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This paper presents new data derived from pollen, starch and radiocarbon samples that were collected from six Aboriginal earth (oven) mounds and middens on the Calperum and Pike floodplains, Murray-Darling Basin (MDB), South Australia (SA). Analyses of these samples were undertaken in order to enhance our understanding about the Holocene lifeways of Aboriginal people living in this region. The results from these analyses, combined with published data about the mounds' contents, relevant ethno-historical information and climate data, allow us to infer that Aboriginal people adopted, in this region, an innovative food production system about 3800 years ago. Further, we argue that the timing of the change suggests this was in response to adverse El Niño Southern Oscillation (ENSO) related weather patterns and consequently fluctuations in both terrestrial and aquatic food resources. [ABSTRACT FROM AUTHOR]

Recently, the North Tropical Atlantic (NTA) Sea Surface Temperature (SST) anomalies emerge as a key-driver in the biennial transitions of El Niño Southern Oscillation (ENSO) and by extension of the whole ENSO-Indian Summer Monsoon (ISM) system. In this context, we utilized a suite of Coupled Model Intercomparison Project Phase 6 (CMIP6) models with the Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) and historical simulations to investigate whether the ENSO-ISM teleconnections as well as its biennial signature undergoes significant modulations in the future warming climate as mediated through the NTA SSTs. Our results reveal a pronounced increase in NTA variability under greenhouse warming, associated with an enhanced two-way teleconnection between NTA and ENSO, while the increase of ENSO variability is more modest. There is an exaggerated signature for the previous ENSO SSTs impacting boreal spring NTA SSTs, compared to a modest enhancement in NTA forcing on the following ENSO state. However, intriguingly, this later signature of NTA damping the ENSO variability seems to strengthen steadily from the historical simulation to the SSP5-8.5, implying an enhanced NTA forcing and biennial rhythm in future projections. In consonance with this emerging NTA signal, there is a significant increase in the variability of ISM rainfall by 21st century, together with a modest strengthening of the ENSO-ISM relationships in the future warming scenarios. We also noted consistent future strengthening of a biennial signature in the ENSO-ISM teleconnection. It is further inferred that the Pacific equatorial zonal SST gradient in conjunction with the NTA relative warming act as important sources for the future intensification of this biennial signal in ENSO variability and for the inter-model spread in the projections. In contrast to this, the future intensification in ISM rainfall variability and its biennial signature are not uniquely driven by these factors. [ABSTRACT FROM AUTHOR]

The relationship between the South Asian summer monsoon (SASM) and El Niño Southern Oscillation (ENSO) is crucial for understanding and predicting changes of monsoon rainfall, as water resources strongly affect the living of billions of populations in South Asia. Although many mechanisms have been proposed to understand variations of SASM-ENSO relationship in previous studies, the impact of internal variability and primary control factor are under debate. Here, the potential contribution of internal variability to SASM-ENSO relationship is analyzed by using large-ensemble historical simulations in which members only differ for initial conditions, from two coupled climate models FGOALS-g3 and CESM2. While both ensembles show reasonable skills in monsoon climatology and basic ENSO characteristics, there is considerable spread of SASM-ENSO correlation coefficient across ensemble members: −0.57 to -0.01 (−0.66 to −0.25, range between 5 and 95%) in FGOALS and −0.74 to −0.36 (−0.83 to −0.51) in CESM2 for precipitation (circulation). We find that ENSO intensity is the primary factor significantly influencing the diversity of the SASM-ENSO relationship by modulating the Walker circulation, which is associated with the spread in mean state of tropical Pacific sea surface temperature zonal gradient. The ENSO intensity can explain about 8% and 20% of the spread in the SASM-ENSO relationship in the FGOALS and CESM2 large ensembles, respectively. Taking the mean of 110 and 100 members as a referenced truth, approximately 75 and 60 members are necessary to accurately estimate the SASM-ENSO relationship for FGOALS and CESM2, respectively, affirming the importance of sufficiently large ensemble sizes to reasonably address the interannual SASM-ENSO relationship. [ABSTRACT FROM AUTHOR]

Using a new measure that relates tropical cyclone (TC)-induced sea surface cooling with the strength of TCs, interannual variations in potential impacts of the upper ocean stratification on TC-induced sea surface cooling associated with the evolution of El Niño/Southern Oscillation (ENSO) are investigated in the northwestern Pacific using an ocean reanalysis product, with a special focus on haline effects. It is found that the haline stratification could suppress the sea surface cooling by as much as 20% to the south of 20°N in the peak typhoon season (July-October), and this contribution is different between their developing years (September-October) and decaying years (July-August). More specifically, the haline effects may vary up to 25% (40%) during the decaying years of El Niño (La Niña). Due to anomalous haline effects, the region to the west of 160°E is susceptible to the sea surface cooling during the developing and decaying years of El Niño, while the cooling could be suppressed in this region during the decaying years of La Niña. Although the effects of haline stratification have been found less important than those of thermal stratification, potential impacts of the upper ocean salinity on TC-induced sea surface cooling associated with the ENSO have been quantitatively estimated for the first time. Since the main focus of this paper is to present the new measure and discuss potential impacts of the upper ocean salinity stratification, further verifications need to be conducted once more observational data is accumulated or through numerical simulations. [ABSTRACT FROM AUTHOR]

In this study, the relationship between large-scale climatic drivers and streamflow of the Chalakudy River Basin, Kerala, was analysed using methods such as bivariate wavelet coherence (BWC), multiple wavelet coherence (MWC), and partial wavelet coherence (PWC) analysis. The four prominent global climate indices chosen are the Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO), along with other local climate drivers. The BWC analysis showed that streamflow and rainfall had a very strong in-phase relationship, whereas the maximum temperature and average temperature showed an anti-phase relationship. In the case of global climate drivers, ENSO has a significant impact on the streamflow of the Chalakudy River Basin. The average wavelet coherence (AWC), which quantifies the teleconnections, confirms the observation with a high coherency of 0.75 between streamflow and rainfall and 0.51 for streamflow and ENSO. Streamflow prediction models were developed using random forest (RF) and artificial neural network (ANN) techniques by considering the influence of significant global and local climatic drivers. It was observed that the RF model performed slightly better than the ANN model, with R = 0.875, NSE = 0.766, RMSE = 23.468, and RSR = 0.524. [ABSTRACT FROM AUTHOR]

Several recent high intensity ENSO events have caused strong negative impacts on the adult phases of foundational species in coral reef ecosystems, but comparatively little is known about how climatic variables related to recent ENSOs are impacting the supply of larvae to benthic populations. In marine fishes and invertebrates, reproductive adults and planktonic larvae are generally more sensitive to environmental variability than older, non-reproductive adults. Further, the transport of larvae in ocean currents may also be strongly ENSO dependent. The interactions between the dynamics of larval survivorship and larval transport could lead to population bottlenecks as stronger ENSO events become more common. We tested the predictions of this hypothesis around the Main Hawaiian Islands (MHI) by constructing a correlation matrix of physical and biological time series variables that spanned 11 years (2007–2017) and multiple ENSO events. Our correlation matrix included four types of variables: i. published ENSO indices, ii. satellite-derived sea surface temperature (SST) and chlorophyll variables, iii. abundance and diversity of larval fishes sampled during the late winter spawning season off Oahu, and iv. abundance and diversity of coral reef fish recruits sampled on the western shore of the Big Island of Hawaii. We found that the abundance and diversity of larval fishes was negatively correlated with the Multivariate El Niño Index (MEI), and that larval variables were positively correlated with measures of fall recruitment (September & November), but not correlated with spring-summer recruitment (May & July). In the MHI, SST variables were not correlated with the MEI, but two successive El Niño events of 2014–15 and 2015–2016 were characterized by SST maxima approaching 30°C. Two large pulses of benthic recruitment occurred in the 2009 and 2014 recruitment seasons, with > 8000 recruits observed by divers over the summer and fall months. Both events were characterized by either neutral or negative MEI indices measured during the preceding winter months. These patterns suggest that La Niña and the neutral phases of the ENSO cycle are generally favorable for adult reproduction and larval development in the spring and summer, while El Niño phases may limit recruitment in the late summer and fall. We hypothesize that episodic recruitment during non-El Niño phases is related to favorable survivorship and transport dynamics that are associated with the formation of pairs of anticyclonic and cyclonic eddies on the leeward sides (western shores) of the Main Hawaiian Islands. [ABSTRACT FROM AUTHOR]

