Search Results (168)

Since the 1980s, earlier European springs have led to the earlier arrival of migrant passerines. We predict that arrival is related to a suite of climate indices operating during the annual cycle (breeding, autumn migration, wintering, spring migration) in Europe and Africa over the year preceding arrival. The climate variables include the Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), and North Atlantic Oscillation (NAO). Furthermore, because migrants arrive sequentially from different wintering areas across Africa, we predict that relationships with climate variables operating in different parts of Africa will change within the season. We tested this using daily ringing data at Bukowo, a spring stopover site on the Baltic coast. We calculated an Annual Anomaly (AA) of spring passage (26 March–15 May, 1982–2024) for four long-distance migrants (Blackcap, Lesser Whitethroat, Willow Warbler, Chiffchaff). We decomposed the anomaly in two ways: into three independent main periods and nine overlapping periods. We used multiple regression to explore the relationships of the arrival of these species at Bukowo. We found sequential effects of climate indices. Bukowo is thus at a crossroads of populations arriving from different wintering regions. The drivers of phenological shifts in passage of wide-ranging species are related to climate indices encountered during breeding, wintering, and migration. Full article

In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). Winter (0.27 °C decade⁻¹) and spring (0.20 °C decade⁻¹) exhibited the strongest warming, followed by autumn (0.18 °C decade⁻¹) and summer (0.10 °C decade⁻¹). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors. Full article

Based on the Clouds and the Earth’s Radiant Energy System (CERES) satellite data from 2001 to 2023 and the climate indices from the National Oceanic and Atmospheric Administration (NOAA), this study analyzes the solar irradiance over mainland China and the impacts of clouds and aerosols on it and constructs monthly forecasting models to analyze the influence of climate indices on irradiance forecasts. The irradiance over mainland China shows a spatial distribution of being higher in the west and lower in the east. The influence of clouds on irradiance decreases from south to north, and the influence of aerosols is prominent in the east. The average explained variance of clouds on irradiance is 86.72%, which is much higher than that of aerosols on irradiance, 15.62%. Singular Value Decomposition (SVD) analysis shows a high correlation between the respective time series of irradiance and cloud influence, with the two fields having similar spatial patterns of opposite signs. The variation in solar irradiance can be attributed mainly to the influence of clouds. Empirical Orthogonal Function (EOF) analysis indicates that the variation in the first mode of irradiance is consistent in most parts of China, and its time coefficient is selected for monthly forecasting. Both the traditional multiple linear regression method and the Long Short-Term Memory (LSTM) network are used to construct monthly forecast models, with the preceding time coefficient of the first EOF mode and different climate indices as input. Compared with the multiple linear regression method, LSTM has a better forecasting skill. When the input length increases, the forecasting skill decreases. The inclusion of climate indices, such as the Indian Ocean Basin (IOB), El Nino–Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), can enhance the forecasting skill. Among these three indices, IOB has the most significant improvement effect. The research provides a basis for accurate forecasting of solar irradiance over China on monthly time scale. Full article

Moisture transports play a key role in maintaining the hydrometeorological cycle and forming its climate variability over the Tibetan Plateau (TP), also known as the “Asian water tower”. This study focuses on understanding the interannual variability mode characteristics of moisture transport in the TP in boreal summer, using satellite-based analysis and reanalysis data from 1983 to 2022 with a combined empirical orthogonal function (EOF) analysis. We identified the first two primary interannual modes of TP summer water vapor fluxes, which are primarily characterized by zonal and meridional dipole patterns, respectively. The zonal pattern of the TP water vapor flux dominates the TP and East Asian summer rainfall variability, while the meridional pattern of the TP water vapor flux tends to be a result of the South Asian summer rainfall and its circulation anomalies. The tropical Indo-Pacific sea surface temperature (SST) variations, such as El Niño and Indian Ocean SST modes, have significantly delayed relationships with the interannual variability modes of the summer water vapor fluxes over the TP, indicating a significant modulation effect of the low-latitude oceanic variability on the interannual variations in TP summer moisture transport. These results deepen our understanding of the relationship between TP moisture transport and summer monsoonal rainfall variability, as well as the influence of the tropical oceans. Full article

