The chemical index of alteration (CIA) has been used to reconstruct the monsoon rainfall. However, the relationship between the CIA and monsoon rainfall has not been fully understood. In this study, we investigated a sedimentary CIA record from Lake Jingpo during the past 6.1 ka in northeast China. The CIA values ranged from 80 to 88, reflecting the topsoil of basaltic clays in the watershed. The increase in CIA between the mid-1960s and the mid-1980s corresponds to reduced annual (summer) precipitation from the CRU reanalysis datasets, suggesting that the supply of suspended particulates matter into the lake may have resulted from the weakened transport of fresh debris into the river. In other words, the change in chemical weathering intensity in this region is an inverse response to East Asian summer monsoon (EASM) circulation. The results indicate that the EASM precipitation broadly follows the decreasing trend in high-latitude Northern Hemisphere summer insolation, and the abrupt reduction at 4.2 ka may have coincided with the tipping point of the EASM since the mid-Holocene. EASM precipitation increased from 1.5 ka and broadly corresponds to the so-called 2-kyr shift. This shift may be related to Atlantic Meridional Overturning Circulation (AMOC) oscillation. A comparison of the sub-millennial-scale EASM record with the total solar irradiance and El Niño-Southern Oscillation (ENSO) records suggests that the EASM in northeast China was potentially driven by solar activity, whereas the EASM–ENSO teleconnection in this region has become pronounced since the late Holocene ( ca. 4.2 ka). • CIA values of lake sediment are inversely correlated to atmospheric precipitation. • EASM precipitation in northeast China abruptly decreased at 4.2 ka. • The EASM precipitation gradually increased since 1.5 ka, corresponding to 2-kyr shift. • The EASM precipitation was potentially driven by solar acidity since 6.1 ka. • The EASM-ENSO teleconnection became pronounced since 4.2 ka alone.

There is limited evidence regarding the association between weather and Plasmodium vivax (Pv), particulary in Latin America where Pv is the predominant malaria species and key challenge for countries to achieve malaria elimination. We analyzed the association between weather and Pv malaria incidence from 2017 to 2024 in 136 communities in the Peruvian Amazon. Monthly community-level incidence was calculated using Pv case data from Notiweb, the national epidemiological surveillance system, and population census data. Predictors included weekly minimum and maximum temperature and total weekly precipitation and were calculated using hourly weather from the climate dataset ERA5. Non-linear distributed lag models were fit using a lookback period of 2-16weeks. Temperature models were adjusted for total precipitation; precipitation models were adjusted for maximum temperature. Sub-group analyses were conducted by community type (adjacent to river versus highway) and El Niño Southern Oscillation (ENSO) period. Minimum temperature at the 90th percentile (23.7°C) was associated with 10% (95% CI 5-14%) higher malaria incidence compared to the 5th percentile (20.5°C) at a 7-week lag. Maximum temperature at the 90th percentile (33.7°C) was associated with 10% (95% CI 8-13%) higher malaria incidence compared to the 5th percentile (29.6°C) at a 9-week lag. Total weekly precipitation at the 90th percentile (1000mm) was associated with 29% (95% CI 24-33%) higher malaria incidence compared to weeks with the 5th percentile (57mm) at an 11-week lag. Incidence was higher and associations were stronger in communities adjacent to rivers versus highways. Malaria incidence was lower during El Niño periods, and there was evidence of interaction on the multiplicative scale for the association between incidence, all weather predictors, and ENSO period. Pv malaria incidence was positively associated with higher temperatures and precipitation in an elimination setting in Peru, particularly in riverine communities during non-El Niño years, with longer lag periods than previously reported for such associations. These findings can inform malaria elimination interventions to combat the long-lasting effects of weather on Pv transmission.

