Search Results (275)

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

Beach erosion and shoreline dynamics are strongly affected by alterations in nearshore wave intensity and energy, especially in the context of global climate change. However, existing works do not thoroughly study the evolution of the sandy coasts of eastern Hainan Island, China, nor their responses to wave climate change driven by climate variability. This study focuses on the open sandy coast and assesses shoreline evolutionary dynamics in response to wave climate variability over a 30-year period from 1994 to 2023, using an open-source software toolkit that semi-automatically identify the shorelines (CoastSat v2.4) and reanalysis wave datasets (ERA5). The shorelines of the study area were extracted from CoastSat, and then tidal correction and outlier correction were performed for clearer shorelines. Combining the shoreline changes and wave conditions derived from ERA5, the dynamics of the shorelines and their response to wave climate change were further studied. The findings reveal that the average long-term shoreline change rate along the eastern coast of Hainan Island is 0.03 m/year, with 44.8% of transects experiencing erosion and 55.2% showing long-term accretion. And distinct evolutionary patterns emerge across different sections. Interannual variability is marked by alternating erosion and siltation cycles, while most sections of the coast experiences clear seasonal fluctuations, with accretion typically occurring during summer and erosion occurring in winter. El Niño–Southern Oscillation (ENSO) cycles drive changes in parameters including significant wave height, mean wave period, wave energy flux, and mean wave direction, leading to long-term changes in wave climate. The multi-scale behavior of the sandy shoreline responds distinctly to the ongoing changes in wave climate triggered by ENSO viability, with El Niño events typically resulting in accretion and La Niña periods causing erosion. Notably, mean wave direction is the metric most closely linked to changes in the shoreline among all the others. In conclusion, the interplay of escalating anthropogenic activities, natural processes, and climate change contributes to the long-term evolution of sandy shorelines. We believe this study can offer a scientific reference for erosion prevention and management strategies of sandy beaches, based on the analysis presented above. Full article

In fisheries’ stock assessments, the concept of “growth plasticity”—the ability of organisms to modulate their growth rates in response to environmental conditions—has gained attention in recent years. Historically, the impacts of fishing activities and environmental fluctuations were considered separately, while their combined effects have recently come into focus. This study collected 834 adult small yellow croakers (Larimichthys polyactis) from the northern Yellow Sea, the central Yellow Sea, the southern Yellow Sea, and the northern East Sea by trawling during 2020–2021. Using otolith increments as a proxy for annual somatic growth, the study reconstructed otolith chronologies during 2015–2020 for these four stocks. The results of the mixed-effects modeling suggested that temperature during spawning and previous overwintering seasons had comparable importance for the annual growth of small yellow croakers, with higher temperature promoting growth. The growth of small yellow croakers was also found to be correlated with ENSO events, with a lag of 1 to 2 years. A further investigation into combined effects revealed that higher fishing pressure might inhibit the small yellow croaker’s response to favorable environmental conditions. Furthermore, considering the potential differences in growth plasticity among stocks, an analysis was conducted on the spatial variations in growth response to these factors. The analysis revealed that, compared to the stocks in the Yellow Sea, the stock from the East China Sea could exhibit higher growth, superior adaptability to temperature, and a distinctive response to fishing pressure. In conclusion, the present study, while primarily focusing on temperature, preliminarily analyzed the combined effects of fishing and environment and underscored the differences in growth plasticity between stocks in the Yellow Sea and the East China Sea. Despite the limited factors analyzed in this study, it suggests a direction for future studies, highlighting the necessity to include more environmental factors, and even population factors (e.g., the biomass of preys), for a more comprehensive understanding of the combined effects. Based on the observed differences between the two potential subpopulations, this study also provides new insights for the management of the small yellow croaker based on metapopulation dynamics. Full article

The Indonesian Throughflow (ITF) is significantly modulated by Indo-Pacific climate forcing, especially the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). However, when ENSO and IOD occur concurrently, they tend to play different roles in the ITF volume transport. By employing an improved Constructed Circulation Analogue (CCA) method, the relative contributions of these climate events to the ITF inflow and outflow transport in the upper and lower layers were quantified. The results indicate that during co-occurring El Niño and positive IOD events, ENSO is the dominant influence, with ratio values of 5.5:1 (3.5:1) in the upper layer and 1.7:1 (1.6:1) in the lower layer of the inflow (outflow). Conversely, during co-occurring La Niña and negative IOD events, the IOD predominates, with ratio values of 1:6 (1:6.5) in the upper layer and 1:4 (1:3) in the lower layer of the inflow (outflow). The mechanisms underlying these variations in the upper and lower layers can be explained by the differences in sea level anomaly (SLA) and wave propagation, respectively. This study provides a new insight into distinct roles of climate forcing on the ITF volume transport during the simultaneous occurrence of multiple climate modes. Full article

