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The Southwest Atlantic (SWA) is an important region for the Caretta caretta characterized by unique genetic lineages; however, their demographic evolution is still misunderstood. In this study, we evaluated the demographic patterns of four SWA rookeries using D-loop and microsatellites data looking for expansion and bottlenecks signals. Then, we simulated several colonization scenarios for the SWA using Approximate Bayesian Computation. The best-supported scenario indicated that loggerheads might have colonized the SWA region once by the ancient lineage of ES/k3 that signals a sharing ancestry history, and from it originated the other lineages by divergence and introgression processes, explaining the high admixture levels between their rookeries and genetic clusters. The D-loop recovered population stability in the past. Still, microsatellites identified sharp recent bottleneck events, which the Last Glacial Maximum, El Niño Southern Oscillation, and anthropogenic actions may have triggered. Thus, we provide, for the first time, a complete assessment of the life history and colonization of loggerhead into the SWA, demonstrating differences between markers (matrilinear and biparental) that may bias our understanding of their genetic and demographic patterns, and which should be considered for conservation programs at a global scale. [ABSTRACT FROM AUTHOR]

Using a Green's function‐like approach, this study identifies optimal atmospheric heat sources for the two leading modes of South Asian Summer Monsoon (SASM) interannual variability. Optimal heating for the first mode, characterized by a lower‐level anomalous anticyclone over northern Bay of Bengal (BOB), is distributed over the Arabian Sea and tropical eastern Indian Ocean (EIO)‐Maritime Continent, with cooling over the BOB‐western North Pacific. In contrast, heating over the tropical southwestern Indian Ocean and equatorial Atlantic, along with cooling over the tropical EIO‐western Pacific, optimally drives the second mode, featuring an anomalous anticyclone over central‐northern India. El Niño/Southern Oscillation indirectly influences SASM by triggering heat sources resembling these optimal patterns. Other sea surface temperatures (SSTs), like those over equatorial Atlantic, can also generate similar heating structures, causing corresponding SASM anomalies. This suggests that the impact of SST modes on SASM depends on the similarity of induced heat sources to optimal patterns. Plain Language Summary: The principal origins of South Asian summer monsoon (SASM) interannual variability are rooted in the combined modulation of three tropical oceans, which challenges its prediction due to complex multi‐ocean interactions. Atmospheric heat sources, however, can directly impact SASM, acting as a bridge connecting sea surface temperature (SST) and SASM. Here we identify optimal atmospheric heat sources for the two leading modes of SASM interannual variability using a Green's function‐like approach. Optimal heating for the first mode, characterized by a lower‐level anomalous anticyclone over northern Bay of Bengal (BOB), is distributed over the Arabian Sea and the tropical eastern Indian Ocean (EIO)‐Maritime Continent, with cooling over the BOB‐western North Pacific. In contrast, heating over the tropical southwestern Indian Ocean and equatorial Atlantic, along with cooling over the tropical EIO‐western Pacific, optimally drives the second SASM mode, featuring a lower‐level anomalous anticyclone over central‐northern India. El Niño/Southern Oscillation indirectly influences SASM by triggering heat sources resembling these optimal patterns. Other SSTs, such as those over the equatorial Atlantic, can also generate similar heating structures as the optimal heat sources, further impacting corresponding SASM modes. Optimal heat sources provide a possible way to understand the combined effects of different SST modes on SASM. Key Points: Optimal heat sources for the South Asian summer monsoon (SASM) interannual variability are identified using a Green's function‐like methodThe El Niño/Southern Oscillation indirectly impacts SASM by triggering heat sources resembling the optimal patternsOther sea surface temperature anomalies can also generate similar heating structures to the optimal patterns, leading to SASM anomalies [ABSTRACT FROM AUTHOR]

The Eastern South Pacific Right Whale (SRW) (Eubalaena australis) population has gained interest due to its Critically Endangered conservation status. So far, this population has been confirmed only along the coasts of Chile (18°20'S to 56°30'S) and from southern to central Peru (17°38'S to 12°11'S). Recent records have extended the species' known range, highlighting its geographic distribution, now reaching 1500 km north. Here, we report six recent records, consisting of five sightings and one stranding, that expand the documented range to northern Ecuador (0.6°N). The northern extension of the population may be associated with the unusual three-year-long cold phase (La Niña) of the El Niño Southern Oscillation (ENSO) in the eastern South Pacific, population expansion, movement and re-distribution of the species, increased monitoring effort, or a combination of these factors. These observations raise hope for the Critically Endangered SRW population, as the occurrence of mother-calf pairs may indicate a potential for population recovery. Nevertheless, these findings intensify concerns for what is still the least abundant SRW population, underscoring the urgency for more targeted research and conservation measures. [ABSTRACT FROM AUTHOR]

