EI Nino-Southern Oscillation Research Articles

Effects of interaction of multiple large-scale atmospheric circulations on precipitation dynamics in China

Different scenarios of precipitation, that lead to such phenomena as droughts and floods are influenced by concurrent multiple teleconnection factors. However, the multivariate relationship between precipitation indices and teleconnection factors, including large-scale atmospheric circulations and sea surface temperature signals in China, is rarely explored. Understanding this relationship is crucial for drought early warning systems and effective response strategies. In this study, we comprehensively investigated the combined effects of multiple large-scale atmospheric circulation patterns on precipitation changes in China. Specifically, Pearson correlation analysis and Partial Wavelet Coherence (PWC) were used to identify the primary teleconnection factors influencing precipitation dynamics. Furthermore, we used the cross-wavelet method to elucidate the temporal lag and periodic relationships between multiple teleconnection factors and their interactions. Finally, the multiple wavelet coherence analysis method was used to identify the dominant two-factor and three-factor combinations shaping precipitation dynamics. This analysis facilitated the quantification and determination of interaction types and influencing pathways of teleconnection factors on precipitation dynamics, respectively. The results showed that: (1) the Atlantic Multidecadal Oscillation (AMO), EI Niño-Southern Oscillation (ENSO), East Asia Summer Monsoon (EASM), and Indian Ocean Dipole (IOD) were dominant teleconnection factors influencing Standardized Precipitation Index (SPI) dynamics; (2) significant correlation and leading or lagging relationships at different timescales generally existed for various teleconnection factors, where AMO was mainly leading the other factors with positive correlation, while ENSO and Southern Oscillation (SO) were mainly lagging behind other factors with prolonged correlations; and (3) the interactions between teleconnection factors were quantified into three types: enhancing, independent and offsetting effects. Specifically, the enhancing effect of two-factor combinations was stronger than the offsetting effect, where AMO + NAO (North Atlantic Oscillation) and AMO + AO (Atlantic Oscillation) had a larger distribution area in southern China. Conversely, the offsetting effect of three-factor combinations was more significant than that of the two-factor combinations, which was mainly distributed in northeast and northwest regions of China. This study sheds new light on the mechanisms of modulation and pathways of influencing various large-scale factors on seasonal precipitation dynamics.

Responses of annual streamflow variability to annual precipitation, extreme climate events and large-scale climate phenomena in the Qinghai-Tibet Plateau

Solar activity and large-scale climate phenomena have significant effects on extreme climate events and streamflow variability. As the roof of the world and the water tower of Asia, the Qinghai-Tibet Plateau (QTP) is highly sensitive to climate change. Therefore, it is of great significance to study the relationship between extreme hydrometeorological events of the QTP and climate change for global hydroclimate research. In this study, we analyzed the spatiotemporal variation and significant oscillation period of several hydrometeorological variables such as extreme precipitation indices (EPIs), extreme temperature indices (ETIs) and annual streamflow variability based on the observation data of hydrometeorological stations in the QTP during 1962–2019 using Sen’s slope estimator, Mann-Kendall test and continuous wavelet analysis (CWT). And the teleconnection patterns and the lead-lag relationship between annual precipitation, extreme climate events, multiple nonstationary climate mode and annual streamflow were evaluated using wavelet coherence (WTC). The results show that the climate of the QTP has been wetter and warmer in the past 58 years. The annual streamflow of several natural rivers in the QTP mainly show an increasing trend over the last 60 years. The annual streamflow of natural river (except Yellow River) increases significantly in three-rivers headwater region, and the rivers in the northeast and northwest also show a significant increase trend, but there is no significant change in the natural river streamflow in the south of QTP. In addition, the annual streamflow of natural rivers had significant periodicities in the bands of 2–4 years, 4–7 years and 7–11 years. The significant coherence of WTC spectra between annual precipitation and streamflow indicate that precipitation is the main reason for annual streamflow variation in the QTP. The variation of extreme climate events significantly regulates the regional annual streamflow. All EPIs except consecutive dry days show an in-phase correlation with annual streamflow of most headwater. Annual streamflow of Naijin River is positively and negatively correlated with extreme warm and extreme cold, respectively, and has a clearer lead-lag relationship than that with EPIs. Annual streamflow of headwater has higher correlations with EI Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) than with sunspot number (SSN). Solar activity may first affect ENSO and PDO, and then this effect is transferred sequentially to the hydrological cycle of the QTP by SSN-modulated PDO and PDO-SSN co-modulated ENSO. It is of great significance to investigate the response of streamflow variation to annual precipitation, extreme climate events and large-scale climate phenomena for water resources management, flood control, ecosystem restoration, and the well-being of surrounding residents and sustainable development in the QTP.

