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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.

Identifying the processes that limit populations is a foundational objective of ecology and an urgent need for conservation. For migratory animals, researchers must study individuals throughout their annual cycles to determine how environmental conditions limit demographic rates within each period of the annual cycle and also between periods through carry-over effects and seasonal interactions. 1,2,3,4,5,6 Our poor understanding of the rates and causes of avian migration mortality 7 hinders the identification of limiting factors and the reversal of widespread avian population declines. 8,9 Here, we implement new methods to estimate apparent survival (hereafter survival) during migration directly from automated telemetry data 10 in Kirtland's Warblers (Setophaga kirtlandii) and indirectly from mark-recapture data in Black-throated Blue Warblers (S. caerulescens). Previous experimental and observational studies of our focal species and other migratory songbirds have shown strong effects of Caribbean precipitation and habitat quality on food availability, 11,12,13,14 body condition, 12,13,14,15,16,17,18,19 migration timing, 11,12,15,16,20,21,22,23 natal dispersal, 24,25 range dynamics, 26 reproductive success, 20,22,27 and annual survival. 18,19,20,23,28,29,30,31 Building on this research, we test the hypotheses that environmental conditions during the non-breeding period affect subsequent survival during spring migration and breeding. We found that reduced precipitation and environmental productivity in the non-breeding period strongly influenced survival in both species, primarily by reducing survival during spring migration. Our results indicate that climate-driven environmental conditions can carry over to affect survival in subsequent periods and thus likely play an important role in year-round population dynamics. These lethal carry-over effects may be widespread and are likely magnified by intensifying climate change. • New methods allow for direct and indirect estimation of survival during migration • Poor non-breeding conditions affect songbird survival during migration and breeding • Lethal carry-over effects may be widespread in other migratory taxa • Climate change is likely to magnify these effects and endanger animal populations Using new methods to estimate songbird survival during the non-breeding, spring migration, and breeding periods, Cooper et al. show that drought and poor environmental productivity in winter carry over to reduce survival during migration and breeding. These lethal carry-over effects may be widespread and will likely be magnified by climate change. [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]

Radiolysis of transplutonium elements and rare earths (REE)-containing solutions in radiochemical processes destroys complexing reagents and precipitates target products. We investigated the effect of the radiation-absorbed dose (AD) and acidity on the behavior of simulated DTPA and NTA REE-containing solutions to eliminate precipitation. We measured the AD by bichromate dosimetry. Without nitric acid, the AD = 0.12–0.24 MGy causes abundant precipitation of REEs. In the presence of HNO3 up to 2 mol L−1, no precipitation occurred up to 1.53 MGy. At HNO3 1 mol L−1 and AD = 1.02 MGy, DTPA is entirely decomposed. We have suggested a method to avoid precipitation. [ABSTRACT FROM AUTHOR]

Aim: 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. Location: Europe. Time Period: 2004–2021. Major Taxa Studied: Long‐distance migratory passerine birds. Methods: 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. Results: 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. Main Conclusions: 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]

Under global warming, extreme events have been increasing in the last decade and are projected to increase in the future with every increment of global warming. The potential increase in compound drought and hot events may induce a complex web of impacts on societies, ecosystems, and economies, including crop failure, wildfires, and water scarcity. This is particularly concerning for Brazil, where it has been demonstrated to be vulnerable to recent extreme climate events. Using an ensemble of CORDEX-CORE simulations over Tropical Brazil, we investigate changes in compound events in response to changes in radiative forcing and their impact on climate extreme events, including drought and extreme heat. The simulations are conducted at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Each model covers the period from 1980 to 2100 under two Radiative Concentration Pathways (RCP2.6 and RCP8.5) in the 21st-century projection period. We used observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) to evaluate the simulations and perform a quantitative assessment of areas affected by these compound events during the present day. The study finds a generally good agreement between RCM simulations and observed data, with moderate to high correlation coefficients for precipitation, though the strength of these correlations varies across different regions and seasons. The analysis emphasizes the prevalence of compound climate events during the Austral summer season and projects a significant increase in both extreme heat and drought events in the coming decades. These findings underscore Brazil's vulnerability to compound climate events, highlighting the need for adaptive strategies and policy interventions to mitigate the socio-economic and environmental impacts across various sectors. [ABSTRACT FROM AUTHOR]

The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons. [ABSTRACT FROM AUTHOR]

A correlation study was conducted between SPOTVEGNDVI monthly data and rainfall data in Yulin, Yan 'an, Xi 'an and Ankang of Shaanxi Province from 2015 to 2022. The ecological effects of urban vegetation cover were studied using the revised standardized Vegetation Index (NDVIC). It is found that NDVIC has a strong correlation with precipitation, and its correlation increases with the increase of altitude. Vegetation cover in the Ankang area increased significantly from 2016 to 2022. The net primary productivity index of urban green space increased significantly in October 2016-2022, which is an objective reflection of the effectiveness of urban green construction. This study can lay a theoretical foundation for the objective quantitative assessment of the ecological effects of urban vegetation cover. [ABSTRACT FROM AUTHOR]

The paper determines the effect of daily precipitation, occurring in the sewerage catchment area located in the Jastków Commune, on the volume of wastewater, flowing into the treatment plant in Snopków. Daily and annual volumes of incidental water, caused by precipitation, flowing into the selected sewerage system were determined, and then the annual costs incurred for their treatment were calculated. The sewerage system selected for the study is located in the eastern part of Poland, in the Lublin Voivodeship, within the Jastków Commune. The design average daily inflow of wastewater to the classical treatment plant with activated sludge is 1200 m³·d-1. To the sewage network made of PVC with a length of 67.22 km domestic wastewater from 1119 single-family buildings from 3350 residents are discharged. The study was carried out in 2019–2022, and the daily precipitation totals and daily sewage inflows to the treatment plant were analyzed. The results of the calculations were related to the humidity classification of the year (dry, average and wet) under temperate climate conditions. The study showed that during precipitation events, the share of incidental water in the average daily volume of wastewater flowing into the treatment plant ranged from 3 to 26%. Annually, 7,400 to 10,325 m³ of rainwater flowed into the studied treatment plant, which accounted for 5.0 to 6.3% of the annual volume of wastewater. The incidental waters flowing into the sewage system resulted in the need to spend additional sums – from PLN 47,814 (€10,897) to PLN 71,232 (€16,234) per year. Thus, the annual cost of wastewater treatment in the studied system increased by about 5–6%. The results of the study are important and valuable information for the operators of the analyzed sewerage network and the wastewater treatment plant, as well as for other units that use similar systems. They will allow to initiate actions to detect and eliminate illegal connections of roof gutters and/or yard drains to sanitary sewers in the analyzed area, which would then allow to reduce the cost of wastewater treatment in the studied case. [ABSTRACT FROM AUTHOR]

