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Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020-2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015-2020) to 56 ± 12 cm yr−1 (2020-2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Abstract: The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal‐mean precipitation‐minus‐evaporation (P − E) under greenhouse‐gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P − E and increase P − E in the deep tropics. The surface‐albedo feedback shifts the location of maximum tropical P − E and increases P − E in the polar regions. The intermodel spread in the P − E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse‐rate and surface‐albedo feedbacks playing important roles in the polar regions. The P − E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet‐gets‐wetter, dry‐gets‐drier” paradigm.

Abstract: Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

Precipitation is a critical part of the global hydrological cycle that determines the distribution of water resources. It is also an essential meteorological variable used as input for hydroclimatic models and projections. However, precipitation data frequently lack complete series, especially at daily and sub‐daily precipitation stations, which are usually large, bulky, and complex. To address this, gap filling is commonly used to produce complete hydrometeorological data series without missing values. Several gap‐filling methods have been developed and improved. This study seeks to fill the gaps of 201 daily precipitation time series in Central Italy by localizing the approach used to generate the Serially Complete dataset for the Planet Earth (SC‐Earth). This method combines the outcome of 15 strategies based on four various gap‐filling techniques (quantile mapping, spatial interpolation, machine learning, and multi‐strategy merging). These strategies employ the daily dataset of the neighbouring stations and the matched ERA5 data to estimate missing values at the target stations. Both raw data and the final serially complete station datasets (SCDs) underwent comprehensive quality control. Many accuracy indicators have been utilized to evaluate the performance of the strategies' estimations and the final SCD, such as Correlation Coefficient (CC), Root mean square error (RMSE), Relative bias (Bias %), and Kling‐Gupta efficiency (KGE″). Multi‐strategy merging strategy based on the Modified Kling‐Gupta efficiency (MS1) shows the highest performance as an individual precipitation gap‐filling strategy. However, the machine learning strategy using random forest (ML3) has the most outstanding share in the final estimates among all other strategies. In the end, the temporal–spatial performance of the final SCD is promising and depends on the pattern of the missing values (MV%). The mean values of KGE″, CC, variability (α), and bias term (β) are 0.9, 0.93, 1.064, and 4.98 × 10−7, respectively. [ABSTRACT FROM AUTHOR]

The nonequilibrium microstructures produced by laser‐based additive manufacturing can enhance the mechanical properties of metallic alloys. However, the optimal post‐treatment conditions will change according to the initial microstructure induced by the manufacturing process employed. Therefore, this study investigates the optimal direct aging conditions for the post‐treatment of laser powder bed fusion‐processed 18Ni300 maraging steel by changing the aging temperature and time. The aging time required to achieve maximum tensile strength decreased as the aging temperature increased, and an increase in the aging temperature accelerated the evolution of nanoscale precipitates. The results indicate that the optimal direct aging conditions of 520 °C for 2 h achieved the largest tensile strength (≈2100 MPa) and precipitate area fraction. Notably, the reduced aging time at this temperature compared with that under conventional aging conditions (8 h at 460 °C or 490 °C) minimizes the thickening of the (Mo,Ti)‐rich segregation, which otherwise contributes to austenite reversion and strength reduction owing to overaging. Thus, the optimal direct aging conditions for additively manufactured maraging steel components are different from those for conventionally produced maraging steel components. This understanding of the microstructural changes induced by direct aging according to the initial non‐equilibrium state provides vital information for enhancing the performance of additively manufactured metallic alloys. [ABSTRACT FROM AUTHOR]

Precipitation stable-isotope data are often used in hydroclimatic, hydrological and hydrogeological investigations, with measurements typically undertaken on integrated monthly samples. However, daily sampling reveals overlooked aspects of controls on precipitation isotope values, including synoptic meteorological conditions. We present a one-year record of stable isotopes in daily precipitation during 2021, from a site in SE England close to Greater London. We find marked daily variability over the course of the year (–15.62 to +0.92 ‰ for δ18O, –108.7 to +2.9 ‰ for δ2H and –6.5 to +23.1 ‰ for deuterium excess). Correlations with individual meteorological variables including precipitation amount, temperature and weather type are moderate to weak suggesting complex controls on the daily rainfall isotope values. The daily data are compared with three other daily datasets from England and, by conversion to monthly values, directly with data from three long-term collection stations across Britain and Ireland. The scale of variability in the daily data from our site is consistent with that seen in other English records despite them all coming from different time periods. The monthly data show broad consistency, although there are differences that also highlight geographical variability in precipitation values across the British Isles. [ABSTRACT FROM AUTHOR]

This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation. [ABSTRACT FROM AUTHOR]

ABSTRACT Main Location Taxon Methods Results Conclusions Animals not only forage for abundant and nearby resources, but their diets can also be influenced by abiotic and geographic factors. This often results in non‐random interactions among species. We investigate how seed density, distance from nest, abiotic (e.g., climate stability, temperature, precipitation) and geographic factors (e.g., latitude, elevation and continental hemisphere) influence the removal of food items (i.e., seeds and dead arthropods) by Pogonomyrmex species.South and North America, from Patagonia to the Rocky Mountains.Genus Pogonomyrmex (Formicidae: Hymenoptera).Conducting standardised experiments, we performed a seed removal experiment and an assessment of the items retrieved by ant workers of 160 nests from eight Pogonomyrmex spp. at 16 sites extending the American continent.Pogonomyrmex ants native to North America removed more seeds than their South American counterpart. In general, results align with optimal foraging theory, indicating a higher probability of seed removal near ant nests. High climate stability correlated with lower seed predation rates, emphasising seed consumption's significance in historically arid environments. Increased precipitation and temperature led to reduced removal of food resources, suggesting reduced water availability and lower mean temperatures increases the consumption of seeds by harvester ants.Overall, Pogonomyrmex ants' food resource use is influenced by a combination of factors such as region, distance from the nest and climate. This study underscores harvester ants' potential impact on plant distribution over large spatial scales consuming seeds from the immediate proximity of their nest and preventing establishment. [ABSTRACT FROM AUTHOR]

The constellation approach underpinning precipitation products such as the Integrated Multi‐satellitE Retrievals for GPM (IMERG) is key to achieving high resolution, but the use of data from multiple sources can unintentionally incorporate instrumental artifacts. Here, we introduce a machine learning–based anomaly detection scheme called SPEEDe, which processes a two‐dimensional precipitation field into a re‐estimated precipitation field that can be compared with the input. Large differences identify IMERG fields with bad orbit data, separating most of the bad cases from the good cases. When modified to process the passive microwave inputs, SPEEDe can pick out orbits with bad data, enabling quality control on these IMERG inputs. SPEEDe works by producing a locally realistic‐looking precipitation field when given unphysical data, which results in a larger‐than‐normal difference between the input and the output. SPEEDe is implemented as an automated quality control for GPM precipitation products. Plain Language Summary: A machine learning–based scheme, SPEEDe, has been developed to identify bad data in satellite precipitation products. This scheme is able to pick out precipitation maps that have unrealistic patterns better than a conventional approach based on the distribution of values. It can be run as the precipitation data are being produced, thus allowing for automated quality control. Avoiding manual intervention is important in computing quality‐controlled precipitation data in near‐real time for applications such as flood monitoring that require highly reliable data with minimal delay. Key Points: A machine learning scheme called SPEEDe can detect artifacts in satellite precipitation fields based on an aggregate grid‐wide scoreSPEEDe identifies bad data by modifying anomalous inputs substantially to produce realistic‐looking outputs, leading to a large scoreSPEEDe is being applied operationally in GPM data production for quality control [ABSTRACT FROM AUTHOR]