Seasonal precipitation forecasting (SPF) is critical for effective water resource management and risk mitigation. Large-scale climate drivers significantly influence regional climatic patterns and forecast accuracy. This study establishes relationships between key climate drivers—El Niño–Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), Real-time Multivariate Madden–Julian Oscillation (MJO), and Multivariate ENSO Index (MEI)—and seasonal precipitation anomalies (rainy, summer, and winter) in Eastern Thailand, utilizing Pearson's correlation coefficient. Following the establishment of these correlations, the most influential drivers were incorporated into the forecasting models. This study proposed an advanced SPF methodology for Eastern Thailand through a Seasonal WaveNet-LSTM model, which integrates Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs) with Wavelet Transformation (WT). By integrating large-scale climate drivers alongside key meteorological variables, the model achieves superior predictive accuracy compared to traditional LSTM models across all seasons. During the rainy season, the WaveNet-LSTM model (SPF-3) achieved a coefficient of determination (R2) of 0.91, a normalized root mean square error (NRMSE) of 8.68%, a false alarm rate (FAR) of 0.03, and a critical success index (CSI) of 0.97, indicating minimal error and exceptional event detection capabilities. In contrast, traditional LSTM models yielded an R2 of 0.85, an NRMSE of 10.28%, a FAR of 0.20, and a CSI of 0.80. For the summer season, the WaveNet-LSTM model (SPF-1) outperformed the traditional model with an R2 of 0.87 (compared to 0.50 for the traditional model), an NRMSE of 12.01% (versus 25.37%), a FAR of 0.09 (versus 0.30), and a CSI of 0.83 (versus 0.60). In the winter season, the WaveNet-LSTM model demonstrated similar improvements, achieving an R2 of 0.79 and an NRMSE of 13.69%, with a FAR of 0.23, compared to the traditional LSTM's R2 of 0.20 and NRMSE of 41.46%. These results highlight the superior reliability and accuracy of the WaveNet-LSTM model for operational seasonal precipitation forecasting (SPF). The integration of large-scale climate drivers and wavelet-decomposed features significantly enhances forecasting performance, underscoring the importance of selecting appropriate predictors for climatological and hydrological studies. [ABSTRACT FROM AUTHOR]

Numerous studies, spanning experimental, instrumental, historical, and modeled approaches, have delved into understanding climate change across the Holocene era and millennial-scale occurrences. However, the chronology and causes of centennial-scale climate events during the Holocene remain controversial. In this study, we overviewed 10 of the best-resolved and most accurately dated records detailing climate change in the Northern Hemisphere (NH) over the Holocene, obtained from different proxies across different climatic zones, and constructed a stack of temperature changes in the NH. Based on the constructed stack, we identified and categorized 15 notable Holocene centennial cooling events (HCCEs) in the NH (period with temperature decreases). To test the chronological validity of the constructed HCCEs, we compared them with the most accurately dated and highly resolved climate records during the last 3 kyr, which have been extensively investigated by the scientific community. Based on the close alignment of the outlined HCCEs with temperature records, we suggest that other HCCEs also match centennial climate cooling events over the last 10 kyr. To understand the origins of the established HCCEs, we compared them with potential climate influencing factors: total solar irradiance (TSI), explosive volcanic activity, Atlantic meridional overturning circulation (AMOC)-limited slowdowns, Intertropical Convergence Zone (ITCZ) fluctuations, and El Niño/Southern Oscillation (ENSO variability. Early Holocene HCCE 5, terminated by a prominent 8.2-ka cold event, was likely driven by the superposition of the AMOC limited slowdown, TSI minimum, and volcanic activity. The Holocene Thermal Maximum (HTM) happened between HCCEs 5 and 4a and was interrupted by HCCE 4c and 4b, coeval, with a significant southward shift of the ITCZ, likely related to cooling in the tropical zone. However, the sequence of HCCEs 3b, 3a, and 2b (over 4.53–3.42 BP), accompanied by small changes in the TSI, was likely forced by an increase in ENSO variability, leading to remarkable changes in the tropical processes and a southward shift of the ITCZ, coeval with the collapse of the Chinese Neolithic cultures and onset of the Holocene Neoglacial. Subsequent HCCEs 2a–0a were likely forced by the TSI minimum combined with the influence of ENSO and volcanism over the last 2 ka. [ABSTRACT FROM AUTHOR]

