El Nino Southern Oscillation Index Research Articles

Analysis of ENSO Event Intensity Changes and Time–Frequency Characteristic Since 1875

This study investigates the characteristics and intensity of El Niño–Southern Oscillation (ENSO) events from January 1875 to December 2023, employing an advanced method for intensity determination based on various ENSO indices defined as a continuous five-month period with temperatures exceeding 0.5 °C for warm events or falling below −0.5 °C for cold events. A total of 40 warm and 41 cold events were identified, with further classification revealing seven extreme warm events and five extreme cold events. The analysis shows a positive skewness in frequency distribution, indicating a predominance of strong warm events. The primary mode of variability is found to be interannual oscillation in the 3–8 year range, with significant decadal oscillations in the 10–16 year range. This study highlights the importance of methodological rigor in evaluating ENSO dynamics, contributing to a more comprehensive understanding of climate variability and offering a reliable framework for future research.

Spatial and Temporal Characterization of Near Space Temperature and Humidity and Their Driving Influences

Near space refers to the atmospheric region 20–100 km above Earth’s surface, encompassing the stratosphere, mesosphere, and part of the thermosphere. This region is susceptible to surface and upper atmospheric disturbances, and the atmospheric temperature and humidity profiles can finely characterize its complex environment. To analyze the relationship between changes in temperature and humidity profiles and natural activities, this study utilizes 18 years of temperature and water vapor data from the TIMED/SABER and AURA/MLS instruments to investigate the variations in temperature and humidity with altitude, time, and spatial distribution. In addition, multiple linear regression analysis is used to examine the impact mechanisms of solar activity, the El Niño–Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO) on temperature and humidity. The results show that in the mid- and low-latitude regions, temperature and water vapor reach their maxima at an altitude of 50 km, with values of 265 K and 8–9 × 10⁻⁶ ppmv, respectively; the variation characteristics differ across latitudes and altitudes, with a clear annual cycle; the feedback effects of solar activity and the ENSO index on temperature and humidity in the 20–40 km atmospheric layer are significantly different. Among these factors, solar activity is the most significant influence on temperature and water vapor, with response coefficients of −0.2 to −0.16 K/sfu and 0.8 to 4 × 10⁻⁶ ppmv/sfu, respectively. Secondly, in the low-latitude stratospheric region, the temperature response to ENSO is approximately −1.5 K/MEI, while in the high-latitude region, a positive response of 3 K/MEI is observed. The response of water vapor to ENSO varies between −1 × 10⁻⁷ and −4 × 10−⁷ ppmv/sfu. In the low-latitude stratospheric region, the temperature and humidity responses to the QBO index exhibit significant differences, ranging from −1.8 to −0.6 K/10 m/s. Additionally, there are substantial differences in responses between the polar regions and the low-latitude equatorial region. Finally, a three-dimensional model coefficient was constructed to illustrate the influence of solar activity, ENSO, and QBO on temperature and humidity in the near space. The findings of this study contribute to a deeper understanding of the temperature and humidity variation characteristics in near space and provide valuable data and model references for predicting three-dimensional parameters of temperature and humidity in this region.

El Niño southern oscillation, weather patterns, and bacillary dysentery in the Yangtze River Basin, China

BackgroundIncreasingly intense weather anomalies associated with interannual climate variability patterns, like El Niño-southern oscillation (ENSO), could exacerbate the occurrence and transmission of infectious diseases. However, research in China remains limited in understanding the impacts and intermediate weather changes of ENSO on bacillary dysentery (BD). This study aimed to reveal the relationship between ENSO, weather conditions, and the incidence of BD, and to identify the potential meteorological pathways moderated by ENSO in the ENSO-BD connections.MethodsBD disease data and meteorological data, as well as ENSO index, from 2005 to 2020 were obtained for 95 cities in the Yangtze River Basin. We first established the associations between ENSO events and BD, ENSO and weather, as well as weather and BDs using two-stage statistical models. Then, we applied a causal mediation analysis to identify the specific meteorological changes in the ENSO-BD relationship.ResultsIn the Yangtze River Basin, both El Niño (IRR: 1.06, 95%CI: 1.04 ~ 1.08) and La Niña (IRR: 1.03, 95%CI: 1.02 ~ 1.05) events were found to increase the risk of BD. Variations of ENSO index were associated with changes in local weather conditions. Both the increases in regional temperatures and rainfall were associated with a higher risk of BD. In the casual mediation analyses, we identified that higher temperatures and excessive rainfall associated with La Niña and El Niño events mediated the ENSO’s effect on BD, with mediation proportions of 38.58% and 34.97%, respectively.ConclusionsLong-term climate variability, like ENSO, can affect regional weather conditions and lead to an increased risk of BD. We identified the mediating weather patterns in the relationship between ENSO and BD, which could improve targeted health interventions and establish an advanced early warning system in response to the BD epidemic.

