Search Results (149)

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

A long-wavelength geoidal geometry reflects mainly lateral density variations in the Earth’s mantle, with the most pronounced features of the Indian Ocean Geoid Low and the West Pacific and North Atlantic Geoid Highs. Despite this spatial pattern being clearly manifested in the global geoidal geometry determined from gravity-dedicated satellite missions, the gravitational signature of the deep mantle could be refined by modelling and subsequently removing the gravitational contribution of lithospheric geometry and density structure. Nonetheless, the expected large uncertainties in available lithospheric density models (CRUST1.0, LITHO1.0) limit, to some extent, the possibility of realistically reproducing the gravitational signature of the deep mantle. To address this issue, we inspect an alternative approach. Realizing that the gravity geopotential field (i.e., gravity potential) is smoother than its gradient (i.e., gravity), we apply the integral operator to geopotential and then investigate the spatial pattern of this functional (i.e., radially integrated geopotential). Results show that this mathematical operation enhances a long-wavelength signature of the deep mantle by filtering out the gravitational contribution of the lithosphere. This finding is explained by the fact that in the definition of this functional, spherical harmonics of geopotential are scaled by the factor 1/n (where n is the degree of spherical harmonics), thus lessening the contribution of higher-degree spherical harmonics in the radially integrated geopotential. We also demonstrate that further enhancement of the mantle signature in this functional could be achieved based on modelling and subsequent removal of the gravitational contribution of lithospheric geometry and density structure. 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

Sri Lanka typically experiences anomalously wet conditions during the summer following El Niño events, but this response varies due to El Niño complexity. This study investigates the impact of post-El Niño conditions on Sri Lanka’s Monsoon rainfall, contrasting summers after fast- and slow-decaying El Niño events. Results indicate that fast-decaying El Niño events lead to wet and cool summers while slow-decaying events result in dry and warm summers. These contrasting responses are linked to sea surface temperature (SST) changes in the central to eastern Pacific. During the fast-decaying El Niño, the transition to La Niña generates strong easterlies in the central and eastern Pacific, enhancing moisture convergence, upward motion, and cloud cover, resulting in wetter conditions over Sri Lanka. During the fast-decaying El Niño, enhanced precipitation over the Maritime Continent acts as a diabatic heating source, inducing Gill-type easterly wind anomalies over the tropical Pacific. These winds promote coupled feedbacks that accelerate the transition to La Niña, strengthening moisture convergence and upward motion over Sri Lanka. Conversely, slow-decaying El Niño events are associated with cooling in the western North Pacific and warming in the Indian Ocean, which promotes the development of the western North Pacific anticyclone, suppressing upward motion and reducing cloud cover, leading to conditions over Sri Lanka. Changes in the Walker circulation further contribute to these distinct rainfall patterns, highlighting its influence on regional climate dynamics. These findings enhance our understanding of the seasonal predictability of rainfall in Sri Lanka during post-El Niño Summers. Full article

Sea surface temperature (SST) is crucial for weather forecasting, climate modeling, and environmental monitoring. This study proposes a novel prediction model that achieves a 60-day forecast with a root mean square error (RMSE) consistently below 0.9 °C. The model combines the nonlinear feature extraction of a deep belief network (DBN) with the high-precision regression of support vector regression (SVR), enhanced by spatiotemporal secondary calibration (SSC) to better capture SST variation patterns. Using satellite-derived remote sensing data, the DBN-SVR model outperforms baseline methods in both the Indian Ocean and North Pacific regions, demonstrating strong applicability across diverse marine environments. This work advances long-term SST prediction capabilities, providing a reliable foundation for extended-range marine forecasts. Full article

