Search Results (139)

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

The barotropic sea level difference (SLD) across the Korea/Tsushima Strait (KTS) is considered an index of the volume transport into the East/Japan Sea. This study investigates the interannual variability of the barotropic SLD (the KTS inflow) from 1985 to 2017 and its relationship to upper-ocean (<300 m) current variability in the western North Pacific. An increase in the KTS inflow is associated with a weakening of the Kuroshio current through the Tokara Strait and upper-ocean cooling in the North Pacific Subtropical Gyre, characteristic of a La Niña-like state. Diagnostic analysis reveals that the KTS inflow variability is linked to at least two statistically distinct and concurrent modes of oceanic variability. The first mode is tied to the El Niño–Southern Oscillation through large-scale changes in the Kuroshio system. The second mode, which is linearly uncorrelated with the first, is associated with regional eddy kinetic energy variability in the western North Pacific. The identification of these parallel pathways suggests a complex regulatory system for the KTS inflow. This study provides a new framework for understanding the multi-faceted connection between the KTS and upstream oceanic processes, with implications for the predictability of the ocean environmental conditions in the East/Japan Sea. Full article

The aim of this study is to quantitatively analyze the long-term climate variability in Poland during the period 1901–2010, using Shannon entropy as a measure of uncertainty and complexity within the atmospheric system. The analysis is based on the premise that variations in temperature and precipitation reflect the dynamic nature of the climate, understood as a nonlinear system sensitive to fluctuations. This study focuses on monthly distributions of temperature and precipitation, modeled using the bivariate Clayton copula function. A normal marginal distribution was adopted for temperature and a gamma distribution for precipitation, both validated using the Anderson–Darling test. To improve estimation accuracy, a bootstrap resampling technique and numerical integration were applied to calculate Shannon entropy at each of the 396 grid points, with a spatial resolution of 0.25° × 0.25°. The results indicate a significant increase in Shannon entropy during the summer months, particularly in July (+0.203 bits) and January (+0.221 bits), compared to the baseline period (1901–1971), suggesting a growing unpredictability of the climate. The most pronounced trend changes were identified in the years 1985–1996 (as indicated by the Pettitt test), while seasonal trends were confirmed using the Mann–Kendall test. A spatial analysis of entropy at the levels of administrative regions and catchments revealed notable regional disparities—entropy peaked in January in the West Pomeranian Voivodeship (4.919 bits) and reached its minimum in April in Greater Poland (3.753 bits). Additionally, this study examined the relationship between Shannon entropy and global climatic indicators, including the Land–Ocean Temperature Index (NASA GISTEMP) and the ENSO index (NINO3.4). Statistically significant positive correlations were observed between entropy and global temperature anomalies during both winter ($\rho$ = 0.826) and summer ($\rho$ = 0.650), indicating potential linkages between local climate variability and global warming trends. To explore the direction of this relationship, a Granger causality test was conducted, which did not reveal statistically significant causality between NINO3.4 and Shannon entropy ($p$ > 0.05 for all lags tested), suggesting that the observed relationships are likely co-varying rather than causal in the Granger sense. Further phase–space analysis (with a delay of $\tau$ = 3 months) allowed for the identification of attractors characteristic of chaotic systems. The entropy trajectories revealed transitions from equilibrium states (average entropy: 4.124–4.138 bits) to highly unstable states (up to 4.768 bits), confirming an increase in the complexity of the climate system. Shannon entropy thus proves to be a valuable tool for monitoring local climatic instability and may contribute to improved risk modeling of droughts and floods in the context of climate change in Poland. Full article