The precipitation changes in arid and semiarid central Asia have great impacts on the local fragile ecosystem. The summer precipitation in central Asia shows obvious interannual variations, but the corresponding crucial moisture transporting processes remain unclear. Therefore, this study employs the Lagrangian flexible particle (FLEXPART) diffusion model to achieve this goal. Results show that the moisture of climatological summer precipitation in central Asia is mainly from the local regions, the surrounding western and northern Eurasian regions. The contribution of local evaporation from western central Asia is about 2 times that from eastern central Asia. At the interannual time scale, the moisture variations are mainly influenced by the local regions and western Eurasia, while the local evaporation is mainly from western central Asia. Totally, the Eurasian evaporation plays a dominant role in the interannual variations of central Asian summer precipitation by contributing more than 90% of the total moisture. The moisture transports associated with central Asian summer precipitation interannual variations are impacted by the anomalous cyclones over the western and northeastern parts of central Asia during the wet years, which enhance the moisture convergence and hence increase the summer precipitation in central Asia. The anomalous cyclone over the western part of central Asia is correlated with the changes in the intensity of Eurasian summer subtropical westerly jet (ESSWJ), while the anomalous cyclone over the northeastern part of central Asia is correlated with both ESSWJ and the British–Baikal Corridor pattern teleconnection in association with the polar front jet. [ABSTRACT FROM AUTHOR]

Biological particles, as a fraction of organic particles, potentially play a crucial role in ice nucleation processes. However, the contributions and relationships of biological components and organic matter (OM) to atmospheric ice nucleation remain largely unexplored. Here, total ice nucleating particles (INPs), heat‐resistant INPs, lysozyme‐resistant INPs, nanoscale INPs (<0.22 μm), and heat‐resistant nanoscale INPs in precipitation collected at the summit of Mt. Lu, China, were determined using droplet freezing assays coupled with corresponding pretreatments. Heat‐sensitive INPs and lysozyme‐sensitive INPs were considered as biological INPs and bacterial INPs, respectively. Microorganisms and OM molecules in precipitation were identified by high‐throughput sequencing technology and ultrahigh‐resolution mass spectrometry, respectively. Results revealed a predominant biological (heat‐sensitive) composition (78.8% and 93.2%) of total and nanoscale INPs at temperatures above −15°C. Specifically, bacterial (lysozyme‐sensitive) INPs accounted for 36.1% of the biological INPs at temperatures above −15°C. A notable correlation between sulfur‐containing organic compounds, mainly proteinaceous and lignin‐like substances, and INPs was uncovered, with a co‐occurrence network linking these compounds to Gram‐positive bacteria and Agaricomycetes. This study underscored the possible significance of sulfur‐containing organic compounds in the ice nucleation capacity of biological INPs, further shedding light on the ice nucleation mechanisms and potential sources of biological INPs. Plain Language Summary: Ice nucleating particles (INPs) are particles that facilitate the freezing of water at temperatures above the homogenous freezing point, impacting cloud formation and precipitation processes in the atmosphere. This study identified different types of INPs, microbes, and organic matter in precipitation sampled from Mt. Lu in southeastern China and investigated the connections among them. The findings suggested that a significant portion of INPs were of biological origin. Sulfur‐containing organic compounds likely played an important role in ice nucleation, which may originate from certain microbial taxa. This study helps us understand the role of microbes and organic molecules in ice formation, which has broader implications in areas such as preserving biological materials at low temperatures or facilitating artificial snow production. Key Points: Biological materials predominated among the ice nucleating particles (INPs) identified in precipitationSulfur‐containing organic compounds, associated with specific microbial taxa, likely acted as efficient INPs in precipitationThis study provides a preliminary understanding of the connections among INPs, organic matter, and microorganisms [ABSTRACT FROM AUTHOR]

We describe the bias adjustment and downscaling to a resolution of 1 × 1 km of daily temperature, precipitation and solar radiation of a multimodel ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs with the local observational data set BhutanClim for Bhutan and an analysis of the results for 1996–2100. The GCMs were selected based on their ability to reproduce local temporal and spatial precipitation patterns, resulting in a total of 14 models for each shared socioeconomic pathway (SSP). The results are shown for five global warming levels (GWLs) 1.5, 2, 3, 4 and 5°C, providing insight into the projected impacts of climate change at different warming levels. The daily temperature and precipitation data are publicly available, which is of great benefit for climate impact studies, such as ecological, hydrological or infrastructure‐related studies. In this paper, we discuss showcase climate indicators. Peak‐over‐threshold climate indicators such as hot days above 30°C are relevant for human health. In the capital city of Thimphu, these are currently rare, at 4 days per year, but might increase to more than 80 for GWL5.0. Precipitation increases in the summer months, but slightly decreases in the drier winter months. Accordingly, the 3‐month SPEI drought index is projected to increase in summer, but strongly decreases in the winter months, mostly pronounced in February. The increasing severity of late‐winter and spring droughts might have a negative impact on vegetation, wildfires and water supply. [ABSTRACT FROM AUTHOR]

In recent years, the application of machine learning methods in the prediction of hydrological processes such as precipitation has been widely considered. These methods can analyze large volumes of data and detect the existing trends and patterns. Therefore, in the present study, machine learning methods, including random forests (RF), Kstar algorithm and Gaussian process regression (GPR), were used to predict the precipitation of Sindh River basin in India during period of 1901 to 2020. In the next step, three distinct input scenarios include (i) using monthly precipitation data and considering the memory of time series up to 5 months delay, (ii) adding periodic term to the first scenario inputs and (iii) decomposing the data using the Daubechies 4 wavelet function and creating hybrid wavelet-learning machine (W-ML) models, were prepared and introduced to the models. The performance of each method was evaluated using the root mean square error (RMSE), mean absolute error (MAE), Kling–Gupta efficiency score (KGE) and Willmott index (WI). The results showed that single models with the first scenario inputs (without taking into account the periodicity of the data) did not have good accuracy, but by adding the periodicity, the performance of these models was significantly improved, and the average value of KGE index for all studied stations increased from 0.466 to 0.672. It was also found that the GPR model for all stations could not have good performance and RF and Kstar models are the most appropriate methods for predicting precipitation in the Sindh River basin, respectively. With the application of the third scenario and the development of W-ML hybrid models, the accuracy of precipitation forecasting was significantly improved, especially the maximum precipitation values were estimated with higher accuracy than standalone models. [ABSTRACT FROM AUTHOR]

Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015–2100 relative to the historical period (1980–2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test. Both observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region. [ABSTRACT FROM AUTHOR]

The rhizosphere is the hotspot for microbial enzyme activities and contributes to carbon cycling. Precipitation is an important component of global climate change that can profoundly alter belowground microbial communities. However, the impact of precipitation on conifer rhizospheric microbial populations has not been investigated in detail. In the present study, using high-throughput amplicon sequencing, we investigated the impact of precipitation on the rhizospheric soil microbial communities in two Norway Spruce clonal seed orchards, Lipová Lhota (L-site) and Prenet (P-site). P-site has received nearly double the precipitation than L-site for the last three decades. P-site documented higher soil water content with a significantly higher abundance of Aluminium (Al), Iron (Fe), Phosphorous (P), and Sulphur (S) than L-site. Rhizospheric soil metabolite profiling revealed an increased abundance of acids, carbohydrates, fatty acids, and alcohols in P-site. There was variance in the relative abundance of distinct microbiomes between the sites. A higher abundance of Proteobacteria, Acidobacteriota, Ascomycota, and Mortiellomycota was observed in P-site receiving high precipitation, while Bacteroidota, Actinobacteria, Chloroflexi, Firmicutes, Gemmatimonadota, and Basidiomycota were prevalent in L-site. The higher clustering coefficient of the microbial network in P-site suggested that the microbial community structure is highly interconnected and tends to cluster closely. The current study unveils the impact of precipitation variations on the spruce rhizospheric microbial association and opens new avenues for understanding the impact of global change on conifer rizospheric microbial associations. [ABSTRACT FROM AUTHOR]