ABSTRACT Precipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale. [ABSTRACT FROM AUTHOR]

Green rusts (GR) are mixed-valence iron (Fe) hydroxides which form in reducing redox environments like riparian and wetland soils and shallow groundwater. In these environments, silicon (Si) can influence Fe oxides' chemical and physical properties but its role in GR formation and subsequent oxidative transformation have not been studied starting at initial nucleation. Green rust sulfate [GR(SO4)] and green rust carbonate [GR(CO3)] were both coprecipitated from salts by base titration in increasing % mol Si (0, 1, 10, and 50). The minerals were characterized before and after rapid (24 h) aqueous air-oxidation by x-ray diffraction (XRD), scanning electron microscopy (SEM), Fe extended x-ray absorption fine structure spectroscopy (EXAFS), and N2-BET surface area. Results showed that only GR(SO4) or GR(CO3) was formed at every tested Si concentration. Increasing % mol Si caused decreased plate size and increased surface area in GR(CO3) but not GR(SO4). GR plate basal thickness was not changed at any condition indicating a lack of Si interlayering. Air oxidation of GR(SO4) at all % mol Si contents transformed by dissolution and reprecipitation into lepidocrocite and goethite, favoring ferrihydrite with higher % Si content. Air oxidation of GR(CO3) transformed into magnetite and goethite but increasing Si caused GR to oxidize while retaining its hexagonal plate structure via solid-state oxidation. Our results indicate that Si has the potential to cause GR to form in smaller particles and upon air oxidation, Si can either stabilize the plate structure or alter transformation to ferrihydrite. [ABSTRACT FROM AUTHOR]

The uncertainty in precipitation projections across the Sahel has persisted across generations of climate models. Many projections show a zonal dipole in the sign of precipitation change, with wetting across the Central Sahel and drying across the Western Sahel. We analyze the outputs from an ensemble of current climate models to explain why some produce this dipole and understand the inter‐model variability in the transition region for these models. Models projecting the dipole tend to shift the Atlantic Intertropical Convergence Zone (ITCZ) to the south, while models that do not tend to shift the ITCZ to the north. We find that the strong relationship between the change in Sahel precipitation and surface temperature‐based indices is highly driven by the non‐dipole models. These indices do not explain most of the variance in where models transition. This suggests that understanding zonal variability in Sahel precipitation change must go beyond these temperature‐based analysis. Plain Language Summary: Precipitation across the Sahel is vital for sustaining a rapidly growing population and the region's sensitive ecosystem. Uncertainty in Sahel rainfall projections presents a significant challenge for stakeholders. At the end of the 21st century, many climate models predict a dipole pattern, characterized by a drought across western Sahel and an increased rainfall across the central Sahel. This study delves into the outputs of the current state‐of‐the‐art global climate models to investigate and explain why certain models show this drought and increased rainfall pattern across the Western and Central Sahel, respectively, while others do not. Additionally, we seek to comprehend the substantial disparity in the location where these models transition from drought to increased rainfall in this region. Our findings indicate that models depicting these spatial distribution patterns tend to position their band of maximum rainfall farther south compared to models that do not exhibit this pattern. Furthermore, we observe that the strong correlation between Sahel rainfall and temperature‐based indices is predominantly heightened by the group of models lacking this rainfall dipole pattern. This study suggests that to gain a deeper understanding of this drought and increased rainfall projections in the Sahel, we must explore factors beyond these temperature‐based indices. Key Points: Zonal intermodel variability in projections of Sahelian precipitation cannot be fully explained using traditional temperature‐based indicesModels with non‐dipole Sahel precipitation change drive strong correlations between temperature‐based indices and Sahel precipitation changeModels with (without) the zonal dipole pattern tend to produce a southward (northward) shift in the rainbands across the tropical Atlantic [ABSTRACT FROM AUTHOR]

In the mid‐latitudes extreme precipitation events are strongly associated with cold fronts. By exploring drivers across different scales and relating them to precipitation, we aim to improve our understanding of processes influencing cold frontal extremes. Using hourly ERA5 data over Europe and the North Atlantic, cold fronts are detected and the associated conditions are identified. Quantile regression models are employed to find drivers of frontal precipitation and to quantify these relations. Additionally, we use composites to study the synoptic conditions and meso‐scale structure of extreme events. We find that humidity close to the detected fronts, convergence and the low level jet speed contribute most to the formation of extreme precipitation. Synoptic conditions favoring the formation of extreme events are also identified. These results improve our understanding of cold frontal processes leading to precipitation. Additionally, they provide the foundation for a process‐based evaluation of frontal dynamics in climate models. Plain Language Summary: Extreme rain, snow and hail events in the mid‐latitudes are often caused by atmospheric cold fronts. To understand what makes certain events extreme, we need to study the processes and conditions influencing these fronts. In this study, we analyze the characteristics of cold fronts and their connection to extreme rain. First, we identify fronts in the ERA5 climate reanalysis data by finding strong changes in temperature and humidity over Europe and the North Atlantic. Next, we examine the conditions near these fronts. Using statistical models, we link these conditions to rain intensities. Additionally, the large‐scale weather patterns are studied by looking at the average conditions. We find that atmospheric moisture content and the strength of certain frontal circulation patterns play the biggest role in creating extreme weather. These results help us to better understand cold fronts and provide the basis for evaluating frontal conditions in climate models. Key Points: Cold fronts are responsible for up to 70% of hourly extreme precipitation over EuropeERA5 captures the key structural and circulation features of cold fronts that are expected from theoryMoisture content close to the front, low‐level jet speed and convergence are identified as the main drivers of frontal precipitation [ABSTRACT FROM AUTHOR]