As important components of the equatorial current system in the Indian Ocean, Wyrtki jets (WJs) play a significant role in distributing heat and matter in the East and West Indian Oceans. By dividing the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events into several phases, we find that the spring branch exhibits positive (negative) anomalies during the El Niño (La Niña) decaying phase, while the fall branch exhibits negative (positive) anomalies during the El Niño (La Niña) developing phase. The spring and fall branches are characterized by negative (positive) anomalies under the influence of positive (negative) dipole events, and these anomalies are particularly pronounced during fall. This study systematically analyzes the characteristics of WJs under the interactions between the Indo-Pacific ocean and the atmosphere, based on the phase-locking characteristics of ENSO, and reveals the regulatory mechanisms underlying their different response patterns. Full article

This study investigates teleconnections between rainfall in the Ogooué River Basin (ORB) and sea surface temperature (SST) in the tropical ocean basins. The Maximum Covariance Analysis (MCA) is used to determine coupled patterns of SST in the tropical oceans and rainfall in the ORB, depicting regions and modes of SST dynamics that influence rainfall in the ORB. The application of MCA to rainfall and SST fields results in three coupled patterns with squared covariance fractions of 84.5%, 76.5%, and 77.5% for the Atlantic, Pacific, and Indian tropical basins, respectively. Computation of the correlations of the Savitzky–Golay-filtered resulting expansion coefficients reached 0.65, 0.5 and 0.72, respectively. The SST variation modes identified in this study can be related to the Atlantic Meridional Mode for the tropical Atlantic and the El Niño Southern Oscillation for the tropical Pacific. Over the Indian Ocean, it is a homogeneous mode over the entire basin, instead of the popular dipole mode. Then, the time-dependent correlation method is used to remove any ambiguity on the relationships established from the MCA. 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 northern Indian Ocean is located in a typical monsoon region that is also influenced by climate events such as the Indian Ocean Dipole (IOD), which makes Sthenoteuthis oualaniensis habitat highly susceptible to changes in climate and marine environmental conditions. This study established a suitability index (SI) model and used the arithmetic average method to construct a comprehensive habitat suitability index (HSI) model based on S. oualaniensis production statistics in the northern Indian Ocean from 2017 to 2019. Variations in the suitability of S. oualaniensis habitat during different IOD events were then analyzed. The results indicate that the model performed best when year, month, latitude, longitude, sea surface temperature (SST), wind speed (WS), and photosynthetically active radiation (PAR) variables were included in the generalized additive model (GAM). SST, WS, and PAR were identified as the most important key environmental factors. The HSI model showed that the most suitable habitat during a positive IOD event was smaller than during a negative IOD event and that the suitable habitat’s center was located west of the positive IOD event and east of the negative IOD event. There was a significant inverse relationship between the area, suitable for habitation, and the north–south shift in the latitudinal gravity center and the Dipole modal index (DMI). The results indicate significant differences in the habitat of S. oualaniensis in the northern Indian Ocean during different IOD events, as well as differences in suitable habitat ranges and the spatial distribution of the species. Full article

The regularity of rainfall seasonality is very important for vegetation growth, the livelihood of the population, agricultural production, and ecosystem sustainability. Changes in precipitation and its extremes have been widely reported; however, the spatial and temporal variations in rainfall seasonality and their underlying mechanisms are less understood. Here, we analyzed the changes in rainfall seasonality and possible teleconnection mechanisms in the eastern China monsoon region during 1981–2022, with a special focus on the El Niño-Southern Oscillation (ENSO), El Niño Modoki (ENSO_M), and Indian Ocean Dipole (IOD). Our results show that due to the changes in rainfall concentration, rainfall magnitude, or both, rainfall seasonality has developed in the northern China (NC, 0.15 × 10⁻³ yr⁻¹) and central China (CC, 0.07 × 10⁻³ yr⁻¹) monsoon regions, and weakened in the northeastern China (NEC, −0.08 × 10⁻³ yr⁻¹) and southern China (SC, −0.15 × 10⁻³ yr⁻¹) monsoon regions during the recent decades. The large-scale circulation and SST anomalies induced by cold or warm phases of the IOD, ENSO_M, and (or) ENSO can explain the enhanced seasonality in the NC and CC monsoon regions and weakened seasonality in the NEC and SC monsoon regions. The wavelet coherence analysis further shows that the dominated climatic factors for rainfall seasonality changes are different in the CC, NC, SC, and NEC monsoon regions, and that rainfall seasonality is also affected by the coupling of the IOD, ENSO_M, and ENSO. Our results highlight that the IOD, ENSO_M, and ENSO are important climatic causes for rainfall seasonality changes in the eastern China monsoon region. Full article

