El Nino Southern Oscillation Index Research Articles

El Niño–Southern Oscillation and the Transatlantic Slave Trade

Abstract The relationship between El Niño–Southern Oscillation (ENSO) and the transatlantic slave trade (TAST) is examined using the Slave Voyages dataset and several reconstructed ENSO indices. The ENSO indices are used as a proxy for West African rainfall and temperature. In the Sahel, the El Niño (warm) phase of ENSO is associated with less rainfall and warmer temperatures, whereas the La Niña (cold) phase of ENSO is associated with more rainfall and cooler temperatures. The association between ENSO and the TAST is weak but statistically significant at a 2-yr lag. In this case, El Niño (drier and warmer) years are associated with a decrease in the export of enslaved Africans. The response of the TAST to El Niño is explained in terms of the societal response to agricultural stresses brought on by less rainfall and warmer temperatures. ENSO-induced changes to the TAST are briefly discussed in light of climate-induced movements of peoples in centuries past and the drought-induced movement of peoples in the Middle East today. Significance Statement The transatlantic slave trade was driven by economic and political forces, subject to the vagaries of the weather; it spanned two hemispheres and four continents and lasted more than 400 years. In this study we show that El Niño–Southern Oscillation, and its proxy association with West African rainfall and temperature, are significantly associated with the number of enslaved Africans that were transported from West Africa to the Americas. Lessons learned from the effects of weather and climate on the transatlantic slave trade reverberate today: extreme weather and climate change will continue to catalyze and amplify human conflict and migrations.

Link of ENSO and Southern Annular Mode as elements of Global Atmospheric Oscillation

Based on the monthly average data of NCEP/NCAR Reanalysis, using a specially developed technique, the interannual variability of the sea level pressure (SLP) anomalies associated with the Global Atmospheric Oscillation (GAO) for the period 1950-2021 was studied. The field of mean deviations of the SLP anomalies during the El Niño – Southern Oscillation (ENSO) from the interannual GAO variability has been calculated. It is shown that the variability of the SLP anomalies during the GAO contains, in addition to the mode associated with the ENSO, also the mode of interannual climatic variability associated with the Antarctic Oscillation (AAO) – the Southern Annular Mode (SAM). It was found that the GAO does not include a part of the SLP anomaly variability associated with El Niño and La Niña events of the Central Pacific type. At all other grid nodes, the field of mean deviations of the SLP anomalies in the ENSO from the GAO has SLP differences slightly different from zero and formally statistically insignificant. This means that in the interannual climatic variability of the SLP associated with the GAO, there are only two modes: ENSO and SAM. With the help of cross-wavelet analysis, the relationships between the ENSO and SAM indices were studied. Negative relationships were found between these indices on fluctuation periods of about 11 years. An analysis of the time series of these indices and the time series of total solar irradiance (TSI) made it possible to put forward a hypothesis about the influence of synchronization and desynchronization of quasi-11-year changes in solar activity and ENSO on the weakening and strengthening of negative relationships between ENSO and SAM at periods of oscillations of about 11 years.

Forecasting commodity returns by exploiting climate model forecasts of the El Niño Southern Oscillation

Abstract The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.

Vertical variation of tropical cyclone size in the western North Pacific

AbstractBased on the ERA5 reanalysis dataset, this paper examines the vertical variation of tropical cyclone (TC) size in the western North Pacific during 1998–2018. The TC size is defined as the distance from the TC centre to the radius at which the azimuthally averaged relative vorticity decreases to 1 × 10−5 s−1 (Rvor). It is found that TCs with similar sizes at the surface can be significantly different in size at higher levels. The Rvor at 10 m height (Rvor10) and that at 200 hPa (Rvor200) are related to TC intensity categories, and Rvor10 and that at 700 hPa (Rvor700) correlate linearly with the relative sea surface temperature (RSST) averaged within a 500‐km radius. Further, a positive correlation is found between the 500‐km radius averaged rain rate and Rvor200, while Rvor10, Rvor700 and Rvor200 all decrease with an increasing vertical wind shear (VWS). The spatial distributions of Rvor10, Rvor700, Rvor200 and Vmax, RSST, and rain rate are all similar to one another, and the spatial correlation coefficient between any pair of variations is larger than 0.91, which is statistically significant at the 0.01 level. The monthly variations of Rvor10, Rvor700 and Rvor200 are generally larger in the months with strong TC activity (May–October) than in the other months, although a peak of Rvor10 during March has been identified for the more recent few TCs. No significant interannual trend is found during 1998–2018 for any of the three variables (Rvor10, Rvor700 and Rvor200). Statistically significant correlations between the ENSO (El Niño–Southern Oscillation) index and Rvor10 and Rvor200 are 0.54 and 0.47, respectively, while no statistically significant correlation is found between the ENSO index and Rvor700. The mechanisms that affect the TC size at different heights are complicated and will be explored in later studies.