Rainfall interception loss (Ei) is a difficult to study and poorly understood flux compared to transpiration and soil evaporation. The influence of climate and vegetation on Ei is not well known at continental‐to‐global and annual‐to‐decadal scales. Here, we use a long‐term multi‐product approach to examine the global trends in Ei, and further utilize a recently developed and validated dataset to isolate the relative contributions of precipitation, vegetation and evaporative demand. At decadal timescales, increasing Ei is largely driven by global vegetation greening through an increase in the intercepting surface and storage capacity, while its inter‐annual variations are mainly controlled by changes in precipitation, largely related to El Niño/Southern Oscillation. Increasing evaporative demand, driven by atmospheric warming, also positively contributes to the global rise in Ei. This study provides new perspectives for further understanding the impacts of climate change on the terrestrial hydrological cycle. Plain Language Summary: Rainfall interception loss is the volume of rain that gets caught by plants before reaching the ground and evaporated back into the atmosphere. It is among the least understood components of the global water cycle. In our research, we used satellite data over a long time (from 1981 to 2020) and a recently developed global model to study how rainfall interception has changed in time and space. We discovered that globally, more rain is being caught by vegetation over the years. This increase happens because our planet is greening, increasing the surface over which rain can be intercepted. On the other hand, changes in how much it rains dominate the year‐to‐year differences in interception loss. At the same time, as the atmosphere gets warmer, water can evaporate faster from vegetation, which adds to the growing trend in interception loss. These results match with the expectation of an intensified water cycle over the continents. Key Points: Rainfall interception loss exhibits increasing trends globallyIts multi‐decadal trends are driven by vegetation greening and warming, whereas interannual variations are controlled by precipitationENSO regulates rainfall interception loss largely through its influence on precipitation dynamics [ABSTRACT FROM AUTHOR]

In this study, we aim to reconstruct southern India's intrinsic environmental changes over the past 1500 years from 3330 to 1830 cal BP by investigating the sedimentation and weathering dynamics in Lake Shantisagara, one of Karnataka's largest lakes. Four distinct climatic phases were delineated based on sedimentological, geochemical, and End Member Modelling Analysis (EMMA) results. Phase 1 (3330-3100 cal BP) is a short-term low rainfall zone characterized by a calm hydrodynamic environment and weak chemical weathering. Phase 2 (3100-2800 cal BP) is a climatically unstable phase, fluctuating between low and high rainfall conditions. Phase 3 (2800-2200 cal BP) is characterized by a stable, low rainfall climate with weak fluvial activity and chemical weathering. It is followed by a highly unstable phase marked by frequent extreme climatic events (Phase 4; 2200-1830 cal BP). Our study reveals a highly unstable hydrodynamic condition that culminated in potentially catastrophic high rainfall events that triggered intense and frequent floods in southern India around ∼2208, 2054, 1958, and 1891 cal BP. Comparative studies of regional records show that the regional climate pattern is similar. There is a strong effect of Total Solar Irradiance (TSI), Sea Surface Temperature (SST) off the Malabar coast, location of the Intertropical Convergence Zone (ITCZ), and the El Niño Southern Oscillation (ENSO) on the monsoon system in southern India. This suggests that there is a global teleconnection. [ABSTRACT FROM AUTHOR]

The electromagnetic waves in the Schumann resonance (SR) frequency range (<100 Hz) radiated by natural "lightning antennas" excite the Earth‐ionosphere cavity confined between the Earth's surface and the lower ionosphere. The peak frequencies of SR are known to vary with source‐observer distance (SOD), while the daily frequency range (DFR: fmax − fmin) is also indicative of the average size of thunderstorm regions. This paper provides observational evidence for these relationships based on SR frequency observations of the vertical electric (EZ) field component at Nagycenk (NCK), Hungary in Central Europe from the period 1994–2015. Variations of the peak frequencies are considered on the annual, seasonal and diurnal time scales as well as during a specific event when squall‐line formation of lightning activity in South America moves toward NCK. DFR is studied in relation to the El Niño Southern Oscillation (ENSO). Increasing area of lightning activity in mid‐high Northern hemisphere latitudes has been identified by DFR variations during the transition from warm to cold episodes of the ENSO in 1998 and 2010. The extension of the lightning area is considered as a consequence of energy released in the tropics and exported to higher latitudes with some months of delay from the end of the El Niño episodes. The frequency variations are interpreted via model calculations and supported with satellite‐based optical lightning observations (Optical Transient Detector, Geostationary Lightning Mapper). The described variations of SR peak frequencies and DFR yield information on the global/regional lightning dynamics and on this basis they have important application to climate issues as well. Plain Language Summary: Schumann resonances (SR) are global electromagnetic resonances maintained by global lightning activity in the cavity resonator formed by the Earth's surface and the lower ionosphere. This paper provides observational evidence for the relationships between the variations of SR peak frequencies of the first three resonance modes and the global/regional lightning dynamics on different time scales based on SR observations of the vertical electric (EZ) field component. As lightning is one of the essential climate variables that provides valuable information on the state of the atmosphere, the lightning source‐observer distance and lightning area‐dependent frequency variations demonstrated in this paper have important applications to climate issues as well. Key Points: Schumann resonance (SR) frequency variations of the EZ field component at nodal distances can be used to study global lightning dynamics at different time scalesThe movement of individual squall‐line formations in South America can cause detectable variations in SR peak frequency at NCK, HungaryExtratropical extension of global lightning activity is revealed during the transition from warm to cold phases of the El Niño Southern Oscillation [ABSTRACT FROM AUTHOR]

This paper uses time-varying smooth transition autoregressive model to investigate the asymmetric nature of El Niño Southern Oscillation (ENSO) —an exogenous climatic factor—with respect to the nonlinear dynamics of food prices in sub-Saharan Africa. Curating food price series from more than 1100 markets from 36 SSA countries, the study finds that ENSO (linearly or nonlinearly) affects roughly half of food prices considered, with most nonlinear models exhibiting strong asymmetric properties with shock-inflicted persistence. Moreover, in terms of the location of the burden of ENSO impact, I find a geographical and food product divide. Specifically, ENSO appears to be more efficacious on maize prices in Southern, Eastern, and some parts of Central Africa. Conversely, local rice, cassava, millet, and animal products are least affected. The policy implication of this dichotomy is that response to ENSO news should be subregion-specific rather than region-specific, depending on how the subregions absorb the shock. [ABSTRACT FROM AUTHOR]

The influences of the tropical Indian and Atlantic oceans on the development of El Niño Southern Oscillation (ENSO), especially regarding super El Niño events, have been a subject of debate. In particular, several studies argue that these cross-basin influences may be mere statistical artifacts resulting from the high auto-correlation of ENSO. To clarify this issue, we conduct a series of perfect model hindcast experiments to untangle the individual and synergistic effects of the tropical Indian and Atlantic oceans. Our results clearly demonstrate that without these cross-basin effects, the Pacific warming would rarely reach super El Niño level. Specifically, the individual effect of the Indian Ocean efficiently enhances the development of super El Niño events. In contrast, the Atlantic's effect is initially limited because it fails to establish the Bjerknes feedback in the Pacific. However, when coupled with the Indian Ocean, the Atlantic's effect becomes more pronounced as it is amplified by the Bjerknes feedback established by the Indian Ocean. [ABSTRACT FROM AUTHOR]

Climate changes caused by the El Niño oscillation southern (ENSO) significantly influence human diseases, because they are related to anomalies in precipitation or periods of drought. The aims of this paper are: to evaluate the anomalies of the average sea surface temperature, to calculate the average sea surface temperature of the Niño and Niño 1+2 regions, to contextualize infectious pathologies according to their classification: viral, bacterial, parasitic and caused by fungi in the geographical areas or countries of the Region where they have been studied in the historical context (1982-1983; 1997-1998 and 2016-2017) periods of the ENSO considered strong. A bibliographic, descriptive, documentary, retrospective and cross-sectional review was carried out during the months of October 2023 to March 2024. Sea Surface Temperatures (SST) are higher than average, observed throughout the equatorial Pacific Ocean, with anomalies being greater in the central and central-eastern Pacific. For ENSO 2023-2024, in the Niño 4 region the last calculated values of the Niño index remained at a temperature of +1.4 °C, in the Niño 3,4 region it was +1.9, in the Niño -3 region it was +2.0 °C while in the Niño-1+2 regions it weakened to +1.0 °C. The highest incidence and prevalence of ENSO infection (1997-1998) in Ecuador stands out, accounting for an increase in 13 infectious pathologies. Attention must be paid and the links between the environment and the risk of infection must be better understood. [ABSTRACT FROM AUTHOR]