This study examines the impact of high-pressure torsion (HPT) processing at various temperatures on the precipitation behavior of Cu-Cr alloys. The introduction of defects through HPT is observed to promote the precipitation of Cr atoms. Unlike the traditional large-scale precipitation that typically occurs around 400 °C, HPT can induce the precipitation of solute atoms even at room temperature. Furthermore, the temperature at which HPT is performed significantly influences the behavior of the precipitated phase during subsequent aging, ultimately affecting the alloy's overall properties. At elevated temperatures (ETs) and room temperature (RT), Cr atoms tend to aggregate, forming Guinier–Preston (GP) zones or precipitates, which coarsen into incoherent precipitates after annealing. In contrast, when HPT is conducted at liquid nitrogen temperature (LNT), Cr atoms are retained in their original positions, leading to the formation of uniformly distributed, high-density small precipitates post-annealing. This phenomenon results in superior properties for HPT-LNT-treated samples, evidenced by a microhardness of 191.8 ± 3.2 HV and an electrical conductivity of 84.6 ± 1.8% IACS. [ABSTRACT FROM AUTHOR]

Determining the responses of non-photosynthetic vegetation (NPV) and photosynthetic vegetation (PV) communities to climate change is crucial in illustrating the sensitivity and sustainability of these ecosystems. In this study, we evaluated the accuracy of inverting NPV and PV using Landsat imagery with random forest (RF), backpropagation neural network (BPNN), and fully connected neural network (FCNN) models. Additionally, we inverted MODIS NPV and PV time-series data using spectral unmixing. Based on this, we analyzed the responses of NPV and PV to precipitation and drought across different ecological regions. The main conclusions are as follows: (1) In NPV remote sensing inversion, the softmax activation function demonstrates greater advantages over the ReLU activation function. Specifically, the use of the softmax function results in an approximate increase of 0.35 in the R2 value. (2) Compared with a five-layer FCNN with 128 neurons and a three-layer BPNN with 12 neurons, a random forest model with over 50 trees and 5 leaf nodes provides better inversion results for NPV and PV (R2_RF-NPV = 0.843, R2_RF-PV = 0.861). (3) Long-term drought or heavy rainfall events can affect the utilization of precipitation by NPV and PV. There is a high correlation between extreme precipitation events following prolonged drought and an increase in PV coverage. (4) Under long-term drought conditions, the vegetation in the study area responded to precipitation during the last winter and growing season. This study provides an illustration of the response of semi-arid ecosystems to drought and wetting events, thereby offering a data basis for the effect evaluation of afforestation projects. [ABSTRACT FROM AUTHOR]

Montane species are generally predicted to respond to climate change via upslope movement. Elevational range shifts of birds rarely have been examined in arid regions. Here, we examine shifts in the elevational distributions of breeding birds from two regions of the Great Basin, a desert in the western USA, over 10 to 20 years. We collected data annually from 2001 to 2020, a relatively long and consistent time series that is uncommon in research on distributional shifts. We used single‐species occupancy models of 32 bird species to examine shifts along the full elevational gradient (1650–3200 m a.s.l.) and within the lowest and highest edges (25%) of the gradient. We then conducted simulations to test whether population stochasticity could confound inferences about shifts. We examined whether temperature, precipitation, and primary productivity (normalized difference vegetation index) were associated with occupancy and shifts. The elevational distributions of 23 species shifted, and simulations indicated that shifts in the distributions of 18 species were unlikely to be stochastic. The majority of shifts in the western Great Basin were downslope, whereas those in the central Great Basin were upslope. More shifts occurred at the edges of the elevational gradient than along the full gradient. Elevational shifts lacked a consistent climate‐response signal, but those of some species appeared to follow changes in primary productivity. We found regional differences in elevational shifts and climate associations, and our work suggests that these desert bird populations may be relatively resilient to climate change. [ABSTRACT FROM AUTHOR]

Understanding the triggering factors leading to rockfall is essential in managing their risk to transportation infrastructure. Precipitation and freeze-thaw (FT) are widely studied rockfall triggers, but developing reliable, quantitative methods to forecast rockfall in response to weather events remains challenging. Terrestrial laser scanning (TLS) is a powerful tool for high-accuracy modeling of rock slopes, but the frequency of scanning is often too low to correlate rockfall behavior with weather events or seasonal trends. We conducted a TLS campaign between 2017 and 2022 in Glenwood Canyon, Colorado, to investigate the seasonal variability in rockfall triggering and conditioning mechanisms. A total of 44 scans were collected over 5 years and were processed to allow for consistent detection of rockfalls larger than 0.0036 m3 in volume. Meteorological variables relating to precipitation, snowpack, and temperature were modeled using the National Weather Service SNODAS product and were used to complete an exploratory analysis of the correlation of various weather indices with rockfall rate over time. It was found that the short-term sum of liquid precipitation and snowmelt (averaged over the scanning interval or the max single-day total) was a strong predictor of rockfall volume rate between 2018 and 2020, especially in the spring and summer months; a linear model of max daily liquid was able to explain 65% of the variance (R2adj) in rockfall volume rate in March through August. This implicates springtime snowmelt and rain-on-snow events as strong predictors of rockfall at the study site. We interpret these observations to indicate that snowmelt and rainfall are acting to trigger blocks that have been conditioned (destabilized) over the preceding winter. [ABSTRACT FROM AUTHOR]

Winter precipitation over eastern China displays remarkable interannual variability, which has been suggested to be closely related to El Niño–Southern Oscillation (ENSO). This study finds that ENSO impacts on eastern China precipitation patterns exhibit obvious differences in early (November–December) and late (January–February) winter. In early winter, precipitation anomalies associated with ENSO are characterized by a monopole spatial distribution over eastern China. In contrast, the precipitation anomaly pattern in late winter remarkably changes, manifesting as a dipole spatial distribution. The noteworthy change in precipitation responses from early to late winter can be largely attributed to the seasonally varying Kuroshio anticyclonic anomalies. During the early winter of El Niño years, anticyclonic circulation anomalies appear both over the Philippine Sea and Kuroshio region, enhancing water vapor transport to the entirety of eastern China, thus contributing to more precipitation there. During the late winter of El Niño years, the anticyclone over the Philippine Sea is further strengthened, while the one over the Kuroshio dissipates, which could result in differing water vapor transport between northern and southern parts of eastern China and thus a dipole precipitation distribution. Roughly the opposite anomalies of circulation and precipitation are displayed during La Niña winters. Further analysis suggests that the seasonally-varying Kuroshio anticyclonic anomalies are possibly related to the enhancement of ENSO-related tropical central-eastern Pacific convection from early to late winter. These results have important implications for the seasonal-to-interannual predictability of winter precipitation over eastern China. [ABSTRACT FROM AUTHOR]

Non-stationary sediment load analysis is critical for river engineering design and water resource management. Traditional sediment load frequency analysis methods usually assume stationarity, which can lead to inconsistent results in a changing environment because they cannot account for factors such as time variations. Here, we use generalized additive models for location, scale and shape (GAMLSS) to establish non-stationary models with time, precipitation and streamflow as covariates (named Model 1 and Model 2, respectively), and compare their fitting effects with stationary models (parameters unchanged: Model 0). In this study, the sediment load of the Jinsha River Basin in southwest China was analyzed. Outcomes indicate that: (1) the research area's sediment load decreased significantly, with a significant change point in 2002 (p < 0.1); (2) the goodness of fit indices (global fitting deviation: GD, AIC criterion and SBC criterion) based on Model 2 are smaller than the values of the other two models. The other two models' sediment load quantile design values are within Model 2's range. (3) Compared with Model1, precipitation and streamflow as covariates in Model 2 are more able to capture the non-stationary features of sediment load frequency. Furthermore, Model 2 can more accurately forecast future changes in sediment load when external physical factors are considered. The findings of this research can serve as a scientific foundation for decision makers to carry out water conservancy planning and design and river management and development. [ABSTRACT FROM AUTHOR]