The examination of the spatiotemporal characteristics and developmental trends of drought is crucial for enhancing water resource management, bolstering drought resistance, and improving disaster prevention capabilities. This study employs the Standardized Precipitation Evapotranspiration Index (SPEI) and grain yield data across various time scales, in conjunction with methodologies such as Run Theory, Mann-Kendall, and Standardized Yield Residual Series, to conduct an in-depth investigation into the spatiotemporal variation characteristics of meteorological drought in Hunan Province and its impact on grain yield. The findings suggest that: (1) Since 1960, the likelihood of seasonal drought occurrence in Hunan Province has been ranked as autumn > winter > spring > summer, with mild drought occurring most frequently, followed by moderate drought, while the frequency of severe and extreme drought remains low. (2) Meteorological drought in Hunan Province exhibits spatial differences at the seasonal scale, with the overall drought changes in spring and summer displaying a non-significant upward trend; the western and southern regions exhibit a trend of aridification in autumn; and in winter, the Zhangjiajie and Xiangxi regions show an insignificant downward trend. (3) From 1960 to 2022, grain production in Hunan Province has demonstrated a pattern of fluctuation and increase. The meteorological yield of grain crops displays a high-low-high spatial distribution from south to north. Concurrently, there is a positive correlation between short-term climate change and meteorological output, while long-term climate change is not evident. (4) El Niño Southern Oscillation (ENSO) is a significant circulation factor affecting meteorological drought in Hunan Province, and the meteorological drought in autumn and winter in Hunan Province is significantly influenced by ENSO. The research findings can provide reference significance and a scientific basis for drought research and comprehensive management in Hunan Province, and offer data and theoretical support for promoting economic development. [ABSTRACT FROM AUTHOR]

Surface air temperature (SAT) variability is investigated for advancing our understanding of the climate patterns over the Kingdom of Saudi Arabia (KSA). SAT variability reveals significant warming trends, particularly from 1994 onward, as demonstrated by nonlinear and linear trend analysis. This warming is linked to global climate patterns, which serve as significant indicators for studying the effects of climate change on surface air temperature patterns across the KSA. The empirical orthogonal function (EOF) method is employed for analyzing SAT due to its effectiveness in extracting dominant patterns of variability during the winter (DJF) and summer (JJA) seasons. The first mode (EOF1) for both seasons shows positive variability across the KSA, explaining more than 45% of the variance. The second mode (EOF2) indicates negative variability in central and northern regions. The third mode (EOF3) describes positive variability but with lower variance over time. PC1 is used to describe the physical mechanism of SAT variability and correlations with global sea surface temperature (SST). The physical mechanism shows that the variability in Mediterranean troughs during the winter season and high pressure over the Indian Ocean and central Asia controls SAT variability over the KSA. The correlation coefficients (CCs) were calculated during the winter and summer season between the SAT of the KSA and six teleconnection indices, El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Atlantic Meridional Mode (AMM), Pacific Warm Pool (PWP), North Atlantic Oscillation (NAO), and Tropical North Atlantic (TNA) SST for the period from 1994 to 2022. ENSO shifts from positive to negative correlations with SAT from winter to summer. IOD shows a diminished correlation with SAT due to the absence of upper air dynamics. PWP consistently enhances surface warming in both seasons through upper air convergence during both seasons. AMM and NAO have a non-significant impact on SAT; however, TNA contributes warming over central and northern parts during winter and summer seasons. The seasonal SAT variations emphasize the significant role of ENSO, PWP, and TNA across the seasons. The findings of this study can be helpful for seasonal predictability in the KSA. [ABSTRACT FROM AUTHOR]