Unravelling the Influence of ENSO and SAM Patterns on Skate Growth: The Case of the Shorttail Yellownose Skate in Patagonia, Argentina

ABSTRACTIn recent decades, the growth, physiology and distribution of many elasmobranch species have been altered as a result of environmental changes that affect prey abundance, availability and composition. Consequently, variations in nutrient input during climate events could manifest in the growth of their hard tissues. This study focuses on assessing the impact of the El Niño‐Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) on the growth of Shorttail Yellownose skate (Zearaja brevicaudata) in Patagonia, Argentina. To achieve this, vertebrae from 115 Z. brevicaudata specimens were analysed, and growth rings were dated and measured. By using cubic splines with varying flexibility, we constructed three standard chronologies. Generalized additive models (GAMs), the chronology with the best the R‐bar () and expressed population signal (EPS) were employed and values obtained from them linked to annual time series data of the Multivariate ENSO Index (MEI) and SAM, considering lags in the biological response. Surprisingly, no significant association was found with the MEI time series. However, a noteworthy positive association emerged between the chronology and the SAM time series lagged by 1 year, suggesting that SAM‐related climatic conditions could delay their transfer into the Patagonian marine ecosystem, subsequently impacting the growth of this ectothermic predator.

Chlorophyll-a and suspended matter variability in a data-scarce coastal-estuarine ecosystem

Analyzing the variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) in estuarine and coastal environments is crucial for understanding ecosystem health, guiding environmental management decisions, and evaluating climate change impacts. Satellite remote sensing offers a powerful tool for this analysis due to its extensive spatial and temporal coverage. Although several algorithms exist for complex coastal and estuarine waters, long-term datasets such as GlobColor's Ocean Color (OC5) and neural network (NN) algorithms are frequently used for robust variability analysis. This study uses the GlobColor NN algorithm to investigate the seasonal and inter-annual variability of Chl-a and TSM in a data-scare region, namely the Meghna estuary in Bangladesh and its adjacent coastal fringe. The other algorithm (i.e. OC5), while offers the longest time series, cannot be used in this region due to the high number of invalid pixels. Therefore, this study examines different environmental factors (i.e. sea surface temperature (SST), photosynthetically active radiation (PAR), rainfall, zonal (ZWC) and meridional (MWC) wind components, and ocean currents) and climatic indices (i.e., El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) to understand their influence on the seasonal and inter-annual variability of Chl-a and TSM derived from the GlobColor NN algorithm. Empirical orthogonal function analysis identifies the seasonal signal as dominant in the study region. The seasonal cycle of Chl-a is influenced by factors including MWC, TSM, SST, and rainfall. In contrast, TSM seasonal variations are primarily driven by rainfall and MWC. Post-monsoon Chl-a inter-annual fluctuations are mainly linked to TSM inter-annual variability, with secondary influences from monsoon rainfall and the winter ENSO index. Inter-annual changes in TSM are primarily associated with the winter ENSO index and monsoon rainfall. This research elucidates the primary mechanisms influencing Chl-a and TSM variability in the Meghna estuary and its adjacent coast, thus advancing the understanding of the dynamics in the study region. The information obtained through this study is valuable for scientists, policymakers, and stakeholders involved in the sustainable management of the Meghna estuary and its coastal resources, particularly in the context of climate change.