Identifying the relationship between extreme precipitation (EP) and large-scale climate circulation is of great significance for extreme weather management and warning. Previous studies have effectively revealed the influence of single climate circulation on EP, although the influence characteristics of multiple climate circulation are still unclear. In this study, seasonal spatiotemporal changes in decadal anomalies of daily EP were analyzed based on quantile perturbation method (QPM) within the Three Gorges Reservoir Area (TGRA) for the period from 1960 to 2020. Sea surface temperature (SST)- and sea level pressure (SLP)-related climate circulation factors were selected to examine their interaction influences on and contributions to EP. The results showed that: (1) Summer EP anomalies exhibited greater temporal variability than those in other seasons, with the cycle duration of dry/wet alternation shortening from 15 years to 5 years. Winter EP anomalies showed pronounced spatial homogeneity patterns, especially in the 1970s. (2) According to the analysis based on a single driver, the Southern Oscillation Index (SOI), the North Atlantic Oscillation (NAO), and the Indian Ocean Dipole (IOD) had prolonged correlations with seasonal EP anomalies. (3) More contributions can be obtained from multiple climate circulations (binary and ternary drivers) on seasonal EP anomalies than from a single driver. Although difference existed in seasonal combinations of ternary factors, their contributions on EP anomalies were more than 60%. This study provides an insight into the mechanisms of modulation and pathways influencing various large-scale climate circulation on seasonal EP anomalies. Full article

Sub-Saharan Africa (SSA) is a region vulnerable to extreme weather events due to its low level of adaptive capacity. In recent decades, SSA has been punctuated by more intense climatic phenomena that severely affect its population. Therefore, this study evaluates the performance of the ERA5 and CHIRPS datasets, and the spatio-temporal evolution of extreme weather indices and their potential relationship/response to climate variability modes in the Pacific, Indian, and Atlantic oceans, namely, the El Niño−Southern Oscillation, Indian Ocean Dipole, and Tropical Atlantic Variability (ENSO, IOD, and TAV). The CHIRPS dataset showed strong positive correlations with CPC in spatial patterns and similarity in simulating interannual variability and in almost all seasons. Based on daily CHIRPS and CPC data, nine extreme indices were evaluated focusing on regional trends and change detection, and the maximum lag correlation method was applied to investigate fluctuations caused by climate variability modes. The results revealed a significant decrease in total precipitation (PRCPTOT) in north−central SSA, accompanied by a reduction in Consecutive Wet Days (CWDs) and maximum 5-day precipitation indices (RX5DAYS). At the same time, there was an increase in Consecutive Dry Days (CDDs) and maximum rainfall in 1 day (RX1DAY). With regard to temperatures, absolute minimums and maximums (TNn and TXn) showed a tendency to increase in the center−north and decrease in the south of the SSA, while daily maximums and minimums (TXx and TNx) showed the opposite pattern. The IOD, TAV, and ENSO modes of climate variability influence temperature and precipitation variations in the SSA, with distinct regional responses and lags between the basins. Full article

Understanding the factors behind urban water leakage is crucial for developing a sustainable climate and protecting civil infrastructure. Water leaks not only waste essential resources but also increase urban vulnerabilities to climate-induced disasters. This study investigates the teleconnection between leakage incidents and climate change indices to establish predictive insight for water management. It focuses on climate phenomena such as El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), which significantly influence global climate dynamics, affecting temperature and precipitation in South Korea. Using Pearson correlation analysis and Granger causality tests, this research examines climate indices and leakage data across South Korea’s inland regions from 2009 to 2022. The results indicate that ENSO indices exhibit a lead time of 6 to 30 months, with significant correlations in coastal areas, particularly Chungnam (west coast) and Gyeongnam (east coast). Inland regions such as Gimcheon and Chuncheon also showed notable correlations influenced by topographical factors. The findings highlight the importance of integrating climate teleconnection indices into risk management strategies. This approach allows for targeted monitoring and predictive modeling, enabling proactive responses to water leakage risks and contributing to sustainable urban development. Full article