Aim: This study investigates the biogeographic patterns of Pacific white‐sided dolphins (Lagenorhynchus obliquidens) in the Eastern North Pacific based on long‐term passive acoustic records. We aim to elucidate the ecological and behavioural significance of distinct echolocation click types and their implications for population delineation, geographic distribution, environmental adaptation and management. Location: Eastern North Pacific Ocean. Time Period: 2005–2021. Major Taxa Studied: Pacific white‐sided dolphin. Methods: Over 50 cumulative years of passive acoustic monitoring (PAM) data from 14 locations were analyzed using a deep neural network to classify two distinct Pacific white‐sided dolphin echolocation click types. The study assessed spatial, diel, seasonal and interannual patterns of the two click types, correlating them with major environmental drivers such as the El Niño Southern Oscillation and the North Pacific Gyre Oscillation, and modeling long‐term spatial‐seasonal patterns. Results: Distinct spatial, diel and seasonal patterns were observed for each click type. Significant biogeographical shifts in presence were observed following the 2014–2016 marine heatwave event. Main Conclusions: Distinct spatial distributions of the two click types support the hypothesis that Pacific white‐sided dolphins produce population‐specific echolocation clicks. Seasonal and diel patterns suggest spatiotemporal niche partitioning between the populations in Southern California. Interannual changes, notably initiated during the 2014–2016 marine heatwave, indicate climate‐driven range expansions and contractions related to gradual tropicalization of the Southern California Bight. [ABSTRACT FROM AUTHOR]

The Nile Basin has long been the subject of extensive research, reflecting its importance, which spans from its historical role in the development of ancient civilizations to its current significance in supporting rapidly changing socioeconomic conditions of the basin countries. This review synthesizes studies focusing on the past and future climate, hydrologic, and drought outlooks of the basin, and explores the roles played by large-scale atmospheric phenomena and water infrastructure on the basin's climate and hydrology. Overall, the studies underscore the complexity of the Nile hydrological system and the necessity for improved modeling and data integration. This review serves as a guide to areas warranting further research by highlighting the uncertainties and inconsistencies among the different studies. It underscores the interconnectedness of climatic and hydrological processes in the basin and encourages the use of diverse data sources to address the data scarcity issue and ensemble models to reduce modeling uncertainty in future research. By summarizing the data and modeling resources employed in these studies, this review also provides a valuable resource for future modeling efforts to understand and explore of the basin's complex climatic and hydrological dynamics. [ABSTRACT FROM AUTHOR]

Atmosphere‐ocean dynamics in the tropics play a key role in global climate change. An abrupt cooling event that occurred between 4500 and 3900a BP, known as the 4.2 ka event, has long been believed to be linked to changes in sea surface temperatures (SSTs) associated with intensified El Niño and Southern Oscillation (ENSO) variability. However, the precise timing and amplitude of ENSO variability during the 4.2 ka event remain uncertain, largely due to the lack of resolution in records from the ENSO‐sensitive regions. Here we present a 104‐year‐long monthly resolved coral record from the South China Sea that spans from 4400 to 4300 years BP, corresponding to the onset of the 4.2 ka event. Using the Sr/Ca paleo‐thermometry, we show a significant decrease in ENSO variability during this time interval compared to the modern period. The ENSO events decrease in the frequency from one every 3.5 years during the modern era to one every 5.6 years during the onset of the 4.2 ka event, with weaker magnitude in the latter period. This reduction in ENSO variability is largely associated with an intensification of the Pacific Walker circulation. We therefore suggest that ENSO variability was a response to, not a driver of, the development of the 4.2 ka event. Plain Language Summary: Abrupt climate change occurred between 4,500 and 3,900 years ago, known as the 4.2 ka event, stands out as one of the most prominent climatic events in the Holocene and witnesses a northern hemisphere cooling and agricultural‐based civilization collapsed. A growing number of terrestrial records have shown that anomalous sea surface temperatures in the tropical oceans associated with intensified El Niño and Southern Oscillation (ENSO) variability triggered this abrupt climatic event. However, precise ENSO variability based on tropical oceanic records is still scarce, hindering our understanding of this event. Here, we evidence a significant reduction in ENSO variability during the onset of the 4.2 ka event using multiple monthly resolved coral records from the tropical oceans. This contrasts with previous studies that suggest increased ENSO variability triggers the 4.2 ka event, implying that anomalous ENSO variability may be a response to the 4.2 ka event. Key Points: Monthly resolved coral Sr/Ca record provides a century‐long evolution of El Niño and Southern Oscillation (ENSO) variability during the onset of the 4.2 ka eventThere is a significant reduction in ENSO variability during the onset of the 4.2 ka eventReduced ENSO variability during this time interval is associated with intensified Pacific Walker circulation [ABSTRACT FROM AUTHOR]

Continental shelves are amongst the most dynamic and vulnerable regions in the oceans, and understanding their dynamics has implications for the transport of organisms and materials in both coastal and offshore zones. Determining the circulation patterns in systems experiencing high variability is crucial, but challenging as continuous and high‐resolution long‐term data sets are still scarce. In this paper, we use 9‐years (2010–2018) of High‐Frequency Radar surface current observations and satellite‐derived Sea Surface Temperature to investigate the spatiotemporal variability of surface currents on the offshore, shelf break and inner continental regions along the Wadjemup (Rottnest) Continental Shelf (WCS), South‐West Australia. We use the indigenous Noongar calendar that defines six‐seasons: Birak (DJ), Bunuru (FM), Djeran (AM), Makuru (JJ), Djilba (AS), and Kambarang (ON); to better represent the seasonal changes in local winds and surface circulation. The surface currents revealed the poleward‐flowing Leeuwin Current (LC), the equatorward‐flowing Capes Current (CC), their interactions, and eddy features (radii >10 km). The LC was the strongest (weakest) over Djeran/Makuru (Birak/Bunuru), whilst CC and offshore eddies were persistent from Kambarang‐Bunuru. Along WCS, CC and offshore eddies locally modulated LC strength and position, whereas the El Niño Southern Oscillation influenced its interannual variability. During El Niño (La Niña) years, LC was weakened (strengthened), whilst CC and offshore eddy activity were strengthened (weakened). Particularly during La Niña years, LC increased its cross‐shore migration and had an unseasonal peak in 2011. Our findings contribute to the understanding of the variability of boundary and coastal currents, and their local impacts on coastal circulation. Plain Language Summary: Variability in surface circulation influences the transport of suspended material and organisms. We investigated how patterns of surface currents change over intra‐seasonal, seasonal to interannual timescales across the Wadjemup (Rottnest) Continental Shelf using 9 years of High‐Frequency Radar maps of surface current and satellite Sea Surface Temperature (SST). The current patterns in the offshore, continental shelf break and the inner continental shelf were compared to local winds, sea level, and El Niño Southern Oscillation (ENSO) events to explain observed patterns. We adopted the 6 Noongar seasons to better represent winds and surface circulation variability. Results revealed distinct surface flow patterns along WCS: the poleward‐flowing Leeuwin Current (LC), the equatorward‐flowing Capes Current (CC) and associated eddy features. LC was the strongest (weakest) over Djeran (AM)/Makuru (JJ) (Birak (DJ)/Bunuru (FM)), whilst CC and offshore eddies were persistent during Kambarang (ON)‐Bunuru. The interannual variability of surface flows were modulated by ENSO. During El Niño (La Niña) years, LC was weakened (strengthened), whilst CC and offshore eddy activity were strengthened (weakened). Particularly during La Niña years, LC increased its cross‐shore migration and had an unseasonal peak in currents in 2011. Key Points: Long‐term HFR‐surface current observations revealed different flow regimes with strong spatiotemporal variability across the continental shelf and slopeSeasonal surface flow patterns reflected wind forcing related to local indigenous seasonsEl Niño Southern Oscillation events controlled the inter‐annual variability in the surface currents [ABSTRACT FROM AUTHOR]

