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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]

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

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

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

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

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

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]

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

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

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

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

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

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

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

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

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]

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

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

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

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

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

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

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]

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]

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

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

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

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

The Pearl River Basin is one of the most important agricultural areas and water sources in the subtropical humid region of southern China. Drought events occur frequently in the Pearl River Basin under the background of global climate, especially for the continuous seasonal drought. The temporal and spatial evolution of drought at different time scales is of great significance to allocate the water resources for the agricultural planting structure in the Pearl River Basin. In this study, the daily observation data was selected from 44 meteorological stations in the Pearl River Basin from 1960 to 2020. The precipitation and potential evapotranspiration (ET0) was then calculated by the FAO Penman-Monteith, in order to obtain the standardized precipitation evapotranspiration index (SPEI) at the cross-seasonal drought time scale. Linear tendency estimation was combined with Mann-Kendall mutation test and Morlet wavelet analysis. The temporal and spatial variation of continuous seasonal drought was then analyzed over the past 60 years. The result showed that: 1) The instable precipitation was led to the complex and diverse dry and wet changes. The growth trend of the continuous drought was found in autumn-winter and autumnwinter- spring. This variation was attributed to the decrease of autumn precipitation and the concentration of winter precipitation. The frequency of drought was also higher than that before the 21st century. Specifically, the frequency of continuous drought in autumn-winter-spring increased from 0.15 times/a before 2000 to 0.25 times/a after 2000. And there were many consecutive seasonal drought events from 2000 to 2010. The intensity of drought was mostly light and moderate and the continuous drought coverage area is mainly characterized by local, followed by regional drought. Particularly, the drought from dry to wet and from wet to dry occurred in the continuous drought of autumn-winter-spring in 1962 and 1997, respectively. 2) There were the outstanding interannual and interdecadal variations of SPEI in the continuous drought of autumn-winter and autumn-winter-spring. The main periods of continuous drought were 9 and 23a, respectively, in autumnwinter and autumn-winter-spring. There was a general cycle of 9-14a. According to the short period, there was some influence of atmospheric circulation on the wet and dry variation. The correlation analysis showed that the dry and wet variations in the continuous drought in autumn-winter were affected mainly by the Southern oscillation index (SOI). And the wet and dry changes in the continuous drought of autumn-winter-spring were significantly correlated with North Atlantic oscillation index (NAO), Southern oscillation index (SOI) and Pacific Decadal oscillation index(PDO). The SOI shared the better characterization for the ENSO. As such, the ENSO events were the main influencing factors on the changes of dry and wet in autumn-winter and autumn-winter-spring. 3) 47.7 % and 54.5 % of the stations showed a downward trend under the continuous drought of autumn-winter and autumn-winter-spring, respectively. Therefore, the spatial distribution pattern of the continuous drought autumn-winter and autumn-winter-spring was roughly dry in the east and west, while wet in the middle. There were the large spatial differences in dry and wet changes in different watersheds. There was the high frequency of the continuous drought of autumn-winter and autumn-winter-spring, indicating the trend of dry early. The Youjiang, Zuojiang and Yujiang River Basin were used to share the great changes in dry and wet alternation, indicating the high frequency of drought. But there was the weakening trend of the continuous drought in autumn-winter and autumn-winter-spring in recent years. Much more attention should be paid to the extreme precipitation and flood in the future. There was some increase in the continuous drought of autumn-winter with a high frequency and a weak trend of early drought rate from northeast to southwest in the Dongjiang River Basin. The high occurrence can be found in the continuous seasonal risk. [ABSTRACT FROM AUTHOR]

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

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

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

The western Pacific warm pool (WPWP) is a major thermal driver of atmospheric deep convection and global atmospheric circulation in the tropics, and changes in its ocean heat content (OHC) affect the local and global climates. Four stateof-the-art ocean reanalyses and one objective analysis were used to study the variations in the WPWP OHC, ocean heat content tendency (OHCT), and ocean heat transport (OHT). The variabilities of both the OHC and OHCT integrated from 0 to 300 m are consistent between the datasets and are closely related to the El-Nino southern oscillation cycle. The integrated OHC from 0 to 2000 m shows an overall increasing trend in the WPWP. The WPWP mainly gains heat from the eastern boundary and loses heat from the northern boundary. The heat transport through the eastern boundary of the WPWP is mainly facilitated by the westward flowing south and north equatorial currents as well as the equatorial countercurrent around the depth of the thermocline, whereas the OHT at the northern boundary is mainly driven by the western boundary current of the Pacific Ocean, which shows complex flow structures. [ABSTRACT FROM AUTHOR]