Interdecadal variations, since the middle of the 20th century, in the seasonal cycle of Southern Hemisphere extratropical synoptic scale weather systems, are studied and related to associated anomalies in Southern Australian rainfall over south-west Western Australia (SWWA) and southeast Australia (SEA). A data-driven method is employed in which atmospheric fluctuations, specified from 6-hourly lower-tropospheric reanalysis data, are spectrally analysed in space and time to determine the statistics of the intensity and growth rates of growing and decaying eddies. Extratropical storms, blocking and north-west cloud band weather types are investigated in two frequency bands, with periods less than 4 days and between 4 and 8 days, and in three growth rate and three decay rate bins. Southern Australian rainfall variability is found to be most related to changes in explosive storms particularly in autumn and winter. During the first 10 years of the Australian Millennium Drought (AMD), from 1997 to 2006, dramatic changes in rainfall and storminess occurred. Rainfall declines ensued over SEA in all seasons, associated with corresponding reductions in the intensity of fast-growing storms with periods less than 4 days. These changes, compared with the 20-year timespans of 1949 to 1968 and 1975 to 1994, also took place for the longer duration of 1997 to 2016, apart from summer. Over SWWA, autumn and winter rainfall totals have decreased systematically with time for each of the 10-year and 20-year timespans analysed. Southern Australian rainfall variability is also found to be closely related to the local, hemispheric or global features of the circulation of the atmosphere and oceans that we characterise by indices. Local circulation indices of sea level pressure and 700 hPa zonal winds are good predictors of SWWA and SEA annual rainfall variability particularly in autumn and winter with vertical velocity generally less so. The new Subtropical Atmospheric Jet (SAJ) and the Southern Ocean Regional Dipole (SORD) indices are found to be the most skilful non-local predictors of cool season SWWA rainfall variability on annual and decadal timescales. The Indian Ocean Dipole (IOD) and Southern Oscillation Index (SOI) are the strongest non-local predictors of SEA annual rainfall variability from autumn through to late spring, while on the decadal timescale, different indices dominate for different 3-month periods. [ABSTRACT FROM AUTHOR]

Precipitation and air temperature have decreased and increased, respectively, in the three biomes of the state of Mato Grosso (Amazon - AMZ, Cerrado - CER, and Pantanal - PAN), making them susceptible to increased drought. The objective of this study was to analyze the trends of severity, intensity and duration of drought events between 1970 and 2022, and their relationships with sea surface temperature anomalies (SSTA) and the Southern Oscillation Index (SOI). The Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) were calculated using precipitation and air temperature data from the ERA5-land product. The trends of the SPI and SPEI were obtained using the Mann-Kendall (MK) test, and the number of occurrences, severity, intensity, and duration were obtained using the Run-Theory method. The results showed that the SPI and SPEI tended to decrease in the three biomes between 1970 and 2022. Moderate, severe and extreme drought events occurred in all biomes, with emphasis on the highest drought severity in the Pantanal between 2019 and 2022. The highest number of occurrences of extreme droughts by the SPI occurred in AMZ and CER between 2020 and 2022, and the highest values of severity, duration and intensity occurred in CER and PAN in the same period. However, the values of severity, duration and intensity by the SPEI occurred in AMZ and CER. The SPI and SPEI had a similar correlation pattern with the SSTA and SOI indices. The SPI had weak negative correlation with the Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index (SOI), Tropical Northern Atlantic Index (TNAI) and Tropical Southern Atlantic Index (TSAI), while the SPEI had moderate negative correlation with AMO and TSAI. In conclusion, the results of this study showed that the severity of drought in Mato Grosso increased between 1970 and 2022. The severity of drought in the Pantanal was particularly high between 2019 and 2022. SSTA anomalies and the SOI had some influence on the seasonal and interannual dynamics of drought in Mato Grosso, but not all SSTA anomalies had influence. [ABSTRACT FROM AUTHOR]