Introduction. Today, the study of climate is one of the most important tasks of modern science. Particular attention is paid to issues of climate variability on different time scales. The El Niño – Southern Oscillation (ENSO) is the leading signal of interannual climate variability that influences the weather and climate of the globe. It has been revealed that during the onset of its different phases, weather and climate anomalies (up to extreme ones) are observed not only over the tropical zone of the Pacific Ocean, but also in extratropical latitudes, which are transmitted through teleconnections in the ocean-atmosphere system. Numerous studies have shown that ENSO modulates tropical cyclone activity in the northwest Pacific Ocean. The purpose of the study is to determine the relationship between sea surface temperature (SST) changes in the equatorial Pacific Ocean and the frequency of tropical cyclones of varying intensity in the northwestern Pacific Ocean under current climatic conditions. Materials and methods of research. The work uses data of the number of cases and intensity of tropical cyclones (TCs) for the period 1991-2020, and series of indices ONI and SOI. Using these data, statistics on the frequency of TCs in the study area were calculated and Pearson correlation coefficients were calculated. The scientific novelty lies in obtaining quantitative characteristics of the relationship between SST and the number of TCs of varying intensity in modern climatic conditions. The practical significance of the work lies in the fact that TCs are associated with the occurrence of severe weather conditions, which, when a TC reaches the densely populated coast of Southeast Asia, can pretend to be significant losses. Therefore, information that can be used in forecasting TCs and their trajectories is very high importance. Research results. Analysis of the frequency of TCs of different intensity in the northwestern Pacific Ocean during the 1991-2020 showed that there was a decrease in the number of tropical depressions and typhoons. However, there has been a slight increase in the occurrence of tropical storms. The results of the analysis of the influence of SST variability in the equatorial Pacific Ocean on the frequency of TCs, showed that there is a statistically significant relationship only with the frequency of typhoons. The establishment of positive SST in the center of the equatorial Pacific Ocean, associated with the El Niño episode, is associated with an increase in typhoons in the northwestern Pacific Ocean.

In this study, we analyze the spatiotemporal patterns of propagation of pre‐monsoon heatwaves and their drivers in India. Using complex networks, we find that heatwaves originate most frequently in northwest India and propagate in the northeast or southeast direction. Heatwaves propagating in the northeast direction have a higher intensity, lower moving distance, smaller areal coverage, and shorter duration than heatwaves moving in the southeast. We find that the larger area and duration of heatwaves propagating southeast are a result of development of larger and more persistent high‐pressure systems extending over entire northern and eastern India, which are influenced by El Niño Southern Oscillation. On the other hand, higher radiative fluxes and larger heat entrainment in the boundary layer in the heatwaves propagating northeast contribute to their higher intensities. Plain Language Summary: We analyze the spatial patterns of where heatwaves originate and how they spread in the pre‐monsoon period in India. We find that most heatwaves originate in northwest India. Some of these heatwaves spread to north and central India while some propagate to south India. Heatwaves in northern and central India have higher intensity and last for a shorter period of time as compared to those which spread to south India. The area and duration of heatwaves moving toward south India are influenced by El Nino events. On the other hand, the intensity of heatwaves of central and north India is higher due to greater shortwave radiation and heat entrainment in the boundary layer. Key Points: Regions of origin and termination of pre‐monsoon heatwaves in India are identified using complex networks and event synchronizationMajority of heatwaves originate from northwest India and propagate in the northeast or southeast directionHeatwaves propagating southwards have larger duration and areal extent and are influenced by El Niño Southern Oscillation [ABSTRACT FROM AUTHOR]

The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year‐to‐year contributions to multi‐model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well‐forecast and poorly‐forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well‐forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi‐model seasonal forecasting perspective. Plain Language Summary: The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are two important indicators of atmospheric variability in the North Atlantic. They can have a substantial impact on European winter weather. The ability of seasonal forecast models to forecast the NAO and EA varies from year to year. This intermittency of forecast skill is investigated in seven different seasonal forecast systems from the Copernicus C3S database, by focusing on the most well‐forecast and poorly‐forecast years. Years where the NAO is well‐forecast are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Similar but weaker results hold for the EA. These results are valuable for increasing the usability of seasonal forecasts by identifying conditions under which forecasts are more likely to be skillful. Key Points: Seasonal forecast skill of the wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) is intermittentWell‐forecast NAO/EA winters generally occur when there is substantial tropical forcing [ABSTRACT FROM AUTHOR]

The temporal variability of fish habitat utilization is poorly understood in tropical deltaic systems due to high water turbidity, which limits visual censuses, and to the lack of long-term data incorporating climate variability events. We aimed to assess the influence of body size and El Niño Southern Oscillation (ENSO) variability on the cross-habitat utilization rate of 14 fish species of commercial relevance in the Ciénaga Grande de Santa Marta (CGSM). We estimated the utilization of mangroves and coastal lagoons based on relative catch frequencies from encircling gillnets used within a long-term catch monitoring program, and then tested for significant changes in each species' habitat utilization as a function of body size and climate variability. Six species showed a high dependence on mangroves and four on coastal lagoons for most body size classes (including juveniles) and ENSO conditions. One species (Elops smithi) showed a high utilization of mangroves in some ENSO phases and body size classes, while three species showed a high utilization of both mangroves and coastal lagoons. Mangrove utilization by six species (Megalops atlanticus, E. smithi, Centropomus undecimalis, Mugil incilis, Mugil liza, and Ariopsis canteri) increased in larger body sizes at low depths, which usually occurs under dry ENSO conditions, when predatory risk is higher in coastal lagoons. Another species (Caquetaia kraussi) increased its mangrove utilization from the body size at which its feeding habits change. Mangroves and coastal lagoons are important nurseries and habitats for adults of the main commercial fish species in the CGSM. Seascape habitats and fringe/riverine mangroves must be conserved in tropical deltas to promote not only nurseries but also fish lifecycles. [ABSTRACT FROM AUTHOR]

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. El Niño Southern Oscillation (ENSO) has been shown to relate to the epidemiology of child infectious diseases, but evidence for child deaths is limited. Here, the authors show that maternal exposure to high levels of ENSO before conception and giving birth may increase under-five mortality. [ABSTRACT FROM AUTHOR]

To better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales. Plain Language Summary: While seasonal temperature forecasting is crucial for decision makers, it is generally a difficult task. A major setback to statistical machine learning methods for forecasting is the limited observational record. In our study, to bypass this, we used simulated climate model data, not historical data, to train machine learning (ML) models for seasonal surface temperature forecasts. We applied this method to train convolutional neural networks (CNN), a type of ML model that can analyze spatial patterns in data. We found that using CNN models greatly improves seasonal temperature predictions compared to standard linear models and that the CNN models effectively analyze spatial patterns in ENSO to make more accurate predictions. We concluded that using ML models is a promising method to improve seasonal temperature forecasts over the US. Key Points: Seasonal predictability of US temperature is compared between CNN, statistical, and dynamical modelsTraining CNN models on CESM2 LENS improves the seasonal predictability of US temperatureConsidering the spatiotemporal variability of tropical Pacific SST contributes to seasonal predictability improvement [ABSTRACT FROM AUTHOR]

The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO Indices data from December 1950 to November 2000 as a reference. The latter is to construct monthly precipitation hindcasts for Mexico spanning from December 2000 to November 2023 through historically high-resolution monthly precipitation rasters. The model's performance is evaluated at a global and local scale across seasonal periods (winter, spring, summer, and fall). Assessment using global Hansen–Kuiper Skill Score and Heidkee Skill Score metrics indicates skillful performance across all seasons (>0.3) nationwide. However, local metrics reveal a higher spatial percent of corrects (>0.40) in winter and spring, corresponding to dry seasons, while a lower percent of corrects (<0.40) are observed in more extensive areas during summer and fall, indicative of rainy seasons, due to increased variability in precipitation. The choice of averaging method influences the degree of underestimations and overestimations, impacting the model's variability. Spearman correlations highlight regions with significant model performance, revealing potential misinterpretations of high hit rates during winter and spring. Notably, during the fall, the model demonstrates spatial skill across most of Mexico, while in the spring, it performs well in the southern and northeastern regions and, in the summer, in the northwestern areas. Integration of accurate forecasts of ENSO Indices to predict precipitation months ahead is crucial for the operational efficacy of this model, given its heavy reliance on anticipating ENSO behavior. Overall, the empirical method exhibits great promise and potential for application in other developing countries directly impacted by the El Niño phenomenon, owing to its low resource costs. [ABSTRACT FROM AUTHOR]