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.

Analysis of Relationships Between ENSO Events and Atmospheric Angular Momentum Variations

AbstractThe El Niño–Southern Oscillation (ENSO) event has a long incubation process, during which the interannual variation of length‐of‐day (LOD) and the atmospheric angular momentum (AAM) series will be quickly affected due to the interaction between sea and air. Based on the comparisons between filtered interannual LOD variation, the AAM, the Oceanic Niño Index (ONI), and the Southern Oscillation Index (SOI) for the period of January 1953 and December 2013, the relationships among them and ENSO are studied. The results demonstrate that AAM changes and SOI/ONI series have similar waveform structures and trends. Interannual LOD variations, atmospheric LOD excitations, and ENSO indices such as the SOI and ONI are well correlated as a consequence of angular momentum conservation. AAMs can be accurately modeled at present, taking ESMGFZ as an example, its sampling interval can reach 3 hr, and can realize accurate prediction in the next 90 days. Consequently, AAM series also can be used as an index for ENSO events and has its own advantages compared with SOI/ONI.

Changes of Extreme Precipitation and Possible Influence of ENSO Events in a Humid Basin in China

In this study, 11 extreme precipitation indices were selected to examine the spatiotemporal variation of extreme precipitation in the Poyang Lake Basin during 1960–2017. The responses of extreme precipitation indices to El Nino/Southern Oscillation (ENSO) events of different Pacific Ocean areas were further investigated. The results show that the temperature in the Poyang Lake Basin has increased significantly since the 1990s, and the inter-decadal precipitation fluctuated. Most extreme precipitation indices showed an increasing trend with abrupt changes occurring around 1991. Spatially, most of the extreme precipitation indices decreased from northeast to southwest. The increasing trend of most indices in the center and south of the basin was relatively prominent. The linear correlations between the extreme precipitation indices and Nino 1 + 2 were the most significant. On the timescale of 2–6 years, a common oscillation period between the extreme precipitation of the basin and the four ENSO indices can be observed. After 2010, the positive correlation between the precipitation of the Poyang Lake Basin and the SST (sea surface temperature) anomalies in the equatorial Pacific increased significantly. Additionally, annual total wet–day precipitation in most areas of the Poyang Lake Basin increased with varying degrees in warm ENSO years. The results of this study will improve the understanding of the complex background and driving mechanism of flood disasters in the Poyang Lake Basin.

Investigating links between rainfall variations in the Ogooué River basin and ENSO in the Pacific Ocean over the period 1940–1999

Abstract. This paper investigates links between rainfall variability in the Ogooué River Basin (ORB) and El Niño Southern Oscillation (ENSO) in the Pacific Ocean. Recent hydroclimatology studies of the ORB and surrounding areas resulting in contrasting conclusions about links between rainfall variability and ENSO. Thus, to make the issue clearer, this study investigates the links between ENSO and rainfall in the ORB over the period 1940–1999. The principal component analysis of monthly rainfall in the ORB was done. The temporal mode of the first component corresponds to the interannual variations of rainfall on the ORB. Also, the pattern of the spatial mode of the first component shows that the ORB is a homogeneous hydroclimatic zone. However, no leading mode is significantly correlated to the ENSO index. A cross-wavelet analysis of the time series of basin-scale rainfall and the ENSO index was therefore carried out. The result is a set of periodogram structures corresponding to some ENSO episodes recorded over the study period. And wavelet coherence analysis of both time series confirms that there are significant links between ENSO and rainfall in the ORB.

