Search Results (965)

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 atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens’ theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments. Full article

The Nile Basin has long been the subject of extensive research, reflecting its importance, which spans from its historical role in the development of ancient civilizations to its current significance in supporting rapidly changing socioeconomic conditions of the basin countries. This review synthesizes studies focusing on the past and future climate, hydrologic, and drought outlooks of the basin, and explores the roles played by large-scale atmospheric phenomena and water infrastructure on the basin’s climate and hydrology. Overall, the studies underscore the complexity of the Nile hydrological system and the necessity for improved modeling and data integration. This review serves as a guide to areas warranting further research by highlighting the uncertainties and inconsistencies among the different studies. It underscores the interconnectedness of climatic and hydrological processes in the basin and encourages the use of diverse data sources to address the data scarcity issue and ensemble models to reduce modeling uncertainty in future research. By summarizing the data and modeling resources employed in these studies, this review also provides a valuable resource for future modeling efforts to understand and explore of the basin’s complex climatic and hydrological dynamics. Full article

This study investigates the dust aerosol optical depth (DAOD) variability over the Arabian Peninsula (AP) in the spring season, a region profoundly affected by dust activity due to its desert terrain. Employing the MERRA-2 DAOD reanalysis dataset for the period 1981–2022, a significant trend in DAOD is noted in the spring season compared to the other seasons. The leading Empirical Orthogonal Function (EOF) explains 67% of the total DAOD variance during the spring season, particularly over the central and northeastern parts of AP. The analysis reveals the strengthening of upper-level divergence over the western Pacific, favoring mid-tropospheric positive geopotential height anomalies over the AP, leading to warm and drier surface conditions and increased DAOD. A statistically significant negative relationship (correlation = −0.32, at 95% confidence level) is noted between DAOD over AP and the El Niño-Southern Oscillation (ENSO), suggesting that La Niña conditions may favor higher dust concentrations over the AP region and vice versa during El Niño phase. The high (low) DAOD over the region corresponds to mid-tropospheric positive (negative) geopotential height anomalies through strengthening (weakening) of the upper-level divergence (convergence) over the western Pacific during the La Niña (El Niño) phase. This study shows that ENSO could be a possible precursor to predicting dust variability on a seasonal time scale. Full article

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region’s reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region’s flood preparedness, response strategies, and overall disaster management. Full article

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and the El Niño phenomenon. This paper proposes an improved double-output residual neural network (DO-ResNet) model to concurrently estimate the subsurface temperature (ST) and subsurface salinity (SS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), sea surface wind (SSW), and geographical information (including longitude and latitude). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R²) were employed to evaluate the model’s performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating ST (SS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of ST and SS across different depths with Argo data, demonstrating the model’s ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model demonstrates strong adaptability to seasonal variations. In conclusion, this research introduces a novel artificial intelligence technique for estimating ST and SS in the tropical Western Pacific Ocean. 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

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness. Full article

Currently, the “spring predictability barrier” (SPB) is still a controversial problem in many atmosphere–ocean coupled models and has significant impacts on degrading the El Niño–Southern Oscillation (ENSO) predictions across the boreal spring. In this study, unlike previous studies that viewed the SPB issue from the perspective of sea surface temperature (SST), based on the Bjerknes feedback theory and the decadal variations in Walker circulation over the tropical Pacific, a new perspective of the SPB is revealed by the seasonal variations in the observed zonal sea level pressure (SLP) gradient, which can reflect the stability and variability of the atmosphere–ocean interactions during the ENSO’s evolution. More importantly, a significant decadal variation of SPB strength (SPBS) is exhibited in the last 3 decades, from 1991 to 2020, which is strongly controlled by the dominant patterns of sea surface temperature (SST) and Walker circulation, and associated with the background mean atmosphere–ocean states. That is to say, the atmosphere–ocean interaction pattern over the tropical Pacific has undergone decadal variations over the past 3 decades which determine the decadal variations in SPBS. International Research Institute for Climate and Society/Climate Prediction Center (IRI/CPC) multi-models show stronger SPBS during 2001–2010 than during 2011–2020, indicating that the decadal variations in SPBS from statistical analysis also exist in actual model predictions, which further confirms the rationality of this perspective of SPB based on the zonal SLP gradient. 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