Tornado outbreaks (TOs) are highly dangerous meteorological phenomena common in the United States and have limited known relationships with climate variability. Many of the challenges in understanding TOs result from a lack of formal TO quantification (both in definition and impact). Here, we present a TO definition based on a spatially cohesive and distinct region of tornado activity and present a TO intensity index using tornado characteristics within the TO region. In developing this index, we present a support vector regression-based detrending methodology to remove the secular trends within tornado reporting. The resulting TO definition suggests a decline in TO activity of roughly 1 TO per 4–5 years, with a similar decline in TO intensity. In addition, the relationship between this new quantification of TOs and common North American interannual and monthly climate variability indices is explored, namely the El Niño Southern Oscillation, the North Atlantic Oscillation, the Arctic Oscillation, and the Pacific North American Oscillation. In general, the links between these teleconnections and TO frequency and intensity were minimal (and sometimes in opposition when comparing TO frequency and intensity), but interesting patterns emerged that may offer an initial pathway to exploring longer-term TO predictability. [ABSTRACT FROM AUTHOR]

This study explores the features of leading modes of Subtropical Indian Ocean (SIO) sea surface temperature (SST) variability and their representation in CMIP6 models. The first EOF mode of SIO SST, featured by an SST anomaly elongated from the northwestern to the southeastern SIO region is triggered by the El Niño/Southern Oscillation (ENSO). ENSO-induced wind anomalies over the SIO region weaken the climatological southeasterlies, reduce evaporative cooling, and consequently warm the SST in subtropics. The first SIO mode is closely related to the Indian Ocean Basin Mode. The CMIP6 models' skill in simulating this mode is attributable to their accurate representation of ENSO impacts in this region. At the same time, the skill of simulation of the second mode, the Subtropical Indian Ocean Dipole (SIOD), is poor in most models due to the misrepresentation of the pure tropical Indian Ocean Dipole (IOD) events. As pure IOD events generate SIOD, underestimation of the occurrence of pure IOD events will affect the development of SIOD in models. The CMIP6 model experiments reveal the absence of ENSO forcing on SIOD. Notably, regardless of the bias in co-occurred and pure IOD events, most of the models overestimate IOD strength due to the easterly surface wind bias and the associated thermocline depth bias. The easterly bias in the equatorial Indian Ocean surface wind weakens the Wyrtki jet, creating a shallow (deep) thermocline bias in the eastern (western) TIO. This induces an overestimation of warm (cold) SST in the western (eastern) TIO during positive IODs. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillations (ENSO), being the largest global air-sea interaction phenomena, has a profound effect on global monsoon. In this study, for the first time we have examined the ENSO – Monsoon relation through nonlinear kinetic energy (KE) exchanges outlook across scales. Indian summer monsoon (ISM) and Western North Pacific Summer Monsoon (WNPSM) are considered to be the two major components of global monsoon on which ENSO has a huge impact. The ENSO – Monsoon nonlinearity can be explored by unraveling the unstable (stable) relation between ENSO and Monsoon during El Niño (La Niña) period through the mechanism of scale interactions among El Niño / La Niña, dominant high (HFO) and low frequency oscillation (LFO) considering ENSO itself as a LFO of time period 12–18 months. It is found from the analysis that more (less) KE is transferred to synoptic scale and 10-20 day LFO from El Niño 1997-98 compared to that from 2015 - 16 episode over ISM (WNPSM) region. Moreover, the opposite energy exchange between El Niño and 30-60 day wave is observed over these regions, attributing to contrasting rainfall variation over ISM and WNPSM zone. Sharp (comparatively less) variability in energy exchange among El Niño (La Niña), HFO and LFO over ISM and WNPSM regions make El Niño – Monsoon relation highly unstable whereas La Niña – Monsoon relation a stable one. Unveiling El Niño-ISM, El Niño-WNPSM, La Niña-ISM and La Niña-WNPSM relations in terms of inter-scale KE exchanges for the two El Niño and two La Niña events may lead to enhance the understanding of ENSO-monsoon nonlinearity. This work will make an avenue for further research in the space of nonlinearity between the two large scale systems that may be expected to be more complex due to the global warming and climate change as a futuristic study. [ABSTRACT FROM AUTHOR]

Unprecedented precipitation deficits in the 2022-2023 growing season across the primary wheat-producing region in the United States caused delays in winter wheat emergence and poor crop growth. Using an integrated approach, we quantitatively unraveled a 37% reduction in wheat production as being attributable to both per-harvested acre yield loss and severe crop abandonment, reminiscent of the Dust Bowl years in the 1930s. We used random forest machine learning and game theory analytics to show that the main driver of yield loss was spring drought, whereas fall drought dominated abandonment rates. Furthermore, results revealed, across the US winter wheat belt, the La Niña phase of the El Niño Southern Oscillation (ENSO), increased abandonment rates compared to the El Niño phase. These findings underscore the necessity of simultaneously addressing crop abandonment and yield decline to stabilize wheat production amid extreme climatic conditions and provide a holistic understanding of global-scale ENSO dynamics on wheat production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific events are typically weaker than Eastern Pacific events. SSTA pattern and intensity undergo low‐frequency modulations, affecting ENSO prediction skill and remote impacts, and resulting in low‐frequency changes in ENSO variance. Yet, how different ENSO types contribute to these decadal variance changes remains unclear. Here, we decompose the low‐frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non‐binary event category memberships, where each event can belong to different clusters. Our approach identifies two La Niña and three El Niño categories and shows that the major shift of ENSO variance in the mid‐1970s was associated with an increasing likelihood of strong La Niña and extreme El Niño events. Plain Language Summary: The El Niño Southern Oscillation (ENSO) is a climate phenomenon that involves changes in ocean temperatures in the central and eastern tropical Pacific and greatly influences the weather around the globe. ENSO events are usually categorized based on where these temperature variations are the strongest and how intense they are. However, these categories are at times ambiguous and the way ENSO behaves can change over decades. Our study introduces a new way to estimate how different kinds of ENSO events contribute to its overall variation over time. We base our variability analysis on a fuzzy categorization of ENSO events, which results in three different kinds of El Niño events and two kinds of La Niña events. We find that the decadal changes in ENSO variance, including the decadal shift in the mid‐1970s, were primarily associated with strong La Niña and extreme El Niño events. Key Points: Using a fuzzy categorization, five different El Niño Southern Oscillation (ENSO) types are identified in observationsExtreme El Niño events emerge as a distinct categoryStrong La Niña and Extreme El Niño categories mainly contributed to the shift in decadal ENSO variance around 1970 [ABSTRACT FROM AUTHOR]

Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts. Plain Language Summary: In 2017 and 2023, California experienced unexpectedly wet conditions despite predictions of dry winters due to La Niña. In 2016, seasonal predictions in California favored wet conditions due to the very strong El Niño, but the season was normal‐to‐dry statewide. Understanding relationships between El Niño/La Niña and recurring atmospheric weather patterns driving individual storms is needed to improve seasonal forecasts. We studied historical relationships between weather patterns that bring rain and snow to the region and the El Niño Southern Oscillation (ENSO). We find ENSO influences important characteristics of weather patterns once they make landfall in California, making El Niño storms generally wetter in coastal southern California and Desert Southwest. However, ENSO does not strongly affect how often these patterns occur in a season, which makes seasonal precipitation forecasts challenging. The frequency of certain weather patterns not tied to ENSO played important roles in the unusual rainfall of 2017, the heavy snowfall of 2023, and the drier than expected winter of 2016. Understanding these weather patterns provides operationally and scientifically relevant context for future seasonal forecasts by highlighting that while ENSO only minimally influences the frequency of certain impactful storm types, it does change the precipitation characteristics of these storms. Key Points: Weather regime type and frequency are key drivers of winter seasons with anomalous precipitation and/or snow accumulation in CaliforniaENSO does not modulate the seasonal frequency of weather regimes impacting the coast, presenting a challenge for seasonal forecastingENSO modulates synoptic circulation characteristics of key weather regimes which produces the canonical ENSO‐precipitation relationship [ABSTRACT FROM AUTHOR]