ABSTRACT The El Niño Southern Oscillation (ENSO) is a major driver of hydroclimatic variability, yet its influence on streamflow across Peru remains insufficiently quantified at the national scale. This study analyses ENSO-driven streamflow responses in 44 Peruvian catchments during 1981–2024 using observations and hydrological simulations within a multiscale framework. The analysis integrates seasonal regimes, anomaly patterns, and ENSO–streamflow teleconnections using multiple climate indices. Results reveal a clear regional structure, with contrasting responses between the northern Pacific coast and the Andean–Amazonian regions. The northern Pacific shows positive associations with eastern Pacific indices, while other regions exhibit negative relationships with central Pacific indices. These patterns are consistent across temporal scales, with rapid coastal responses and weaker inland signals. The findings highlight regionally differentiated ENSO controls on streamflow variability in Peru, with implications for monitoring and forecasting, and relevance for South America.

Artificial intelligence has advanced global weather forecasting, outperforming traditional numerical models in both accuracy and computational efficiency. Nevertheless, extending predictions beyond subseasonal timescales requires the development of deep learning (DL)–based ocean-atmosphere coupled models that can realistically simulate complex oceanic responses to atmospheric forcing. This study presents KIST-Ocean, a DL-based global three-dimensional ocean general circulation model. Comprehensive evaluations demonstrate the model’s robust ocean simulation skill and efficiency. Moreover, it reproduces ocean responses, such as Kelvin and Rossby wave propagation, and vertical motions induced by wind stress curl, demonstrating its ability to represent key atmospherically forced ocean dynamics underlying climate phenomena, including the El Niño–Southern Oscillation. These findings reinforce confidence in DL-based global weather and climate models by demonstrating their capacity to capture essential ocean-atmosphere relationships. Building on this foundation, the present study paves the way for extending DL-based modeling frameworks toward integrated Earth system simulations, thereby offering substantial potential for advancing long-range climate prediction capabilities.

Marine productivity driven by phytoplankton biomass (chlorophyll-a, Chl) sustains biodiversity, fisheries, and ecosystem services in the Humboldt Archipelago (29°S), an arid coastal upwelling region within the Humboldt Current System. The archipelago is characterized by strong bathymetric gradients and a submarine canyon that generate complex circulation patterns, potentially affecting phytoplankton aggregation. Although upwelling occurs year-round, its efficiency varies seasonally and interannually under the influence of regional atmospheric forcing and large-scale climate modes, including the El Niño-Southern Oscillation (ENSO) and the Pacific Meridional Mode (PMM). We evaluated how local hydrographic structure, upwelling dynamics, and climate variability interact to regulate Chl variability and biomass connectivity within the Coquimbo-Humboldt upwelling system. To address this, CTD-fluorescence profiles collected at a fixed site during 21 near-monthly surveys between November 2022 and December 2024 were combined with satellite observations and atmospheric data. Chlorophyll variability was analyzed using hierarchical Generalized Additive Models, of which the baseline physical model explained 80% of Chl deviance, while inclusion of the PMM increased it to ~ 84% and substantially improved model performance. Seasonal Chl variability was primarily associated with mixed-layer depth shoaling and intermediate Ekman transport, indicating strong control by short-term physical forcing. In contrast, the PMM-Ekman transport interaction revealed that upwelling efficiency depended on the large-scale climatic background. Satellite observations further suggested the episodic export of phytoplankton biomass from the Coquimbo Bay system into the archipelago. These results demonstrate that climate modes modulate local upwelling efficiency, shaping phytoplankton dynamics in one of the most biodiverse coastal regions of the Southeastern Pacific.