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

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

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

Coastal El Niño events—marine heatwaves instances in the far eastern Tropical Pacific during otherwise basin-scale neutral or cold conditions—can have severe societal impacts for countries along the west coast of South America, as exemplified by the 2017 and 2023 Peru-Ecuador floods. Due to the brevity of the observational record, it is not well understood whether these events are driven by local or large-scale processes. Here, to overcome this limitation we use a data-driven modeling approach to address their return period and forcing mechanisms. It is shown that extreme coastal El Niño events are a local manifestation in the eastern tropical Pacific of the constructive interactions of the Pacific Meridional Modes (PMM). Specifically, the North PMM yields a dipole-like anomaly SST pattern along the equator that favors its development, while the positive phase of the South PMM reinforces it. A smaller group of more moderate coastal events are remotely driven by zonal wind anomalies in the western tropical Pacific without the PMMs' influence. The role of PMMs in the development of extreme coastal El Niño suggests that they may be more predictable than previously thought. [ABSTRACT FROM AUTHOR]

The dependence of the southward shift of low-level westerly anomalies over the equatorial central Pacific on El Niño intensity during the mature winter was investigated through observational analyses and air-sea coupled model simulations. El Niño events are categorized into two types based on the presence or absence of such a southward westerly shift (SWS). El Niño events with an evident SWS (SWS El Niño) exhibit strong intensity in sea surface temperature anomalies, whereas those without a remarkable SWS (non-SWS El Niño) are weak. The strength of westerly anomalies of SWS El Niño is twice as large as those of non-SWS El Niño in mature winter and can induce larger growth of westerly anomalies south of the equator by two anomalous southward westerly advections. One is the advection of anomalous westerlies by climatological winter-mean northerlies, and the other is the advection of climatological zonal wind by anomalous westerlies. Observations and model simulations both indicate that the two types of El Niño terminate differently. The strong SWS El Niño decays initially in the equatorial central Pacific, induced by a large local discharge of mass and heat content associated with intense SWS and the subsequent westward sea current anomalies starting in the decaying spring. In contrast, the non-SWS El Niño terminates first in the far eastern Pacific by cool advection carried by local westward sea current anomalies in mature winter, which is then enhanced by poleward discharge associated with weak SWS in the equatorial central Pacific. [ABSTRACT FROM AUTHOR]

The Ogallala aquifer, underlying eight states from South Dakota to Texas, is practically non-recharging south of Nebraska, and groundwater withdrawals for irrigation have lowered the aquifer in western Kansas. Subsequent well-yield declines encourage deficit irrigation, greater reliance on precipitation, and producing profitable drought-tolerant crops like upland cotton (Gossypium hirsutum (L.)). Our objective was to evaluate deficit irrigated cotton growth, yield, and water productivity (CWP) in northwest, west-central, and southwest Kansas in relation to El Niño southern oscillation (ENSO) phase effects on precipitation and growing season cumulative thermal energy (CGDD). Using the GOSSYM crop growth simulator with actual 1961–2000 location weather records partitioned by the ENSO phase, we modeled crop growth, yield, and evapotranspiration (ET) for irrigation capacities of 2.5, 3.75, and 5.0 mmd−1 and periods of 4, 6, and 8 weeks. Regardless of location, the ENSO phase did not influence CGDD, but precipitation and lint yield decreased significantly in southwest Kansas during La Niña compared with the Neutral and El Niño phases. Simulated lint yields, ET, CWP, and leaf area index (LAI) increased with increasing irrigation capacity despite application duration. Southwestern Kansas producers may use ENSO phase information with deficit irrigation to reduce groundwater withdrawals while preserving desirable cotton yields. [ABSTRACT FROM AUTHOR]

Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems. We prove that the optimal solution of the GOAL algorithm consists in piecewise-linear functions in the Euclidean space and that it can be approximated through a monotonically convergent algorithm that presents—under the assumption of a discrete segmentation of the feature space—a closed-form solution for each optimization substep and an overall linear iteration cost scaling. The GOAL algorithm has been compared to other state-of-the-art machine learning tools on both synthetic data and challenging real-world applications from climate science and bioinformatics (i.e., prediction of the El Niño Southern Oscillation and inference of epigenetically induced gene-activity networks from limited experimental data). The experimental results show that the proposed algorithm outperforms the reported best competitors for these problems in both learning performance and computational cost. [ABSTRACT FROM AUTHOR]