We investigate why the North American Multi‐Model Ensemble (NMME) upper‐level height forecast for December–February (DJF) 2023/24 differs from the expected El Niño response. These atypical height anomalies emerged despite the fact a strong El Niño was forecast. The analysis focuses on diagnosing the NMME forecasts of DJF 2023/24 for SSTs and 200‐hPa heights initialized at the beginning of November 2023 relative to other ensemble mean NMME DJF forecasts dating back to 1982. The results demonstrate that forecasts of the 200‐hPa height anomalies had a large contribution from warming trends in global SSTs. It is the combination of trends and the expected El Niño teleconnection that results in the forecast height anomalies. Increasingly, for forecasts of geopotential height anomalies during the recent El Niño winters, the amplitude of trends is nearly equal to the signal from El Niño and has implications for the climatological base period selection for seasonal forecasts. Plain Language Summary: Seasonal forecasts are cast as anomalies as users want to know what can be expected beyond the typical seasonal swings of the climate. This necessitates a choice for the climatological base period relative to which forecast anomalies are computed. It, however, poses a challenge under rapid climate change. In this scenario, climate trends become part of the real‐time forecast anomalies, and if the climatological base period is sufficiently different, may even start to dominate. This was the case for the NMME DJF 2023/24 forecast of 200‐hPa heights which was forecast to be a strong El Niño, and yet, forecast for 200‐hPa heights differed from typical El Niño signal. The analysis implies that seasonal forecasts for some variables, consideration of trends is important and reliance on expected signal from El Niño—Southern Oscillation alone may not be sufficient. Key Points: The North American Multi‐Model Ensemble (NMME) seasonal forecasts for December–February (DJF) 2023/24 upper‐level height differ from the expected El Niño signalIt is the combination of trends in heights and the expected El Niño signal that results in the forecast NMME ensemble mean heights anomaliesThe forecast of trends is increasingly important to account for NMME forecast anomaly and their amplitude in recent years can be of same magnitude as the signal from El Niño [ABSTRACT FROM AUTHOR]

The position of the subtropical jet over the Himalayas (Himalayan jet) affects extreme precipitation and heat over Central and South Asia. We examine the influence of two major natural factors‐the El Niño/Southern Oscillation (ENSO) and explosive volcanic eruptions—on Himalayan jet interannual variability during the past millennium using simulations from the Community Earth System Model. We find that both El Niño events and eruptions shift the Himalayan jet equatorward by up to 3°. If an El Niño occurs following an eruption, this enhances the equatorward Himalayan jet shift, while La Niña tends to favor poleward jet migration. Subtropical cooling during El Niño or following eruptions is the primary cause of equatorward Himalayan jet shifts, while poleward shifts are associated with subtropical warming. Consistent across the CMIP6 models over the historical period, our results suggest that both ENSO and eruptions are the key drivers of interannual Himalayan jet variability. Plain Language Summary: The latitudinal position of the jet stream over the Himalayas (the Himalayan jet) affects storms and heat waves in Central and South Asia. Because changes in the sea surface temperature over the tropical Pacific Ocean (the El Niño Southern Oscillation, or ENSO) and large volcanic eruptions are the major sources of year‐to‐year climate variability, we examined their impacts on north‐south displacements of the Himalayan jet latitude over the past millennium using climate model simulations from the Community Earth System Model. We find that both El Niño events and volcanic eruptions move the jet toward the equator, while La Niña events move the jet poleward. These jet displacements happen because both ENSO and volcanic eruptions change subtropical temperatures. When the tropospheric subtropics cool, equatorward jet shifts occur, while the reverse is true for poleward jet shifts. Our results are also consistent across a set of global climate models over the past 150 years. Key Points: Climate models show equatorward shifts of Himalayan jet during El Niño events and in the 0–2 years following volcanic eruptionsEl Niño events following eruptions amplify jet migration, while ENSO‐neutral post‐eruption conditions tend to have a weaker impactPoleward (equatorward) displacement of the jet is caused by tropospheric warming (cooling) over the subtropics [ABSTRACT FROM AUTHOR]

The Spring Predictability Barrier (SPB) phenomenon is characterized by the reduced accuracy of El Niño/Southern Oscillation (ENSO) forecasts during the spring, which substantially limits our ability to predict ENSO events. By investigating the nonlinear dynamic characteristics of ENSO systems simulated by a box model, we found that the strong surface heating process in spring may contribute to the SPB by regulating the different coupling processes between the ocean and atmosphere. Specifically, the intensified springtime surface heating increases the Sea Surface Temperature (SST), further amplifying the thermal damping effect of SST anomalies and reducing the dynamic connection between zonal SST gradient and upwelling process, and finally increasing the chaotic degree of ENSO systems simulated by the box model. The enhanced chaotic degree of ENSO systems leads to a more rapid growth of initial errors in the forecast model in spring, potentially leading to the SPB phenomenon. Plain Language Summary: The El Niño‐Southern Oscillation (ENSO) is a climate phenomenon characterized by the periodic fluctuation of Sea Surface Temperature (SST) anomaly in the tropical Pacific, which has a significant impact on global weather and climate patterns. However, predicting ENSO evolution several seasons in advance is challenging due to the Spring Predictability Barrier (SPB) phenomenon, which refers to the reduced accuracy of ENSO forecasts during the spring. Our investigation into the nonlinear dynamic characteristics of ENSO systems revealed that the strong surface heating process in spring could be a contributing factor to the SPB. The strong springtime surface heating increases the SST in the eastern tropical Pacific and further affects the different coupled processes between ocean and atmosphere, finally raising the chaotic degree of ENSO systems. As the chaotic degree of ENSO systems increases, initial errors in the forecast model grow more rapidly, which may lead to the SPB phenomenon. Key Points: The box model was utilized to investigate the physical mechanisms for the Spring Predictability Barrier phenomenonThe El Niño/Southern Oscillation systems undergo a chaotic regime transition when the couple processes between the ocean and atmosphere change in springThe strong surface heating in spring may be responsible for the change of couple processes [ABSTRACT FROM AUTHOR]

A machine learning method is used to identify sources of long‐term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near‐surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has apredictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long‐lead signal originates from coupled wind‐SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO. Plain Language Summary: Many extreme events, such as floods or droughts, can be attributed to the El Niño Southern Oscillation, a mode of large‐scale ocean‐atmosphere coupled variability in the tropical Pacific Ocean occurring with a period of approximately 4 years. In this analysis, we use a machine learning methodology to disentangle the key atmospheric and oceanic ENSO components' relative contribution to its predictability, particularly the role of near‐surface 10‐m zonal wind. We quantify the potential for improved ENSO predictions for up to 2 years in advance and present a mechanistic understanding of the location of the sources of predictability. While equatorial sea surface temperature represents the primary source of ENSO predictability, the equatorial U10 plays a vital role from late spring to fall, from 1 to 2 years in advance. The enhanced predictability skill is shown to be linked to an SST anomaly originating in the Indian Ocean. The ML model used provides a new way to get new insights into the sources of predictability for ENSO and can be used as a simple but powerful tool to improve the underlying mechanistic understanding. Key Points: A deep learning‐based approach suggests near‐surface 10 m wind to play a significant role in providing skills for long‐term ENSO predictabilityThe skill of the 10‐m zonal wind is large from 12 months up to 23 months in advance (depending on the season)The signal is generated by coupled wind‐SST interactions in the Indian Ocean and later propagates across the Pacific [ABSTRACT FROM AUTHOR]

The role of chaos in the climate system has been dismissed as high dimensional turbulence and noise, with minimal impact on long-term climate change. However theory and experiment show that chaotic systems can be reduced or "controlled" from high to low dimensionality by periodic forcings and internal feedbacks. High dimensional chaos is somewhat featureless. Conversely low dimensional borderline chaos generates pattern such as oscillation, and is more widespread in climate than is generally recognised. Thus, oceanic oscillations such as the Pacific Decadal and Atlantic Multidecadal Oscillations are generated by dimensionality reduction under the effect of known feedbacks. Annual periodic forcing entrains the El Niño Southern Oscillation. Numerous oceanic and Arctic centered climate oscillations have recently been observed to exhibit delayed synchrony in the manner of a "stadium wave". Here, three studied systems with chaotic-nonlinear dynamics (physico-chemical and biological) are reviewed which show dimensionality reduction as a source of emergent pattern. This alongside mathematical studies of chaotic Lorenz models whose trajectories escape under controlling influences from chaotic to non-chaotic phase space regions – incurring a dimensional haircut in the process. Control of chaos by external and internal influences results in lowered phase-space dimensionality with emergent dissipative structure. Dimensionality reduction is proposed as a source of natural climate change. Thus the effect of astrophysical periodic forcing and oceanic and other feedbacks in climate is not so much to generate chaos as to lower dimensionality in a chaotic system, engendering the export of entropy and emergent regular spatiotemporal pattern. [ABSTRACT FROM AUTHOR]