Snakebite envenoming is a neglected public health issue influenced by both environmental and socioeconomic variables. In Pará, the Brazilian state with the highest number of cases, knowledge gaps remain regarding the relationship between these factors and the temporal and geospatial distribution of accidents. This study investigates the influence of the El Niño-Southern Oscillation (ENSO), rainfall, river levels and socioeconomic indicators on snakebite incidence over nearly two decades (2007-2023). Data were obtained from official public health, environmental and socioeconomic databases. A Generalized Additive Model (GAM) was used to assess social variables, cross-correlation analyses evaluated associations with rainfall and river levels and wavelet analysis was applied to explore temporal patterns linked to ENSO variability. A total of 87,267 cases were recorded during the study period, with the highest incidence in northeastern Pará, particularly in the municipality of Santarém. Snakebite occurrence showed positive correlations with poor sanitation, rural population density and illiteracy rates. Temporal associations were also observed with river levels, rainfall and ENSO variability. The most affected municipalities should be prioritised for preventive education and improved antivenom distribution. The association with poverty indicators underscores the need for structural improvements in health and social assistance systems. Climatic influences highlight the role of environmental variability in shaping snakebite incidence in the Amazon and raise concerns about potential impacts of ongoing climate change on snakebite risk.

Understanding how ENSO predictability responds to climate change is essential for improving future climate projections. In this study, we apply a deep learning model—convolutional neural network with a leave-one-out model strategy to Coupled Model Intercomparison Project Phase 6 (CMIP6) historical and preindustrial control simulations. We find that El Niño–Southern Oscillation (ENSO) predictability is statistically enhanced by 14.0 ± 1.8% enhancement under historical anthropogenic forcing. This improvement is linked to changes in key ocean-atmosphere feedbacks. Under historical forcing, the equatorial Pacific shows a shoaling of the thermocline. As a result, the surface ocean becomes more sensitive to wind forcing, and subsurface temperature becomes more responsive to thermocline variations. These processes strengthen the three-dimensional advective and thermocline feedbacks, which together enhance ENSO predictability. These findings highlight the importance of anthropogenic forcing in shaping ENSO predictability in a warming climate.

ENSO-driven distribution shifts in the critically endangered Mustelus whitneyi along the Northern Peruvian Coast.

Marine radionuclides in climate change studies: Pacific Ocean and marginal seas.

Abstract Time of emergence (ToE) – the time when climate change signals become distinguishable from natural variations – offers key insight into when climate impacts will become noticeable. Under the SSP585 scenario, ToE of strengthened El Niño–Southern Oscillation (ENSO) teleconnections to surface air temperature anomalies (TAS) over tropical rainforests and the Sahel transition zone (including northern South America, central and western Africa and Southeast Asia) in CMIP6 models appears notably earlier than other regions – as early as in the 2040s. ENSO-related precipitation anomalies in the deep tropics also emerge early in around the same period. These changing patterns are associated with a strengthened anomalous Walker Circulation in future ENSO and more robust ENSO TAS responses to global warming over the tropical rainforests/transition zone. It is found that Earth System models incorporating more comprehensive vegetation representation exhibit stronger responses in these areas and earlier ENSO TAS ToEs than models with limited vegetation feedbacks. This suggests that the early emergence of ENSO TAS teleconnections there can be attributed to atmosphere-land-vegetation feedbacks which amplify ENSO TAS responses. Our findings indicate that a strengthened relationship between ENSO and tropical rainforests and the Sahel transition zone could emerge in the coming decades.

Oceans exhibit complex dynamics influenced by climate change, anthropogenic activities, and natural phenomena. Understanding these dynamics is critical for ensuring the sustainability of marine environments and their optimal utilization. This research aims to study and monitor upwelling phenomena in the South Sea of Java. Upwelling, the exchange of nutrient-rich, cold water from deeper layers to the surface, enhances marine biological productivity; Sea Surface Temperature (SST) serves as a key indicator for its detection. To achieve these objectives, this study employs both ConvLSTM and 3D-CNN. ConvLSTM, a deep learning architecture that integrates convolutional structures within LSTM units, effectively captures spatiotemporal dependencies in sequential data. 3D-CNN, a deep learning model extending traditional 2D convolutional neural networks, processes volumetric data, enabling the extraction of spatial features across three dimensions. Analysis reveals that ConvLSTM outperforms 3D-CNN in modeling upwelling data in the South Sea of Java. This is evidenced by lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The ConvLSTM method was then used for forecasting, and the results were validated with data obtained from local fishermen regarding their fishing expeditions. Visual analysis confirms that the ConvLSTM method accurately models upwelling data in the South Sea of Java with fishermen's schedules. ConvLSTM and 3D-CNN methods were comparatively evaluated for modeling Sea Surface Temperature (SST) data, considering wind speed, sea surface salinity, and the El Niño-Southern Oscillation (ENSO) phase as influential factors. Based on Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, the ConvLSTM method exhibited lower values, indicating superior performance compared to the 3D-CNN approach. Specifically, RMSE and MAE values for ConvLSTM were 0.4161 and 0.3017, respectively, while for 3D-CNN, the corresponding values were 0.6095 and 0.4259. Upwelling data forecasting results were validated against local fishermen's schedules, with data collected in July 2022. Visual inspection confirmed alignment between the forecasted upwelling patterns and the fishermen's activity.