Assessment of flood discharge sensitivity to climate indexes in West Africa

Abstract. Floods are natural disasters that widely affect people and goods. Its frequency and magnitude are projected to substantially increase due to the ongoing environmental change. At regional and national levels, some efforts have been made in predicting floods at a short-term range. However, the usefulness of flood prediction increases as the time lead increases. The objective of this work is therefore to investigate flood sensitivity to climate indexes in West Africa as a basis for seasonal flood forecasting. The methodology consists of optimizing the relationship between Annual Maximal Discharge (AMD), a proxy for flood discharge and various climate indexes using correlation coefficient, linear regression and statistical modeling based on 56 river gauging stations across West Africa. The climate indexes considered are the Sea Surface Temperature (SST) of the Tropical Northern Atlantic (TNA), SST of the Tropical Southern Atlantic (TSA), the Sea Level Pressure (SLP) of the Southern Oscillation Indexes (SOI) and the detrended El-Nino Southern Oscillation indexes. It was found that SOI/SLP indexes are the most strongly related to the AMD for the investigated stations with generally high, positive, and statistically significant correlation. The TSA/SST indexes indicated both positive and negative statistically significant correlations with river discharge in the region. The percentage change in AMD per unit change in SOI/SLP for most of the statistically significant stations is within 10 % and 50 % indicating a strong relationship between these two variables. This relationship could serve as a basis for seasonal flood forecasting in the study area.

El Niño Southern Oscillation (1896 to 2016): Quantifying Effects on Winter Precipitation and Temperature in Southwest Ohio, USA

Continental-scale studies of North America suggest that the El Niño Southern Oscillation (ENSO) can cause winters to be warmer, with less precipitation, during El Niño conditions and colder, with more precipitation, during La Niña conditions in the Midwest United States. Two sources of historical records of precipitation and temperature in southwest Ohio from 1896 to 2016 were analyzed. Three statistical methodologies were used to test the hypothesis that anomalies in winter temperature and precipitation occurred in relation to ENSO phases. Eighty percent of El Niño winters had below-average winter precipitation; the average anomaly was −5 cm. Precipitation decreased with increase in El Niño strength as measured by the Multivariate ENSO Index (MEI). These results were statistically significant beyond the 95% level. However, variation in MEI only accounted for 3% of the overall variability in winter precipitation. Many of the drier winters on record, including the extrema, occurred during neutral winters. During La Niña winters precipitation was not statistically significantly different from that in neutral winters. Winter temperature was not statistically significantly different during El Niño and La Niña winters within the century of record. The results were consistent between separate analyses of data from the 2 different sources.

Improved El Niño Southern Oscillation signals extracted by principal component analysis of tree-ring oxygen isotope records from the East Asian monsoon region of China

There is a great need for the reconstruction of independent El Niño Southern Oscillation (ENSO) from different archives and locations to provide a robust understanding of its driving and feedback mechanisms on long time scales. Though tree-ring cellulose δ18O (δ18OTR) records from the East Asian monsoon (EAM) region of China have the potential to reconstruct ENSO on an annual time scale, δ18OTR ENSO signals may be masked by local climate effects since any individual δ18OTR record is influenced by both large-scale atmospheric circulation and local climate. Few attempts have been made to reduce the uncertainties of the reconstructed ENSO index by δ18OTR records from the EAM region of China by ensemble data analysis. In this study, twelve published δ18OTR records spanning over 100 years (1902–2003 CE) have been collected and processed using principal component analysis (PCA). The first principal component (PC1) extracted from the δ18OTR records shows a positive/negative relationship with the annual Niño 3.4 index/southern oscillation index (SOI), with correlation coefficients higher than those between single δ18OTR record and the Niño 3.4 index/SOI, suggesting the ENSO signal is improved by PCA. A 31-year running correlation on PC1- Niño 3.4 index/SOI reveals an increasingly stronger correlation between δ18OTR and ENSO since the last century, inferring that, in the EAM region, the δ18OTR-ENSO correlation may become much stronger under continuous global warming in the future. Spectrum and spatial correlation analysis further support the idea that PC1 responds to ENSO. The significant relationships between observational precipitation δ18O and Niño 3.4 SST anomaly and PC1 from 1973–2003 CE suggest that precipitation δ18O is a key link to connect δ18OTR with ENSO. This study shows the advantage of ensemble data analysis on δ18OTR records in EAM region of China, which could better extract ENSO signals and reduce the uncertainties of reconstructed ENSO variations by individual δ18OTR records from the EAM region of China. It provides an alternative independent annual indicator for ENSO reconstruction on long time scales when more δ18OTR records from this region are extended in the near future.

