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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]

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]

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]

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]

Polar sea surface height observation by radar altimeters requires missions with high-latitude orbit and specific processing to observe the sea-ice-covered region within fractures in the ice. Here, we combine sea surface height estimates from different radar satellites over the ice-free and ice-covered polar oceans to create cross-calibrated along-tracks and gridded products over the Arctic Ocean (2011-2021) and the Southern Ocean (2013-2021). The sea surface height from our regional polar products is in great agreement with tide gauges and bottom pressure recorders at monthly timescales in seasonally to year-round icecovered regions. Thanks to the use of several missions and the mapping strategy, our multi-mission products have a greater resolution than monomission products. Part of the sea level variability of the Arctic Ocean product is related to the Arctic Oscillation atmospheric circulation. At long term, the Arctic altimetry sea level is coherent with in-situ steric height evolution in the Beaufort gyre, and negative sea level trends over the 10-year period are observed in the East Siberian slope region, which may be related to the local freshwater decrease observed by other studies. Our regional polar sea level products are limited by current understanding of the sea-ice lead measurements, and homogenization of these polar products with global sea level products needs to be tackled. [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]

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]

Polar sea surface height observation by radar altimeters requires missions with high-latitude orbit and specific processing to observe the sea-ice-covered region within fractures in the ice. Here, we combine sea surface height estimates from different radar satellites over the ice-free and ice-covered polar oceans to create cross-calibrated along-tracks and gridded products over the Arctic Ocean (2011-2021) and the Southern Ocean (2013-2021). The sea surface height from our regional polar products is in great agreement with tide gauges and bottom pressure recorders at monthly timescales in seasonally to year-round icecovered regions. Thanks to the use of several missions and the mapping strategy, our multi-mission products have a greater resolution than monomission products. Part of the sea level variability of the Arctic Ocean product is related to the Arctic Oscillation atmospheric circulation. At long term, the Arctic altimetry sea level is coherent with in-situ steric height evolution in the Beaufort gyre, and negative sea level trends over the 10-year period are observed in the East Siberian slope region, which may be related to the local freshwater decrease observed by other studies. Our regional polar sea level products are limited by current understanding of the sea-ice lead measurements, and homogenization of these polar products with global sea level products needs to be tackled. [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]

This study aimed to analyse the temporal and spatial characteristics of drought in the Mekong Delta region. The influence of Sea Surface Temperature Anomalies (SSTAs) and the Southern Oscillation Index (SOI) in the Pacific and Indian Oceans on drought was also investigated. The drought was calculated using the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) indices at one-month and three-month timescales, respectively. A correlation equation is used to determine the relationships between the SSTA/SOI and the SPI/SPEI. The results showed that drought occurred in several months from 1990–1991, 1992–1993, 1995, 1998, 2002–2003, 2005–2006, 2010–2011, and 2015–2016, consistent with the occurrence of drought in the Mekong Delta. However, the SPEI3 reflected drought events more accurately than the other indices. The El Nino phenomenon had a relatively significant impact on drought in the study area. When an El Nino occurred, drought in the Mekong Delta lasted several months but often occurred later than the El Nino. Examining the relationships between the SSTA/SOI and the SPI/SPEI, we find that when the SST increases in the Nino 3.4 region, the NinoW decreases, and a negative SOI affects all drought events. Particularly when the SST increases in the Nino 3.4 region, there is a high probability of drought occurrence in the Mekong Delta. However, drought occurrence is typically 2 to 3 months later than the El Nino. The results will help managers find appropriate solutions to minimize the effects of drought on the research area. [ABSTRACT FROM AUTHOR]