Epipelagic fish communities dominate fish assemblages and are an important part of marine ecosystems due to their high abundance, vertical migration behavior, and global distribution. Purse seine fisheries are key components of marine fisheries in the tropical Western and Central Pacific Ocean (WCPO), primarily targeting skipjack tuna (Katsuwonus pelamis, SKJ), yellowfin tuna (Thunnus albacares, YFT), and bigeye tuna (Thunnus obesus, BET). In this study, WCPO purse seine fishery data from 2014 to 2022, combined with environmental factor data, were used, and Mantel tests and correlation analysis were employed to analyze the diversity, fish coexistence mechanisms, and environmental responses of catch communities under the following two different fishing strategies: free–swimming schools (FSCs) and drifting fish aggregating devices (DFADs). Mantel tests indicated that nitrate (NO_3⁻), the Oceanic Niño Index (ONI), and pH significantly impact the diversity of the FSCs community, whereas NO_3⁻ significantly affects the diversity of the DFADs community. Based on the correlation analysis results, in the FSCs community, yellowfin tuna was positively correlated with bigeye tuna, and yellowfin tuna was negatively correlated with skipjack tuna and black marlin (Istiompax indica, BLM). In the DFADs community, yellowfin tuna was only positively correlated with skipjack tuna and bigeye tuna. In addition, species with high correlations were also positively correlated. The results of this study provide a theoretical basis for the biodiversity conservation of catch communities under two different purse seine fishing strategies in the WCPO. Full article

The coral reefs in the Philippines are facing an unprecedented crisis. This study, based on a comprehensive analysis of marine heatwaves (MHWs), degree heating weeks (DHWs), and ocean acidification (OA) indices derived from satellite observations and reanalysis data, reveals how thermal stress and OA have progressively eroded coral ecosystems from 1985 to 2022. This study analyzed 12 critical coral habitats adjacent to the Philippines. The monthly average sea surface temperature (SST) in the study area ranged from 26.6 °C to 29.3 °C. The coast of Lingayen Gulf was identified as the most vulnerable coral reef site in the Philippines, followed by Davao Oriental and Polillo Island. The coast of Lingayen Gulf recorded the highest total MHW days in 2022, amounting to 293 days. The coast of Lingayen Gulf also reached the highest DHW values in July and August 2022, with 8.94 °C weeks, while Davao Oriental experienced the most extended average duration of MHWs in 2020, lasting 90.5 days per event. Large-scale climate features such as the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) significantly influenced the study area’s SST anomalies and MHW events. High-risk coral bleaching periods, such as 1988–1989, 1998–1999, 2007–2008, and 2009–2010, were characterized by transitions from El Niño and positive PDO phases, to La Niña and negative PDO phases. However, since 2015, global warming has led to high cumulative heat stress without specific climate background patterns. We propose a Coral Marine Environmental Vulnerability Index (CoralVI) to integrate the spatiotemporal dynamics of warming and acidification and their impacts on coral habitats. The data show a rapid increase in the marine environmental vulnerability of coral habitats in the Philippines in recent years, extending to almost the entire coastline, posing significant threats to coral survival. Full article

In arid regions, water scarcity necessitates reliance on surface runoff as a vital water source. Studying the impact of climate change on surface runoff can provide a scientific basis for optimizing water use and ensuring water security. This study investigated runoff patterns in the upper-middle Amu Darya River (UADR) from 1960 to 2015. Special emphasis was placed on the effects of climatic factors and the role of major atmospheric circulation indices, such as the Eurasian Zonal Circulation Index (EZI), Niño 3.4, and the Indian Ocean Dipole (IOD). The results show a significant linear decreasing annual trend in runoff at a rate of 2.5 × 10⁸ m³/year, with an abrupt change in 1972. Runoff exhibited periodic characteristics at 8–16 and 32–64 months. At the 8–16-month scale, runoff was primarily influenced by precipitation (PRE), actual evapotranspiration (AET), and snow water equivalent (SWE), and, at the 32–64-month scale, Niño 3.4 guided changes in runoff. In addition, El Niño 3.4 interacted with the EZI and IOD, which, together, influence runoff at the UADR. This study highlights the importance of considering multiple factors and their interactions when predicting runoff variations and developing water resource management strategies in the UADR Basin. The analysis of nonlinear runoff dynamics in conjunction with multiscale climate factors provides a theoretical basis for the management of water, land, and ecosystems in the Amu Darya Basin. Full article