The Hydrologic Modeling System (HEC-HMS), designed to accurately simulate precipitation-runoff processes in various watershed types, was employed in our study to establish a model for a particular watershed. Consequently, we planned to create a rainfall-runoff model to investigate the specific characteristics of floods, assess some pre-diction models, and issue assumptions about their viability, which could be beneficial in establishing flood warning systems. The model was developed using daily precipitation data collected from different rain gauge stations in the Gilort watershed, located in southern Romania. The study employed the HEC-GeoHMS terrain processing tool, utilizing a digital elevation design to build a hydrological model. The statistical indicators used to evaluate the runoff mechanisms, specifically regression, coefficient of determination, correlation coefficient, index of agreement (Willmott index), and the root mean squared error (RMSE), showed a strong relationship between the simulated and recorded flow of the watershed. The leaking model was assessed using other statistical parameters, including the deviation of runoff volumes (Dv = 6.40%), Nash-Sutcliffe efficiency (NSE = 0.908), and Kling-Gupta efficiency (KGE = 0.901). These parameters confirmed that the simulated data closely matched the observed data, indicating an effective association, and were considered reliable indicators of the model's goodness of fit, ensuring its reliability and efficacy. [ABSTRACT FROM AUTHOR]

The study evaluated the use of Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) data for monitoring rainfall data in Eswatini. Various statistical metrics such as Bias, correlation coefficient (r), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the CHIRPS 2.0 data against 14 rain gauge observations acquired during 1981–2020. CHIRPS 2.0 rainfall agrees well with rain gauge precipitation at monthly (r = 0.73, Bias = 1.02, RMSE = 50.44 and MAD = 31.44), seasonal (r = 0.77, Bias = 1.01, RMSE = 36.99 and MAD = 24.15) and annual scales (r = 0.65, Bias = 2.46, RMSE = 500.78 and MAD = 468.06). Moreover, areas characterized by complex topography and land use, and areas in transition zones (to a different agroecological zone) had generally poor correlations. Nonetheless, CHIRPS 2.0 captures well the spatial distribution of rainfall in the different agroecological zones of Eswatini, even in areas with no rain gauge data. In conclusion, CHIRPS 2.0 can be a very valuable tool in filling gaps created by poor spatial coverage of ground‐based rain gauges, especially in the developing world where this is often the case. [ABSTRACT FROM AUTHOR]

ABSTRACT This work studies the simultaneous and sequential occurrence of hot and dry months in the summer season in Argentina, north of 40°S, based on three different databases: meteorological stations, a gridded observational dataset and a reanalysis product. The influence of the El Niño‐Southern Oscillation (ENSO) over the occurrence of these compound events is specially analysed using a logistic regression model. Monthly maximum temperature and precipitation data are used for the period 1979–2022 in four sub‐regions of Argentina: Northwestern Argentina (NOA), Northeastern Argentina (NEA), Cuyo (central‐western Argentina) and the Pampas (central‐eastern Argentina). Simultaneous hot and dry months and hot months preceded by dry months are the most frequent compound events. The highest frequencies are found in the centre part of the study region and NEA for simultaneous compound events, and in NOA and the Pampas region for sequential ones. In general terms, all datasets show a good representation of the spatio‐temporal variability of hot and dry months. The insights of the influence of ENSO on compound events revealed that La Niña enhances the occurrences of hot and dry months throughout the study region, with the exception of NOA, where El Niño conditions promote the occurrence of these events. Based on logistic regression models, we successfully quantify the relationship between ENSO and hot and dry months and demonstrate that ENSO plays a significant role as a driver of compound hot and dry events in the central region, Cuyo, NEA and a portion of the Pampas. This research contributes to the understanding of compound events in Argentina and how they are influenced by major drivers of climate variability providing useful information for the development of a predictive system for such events. [ABSTRACT FROM AUTHOR]

The diversity of the Coreidae family in the tropical deciduous forest (TDF) of Coaxitlán, Tlaquiltenango, Morelos, Mexico, is presented. From the collection carried out during an annual cycle, 34 species, 18 genera, 8 tribes, and 2 subfamilies were recorded. The sample coverage was 99% and according to the Chao 1 richness estimator, 90% of the species in the study area were represented. The greatest richness, abundance, and diversity occur during the months of greatest rainfall. The faunal similarities between months show groupings in species composition corresponding to the TDF’s seasonality. The community structure reflects a high diversity, integrated by a high proportion (>60%) of species with a low abundance. The data show that the diversity of Coreidae in the TDF is high, but the environmental heterogeneity of this type of vegetation suggests that the species composition could still be different in other sites, which shows the need to continue documenting it. [ABSTRACT FROM AUTHOR]

It has previously been shown that trends in sensible heat from climate models have had a substantial contribution to global precipitation changes. We illustrate that this is the case also in the most recent Coupled Model Intercomparison Project Phase 6 (CMIP6). However, we find that over the period since 1980 reanalyses do not support the reduction in sensible heat from the CMIP6 models and rather estimate a global increase in sensible heat which would contribute to a precipitation reduction. Satellite data over a period of two decades over global ocean generally show an opposite sign of the sensible heat trend to the CMIP6 models, similarly to the reanalyses. [ABSTRACT FROM AUTHOR]

Geochemical investigation of hydrogen (H 2), brine, rock formations, and residual gases (nitrogen – N 2 , methane – CH 4 , and carbon dioxide – CO 2) is crucial for understanding H 2 storage processes in depleted gas reservoirs. This study examines the effects of injecting a gas mixture (60% H 2 + 30% N 2 + 5% CH 4 + 5% CO 2) into Bandera Grey sandstone with notable clay mineral content. The experiment lasted 50 days at 42 °C, 1350 psi, and 5 wt% NaCl brine, with detailed analytical characterization before and after aging. Results show minimal sample reactivity, causing slight alterations in petrophysical characteristics. Dissolution primarily affected dolomite and barite minerals, while illite and kaolinite caused pore blockage. Gas chromatography data indicate only 0.45% of injected H 2 is lost (via dissolution in brine and reaction with CO 2), CH 4 increased by 6.22%, N 2 remained stable, and CO 2 decreased by 1%. These results provide valuable insights into the feasibility and safety of utilizing depleted gas reservoirs for H 2 storage. • Effect of storing 60% H 2 and 40% cushion gas in clay-rich sandstone is investigated. • Geochemical reactions (dissolution/precipitation) induced by the mixture are very weak. • PH increased from 6.13 to 7.24 led to mineral dissolution and precipitation. • Only 0.45% of injected H 2 was lost based on gas chromatography. • Findings provide insights into the feasibility and safety of depleted gas reservoirs for H 2 storage. [ABSTRACT FROM AUTHOR]