Statistical descriptions of extreme met-ocean conditions are essential for the safe and reliable design and operation of structures in marine environments. The significant wave height ( H S ) is one of the most essential wave parameters for coastal and offshore structural design. Recent studies have reported that a time-varying component exists globally in the H S . Therefore, the non-stationary behavior of an annual maximum series of H S is important for various ocean engineering applications. This study aims to analyze the frequency of H S over the northern Indian Ocean by modeling the non-stationarity in the H S series using a non-stationary Generalized Extreme Value (GEV) distribution. The hourly maximum H S data (with a spatial resolution of 0.5° longitude × 0.5° latitude) collected from the global atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for the study. To model the annual maximum series of H S using a non-stationary GEV distribution, two physical covariates (El-Ni n ~ o Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) and time covariates are introduced into the location and scale parameters of the GEV distribution. The return levels of various frequencies of H S are estimated under non-stationary conditions. From the results, average increases of 13.46%, 13.66%, 13.85%, and 14.02% are observed over the study area for the 25-year, 50-year, 100-year, and 200-year return periods, respectively. A maximum percentage decrease of 33.3% and a percentage increase of 167% are observed in the return levels of various return periods. The changes in the non-stationary return levels over time highlight the importance of modeling the non-stationarity in H S . [ABSTRACT FROM AUTHOR]

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A comprehensive analysis was carried out to investigate the driving factors and influencing mechanisms of spatiotemporal variation of sea level at multiple scales in the East China Sea (ECS) via satellite altimetry datasets from 1993 to 2020. Based on the altimetry grid data processed by the local mean decomposition method, the spatiotemporal changes of ECS sea level are analyzed from the multi-scale perspective in terms of multi-year, seasonal, interannual, and multi-modal scales. The results revealed that the ECS regional mean sea level change rate is 3.41 ± 0.58 mm/year over the 28-year period. On the seasonal scale, the regional mean sea level change rates are 3.45 ± 0.66 mm/year, 3.35 ± 0.60 mm/year, 3.39 ± 0.71 mm/year, and 3.57 ± 0.75 mm/year, for the four seasons (i.e., spring, summer, autumn, and winter) respectively. The spatial distribution analysis showed that ECS sea level changes are most pronounced in coastal areas. The northeast sea area of Taiwan and the edge of the East China Sea shelf are important areas of mesoscale eddy activity, which have an important impact on regional sea level change. The ECS seasonal sea level change is mainly affected by monsoons, precipitation, and temperature changes. The spatial distribution analysis indicated that the impact factors, including seawater thermal expansion, monsoons, ENSO, and the Kuroshio Current, dominated the ECS seasonal sea level change. Additionally, the ENSO and Kuroshio Current collectively affect the spatial distribution characteristics. Additionally, the empirical orthogonal function was employed to analyze the three modes of ECS regional sea level change, with the first three modes contributing 26.37%, 12.32%, and 10.47%, respectively. Spatially, the first mode mainly corresponds to ENSO index, whereas the second and third modes are linked to seasonal factors, and exhibit antiphase effects. The analyzed correlations between the ECS sea level change and southern oscillation index (SOI), revealed the consistent spatial characteristics between the regions affected by ENSO and those by the Kuroshio Current. [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]

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks. [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]

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]

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]

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]

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]

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]

Low–lying coral islands are susceptible to rising sea levels and climate change, posing risks to island habitability over the next century. The decadal timescale evolution of the islands can provide an understanding of the governing controls of change and island response. This study investigates variations in the shoreline morphology of the Lakshadweep coral islands (Northern Indian Ocean/Arabian Sea) using satellite datasets (2003–2022; CNES/Airbus;0.3m – 0.7m resolution) and sedimentological data. Of the thirty islands (ten inhabited), six islands (Bangaram, Thinnakara, Suheli, Minicoy, Androth and Bitra) have been studied. Most of these islands are less than 1 km² in size except Androth and Minicoy (>4 km² ). While the data is discontinuous for the islands due to cloud obscuring, the shoreline morphology depicts variations for all the islands’ studied, irrespective of habitation and size. Larger islands have undergone consistent erosion since 2007 (total land loss is approximately 3 - 5% cumulatively). Smaller islands have undergone lateral migration, with sediment erosion usually along the southern edge and sediment migration and accumulation northwards, however, overall, their size remains consistently stable. The migration of sediments is observed only from 2007–2017, which coincides with severe El–Nino Southern Oscillation (ENSO) and low amplitude positive Indian Ocean Dipole (IOD) events. Furthermore, sedimentological analysis along Bangaram’s (small island) accreting margin reveals unconsolidated bio-detrital grainstones with predominant sand-sized fractions (2 – 0.25mm). Corals (43%), molluscs (33%), forams (13%), and algae (8%) are the major sediment producers (with 3% unidentified bioclasts). The ongoing climate warming and ocean acidification will influence the carbonate sediment budget in addition to the changing hydrodynamics owing to monsoons, cyclones, and coupled ocean-atmospheric regional phenomenon, altering the sediment production, transport and accumulation on these islands. Furthermore, rising sea levels and consequent erosion by wave action might lead to rapid drowning in the next few centuries. Our study identifies the current status of the island size variability in the Lakshadweep Archipelago and how site-specific and global factors influence them, thus providing insights into assessing the habitability within these low-lying coral islands. [ABSTRACT FROM AUTHOR]

