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Extreme weather events, such as those associated with winds and precipitation, result in billions of euros in damages annually. While changes in extreme precipitation due to global warming have already been detected at sub-continental scales, their complex characteristics make them a challenges to asses at more regional scales. Extreme winds present an even greater challenge as the varying dynamical response to global warming exhibits high levels of uncertainty. This situation is complicated by local scale interactions with orography, cities, land-sea contrasts, etc. The dataset presented here attempts to address these challenges and provide information that will allow robust assessment of extreme winds and precipitation (maximum five day precipitation). We achieve this by leveraging a large ensemble (52 members) of high resolution (12 km) EURO-CORDEX simulations. The dataset will be of value, not only to the scientific community, but also practitioners in the public (e.g., municipal planners, government agencies) and private sectors (e.g., insurers and reinsurers).

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The co-occurrence of multiple hazards can either exacerbate or mitigate risks. The interrelationships between multiple hazards greatly depend on the spatiotemporal scale and can be difficult to detect from large to local scales. In this paper, we identified coastal regions worldwide where the leading tropical (El Niño-Southern Oscillation, ENSO) and polar (Arctic Oscillation, AO; Southern Annular Mode, SAM) modes of climate variability simultaneously modify the seasonal conditions of multiple hazards, including the near-surface wind speed and swell and wind-sea wave powers. We classified the results at the national and municipal levels, with a focus on multiple hazards simultaneously occurring in space and time. The results revealed that the ENSO modulates multiple hazards, affecting approximately 40% of coastal countries, while the polar annular modes affect approximately 30% of coastal countries. The ENSO induced a greater diversity of multiple hazards, with Asian countries (e.g., Indonesia experienced increases of + 2% in wind and + 7% in swell) and countries in the Americas (e.g., Peru exhibited increases of + 1.5% in wind and + 6% in wind-sea) the most notably affected. The SAM imposed a greater influence on swells in the eastern countries of ocean basins (+ 2.5% in Chile) than in other countries, while the influence of the AO was greater in Norway and the UK (+ 12% for wind-sea and 8% for swell). Low-lying islands exhibited notable variations in pairwise hazards between phases and seasons. Our results could facilitate the interpretation of multihazard interactions and pave the way for a wide range of potential implementations of different coastal industries.

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Aerosol proteins, as core biological components of bioaerosols, are garnering increasing attention due to their environmental significance, including their roles in atmospheric processes and associated health risks. However, observational data on the proteins are very limited, leaving their distribution and variation in the atmosphere poorly understood. To investigate the long-distance transport of proteins with Asian dust in the Northern Hemisphere middle latitude westerlies to remote downwind areas, we quantified the soluble proteins in aerosol particles, referred to as aerosol soluble proteins (ASPs), collected in the coastal city of Kumamoto, Japan, during the spring of 2023, when three dust events occurred. The concentration of ASPs ranged from 0.22 to 1.68 µg m-3, with an average concentration of 0.73 ± 0.36 µg m-3 under dust conditions and 0.31 ± 0.05 µg m-3 under non-dust conditions. During the dust periods, the largest concentration of ASPs (1.68 µg m-3) coincided with the peak concentration of suspended particulate matter, and the concentration strongly correlated with the mass concentration of particles larger than 2.5 µm, indicating a close dependence of ASPs on dust particles. Primary estimations indicated a dry deposition flux of ASPs at approximately 1.10 ± 0.87 mg m-2 d-1 under the dust conditions. These results prove that Asian dust efficiently transports proteins, facilitating their dispersion in the atmosphere.

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The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 µg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 µg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

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Organophosphate esters (OPEs) are extensively applied in various materials as flame retardants and plasticizers, and have high biological toxicity. OPEs are detected worldwide, even in distant polar regions and the Tibetan Plateau (TP). However, few studies have been performed to evaluate the distribution patterns and origins of OPEs in different climate systems on the TP. This study investigated the distribution characteristics, possible sources, and ecological risks of OPEs in soils from the different climate systems on the TP and its surroundings. The total concentrations of OPEs in soil varied from 468 to 17,451 pg g-1 dry weight, with greater concentrations in southeast Tibet (monsoon zone), followed by Qinghai (transition zone) and, finally, southern Xingjiang (westerly zone). OPE composition profiles also differed among the three areas with tri-n-butyl phosphate dominant in the westerly zone and tris(2-butoxyethyl) phosphate dominant in the Indian monsoon zone. Correlations between different compounds and altitude, soil organic carbon, or longitude varied in different climate zones, indicating that OPE distribution originates from both long-range atmospheric transport and local emissions. Ecological risk assessment showed that tris(2-chloroethyl) phosphate and tri-phenyl phosphate exhibited medium risks in soil at several sites in southeast Tibet. Considering the sensitivity and vulnerability of TP ecosystems to anthropogenic pollutants, the ecological risks potentially caused by OPEs in this region should be further assessed.