Influence of El Niño southern oscillation on precipitation variability in Northeast Thailand

This study investigates the influence of El Niño Southern Oscillation (ENSO) on monthly precipitation anomaly (PPTA) in Northeast Thailand using 30 years (1993–2022) data obtained from 27 weather stations of the Thai Meteorological Department (TMD). Pearson correlation analysis elucidates the relationship between ENSO indices and PPTA. Results reveal significant correlations between ENSO indices and PPTA, providing insights into their intricate relationship. La Niña events have a stronger relationship with PPTA than El Niño events, with notable correlations observed.•Strong La Niña phases, PPTA tends to increase across Northeast Thailand when Niño 3, 3.4, and 4 are more strongly negative -0.64, -0.50, and -0.65, respectively. These correlations provide valuable insights into the hydrological influence of ENSO in Thailand.•After the ENSO event, there appears to be a 4–5 month lag period from the impact on PPTA that shows a higher correlation.•Wavelet transform coherence (WTC) analysis dissects the temporal and frequency-specific nature of the relationship between ENSO and PPTA found during the frequency of 2–7 years. These findings underscore the importance of studying regional variations in ENSO impacts for PPTA in Northeast Thailand.

Similar teleconnection patterns of ENSO-NAO and ENSO-precipitation in Colombia: linear and non-linear relationships.

The Central-Pacific (CP) and Eastern-Pacific (EP) types of El Niño-Southern Oscillation (ENSO) and their ocean-atmosphere effect cause diverse responses in the hydroclimatological patterns of specific regions. Given the impact of ENSO diversity on the North Atlantic Oscillation (NAO), this study aimed to determine the relationship between the ENSO-NAO teleconnection and the ENSO-influenced precipitation patterns in Colombia during the December-February period. Precipitation data from 1981 to 2023, obtained from the Climate Hazards Group (CHIRPS), were analyzed using nine ENSO and NAO indices spanning from 1951 to 2023. Using Pearson's correlation and mutual information (MI) techniques, nine scenarios were devised, encompassing the CP and EP ENSO events, neutral years, and volcanic eruptions. The results suggest a shift in the direction of the ENSO-NAO relationship when distinguishing between the CP and EP events. Higher linear correlations were observed in the CP ENSO scenarios (r > 0.65) using the MEI and BEST indices, while lower correlations were observed when considering EP events along with the Niño 3 and Niño 1.2 indices. MI show difference in relationships based on the event type and the ENSO index used. Notably, an increase in the non-linear relationship was observed for the EP scenarios with respect to correlation. Both teleconnections followed a similar pattern, exhibiting a more substantial impact during CP ENSO events. This highlights the significance of investigating the impacts of ENSO on hydrometeorological variables in the context of adapting to climate change, while acknowledging the intricate diversity inherent to the ENSO phenomenon.

Linkage of El Niño-Southern Oscillation to astronomic forcing

Abstract Deciphering the causes of El Niño-Southern Oscillation (ENSO) represents one of the greatest scientific and societal challenges because ENSO impacts people’s safety, food, water, health, and economy. Traditionally, ENSO has been considered a phenomenon that is mostly influenced by the interactions between oceanic and atmospheric processes, i.e. by the internal variability in Earth’s ocean-atmosphere system. However, dendrochronological records of climate indices, ENSO among them, have statistically significant variance at periodicities related to solar activity (sunspots) and lunar precessions. Other studies indicate a modulation by the lunar nodal cycle of ocean’s mixing and its implications on water and air temperatures, steric sea levels, coastal flooding, rain, river discharge and heat transport. Those findings suggest that astronomic forces may influence ENSO. In fact, this study shows that a fit to well-established periodicities from lunar precessions, solar activity and their interactions explains 91% of the variance of an ENSO index smoothed at 5 years, and 67% of a 3 yr filtered ENSO index. Provided that the future Earth’s ocean-atmospheric system remains roughly within historical bounds under a changing climate in the next 2–3 decades, one can venture a projection into the future of 3- and 5 yr smoothed ENSO variability. Such projection is given here and might help in preparations for adaptation and mitigation measures caused by ENSO-related coastal hazards.