In this study, hindcasts from eight Copernicus Climate Change Service (C3S) and three North American Multi‐Model Ensemble (NMME) operational seasonal forecast systems, based on dynamical climate models, are employed to investigate the influence of the South Atlantic Ocean Dipole (SAOD) on the predictive skill of Central Africa (CA) rainfall. The focus is primarily on the June–July–August season for 1993–2016. The findings reveal that, when regionally averaged, all models exhibit positive skill in predicting CA rainfall, except for the Geophysical Fluid Dynamics Laboratory (GFDL‐SPEAR) model. Notably, there are significant spatial variations in skill across different regions. Model performance is particularly low (high) in the Central African Republic and Congo Basin (Gabon and Chad) and tends to deteriorate with increasing lead‐time. Models that demonstrate a strong connection between SAOD and CA rainfall tend to exhibit better predictive skills in forecasting rainfall, in contrast to models with weaker connections. This leads to a significant in‐phase relationship between the predictive skills of rainfall and the strength of the SAOD–rainfall connection among the models. Furthermore, the atmospheric circulation responding to SST forcing associated with the El Niño–Southern Oscillation exerts a significant influence on the robust atmospheric circulation associated with the climatological mean of SST over the SAO. This suggests that mean state bias in the SAO/equatorial Pacific region plays a role in modulating the strength of the simulated SAOD–CA rainfall connection and, consequently, the prediction skill of CA rainfall. In general, both NMME and C3S models appear to be valuable tools capable of providing essential seasonal information several months in advance. These insights can aid decision‐makers in the region in making informed decisions regarding adaptation and mitigation measures. [ABSTRACT FROM AUTHOR]

The profound impact of the Khmer Empire on Southeast Asia renders the Angkor Period particularly significant in guiding contemporary societies to advocate and promote policies that respond to climate change. We present a new continuous multi-proxy speleothem dataset from Tonle Sap Lake Basin, investigating hydroclimate variability and the paleoenvironment of Cambodia during the Angkor Period from the 9th to 15th centuries. In addition, two important climatic events on a decadal scale are clearly reconstructed. The first is the reduction of precipitation between 800 and 1000 AD and the relatively significant drought that the regional environment may have experienced from 950 to 1000 AD. The second is the anomalous wet period between 1000 and 1200 AD, during which the Southern Oscillation Index also reached its negative peak after a thousand years. The wet and dry conditions are highly consistent with the El Niño-dominated and northward Intertropical Convergence Zone. [ABSTRACT FROM AUTHOR]

Teleconnections like the El Niño/Southern Oscillation affect climate and weather conditions across the globe, including conditions that modulate tornado activity. Early studies of teleconnection/tornado activity relationships provided evidence of links between one teleconnection and tornado activity. Later attempts introduced multivariate approaches by analyzing bivariate distributions and integrating multiple teleconnections in statistical models to predict variability in tornado activity. However, little attention has been given to teleconnection interactions and the role of these interactions in modulating tornado activity. Here, we employ a data-driven, multiple logistic regression modelling approach to explore the interactions between the El Niño/Southern Oscillation, North Atlantic Oscillation, Artic Oscillation, and Pacific North American pattern and their ability to predict the odds of an active tornado period in the southeastern United States. We develop models at the annual, seasonal, and monthly scales and, in doing so, illustrate that the teleconnections and teleconnection interactions that best predict the odds of an active tornado period differ across timescales and that the relationships exhibit clear seasonality. We also show climate conditions associated with select interactions, like the ENSO-NAO interaction in spring, that help explain the elevated tornado activity, namely anomalously high near-surface air temperature and humidity steered by an anomalously strong subtropical high. [ABSTRACT FROM AUTHOR]