The Arabian Sea (AS), a basin in the North Indian Ocean (NIO), possesses strong coastal upwelling in its eastern and western margins. The spatio-temporal variability of upwelling over Somalia, Oman and the southwest coast of India is analysed using observed datasets. The spectral analysis of upwelling indices highlights that the semi-annual and annual frequencies were dominant by two orders of magnitude compared with the inter-annual variability over these upwelling zones. The inter-annual variability of coastal upwelling was demarcated with variations in the causative forcing of upwelling associated with the positive modes of Indian Ocean Dipole (pIOD), El-Niño Southern Oscillation (El-Niño) and combined pIOD and El-Niño events over the southwest coast of India (SWCI) and Somalia. Along SWCI, the sea surface temperature and meridional wind stress weakened leading to warming associated with the suppression of upwelling during El-Niño, pIOD and combined events. Along Somalia, the upwelling favourable wind stress showed slight weakening due to pIOD and strong decline due to El-Niño and combined modes. Along Oman, the upwelling favourable wind stress indicated negligible changes due to pIOD, an increase due to El-Niño and a slight decline due to combined modes. [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]

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]

Droughts have a substantial impact on water supplies, agriculture, and ecosystems worldwide. Agricultural sustainability and production in the Mekong Delta of Vietnam are being jeopardized by droughts caused by climate change. Conventional forecasting methods frequently struggle to comprehend the intricate dynamics of meteorological occurrences connected to drought, necessitating the use of sophisticated prediction techniques. This study assesses the effectiveness of various statistical models (ARIMA), machine learning, and deep learning models (Gradient Boosting, XGBoost, RNN, and LSTM) in forecasting the SPEI over different time periods (1, 3, 6, and 12 months) across six prediction intervals. The models were developed and evaluated using data from 11 meteorological stations spanning from 1985 to 2022. These models incorporated various climatic variables, including precipitation, temperature, humidity, potential evapotranspiration (PET), Southern Oscillation Index (SOI) Anomaly, and sea surface temperature in the NINO4 region (SST_NINO4). The results demonstrate that XGBoost and LSTM models exhibit outstanding performance, showcasing lower error metrics and higher R² values compared to Gradient Boosting and RNN. The performance of the model fluctuated depending on the forecast step, with error metrics often increasing with longer prediction horizons. The use of climatic indices improved the accuracy of the model. These findings are consistent with earlier research on drought episodes in the Mekong Delta and support studies from other areas that show the effectiveness of advanced modeling tools for predicting droughts. The work emphasizes the capacity of machine learning and deep learning models to enhance the precision of drought forecasting, which is vital for efficient water resource management and agricultural planning in places prone to drought. [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]

Using fifth-generation ECMWF Atmospheric Reanalysis (ERA-5) we investigate the topical changes in global teleconnection with seasonal precipitation dynamics in the backdrop of climate change particularly for the recent 20 years (L20:2002 to 2021) with respect to the previous 20 years (F20: 1982–2001). It unveils a considerable decrease in both monsoon and pre-monsoon precipitation over the Northwest Himalaya (NWH) in the Indian region and a decline in winter precipitation over the Tibetan Plateau (TP). Statistically robust K-S test shows notable changes (5% significance level) in the regional distribution of the atmospheric parameters in parts of the High Mountain Asia (HMA) region in recent decades (L20). Further, a weakening of the North Atlantic Oscillation (NAO) and El-Nino Southern Oscillation (ENSO) influence on precipitation pattern is noted in the L20 (2002–2021) compared to the F20 (1982–2001) years over parts of the HMA region. A regional heterogeneity is perceptible among different parts of HMA i.e. NWH in the Indian region and the TP. The NWH precipitation shows a diametrically opposite relation with the NAO index for winter precipitation in L20 (correlation coefficient R= -0.18, sample size: 240) compared to a positive correlation in F20 (correlation coefficient R = 0.15, sample size: 240) which is significant at 5% significance level. The plausible reasons for such a diametrical change in the relationship during winter are addressed by examining the changing signature of the subtropical jet stream and its relationship with NAO and ENSO. Sea surface temperature shows cooling of the tropical Pacific and the resulting weakening in El-Nino signatures in recent years. Our results point to the noteworthy changes in the atmospheric circulation pattern over the HMA region in recent decades which is attributed to the alteration in the global teleconnection patterns and subsequent their association with the precipitation distribution over the HMA region. This study would be useful for an improved understanding of the precipitation dynamics over the HMA region and global teleconnections in the future. [ABSTRACT FROM AUTHOR]