In this study we analyse projections of future changes to the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM) using the latest generation of climate models. Multiple future scenarios are considered. We quantify the fraction of models that project future increases or decreases in the frequency and amplitude of ENSO, IOD, and SAM events in the late 21st century. Changes to the frequency of co‐occurring and consecutive driver phases are also examined. We find that while there is large inter‐model spread, the most common pathways correspond to more frequent ENSO events; weaker, less frequent IOD events; and stronger, but less frequent austral spring SAM events. There is no clear consensus on the change to the frequency of concurrent events, though we find a significant increase in La Niña‐ and El Niño‐only events occurring with neutral IOD and SAM. We also find a significant increase to the frequency of consecutive positive IOD events under a high emissions scenario, but no significant change to the frequency of consecutive ENSO or negative IOD events. In most models, the correlation between drivers, that is, ENSO and IOD, and ENSO and SAM, does not significantly change between the late 20th and late 21st century. These results indicate a high degree of internal variability in the models. Plain Language Summary: Year‐to‐year climate variability impacts ecosystems and billions of people around the world. Major drivers of this variability in the Southern Hemisphere include the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Southern Annular Mode (SAM). As anthropogenic activities continue to impact the climate, and further change is inevitable, we need to improve understanding of how these climate drivers will change. Here we use the latest generation of climate models to investigate how the strength and frequency of these three climate drivers are projected to change over the remainder of the 21st century. We investigate changes to these drivers individually, as well as changes to the frequency of these drivers occurring simultaneously or consecutively. While there is still a large amount of inter‐model spread, the most common model projections are: more frequent ENSO events; weaker and less frequent IOD events; and stronger, but less frequent SAM events. A statistically significant increase in the frequency of consecutive positive IOD events in the high emissions scenario is also found. Key Points: Using all available models and ensemble members, the latest generation of climate models project an overall increase to the frequency of El Niño‐Southern Oscillation (ENSO) events but a weakening and decrease in Indian Ocean Dipole (IOD) events in the late 21st century. The Southern Annular Mode (SAM) is also projected to strengthen in austral springThere is less consensus among models on projections of frequency change of concurrent (co‐occurring) climate driver phases and consecutive ENSO and IOD phasesInternal variability remains a large source of uncertainty. Multi‐model mean results can differ markedly depending on the subset of models used [ABSTRACT FROM AUTHOR]

Ice core oxygen isotope (δ18O) records from low-latitude regions preserve high-resolution climate records in the past, yet the interpretation of these ice core δ18O records is still facing difficulty due to the uncertainty of ice core dating. Here we present a new established δ18O time series from Qiangtang (QT) No. 1 ice core retrieved from the central Tibetan Plateau. Due to the vague seasonal signals in the QT ice core, we investigated the spectral properties of δ18O record with depth and discussed the implications of significant spectral power peaks in the QT ice core. We employed a variational mode decomposition (VMD) analysis for the upper part of the QT ice core to decompose the δ18O depth series in order to separate the El Niño Southern Oscillation (ENSO) mode, a signal strongly preserved in the QT ice core δ18O record. With this approach, we established a time series of 335 years (1677–2011 CE) for the upper 50 m of the QT ice core. Subsequently, we examined the frequency of the new established δ18O time series and detected strong signals of the bidecadal and multidecadal modes of Pacific Decadal Oscillation (PDO). The PDO consists of two modes with periods of approximately 25–35 years and 50–70 years, and we found that the 50–70 years periodicity has persisted since 1700 CE, succeeded by dominance of the 25–75 years periodicity after 1900 CE. Additionally, we analyzed the δ18O series of the QT ice core during the past century and determined that the increasing frequency of El Niño events is an important factor contributing to the increase in recent ice core δ18O. [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 spatial and temporal distribution of heavy rainfall across the Taihang Mountains exhibits significant variation. Due to the region's unstable geological conditions, frequent heavy rainfall events can lead to secondary disasters such as landslides, debris flows, and floods, thus intensifying both the frequency and severity of extreme events. Understanding the spatiotemporal evolution of heavy rainfall and its response to atmospheric circulation patterns is crucial for effective disaster prevention and mitigation. This study utilized daily precipitation data from 13 meteorological stations in the Taihang Mountains spanning from 1973 to 2022, employing Rotated Empirical Orthogonal Function (REOF), the Mann–Kendall Trend Test, and Continuous Wavelet Transform (CWT) to examine the spatiotemporal characteristics of heavy rainfall and its relationship with large-scale atmospheric circulation patterns. The results reveal that: (1) Heavy rainfall in the Taihang Mountains can be categorized into six distinct regions, each demonstrating significant spatial heterogeneity. Region I, situated in the transition zone between the plains and mountains, experiences increased rainfall due to orographic lifting, while Region IV, located in the southeast, receives the highest rainfall, driven primarily by monsoon lifting. Conversely, Regions III and VI receive comparatively less precipitation, with Region VI, located in the northern hilly area, experiencing the lowest rainfall. (2) Over the past 50 years, all regions have experienced an upward trend in heavy rainfall, with Region II showing a notable increase at a rate of 14.4 mm per decade, a trend closely linked to the intensification of the hydrological cycle driven by global warming. (3) The CWT results reveal significant 2–3-year periodic fluctuations in rainfall across all regions, aligning with the quasi-biennial oscillation (QBO) characteristic of the East Asian summer monsoon, offering valuable insights for future climate predictions. (4) Correlation and wavelet coherence analyses indicate that rainfall in Regions II, III, and IV is positively correlated with the Southern Oscillation Index (SOI) and the Pacific Warm Pool (PWP), while showing a negative correlation with the Pacific Decadal Oscillation (PDO). Rainfall in Region I is negatively correlated with the Indian Ocean Dipole (IOD). These climatic factors exhibit a lag effect on rainfall patterns. Incorporating these climatic factors into future rainfall prediction models is expected to enhance forecast accuracy. This study integrates REOF analysis with large-scale circulation patterns to uncover the complex spatiotemporal relationships between heavy rainfall and climatic drivers, offering new insights into improving heavy rainfall event forecasting in the Taihang Mountains. The complex topography of the Taihang Mountains, combined with unstable geological conditions, leads to uneven spatial distribution of heavy rainfall, which can easily trigger secondary disasters such as landslides, debris flows, and floods. This, in turn, further increases the frequency and severity of extreme events. [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]