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With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.

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Previous research methodologies for quantifying Suspended Sediment Concentration (SSC) have encompassed in-situ observations, numerical simulations, and analyses of remote sensing datasets, each with inherent constraints. In this study, we have harnessed Convolutional Neural Networks (CNNs) to create a deep learning model, which has been applied to the remote sensing data procured from the Geostationary Ocean Color Imager (GOCI) spanning April 2011 to March 2021. Our research indicates that on a small time scale, wind and hydrodynamic forces both have a significant impact on the prediction results of CNNs model. Considering both wind and hydrodynamic forces can effectively improve the model's prediction efficiency for SSC. Moreover, we have employed CNNs to interpolate absent values within the remote sensing datasets, yielding enhancements superior to those attained via linear or multivariate regression techniques. Finally, the correlation coefficient between CNN-derived SSC estimates for Jiaozhou Bay (JZB) and its corresponding remote sensing data is 0.72. Correlation coefficient and root mean square error differ in different regions. In the shallow water of JZB, due to water level changes, there is limited data, and the correlation coefficient in this area is about 0.5-0.6. In the central region of JZB with sufficient data, the correlation coefficient is generally higher than 0.75. Therefore, we believe that this CNNs model can be used to predict the hourly variation of SSC. When juxtaposed with alternative methodologies, the CNN approach is found to economize computational resources and enhance processing efficiency.

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This paper proposes a wind-speed-adaptive resonant piezoelectric energy harvester for offshore wind energy collection (A-PEH). The device incorporates a coil spring structure, which sets the maximum threshold of the output rotational frequency, allowing the A-PEH to maintain a stable output rotational frequency over a broader range of wind speeds. When the maximum output excitation frequency of the A-PEH falls within the sub-resonant range of the piezoelectric beam, the device becomes wind-speed-adaptive, enabling it to operate in a sub-resonant state over a wider range of wind speeds. Offshore winds exhibit an annual average speed exceeding 5.5 m/s with significant variability. Drawing from the characteristics of offshore winds, a prototype of the A-PEH was fabricated. The experimental findings reveal that in wind speed environments, the device has a startup wind speed of 4 m/s, and operates in a sub-resonant state when the wind speed exceeds 6 m/s. At this point, the A-PEH achieves a maximum open-circuit voltage of 40 V and an average power of 0.64 mW. The wind-speed-adaptive capability of the A-PEH enhances its ability to harness offshore wind energy, showcasing its potential applications in offshore wind environments.

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Using WRF as a benchmark, GRAMM-SCI simulations are performed for a case study of thermally driven valley- and slope winds in the Inn Valley, Austria. A clear-sky, synoptically undisturbed day was selected when large spatial heterogeneities occur in the components of the surface-energy budget driven by local terrain and land-use characteristics. The models are evaluated mainly against observations from four eddy-covariance stations in the valley. While both models are able to capture the main characteristics of the surface-energy budget and the locally driven wind field, a few overall deficiencies are identified: (i) Since the surface-energy budget is closed in the models, whereas large residuals are observed, the models generally tend to overestimate the daytime sensible and latent heat fluxes. (ii) The partitioning of the available energy into sensible and latent heat fluxes remains relatively constant in the simulations, whereas the observed Bowen ratio decreases continuously throughout the day because of a temporal shift between the maxima in sensible and latent heat fluxes, which is not captured by the models. (iii) The comparison between model results and observations is hampered by differences between the real land use and the vegetation type in the model. Recent modifications of the land-surface scheme in GRAMM-SCI improve the representation of nighttime katabatic winds over forested areas, reducing the modeled wind speeds to more realistic values.