The Ogasawara (Bonin) Islands, consisting of more than 30 islands and located approximately 1000 km south of central Tokyo, occasionally experience severe droughts. Severe drought does not typically occur during El Niño (EN) events in the Ogasawara Islands because convective activity around the tropical western Pacific is inactive during EN events and correspondingly induces substantial precipitation around the Ogasawara Islands through the Pacific–Japan (P-J) pattern. However, a severe drought in 2018–2019 occurred during EN. In this study, we investigated the causes of drought occurrence. In 2018–2019, the El Niño Modoki (EN Modoki) event occurred simultaneously with EN, which decreased precipitation around the Ogasawara Islands from autumn to the following spring. This was induced by the positive sea level pressure anomaly and anticyclonic circulation around the Ogasawara Islands peculiar to the EN Modoki condition. In relation to the 2018–2019 drought, the investigation of past drought events at the Ogasawara Islands revealed that the drought in the spring and summer of 1991 also occurred during the simultaneous occurrence of the EN and EN Modoki events. Full article

To measure the long-range transport of PCDD/Fs, a background sampling site at Mt. Lulin station (Taiwan) was selected based on meteorological information and its location relative to burning events in Southeast Asia. During regular sampling periods, a higher concentration of PCDD/Fs was recorded in 2008 at Mt. Lulin station during La Niña events, with levels reaching 390 fg I-TEQ/m³. In contrast, a higher concentration of 483 fg I-TEQ/m³ was observed in 2013 during biomass burning events. This indicates that La Niña affects the ambient PCDD/F concentrations. The ratio of ΣPCDD/ΣPCDF was 0.59, suggesting significant long-range transport contributions from 2007 to 2023. From 2007 to 2015, the predominant species was 2,3,4,7,8-PCDF, accounting for 25.3 to 39.6% of the total PCDD/Fs. From 2018 onward, 1,2,3,7,8-PCDD became more dominant, accounting for 15.0 to 27.1%. According to the results from the receptor model PMF (n = 150), the sources of PCDD/Fs were identified as dust storms and monsoon events (19.3%), anthropogenic activity (28.5%), and biomass burning events (52.2%). The PSCF values higher than 0.7 highlighted potential PCDD/F emission source regions for Mt. Lulin during biomass burning events, indicating high PSCF values in southern Thailand, Cambodia, and southern Vietnam. Full article

(1) Background: Could compounds such as monoterpenes and sesquiterpenes present in essential plant oils inhibit bacterial growth as an alternative to help mitigate bacterial resistance? The purpose of this study is evaluating the in vitro antibacterial effect of Lippia organoides EO (LEO) and Thymus vulgaris EO (TEO), individually and in combination with ampicillin, against extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli strains; (2) Methods: Experimental in vitro design with post-test. The EOs were obtained by hydrodistillation and were analyzed by GC. ESBL-producing E. coli strains used were selected from urine cultures and the bla_CTX-M and bla_TEM resistance genes were identified by end point PCR. The disk diffusion method was used for the susceptibility tests. The MICs and MBCs were determined by microdilution test. Finally, the interaction effect was observed by checkerboard assay; (3) Results: A 39.9% decrease in the growth of the strain thymol in TEO and 70.4% in carvacrol in LEO was shown, observing inhibition halos of 32 mm for both EOs. MICs of 632 and 892 μg/mL for LEO and 738 and 940 μg/mL for TEO were determined. Finally, it was observed that, at low doses, there is a synergistic effect between TEO + LEO and EOs + ampicillin; (4) Conclusions: The findings demonstrate that TEO and LEO have an inhibitory effect on ESBL-producing E. coli, suggesting that they are candidates for further studies in the formulation of antibiotics to reduce bacterial resistance to traditional antibiotics. Full article