Impact of Enso on Indian Monsoon and Food Production

El Nino is a big ocean-atmosphere climate interaction It happens when Sea Temperatures (SST) warm up in central and eastern parts of the Equatorial Pacific. Whereas La Nina is the opposite event of it, when ocean SST cools down in those same areas. Together, El Nino and La Nina are known as the El Nino Southern Oscillation Index (O). Usually, El Nino brings warm and dry weather to southern & eastern inland regions of India, Australia, Indonesia, Philippines, Malaysia, and the central Pacific islands. The variations of Indian summer monsoon Rainfall (ISMR) are linked to changes in sea surface temperatures over the equatorial Pacific & Indian Oceans. ENSO events really affect summer rainfall in India and major droughts have faced during El Nino events. When El Nino kicks in, it often leads to weak monsoons & higher temperatures in India. This raises the chances of droughts, which could upset crop production & water supply in India. The lack of rain because of El Nino causes crop production to drop, especially short-duration Kharif crops. This situation can lead to higher inflation rates & slower GDP growth, since agriculture is such a huge part of India’s economy. Therefore, this paper provide information about how El Nino affects climate throughout various regions of India, especially regarding monsoons and growing food.

Temporal and Spatial Variations in Rainfall Erosivity on Hainan Island and the Influence of the El Niño/Southern Oscillation

Rainfall erosivity (RE), a pivotal external force driving soil erosion, is impacted by El Niño/Southern Oscillation (ENSO). Studying the spatiotemporal variations in RE and their response to ENSO is essential for regional ecological security. In this study, a daily RE model was identified as a calculation model through an evaluation of model suitability. Daily precipitation data from 1971 to 2020 from 38 meteorological stations on Hainan Island, China, were utilized to calculate the RE. The multivariate ENSO index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI) were used as the ENSO characterization indices, and the effects of ENSO on RE were investigated via cross-wavelet analysis and binary and multivariate wavelet coherence analysis. During the whole study period, the average RE of Hainan Island was 15,671.28 MJ·mm·ha−1·h−1, with a fluctuating overall upward trend. There were spatial and temporal distribution differences in RE, with temporal concentrations in summer (June–August) and a spatial pattern of decreasing from east to west. During ENSO events, the RE was greater during the El Niño period than during the La Niña period. For the ENSO characterization indices, the MEI, SOI, and ONI showed significant correlations and resonance effects with RE, but there were differences in the time of occurrence, direction of action, and intensity. In addition, the MEI and MEI–ONI affected RE individually or jointly at different time scales. This study contributes to a deeper understanding of the influence of ENSO on RE and can provide important insights for the prediction of soil erosion and the development of related coping strategies.

Nonlinear impacts of climate anomalies on oil palm productivity

Oil palm contributes to various global needs as one of the most productive oil crops, but there exist ongoing concerns regarding its yield reductions and associated environmental impacts resulting from land conversion. This is the first detailed report investigating the nonlinear threats to estate-level oil palm yields posed by El Niño Southern Oscillation (ENSO) in the equatorial Pacific Ocean, a major driver of climate variability. Using the Malaysian Palm Oil Board administrative records on monthly performances reported by oil palm estates through the e-submissions portal spanning from January 2015 to June 2023, we focused on elucidating the impacts of ENSO on fresh fruit bunch yield, oil extraction rate, and oil yield. We found that both El Niño and La Niña conditions, characterized by extreme levels of ENSO indices cumulated over lags of 0–23 months prior to harvest, were associated with statistically significant reductions in yields. Lag association patterns unveiled that production risks were linked to pre-harvest exposure to extreme ENSO indices in various time windows. Subgroup analyses further revealed that the effects were pronounced in labor-intensive estates and those lacking fertilizer investments. This study underscores the necessity for adaptation strategies in response to future climate anomalies.