A novel model in predicting dengue fever considering spatial heterogeneity of nine impact factors and four AI algorithms in China.

Age at first reproduction is an important life-history trait that marks the beginning of reproductive allocation in long-lived organisms and drives patterns of life-history strategies. Demographic factors and environmental conditions likely affect age at first reproduction through multiple pathways: food resources availability and energy storage from birth to recruitment, competition for breeding sites and mate availability. Using a unique 35-year dataset of individual-based mark-recapture data from a wandering albatross (Diomedea exulans) population at Crozet (southern Indian Ocean), we investigated how demographic factors and environment influence age at first reproduction. The population experienced major fluctuations, declining by 50% in the 1970s before partially recovering in the 1980s. It was also exposed to important environmental changes, including variations in large-scale climate phenomena and changes in subtropical anticyclone systems like the Mascarene high pressure system. We used multi-event hidden Markov models to estimate age-specific survival and breeding probabilities for each sex separately. From these models, we estimated the age at first reproduction through absorbing Markov chains while accounting for imperfect detection. We investigated how demographic factors (population density at birth and mate availability at recruitment) and environmental conditions (at birth and recruitment) influenced age at first reproduction through their effects on survival and breeding probabilities. Age at first reproduction declined across cohorts for both sexes from 1970 to the mid-1980s, then stabilized. Females recruited at 9.0 years in early cohorts versus 7.5 years in later ones; males declined from 10.2 to 9.2 years. Environmental conditions at birth, particularly the El Niño Southern Oscillation and the Mascarene high, influenced recruitment timing through delayed effects of natal condition on breeding probability rather than survival. Mate availability strongly facilitated earlier recruitment in both sexes, while natal population density delayed male recruitment specifically. Recruitment timing in wandering albatrosses is shaped primarily by developmental programming during the natal period rather than by immediate environmental triggers at sexual maturity, with mate availability and population density modulating these early-life effects in sex-specific ways. Given that recruitment is an important life-history event linked to population-level reproductive rates, accurate demographic projections require models accounting for cohort-specific effects under changing environments.

A novel framework for estimating probable maximum tropical cyclone parameters for ocean basins with sparse records

ObjectivesUrban heat island effects are intensifying under climate change and rapid urbanization. However, how large-scale climate anomalies such as the El Niño–Southern Oscillation (ENSO) interact with urban morphology to shape surface urban heat island intensity (SUHII) remains unclear. This study compares Shanghai and Suzhou to examine how SUHII responds to ENSO intensity across Local Climate Zones (LCZs).MethodsSummer (June–August) land surface temperature data (2018–2022) were downscaled from MODIS using a random forest model and integrated with 100 m LCZ maps. SUHII was calculated relative to LCZ D and decomposed into inter- and intra-LCZ components. Linear and quadratic regressions were applied to quantify SUHII sensitivity to ENSO intensity, represented by the Oceanic Niño Index (ONI).ResultsENSO intensity appears to modulate SUHII. La Niña phases strengthen inter-SUHII in both cities, whereas El Niño generally weakens it. The ONI–SUHII linkage is LCZ-dependent, with compact built types exhibiting the strongest sensitivity; in Shanghai, compact LCZs show a response slope of −0.98 °C per ONI, exceeding open, industrial, and vegetated types. Intra-SUHII follows a nonlinear pattern, reaching minima under near-neutral ENSO conditions and increasing during stronger El Niño or La Niña phases. City-scale morphology further appears to condition this sensitivity: Shanghai’s monocentric and high-density structure may contribute to the concentration of heat cores and amplifies ENSO-related variability, resulting in higher mean inter-SUHII (by 0.54 °C) and stronger ONI sensitivity than in polycentric Suzhou, where more dispersed urban form and cooling elements may help limit heat buildup.ConclusionsThese findings underscore the critical role of urban morphology in modulating climate-induced surface heat burden, offering valuable insights for climate-resilient urban planning in rapidly developing regions.