The study is about the influence of climate change on tree growth in urban forests in Cuiabá, Mato Grosso, Brazil, using dendrochronology. The study focuses on two species, Cedrela fissilis Vell. and Copaifera langsdorffii Desf., both with dendrochronological potential. Samples were collected from an urban forest fragment, and local (temperature and precipitation) and global (ocean surface temperature—SST and Niño 3.4 index) meteorological data were analyzed to correlate with ring width. The methodology involved collecting, preparing, polishing, and marking the rings. The data series were analyzed using the COFECHA, Arstan, and CooRecorder programs to verify the accuracy of ring dating and SAS program for correlations with climatic variables. Both species exhibited good correlations between growth rings and climatic conditions. Cedrela fissilis and Copaifera langsdorffii were positively correlated with precipitation during the dry season and generally negatively correlated with temperatures. Negative correlations were identified with SST and Niño 3.4 for both species. These results are important for understanding how urban forests respond to climate change and how the study of growth rings can be used to predict the future impacts of these changes on plant species. Full article

In this work, the preindustrial control (piControl) and Historical experiments results from climatic Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are analyzed for their ability to predict the El Niño–Southern Oscillation (ENSO). Using the principal component method, it is shown that the Global Atmospheric Oscillation (GAO), of which the ENSO is an element, is the main mode of interannual variability of planetary anomalies of surface air temperature (SAT) and atmospheric sea level pressure (SLP) in the ensemble of 50 CMIP6 models. It turns out that the CMIP6 ensemble of models reproduces the planetary structure of the GAO and its west–east dynamics with a period of approximately 3.7 years. The models showed that the GAO combines ENSO teleconnections with the tropics of the Indian and Atlantic Oceans, and with temperate and high latitudes. To predict strong El Niño and La Niña events, we used a predictor index (PGAO) obtained earlier from observation data and reanalyses. The predictive ability of the PGAO is based on the west–east propagation of planetary structures of SAT and SLP anomalies characteristic of the GAO. Those CMIP6 models have been found that reproduce well the west–east spread of the GAO, with El Niño and La Niña being phases of this process. Thanks to this, these events can be predicted with approximately a year’s lead time, thereby overcoming the so-called spring predictability barrier (SPB) of the ENSO. Thus, the influence of global anomalies of SAT and SLP on the ENSO is shown, taking into account that it may increase the reliability of the early forecast of El Niño and La Niña events. Full article

This study utilizes high-resolution sea surface temperature (SST) reanalysis data (0.25° × 0.25°) to investigate the relationship between SST anomalies in the South China Sea and ENSO events. The main findings are as follows: First, there is a delayed correlation between ENSO and SST anomalies in the South China Sea, with the correlation being stronger during El Niño years than during La Niña years. Second, the correlation with the peak values of the Oceanic Niño Index (ONI) is strongest for El Niño events with a 9-month lead, while for La Niña events, it is strongest with a 2-month lead. Seasonally, during El Niño events, the strongest correlations are observed in summer with a 3-month lead and in winter with a 1-month lag. For La Niña events, the strongest correlations are seen in summer with an 8-month lag and in winter with a 9-month lag. Finally, an analysis of atmospheric anomalies and shear kinetic energy anomalies relative to SST anomalies reveals a significant seasonal SST response, particularly during the summer of El Niño years and the winter of La Niña years. Overall, these results enhance our understanding of ENSO’s influence on the South China Sea and provide valuable insights for climate prediction and ecosystem protection in the region. Full article

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

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

This paper demonstrates the effectiveness and superiority of Empirical Mode Decomposition (EMD) in projecting non-stationary hydrological data. The study focuses on daily Sea Surface Temperature (SST) sequences in the Niño 3.4 region and uses EMD to forecast the probability of El Niño events. Applying the Mann–Kendall test at the 5% significance level reveals a significant increasing trend in SST changes in this region, particularly noticeable after 1980. This trend is associated with the occurrence of El Niño and La Niña events, which have a recurrence interval of approximately 8.4 years and persist for over a year. The modified Oceanic Niño Index (ONI) proposed in this study demonstrates high forecast accuracy, with 97.56% accuracy for El Niño and 89.80% for La Niña events. Additionally, the EMD of SST data results in 13 Intrinsic Mode Functions (IMFs) and a residual component. The oscillation period increases with each IMF level, with IMF7 exhibiting the largest amplitude, fluctuating between ±1 °C. The residual component shows a significant upward trend, with an average annual increase of 0.0107 °C. These findings reveal that the EMD-based data generation method overcomes the limitations of traditional hydrological models in managing non-stationary sequences, representing a notable advancement in data-driven hydrological time series modeling. Practically, the EMD-based 5-year moving process can generate daily sea temperature sequences for the coming year in this region, offering valuable insights for assessing El Niño probabilities and facilitating annual updates. 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