Comparison of machine learning models in forecasting different ENSO types

Accurate forecasting of the El Niño Southern Oscillation (ENSO) plays a critical role in mitigating the impacts of extreme weather conditions linked to ENSO variability on ecosystems. This study evaluates the performance of six machine learning models in forecasting two ENSO types: the Central Pacific El Niño (Niño 4 index) and the East Central Pacific El Niño (Niño 3.4 index). The models analyzed include the Feed Forward Neural Network (FFNN), Long Short-term Memory (LSTM) neural network, eXtreme Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Gradient Boosting Regressor, and Support Vector Regressor, using the ENSO index lagged by six months as the predictor. The models were trained on the monthly ENSO indices from 1870 to 1992 and tested from 1993 to 2023. We also assess the relative predictability of the two ENSO types. Events were defined as when the ENSO index exceeded ±0.4. Our evaluation during the testing period reveals that for the analyzed models, the deep neural network models (LSTM and FFNN) demonstrated superior performance in forecasting ENSO at a 6-month lead time. Furthermore, all models achieved impressive all-season correlations ranging from 0.93 to 0.97 and threat score for the ENSO phases between 0.71 to 0.88 for Niño 3.4 events, and 0.72 to 0.93 for Niño 4 events. The predictability of the two ENSO types depended on the model and strength of the ENSO event. Considering both ENSO phases, La Niña events were forecasted with a higher accuracy relative to El Niño events, and all models, besides the deep learning models, notably fell short in capturing the extreme 2015/2016 Central Pacific El Niño event. These results highlight the potential of machine learning models, particularly the deep learning approaches, for skillful ENSO forecasting, by leveraging its historical data.

Maternal preconceptional and prenatal exposure to El Niño Southern Oscillation levels and child mortality: a multi-country study

El Niño Southern Oscillation (ENSO) has been shown to relate to the epidemiology of childhood infectious diseases, but evidence for whether they increase child deaths is limited. Here, we investigate the impact of mothers’ ENSO exposure during and prior to delivery on child mortality by constructing a retrospective cohort study in 38 low- and middle-income countries. We find that high levels of ENSO indices cumulated over 0–12 lagged months before delivery are associated with significant increases in risks of under-five mortality; with the hazard ratio ranging from 1.33 (95% confidence interval [CI], 1.26, 1.40) to 1.89 (95% CI, 1.78, 2.00). Child mortality risks are particularly related to maternal exposure to El Niño-like conditions in the 0th–1st and 6th–12th lagged months. The El Niño effects are larger in rural populations and those with unsafe sources of drinking water and less education. Thus, preventive interventions are particularly warranted for the socio-economically disadvantaged.

Teleconnection Between Coastal Phytoplankton Blooms Phenomenon in Western North Pacific and El Niño–Southern Oscillation by Time‐Frequency Analysis

AbstractPhytoplankton blooms in coastal marine environments are regard as a double‐edged sword, offering benefits to coastal ecosystems while also posing significant environmental challenges. The occurrence and severity of these blooms are influenced by environmental factors, with climate oscillations playing a crucial role. In this study, we employed two time‐frequency analysis methods to investigate the teleconnection between phytoplankton blooms (denoted by bloom intensity, BI) and El Niño–Southern Oscillation (ENSO) from 2003 to 2020 in eight large marine ecosystems (LMEs) of Western North Pacific which are particularly sensitive to the impacts of climate oscillations and reflect well the impacts on marine ecosystems at different latitudes. These analytical methods unveiled these time series exhibited similar periodicity and synchronization patterns, which indicated the teleconnection between ENSO and phytoplankton blooms in the Western North Pacific. The Hilbert‐Huang Transformation classified the eight LMEs into two categories based on their similarity to BI and MEI in the time‐frequency domain. Specifically, one category is similar to MEI with energy concentrated at low frequencies, while the other category exhibits significant dissimilarity with dispersed energy. Wavelet analysis uncovered a robust coherence between the ENSO and BI across all LMEs, with a 57‐month periodicity. Furthermore, phytoplankton blooms in the Western North Pacific Ocean strengthen with increasing Multivariate ENSO Index on the large spatiotemporal scale due to a decrease in sea surface temperatures. The findings will be useful as a reference for improving understanding of the teleconnection between coastal phytoplankton blooms and climate oscillations in other coastal zones.

The dynamics of the El Niño–Southern Oscillation diversity in the recharge oscillator framework