Background and Objectives: New Caledonia, an archipelago in the South Pacific, experienced an unprecedented conjunction of prolonged border closure during the COVID-19 pandemic (2020 to 2022) and marked influence of the El Niño/Southern Oscillation (ENSO). This context provided a unique opportunity to explore how environmental drivers, island isolation, and socio-demographic factors interact to shape infectious disease dynamics. This study aimed to assess the respective and combined effects of climatic variability, travel restrictions, and socio-demographic factors on the dynamics of four priority infectious diseases. Materials and Methods: We retrospectively analysed data from 2017 to 2023 on four infectious diseases: leptospirosis, dengue, influenza, and hepatitis A (HAV). Satellite precipitation data and the Multivariate El Niño/Southern Oscillation Index (MEI) were used. Socio-demographic and economic variables were gathered. Statistical analyses employed descriptive analysis and Generalized Additive Mixed Models to evaluate the associations between climatic events, travel restrictions, and disease circulation using the communal level as a random effect and time (daily) as a spline effect. Results: We analysed 878 cases of leptospirosis, 165 of HAV, 6607 of influenza, and 7377 dengue cases. Influenza was associated with rainfall before lockdown (Odds Ratio (OR) 0.7, Confidence interval 95%, (CI95%), (0.6–0.8)) and disappeared during lockdown but resurged post-reopening losing its meteorological association. Dengue epidemics declined, coinciding with the Wolbachia program and border closure, and were associated with lower MEI (OR 0.78, CI95% (0.6–1) during the 2017 to 2020 period. HAV cases were correlated with the MEI (OR: 1.8, CI95% (1–3.3)). Leptospirosis cases were associated with cumulative rainfall (OR 1.12 (1.1–1.2)) and lower education (OR 1.04, CI95% (1–1.1)) and decreased with water supply (OR 0.7, CI95% (0.5–0.8)). Conclusions: Our findings highlight how climatic conditions, mobility restrictions, and socio-environmental inequities differentially shape infectious disease risks in island ecosystems. These results reinforce the need for integrated One Health surveillance that jointly addresses environmental change, social vulnerability, and infectious disease prevention.

The El Niño–Southern Oscillation (ENSO) exhibits a pronounced decline in predictability during boreal spring, referred to as spring predictability barrier (SPB). While tropical basin interactions among the Indian, Atlantic, and Pacific Oceans potentially enhance ENSO predictability, their roles in mitigating SPB within deep learning (DL) frameworks remain underutilized. Here, we introduce GL-Geoformer, a DL model for global tropical ocean-atmosphere prediction. GL-Geoformer captures spatiotemporal evolutions of wind and three-dimensional temperature anomalies across the tropical basins. Our modeling demonstrates that incorporating tropical basin interactions substantially reduces SPB, enabling GL-Geoformer to achieve skillful ENSO predictions up to 16 months in advance when initiated in spring. Pacemaker experiments are performed to quantify individual and synergistic contributing nonlinearities of Indian Ocean Dipole and Atlantic Niño via subsurface heat transport and Walker circulation mechanisms, respectively. This study provides a data-driven framework to represent tropical basin interactions and reduce SPB, thereby deepening understanding of ENSO predictability.