Supersaturation and precipitation within the gastrointestinal tract can influence oral absorption of active pharmaceutical ingredients (APIs). Supersaturation of weakly basic APIs upon transfer from the stomach into the small intestine may enhance their absorption, while salt forms of poorly soluble weak acids may generate supersaturated solutions in both stomach and intestine. Likewise, APIs with solubility-limited absorption may be developed as enabling formulations intended to produce supersaturated solutions of the API in the gut. Integrating the supersaturation/precipitation characteristics of the API into the biopharmaceutical risk classification enables comprehensive mapping of potential developability risks and guides formulation selection towards optimizing oral bioavailability (BA). The refined Developability Classification System (rDCS) provides an approach for this purpose. In this work, the rDCS strategy is revisited and a stratified approach integrating the in vitro supersaturation and precipitation behavior of APIs and their formulations is proposed. [Display omitted] [ABSTRACT FROM AUTHOR]

The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses. [ABSTRACT FROM AUTHOR]

Climate change affects the spread of waterborne infectious diseases, yet research on vulnerability to outbreaks remains limited. This integrative review examines how climate variables (temperature and precipitation) relate to human vulnerability factors in Pakistan. By 2060, mean temperatures are projected to rise from 21.68°C (2021) to 30°C, with relatively stable precipitation. The epidemiological investigation in Pakistan identified Diarrhea (119,000 cases/year), Malaria (2.6 million cases/year), and Hepatitis (A and E) as the most prevalent infections. This research highlighted vulnerability factors, including poverty (52% of the population), illiteracy (59% of the population), limited healthcare accessibility (55% of the population), malnutrition (38% of the population), dietary challenges (48% of the population), as well as exposure to water pollution (80% of the population) and air pollution (55% of the population). The findings suggest that the coordinated strategies are vital across health, environmental, meteorological, and social sectors, considering climatic variability patterns and population vulnerability determinants. [ABSTRACT FROM AUTHOR]

The stable hydrogen isotope in precipitation is an effective environmental tracer for climatic and hydrologic studies. However, accurate and high-precision precipitation hydrogen isoscapes are currently unavailable in China. In this study, a data fusion method based on Convolutional Neural Networks (CNN) is used to fuse the hydrogen isotopic composition (δ2Hp) of observations and isotope-equipped general circulation model (iGCM) simulations. A precipitation hydrogen isoscape with a temporal resolution of monthly and a spatial resolution of 50–60 km is established for East China for the 1969–2017 period. Prior to building the isoscape, the performance of three data fusion methods (DFMs) and two bias correction methods (BCMs) is compared. The results indicate that the CNN fusion method performs the best with a correlation coefficient larger than 0.90 and root mean square error smaller than 10.5‰when using observation as a benchmark. The fusion methods based on back propagation and long short-term memory neural network perform similarly, while slightly outperforming the bias correction methods. Thus, the CNN method is used to generate the hydrogen isoscape, and the temporal and spatial distribution characteristics of the hydrogen isotope in precipitation are analyzed based on this dataset. The generated isoscape shows similar spatial and temporal distribution characteristics to observations. In general, the distribution pattern of δ2Hp is consistent with the temperature effect in northern China, and consistent with the precipitation amount effect in southern China. The trend of the δ2Hp time series is consistent with that of observed precipitation and temperature. Overall, the generated isoscape effectively reproduces the observations, and has the characteristics of time continuity and relative spatial regularity, which can provide valuable data support for tracking atmospheric and hydrological processes. [ABSTRACT FROM AUTHOR]

Understanding inter-annual variation in the density of young-of-the-year fish is an important tool for assessing stock status and guiding management decisions. We analyzed data spanning from 2003 to 2022 collected at Lipno Reservoir in Czechia. The study aimed to identify factors influencing the density of pikeperch (Sander lucioperca), a valuable predatory fish species in European waters. A bimodality test for the size class distribution has revealed the existence of two distinct YOY cohorts: extremely small (ES) and ordinary fingerlings. Using the Bayesian horseshoe prior method with 37 potential predictors, followed by linear regression, we observed that certain environmental factors similarly influenced both cohorts. Higher temperatures during spring and summer, coupled with increased densities of large and medium-sized cladocerans, positively affected the density of both cohorts. However, distinct influences were observed: for ordinary fingerlings, increased summer precipitation and a lower abundance of predators were beneficial, whereas, for ES fingerlings, summer copepod density emerged as an additional positive factor. This study emphasizes the importance of a detailed investigation of factors influencing pikeperch recruitment. Examining these drivers provides a clearer insight into the causes of variations in the early life stage, which is crucial for monitoring and managing populations in temperate reservoirs. [ABSTRACT FROM AUTHOR]

• High volume precipitation does not translate into increased moss-associated nitrogen (N) fixation rates. • N fixation increased more in a tropical moss compared to a subarctic moss under all rainfall regimes. • Less than 1 % of fixed N was leached from mosses within a 1-month period. • N leaching from mosses was more sensitive to watering volume than frequency. Nitrogen (N) fixation by moss-cyanobacteria associations has been recognized as an important N input pathway in many ecosystems from arctic tundra to tropical forests. However, the transfer of fixed N 2 from mosses to the soil as well as the effects of rainfall frequency and volume on this N transfer has hardly been studied – even though mosses can leach nutrients upon rewetting. In this study, we investigated the transfer of fixed N 2 by moss-cyanobacteria associations in one month under four watering regimes with a combination of high and low volume and frequency. For this, we used two morphologically similar moss species collected from ecosystems with different climate and N availability (subarctic - Hylocomium splendens ; and tropical - Thuidium delicatulum). Acetylene reduction assays were conducted as a measure of N 2 fixation rates in mosses, and 15N-N 2 tracing was used to follow the fixed N 2 from moss to the underlying substrate. Nitrogen fixation rates were higher in T. delicatulum than in H. splendens , but rainfall volume and frequency did not show strong effects on N 2 fixation rates. Nonetheless, the extent of N leached from mosses was more sensitive to an increase in rainfall volume than to an increase in frequency, and more N was lost from T. delicatulum under high volume precipitation than from H. splendens. Both total nitrogen and 15N enrichment results demonstrate that the fixed N 2 was mostly stored in moss tissues with less than 1 % leached to the substrate. Our results show that both moss species retain almost all fixed N 2 within their tissues under small rainfall disturbances within one month, while increased N availability under higher precipitation volume renders some moss species an important N source for the soil. [Display omitted] [ABSTRACT FROM AUTHOR]

In tropical forests, deciduous and evergreen leaf habits represent contrasting tree adaptations to precipitation seasonality. Both rainfall seasonality and interannual variation in rainfall are determinants of forest deciduousness, but their relative influence is not well understood. In this study, we evaluate the extent of deciduous-evergreen coexistence in tropical forests and develop a simple model of competition for water between leaf habits. Using this model, we formalize two mechanisms representing rainfall variability across time scales that may explain their stable coexistence: the temporal storage effect via interannual variability in rainfall vs. rainfall partitioning via evergreen access to dry-season rainfall. In our model, both mechanisms resulted in coexistence, but coexistence was more robust via resource partitioning. Empirically, remotely sensed deciduousness increased with precipitation seasonality, but effects of interannual rainfall variability on deciduousness were minor. We hypothesize that dry-season rainfall may prove a stronger influence on coexistence between leaf habits, and that changes in rainfall seasonality will have a greater impact on forest deciduousness than changes in the interannual variability of rainfall. [ABSTRACT FROM AUTHOR]