Forecasting river basin yield using information of large-scale coupled atmospheric–oceanic circulation and local outgoing longwave radiation

ABSTRACT Global and local climate parameters influence the distribution of precipitation over continents. The spatio-temporal distribution of rainfall and the depth of rainfall over a river basin influence the river basin yield. This study deals with the prediction of the river basin scale yield of the ‘Upper Bhima River basin’ from the Maharashtra State of India. The information on large-scale circulation patterns El Nino-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO) index, Multivariate ENSO Index (MEI), and local meteorological input viz. Outgoing longwave radiation (OLR) has been used to predict the river basin scale yield. The Artificial Intelligence (AI) tool – Genetic Programming (GP) – is used for developing prediction models. Ten different combinations of input variables are attempted for the development of monthly river basin yield models to arrive at the best input variable combination for the best predictions with varying lead times. Also, three combinations of input variables were tested for the prediction of ‘Seasonal Yield’. The findings of this research work indicate that GP-derived monthly River Basin Scale Yield forecasting models are successful in the prediction of yield with a correlation coefficient of 0.83. The seasonal yields could be predicted with a correlation coefficient of 0.75.

Spatiotemporal Variation in Rainfall Erosivity and Correlation with the ENSO on the Tibetan Plateau since 1971.

Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971–2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen’s slope estimator, and the Mann–Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.

Spatiotemporal dynamics of dengue in Colombia in relation to the combined effects of local climate and ENSO

Dengue virus (DENV) is an endemic disease in the hot and humid low-lands of Colombia. We characterize the association of monthly series of dengue cases with indices of El Niño/Southern Oscillation (ENSO) at the tropical Pacific and local climatic variables in Colombia during the period 2007-2017 at different temporal and spatial scales. For estimation purposes, we use lagged cross-correlations (Pearson test), cross-wavelet analysis (wavelet cross spectrum, and wavelet coherence), as well as a novel nonlinear causality method, PCMCI, that allows identifying common causal drivers and links among high dimensional simultaneous and time-lagged variables. Our results evidence the strong association of DENV cases in Colombia with ENSO indices and with local temperature and rainfall. El Niño (La Niña) phenomenon is related to an increase (decrease) of dengue cases nationally and in most regions and departments, with maximum correlations occurring at shorter time lags in the Pacific and Andes regions, closer to the Pacific Ocean. This association is mainly explained by the ENSO-driven increase in temperature and decrease in rainfall, especially in the Andes and Pacific regions. The influence of ENSO is not stationary, given the reduction of DENV cases since 2005, and that local climate variables vary in space and time, which prevents to extrapolate results from one region to another. The association between DENV and ENSO varies at national and regional scales when data are disaggregated by seasons, being stronger in DJF and weaker in SON. Overall, the Pacific and Andes regions control the relationship between dengue dynamics and ENSO at national scale. Cross-wavelet analysis indicates that the ENSO-DENV relation in Colombia exhibits a strong coherence in the 12 to 16-months frequency band, which implies the frequency locking between the annual cycle and the interannual (ENSO) timescales. Results of nonlinear causality metrics reveal the complex concomitant effects of ENSO and local climate variables, while offering new insights to develop early warning systems for DENV in Colombia.