Abstract El Niño–Southern Oscillation (ENSO) is a primary driver of interannual climate variability worldwide. However, the observed pattern of ENSO teleconnections can deviate significantly from the average or canonical teleconnection pattern from one event to another. It is unclear what controls the consistency in the pattern of ENSO teleconnections and to what extent this consistency may be affected by global warming. Here, we find that the pattern correlation between climate anomalies for a single event with the canonical ENSO teleconnections depends strongly on the intensity of the event, consistent with observations and climate model simulations. Under a high-emission scenario, significantly more ENSO events will exhibit a high pattern correlation, indicating that ENSO’s control on global climate anomalies in the twenty-first century will be substantially enhanced compared to the twentieth century. This strengthened control is attributed to both increased ENSO variability and a permanent El Niño–like warming pattern in the tropical Pacific. Our results suggest that climate predictions based on ENSO signals are likely to become more reliable in a warming climate.

Abstract El Niño–Southern Oscillation (ENSO) is the leading mode of coupled ocean–atmosphere climate variability on interannual time scales over the tropical Pacific and substantially influences the global climate system. There is great heat redistribution between the equatorial and off-equatorial Pacific regions during ENSO events, which can be described by the “recharge–discharge oscillator” paradigm. By analyzing a 35-member Community Earth System Model (CESM) Large Ensemble (CESM-LE) and 25 Coupled Model Intercomparison Model phase 6 (CMIP6) models, this study investigated the changes in ENSO-induced ocean heat content (OHC) redistribution under global warming. We find decreased heat convergence at 5°–20°N during El Niño and remarkably increased heat convergence at 10°–20°S during the decay phase of El Niño. These changes can be explained by the weakened poleward meridional heat transport (MHT) between the equator and 10°N and the enhanced poleward heat transport in the Southern Hemisphere, which can extend to higher latitudes and reach 10°–20°S. The changes in the MHT are associated with the negative wind stress curl anomaly induced by the reduction in precipitation near the intertropical convergence zone (ITCZ) and the enhanced negative wind stress curl anomaly at approximately 10°S induced by the increased precipitation according to the Sverdrup relation. Furthermore, the response of the potential temperature to the recharge–discharge process has a shallower vertical structure within 10°S–10°N, and the corresponding MHT also shoals, with enhancement above 150 m and suppression below it. These changes in the vertical structure imply that the ENSO-driven vertical thermal response of the ocean will shoal and the recharge–discharge process above the thermocline will become more important in a warmer climate.

Because of their impacts on droughts, famines, and floods, modes of climate variability can shape patterns of social instability. Yet the mechanisms linking climate variability to armed conflict remain contested, especially relative to the myriad sociopolitical and economic determinants of conflict. A key challenge is that most studies rely on coarse, state-level data and treat climatic teleconnections as invariant. As such, it is unknown whether there are distinct climate hazards that select for conflict risk; whether conflict scales with climate hazard exposure; and whether such associations exist for more regional forms of climate variability. Here we leverage empirical modeling using a high-resolution gridded dataset of armed conflicts and the natural experiment afforded by two major climate modes-the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD)-to clarify how systematic hydroclimatic anomalies influence conflict emergence. Our results reveal the following: First, conflict risk heightens during El Niño, but ENSO-associated risk does not scale linearly with teleconnection strength; and, evidence for threshold behavior varies with spatial aggregation. Second, El Niño-related increases in conflict risk arise through its dry teleconnections, with limited evidence for wet teleconnections. Finally, the more regionally confined IOD also influences conflict, with both positive and negative phases elevating risk in strongly teleconnected regions, namely the Horn of Africa and Southeast Asia. These results reveal that modes of climate variability can differentially shape conflict risk, offering insight into societies' vulnerabilities to natural climate fluctuations and, by extension, anthropogenic climate change.