Featured Application: Polystyrene recycling. This work proposes a conceptual design for recovering polystyrene (PS) using solvents of agro-industrial origin. The literature describes the dilution of expanded polystyrene (EPS) in limonene, followed by its insolubilization with alcohols for recovery. However, there is no information on the solubility limit for the PS + limonene + alcohol system, which is critical for the process design. To determine the solubility limit, we diluted the waste EPS in D-limonene, mixed it with ethanol to form a precipitate, and used a gravimetric method to measure the mass of the compounds. These results allowed for the conceptual design of an EPS recycling process using a chemical process simulator, which includes a separator, a distillation column, and auxiliary equipment such as heaters, coolers, and pumps. An empirical correlation was obtained for the solubility limit, which enabled the design of a process for the treatment of 52 kg/s of PS using 0.75 kg/s of ethanol and 2.4 kg/s of D-limonene once the stationary state had been reached. The distillation column is six-stage, with a reflux ratio of 1.5 and duties of 30,000 and −25 847 kW for the condenser and reboiler, respectively. [ABSTRACT FROM AUTHOR]

Studying long-term precipitation trends is crucial for sustainable development, as the proper utilization of water resources is essential for maintaining a sustainable water supply. The objective and novelty of this paper was to reveal the gradual mutation process of precipitation in China over a century. This study utilized monthly precipitation data from 1901 to 2022 (at a century scale) to analyze and explore the spatiotemporal variability in precipitation across different time scales and regions with a trend analysis, an abrupt change analysis, and gravity center models. The findings indicate that (1) from 1901 to 2022, the precipitation in China generally decreased from the southeast coastal areas toward the northwest inland regions. (2) There were significant differences in the migration of precipitation gravity centers among the different study regions, with the least dispersion being observed in the Liao River basin, while the Hai River basin, various river basins in the northwest, and the Pearl River basin exhibited certain regularities in gravity center movement, and other regions showed periodic variations. (3) Over the period from 1901 to 2022, there was a trend of transitioning from lower to higher precipitation levels. (4) According to continuous long-term abrupt change tests, the timing of precipitation shifts varied across different basins. Precipitation, as a crucial component of natural resources, directly impacts various aspects of socio-economic life. Research findings provide decision support for regional flood control and disaster reduction and offer scientific decisions for ecological security. [ABSTRACT FROM AUTHOR]

The aerosols play a main role in precipitation formation in the atmosphere. Organic Carbon (OC) is a must-imported aerosol because it is considered a cloud condensation nuclear CCN. This work dealt with the relationship between Organic Carbon (OC) and many variables that affect precipitation formation in South Iraq (Basra and AL-Muthana) including Cloud Top Temb Cloud Top Temperature CTT, Total PrecipitableWater TPW, and Precipitation. The cases selected are 14 – 15 Nov. 2018 and 26 – 29 Jan. 2019 to test the relationship between variables. Data obtained from retrospective analysis for research and applications, version 2 (MERRA-2). Including OC, total Precipitablewater (TPW), and precipitation amount of 3 hours. The times are fixed according to the cases. The cloud top temperature (CTT) was obtained by the infrared satellite images provided by the European Space Agency, using a group of satellites. The result indicates a strong effect of OC on the cloud depth and the TPW when finally increases the precipitation amount. [ABSTRACT FROM AUTHOR]

Quantifying the vulnerability of population to multi-faceted climate change impacts on human well-being remains an urgent task. Recently, weather and climate extremes have evolved into bivariate events that heighten climate risks in unexpected ways. To investigate the potential impacts of climate extremes, this study analyzes the frequency, magnitude, and severity of observed and future compound hot-dry extremes (CHDEs) over East Africa. The CHDE events were computed from the observed precipitation and maximum temperature data of the Climatic Research Unit gridded Timeseries version five (CRU TS4.05) and outputs of climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, this study quantifies the population exposure to CHDE events based on future population density datasets under two Shared Socioeconomic Pathways (SSPs). Using the 75th/90th and 25th/10th percentile of precipitation and temperature as threshold to define severe and moderate events, the results show that the East African region experienced multiple moderate and severe CHDE events during the last twenty years. Based on a weighted multi-model ensemble, projections indicate that under the SSP5-8.5 scenario, the frequency of moderate CHDE will double, and severe CHDE will be 1.6 times that of baseline (i.e., an increase of 60%). Strong evidence of an upward trajectory is noted after 2080 for both moderate and severe CHDE. Southern parts of Tanzania and northeastern Kenya are likely to be the most affected, with all models agreeing (signal-to-noise ratio, SNR > 1), indicating a likely higher magnitude of change during the mid- and far-future. Consequentially, population exposure to these impacts is projected to increase by up to 60% for moderate and severe CHDEs in parts of southern Tanzania. Attribution analysis highlights that climate change is the primary driver of CHDE exposure under the two emission pathways. The current study underscores the urgent need to reduce CO2 emissions to prevent exceeding global warming thresholds and to develop regional adaptation measures. [ABSTRACT FROM AUTHOR]

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Time of Concentration (TC) is a widely utilized time variable in hydrological studies. Various approaches exist to estimate TC, including direct measurements, empirical relations, and graphical method (GM). The GM is commonly regarded as a standard by researchers. Nevertheless, this approach faces obstacles due to the scarcity of records in many watersheds.. This research explores a novel technique for TC estimation, focusing on the examination and recommendation of two-dimensional (2D) hydraulic modeling. The proposed method leverages available satellite data to obtain the necessary input for the 2D simulation. Additionally, several empirical relations were evaluated for TC determination. The GM served as reference point for evaluating the results obtained from empirical relations and hydraulic modeling. To accomplish this, analysis was conducted on four distinct watersheds in Iran, comprising a total of 28 recorded rainfall-runoff data.. In numerical modeling, TC was defined as the duration required for a water parcel to traverse from the headwater to the outlet of a basin. In all studied watersheds, the maximum relative error between the benchmark (GM) and the 2-D modeling was limited to less than 10%. This observation establishes the reliability of hydraulic modeling as a viable method for TC calculation, particularly in ungauged watersheds where the GM cannot be employed. [ABSTRACT FROM AUTHOR]

The removal of phosphorous from wastewater in metal-orthophosphate systems typically occurs by simultaneous adsorption on poorly soluble metal hydroxides and by precipitation reactions between metal ions and orthophosphates in solution. To understand the individual contribution of these mechanisms to the removal of phosphorus, the main aim of this study was to determine the kinetics of consumption of contaminant orthophosphates by the precipitation reaction with aluminium ions in a solution free of insoluble aluminium hydroxide. To define the amount of aluminium and phosphorous compounds to be dissolved in water to have this desired reacting condition at a given pH, the solubilities of KH2PO4(s), Al(OH)3(s) and AlPO4(s) were examined at 25 °C in the pH range ∼2.6 to 7.9. pH-solubility diagrams for these ionic solids were made by solving a system of nonlinear algebraic equations involving dissolution, dissociation and hydrolysis reactions at equilibrium. The kinetics of the reaction between aluminium and orthophosphate ions at a reacting condition free of solids except for the product AlPO4(s) was investigated in a well-stirred batch reactor at pH ∼3.1 and 3.5 at 25 °C. A detailed kinetic model involving ten species, seven reversible reactions of hydrolysis of soluble aluminium and orthophosphate species and one reversible precipitation reaction between aluminium and phosphate ions revealed a rate constant for the latter reaction of 5.968 × 1010 L mol−1 s−1 (p = 0.191). XRD, TGA/DTGA and EDX analyses of the filtered and dried reacting mixture confirmed that the only solid product of the precipitation reaction was hydrated AlPO4(s). [ABSTRACT FROM AUTHOR]