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Understanding the physical mechanisms that control galaxy formation is a fundamental challenge in contemporary astrophysics. Recent advances in the field of astrophysical feedback strongly suggest that cosmic rays (CRs) may be crucially important for our understanding of cosmological galaxy formation and evolution. The appealing features of CRs are their relatively long cooling times and relatively strong dynamical coupling to the gas. In galaxies, CRs can be close to equipartition with the thermal, magnetic, and turbulent energy density in the interstellar medium, and can be dynamically very important in driving large-scale galactic winds. Similarly, CRs may provide a significant contribution to the pressure in the circumgalactic medium. In galaxy clusters, CRs may play a key role in addressing the classic cooling flow problem by facilitating efficient heating of the intracluster medium and preventing excessive star formation. Overall, the underlying physics of CR interactions with plasmas exhibit broad parallels across the entire range of scales characteristic of the interstellar, circumgalactic, and intracluster media. Here we present a review of the state-of-the-art of this field and provide a pedagogical introduction to cosmic ray plasma physics, including the physics of wave-particle interactions, acceleration processes, CR spatial and spectral transport, and important cooling processes. The field is ripe for discovery and will remain the subject of intense theoretical, computational, and observational research over the next decade with profound implications for the interpretation of the observations of stellar and supermassive black hole feedback spanning the entire width of the electromagnetic spectrum and multi-messenger data.

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The Mauritania-Senegalese upwelling region (MSUR), the southernmost region of the Canary current upwelling system, is well-known for its coastal productivity and the key role it plays in enriching the oligotrophic open ocean through the offshore transport of the upwelled coastal waters. The great ecological and socio-economic importance makes it necessary to evaluate the impact of climate change on this region. Hence, our main objective is to examine the climate change signal over the MSUR with a high resolution regional climate system model (RCSM) forced by the Earth system model MPI-ESM-LR under RCP8.5 scenario. This RCSM has a regional atmosphere model (REMO) coupled to a global ocean model (MPIOM) with high-resolution in the MSUR, which allows us to evaluate the wind pattern, the ocean stratification, as well as the upwelling source water depth, while maintaining an ocean global domain. Under RCP8.5 scenario, our results show that the upwelling favourable winds of the northern MSUR are year-round intensified, while the southern MSUR presents a strengthening in winter and a weakening in March-April. Along with changes in the wind pattern, we found increased ocean stratification in the spring months. In those months southern MSUR presents a shallowing of the upwelling source water depth associated to changes in both mechanisms. However, in winter the whole MSUR shows a deepening of the upwelling source water depth due to the intensification of the upwelling favourable winds, with the increased ocean stratification playing a secondary role. Our results demonstrate the need to evaluate the future evolution of coastal upwelling systems taking into account their latitudinal and seasonal variability and the joint contribution of both mechanisms.

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No abstract available.

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Southern South America is the only large landmass that extends through the core of the Southern Westerly Winds (SWW), controlling hydrological and ecosystem variability in the region. In fact, the vegetation along the west coast changes from Temperate and Valdivian Rain Forest to the North Patagonian Evergreen Forest (ca. 42°S) due to the latitudinal influence of the SWW. Climate is an important driver of organic matter accumulation in lakes, hence changes in vegetation would be recorded in lacustrine sedimentary archives. This study evaluated leaf waxes contained in lake surface sediments as indicators of climate change along the west coast of southern South America, providing a biogeochemical dataset for ongoing and future (paleo)climate and environmental research. The fatty acid and n-alkane sediment leaf wax datasets are compared with latitudinal, orographic, and climatic (Mean Annual air Temperature [MAT] & Precipitation [MAP]) trends extracted from a monthly gridded reanalysis product of the Climate Forecast System Reanalysis. Fatty acids are more abundant than n-alkanes, with high abundances characterizing the transition between seasonal and year-round precipitation along the coast (ca. 42°S). The abundance of both leaf wax groups increases with MAP, suggesting precipitation as the main control on sedimentary leaf wax delivery to the lake sediments in the study area. The Carbon Preference Index (CPI) of the two groups show opposite trends, but both highlight the climate transition at ca. 42°S, and have a linear relationship with MAP. The opposite significant trends between n-alkane CPI and fatty acid CPI with MAP are interpreted as higher n-alkane production at much higher precipitation because leaf wax fatty acids are the precursors of n-alkanes. Hence, past periods during which these leaf waxes show opposite trends in CPI might be interpreted as a precipitation change, especially if additional information such as pollen, diatoms, chironomids and stable isotopes is available.