Simple Summary: This work investigated lameness on sheep and goat farms in Greece in an extensive study throughout the country. The prevalence of lameness was found to be low, specifically 1.9% among sheep farms and 2.6% among goat farms. Application of vaccination against foot-rot, increased precipitation at farm locations and longer annual grazing periods for sheep and increased precipitation at farm locations for goats emerged as significant predictors for the prevalence of lameness among the farms in the study. Antibiotic administration (mainly lincomycin or oxytetracycline) was the preferred method for treatment of the disorder. Lameness was considered to be an important health problem by 5.6% of farmers, whilst mastitis was ranked first, which reflects the dairy production type prevailing in small ruminant farms in Greece. The present study refers to an extensive investigation of lameness performed countrywide in Greece, on 325 sheep and 119 goat farms. The specific objectives of this work were to present data on the occurrence of lameness on sheep and goat farms and to identify variables (including variables related to climatic factors) associated with the disorder on the farms. Farms were visited and animals on the farm were assessed for the presence of lameness; further, an interview was carried out with the farmer to obtain information regarding practices applied on the farm. Climatic variables at the location of each farm were derived from NASA research. The within farm prevalence rate varied from 0.0% to 25.0% in sheep flocks and from 0.0% to 30.0% in goat herds. The mean ± standard error (median (interquartile range)) within farm prevalence rate among sheep farms was 1.9% ± 0.2 (0.0% (0.0%)); among goat farms, it was 2.6% ± 0.5% (0.0% (0.0%)). Multivariable analysis for within farm prevalence of lameness revealed three significant predictors in sheep farms: application of vaccination against foot-rot, increased precipitation at the farm location and longer annual grazing period for sheep, and one in goat farms: increased precipitation at the farm location. Treatment of lameness involved mostly administration of antibiotics (on 104 farms); the antibiotics administered most often were lincomycin (on 69 farms) and oxytetracycline (on 33 farms). There was a tendency for higher median within farm prevalence of lameness among farms where no antibiotic administration was practiced. Finally, 6.2% of sheep farmers and 4.2% of goat farmers considered lameness as an important health problem for the animals, specifically the third and fifth most important problem on the respective farms. [ABSTRACT FROM AUTHOR]

Accurate monitoring and assessment of precipitation distribution can be vital to effectively manage water resources and disaster preparedness for high productivity in modern agriculture against global climate change. Among them, Guangxi in southern China is in a monsoonal climate. A great challenge remains on the notable temporal and spatial fluctuations in precipitation, particularly for agricultural productivity and ecological integrity. The purpose of this study is to assess the suitability and precision of the monthly IMERG (V06) precipitation dataset in the Guangxi region. A scientific foundation was then established to manage the local water resources and disaster preparedness. The study area was characterized by low rainfall (≤1300 mm/a), moderate rainfall (>1300-1700 mm/a), and high rainfall (>1700 mm/a). The performance of the IMERG Early, Late, and Final products was also elucidated across the different zones. The baseline data of monthly precipitation was collected from 91 meteorological stations in the period from 2001 to 2020. Three quantitative indexes were employed, including the metrics-correlation coefficient (CC), root mean square error (RMSE), and relative error (RE). A comprehensive analysis was conducted on the precision of IMERG products at the various spatial and temporal scales, as well as precipitation intervals. Results indicate that: 1) Both Early and Late products exhibited similar precision to underestimate the overall precipitation, whereas the Final product tended to overestimate the precipitation. 2) The increase in precipitation caused a decrease in the CC and RE for all products, while an increase in RMSE. The deviation thresholds after monthly precipitation estimation were 271, 272, and 351 mm for the Early, Late, and Final products, respectively. Once the actual monthly precipitation fell below these thresholds, an overestimation occurred and vice versa. 3) Spatially, the IMERG products demonstrated that there was a stronger correlation and greater variability in the areas with the higher precipitation, with the overestimation in the regions under low or moderate rainfall, and the underestimation under high rainfall. 4) Temporally, the precision of products was higher in the dry season during drought years. The Early and Late products performed adequately in autumn, indicating noticeable underestimations. The final product excelled in winter. 5) A decreasing trend was found in the precision of the Final product over the whole years, whereas, an ever-growing overestimation was in the Early and Late products. In conclusion, the precision of the monthly-scale IMERG product was notably influenced by the precipitation, indicating considerable discrepancies in the various regions and temporal scopes. Hence, it is recommended to conduct customized evaluations of product precision for the specific regions and time frames in practical applications. This finding can serve as a significant reference to monitor and manage the water resources for beneficial insights into the climate zones. [ABSTRACT FROM AUTHOR]

Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China's temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales. Significance Statement: The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported. [ABSTRACT FROM AUTHOR]

This study explores how future SST warming in remote ocean basins may affect the western North Pacific (WNP) wet season climate by applying a high-resolution atmospheric general circulation model to conduct a series of numerical experiments. A marked precipitation and tropical cyclone (TC) activity reduction, as well as enhanced anticyclonic circulation, in the WNP is projected in AMIP experiments forced by SST change in a future warming scenario. The sensitivity experiments reveal that various SST warming phenomena (e.g., in the global SST warming pattern, the tropical ocean belt, the Indian Ocean, the tropical Atlantic, and the subtropical northeast Pacific) and the increase in greenhouse gas concentration could weaken the precipitation, TC activity, and circulation. By contrast, the SST warming in the WNP and eastern equatorial Pacific has opposite and mixed effects, respectively, and tends to weakly offset the dominant influences of remote ocean warming. These results indicate that the WNP, being the epicenter of the global teleconnection of divergent and rotational flow, is susceptible to the influence of the SST warming in remote ocean basins. The remote forcing as projected in future scenarios would overwhelm the enhancing effect of local SST warming and weaken the circulation, convection, and TC activity in the WNP. These findings further the understanding of how the decreased precipitation and enhanced subtropical high in the WNP may be easily triggered by remote SST warming as revealed in the AMIP-type simulations. How this effect would be affected by air–sea coupling needs further investigation. [ABSTRACT FROM AUTHOR]

Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning. [ABSTRACT FROM AUTHOR]

Groundwater recharge zone identification is vital for managing water resources, particularly in semi-arid and dry climates. Accurate and quantifiable assessment is necessary for the sustainable management of groundwater resources, and it is possible to carry this method out using modern techniques and technical standards. To identify likely groundwater locations in the Diyala River Catchment, Iraq, which serves as an example study basin, the current research examines a new methodology that employs a geographic information system, and an Analytical Hierarchy Process connected with remote sensing data. The technique of ArcGIS was employed to generate spatially distributed thematic layers of rainfall, lithology, slope, drainage density, land use/land cover, relief and soil. The raster data from these layers were then converted and categorized. The weights assigned to thematic strata depended on their significance relative to groundwater occurrence. A pairwise judgement matrix for the Analytical Hierarchy Process was used, with the categorized ranking, to assess the standardized weights of the layers under consideration. The layers for the formation of groundwater zones have then been placed using the overlay-weighted summation approach. Three regions, which are classed as excellent, good and moderate, have been identified on the resulting groundwater potential zones map, representing roughly 29, 69 and 2% of the basin's total area, respectively. The study's conclusions indicate that, in such a climate, the adopted strategy would produce favourable results to promote the organizing of opinions and the sustainable use of groundwater resources. [ABSTRACT FROM AUTHOR]