The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose‐beaked whales (Ziphius cavirostris). Goose‐beaked whales are deep‐diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose‐beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose‐beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose‐beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose‐beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose‐beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose‐beaked whales. [ABSTRACT FROM AUTHOR]

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ −0.7, p ≤ 0.05). [ABSTRACT FROM AUTHOR]

Sandy coastlines are very dynamic spaces affected by a variety of natural and human factors. In Central Chile, changes in oceanographic and wave conditions, modes of inter-annual climate variability such as El Niño Southern Oscillation (ENSO), and extreme events such as earthquakes and tsunamis condition the beach morphology. At the same time, direct human actions alter the arrival of sediments to the coast and their alongshore distribution. Despite the relevance of the beaches for this coastal region and the interesting relationship their morphology has with the aforementioned factors, there is a lack of robust morphological datasets to provide a deep characterization and understanding of the dynamism of the Chilean coast. Based on the information provided by satellite-derived shorelines (SDSs) defined by using the SHOREX algorithm, this paper characterizes the morphological changes of Playa Grande in Cartagena Bay (Central Chile) during the period 1985–2019. The shoreline position data are analyzed in the context of changing beach transforming elements, allowing for a better understanding of the changes according to multiple drivers. While some of these factors, such as earthquakes or coastal storms, have a punctual character, changes in wave patterns vary at different time scales, from seasonal to multi-annual, linked to climate phases such as ENSO. Its effects are translated into shoreline erosion and accretion conditioned by the morphology and orientation of the coast while influenced by the availability of sediment in the coastal system. According to that, a conceptual model of the dynamism and redistribution of sediment in the Bay of Cartagena is proposed. The work proves the high utility that the systematic analysis of multi-decadal SDS datasets obtained from the images acquired in the optical by the Landsat and Sentinel-2 offer for beach monitoring and understanding the coastal dynamism. [ABSTRACT FROM AUTHOR]

A comprehensive study was conducted to analyse the impact of different drivers on streamflow downstream of the Magdalena River (MR), Colombia, between 1971 and 2020. The study considered El Niño Southern Oscillation (ENSO) events as a climate-forcing driver and evaporation from the reservoir network as a human-induced driver. Streamflow data was linked to previously categorized ENSO events, then standardized and classified based on date and year to evaluate inter- and intra-annual changes in streamflow. Daily evaporation losses were calculated using meteorological records and the aerodynamic method. The study also grouped 10-year periods of evaporation and streamflow to assess their ranges, rates, and water volumes. The results showed a decrease in means, volume, and maximum streamflow, while evaporation and minimum streamflow increased, especially during positive ENSOs. These changes in streamflow could have a significant impact on river-dominated dependent ecosystems, such as the MR downstream wetland complex, putting these ecosystems at risk. [ABSTRACT FROM AUTHOR]

The La Plata basin is one of the largest river basins in the world, draining about a fifth of the South American continent. It is a system of great hydrological significance, sensitive to extreme events, and also has the most economic development in the region. This work analyses the total water storage (TWS) changes over the La Plata basin from the GRACE (Gravity Recovery And Climate Experiment) mission and compares it with TRMM (Tropical Rainfall Measuring Mission) rainfall data. Subsequently, a study was conducted on the main sub-basins for the period 2003 to 2017 to identify regional-scale variations in TWS. Precipitation and GLDAS (Global Land Data Assimilation System) runoff data was evaluated in these areas, along with their connections to ENSO (El Niño Southern Oscillation) episodes. The results suggest that the majority of TWS variations are associated with extreme climatic events, such as the 2009 drought, and are linked to ENSO periods. The statistical analysis indicates a significant correlation between severe Niño/Niña episodes and changes in TWS in the sub-basins. [ABSTRACT FROM AUTHOR]

For the present study, a quantitative statistical approach has been used to understand the spatiotemporal hydrological variability in the Luni River Basin from 1980 to 2020. To delineate homogeneous precipitation regions, we utilized the Principal Component Analysis (PCA) and Ward's method of Hierarchical Clustering Analysis (HCA) on the precipitation-derived variables. Five homogenous precipitation regions were identified for the Luni River Basin, Pali-Ajmer (Region 1), Jodhpur-Nagaur (Region 2), Jodhpur-Jaisalmer (Region 3), Barmer-Balotra (Region 4) and northern part of the coastal reaches of Gujarat (Region 5). The calculation of wet and dry periods for a 3-month (or seasonal) scale has been undertaken using metrics like the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and self-calibrated Palmer Drought Severity Index (scPDSI) spanning for a period from 1980 to 2020. These indices indicate significant occurrences of major floods in the years 1990, 1996, 2001, 2006, 2010, 2016, and 2019, along with major droughts during 1984, 1987, and 2002. Cross-Wavelet Analysis (CWA) was utilized to discern the impact of large-scale climatic anomalies, including the Southern Oscillation Index (SOI), Pacific Sea Surface Temperature (SST), Multivariate ENSO Index (MEI), and Indian Ocean Dipole (IOD), on 3-month precipitation, revealing strong teleconnections with the basin's topographic variations and dynamic hydrology. Finally, the present study portrays the occurrences of various hydrological events, including floods and droughts, spanning the last four decades in an inland river basin such as the Luni, which is susceptible to major climatic phenomena such as El Niño, ENSO, SOI, SST and IOD. [ABSTRACT FROM AUTHOR]

The high diversity and biomass of organisms associated with coral communities depend directly on the maintenance or changes in the benthic composition. Over a decade, we evaluated the spatiotemporal variation in the benthic structure and composition of an insular coral community in the Northeastern Tropical Pacific. Our results show that local conditions drive spatiotemporal differences, and benthic organisms such as sponges, crustose coralline algae, octocorals, and hydrocorals all increased in abundance (cover) in response to negative thermal anomalies caused by the 2010–2011 La Niña event. In contrast, abnormally high temperatures, such as those recorded during the 2015–2016 El Niño Southern Oscillation (ENSO) event, explain the loss of scleractinian corals and crustose coralline algae coverage, which reduced the benthic groups' richness (BGR), diversity (H'BG), and evenness (J'BG), with evidence of a consequent decrease in ecosystem function recorded the following year. Our analysis also showed that sites with high habitat heterogeneity harbored higher average BRG and H'BG values and were less affected by environmental fluctuations than sites with high live scleractinian coral cover and lower BRG and H'BG values. Therefore, the benthic structure was impacted differently by the same perturbation, and changes in the benthic community composition affected the groups associated with the community and ecological functions. More importantly, regional stressors such as the ENSO event caused only temporary changes in the benthic community structure, demonstrating the high resilience of the community to annual and interannual stressors. [ABSTRACT FROM AUTHOR]

We explore the links between elevation variability of the Antarctic Ice Sheet (AIS) and large‐scale climate modes. Using multiple linear regression, we quantify the time‐cumulative effects of El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) on gridded AIS elevations. Cumulative ENSO and SAM explain a median of 29% of the partial variance and up to 85% in some coastal areas. After spatial smoothing, these signals have high spatial correlation with those from GRACE gravimetry (r∼ = 0.65 each). Much of the signal is removed by a firn densification model but inter‐model differences exist especially for ENSO. At the lower parts of the Thwaites and Pine Island glaciers, near their grounding line, we find the Amundsen Sea Low (ASL) explains ∼90% of the observed elevation variability. There, modeled firn effects explain only a small fraction of the variability, suggesting significant height changes could be a response to climatological ice‐dynamics. Plain Language Summary: This study investigates how variations in the height of the Antarctic Ice Sheet (AIS) are connected to large‐scale climate patterns. We used a statistical method to measure the effects of two climate phenomena: El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). We found that the cumulative effects of these phenomena account for about 29% of the variations in AIS height on average, and up to 85% in some coastal areas. These patterns match well with independent data from the GRACE satellites over the same period. Applying a model that considers the accumulation of snow and its compaction into ice (firn densification) removes much of this signal, suggesting much, but not all, of the signals are related to snowfall variations. At the fronts of the rapidly changing Thwaites and Pine Island glaciers, the dominant climate phenomenon is the Amundsen Sea Low (ASL), which varies in strength and location. Here, the cumulative effects of the ASL changes explain about 90% of the variations in height of these glaciers, with only a small part explained by firn effects. We suggest the unexplained variability could be partly due to changes in ice flow. Key Points: Cumulative effects of large‐scale climate modes dominate detrended altimeter time series of Antarctic ice elevation 2002–2020These decadal signals have the same spatial pattern in altimeter ice height and GRACE mass time seriesThese decadal signals are largely due to surface mass balance, but ice dynamic changes may play a role in the Amundsen Sea Embayment [ABSTRACT FROM AUTHOR]