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The Southern Ocean has warmed substantially, and up to early 21st century, Antarctic stratospheric ozone depletion and increasing atmospheric CO2 have conspired to intensify Southern Ocean warming. Despite a projected ozone recovery, fluxes to the Southern Ocean of radiative heat and freshwater from enhanced precipitation and melting sea ice, ice shelves, and ice sheets are expected to increase, as is a Southern Ocean westerly poleward intensification. The warming has far-reaching climatic implications for melt of Antarctic ice shelf and ice sheet, sea level rise, and remote circulations such as the intertropical convergence zone and tropical ocean-atmosphere circulations, which affect extreme weathers, agriculture, and ecosystems. The surface warm and freshwater anomalies are advected northward by the mean circulation and deposited into the ocean interior with a zonal-mean maximum at ∼45°S. The increased momentum and buoyancy fluxes enhance the Southern Ocean circulation and water mass transformation, further increasing the heat uptake. Complex processes that operate but poorly understood include interactive ice shelves and ice sheets, oceanic eddies, tropical-polar interactions, and impact of the Southern Ocean response on the climate change forcing itself; in particular, limited observations and low resolution of climate models hinder rapid progress. Thus, projection of Southern Ocean warming will likely remain uncertain, but recent community effort has laid a solid foundation for substantial progress.

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BACKGROUND: Based on previous studies (regional and national), Tijuana, Baja California, Mexico (across the border from San Diego, California, USA), has been shown to have the highest rate of meningococcal meningitis (MeM) in the country. However, the reason for this high incidence has not yet been established. To explain this regional/endemic public health problem, we aimed to evaluate whether there is a climatic association with MeM in the region. In the "African Meningitis Belt," the Harmattan seasons are associated with MeM outbreaks; similarly, the Santa Ana winds (SAWs) seasons are characterized by hot and dry winds (similar to Harmattan seasons) that occur seasonally in Southwest California, USA, and Northwest Baja California, Mexico. OBJECTIVES: We aimed to determine a potential association of SAWs with MeM in Tijuana, Baja California, Mexico, which in turn may partially explain the high rate of this disease in the region. METHODS: Based on our previously published data obtained from thirteen years of active surveillance of MeM and a 65-year review showing the seasonal occurrence of SAWs, we estimated the risk ratio (RR) for the total case numbers of MeM (51 cases of children < 16 years old) vs. bacterial meningitis not caused by Neisseria meningitidis (NMeM, 30 cases, same age group) during seasons with and without SAWs. RESULTS: We found an association between SAWs and MeM, but not with NMeM (RR = 2.06, p = 0.02 (95% CI 1.1 to 3.8), which may partially explain the high endemicity of this deadly disease in this part of the globe. CONCLUSION: This study shows a new potential climatic association with MeM and provides more information that justifies universal meningococcal vaccination in Tijuana, Mexico.

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Estimating the quantitative distribution of wind erosion rates is one of the most important requirements for managing affected environments and optimizing locations to control wind erosion. This study develops high-resolution maps for wind erosion with unmanned aerial vehicles or UAV images in the Sistan region-an area in southeastern Iran with severe wind erosion. Aerial imaging by UAV was done during period of erosive winds. Changes in the amount of wind erosion were measured for 7 months. Digital elevation models or DEM with a spatial resolution of 6 mm, orthophoto mosaic images with a resolution of 3 mm, were prepared before and after the erosion event. Three erosive facies consisting of surface, edge, and blowout were identified. The amount of erosion in different geomorphological landscapes or facies was measured according to differences of DEMs (DOD). The effect of physical factors of the geomorphological landscapes on wind erosion was investigated by calculating the correlation between the erosion, roughness, and slope in the geomorphological landscapes. The results showed that the highest and lowest mean of eroded soil were 22 mm and 4 mm in the blowout and surface facies, respectively. The average rate of wind erosion was 201 t/ha during the study period, which indicates the high intensity of wind erosion in the Sistan plain. Overall, UAV-as an aerial imaging technique collecting ground data-can be a helpful tool in the aeolian geomorphology especially for collecting data for measuring the rate of soil erosion by the wind in the aeolian landscapes located in remote regions.