Drought prediction is crucial for early risk assessment, preventing negative impacts and the timely implementation of mitigation measures for sustainable water management. This study investigated the relationship between climate variations in three seas and the prediction of December meteorological droughts in South Korea, using the Standardized Precipitation Evapotranspiration Index (SPEI). Climate indices with multiple time lags were integrated into multiple linear regression (MLR) and Random Forest (RF) models and evaluated using Pearson’s correlation coefficients (PCCs) and the Root Mean Square Error (RMSE). The results indicated that the MLR model outperformed RF model in the western inland region with a PCC of 0.52 for predicting SPEI-2. On the other hand, the RF model effectively predicted drought states of ‘moderate drought’ or worse (SPEI < −1) nationwide, achieving an average hit rate of 47.17% and Heidke skill score (HSS) of 0.56, particularly excelling in coastal areas. Nino 3.4 turned out to be the most influential factor for short-period extreme droughts (SPEI-2) with a three-month lag, contributed by the Pacific, Atlantic, and Indian Oceans. For periods of four months or longer, climate variations had a lower predictive value. However, integrating autocorrelation functions to account for the previous month’s drought status improved the accuracy. A HYBRID model, which blends linear and nonlinear approaches, further enhanced reliability, making the proposed model more applicable for drought forecasting in neighboring countries and valuable for South Korea’s drought monitoring system to support sustainable water management. Full article

The yellowfin tuna represents a significant fishery resource in the Pacific Ocean. Its resource endowment status and spatial variation mechanisms are intricately influenced by marine environments, particularly under varying climate events. Consequently, investigating the spatial variation patterns of dominant environmental factors under diverse climate conditions, and understanding the response of yellowfin tuna catch volume based on the spatial heterogeneity among these environmental factors, presents a formidable challenge. This paper utilizes comprehensive 5°×5° yellowfin tuna longline fishing data and environmental data, including seawater temperature and salinity, published by the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) for the period 2000–2021 in the Pacific Ocean. In conjunction with the Niño index, a multiscale geographically weighted regression model based on geographically weighted principal component analysis (GWPCA-MGWR) and spatial association between zones (SABZ) is employed for this study. The results indicate the following: (1) The spatial distribution of dominant environmental factors affecting the catch of Pacific yellowfin tuna is primarily divided into two types: seawater temperature dominates in the western Pacific Ocean, while salinity dominates in the eastern Pacific Ocean. When El Niño occurs, the area with seawater temperature as the dominant environmental factor in the western Pacific Ocean further extends eastward, and the water layers where the dominant environmental factors are located develop to deeper depths; when La Niña occurs, there is a clear westward expansion in the area with seawater salinity as the dominant factor in the eastern Pacific Ocean. This change in the spatial distribution pattern of dominant factors is closely related to the movement of the position of the warm pool and cold tongue under ENSO events. (2) The areas with a higher catch of Pacific yellowfin tuna are spatially associated with the dominant environmental factor of mid-deep seawater temperature (105–155 m temperature) to a greater extent than other factors, the highest correlation exceeds 70%, and remain relatively stable under different ENSO events. The formation of this spatial association pattern is related to the vertical movement of yellowfin tuna as affected by subsurface seawater temperature. (3) The GWPCA-MGWR model can fully capture the differences in environmental variability among subregions in the Pacific Ocean under different climatic backgrounds, intuitively reflect the changing areas and influencing boundaries from a macro perspective, and has a relatively accurate prediction on the trend of yellowfin tuna catch in the Pacific Ocean. Full article