Teleconnections of Atmospheric Circulations to Meteorological Drought in the Lancang-Mekong River Basin

The Lancang-Mekong River Basin (LMRB) is one of the major transboundary basins globally, facing ongoing challenges due to flood and drought disasters. Particularly in the past two decades, the basin has experienced an increased frequency of meteorological drought events, posing serious threats to the local socio-economic structures and ecological systems. Thus, this study aimed to analyze the meteorological drought characteristics in the LMRB and identify the impact and correlation of atmospheric circulation on the meteorological drought in the basin. Specifically, the different levels of meteorological drought events were defined using the Run Theory based on the seasonal and annual SPEI from 1980 to 2018. The time lag correlation between meteorological drought events and the EI Nino-Southern Oscillation (ENSO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO), were analyzed in the LMRB. Our results indicated that, from a temporal perspective, the period from November to April of the following year was particularly prone to meteorological droughts in the basin. In terms of spatial distribution, the primary agricultural regions within the basin, including Thailand, Eastern Cambodia, and Vietnam, were highly susceptible to meteorological droughts. Further analysis revealed a teleconnection between drought events in the LMRB and atmospheric circulation factors. The sensitivity of the basin’s drought timing to its response decreased in the order of the ENSO > AO > NAO > PDO. In general, the ENSO had the most substantial influence on drought events in the basin, with the strongest response relationship, while the upper reaches of the basin displayed the most significant response to the AO; the occurrence and progression of meteorological droughts in this area synchronized with the AO. These findings enhance our understanding of drought-prone areas in the LMRB, including the meteorological factors and driving mechanisms involved. This information is valuable for effectively mitigating and managing drought risks in the region.

Monthly Precipitation Reconstruction in Subtropical South America Using Seasonal Tree‐Ring Oxygen Isotopes

AbstractThe South American Summer Monsoon (SASM) is one of the most important climate systems in the tropical and subtropical regions of South America, and transports Atlantic moisture to the South American continent. However, there are few high‐resolution tree ring based studies of the SASM due to the difficulty of crossdating and the lack of dependable moisture proxies based on tree‐ring width. In this study, we use intra‐annual tree‐ring oxygen isotope (δ18OTR) data (E.V.A 03w, E.V.Ar11e, E.V.Ar12e; the full name of sample code is Erval Velho ‐ Araucaria) for crossdating, and to extract climate signals and reconstruct monthly precipitation. Our data are from Araucaria angustifolia in southern Brazil, and each annual tree‐ring comprises four parts (parts 1, 2, 3, 4) during 1960–2016. The intra‐annual δ18OTR data show pronounced seasonal variations with a decreasing trend in the growing season. The intra‐annual δ18OTR data record variations in precipitation δ18O values, and the interannual δ18OTR data record a precipitation amount. The δ18OTR values of parts 1 and 2 of each ring show the strongest correlation with precipitation at the beginning of the SASM (i.e., December). δ18OTR values of Part 3 have the strongest correlation with precipitation during January–February, and those of Part 4 are strongly correlated with precipitation during February–June. The seasonal δ18OTR values can be used to reconstruct monthly precipitation in the growing season. Spatial correlations between monthly precipitation reconstruction and global sea surface temperatures reveal that EI Niño‐Southern Oscillation (ENSO) regulate seasonal precipitation and affect the δ18OTR values in southern Brazil.