Cloud seeding involves boosting precipitation by releasing substances into the air that act as cloud condensation or ice nuclei. These substances encourage the development of clouds and precipitation. It's like giving Mother Nature a gentle push to assist with rainfall in specific areas. The current work aimed to suggest Al2O3 as an alternate compound in cloud seeding rather than silver iodide. In this research, a unique approach is used to identify condensation nuclei, which play a crucial role in cloud formation and droplet growth. Various samples and four sources were included in the current study; refrigerated helfa powder, Himalayan salt, generator powder, and pollen, were analyzed using different physicochemical instruments. The proportions of chemical compounds in the samples show that there is 1.392% of Al2O3 in Refrigerated helfa which is the highest than in the other 3 sources, while the proportions of elements in the samples indicate that refrigerated helfa contains the lowest toxic compound, and although Al2O3 is insoluble in water, it is hygroscopic and can absorb 6.4% of humidity within 24 hours. As for the surface tension, refrigerated helfa shows lower density and surface tension than the other three sources with values of 0.9480 and 47.89 respectively. Al2O3 shows high humid absorptivity and refrigerated helfa can be used as a main source for Al2O3 which has a low effect on biota and is recommended for use in cloud seeding. However further work is recommended to be carried out in using Al2O3 as an alternative compound to silver iodide in cloud seeding. [ABSTRACT FROM AUTHOR]

Idealized general circulation models (GCMs) suggest global‐mean precipitation ceases to increase with warming in hot climates because evaporation is limited by the available solar radiation at the surface. We investigate the extent to which this generalizes in comprehensive GCMs. We find that in the Community Atmosphere Model, global‐mean precipitation increases approximately linearly with global‐mean surface temperatures up to about 330 K, where it peaks at 5 mm day−1. Beyond 330 K, global‐mean precipitation decreases substantially despite increasing surface temperatures because of increased atmospheric shortwave absorption by water vapor, which decreases the shortwave radiation available for evaporation at the surface. Precipitation decreases in the tropics and subtropics but continues to increase in the extratropics because of continuously strengthening poleward moisture transport. Precipitable water increases everywhere, resulting in longer water‐vapor residence times and implying more episodic precipitation. Other GCMs indicate global‐mean precipitation might exhibit a smaller maximum rate and begin to decrease at lower surface temperatures. Plain Language Summary: Earth's climate has experienced substantial changes over its history, including periods of extremely cold temperatures where most regions contained ice, and periods of extremely warm temperatures where most regions contained no ice. In this study, we explore how precipitation changed in extremely cold and warm climates using a unique set of coupled climate model simulations. We find that global‐mean precipitation increases linearly with global‐mean surface temperatures up to 330 K, where it peaks at 5 mm day−1 and then decreases as surface temperatures further increase. This occurs because in hot climates, global‐mean precipitation is almost entirely balanced by absorbed shortwave radiation at the surface. As the climate warms, the atmosphere contains more water vapor, resulting in increased absorption of shortwave radiation within the atmosphere and decreased absorption of shortwave radiation at the surface. This decreases the energy available for surface evaporation. We show that other climate models exhibit qualitatively similar behavior but indicate global‐mean precipitation might exhibit a smaller maximum rate and begin to decrease at lower surface temperatures. These results demonstrate the need to better understand Earth's hydrological cycle in hot climates. These results also have large implications for understanding weathering in past climates and the habitability of other Earth‐like planets. Key Points: In CAM4, global‐mean precipitation increases linearly with surface temperatures up to 330 K, then decreases with higher temperaturesPrecipitation decreases at high temperatures due to increased atmospheric shortwave absorption by water vapor, decreasing surface absorptionAt high temperatures, precipitation decreases in most regions, but continues to increase in the extratropics due to eddy moisture transport [ABSTRACT FROM AUTHOR]

Colorado River streamflow has decreased 19% since 2000. Spring (March‐April‐May) weather strongly influences Upper Colorado River streamflow because it controls not only water input but also when snow melts and how much energy is available for evaporation when soils are wettest. Since 2000, spring precipitation decreased by 14% on average across 26 unregulated headwater basins, but this decrease did not fully account for the reduced streamflow. In drier springs, increases in energy from reduced cloud cover, and lowered surface albedo from earlier snow disappearance, coincided with potential evapotranspiration (PET) increases of up to 10%. Combining spring precipitation decreases with PET increases accounted for 67% of the variance in post‐2000 streamflow deficits. Streamflow deficits were most substantial in lower elevation basins (<2,950 m), where snowmelt occurred earliest, and precipitation declines were largest. Refining seasonal spring precipitation forecasts is imperative for future water availability predictions in this snow‐dominated water resource region. Plain Language Summary: With over 40 million people dependent on the Colorado River, the 19% drop in streamflow since 2000 has been worrying, especially because its cause is not well understood. To explain this drop, we focused on changes to spring weather in snow‐dominated basins, which contribute over 80% of the river's water. We found spring precipitation decreases since 2000 not only reduced streamflow but also correlated with higher temperatures and evaporation rates and less cloudiness. These impacts combined to intensify streamflow declines in basins with earlier snowmelt. The importance of spring precipitation to Colorado River streamflow underscores the need to improve seasonal precipitation forecasts. Such improvements would enhance water availability predictions for the one billion people worldwide reliant on snow for water resources. Key Points: Significant decreases in spring precipitation have been observed since 2000 in headwater basins of the Upper ColoradoDrier springs have corresponded with greater spring potential evapotranspiration (PET)Spring precipitation decreases and PET increases explain much of the variability in observed streamflow deficits in these headwater basins [ABSTRACT FROM AUTHOR]

In this study, we investigate the changing relationship between the boreal winter Arctic Oscillation (AO) and eastern African precipitation. The results show that the negative correlation has significantly weakened since the late 1970s. And the Arabian anticyclonic circulation changed concurrently with the AO. In the mid‐high troposphere, the anomalous anticyclone centred over the Arabian Peninsula in 1980–2020 moved northeastward and weakened compared to 1950–1979. Correspondingly, the anomalous downward motion over eastern Africa significantly weakened compared with that in 1950–1979, corresponding to insignificant precipitation. A further analysis suggests that AO‐related Rossby wave trains in the upper troposphere may modulate the Arabian anticyclone. From 1950 to 1979, the wave activity preferred propagating eastward from the Mediterranean to Russia along a high‐latitude path. In contrast, during 1980–2020, it tended to emanate southeastward towards Saudi Arabia, with a notably stronger and more eastward extension than in the earlier period. [ABSTRACT FROM AUTHOR]

Weather features, such as extratropical cyclones, atmospheric rivers (ARs), and fronts, contribute to substantial amounts of precipitation globally and are associated with different precipitation characteristics. However, future changes in these characteristics, as well as their representation in climate models, remain uncertain. We attribute 6‐hourly accumulated precipitation to cyclones, moisture transport axes (AR‐like features), fronts, and cold air outbreaks, and the combinations thereof in 10 ensemble members of the CESM2‐LE between 1960 and 2100 under the SSP3‐7.0 scenario. We find that, despite some biases in both precipitation and weather features, CESM2‐LE adeptly represents the precipitation characteristics associated with the different combinations of weather features. The combinations of weather features that contribute most to precipitation in the present climate also contribute the most to future changes, both due to changes in intensity as well as frequency. While the increase in precipitation intensity dominates the overall response for total precipitation in the storm track regions, the precipitation intensity for the individual weather features does not necessarily change significantly. Instead, approximately half of the increase in precipitation intensity in the storm track regions can be attributed to a higher occurrence of the more intensely precipitating combinations of weather features, such as the co‐occurrence of extratropical cyclones, fronts, and moisture transport axes. Plain Language Summary: Most precipitation is associated with weather features such as storms, atmospheric rivers, and fronts. Different combinations of these weather features are associated with different precipitation characteristics, but how these characteristics are represented in climate models as well as their possible future changes is not known. We attribute 6‐hourly accumulated precipitation to weather features, such as storms, fronts, and atmospheric rivers, from 1960 to 2100 under a high greenhouse emission scenario in a climate model. Despite some biases, the climate model represents the precipitation characteristics associated with these weather features well. We find that the weather features with the largest contribution to precipitation in the current climate also contribute the most to future changes in precipitation. The changes are caused by changes in both frequency of occurrence and precipitation intensity. Key Points: We attribute precipitation to cyclones, fronts, moisture transport axes, and cold air outbreaks in 10 ensemble members CESM2‐LE in 1960–2100CESM2‐LE adeptly represents the precipitation characteristics associated with the different weather features and their combinationsCo‐occurring weather features, associated with intense precipitation, contribute to a larger fraction of the precipitation in the future [ABSTRACT FROM AUTHOR]