Studies have analyzed U.S. tornado variability and correlated F1–F5 tornado occurrence with various natural climate oscillations and anthropogenic factors. Using a relatively new empirical mode decommission (EMD) method that extracts time-frequency modes adaptively without priori assumptions like traditional time-series analysis methods, this study decomposes U.S. tornado variability during 1954–2022 into intrinsic modes on specific temporal scales. Correlating the intrinsic mode functions (IMFs) of EMD with climate indices found that 1. the U.S. overall tornado count is negatively (positively) correlated with the Atlantic Multidecadal Oscillation (AMO) index (the Southern Oscillation Index (SOI)); 2. the negative (positive) correlation tends to be more prevalent in the western (eastern) U.S.; 3. the increase in weak (F1–F2) and decrease in strong (F3–F5) tornadoes after around 2000, when both the AMO and the Pacific Decadal Oscillation (PDO) shifted phases, are likely related to their secular trends and low-frequency IMFs; and 4. the emerging Dixie Tornado Alley coincides with an amplifying intrinsic mode of the SOI that correlates positively with the eastern U.S. and Dixie Alley tornadoes. The long-term persistence of these climate indices can offer potential guidance for future planning for tornado hazards. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis on the variations in the tropical cyclone (TC) frequencies during 1980–2021, including the linear trends, periodicities, and their variabilities on both global and basin-wise scales. An increasing trend in the annual number of global TCs is identified, with a significant rising trend in the numbers of tropical storms (maximum sustained wind 35   k t s ≤ U m a x < 64   k t s ) and intense typhoons ( U m a x ≥ 96   k t s ) and a deceasing trend for weak typhoons ( 64   k t s ≤ U m a x < 96   k t s ). There is no statistically significant trend shown in the global Accumulated Cyclone Energy (ACE). On a regional scale, the Western North Pacific (WNP) and Eastern North Pacific (ENP) are the regions of the first- and second-largest numbers of TCs, respectively, while the increased TC activity in the North Atlantic (NA) contributes the most to the global increase in TCs. It is revealed in the wavelet transformation for periodicity analysis that the variations in the annual number of TCs with different intensities mostly show an inter-annual period of 3–7 years and an inter-decadal one of 10–13 years. The inter-annual and inter-decadal periods are consistent with those in the ENSO-related ocean drivers (via the Niño 3.4 index), Southern Oscillation Index (SOI), and Inter-decadal Pacific Oscillation (IPO) index. The inter-decadal variation in 10–13 years is also observed in the North Atlantic Oscillation (NAO) index. The Tropical North Atlantic (TNA) index and Atlantic Multi-decadal Oscillation (AMO) index, on the other hand, present the same inter-annual period of 7–10 years as that in the frequencies of all the named TCs in the NA. Further, the correlations between TC frequencies and ocean drivers are also quantified using the Pearson correlation coefficient. These findings contribute to an enhanced understanding of TC activity, thereby facilitating efforts to predict particular TC activity and mitigate the inflicted damage. [ABSTRACT FROM AUTHOR]