Transformer for EI Niño-Southern Oscillation Prediction

Accurate prediction of EI Niño-southern oscillation (ENSO) is of great significance to seasonal climate forecast. Recently, a convolutional neural network (CNN) has shown an optimal skill for ENSO prediction. However, it is difficult for the convolutional kernel to capture long-range precursors of ENSO due to its build-in local property. The transformer model has long been used in natural language processing (NLP) for its ability to focus on global features. Here, we introduce it to the ENSO research community and propose the ENSO transformer (ENSOTR). We show that using the ENSOTR model, the monthly average Niño3.4 index can be skillfully predicted up to one and a half years ahead. The model can also predict strong EI Niño cases more than a year ahead, such as 1997–1998. Experimental results show that our model achieves better skill than CNN for ENSO prediction.

Deep Residual Convolutional Neural Network Combining Dropout and Transfer Learning for ENSO Forecasting

AbstractTo improve EI Niño‐Southern Oscillation (ENSO) amplitude and type forecast, we propose a model based on a deep residual convolutional neural network with few parameters. We leverage dropout and transfer learning to overcome the challenge of insufficient data in model training process. By applying the dropout technique, the model effectively predicts the Niño3.4 Index at a lead time of 20 months during the 1984–2017 evaluation period, which is three more months than that by the existing optimal model. Moreover, with homogeneous transfer learning this model precisely predicts the Oceanic Niño Index up to 18 months in advance. Using heterogeneous transfer learning this model achieved 83.3% accuracy for forecasting the 12‐month‐lead EI Niño type. These results suggest that our proposed model can enhance the ENSO prediction performance.

Influence of solar activity and EI Niño-Southern Oscillation on precipitation extremes, streamflow variability and flooding events in an arid-semiarid region of China

Solar activity including sunspot number (SSN) and EI Niño-Southern Oscillation (ENSO) affect both surface and subsurface hydrological processes in arid-semiarid regions of the world, thus evaluating multi-scale hydrological response to global climate changes can provide valuable information on regional water resources management and ecological restoration. In this study, we utilize wavelet coherence together with continuous wavelet transform to analyze the influence of SSN and ENSO on multiple interacting hydrological processes and distinguish nonstationary patterns in annual precipitation and extreme precipitation events and their effects on short-term or long-term streamflow variations and periodic flooding in Xinjiang, an arid-semiarid region of China. The results indicate that the climate in Xinjiang has become wetter during the period 1960–2019. Extreme precipitation events in Xinjiang have significant oscillation periods at the 2–4 years band, and stations with strong extreme precipitation events are located in northern Xinjiang and Tianshan mountain area, while stations with weak extreme precipitation events are found in southern Xinjiang. The annual precipitation has significant multiple-periodicities at the 2–7-year band, at the 7–11-year band, and at the 13–15-year band. Furthermore, annual streamflow data for nine natural rivers all have significant dominant periodicities at inter-annual scale of 2–4-year and 4–7-year band, and monthly streamflow data for Yarkand river has significant periodicities at intra-annual scale of 3–7-month and 8–14-month band. Since evolution of annual streamflow variability and monthly streamflow variability all show direct response to precipitation features, increasing extreme precipitation events significantly affect streamflow fluctuation of natural rivers in Xinjiang. Overall, SSN and ENSO is viewed as one of the main drivers for annual precipitation or extreme precipitation periodic variation by either enhancing or suppressing convection, and the SSN and ENSO-modulated precipitation anomaly contributes to significant streamflow variation or regional flooding disasters. Investigating the detailed linkage between interannual to multidecadal climate variability and hydrometeorological variables will help decision-makers and scientists to better understand variability in regional hydrological processes, which has great implications for water resources planning and operations in Xinjiang under future climate uncertainty.

The Influence of ENSO and MJO on Drought in Different Ecological Geographic Regions in China