This study investigates the observed El Niño-Southern Oscillation (ENSO) dynamics for the eastern Pacific (EP) and central Pacific (CP) events in reference to the canonical ENSO (T). We use the recharge oscillator (ReOsc) model concept to describe the ENSO phase space and polar coordinate statistics, based on the interaction of sea surface temperature (T) and thermocline depth (h), for the different types of ENSO events. We further look at some important statistical characteristics, such as power spectrum and cross-correlation as essential parameters for understanding the dynamics of ENSO. The results show that the dynamics of the EP and CP events are very different from each other and from the canonical ENSO events. The canonical ENSO (T) events fit closest to the idealised ReOsc model and has the most clearly oscillating ENSO phase space, suggesting it is the most predictable ENSO index. The EP phase space evolution is similar to the canonical ENSO, but the phase transitions are less clear, suggesting less of an oscillatory nature and that the index is more focussed on extreme El Niño and discharge states. The CP phase space, in turn, does not have a clear propagation through all phases and are strongly skewed towards the La Niña state. The interaction between CP and h are much weaker, making the mode less predictable. Wind forced shallow water model simulations show that the CP winds spatial pattern do not force significant h tendencies, strongly reducing the delayed negative feedback, which is essential for the ENSO cycle.

Space-time analysis of the relationship between landslides occurrence, rainfall variability and ENSO in the Tropical Andean Mountain region in Colombia

According to studies by the Intergovernmental Panel on Climate Change (IPCC), tropical mountainous areas are experiencing increasingly unfavorable climatic conditions regarding geohazards due to a heightened occurrence of intense rainfall events. These climatic shifts contribute to heightened geological risks, notably an elevated frequency of landslides, exacerbating the challenges faced by these regions. There is an urgent need to understand and measure how rainfall variability affects geo-hydrological hazards, which remain difficult to determine and predict. The complex and non-linear space-time relationships and dynamics of rainfall, El Niño-Southern Oscillation (ENSO), and landslides in the Tropical Andean Mountain region in Colombia require an adequate analysis and understanding of their link in terms of its spatial and temporal component at different scales. By evaluation in annual, seasonal, and monthly scales, additional insights on the relationships using a wavelet spectral analysis and a space-time permutation scan statistics method using SaTScan™ are provided. In this study, a space-time and frequency analysis of landslides using a 42-year (1981–2022) rainfall and Multivariate ENSO Index v2.0 time series is presented. According to the results, landslides closely aligned with rainfall patterns, exhibiting a bimodal annual cycle. The ENSO added complexity, with La Niña years leading to more frequent landslides throughout the year and El Niño years showing concentrated occurrences in specific months. This study highlights the influence of rainfall patterns and antecedent rainfall on landslide occurrence, the impact of ENSO phases on rainfall and landslides, and the increasing trend of landslides in Colombia.

Augmented human thermal discomfort in urban centers of the Arabian Peninsula

Anthropogenic climate change has amplified human thermal discomfort in urban environments. Despite the considerable risks posed to public health, there is a lack of comprehensive research, evaluating the spatiotemporal changes in human thermal discomfort and its characteristics in hot-hyper arid regions, such as the Arabian Peninsula (AP). The current study analyzes spatiotemporal changes in human thermal discomfort categories and their characteristics in AP, using the newly developed high-resolution gridded ERA5-HEAT (Human thErmAl comforT) dataset for the period 1979–2022. In addition, the study assesses the interplay between the Universal Thermal Climate Index (UTCI) and El Niño-Southern Oscillation (ENSO) indices for the study period. The results reveal a significant increase in human thermal discomfort and its characteristics, with higher spatial variability in the AP region. The major urban centers in the southwestern, central, and southeastern parts of AP have experienced significant increases in human thermal discomfort (0.4–0.8 °C), with higher frequency and intensity of thermal stress during the study period. The temporal distribution demonstrates a linear increase in UTCI indices and their frequencies and intensities, particularly from 1998 onward, signifying a transition towards a hotter climate characterized by frequent, intense, and prolonged heat stress conditions. Moreover, the UTCI and ENSO indices exhibit a dipole pattern of correlation with a positive (negative) pattern in the southwestern (eastern parts) of AP. The study’s findings suggest that policymakers and urban planners need to prioritize public health and well-being in AP’s urban areas, especially for vulnerable groups, by implementing climate change adaptation and mitigation strategies, and carefully designing future cities to mitigate the effects of heat stress.