The Southeastern Tropical Indian Ocean (SETIO) forms part of the global ocean conveyor belt and thermohaline circulation that has a significant influence in controlling the global climate. This region of the ocean has very few observations using surface drifters, and this study presents, for the first time, paths of satellite tracked drifters released in the Timor Sea (123.3° E, 13.8° S). The drifter data were used to identify the ocean dynamics, forcing mechanisms and connectivity in the SETIO region. The data set has high temporal (~5 min) and spatial (~120 m) resolution and were collected over an 8-month period between 17 September 2020 and 25 May 2021. At the end of 250 days, drifters covered a region separated by ~8000 km (83–137° E, 4–21° S) and transited through several forcing mechanisms including semidiurnal and diurnal tides, submesoscale and mesoscale eddies, channel and headland flows, and inertial currents generated by tropical storms. Initially, all the drifters moved as a single cluster, and at 120° E longitude they entered a region of high eddy kinetic energy defined here as the ‘SETIO Mixing Zone’ (SMZ), and their movement was highly variable. All the drifters remained within the SMZ for periods between 3 and 5 months. Exiting the SMZ, drifters followed the major ocean currents in the system (either South Java or South Equatorial Current). Two of the drifters moved north through Lombok and Sape Straits and travelled to the east as far as Aru Islands. The results of this study have many implications for connectivity and transport of buoyant materials (e.g., plastics), as numerical models do not have the ability to resolve many of the fine-scale physical processes that contribute to surface transport and mixing in the ocean. Full article

Cyclone Mocha, classified as an Extremely Severe Cyclonic Storm (ESCS), followed an unusual northeastward trajectory while exhibiting a well-defined eyewall structure. It experienced rapid intensification (RI) before making landfall along the Myanmar coast. It caused heavy rainfall (~90 mm) and gusty winds (~115 knots) over the coastal regions of Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries, such as the coasts of Bangladesh and Myanmar. The factors responsible for the RI of the cyclone in lower latitudes, such as sea surface temperature (SST), tropical cyclone heat potential (TCHP), vertical wind shear (VWS), and mid-tropospheric moisture content, are studied using the National Ocean and Atmospheric Administration (NOAA) SST and National Center for Medium-Range Weather Forecasting (NCMRWF) Unified Model (NCUM) global analysis. The results show that SST and TCHP values of 30 °C and 100 (KJ cm⁻²) over the Bay of Bengal (BoB) favored cyclogenesis. However, a VWS (ms⁻¹) and relative humidity (RH; %) within the range of 10 ms⁻¹ and >70% also provided a conducive environment for the low-pressure system to transform into the ESCS category. The physical mechanism of RI and recurvature of the Mocha cyclone have been investigated using forecast products and compared with Cooperative Institute for Research in the Atmosphere (CIRA) and Indian Meteorological Department (IMD) satellite observations. The key results indicate that a dry air intrusion associated with a series of troughs and ridges at a 500 hPa level due to the western disturbance (WD) during that time was very active over the northern part of India and adjoining Pakistan, which brought north-westerlies at the 200 hPa level. The existence of troughs at 500 and 200 hPa levels are significantly associated with a Rossby wave pattern over the mid-latitude that creates the baroclinic zone and favorable for the recurvature and RI of Mocha cyclone clearly represented in the NCUM analysis. Moreover the Q-vector analysis and steering flow (SF) emphasize the vertical motion and recurvature of the Mocha cyclone so as to move in a northeast direction, and this has been reasonably well represented by the NCUM model analysis and the 24, 7-, and 120 h forecasts. Additionally, a quantitative assessment of the system indicates that the model forecasts of TC tracks have an error of 50, 70, and 100 km in 24, 72, and 120 h lead times. Thus, this case study underscores the capability of the NCUM model in representing the physical mechanisms behind the recurving and RI over the BoB. Full article