Taiwan Bank (TB) is located in the southern Taiwan Strait (TS). The uplifted continental slope and bottom currents in this area result in the formation of upwelling areas, which serve as crucial fishing grounds. Climate-induced fluctuations in fish populations occur in the TS. However, how predation and competition affect the interspecies relationships in the TB ecosystem warrants clarification. In this study, we collected high-grid-resolution data on fishery activity (2013–2019) and constructed ecosystem models using Ecopath with Ecosim (EwE). Three mass-balanced models for determining the influence of El Niño–Southern Oscillation (ENSO) events on the TB ecosystem were constructed using EwE. A range of groups, including representative pelagic, benthic, and reef species, were collected for analyzing the relationship between migratory and sedentary species in terms of ecosystem structure variation due to climate change. The results demonstrated that the total system throughput (TST) was 10,556–11,122 t km⁻² year⁻¹, with an average transfer efficiency of 12.26%. According to the keystoneness index, calculated through mixed trophic impact analysis, Polydactylus sextarius and Scomber japonicus were the key species with top–down control and relatively high impact on the ecosystem in normal years. The keystone species also shifted to the predator fish Thunnus albacares and Katsuwonus pelamis during El Niño and La Niña events, respectively. Moreover, total biomass, TST, consumption, and respiration were noted to increase during ENSO events. However, during La Niña events, the diversity and connectance indexes were relatively low but pelagic species’ biomass was relatively high, whereas the biomass of most benthic and reef species was relatively high during El Niño events. Full article

The analysis of rainfall patterns in the Indonesian region utilized the Empirical Orthogonal Function (EOF) method to identify spatial and temporal variations. The study evaluated the dynamic influence of the Tropical Indian Ocean (TIO) and the Tropical Pacific Ocean (TPO) on Indonesian rainfall using monthly data from the Southeast Asian Climate Assessment and Dataset (SACA&D) spanning from January 1981 to December 2016 and encompassing three extreme El Niño events in 1982/1983, 1997/1998 and 2015/2016. Using combined reanalysis and gridded-observation data, this study evaluates the potential impact of the two primary modes in the tropical Indo-Pacific region, namely the Indian Ocean Dipole (IOD) and the El Niño-Southern Oscillation (ENSO) on Indonesian rainfall. The analysis using the EOF method revealed two main modes with variances of 35.23% and 13.07%, respectively. Moreover, the results indicated that rainfall in Indonesia is highly sensitive to sea surface temperatures (SST) in the southeastern tropical Indian Ocean and the central Pacific Ocean (Niño3.4 and Niño3 areas), suggesting that changes in SST could significantly alter rainfall patterns in the region. This research is useful for informing government policies related to anticipating changes in rainfall variability as part of Indonesia’s preparedness for hydrometeorological disasters. Full article

Drought constitutes one of the natural phenomena that causes the greatest socio-economic, and environmental losses in both the short and long term worldwide. Each year, these events are related to the presence of “El Niño—Southern Oscillation” (ENSO), which occurs throughout Colombia and has serious consequences in the agricultural and food sectors, as well as in most of the country’s population. Farmers have adopted a number of strategies to mitigate the negative impact of droughts on food production. Certainly, when implementing future strategies, such strategies will be less effective if farmers’ insights on ENSO are not considered. Consequently, this study was carried out to analyze the variables that predict adaptation to droughts in the dry zones of the department of Tolima. Three questionnaires were designed: socioeconomic vulnerability (SVT), risk perception (SRPT) and drought adaptation (SAT). A non-probability sample of 538 farmers was surveyed. Socio-economic vulnerability and drought perception were found to be predictive of drought adaptation in the study sample, and older people were found to be resilient to adaptation. The results of this research provide empirical evidence to analyze and formulate public policies about the impact of droughts on the most vulnerable populations. Full article

As governments continue to address climate change when formulating policy, there remains a need to determine if such a change exists in the historical record to inform clear indices for monitoring the present climate for site-specific interventions. This study characterised trends and anomalies in rainfall and dry spells, providing local information often projected from satellites or regional data in data-scarce regions. From 1961 to 2007, daily rainfall records in Central Malawi were used to calculate indices for low-(Balaka), medium-(Bunda, Chitedze, KIA), and high-altitude (Dedza) sites, which were then subjected to Mann–Kendall’s, Cramer’s, and Spearman-Rho’s trend tests. Significant decreasing trends in terms of wet days and growing season length were evident across locations. Seasonal and extreme rainfall, dry spells, and inter-seasonal and near-decadal anomalies were not consistently or inevitably significant. Unexpectedly, rainfall anomalies were largest in Bunda and KIA, which have mild climatic regimes, while the lowest were in Balaka, a rainfall-averse zone. The relationship between El Niño-Southern Oscillation (ENSO) and extreme rainfall and dry spell events did not reach statistical significance. In conclusion, extreme precipitation and dry spell events show varied intensities and proportions rather than increased frequency. The disparate results largely justify the need for in-depth local-scale assessments for agroclimatic applications. Full article