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds. [ABSTRACT FROM AUTHOR]

To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features. Plain Language Summary: We tracked large‐size rain storms called mesoscale convective systems (MCSs) in the central Amazon during the wet seasons of 2014 and 2015. Data generated from the MCS tracking helps us understand how rainfall is produced as a function of the number of storms, as well as their size, rain intensity, and motion, and how those can be better simulated by weather and climate models. We found that the model produces fewer and smaller MCSs than in reality, but the total rainfall amount is often overestimated. This is because simulated MCSs produce unrealistically intense heavy rainfall events. On the other hand, light rainfall events are mostly underrepresented by the model. Thus, the model error in total precipitation is determined by how these two counterbalance each other. Our analysis also suggests model representation of the background environment can be critical for simulating realistic MCS properties. Key Points: Simulated and observed mesoscale convective system (MCS) clouds and precipitation are tracked during the 2014/15 wet seasons in the central AmazonExcessive heavy rain intensity (≥10 mm hr−1) of simulated MCS lead to an overprediction of precipitationDry bias in stratiform rainfall can also drive MCS precipitation bias when stratiform cloud cover is substantially underpredicted [ABSTRACT FROM AUTHOR]

The diurnal cycle of precipitation in the Amazon Basin (AB) is not homogeneous, varying in its intensity, time of occurrence of precipitation peaks and in the shape of its diurnal distribution. This study presents a seasonal characterization of the mean diurnal cycle of precipitation in the AB from IMERG Final Run (Δx = 0.1° and Δt = 30 min) database from 2001 to 2020. Diurnal and semi-diurnal oscillations were studied by harmonics analysis, i.e., using the first and second harmonics, respectively. Harmonic metrics of normalized amplitude (AN), phase and mean hourly precipitation rate were analyzed. The AN showed pixels within the AB with bimodal/uniform or unimodal distribution associated with the occurrence of two peaks (or none) or a single peak during the day. The phase of the first harmonic shows the time of occurrence of the precipitation rates peaks, as well as the displacement of the precipitation systems. The regionalization of the diurnal cycle was performed using the K-Means technique, showing that AB presents six clusters along its domain based mainly on the phase of the first harmonic. The spatial configuration of clusters showed seasonal variation, being modulated by the South American Monsoon System and the large-scale mechanisms responsible for triggering convection. However, their intensity, the shape of the diurnal distribution and the timing of precipitation peaks are modulated by local factors. [ABSTRACT FROM AUTHOR]

ABSTRACT The present study examines the effects of the central Atlantic Niño (CAN) and eastern Atlantic Niño (EAN) events on the seasonal precipitation in South America (SA) during the 1951–2020 period. For the CAN during the summer and autumn, an interhemispheric sea surface temperature (SST) dipole mode induces an anomalous thermally direct circulation in the 10° N–10° S band and is the main factor causing precipitation anomaly patterns with a dipole structure between northern (negative) and northeastern (positive) SA. For winter and spring, the SST pattern featuring a South Atlantic dipole induces meridional and zonal anomalous circulations, which are the mechanisms causing positive precipitation anomalies in tropical SA to the north of 20° S. In contrast, for the EAN, the precipitation anomaly patterns show large areas with anomalous dryness, particularly during summer, autumn, and spring. For summer and autumn, the east–west SST anomaly gradient in the equatorial Atlantic and the associated sea level pressure (SLP) anomalies induce equatorial westerlies and a regional Walker cell with descending motions in most tropical SA, where large areas with anomalous dryness are noted. During spring, a northward SST gradient in the tropical Atlantic induces a meridional cell with descending motions in the 0°–10° S band; meanwhile, the westward SST gradient in the equatorial Atlantic and tropical South Atlantic induces a zonal circulation with descending motions over northeastern and eastern Brazil. These descending motions extend the anomalous dryness over a large area. For the EAN events, the east–west SST gradient and the associated east–west circulation in the South American/Atlantic region are crucial elements to modulate precipitation variability in SA. Therefore, the CAN and EAN events induce distinct precipitation anomaly patterns in SA due to distinct associated regional circulation patterns. The results presented here have not been discussed before and might have relevant implications for climate monitoring and modelling studies. [ABSTRACT FROM AUTHOR]

Objectives: Accurate monitoring of drought is of great practical significance for agricultural production around the world, and it is beneficial to the sustainable development of an economy. As a vital tool to measure the degree of regional drought, the construction of a comprehensive drought index with high monitoring accuracy is one of the research hotspots nowadays. Methods: We constructed a new comprehensive drought index meteorology-agriculture drought index (MADI) combined with the normalized difference vegetation index (NDVI) and monthly mean precipitation based on the Gaussian Copula function. The superiority of MADI in capturing drought events was confirmed by statistical comparison with standardized precipitation index (SPI) and vegetation condition index (VCI) at monthly scale and by testing in historical events. Results: The research result the research shows that: (1) MADI has high consistency with traditional drought indexes SPI and VCI. The months with a consistent characterization of MADI and SPI accounted for 62.24% of the total months. And the figure is 83.82% taking into account the MADI and VCI. And the months that MADI with either consistent characterization of SPI or VCI to characterize drought accounted for 97.83% of the total months.(2) MADI has high sensitive to capturing drought. In the months when MADI is consistent with SPI and VCI characterization, the absolute value of MADI is greater than that of SPI or VCI in more than 60% of the months, which means that the MADI is clearer in drought characterization. (3) MADI is more accurate in contrast to the record of historical drought events in the meteorologic yearbook. MADI successfully capture two major drought events from January to April 2012 and August to October 2007. However, the SPI and VCI fail to characterize the above events. Conclusions: Therefore, the MADI can serve as a helpful complement to SPI and VCI since it can provide an auxiliary decision for the accurate judgment of drought disasters in the Yangtze River Basin. [ABSTRACT FROM AUTHOR]

In numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate (Au) $(Au)$, which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local Au $Au$ is commonly parameterized as a function of DSD moments: cloud droplet number nc $\left({n}_{c}\right)$ and mass qc $\left({q}_{c}\right)$ concentrations. When applied in a large‐scale model, the grid‐mean Au $Au$ must also include a correction, or enhancement factor, to account for the horizontal variability of the cloud properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large‐eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local Au $Au$, as well as its spatial variability. The analysis of an ensemble of warm low‐level cloud cases shows that the E3SMv2 formulation represents the Au $Au$ reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM‐tuned local Au $Au$ rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of nc ${n}_{c}$ and its co‐variability with qc ${q}_{c}$ in parameterizing the grid‐mean Au $Au$. Plain Language Summary: When representing clouds in large‐scale weather and climate systems, models often compute the rate of formation of rain droplets through cloud droplet collisions as a function of cloud droplet number concentration and liquid water content averaged over the model horizontal grid, typically extending to 10–100 km. This rate, also known as an autoconversion rate, is then multiplied by an enhancement factor that accounts for the non‐linearity of that function and the cloud variability at smaller unresolved scales. We assess how well this rain initiation process is captured in the Energy Exascale Earth System Model (E3SM) by utilizing advanced high‐resolution cloud simulations that explicitly resolve cloud droplet spectra and spatial variability on scales from few tens of meters to 10 km. Our findings reveal that, while E3SM reasonably represents the overall rain droplet formation rate, it stems from a combination of underestimating the local rate and overestimating the enhancement factor accounting for unresolved subgrid cloud variability. This latter overestimation is traced to neglecting horizontal variability in the cloud droplet number concentration, offering valuable insights for improving the performance of global atmospheric models. Key Points: Formulations of a local autoconversion rate (Au) and its enhancement factor in E3SM are comprehensively evaluated using large‐eddy simulationsA reasonable grid‐mean Au in E3SM is produced by an underestimated local rate combined with an overestimated enhancement factorDroplet number concentration variability and co‐variability with liquid water content must be considered to improve the enhancement factor [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low‐cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low‐cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio‐temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity. Plain Language Summary: Ideally, to study how damaging phenomena like heatwaves, droughts and downpours will change in the future under global warming, we would want a large number of climate model runs producing many realizations of climate futures that we can analyze and from which the new characteristics of climate extremes can be quantified. Currently, emulators can rapidly generate simulations of future climate, but often to relatively low frequencies, as decadal, annual or monthly output at best in most cases, which is insufficient for studying extreme events that occur on a daily timescale. We show how it is possible to train a machine learning model to produce daily series of temperature or precipitation from monthly averages, thus facilitating a more robust investigation into how extreme events will change in the future. Key Points: Earth system models (ESMs) are key devices for understanding how human actions will affect the future global climateComputational demands prevent us from running them for more than a handful of scenarios. ESM emulators are most commonly limited to the monthly frequencyWe present DiffESM as a data‐driven emulator of ESMs that closely matches the spatiotemporal distributions of ESMs at daily frequency [ABSTRACT FROM AUTHOR]

Background/Objectives: Parenteral nutrition (PN) is used when enteral feeding is not possible. It is a complex mixture of nutrients that must meet a patient's needs but can face stability issues, such as lipid emulsion destabilisation and precipitate formation. Stability studies are complex, and the methodologies used are very varied in the literature. In addition, many studies are outdated and use outdated components. This study conducts a stability analysis of PN solutions using optical microscopy. Methods: Samples were prepared according to clinical practice standards and previous studies. We used a counting chamber for optical microscopic observations and different storage conditions (RT, 4 °C 1–14 days). Results: Precipitates larger than 5 µm were found in 8 out of 14 samples after 14 days of storage at room temperature, and none were observed in refrigerated samples. More lipid globules larger than 5 µm were detected in samples stored at room temperature than in those stored in a refrigerator after 14 days. Additionally, the number of large globules generally increased from day 1 to day 14 in most samples. Conclusions: The observed precipitates were probably calcium oxalate crystals, the formation of which is possible in PN but is not expected under the usual storage conditions in a hospital environment. Prolonged storage time and storage at room temperature increases the formation of these precipitates. These findings highlight the importance of using filters during both the preparation and administration of PN to prevent large particles from reaching patients. [ABSTRACT FROM AUTHOR]

This study uses idealized simulations to investigate the impact of cloud condensation nuclei (CCN) on a cumulus congestus. Thirteen cases with the initial CCN_C, which is the CCN concentration at 1% supersaturation with respect to water, from 10 to 10,000 cm−3 are simulated. The analysis focuses on the liquid phase due to the negligible ice phase in this study. A non-monotonic response of cloud properties and precipitation to CCN concentration is observed. When CCN_C is increased from 10 to 50 cm−3, the enhanced condensation due to the more numerous droplets invigorates the cumulus congestus. The delayed precipitation formation due to the smaller droplets also facilitates the cloud development. The two processes together lead to a higher liquid water path (LWP), higher cloud top, and heavier precipitation. The cumulus congestus has the highest cloud top, the strongest updraft, and the most accumulated precipitation and at CCN_C = 50 cm−3. When CCN_C is increased from 50 to 500 cm−3, the condensation near the cloud base is further enhanced and the precipitation is further delayed, both of which lead to more liquid water remaining in the cloud, and thus an even higher LWP and heavier precipitation rate in the later stage. However, the significantly enhanced evaporation near the cloud top limits the vertical development of the cumulus congestus, leading to a lower cloud top. When CCN_C is further increased to be higher than 1000 cm−3, the cumulus congestus is strongly suppressed, and no precipitation forms. The ratio of the precipitation production rate to vertical cloud water flux in the updraft is not a constant, as is generally assumed in cumulus parameterization schemes, but decreases significantly with increasing CCN concentration. It is also found that the CCN effect on the cumulus congestus relies on which parameters are used to describe the cloud strength. In this study, as CCN_C increases, the LWP and the maximum precipitation rate peak at CCN_C = 500 cm−3, while the cloud top height, maximum updraft, and accumulated precipitation amount peak at CCN_C = 50 cm−3. [ABSTRACT FROM AUTHOR]