Rain‐on‐snow (ROS) events occur when rain falls on snowpack and can have substantial ecological and social impacts. During ROS events, liquid water in the snowpack can decrease the surface albedo, which contributes to the positive snow‐albedo feedback and further accelerates snowmelt. In a warming climate, the frequency and spatial coverage of ROS events are projected to increase in the high‐latitude regions, especially in northern Alaska. Multi‐year ground observations at two northern Alaska sites are utilized to evaluate 59 ROS events from 2012 to 2022. Results show that ROS events lead to dramatic snow albedo changes with a mean decline of −0.04 per day, which is considerably larger than the multi‐year mean of −0.005 in May and −0.008 in June. A snow albedo model is used to simulate the daily snow albedo changes due to snowpack liquid water content. The simulated impact of liquid water content accounts for only 10% of the observed snow albedo changes. In addition, composite synoptic conditions from reanalysis products reveal different moisture sources for ROS events. ROS events in May are associated with anomalous high pressure systems over the site and meridional transport of warm and moist air from lower latitudes. While the June synoptic conditions for ROS events show little deviation from the climatological mean and suggest local moisture contributions. ROS events in June show comparable snow albedo changes as in May despite the difference in moisture sources, which implies a prolonged impact of ROS events on rapid snow deterioration during late spring. Plain Language Summary: Rain‐on‐snow (ROS) events occur when rain falls on a snow‐covered surface, and can decrease surface reflectivity, accelerate snowmelt, exacerbate flooding, and deeply affect the local ecology and society. As global temperature increases, ROS events will become more frequent and intense at high latitudes, especially in northern Alaska. This study leverages a long record of ground observations in northern Alaska from 2012 to 2022 to detect ROS events and evaluate their impacts on snow cover. Results show that ROS events can cause distinct declines in snow reflectivity. The influence of liquid water in the surface snow has been simulated by a model and compared with observations. Additionally, analyses of composite environmental conditions reveal different moisture sources for ROS events. ROS events in May rely on the remote transport of moisture from lower latitudes, while those in June are associated with local moisture sources. Our results have indicated that ROS events will play a more influential role in accelerating surface snow melting at high latitudes in a warming climate. Key Points: Rain‐on‐snow events accelerate albedo decline during May and June at the North Slope of AlaskaSimulated influence of liquid water content explains 10% of the observed snow albedo changesRain‐on‐snow events in May rely on anomalous remote moisture transport, while those in June are associated with local moisture sources [ABSTRACT FROM AUTHOR]

The Tibetan Plateau, often referred to as Asia's water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study's findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Reliable hydrological simulations require accurate precipitation data. However, data uncertainties due to the indirect nature of satellite estimates can propagate through hydrological models and lead to simulation errors. This study assesses the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products, comparing them directly with ground-based precipitation data and evaluating their performance in rainfall–runoff modelling across Great Britain. Three IMERG V06 products (IMERG-Early, IMERG-Late, and IMERG-Final) are examined. Utilizing the simple water balance model (SWBM), the analysis covers 250 basins, revealing that the SWBM performs well in over 50% of the basins. Runoff estimations show that European Observation (E-OBS) ground-based data yield the highest Nash-Sutcliffe efficiency (NSE) score (0.91), followed by IMERG-Final (0.85), IMERG-Late (0.82), and IMERG-Early (0.73). The findings underscore IMERG’s utility in hydrological modelling for ungauged or poorly gauged basins. [ABSTRACT FROM AUTHOR]

Due to the depletion of fossil fuels, extensive CO2 emissions, and growing demand for electrochemical energy, it is important to develop further utilization of renewable energy, as well as advanced energy storage techniques. To this end, the optically chargeable supercapacitor, which can convert the photon energy of solar light into electrochemical energy and store it for long‐term usage, attracts extensive investigations. Among these, heterojunction construction was revealed as an effective method to enhance capacity. Recent works have reported the implementations of precipitation in ceramics, where the introduced matrix‐precipitate two‐phase structure is inspiring for constructing heterojunction, whereas it was seldom reported. In this work, Li‐doped NaNbO3 electrode material was prepared by adopting a multi‐step solid reaction to precipitate LiNbO3 and construct LiNbO3/NaNbO3 heterojunctions. The electrochemical measurement demonstrates an enhanced optically chargeable capacity of roughly 85% increase at a scan rate of 1 mV/s and a four‐fold increase at a charging–discharging current density of 1 A/g, as well as outstanding stability by introducing precipitates and constructing heterojunctions. This finding proposes a novel methodology design of heterojunction construction via precipitation to enhance the optically chargeable capacity of supercapacitor electrode materials and, therefore, may open up a potentially new application field for precipitate‐tuned functionality in functional ceramics. [ABSTRACT FROM AUTHOR]

Compared to the traditional alloys, high entropy alloys exhibit exceptional strength and outstanding ductility, making them highly attractive for use in demanding engineering applications. However, the atomic-scale deformation behavior of HEAs with precipitate under the low-cycle loading conditions has not been well studied. Here, we utilize molecular dynamics simulations to investigate the low cycle fatigue behavior of AlCoCrFeNi HEAs with AlNi-rich phase, in order to better understand the cyclic deformation, work hardening, and damage mechanisms. In the stress-strain hysteresis loops, the stress in the elastic stage exhibits a gradual linear increase, followed by fluctuations at yielding and plastic deformation. The strain hardening depends on the cycle number after the yielding stage. With an increase in the number of cycles, the activation mode of stacking faults gradually transitions from a multi-slip system to a single-slip system, attributed to the gradual phase transformation. A thorough examination of dislocation evolution is crucial in understanding the strengthening and plastic behavior of materials under cyclic loading. The generation of more stair-rod dislocations further suppresses the movement of dislocations. The combined effects of element diffusion, structural transformation, and incoherent precipitation play a critical role in enhancing the mechanical properties of AlCoCrFeNi HEAs. The strength of high entropy alloys is improved through interface strengthening caused by element diffusion and structural transformation, along with dispersion induced by incoherent precipitation. This work provides a detailed atomic-level understanding of the cyclic deformation-induced strengthening mechanism, in order to design highstrength and ductile HEAs with specific desired properties. [ABSTRACT FROM AUTHOR]

