong southeasterly wind anomalies in the southeastern Pacific.Exploring possible underlying causes of this distinct difference in the FEP may improve understanding of the diversity of extreme El Niños.Here,we employ observational analyses and numerical model experiments to tackle this issue.Mixed-layer heat budget analysis suggests that compared to the 1997/98 event,the modest FEP SST warming in the 2015/16 event was closely related to strong vertical upwelling,strong westward current,and enhanced surface evaporation,which were caused by the strong southeasterly wind anomalies in the southeastern Pacific.The strong southeasterly wind anomalies were initially triggered by the combined effects of warm SST anomalies in the equatorial central and eastern Pacific(CEP)and cold SST anomalies in the southeastern subtropical Pacific in the antecedent winter,and then sustained by the warm SST anomalies over the northeastern subtropical Pacific and CEP.In contrast,southeasterly wind anomalies in the 1997/98 El Niño were partly restrained by strong anomalously negative sea level pressure and northwesterlies in the northeast flank of the related anomalous cyclone in the subtropical South Pacific.In addition,the strong southeasterly wind and modest SST anomalies in the 2015/16 El Niño may also have been partly related to decadal climate variability.

The role of oceanic feedbacks in the 2014–2016 El Niño events as derived from ocean reanalysis data

Why did the predicted“super El Niño”fade out in the summer 2014 and the following year develop into one of the three strongest El Niño on record?Although some hypotheses have been proposed in previous studies,the quantitative contribution of oceanic processes to these events remains unclear.We investigated the role of various oceanic feedbacks,especially in response to intra-seasonal westerly wind busts,in the evolution of the 2014–2016 El Niño events,through a detailed heat budget analysis using high temporal resolution Estimating the Circulation and Climate of the Ocean—Phase II(ECCO2)simulation outputs and satellite-based observations.Results show that the Ekman feedback and zonal advective feedback were the two dominant oceanic processes in the developing phase of the warm event in the spring of 2014 and its decay in June.In the 2015–2016 super El Niño event,the zonal advective feedback and thermocline feedback played a signifi cant role in the eastern Pacifi c warming.Moreover,the thermocline feedback tended to weaken in the central Pacifi c where the zonal advection feedback became the dominant positive feedback.

观测分析El Niño衰减早晚对南亚与青藏高原夏季降水和气温的影响

El Niño(厄尔尼诺)事件对东亚和南亚次年夏季降水影响及其机理已经得到充分研究,但其对夏季青藏高原降水是否有显著影响还不清楚。本研究根据1950年后El Niño事件次年衰减期演变速度,对比分析衰减早型与晚型El Niño事件对南亚季风区与青藏高原夏季(6~9月)季节平均和月平均气候影响差异。结果显示在衰减早型次年夏季热带太平洋海温转为La Niña(拉尼娜)型且持续发展,引起Walker环流上升支西移,印度洋和南亚季风区上升运动加强,同时激发异常西北太平洋反气旋(NWPAC),阿拉伯海异常气旋和伊朗高原异常反气旋性环流响应,增加7~9月对流层偏南气流和印度洋水汽输送,导致南亚和高原西南侧降水偏多。衰减晚型次年6~8月热带太平洋El Niño型海温仍维持,印度洋暖异常海温显著,对应的印度洋和南亚季风区上升运动较弱,NWPAC西伸控制南亚季风区,阿拉伯海和中西亚分别呈现异常反气旋和气旋性环流,导致青藏高原西风加强,水汽输送减少,南亚北部和高原降水一致偏少。结果表明:(1)El Niño显著影响次年青藏高原西南部夏季季节和月平均降水与温度,是印度和高原西南部夏季降水显著相关的重要原因;(2)El Niño衰减快慢速度对南亚和青藏高原西南部夏季季节内降水的影响有着重要差异。

Why Does Extreme Rainfall Occur in Central China during the Summer of 2020 after a Weak El Niño?

In summer 2020,extreme rainfall occurred throughout the Yangtze River basin,Huaihe River basin,and southern Yellow River basin,which are defined here as the central China(CC)region.However,only a weak central Pacific(CP)El Niño happened during winter 2019/20,so the correlations between the El Niño–Southern Oscillation(ENSO)indices and ENSO-induced circulation anomalies were insufficient to explain this extreme precipitation event.In this study,reanalysis data and numerical experiments are employed to identify and verify the primary ENSO-related factors that cause this extreme rainfall event.During summer 2020,unusually strong anomalous southwesterlies on the northwest side of an extremely strong Northwest Pacific anticyclone anomaly(NWPAC)contributed excess moisture and convective instability to the CC region,and thus,triggered extreme precipitation in this area.The tropical Indian Ocean(TIO)has warmed in recent decades,and consequently,intensified TIO basinwide warming appears after a weak El Niño,which excites an extremely strong NWPAC via the pathway of the Indo-western Pacific Ocean capacitor(IPOC)effect.Additionally,the ENSO event of 2019/20 should be treated as a fast-decaying CP El Niño rather than a general CP El Niño,so that the circulation and precipitation anomalies in summer 2020 can be better understood.Last,the increasing trend of tropospheric temperature and moisture content in the CC region after 2000 is also conducive to producing heavy precipitation.