The Yangtze River Basin (YRB), China, experienced record-breaking multiple season droughts in 2022, but also other severe drought events in recent history. This study examined the spatiotemporal characteristics of the 2022 drought in the YRB and compared this event with other extreme drought events in 1951 to 2022 from multiple perspectives, including spatial distribution, temporal evolution, return period, and drought losses. Six other extreme drought events were selected by the severity of water deficiency. The results showed that the return period was more than 1000 years (considering both temperature and precipitation), severer than the six other drought years. And a "whole-basin" drought, which covered nearly the entire region, was evident in the summer and autumn of 2022 compared with other drought years. Although the 2022 drought was much more extreme than other drought years from a natural perspective, the actual crop impacted area ratio was less than those in other drought years. This indicates the importance of drought relief measures. As for the drought attribution in the YRB, the El Niño/Southern Oscillation (ENSO) played a key role in explaining its occurrence, significant at different lag times. These results may help policymakers to comprehensively understand the typical extreme droughts in the YRB and rationally allocate funds for drought relief. [ABSTRACT FROM AUTHOR]

To enhance the capabilities of the Subseasonal to Seasonal (S2S) database for forecasting Iran's southwest precipitation from 1 to 4 weeks ahead, we compared observed precipitation and atmospheric variables with the corresponding hindcasts generated by the KMA, UKMO, ECWMF, and Meteo-France (MF) research centers. This analysis involved several deterministic and probabilistic metrics. Our reference datasets included daily precipitation data from 176 rain gauge stations and the NOAA-based atmospheric circulations data for Dec-April 1995–2014. Most hindcasts underestimated wet events in southern and eastern districts but overestimated them in the western and northern regions. Additionally, all hindcasts overforecasted the frequency of wet events across all lead times. The correlation scores were highest in the first week and declined as lead times increased. The ECMWF had the best correlation in all regions, showing superior deterministic and probabilistic forecast skills in western districts. The UKMO hindcasts, whose accuracy has the highest dependancy on precipitation amount, effectively captured signals of the El-Niño Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) over the study area and the Middle East. They accurately forecasted the 850 hPa moisture transport and 500 hPa vertical velocity features in these regions, particularly during the rainy phases of MJO. These findings offer valuable insights to enhance the accuracy of operational S2S precipitation forecasts for planning and decision-making in Iran and the Middle East. [ABSTRACT FROM AUTHOR]

This paper analyzes spatial and temporal changes of aridity in Argentina (1961–2022). The aridity degree, using Climatic Research Unit (CRU) monthly data, was defined through six climate types classified by De Martonne Aridity Index (DMI). Argentina presents a very arid, arid and semiarid region that extends from Puna to Patagonia, alongside two humid and very humid regions: one located in the Chaco-Pampas Plains and Mesopotamia, and another in the Patagonian Andes. Between these regions, there are subhumid areas with marked aridity variations. These structures persist over time, but advances or setbacks were observed in their bordering areas, with significant changes in the Andes (leading to more arid conditions) and the southwestern Pampas Plains (leading to more humid conditions) during the historical period. The contribution of temperature and precipitation changes to these DMI changes was quantified, indicating that precipitation modulated the DMI spatial changes, while temperature intensified or weakened the change magnitudes. The extension variations of the arid and semiarid regions in Argentina were related to three climate variability modes (El Niño – Southern Oscillation [ENSO], Pacific Decadal Oscillation [PDO] and South Atlantic Ocean Dipole [SAOD]). Significant correlations were found with PDO (r < 0) and SAOD (r > 0), which indicate that an increase in the area occupied by arid and semiarid climates are associated with PDO negative phase or SAOD positive phase. Moreover, when these phases occur simultaneously, the expansion of arid and semiarid regions is larger than under the action of an individual forcing. [ABSTRACT FROM AUTHOR]

The contribution of the El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) to the variations of the leading modes of atmospheric circulation in Africa south of the equator, during austral summer (i.e., from December to March), is examined in this study. The rotated principal component analysis is applied to classify the leading modes of atmospheric circulation in the study region. The result showed that relatively, through the control of sea level pressure in the mid-latitudes, the SAM is more related to the variability of the austral summer leading modes of atmospheric circulation in the study region. Overall, during the analysis period, the SAM explained about 20% to 46% variance of the leading atmospheric circulation modes. ENSO rather explains up to about 10% to 20% of the variance. Due to the continuous nature of atmospheric circulation, incorporating the co-variability of the classified circulation patterns adds skill to the predictability of the classified leading modes. Overall, the joint variations of the SAM, ENSO, and other less frequent regional circulation patterns that are related to classified leading modes, explained up to 46% to 80% variance of the leading modes—which is indeed an added value in further considering the natural gradient of the classified circulation patterns. [ABSTRACT FROM AUTHOR]