Premise: Seed germination involves risk; post‐germination conditions might not allow survival and reproduction. Variable, stressful environments favor seeds with germination that avoids risk (e.g., germination in conditions predicting success), spreads risk (e.g., dormancy), or escapes risk (e.g., rapid germination). Germination studies often investigate trait correlations with climate features linked to variation in post‐germination reproductive success. Rarely are long‐term records of population reproductive success available. Methods: Supported by demographic and climate monitoring, we analyzed germination in the California winter‐annual Clarkia xantiana subsp. xantiana. Sowing seeds of 10 populations across controlled levels of water potential and temperature, we estimated temperature‐specific base water potential for 20% germination, germination time weighted by water potential above base (hydrotime), and a dormancy index (frequency of viable, ungerminated seeds). Mixed‐effects models analyzed responses to (1) temperature, (2) discrete variation in reproductive success (presence or absence of years with zero seed production by a population), and (3) climate covariates, mean winter precipitation and coefficient of variation (CV) of spring precipitation. For six populations, records enabled analysis with a continuous metric of variable reproduction, the CV of per‐capita reproductive success. Results: Populations with more variable reproductive success had higher base water potential and dormancy. Higher base water potential and faster germination occurred at warmer experimental temperatures and in seeds of populations with wetter winters. Conclusions: Geographic variation in seed germination in this species suggests local adaptation to demographic risk and rainfall. High base water potential and dormancy may concentrate germination in years likely to allow reproduction, while spreading risk among years. [ABSTRACT FROM AUTHOR]

The deformation behavior of slow‐moving large landslides is often governed by rainfall characteristics. Based on observational data such as precipitation, deformation measurement, and pore water pressure measurements in the slip zone, in many cases a strong correlation between strong rainfall events, a time‐delayed increase of pore water pressures in the slip zone, and, simultaneously to this, an increase of the deformation rate of the landslide can be found. Based on such detailed data, calculation models, which couples the relation between rainfall characteristics and the development of pore water pressures in the slip zone on one hand and the deformation behavior of the slope on the other, can be developed and be used for a better understanding and a prediction of deformation behavior of such slow‐moving landslides. Climate change issues will lead to a change in rainfall frequency and magnitude and annual temperature distribution characteristics in several regions worldwide, which will also lead to changes in the deformation behavior of such large landslides. In this contribution, satellite‐based interferometric synthetic aperture radar (InSAR) data are discussed to be used as source for deformation measurements as bases for prediction models describing the rainfall‐triggered deformation behavior of slow‐moving landslides. [ABSTRACT FROM AUTHOR]

Global warming and climate change impacts intensify hydrological cycle and consequently unprecedented drought and flood appear in different parts of the world. Meteorological drought assessments are widely evaluated by the concept of standardized precipitation index (SPI), which provides drought classification. Its application is based on the probabilistic standardization procedure, but in the literature, there is a confusion with the statistical standardization procedure. This paper provides distinctive differences between the two approaches and provides the application of a better method. As a novel approach, SPI classification is coupled with fuzzy clustering procedure, which provides drought evaluation procedure based on two variables jointly, precipitation and temperature, which is referred to as the standard precipitation-temperature index (SPTI). The final product is in the form of fuzzy c-means clustering in five clusters with exposition of annual drought membership degrees (MDs) for each cluster and resulting objective function. The application of the proposed fuzzy methodology is presented for the long-term annual precipitation and temperature records from New Jersey Statewide records. [ABSTRACT FROM AUTHOR]

A tank test was performed simulating two-dimensional planar flow conditions at a meter scale to evaluate the effectiveness of microbial-induced desaturation and precipitation (MIDP) in stratified soil conditions. The tank setup (116.5 cm tall, 122 cm wide, and 5.25 cm thick) was filled with two layers of fine sand (a target layer of 40 cm and nontarget layer of 21 cm above) that were confined by silt layers above (6 cm), between (9 cm) and below (9 cm) the sand layers. Multiple flushes of substrate solution, containing calcium, nitrate, and acetate, were injected into the lower sand layer to stimulate indigenous nitrate-reducing bacteria to produce biogenic gas, biominerals, and biomass. Embedded sensors were used to measure the changes in electrical conductivity, volumetric water content, and pore pressure in both the target and nontarget sand layers during and between treatment cycles. Time-lapse camera images were used to determine flow velocity distributions in the target layer and identify modes of gas migration. At the end of the test, hydraulic conductivity, calcium carbonate content, and soil–water characteristic curves (SWCCs) were measured on intact samples of the treated material. The results showed that most of the reaction products were formed in the targeted sand layer. During the first treatment cycle, the degree of saturation in the target sand layer decreased to 75% within 5–12 days, at which point it started to migrate upwards until it got trapped and formed a lens underneath the silt layer above. During the second and subsequent treatment cycles, seepage velocity increased due to the entrapment of biogenic gas, the reaction rate increased due to the accumulation of biomass, and the gas formed channels through the silt and migrated further upwards into and through the upper sand and silt layers by irregular venting events. After 4–5 cycles, an equilibrium condition was reached at which the degree of saturation fluctuated from 65 to 80% when gas was being produced and vented to 80–90% when substrates were depleted. The CaCO3 content after 10 cycles over 12 weeks ranged from 1.6% close to the inlet to 0.5% close to the outlet, with an average of 0.68%. The formation of biomass and CaCO3 had a relatively large impact on the saturated hydraulic conductivity but a very limited impact on the SWCC. [ABSTRACT FROM AUTHOR]

This paper introduces a community‐accessible dataset comprising daily hydroclimatic variables (precipitation, temperature, and streamflow) observed in 50 catchments in Sweden (median size of 1019 km2). The dataset covers a 60‐year period (1961–2020) and includes information on geographical location, landcover, soil classes, hydrologic signatures, and regulation for each catchment. Data were collected from various sources, such as the Swedish Meteorological and Hydrological Institute, the Swedish Geological Survey, and several Copernicus products provided by the European Environment Agency. The compiled, spatially‐matched, and processed data are publicly available online through the Swedish National Data Service (https://snd.se/en), contributing a new region to the collection of existing CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) datasets. The CAMELS‐SE dataset spans a wide range of hydroclimatic, topographic, and environmental catchment properties, making it a valuable resource for researchers and practitioners to study hydrological processes, climate dynamics, environmental impacts, and sustainable water management strategies in Nordic regions. [ABSTRACT FROM AUTHOR]