Malaria, a severe vector-borne disease, affects billions of people globally and claims over half a million lives annually. Climate change can impact lifespan and the development of vectors. There is a gap in organized, multidisciplined research on climate change’s impact on malaria incidence and transmission. This review assesses and summarizes research on the effects of change in climate on human health, specifically on malaria. Results suggest that higher temperatures accelerate larval development, promote reproduction, enhance blood feed frequency, increase digestion, shorten vector life cycles, and lower mortality rates. Rainfall provides aquatic stages, extends mosquitoes’ lifespans, and increases cases. Mosquito activity increases with high humidity, which facilitates malaria transmission. Flooding can lead to increased inhabitation development, vector population growth, and habitat diversion, increasing breeding sites and the number of cases. Droughts can increase vector range by creating new breeding grounds. Strong storms wash Anopheles’ eggs and reproduction habitat. It limits reproduction and affects disease outbreaks. The Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) indirectly alter malaria transmission. The study recommends strengthening collaboration between policymakers, researchers, and stakeholders to reduce malaria risks. It also suggests strengthening control mechanisms and improved early warnings. 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

The spatial and temporal distribution of heavy rainfall across the Taihang Mountains exhibits significant variation. Due to the region’s unstable geological conditions, frequent heavy rainfall events can lead to secondary disasters such as landslides, debris flows, and floods, thus intensifying both the frequency and severity of extreme events. Understanding the spatiotemporal evolution of heavy rainfall and its response to atmospheric circulation patterns is crucial for effective disaster prevention and mitigation. This study utilized daily precipitation data from 13 meteorological stations in the Taihang Mountains spanning from 1973 to 2022, employing Rotated Empirical Orthogonal Function (REOF), the Mann–Kendall Trend Test, and Continuous Wavelet Transform (CWT) to examine the spatiotemporal characteristics of heavy rainfall and its relationship with large-scale atmospheric circulation patterns. The results reveal that: (1) Heavy rainfall in the Taihang Mountains can be categorized into six distinct regions, each demonstrating significant spatial heterogeneity. Region I, situated in the transition zone between the plains and mountains, experiences increased rainfall due to orographic lifting, while Region IV, located in the southeast, receives the highest rainfall, driven primarily by monsoon lifting. Conversely, Regions III and VI receive comparatively less precipitation, with Region VI, located in the northern hilly area, experiencing the lowest rainfall. (2) Over the past 50 years, all regions have experienced an upward trend in heavy rainfall, with Region II showing a notable increase at a rate of 14.4 mm per decade, a trend closely linked to the intensification of the hydrological cycle driven by global warming. (3) The CWT results reveal significant 2–3-year periodic fluctuations in rainfall across all regions, aligning with the quasi-biennial oscillation (QBO) characteristic of the East Asian summer monsoon, offering valuable insights for future climate predictions. (4) Correlation and wavelet coherence analyses indicate that rainfall in Regions II, III, and IV is positively correlated with the Southern Oscillation Index (SOI) and the Pacific Warm Pool (PWP), while showing a negative correlation with the Pacific Decadal Oscillation (PDO). Rainfall in Region I is negatively correlated with the Indian Ocean Dipole (IOD). These climatic factors exhibit a lag effect on rainfall patterns. Incorporating these climatic factors into future rainfall prediction models is expected to enhance forecast accuracy. This study integrates REOF analysis with large-scale circulation patterns to uncover the complex spatiotemporal relationships between heavy rainfall and climatic drivers, offering new insights into improving heavy rainfall event forecasting in the Taihang Mountains. The complex topography of the Taihang Mountains, combined with unstable geological conditions, leads to uneven spatial distribution of heavy rainfall, which can easily trigger secondary disasters such as landslides, debris flows, and floods. This, in turn, further increases the frequency and severity of extreme events. Full article