Walking has been studied extensively in recent years. However, one is still hard pressed to find research examining what makes urban settings of different sizes across the Atlantic Ocean conducive to walking, especially in the presence of greenways and green open spaces. Streets and urban greenways both enable flows. Streets are mostly utilized to enable the flow of motorized traffic and people while greenways aim to guarantee the flow of water, nature, biodiversity, and people. Streets are designed to artificially separate motorized traffic from pedestrians, greenways are designed to create the natural conditions for a harmonious co-existence of people with nature. How would street users benefit from streetscape and urban design improvements aimed at promoting the peaceful, silent, and harmonious co-existence of nature, people, and vehicles? Distinct sets of codes and norms dictate how individuals ought to utilize urban public spaces and greenways. We argue that said codes ought to also be aimed at increasing the quality of public spaces and not only their flow capacities. This paper examines streets and greenways in Ballyclare, Leiria, and Scottsdale. We utilize Ballyclare’s High Street and Six Mile Water greenway, Leiria’s city centre and Lis River greenway, as well as Scottsdale Road and the Indian Bend Wash greenbelt and a segment of the Arizona Canal to analyse the positive characteristics and shortcomings of successful streets and greenway systems in three distinct geographic contexts: U.K., Southern European, and North American. The findings comprise the distillation of new results in the analysis of spaces of flows and permanence across the Atlantic Ocean. 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

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

Two Indian Ocean surgeon fish Paracanthurus hepatus individuals were observed near Saint-Raphaël (Provence, France, north-western Mediterranean Sea) in the late summer of 2024 in Posidonia oceanica seagrass and reef habitats. This species is very popular among aquarium hobbyists in Europe, and a growing number of mega-yachts, such as those which moor in the Saint-Raphaël marina, have seawater aquariums on board. Accidental or deliberate release from one such aquarium is the most probable origin of these individuals. The first individual was speared and the second one was no longer sighted after a September storm. Their establishment is unlikely; however, in the future, with the warming of Mediterranean waters and the rapid increase in the number of mega-yachts, this could change. Yacht owners and their staff should be informed of the risk posed by aquarium discharges. 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

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement. Full article

This study investigates the climatic background of winter PM2.5 (particulate matter with a diameter of 2.5 micrometers or smaller) concentrations in Hubei Province (DJF-HBPMC) and evaluates its predictability. The key findings are as follows: (1) Elevated DJF-HBPMC levels are associated with an upper-tropospheric northerly anomaly, a deepened southern branch trough (SBT) that facilitates southwesterly flow into central and eastern China, and a weakened East Asian winter monsoon (EAWM), which reduces the frequency and intensity of cold air intrusions. Near-surface easterlies and an anomalous anticyclonic circulation over Hubei contribute to reduced precipitation, thereby decreasing the dispersion of pollutants and leading to higher PM2.5 concentrations. (2) Significant correlations are observed between DJF-HBPMC and sea surface temperature (SST) anomalies in specific oceanic regions, as well as sea-ice concentration (SIC) anomalies near the Antarctic. For the atmospheric pattern anomalies over Hubei Province, the North Atlantic SST mode (NA) promotes the southward intrusion of northerlies, while the Northwest Pacific (NWP) and South Pacific (SPC) SST modes enhance wet deposition through increased precipitation, showing a negative correlation with DJF-HBPMC. Conversely, the South Atlantic–Southwest Indian Ocean SST mode (SAIO) and the Ross Sea sea-ice mode (ROSIC) contribute to more stable local atmospheric conditions, which reduce pollutant dispersion and increase PM2.5 accumulation, thus exhibiting a positive correlation with DJF-HBPMC. (3) A multiple linear regression (MLR) model, using selected seasonal SST and SIC indices, effectively predicts DJF-HBPMC, showing high correlation coefficients (CORR) and anomaly sign consistency rates (AS) compared to real-time values. (4) In daily HBPMC forecasting, both the Reversed Unrestricted Mixed-Frequency Data Sampling (RU-MIDAS) and Reversed Restricted-MIDAS (RR-MIDAS) models exhibit superior skill using only monthly precipitation, and the RR-MIDAS offers the best balance in prediction accuracy and trend consistency when incorporating monthly precipitation along with monthly SST and SIC indices. Full article