In northeastern China, simultaneous reconstruction of temperature and precipitation changes in the same region using tree ring data has not yet been reported, limiting our understanding of the historical climate. Using tree ring samples from the Greater Khingan Mountains, it was established that there are five standardized tree ring width chronologies of Pinus sylvestris var. mongolica at five elevations. Correlation analyses revealed significant relationships between the tree ring chronologies and climate data for multiple months. Specifically, the correlation coefficient between the average minimum temperature from May to July and the composite chronologies of mid–high and mid-elevations was 0.726, whereas that between the total precipitation from August to July and the low-elevation chronology was 0.648 (p < 0.01). Based on these findings, we reconstructed two series: the average minimum temperature from May to July over the past 211 years and the total precipitation from August to July over the past 214 years. The reconstructed sequences revealed changes in the average minimum temperature from 1812 to 2022 and precipitation from 1809 to 2022 in the northern part of the Greater Khingan Mountains. The variances explained by the reconstruction equations were 0.528 and 0.421 (adjusted R-squared: 0.520 and 0.411), with F-test values of 65.896 and 42.850, respectively, exceeding the significance level of 0.01. The reliability of the reconstructed sequences was validated by historical records of meteorological disasters and the reconstruction results in the surrounding area. The reconstructed temperature and precipitation sequences exhibited distinct patterns of temperature fluctuations, dry–wet changes, and periodic oscillations. The region experienced two warm periods (1896–1909 and 2006–2020), two cold periods (1882–1888 and 1961–1987), a wet period (1928–1938), a drought period (1912–1914), and a period prone to severe drought events (1893–1919) during the past 210 years. The temperature series showed periodicities of 2–2.5 years, 3.9 years, 5.2 years, and 68 years, while the precipitation series exhibited periodicities of 2.1 years, 2.5 years, and 2.8 years, possibly related to El Niño–Southern Oscillation (ENSO) events, quasi-biennial oscillation, and Pacific Decadal Oscillation (PDO). Spatial correlation analysis indicated that the reconstructed temperature and precipitation sequences accurately represented the hydrothermal changes in the study area. Full article

Turbidity serves as a crucial indicator of coastal water health and productivity. Twenty years of remote sensing data (2003–2022) from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to analyze the spatial and temporal variations in turbidity, as measured by total suspended matter (TSM), in the Berau Coastal Shelf (BCS), East Kalimantan, Indonesia. The BCS encompasses the estuary of the Berau River and is an integral part of the Coral Triangle, renowned for its rich marine and coastal habitats, including coral reefs, mangroves, and seagrasses. The aim of this research is to comprehend the seasonal and interannual patterns of turbidity and their associations with met-ocean parameters, such as wind, rainfall, and climate variations like the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The research findings indicate that the seasonal spatial pattern of turbidity is strongly influenced by monsoon winds, while its temporal patterns are closely related to river discharge and rainfall. The ENSO and IOD climate cycles exert an influence on the interannual turbidity variations, with turbidity values decreasing during La Niña and negative IOD events and conversely increasing during El Niño and positive IOD events. Furthermore, the elevated turbidity during negative IOD and La Niña coincides with rising temperatures, potentially acting as a compound stressor on marine habitats. These findings significantly enhance our understanding of turbidity dynamics in the BCS, thereby supporting the management of marine and coastal ecosystems in the face of changing climatic and environmental conditions. Full article

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. Full article

Extreme precipitation events induced by tropical cyclones have increased frequency and intensity, significantly impacting human socioeconomic activities and ecological environments. This study systematically examines the spatiotemporal characteristics of these events across Hainan Island and their influencing factors using GsMAP satellite precipitation data and tropical cyclone track data. The results indicate that while the frequency of typhoon events in Hainan decreased by 0.3 events decade⁻¹ from 1949 to 2020, extreme precipitation events have increased significantly since 2000, especially in the eastern and central regions. Different typhoon tracks have distinct impacts on the island, with Track 1 (Northeastern track) and Track 2 (Central track) primarily affecting the western and central regions and Track 3 (Southern track) impacting the western region. The impact of typhoon precipitation on extreme events increased over time, being the greatest in the eastern region, followed by the central and western regions. Incorporating typhoon precipitation data shortened the recurrence interval of extreme precipitation in the central and eastern regions. Diurnal peaks occur in the early morning and late evening, primarily affecting coastal areas. Typhoon duration (CC_max = 0.850) and wind speed (CC_max = 0.369) positively correlated with extreme precipitation, while the pressure was negatively correlated. High sea surface temperature areas were closely associated with extreme precipitation events. The atmospheric circulation indices showed a significant negative correlation with extreme precipitation, particularly in the western and central regions. ENSO events, especially sea surface temperature changes in the Niño 1 + 2 region (−0.340 to −0.406), have significantly influenced typhoon precipitation characteristics. These findings can inform region-specific disaster prevention and mitigation strategies for Hainan Island. Full article