Predicting Interannual Variability in Sea Surface Height Along the West Coast of Australia Using a Simple Ocean Model

AbstractSea surface height (SSH) along the west coast of Australia is key to local climate and is strongly forced by remote surface wind variability related to El Niño Southern Oscillation (ENSO) in the tropical Pacific Ocean. This study provides a method to predict interannual variability in SSH along the west coast of Australia using a simple 1.5‐layer dynamical ocean model forced by a statistical atmospheric model for ENSO‐related winds. The model has realistic coastlines and is driven by reanalysis surface winds regressed onto an ENSO index. The model when run in hindcast mode to predict past variability can simulate tide gauge observations at Fremantle along the west coast of Australia up to 13 months in advance, which outperforms persistence. We conclude that this methodology can be useful as a baseline for gauging the performance of more sophisticated forecast models for predicting SSH variations along the west coast of Australia.

Time‐Varying Upper Ocean Circulation and Control of Coral Bleaching in the Western Tropical Pacific

AbstractThe western tropical Pacific Ocean (WTPO) features complicated ocean circulation systems and has the warmest world open‐ocean waters. Small upper ocean temperature change there can exert significant impact on the regional coral reef ecosystems. In the past three decades, moderate to severe coral bleaching events have been observed in the WTPO surrounding Palau in 1998, 2010, 2016, 2017, and 2020. Reflecting the diversity of El Niño‐Southern Oscillation (ENSO) variability, the observed coral bleaching severity does not correspond simply to the amplitude of an ENSO index, such as Niño‐3.4. By conducting an upper ocean temperature budget, we found the time‐varying upper ocean circulation advection acted to damp the anomalous surface heat flux forcing and played critical roles in controlling the surface ocean thermal conditions around Palau. This happened either directly via the advective temperature flux convergence, or indirectly through the pre‐conditioning of upper ocean thermal structures.

Functional Data Visualization and Outlier Detection on the Anomaly of El Niño Southern Oscillation

The El Niño Southern Oscillation (ENSO) is a well-known cause of year-to-year climatic variations on Earth. Floods, droughts, and other natural disasters have been linked to the ENSO in various parts of the world. Hence, modeling the ENSO’s effects and the anomaly of the ENSO phenomenon has become a main research interest. Statistical methods, including linear and nonlinear models, have intensively been used in modeling the ENSO index. However, these models are unable to capture sufficient information on ENSO index variability, particularly on its temporal aspects. Hence, this study adopted functional data analysis theory by representing a multivariate ENSO index (MEI) as functional data in climate applications. This study included the functional principal component, which is purposefully designed to find new functions that reveal the most important type of variation in the MEI curve. Simultaneously, graphical methods were also used to visualize functional data and capture outliers that may not have been apparent from the original data plot. The findings suggest that the outliers obtained from the functional plot are then related to the El Niño and La Niña phenomena. In conclusion, the functional framework was found to be more flexible in representing the climate phenomenon as a whole.

Space- and time-varying associations between Bangladesh’s seasonal rainfall and large-scale climate oscillations

Understanding teleconnections of a region’s climate can be beneficial to seasonal outlooks and hydro-climate services. This study aims at analyzing the teleconnections of seasonal rainfall over Bangladesh with selected climate indices, including El Niño/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) indices. Rainfall data spanning from 1965 to 2017 in the seven hydrological regions are used to derive three seasonal rains, namely the pre-monsoon (March–May), monsoon (June–September), and post-monsoon (October and November) rains, for correlation- and wavelet coherence (WC)-based teleconnection analyses. Among the three seasonal rains, the post-monsoon rain shows the negative correlations, strongest with the IOD and ENSO indices. Correlations between the pre-monsoon/monsoon rain and climate indices are subject to notable spatial and temporal variations. For instance, correlations between the pre-monsoon (monsoon) rain in the South Central (South West) region and the IOD (ENSO) index shift from negative to positive after the 1980s, whereas the comprehensive negative correlations of the post-monsoon rain with the IOD and ENSO indices further enhanced from the early to recent epochs. WC analysis not only corroborates the findings of correlation analysis at shorter time scales (e.g., 1–4 years), but also reveals significant coherence at longer time scales (e.g., 8–16 years). We find that the pre-monsoon and monsoon rains experience the phase change in WC from shorter to longer scales. In contrast, the post-monsoon rain shows the consistent anti-phase WC, more dominant at the longer time scale. Both correlation and WC analyses indicate that the association patterns of the PDO mimic those of ENSO. Lastly, the analysis results of the AMO suggest quite distinct and significant association between Bangladesh’s rainfall and the Atlantic Ocean.