This study investigated the sociological dimensions informing public perceptions of water scarcity during the El Niño drought period in Colombia. We conducted this study in Santa Marta, Colombia, and surveyed 405 urban and rural residents to understand their perceptions of water scarcity, management, and the impacts of the El Niño phenomenon. The survey used a Likert scale to measure responses and employed a multivariate analysis of variance to analyze the data while considering factors such as location (urban versus rural) and gender. The study results indicated that urban residents often experience an irregular water supply all year, whereas most rural respondents noted a more consistent availability of water. The perception of water scarcity also differed notably between urban and rural areas due to their different historical and cultural experiences (habitus). Urban respondents mostly recognized the presence of water, while rural perspectives were less conclusive, likely influenced by their direct access to natural water sources. Participants across various demographics consistently reported that poor management by local, regional, and national governments contributes to the water scarcity crisis, highlighting the importance of improving communication about climate events like El Niño and water management to increase community engagement in public policies. Our research suggests that better understanding the social foundations of such perceptions using Bourdieu's concepts of social fields, habitus, and capital forms can significantly enhance water management strategies. [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]

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]

Indonesia's marine resources are abundant, with fishing being a primary focus. The effective management of these resources requires an understanding of the factors that influence them, such as sea surface temperature (SST), El Niño-Southern Oscillation (ENSO) as indicated by the Southern Oscillation Index (SOI), and chlorophyll-a levels as food sources. This research aimed to elucidate the relationships among those factors at Madura Strait by utilizing their characteristics in response to the electromagnetic wavelengths that can be found Landsat 9 OLI/TIRS satellite images. This research utilized the satellite's Thermal Infrared Sensor (or Band 10) (10.6-11.19 µm) to obtain the SST levels as well as the OceanColor 2 (OC2) algorithm to process Band 2 (0.45-0.51 µm) and Band 3 (0.53-0.59 µm) in order to obtain the chlorophyll-a levels. The results were the mean values of the SST (21.42 and 20.60°C) and the chlorophyll-a levels (0.77 and 0.87 mg/m³) from the periods of June through August 2 022 and December 2 022 through February 2023, respectively. Furthermore, a correlation test and t-test were conducted, which indicated that the chlorophyll-a levels were contradictory with the SST, SOI, and total pelagic fish catches (which were in alignment). The t-test results only indicated significant correlations between the SST and chlorophyll-a levels (-0.806) and between the SST and SOI (0.732), while the other correlation was not significant. [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]

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]

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]

Global warming and associated weather changes, notably the El Niño Southern Oscillation (ENSO), significantly impact marine ecosystems by altering water quality parameters such as chlorophyll-a (Chl-a) and sea surface temperature (SST). These changes are crucial in understanding the biogeochemical and ecological dynamics of marine environments, especially in regions affected by upwelling. This study aims to monitor upwelling events on Satonda Island, a volcanic island with unique central lake and status as a protected area using remote sensing. Utilizing Landsat-8 imagery and machine learning regression techniques--Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART)--this research evaluates the water quality in Satonda waters over a decade (2013-2023). The RF method emerged as the most accurate in estimating Chl-a and SST, indicating its efficacy in monitoring marine ecosystems with the result (RMSE = 0.309 and 0.274). The analysis reveals seasonal upwelling patterns, characterized by decreased SST and increased Chl-a concentration, with peaks varying annually between June and November. This study highlights the crucial role of remote sensing and machine learning in monitoring the effects of climate change on marine biodiversity. It provides valuable insights into the temporal dynamics of upwelling in the shallow waters of Indonesia. [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]

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]

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]

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]

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]

Marine finfish landings in Cuba have decreased during the last 30 years. However, in Cuba's most productive fishing region, certain species, including rays, herrings, and snappers, have had increased landings over the past decade. Despite these anomalies, no comprehensive analysis of the interactions among multispecies landing dynamics, environmental factors, and fishing efforts has been carried out. This study estimates the dynamics of multispecies finfish landings between 1981 and 2017 on the southeastern coast of Cuba. A log-normal generalized additive model (GAM) was fit to evaluate the effects of various environmental and effort-related variables on the total landings. During the period analyzed, the finfish landings and fishing effort decreased by 46% and over 80%, respectively. Despite concerns about overfishing, landings per unit of effort (LPUE) increased by 2.8 times. The total fish landings were significantly related to changes in the fishing effort, coastal vegetation, rainfall, chlorophyll-a, and the Southern Oscillation Index (SOI). This study highlights the changing relationship between the landings and fishing effort, suggesting that LPUE may not accurately reflect true stock abundance. The findings of this study will assist in integrating the dynamics of finfish species, ecosystem status, and management actions for Cuba's most productive fishing zone. [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]

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]