This study assesses the skill of existing indices and newly introduced sea surface temperature (SST) indices in predicting rainfall over East Africa (EA) during short rains from September to December (SOND) and long rains from March to May (MAM). The existing and newly introduced indices were selected based on grid point correlation and spatial mean correlation. West–east SST gradient over the Indian Ocean (WEIO), north–south SST gradient over the Pacific Ocean (NSPO), anomalies over the western Indian Ocean (WIO) and anomalies over the eastern Pacific Ocean (EPO) from newly developed indices, and bivariate ENSO time series (BEST), Oceanic Niño Index (ONI), Niño3.4, western Pacific gradient (WPG) and dipole index moment (DMI) from existing indices have a positive correlation with SOND rainfall at 5% (p<0.05) significance level. West–east SST gradient over the Pacific Ocean (WEPO) and SST anomalies over the southern Pacific Ocean (SPO) from new indices and Southern Oscillation Index (SOI) from existing indices have a significant negative correlation with SOND rainfall over EA. SST anomalies over central and eastern Pacific and Indian oceans significantly correlate with SOND rainfall over southern EA (SEA) and equatorial EA (EEA). Rainfall over northern EA (NEA) has a significant negative correlation with SST over the east and central Pacific oceans. Negative correlation extends northeast and southeast from North of Australia over the Pacific Ocean during MAM. WEIO, WIO, NSPO, Niño3.4, Atlantic Oscillation (AO) and WPG have shown strong and significant (p<0.05) relations at least over one subregion. The new SST and existing oceanic indices have a stronger and more significant correlation with short rains than long rains over EA, except in the northern part of EA. Correlation, RMSE and skill score of regression models revealed that new indices showed higher performance than existing indices over EEA and SEA in predicting rainfall, unlike over NEA during both seasons. [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]

Using ancillary datasets and interpolation schemes, 20+ years of the Munida Time Series (MTS) observations were used to evaluate the seasonal to decadal variability in the regional carbon cycle off the southeast coast of New Zealand. The contributions of gas exchange, surface freshwater flux, physical transport processes and biological productivity to mixed layer carbon were diagnostically assessed using a mass-balanced surface ocean model. The seasonal and interannual variability in this region is dominated by horizontal advection of water with higher dissolved inorganic carbon (DIC) concentration primarily transported by the Southland Current, a unique feature in this western boundary current system. The large advection term is primarily balanced by net community production and calcium carbonate production, maintaining a net sink for atmospheric CO2 with a mean flux of 0.84±0.62 mol C m-2 y-1. However, surface layer pCO2 shows significant decadal variability, with the growth rate of 0.53±0.26 μatm yr-1 during 1998–2010 increasing to 2.24±0.47 μatm yr-1 during 2010–2019, driven by changes in advection and heat content. Changes in circulation have resulted in the regional sink for anthropogenic CO2 being 50% higher and pH 0.011±.003 higher than if there had been no long-term changes in circulation. Detrended cross-correlation analysis was used to evaluate correlations between the Southern Annular Mode, the Southern Oscillation Index and various regional DIC properties and physical oceanographic processes over frequencies corresponding the duration of the MTS. The drivers of variability in the regional carbon cycle and acidification rate indicate sensitivity of the region to climate change and associated impacts on the Southern Ocean and South Pacific. [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]

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]

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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 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]

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]