Mastering the spatial and temporal differences of ENSO (EI Niño-Southern Oscillation) and MJO (Madden–Julian Oscillation) and their influence on drought is very important for accurately monitoring and forecasting drought. In this study, spatiotemporal characteristics and variability of the impact of ENSO and MJO on drought were analyzed from the perspectives of meteorological drought and agricultural drought through temporal and spatial correlation analyses of China’s 48 eco-geographical regions. The results show a strong correlation between drought and ENSO and MJO in general. The spatial correlation coefficients are different, and the response of extreme events varies in different regions. The influence of ENSO and MJO on agricultural drought is higher than that on meteorological drought. ENSO and MJO have a considerable influence on agricultural drought in regions such as the Qinghai-Tibet Plateau and Xinjiang, with the highest correlation coefficient of 0.72. A significant influence of ENSO and MJO on meteorological drought was found in the Jiangnan region with the highest correlation coefficient of 0.40. In addition, agricultural drought shows a significant time lag in response to ENSO events. When the lag time is six months, the time series presents the highest correlation coefficient with the mean value of the correlation coefficient reaching 0.38 and the maximum value reaching 0.75. This research is of great significance for understanding the spatiotemporal correlation between climate patterns and drought on a large regional scale and it provides further insights into the teleconnection mechanisms of drought.

Using GRACE Data to Study the Impact of Snow and Rainfall on Terrestrial Water Storage in Northeast China

Water resources are important for agricultural, industrial, and urban development. In this paper, we analyzed the influence of rainfall and snowfall on variations in terrestrial water storage (TWS) in Northeast China from Gravity Recovery and Climate Experiment (GRACE) gravity satellite data, GlobSnow snow water equivalent product, and ERA5-land monthly total precipitation, snowfall, and snow depth data. This study revealed the main composition and variation characteristics of TWS in Northeast China. We found that GRACE provided an effective method for monitoring large areas of stable seasonal snow cover and variations in TWS in Northeast China at both seasonal and interannual scales. On the seasonal scale, although summer rainfall was 10 times greater than winter snowfall, the terrestrial water storage in Northeast China peaked in winter, and summer rainfall brought about only a sub-peak, 1 month later than the maximum rainfall. On the interannual scale, TWS in Northeast China was controlled by rainfall. The correlation analysis results revealed that the annual fluctuations of TWS and rainfall in Northeast China appear to be influenced by ENSO (EI Niño–Southern Oscillation) events with a lag of 2–3 years. In addition, this study proposed a reconstruction model for the interannual variation in TWS in Northeast China from 2003 to 2016 on the basis of the contemporary terrestrial water storage and rainfall data.

Reconstruction of the March-April average maximum air temperature over 165 years based on Pinus tabuliformis tree-rings of Zhen'an County, Shaanxi Province, China

We used tree rings of Pinus tabuliformis sampled in the Muwang National Forest Park to establish a standardized chronology (STD) and calculated the correlation coefficients between the standardized chronology and climatic factors of Zhen'an meteorological station. With linear regression analysis, we reconstructed the March-April mean maximum temperature of Zhen'an over 165 years from 1853 to 2017. The highest correlation coefficient was observed between the standardized chronology and the March-April mean maximum temperature (r=0.596, n=60, P＜0.01). The variance interpretation of the March-April mean maximum temperature reconstruction function was 33.2%, and the reconstruction function and results were credible and reliable. Warm years occurred 25 times and cold years occurred 29 times in the reconstruction sequence. The warm years were more accompanied by flood events, while the cold years were accompanied by more drought events. Temperature fluctuated obviously in the reconstruction sequence, with two cold periods (1902-1917 and 1953-2000) and four warm periods (1868-1892, 1917-1937, 1941-1953 and 2001-2012). The obvious periodic variations of 2-7, 8-15, 18-28, 75-96, and 100-125 years were found in the reconstruction sequence, in which the quasi-113, 88 and 22 years were the first, second and third main periods, respectively. These variations might potentially be the fingerprints of some climate change forces such as solar activity, monsoon and EI Niño-Southern Oscillation (ENSO) activity.