Quantifying the Strength of the El Niño Southern Oscillation and the Indian Ocean Dipole in Influencing the OND Rainfall Season in Tanzania

The current paper examines the strength of variability between the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) in influencing October to December (OND) rainfall over the bimodal rainfall regime of Tanzania. The empirical orthogonal function (EOF), correlation analysis, and composite analysis were used during the data analysis. The results show more rainfall distribution over the western part of the Lake Victoria basin and to the peripheral of the northern coast of the country, thus suggesting that, during the OND rainfall season, the onset starts in the western part of the Lake Victoria basin, then spreads to the rest of the areas under investigation as the season progresses. Furthermore, on the spatial scale, the findings revealed that there is a strong correlation between IOD and ENSO indices and OND rains in the northeastern highlands. Furthermore, a robust temporal correlation is revealed between the mean OND rains over the bimodal rainfall areas and IOD (r = 0.70) compared to ENSO (r = 0.62). The anomalous warming over the western Indian Ocean (positive IOD) has a faster response to OND rains over the bimodal rainfall regime of Tanzania compared to the remote influence induced by anomalous warming from the central equatorial Pacific Ocean (warm phase of ENSO). Meanwhile, dry years are associated with negative IOD and the cold phase of ENSO conditions. The findings offer valuable insights on strategies for mitigating the effects associated with extreme weather events and improving resilience in Tanzania.

Spatial and Temporal Variability Characteristics and Driving Factors of Extreme Precipitation in the Wei River Basin

As global climate change intensifies, the global atmospheric circulation process is undergoing significant changes, and the local water vapor pattern has also changed. This study takes the Wei River Basin as the research area. Firstly, an evaluation index system for extreme precipitation was established, and the time-series characteristics of the magnitude, frequency, and duration of extreme precipitation were analyzed. Statistical methods were used to analyze the non-consistency in time-series changes in extreme precipitation indicators. Using spatial heterogeneity analysis methods, the spatial variation differences in extreme precipitation in the Wei River Basin were identified. This study selected the El Niño-Southern Oscillation (ENSO) index, global land-ocean temperature index (LOTI), and land surface temperature (LST) index to quantitatively evaluate the impact of climate change on regional extreme precipitation and analyzed the correlation between temperature and extreme precipitation, identifying the key driving factors of extreme precipitation changes. The conclusions of this study are as follows: (1) The southern region of the Wei River Basin experiences more frequent and intense precipitation events, while the northern region experiences relatively few. (2) From 1981 to 2021, the intensity, frequency, and duration of precipitation events in the Wei River Basin gradually increased, with the most significant increase in extreme precipitation in the Guanzhong Plain. (3) Global climate change has an important impact on precipitation events in the Wei River Basin. The increase in the ENSO, LOTI, and LST indices may indicate an increase in the probability of drought and flood events in the Wei River Basin. The relationships between extreme precipitation and temperature present a peak structure. This conclusion is helpful to better understand the impact of climate change on extreme precipitation in the Wei River Basin and provides some support for the response to extreme meteorological events under the background of future climate change.

Spatial Patterns of Vegetation Activity Related to ENSO in Northern South America

AbstractInterannual variability of vegetation activity (i.e., photosynthesis) is strongly correlated with El Niño Southern Oscillation (ENSO). Globally, a reduction in carbon uptake by terrestrial ecosystems has been observed during the ENSO warm phase (El Niño) and the opposite during the cold phase (La Niña). However, this global perspective obscures the heterogeneous impacts of ENSO at regional scales. Particularly, ENSO has contrasting impacts on climate in northern South America (NSA) depending on the ENSO phase and geographical location, which in turn affect the activity of vegetation. Furthermore, changes of vegetation activity during multiple ENSO events are not well understood yet. In this study, we investigated the spatial and temporal differences in vegetation activity associated with ENSO variability and its three phases (El Niño, La Niña, Neutral) to identify hotspots of ENSO impacts in NSA, a region dominated by rainforest and savannas. To achieve this, we investigated time series of vegetation variables from 2001 to 2014 at moderate spatial resolution (0.0083°). Data were aggregated through dimensionality reduction analysis (i.e., Global Principal Component Analysis). The leading principal component served as a proxy of vegetation activity (VAC). We calculated the cross‐correlation between VAC and the multivariate ENSO index separately for each ENSO phase. Our results show that El Niño phase has a stronger impact on vegetation activity both in intensity and duration than La Niña phase. Moreover, seasonally dry ecoregions were more susceptible to El Niño impacts on vegetation activity. Understanding these differences is key for regional adaptation and differentiated management of ecosystems.