Evaluation of the influence of ENSO on tropical vegetation in long time series using a new indicator

With the intensification of extreme weather events in recent years, the drought, flood and fire caused by super El Niño - Southern Oscillation (ENSO) led to lot’s of vegetation destruction in multiple tropical regions. However, little is known about the influence of ENSO on tropical vegetation of long time series (especially after 2015) and the influence’s internal mechanism. Based on generating the long-term vegetation data (combined multiple satellite sensors data), a new index (Vegetation - ENSO response index, VERI) was created to evaluate the influence of ENSO on vegetation more accurately and comprehensively, which considering several factors including the correlation between ENSO index and NDVI anomaly, the change amplitude of NDVI in ENSO events and the duration time and lag time of the influence of ENSO on vegetation. The analysis indicated that tropical vegetation exhibited a fluctuating increase state with an overall increasing range of 4.46%, and the greening trends were the most significant and dramatical in the Indian Peninsula (0.0031/y), the coast of the Gulf of Guinea (0.0026/y), and the Amazon River estuary (0.0025/y). ENSO can lead to significant enhancement or degradation of vegetation in at least 10% of tropical regions, especially in East Africa (|VERI| =0.16), Southern Africa (|VERI|=0.18) and Indonesia (|VERI|=0.14). Among them, shrubs are the most widely affected areas (32.08%) due to limited water resources. ENSO's greening effect is mainly accomplished by increasing precipitation (warm/r = 0.166, cold/r = 0.249) and its degradation effect is more closely related to temperature increase (warm/r = −0.374, cold/r = −0.101). The change of VERI in different periods indicated that the ENSO's influence on vegetation has changed after 2000, which meant the improving effect of cold events on tropical vegetation was weakened, while the inhibiting effect was enhanced. These findings can help us better understand and respond to the effects of ENSO on tropical vegetation in the context of global change.

Trend and ENSO-based analysis of last spring frost and chilling in Iran

The frost and chilling phenomena are the important climatic and environmental hazards that cause a lot of damage to the agricultural sector every year. In this respect, the present study intends to highlight the possibility of attributed occurrence to large-scale atmospheric indices. In this study, the probability of last spring frost (LSF) and chilling (LSC) at a critical temperature of + 4.4 to − 3.3 °C and different phases of El Nino Southern Oscillation index (ENSO) were extracted using Weibull Distribution Function (WDF) at 18 synoptic stations in a period of 59 years from 1961 to 2019. Then, the anomaly of LSF and LSC occurrence date was investigated in different ENSO phases (EP) and intensities (EI) and finally the LSF and LSC occurrence trend and the factors affecting this trend were analyzed using Mann–Kendall method and Sen’s slope estimator. Different EP and EI were classified using Oceanic Nino Index (ONI) in Nino 3.4 region. The results showed that the occurrence date and probability of the LSF and LSC with different base and critical temperatures in EP are associated with precedence and latency over the whole period and the anomaly of LSF and LSC occurrence date fluctuates between + 17 to − 12 and + 15 to − 17 days, respectively. In addition, the frequency and anomaly of the LSF and LSC at different base temperatures in different EI have been intensified compared to its different EP; therefore, the occurrence date of LSF and LSC can be predicted based on the EP and EI. On the other hand, the trend analysis of the date of the LSF and LSC indicates its retreat towards early spring or last winter at the main studied stations, which is due to global warming. According to the results obtained in some of the studied areas, the delay of LSF and LSC is also observed, which is due to local topographic characteristics, altitude and proximity to moisture sources.