Correlations between hydrological drought and climate indices with respect to the impact of a large reservoir

With the construction of numerous water conservation projects, an assessment of the correlation between the evolution of hydrological drought and climate indices under in the context of large reservoirs helps to elucidate the response of the global environment to variations in hydrological drought. This study is focused on the Jinjiang River basin in southeastern China, where the Shilong hydrological station is affected by a large reservoir, and the Anxi hydrological station is not. Based on monthly streamflow data from 1960 to 2010, the Standardized Streamflow Index (SSI) was used to identify hydrological droughts. Pearson correlation coefficients (PCC) and the cross-wavelet transform (XWT) method were used to reveal detailed correlations between the SSI and climate indices. The correlations detected for both stations were then compared. The results indicate the following: (1) the correlations between the SSI and both ENSO (EI Nino-Southern Oscillation) and PDO (Pacific Decadal Oscillation) are more obvious than those of the other two climate indices, NAO (North Atlantic Oscillation) and AMO (Multi-decadal Oscillation). Moreover, the effect of EI Nino was more notable than that of La Nino. (2) Apparent intra-annual (12–48 months) and inter-decadal (128–256 months) common oscillation cycles between the SSI and ENSO were detected. Furthermore, there is an unequal lag time (1.47–1.80 months) between the coherence cycles of the SSI with ENSO. (3) The regulation functions of the large reservoir with respect to streamflow changed the correlations between the SSI and climate indices. The correlations at Shilong were weaker (stronger) than those at Anxi in the dry (wet) season.

Exploring the Sensitivity of Subtropical Stand Aboveground Productivity to Local and Regional Climate Signals in South China

Subtropical forest productivity is significantly affected by both natural disturbances (local and regional climate changes) and anthropogenic activities (harvesting and planting). Monthly measures of forest aboveground productivity from natural forests (primary and secondary forests) and plantations (mixed and single-species forests) were developed to explore the sensitivity of subtropical mountain productivity to the fluctuating characteristics of climate change in South China, spanning the 35-year period from 1981 to 2015. Statistical analysis showed that climate regulation differed across different forest types. The monthly average maximum temperature, precipitation, and streamflow were positively correlated with primary and mixed-forest aboveground net primary productivity (ANPP) and its components: Wood productivity (WP) and canopy productivity (CP). However, the monthly average maximum temperature, precipitation, and streamflow were negatively correlated with secondary and single-species forest ANPP and its components. The number of dry days and minimum temperature were positively associated with secondary and single-species forest productivity, but inversely associated with primary and mixed forest productivity. The multivariate ENSO (EI Niño-Southern Oscillation) index (MEI), computed based on sea level pressure, surface temperature, surface air temperature, and cloudiness over the tropical Pacific Ocean, was significantly correlated with local monthly maximum and minimum temperatures (Tmax and Tmin), precipitation (PRE), streamflow (FLO), and the number of dry days (DD), as well as the monthly means of primary and mixed forest aboveground productivity. In particular, the mean maximum temperature increased by 2.5, 0.9, 6.5, and 0.9 °C, and the total forest aboveground productivity decreased by an average of 5.7%, 3.0%, 2.4%, and 7.8% in response to the increased extreme high temperatures and drought events during the 1986/1988, 1997/1998, 2006/2007, and 2009/2010 EI Niño periods, respectively. Subsequently, the total aboveground productivity values increased by an average of 1.1%, 3.0%, 0.3%, and 8.6% because of lagged effects after the wet La Niña periods. The main conclusions of this study demonstrated that the influence of local and regional climatic fluctuations on subtropical forest productivity significantly differed across different forests, and community position and plant diversity differences among different forest types may prevent the uniform response of subtropical mountain aboveground productivity to regional climate anomalies. Therefore, these findings may be useful for forecasting climate-induced variation in forest aboveground productivity as well as for selecting tree species for planting in reforestation practices.

The correlation between GNSS-derived precipitable water vapor and sea surface temperature and its responses to El Niño–Southern Oscillation