Energy consumption demand has shown successive records during recent months, primarily associated with heat waves in almost all Brazilian states. The effects of climate change induced by global warming and the increasingly frequent occurrence of extreme events, mainly regarding temperature and precipitation, are associated with this increase in demand. In this sense, the impact of meteorological variables on load demand in some substations in the northeast and southeast of Brazil was analyzed, considering the historical series of energy injected into these substations. Fifteen substations were analyzed: three in the state of São Paulo, six in Bahia, three in Pernambuco, and three in Rio Grande do Norte. Initially, essential quality control was carried out on the energy injection data. The SAMeT data sets were used for the variable temperature, and Xavier was used for precipitation and relative humidity to obtain a homogeneous data series. Daily and monthly data were used for a detailed analysis of these variables in energy demand over the northeast and southeast regions of Brazil. Some regions were observed to be sensitive to the maximum temperature variable, while others were sensitive to the average temperature. On the other hand, few cases showed the highest correlation with the precipitation and relative humidity variables, with most cases being considered slight or close to zero. A more refined analysis was based on the type of consumers associated with each substation. These results showed that where consumption is more residential, the highest correlations were associated with maximum temperature in most stations in the northeast and average temperature in the southeast. In regions where consumption is primarily rural, the correlation observed with precipitation and relative humidity was the highest despite being negative. A more detailed analysis shows that rural production is associated with irrigation in these substations, which partly explains consumption, as when rainfall occurs, the demand for irrigation decreases, and thus energy consumption is reduced. [ABSTRACT FROM AUTHOR]

We analyze the calibration stability of the 17-yr precipitation radar (PR) data on board the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM, version 7, is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM, version 8 (or later), takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis. Significance Statement: The stability of long-term data is very important for climate research so that an account of temporal calibration changes in the sensor must be made. In this study, we investigate the calibration stability of the TRMM PR data and evaluate its impact on the precipitation trend analysis. The temporal change of the PR calibration is estimated to be 0.19 dB decade−1. Compensating for this change improves the consistency of precipitation trend analysis between the PR and other precipitation datasets. The reprocessed PR data provide more probable data for long-term precipitation analysis. [ABSTRACT FROM AUTHOR]

This study explored the interannual characteristics of the out‐of‐phase pattern of summer precipitation over northern China during the past years, as well as the possible underlying mechanisms. The out‐of‐phase pattern is characterized by the positive precipitation anomaly in Northwest China (NWC) and the negative anomaly in North China (NC). Our analyses indicate that the variation of Asian westerly jet (AWJ) is found evidently associated with this out‐of‐phase pattern of summer precipitation. The meridional displacement of AWJ induces opposite trends in water vapour transport and circulation anomalies between NWC and NC, leading to the out‐of‐phase pattern. Further analyses suggest that the anomalies of Arctic sea ice concentration (SIC), North Atlantic and Northwest Pacific sea surface temperature (SST) can impact the variation of AWJ by triggering the Eurasian (EU), polar‐Eurasia (POL) and Pacific‐Japan (PJ) teleconnection patterns, respectively, which in turn modulate the formation of this pattern. The precipitation in NWC is primarily affected by the North Atlantic and Northwest Pacific SST anomalies, whereas the precipitation in NC is notably influenced by both SST anomalies and the Arctic SIC anomaly. However, the role of SST anomalies on precipitation in NWC and NC exhibits an evident contrast, leading to the out‐of‐phase pattern of summer precipitation. [ABSTRACT FROM AUTHOR]

A WRF‐based high‐resolution reanalysis of the Irish climate (1981–2010) is used to create proxy daily precipitation observations at the locations of climatological sites used for precipitation monitoring; the data are statistically representative of the real precipitation climate both for mean (over monthly, seasonal and annual periods) and extreme values. The proxy observations are spatially interpolated to the original WRF grid using a typical gridding package and compared against the original data to assess gridding errors. The errors are more complex than the estimates provided by the gridding software; systematic biases are evident which by the inclusion of strategically placed additional observing sites are shown to be greatly reduced. There is also evidence of systematic differences in trend analyses of extreme precipitation over the period. The method provides independent estimates of the errors that arise from actual gridding applications. It also facilitates the testing of the optimality of a network by highlighting possible inadequacies in an existing station layout and suggesting new observing site locations to fill gaps. Uncertainties regarding the errors in real precipitation observations, and possible spurious impacts linked to temporal changes in the real observing network, are avoided by this method. [ABSTRACT FROM AUTHOR]

Due to the less fluctuating environment in the early Holocene (10–8 ka BP) than in the late Holocene (2 ka BP to present), the relationship between EI Nin ̃o-Southern Oscillation (ENSO) and the Australian summer monsoon (AUSM) may be different from that in the late Holocene. But, the questions that how and why the relationship would change remain unknown. In this study, the transient climate simulations over the past 21,000 year (TraCE-21) are analyzed to address these questions. The model can reproduce the negative correlation relationship observed between the ENSO and AUSM. Compared with those in the early Holocene, the precipitation anomalies over the east of NA have shrunk by 5 degrees in the late Holocene. However, from the early to late Holocene, ENSO has significantly intensified by about 30%. Moreover, the maximum sea surface temperature anomalies (SSTAs) have shifted from the eastern to central tropical Pacific, thereby changing the ENSO teleconnection. The central tropical Pacific SSTAs in the late Holocene enhance the AUSM circulation, whereas the eastern tropical Pacific SSTAs in the early Holocene pose nonsignificant impacts on the AUSM. As such, the amplification and structural change of ENSO have altered the AUSM's spatial pattern and the AUSM-ENSO relationship in the Holocene. The results reveal how the AUSM may respond to ENSO in the past and shed light on understanding the future change of the ENSO-AUSM relationship and associated extreme climatic events. [ABSTRACT FROM AUTHOR]

In this research paper, the effect of the addition of copper (0.02 to 3.32 wt.%) on microstructure, mechanical and thermal properties of Al-Zn-Mg alloy are studied using an optical microscope, scanning electron microscope (SEM), X-ray diffractometer (XRD), Vicker's microhardness tester, universal testing machine (UTM) and differential scanning calorimeter (DSC). Precipitates (η-MgZn2 phases) are present in as-cast Al-Zn-Mg alloy. SEM and XRD analyses have shown the presence of η (MgZn2) and θ (Al2Cu) precipitate in the as-cast Al-Zn-Mg-Cu alloy. Micro-Vickers hardness increases from 121.3 HV1 to 153.1 HV1 for the initial increase in copper content (0.02 to 1.54 wt.%) and then decreases to 137.3 HV1 (3.32 wt.% copper). The melting point decreases continuously with increased copper content from 619.8 °C (0.02 wt.% Cu) to 608.5 °C (3.32 wt.% Cu). Peak compressive stress, absorbed energy per unit volume, yield stress is observed to increase by 43.6%, 75.4%, and 59.3%, respectively, for the rise in copper content (0.02 wt.% to 1.54 wt.%) in the alloy. [ABSTRACT FROM AUTHOR]

This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products. [ABSTRACT FROM AUTHOR]

The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling. [ABSTRACT FROM AUTHOR]