Variations of the Eco-Hydro-Climatic Environment Response to the 2015/2016 Super El Niño Event in the Mindanao Dome Upwelling System

A super El Niño event occurred in the equatorial Pacific during 2015-2016,accompanied by considerable regional eco-hydro-climatic variations within the Mindanao Dome(MD)upwelling system in the tropical western Pacific.Using timeseries of various oceanic data from 2013 to 2017,the variability of eco-hydro-climatic conditions response to the 2015/2016 super El Niño in the upper 300 m of the MD region are analyzed in this paper.Results showed that during the 2015/2016 super El Niño event,the upwelling in the MD region was greatly enhanced compared to those before and after this El Niño event.Upwelling Rossby waves and the massive loss of surface water in the western Pacific were suggested to be the main reasons for this enhanced upwelling.De-creased precipitation caused by changes in large-scale air-sea interaction led to the increased surface salinities.Changes in the struc-tures of the thermohaline and nutrient distribution in deep waters contributed to the increased surface chlorophyll a,suggesting a po-sitive effect of El Niño on surface carbon storage in the MD region.Based on the above analysis,the synopsis mechanism illustrating the eco-hydro-climatic changing processes over the MD upwelling system responding to the El Niño event was proposed.It high-lights the prospect for the role played by El Niño in local eco-hydro-climatic effects,which has further profound implications for understanding the influence of the global climate changes on the ocean carbon cycle.

Exploring sensitive area in the tropical Indian Ocean for El Niño prediction: implication for targeted observation

Based on initial errors of sea temperature in the tropical Indian Ocean that are most likely to induce spring predictability barrier(SPB)for the El Niño prediction,the sensitive area of sea temperature in the tropical Indian Ocean for El Niño prediction starting from January is identified using the CESM1.0.3(Community Earth System Model),a fully coupled global climate model.The sensitive area locates mainly in the subsurface of eastern Indian Ocean.The effectiveness of applying targeted observation in the sensitive area is also evaluated in an attempt to improve the El Niño prediction skill.The results of sensitivity experiments indicate that if initial errors exist only in the tropical Indian Ocean,applying targeted observation in the sensitive area in the Indian Ocean can significantly improve the El Niño prediction.In particular,for SPB-related El Niño events,when initial errors of sea temperature exist both in the tropical Indian Ocean and the Pacific Ocean,which is much closer to the realistic predictions,if targeted observations are conducted in the sensitive area of tropical Pacific,the prediction skills of SPB-related El Niño events can be improved by 20.3%in general.Moreover,if targeted observations are conducted in the sensitive area of tropical Indian Ocean in addition,the improvement of prediction skill can be increased by 25.2%.Considering the volume of sensitive area in the tropical Indian Ocean is about 1/3 of that in the tropical Pacific Ocean,the prediction skill improvement per cubic kilometer in the sensitive area of tropical Indian Ocean is competitive to that of the tropical Pacific Ocean.Additional to the sensitive area of the tropical Pacific Ocean,sensitive area of the tropical Indian Ocean is also a very effective and cost-saving area for the application of targeted observations to improve El Niño forecast skills.

El Niño事件对河流入海COD的调控机制——基于高分辨率时间序列观测的证据

入海污染物在线监测是实现海洋污染总量控制和测管协同的关键手段之一。本文基于布设在福建省九龙江口的多参数浮标,获取2014−2018年浮标盐度和荧光溶解有机质(FDOM)数据,结合人工采样测定化学需氧量(COD)浓度,建立了河口区COD浓度的快速反演模型。使用有效浓度法外推得到河端COD浓度,结合流量数据估算了高频率的COD入海通量,利用通量分解模型定量分析了COD入海通量的调控因素。结果表明:(1)COD河端有效浓度的拟合值与实测值的偏差为(10.4±8.8)%,模型能够较好地反演九龙江口河端的COD浓度;(2)在季节尺度上,受2015−2016年超强El Niño事件影响,2016年旱季的异常降雨降低了河流COD浓度,但是径流量的增加仍然显著提高了该季节COD的入海通量;(3)在年际尺度上,2015−2016年El Niño事件所引起的异常降雨事件导致2016年九龙江COD入海通量为(4.4±0.9)×10^(4) t/a,显著高于2014年、2015年及2017年的3.0×10^(4)~3.2×10^(4) t/a。上述研究结果表明,FDOM水质浮标在线监测系统,有助于实现对陆源COD入海通量及其调控因素的长期连续高频监测,可为海洋生态环境保护和管理提供重要技术支撑。