EI Niño–Southern Oscillation (ENSO) is a complex ocean-atmosphere interaction phenomenon occurring in nature that has a profound impact on global atmospheric circulation. As ENSO is a coupled ocean-atmosphere phenomenon, in addition to the commonly used sea surface temperature (SST), water vapor in the atmosphere can be used to monitor the evolution of ENSO and to investigate its consequences (e.g., droughts and flooding). The Global Navigation Satellite System (GNSS), in addition to its applications for positioning, timing, and navigation, is another established atmospheric observing system used to remotely sense precipitable water vapor (PWV) in the atmosphere. The accuracy of the GNSS-derived PWV measurements was assessed from 12 stations based on observations made at co-located radiosonde stations as a reference. The results show that mean values of the root-mean-square error (RMSE) and biases of 6-hourly GNSS-derived PWV derived from all 12 stations are valued at 1.48 mm and −0.30 mm, respectively. Regarding monthly means, mean values of the RMSE and biases of the GNSS-derived PWV are valued at 0.66 mm and −0.23 mm, respectively. The variability in PWV estimated from 56 GNSS stations positioned close to the sea indicates that it is significantly affected by ENSO events. Generally, a 1-K increase in SST will lead to an 11% increase in PWV across all of the stations. A case study conducted at the TOW2 station in Australia shows that the non-linear trend of the PWV depicts the evolution of two severe flood events and one severe drought event occurring in this region. Comparative results derived from TOW2 and from another 24 stations show a good agreement between PWV and total precipitation. These results suggest that GNSS-derived PWV together with other climatic variables (e.g., SST) can be used as an indication of the evolution of ENSO events and as a possible indicator of drought and flood occurrence.

Pollen record of the mid- to late-Holocene centennial climate change on the East coast of South Korea and its influential factors

To understand historical climate change in western Pacific coastal areas, a sediment core (SOJ-2) from the stable sedimentary environment of the Songjiho Lagoon on the east coast of South Korea was obtained for centennial-resolution palynological analysis. The ages of the SOJ-2 core is well controlled by carbon 14 dating with high-resolution accelerator mass spectrometry (AMS), and the results indicated a general warm to cold climate trend from the mid-Holocene to the present, which can be divided into two different stages: a warmer stage between 6842 and 1297 cal yr BP and a colder stage from 1297 cal yr BP to the present, with fluctuations during these stages. The climate was wetter from 6842 to 6227 cal yr BP and 4520 to 1297 cal yr BP and was drier from 6227 to 4520 cal yr BP. The climate changed to cold and dry during the period from 1297–425 cal yr BP. The impact of human activity on the climate began at approximately 1297 cal yr BP and became pronounced starting in 425 cal yr BP. The general cooling trend may represent a response to decreasing solar insolation; however, the relative dryness or wetness of the climate may have been co-determined by westerlies and the East Asian summer monsoon (EASM). The climate had a teleconnection with the North Atlantic region, resulting from changes in solar activity. Nevertheless, EI Niño–Southern Oscillation (ENSO) activity played an important role in impacting the EASM changes in western Pacific coastal areas.

Detection of Antarctic oscillation signals in Earth’s oblateness variations

Variations of Earth’s oblateness (J 2) reflect a large scale mass redistribution within the Earth system. The climate effect causing J 2 interannual variations is still not clear, though previous studies indicated it may be related to EI Nino–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). However, we have a new discovery of the significant Antarctic oscillation (AAO) signals in J 2 interannual variations, especially on 4–6 year scales based on cross wavelet and wavelet coherence analysis with 95% confidence test during 1979–2012. The results additionally indicate that the close phase relationship between J 2 and AAO (AAO leading J 2 variations by 3 ± 2 months in phase) is far superior to that between J 2 and ENSO/PDO on 4–6 year scales. In this work, we discuss, for the first time, a possible geophysical mechanism of AAO effecting J 2 variations. The investigations are based on the definition of AAO and its spatial–temporal behavior influencing the large-scale mass movement. Finally, an approximate quantitative estimate of the AAO imprint on J 2 with an emphasis on the atmospheric contribution is made.

Local temperature and El Niño Southern Oscillation influence migration phenology of East Asian migratory waterbirds wintering in Poyang, China.

Temperature is a critical factor influencing avian phenology, due to its direct impact on food and water availability. Most previous studies have focused on the timing of spring migration and the arrival of birds at breeding grounds along the European and American flyways; little is known about migration ecology at the wintering sites along the Asian flyways. Using linear regression models, this study investigates how local temperature variation and EI Niño Southern Oscillation (ENSO) influences the arrival and departure timing of 9 waterbird species breeding in Mongolia or Siberia and overwintering in Poyang, China from 2002 to 2013. Birds mainly arrive at Poyang in October and depart for their breeding sites in March. Out of the 9 species, 6 show a strong negative relationship between departure time and overwintering temperature in Poyang. Departure dates also show a negative association with overwintering ENSO and March ENSO for two species. Both local and large-scale climate indices show no influence on the arrival timing of waterbirds. We suggest that birds react to the annual variation of overwintering temperature: an earlier departure of waterbirds is facilitated by a warmer overwintering period and vice versa. The long-term accumulated temperature effect is more pronounced than ENSO and the short-term local temperature effect. Our findings could help quantify the potential impact of global warming on waterbirds.