This study investigates the effects of sea spray on extreme precipitation forecast in Beijing of China between 28 July and 2 August 2023 as a case test. In this case, fully coupled model increased upward moisture in the Bohai and Yellow Seas and increased accumulated rainfall by 21% in North China. For the extreme precipitation events with the 5‐day accumulated precipitation exceeding 500 mm, the atmosphere‐only model did not forecast the events; the coupled model without sea spray performed well with the 0.29 threat score (TS) and 88 mm root mean square error (RMSE); in the fully coupled model, the effects of sea spray increased atmospheric instability, which increased the precipitation around Beijing and yielded a more accurate forecast with the 0.37 TS and 65 mm RMSE. This paper suggests a scientific clue to improve numerical simulation for extreme rainfall events, however, more cases are still needed for statistical evaluation. Plain Language Summary: Although meteorological forecasting ability has been improved considerably during the past decades, precipitation during extreme events remains typically underestimated. Improving model accuracy for heavy rainfall events is, indeed, a ground challenge. In this study, we conducted three numerical experiments under different conditions to evaluate the effects of sea spray on extreme precipitation forecasts for the Beijing extreme rainfall event between 28 July and 2 August 2023. Our results indicated that for this 5‐day forecasting case, including sea spray in the simulations enhanced precipitation in North China by transporting more moisture from the Bohai and Yellow Seas upward into the atmosphere. The moister and warmer air caused by sea spray effects was transported to Beijing through northwestward wind and lifted by the local terrain, then caused a more unstable atmosphere and higher intensity of precipitation around Beijing. By using statistical indicators and setting different precipitation thresholds for this extreme rainfall case, we determined that in this case, the fully coupled model including sea spray yielded more accurate forecast. To statistically evaluate the effects of sea spray in extreme precipitation forecasts, more cases are still needed. Key Points: Fully coupled model led more upward moisture in the Bohai and Yellow Seas and increased 5‐day accumulated rainfall by 21% in North ChinaFully coupled model improved the forecasting threat score from 0 to 0.37 with the 5‐day accumulated precipitation exceeding 500 mmSea spray led to more unstable atmosphere for the Beijing extreme rainfall event, yielding a more accurate forecast [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Time Period Major Taxa Studied Methods Results Main Conclusions Ongoing climate changes represent a major determinant of demographic processes in many organisms worldwide. Birds, and especially long‐distance migrants, are particularly sensitive to such changes. To better understand these impacts on long‐distance migrants' breeding productivity, we tested three hypotheses focused on (i) the shape of the relationships with different climate variables, including previously rarely tested quadratic responses, and on regional differences in these relationships predicted by (ii) mean climatic conditions and (iii) by the rate of climate change in respective regions ranging from Spain to Finland.Europe.2004–2021.Long‐distance migratory passerine birds.We calculated breeding productivity from constant effort ringing sites from 11 European countries covering 34° of latitude, and extracted temperature‐ and precipitation‐related climate variables from E‐OBS and NASA MODIS datasets. To test our hypotheses, we fitted GLMM and Bayesian meta‐analytic models.We revealed hump‐shaped responses of productivity to temperature, growing degree‐days, green‐up onset date, and precipitation anomaly, and negative responses to intense and prolonged rains across the regions. The effects of March temperature and April growing degree‐days were more negative in cold than in warm regions, except for the region with the highest accumulated heat, whereas increasing June precipitation anomalies were associated with higher productivity in both dry and wet regions. Productivity responses to climate were unrelated to the rate of climate warming.The influence of climate on bird productivity proved to be frequently nonlinear, as expected by ecological theory. The rate of climate change is less important than regional interannual variability in climate (which is predicted to increase), but this may change with the progression of climate change in the future. Productivity declines in long‐distance migratory songbirds are particularly expected if out‐of‐norm water excess increases in frequency or strength. [ABSTRACT FROM AUTHOR]

Forecasting precipitation accurately poses significant challenges due to various factors affecting its distribution and intensity, including but not limited to subgrid variability. Although higher resolution simulations are often considered to improve precipitation forecasts, it is crucial to note that simply increasing resolution may not suffice without appropriate adjustments to parameterization schemes or tuning. Traditionally, ensembles of simulations are used to generate uncertainty predictions associated with precipitation forecasts, but this approach can be computationally intensive. As an alternative, there is a growing trend towards leveraging neural networks for precipitation prediction, which offers potential computational advantages. We propose a new approach to generating ensemble weather predictions for high‐resolution precipitation without requiring high‐resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble, on which our model was trained on. [ABSTRACT FROM AUTHOR]

The Mediterranean region is experiencing pronounced aridification and in certain areas higher occurrence of intense precipitation. In this work, we analyze the evolution of the precipitation probability distribution in terms of precipitating days (or "wet‐days") and all‐days quantile trends, in Europe and the Mediterranean, using the ERA5 reanalysis. Looking at the form of wet‐days quantile trends curves, we identify four regimes. Two are predominant: in most of northern Europe the precipitation quantiles all intensify, while in the Mediterranean the low‐medium quantiles are mostly decreasing as extremes intensify or decrease. The wet‐days distribution is then modeled by a Weibull law with two parameters, whose changes capture the four regimes. Assessing the significance of the parameters' changes over 1950–2020 shows that a signal on wet‐days distribution has already emerged in northern Europe (where the distribution shifts to more intense precipitation), but not yet in the Mediterranean, where the natural variability is stronger. We extend the results by describing the all‐days distribution change as the wet‐days' change plus a contribution from the dry‐days frequency change, and study their relative contribution. In northern Europe, the wet‐days distribution change is the dominant driver, and the contribution of dry‐days frequency change can be neglected for wet‐days percentiles above about 50%. In the Mediterranean, however, the change of precipitation distribution comes from the significant increase of dry‐days frequency instead of an intensity change during wet‐days. Therefore, in the Mediterranean the increase of dry‐days frequency is crucial for all‐days trends, even for heavy precipitation. Plain Language Summary: The Mediterranean region is facing increased dryness alongside more intense rainfall in certain areas. We delved into the change of precipitation frequency and intensity across Europe and the Mediterranean using ERA5 reanalysis data from 1950 to 2020. Our analysis revealed four distinct patterns in how rainfall is evolving. In much of northern Europe, heavy rainfall events are becoming more frequent, while in the Mediterranean, lighter precipitation is decreasing while heavy rain is either increasing or decreasing. To understand these changes better, we used a mathematical model with two parameters. This model helped us track the types of changes in precipitation patterns. We found that in northern Europe, the increase in rainfall intensity is the primary driver of change, while in the Mediterranean, it's more about the increase of the dry‐days frequency. These findings underscore the importance of considering both the frequency and intensity of precipitation when studying climate change impacts. By understanding these shifts, we can better prepare for future weather extremes in these regions and adapt to the changing climate. Key Points: Four regimes of change for daily precipitation distribution are identified, and are captured by a two parameters analytical modelIn northern Europe, a signal of increasing mean and extreme precipitation has emergedIn the Mediterranean, the changes of precipitation appears dominated by changes in dry‐days frequency [ABSTRACT FROM AUTHOR]

Based on the hourly precipitation data from the observation stations, the accuracy of hourly QPEs from Fengyun geostationary satellites FY-4A, FY-2 H and multi-satellite joint IMERG-Early precipitation products over the southeastern Tibetan Plateau were compared and assessed. And the relationship between the accuracy of various satellites and terrain were discussed. The results show that: (1) The QPEs of FY-4A, FY-2 H and IMERG-Early can reflect the temporal and spatial distribution characteristics of real precipitation; (2) The QPEs of FY-4A and FY-2 H overestimate the precipitation in the researched region, and the overestimation of FY-4A was more obvious, while IMERG-Early QPE underestimates the precipitation. IMERG-Early QPE showed the best overall accuracy performance. (3) All three satellite QPEs were less accurate in July and August. (4) The accuracy of the QPEs of FY-4A and FY −2 H were better than that of IMERG-Early QPE in heavy rainfall. (5) The accuracy of three satellite QPEs increase with the increase of altitude. The results can provide reference for selecting satellite QPEs for operational applications in the southeastern Tibetan Plateau. [ABSTRACT FROM AUTHOR]