El Niño–Southern Oscillation (ENSO) usually peaks in the boreal winter—November to January of the following year. This particular feature of ENSO is known as the seasonal phase locking of ENSO. In this study, based on 34 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the seasonal phase-locking characteristics of the model-simulated El Niño and La Niña events are evaluated in terms of the evolution of the SST anomalies associated with ENSO and the probability distribution of the peak month—the time at which ENSO peaks. It is found that CMIP6 models underestimate the phase-locking strength of ENSO for both El Niño and La Niña events. The ensemble mean peak month matches the observations, but the inter-model spread is large. The models simulate the phase locking of El Nino events better than that of La Niña events, and the large simulation bias of CMIP6 for La Niña phase-locking in the models may have an impact on the simulation of seasonal phase-locking in the ENSO. Full article

Currently there are several precipitation reconstructions for northern Mexico; however, there is a lack of temperature reconstructions to understand past climate change, the impact on ecosystems and societies, etc. The central region of Chihuahua is located in a transition zone between the Sierra Madre Occidental and the Great Northern Plain, characterized by extreme temperatures and marked seasonal variability. The objectives of this study were (1) to generate a climatic association between variables from reanalysis models and the earlywood series for the center of Chihuahua, (2) to generate a reconstruction of mean temperature, (3) to determine extreme events, and (4) to identify the influence of ocean–atmosphere phenomena. Chronologies were downloaded from the International Tree-Ring Data Bank and climate information from the NLDAS-2 and ClimateNA reanalysis models. The response function was performed using climate models and regional dendrochronological series. A reconstruction of mean temperature was generated, and extreme periods were identified. The representativeness of the reconstruction was evaluated through spatial correlation, and low-frequency events were determined through multitaper spectral analysis and wavelet analysis. The influence of ocean–atmosphere phenomena on temperature reconstruction was analyzed using Pearson correlation, and the influence of ENSO was examined through wavelet coherence analysis. Highly significant correlations were found for maximum, minimum, and mean temperature, as well as for precipitation and relative humidity, before and after the growth year. However, the seasonal period with the highest correlation was found from May to June for mean temperature, which was used to generate the reconstruction from 1775 to 2022. The most extreme periods were 1775, 1801, 1805, 1860, 1892–1894, 1951, 1953–1954, and 2011–2012. Spectral analysis showed significant frequencies of 56.53 and 2.09 years, and wavelet analysis from 0 to 2 years from 1970 to 1980, from 8 to 11 years from 1890 to 1910, and from 30 to 70 years from 1860 to 2022. A significant association was found with the Multivariate ENSO Index phenomenon (r = 0.40; p = 0.009) and Pacific Decadal Oscillation (r = −0.38; p = 0.000). Regarding the ENSO phenomenon, an antiphase association of r = −0.34; p = 0.000 was found, with significant periods of 1 to 4 years from 1770 to 1800, 1845 to 1850, and 1860 to 1900, with periods of 6 to 10 years from 1875 to 1920, and from 6 to 8 years from 1990 to 2000. This study allowed a reconstruction of mean temperature through reanalysis data, as well as a historical characterization of temperature for central Chihuahua beyond the observed records. Full article

Much research has raised concerns about how a warming planet will interact with natural cyclical climatic variations, and the implications for the resilience and vulnerability of coastal communities. As the anticipated effects of climate change will continue to intensify, it is necessary to understand the response and adaptive capacity of individuals and communities. Coastal communities in Ecuador have evolved in an environment of such cyclical climatic variations referred to as El Niño-Southern Oscillation (ENSO) and La Niña. These climatic events are frequently characterized by extreme variations in precipitation, violent storms, and coastal flooding during El Niño and lowered sea water temperatures and drought during La Niña. This paper draws on survey data and long-term ethnographic research in Ecuadorian coastal communities to explore how fishers understand the impacts of ENSO and implications for their livelihood decisions and resilience to climate variability. The results suggest that fishers along the coast of Ecuador understand and respond differentially to the impacts of ENSO depending on social, cultural, environmental, and geographical factors. These differential levels of response suggest that livelihood diversification may uphold social resilience, which has implications for how coastal communities may adapt to the increasingly harsh weather conditions predicted by many climate models. Our findings further suggest that the impacts of El Niño are more salient than the impacts of La Niña; these findings have significant implications for fisheries management and science communication. Full article