Numerical Analysis of the Mixed 4th-Order Runge-Kutta Scheme of Conditional Nonlinear Optimal Perturbation Approach for the EI Niño-Southern Oscillation Model

AbstractIn this paper, we proposes and analyzes the mixed 4th-order Runge-Kutta scheme of conditional nonlinear perturbation (CNOP) approach for the EI Niño-Southern Oscillation (ENSO) model. This method consists of solving the ENSO model by using a mixed 4th-order Runge-Kutta method. Convergence, the local and global truncation error of this mixed 4th-order Runge-Kutta method are proved. Furthermore, optimal control problem is developed and the gradient of the cost function is determined.

Interference of natural and anthropogenic forcings on variations in continental freshwater discharge from the Red River (Vietnam) to sea

Continental freshwater discharge is vital to global human development. Intensive studies have been conducted on changes of river discharge around the world (Dai et al., 2009; Milliman and Farnsworth, 2011). However, little work has been carried out on the daily continental freshwater discharge of the Red River in response to influence of the natural and anthropogenic forcings. This paper presents results of change analysis of daily continental freshwater discharge over the period 1956–2009 in the Red River. The wavelet analysis results showed observable periodicities in the continental freshwater discharge series with 4–6 months, 8–12 months, 1.5–2 years, and 4–7 years (48–80 months), respectively. The results from Mann–Kendall test indicated insignificant trends in annual continental freshwater discharge during the study period, but there is clearly decrease in annual suspended particulate matter of the Red River. It is shown that the continental freshwater discharge from the Red River was dominated by precipitation over the catchments. Moreover, changes in the continental freshwater discharge are closely related to SASM (the South Asia Summer Monsoon) and ENSO (EI Niño Southern Oscillation). Variations in daily discharge are lagged behind SASM 13–29 days. It revealed that El Niño and La Niña events are likely to induce extreme low and extreme high continental freshwater discharge from the Red River into the Tokin Gulf, respectively. Meanwhile, continental freshwater discharge from the Red River into the Tonkin Gulf experienced abrupt changes with present characteristics as ‘no flood in the flood season, no drought in the drought season’ due to water regulation induced by dam.

地球动力学扁率与南极涛动年际变化的小波相干分析

Variations in the Earths dynamic oblateness ( J 2) reflect large-scale interactions and dynamic changes in global layers. Previous research on the correlation between 4-6 year variations in J 2 and changes in global atmosphere, marine activity, and surface water, indicates that dominant interannual signals in J 2 arise from latitudes higher than 75°, and that variations in J 2 may be related to the ENSO (EI Nino-Southern Oscillation) phenomenon. This study, for the first time, uses a cross wavelet and wavelet coherence method to analyze the correlation between interannual variability in J 2 and the Antarctic Oscillation Index (AAOI)/Southern Oscillation Index (SOI), representing the ENSO phenomenon. The results demonstrate a strong correlation between J 2 signals and the Antarctic Oscillation (AAO) at 4-6 year time scales during 1986-2010 (correlation coefficient larger than 0.8), and show that J 2 signals lag the AAO by about 0-5 months. However, there is no significant or better correlation between variability in J 2 and other SOI signals over the same time period and scales, which may be related to differences in large scale air movement and its spatial distribution characteristics, that are reflected by the AAO and the SOI, respectively.

Perturbation method of studying the EI Niño oscillation with two parameters by using the delay sea-air oscillator model

The EI Niño-southern oscillation (ENSO) is an interannual phenomenon involved in tropical Pacific ocean-atmosphere interactions. In this paper, we develop an asymptotic method of solving the nonlinear equation using the ENSO model. Based on a class of the oscillator of the ENSO model, a approximate solution of the corresponding problem is studied employing the perturbation method.