Seasonal hindcasts have previously been demonstrated to show multi‐decadal variability in skill across the twentieth century in indices describing El‐Niño Southern Oscillation (ENSO), which drives global seasonal predictability. Here, we analyze the skill of predicting ENSO events' magnitude and spatial pattern, in the CSF‐20C coupled seasonal hindcasts in 1901–2010. We find minima in the skill of predicting the first (in 1930–1950) and second (in 1940–1960) principal components of sea‐surface temperature (SST) in the tropical Pacific. This minimum is also present in the spatial correlation of SSTs, in 1930–1960. The skill reduction is explained by lower ENSO magnitude and variance in 1930–1960, as well as decreased SST persistence. The SST skill minima project onto surface winds, leading to worse predictions in coupled hindcasts compared to hindcasts using prescribed SSTs. Questions remain about the offset between the first and second principal components' skill minima, and how the skill minima impact the extra‐tropics. Plain Language Summary: Seasonal forecasts use slowly changing elements of the climate system to predict average weather and climate several months ahead, and have applications across the energy, agriculture, and insurance industries. The most important signal is El‐Niño Southern Oscillation (ENSO), an ocean temperature pattern in the tropical Pacific which varies between warm and cold phases every 2–7 years. ENSO's specific pattern, including how warm/cold it is compared to normal, and where heating/cooling is concentrated, influences global weather. Seasonal forecasting models are tested by running 'reforecasts': comparing a 'forecast' of the past with observations. In this study, we investigate how skilfully reforecasts from a state‐of‐the‐art seasonal forecasting model reproduce observations, for sea‐surface temperatures in the tropical Pacific (where ENSO occurs). We analyze reforecast skill in every Northern Hemisphere winter from 1901 to 2010 and find periods of high skill at the start and end of the century, with reduced skill in 1930–1960. This was likely caused by a reduction in the size of ENSO events, and decreased sea‐surface temperature memory. This reduction in ocean skill leads to lower skill for surface‐level winds. These results are important for understanding whether season‐ahead predictability changes over decades/centuries, and how climate change might impact this predictability. Key Points: The skill of predicting the spatial pattern of ENSO events in coupled seasonal hindcasts is low in 1930–1960, compared to before and afterThe skill minimum is attributable to lower ENSO variability and decreased SST persistence in the mid‐century periodDecreased skill in the SST spatial pattern is linked to decreased surface wind skill in coupled hindcasts (vs. atmosphere‐only hindcasts) [ABSTRACT FROM AUTHOR]

Extreme El Niño events have outsized global impacts and control the El Niño Southern Oscillation (ENSO) warm/cold phases asymmetries. Yet, a consensus regarding the relative contributions of atmospheric and oceanic nonlinearities to their genesis remains elusive. Here, we isolate the contribution of oceanic nonlinearities by conducting paired experiments forced with opposite wind stress anomalies in an oceanic general circulation model, which realistically simulates extreme El Niño events and oceanic nonlinearities thought to contribute to ENSO skewness (Tropical Instability Waves (TIWs), Nonlinear Dynamical Heating (NDH)). Our findings indicate a weak contribution of oceanic nonlinearities to extreme El Niño events in the eastern Pacific, owing to compensatory effects between lateral (NDH and TIWs) and vertical processes. These results hold across different vertical mixing schemes and modifications of the upper‐ocean heat budget mixed layer criterion. Our study reinforces previous research underscoring the pivotal role of atmospheric nonlinearities in shaping extreme El Niño events. Plain Language Summary: The El Niño‐Southern Oscillation (ENSO) is the primary driver of year‐to‐year climate variations in the tropics and beyond. Originating from air‐sea interactions in the tropical Pacific, ENSO oscillates between warm (El Niño) and cold (La Niña) phases, modulating sea surface temperature in the central and eastern equatorial Pacific. Occasionally, El Niño events intensify into "super" El Niño events, causing widespread impacts globally. Utilizing a state‐of‐the‐art oceanic model, our research challenges previous results suggesting a strong oceanic contribution to the amplitude difference between "normal" and "super" El Niño events. Instead, our findings reveal that potential oceanic influences on "super" El Niño events tend to offset each other. This is consistent with recent research highlighting the crucial role of atmospheric processes in the transformation from a "normal" to a "super" El Niño. Key Points: A state‐of‐the‐art ocean model reproduces extreme El Niño events and the corresponding nonlinear oceanic processes realisticallyContributions from oceanic nonlinearities are isolated using paired simulations forced by opposite wind stress anomaliesEffects of oceanic nonlinearities on extreme El Niño events are small, due to compensation between lateral and vertical processes [ABSTRACT FROM AUTHOR]

The El Niño‐Southern Oscillation causes anomalous atmospheric circulation, temperature and precipitation across southern polar latitudes, but the influence of Central and Eastern Pacific El Niño events on Antarctic surface mass balance and snow accumulation has not yet been assessed. Here, we use reanalysis and reanalysis‐forced regional climate model output and find that Central Pacific El Niño results in significantly increased snow accumulation in the western Ross Sea sector and significantly decreased snow accumulation in the Amundsen Sea sector. Eastern Pacific El Niño is associated with similar but weaker patterns, with some regional exceptions. In some areas, like Dronning Maud Land, or the Wilkes Subglacial Basin, the effect of El Niño on snow accumulation changes from increased to reduced accumulation depending on the type of El Niño. Our results show that projecting El Niño types is important for constraining future changes in Antarctic surface mass balance. Plain Language Summary: The El Niño‐Southern Oscillation influences the climate in Antarctica with impacts on temperature, rainfall and snow accumulation. However, the effect of two different types of El Niño events, Central Pacific and Eastern Pacific El Niño, on the patterns of snow accumulation in Antarctica has not yet been studied. We show from reanalysis and reanalysis‐forced regional climate model output that Central and Eastern Pacific El Niño events have distinct effects on Antarctic snow accumulation patterns. During Central Pacific El Niño events, there is an increase in snow accumulation in the western Ross Sea sector, and decrease in the Amundsen Sea region. Eastern Pacific El Niño events cause similar regional effects, but to a lesser extent. In some areas, like Dronning Maud Land or the Wilkes Subglacial Basin, the effect of El Niño on snow accumulation changes from increased to reduced accumulation depending on the type of El Niño. Our findings emphasize the importance of understanding projected changes in El Niño types, as these will impact the future of Antarctica's snow accumulation. Key Points: Central and Eastern Pacific El Niño events have distinctly different seasonal impacts on Antarctic surface climateCentral Pacific El Niño accumulation anomalies develop earlier than Eastern Pacific El Niño, and persist through austral winter and springCentral Pacific El Niño impacts on West Antarctic accumulation are significantly greater than those of Eastern Pacific El Niño events [ABSTRACT FROM AUTHOR]

This study examines the large-scale atmosphere-ocean environments that led to the winter tornado outbreak across the Ohio Valley on 10–11 December 2021, also known as the Quad-States Tornado Outbreaks. Here, we show that the Quad-States Tornado Outbreaks occurred under an exceptionally strong and prolonged negative Pacific-North American pattern (PNA), which developed around December 1 and persisted for a month. This unusual PNA produced a strong atmospheric ridge along the south and eastern US seaboard, which in turn helped warm the Gulf of Mexico and produced large-scale environments conducive for tornadogenesis across the Ohio Valley. Further analysis shows that a broad region across the Ohio Valley is particularly vulnerable to extensive winter tornado outbreaks during long-lived negative PNA, whereas a limited region in the central US is exposed to winter tornado activity during short-lived negative PNA. Finally, although the PNA is a mode of internal variability that occurs with or without El Niño—Southern Oscillation, the occurrence of prolonged negative PNA is more frequent during La Niña than during El Niño. [ABSTRACT FROM AUTHOR]