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The input of the soluble micronutrients iron (Fe) and/or manganese (Mn) by mineral dust stimulates net primary productivity in the Fe/Mn-deficient Southern Ocean. This mechanism is thought to increase carbon export, thus reducing atmospheric CO2 during the Pleistocene glacial cycles. Yet, relatively little is known about changes in the sources and transport pathways of Southern Hemisphere dust over glacial cycles. Here, we use the geochemical fingerprint of the dust fraction in marine sediments and multiisotope mixture modeling to identify changes in dust transport to the South Pacific Subantarctic Zone (SAZ). Our data show that dust from South America dominated the South Pacific SAZ during most of the last 260,000 a with maximum contributions of up to ∼70% in the early part of the glacial cycles. The enhanced dust-Fe fluxes of the latter parts of the glacial cycles show increased contributions from Australia and New Zealand, but South American dust remains the dominant component. The systematic changes in dust provenance correspond with grain size variations, consistent with the circumpolar transport of dust by the westerly winds. Maximum contributions of dust from more proximal sources in Australia and New Zealand (up to ∼63%) paired with a finer dust grain size indicate reduced westerly wind speeds over the South Pacific SAZ during deglacial and peak interglacial intervals. These quantitative dust provenance changes provide source-specific dust-Fe fluxes in the South Pacific SAZ and show how their systematic changes in magnitude and timing influence the Southern Ocean dust-Fe feedback on glacial-interglacial to millennial time scales.

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We identified anomalously warm sea surface temperature (SST) events during 1980-2019 near the major upwelling center at Punta Lavapié in the central Chile-Peru Current System, using the European Centre for Medium-Range Weather Forecasts reanalysis and focusing on time scales of 10 days to 6 months. Extreme warm SST anomalies on these time scales mostly occurred in the austral summer, December through February, and had spatial scales of 1000s of km. By compositing over the 37 most extreme warm events, we estimated terms in a heat budget for the ocean surface mixed layer at the times of strongest warming preceding the events. The net surface heat flux anomaly is too small to explain the anomalous warming, even when allowing for uncertainty in mixed-layer depth. The composite mean anomaly of wind stress, from satellite ocean vector wind swath data, during the 37 anomalous warming periods has a spatial pattern similar to the resulting warm SST anomalies, analogous to previous studies in the California Current System. The weakened surface wind stress suggests reduced entrainment of cold water from below the mixed layer. Within 100-200 km of the coast, the typical upwelling-favorable wind stress curl decreases, suggesting reduced upwelling of cold water. In a 1000-km area of anomalous warming offshore, the typical downwelling-favorable wind stress curl also decreases, implying reduced downward Ekman pumping, which would allow mixed-layer shoaling and amplify the effect of the positive climatological summertime net surface heat flux.

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Titan is a sedimentary world, with lakes, rivers, canyons, fans, dissected plateaux, and sand dunes. Sediments on Saturn's moon are thought to largely consist of mechanically weak organic grains, prone to rapid abrasion into dust. Yet, Titan's equatorial dunes have likely been active for 10s-100s kyr. Sustaining Titan's dunes over geologic timescales requires a mechanism that produces sand-sized particles at equatorial latitudes. We explore the hypothesis that a combination of abrasion, when grains are transported by winds or methane rivers, and sintering, when they are at rest, could produce sand grains that maintain an equilibrium size. Our model demonstrates that seasonal sediment transport may produce sand under Titan's surface conditions and could explain the latitudinal zonation of Titan's landscapes. Our findings support the hypothesis of global, source-to-sink sedimentary pathways on Titan, driven by seasons, and mediated by episodic abrasion and sintering of organic sand by rivers and winds.

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Long-term measurements at the Mauna Loa Observatory (MLO) show that the CO2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22  ±  0.034 ppm decade-1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO2 fluxes from the Jena CarboScope CO2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of -0.10  ±  0.022 ppm decade-1 from 1959 to 2019, partly offsetting the observed long-term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC-ACTM simulations, the shorter-term variability of MLO SCA is nearly equally driven by varying ecological CO2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U-PDO). Since 1980, 44% of the wind-driven SCA decrease has been tied to a secular trend in the U-PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low-latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO2 fluxes using observations from low latitudes due to the complexity of circulation changes.