Fluid inertia controls mineral precipitation and clogging in pore to network-scale flows.

Mineral precipitation caused by fluid mixing presents complex control and predictability challenges in a variety of natural and engineering processes, including carbon mineralization, geothermal energy, and microfluidics. Precipitation dynamics, particularly under the influence of fluid flow, remain poorly understood. Combining microfluidic experiments and three-dimensional reactive transport simulations, we demonstrate that fluid inertia controls mineral precipitation and clogging at flow intersections, even in laminar flows. We observe distinct precipitation regimes as a function of Reynolds number (Re). At low Reynolds numbers (Re < 10), precipitates form a thin, dense layer along the mixing interface, which shuts precipitation off, while at high Reynolds numbers (Re > 50), strong three-dimensional flows significantly enhance precipitation over the entire intersection, resulting in rapid clogging. When injection rates from two inlets are uneven, flow symmetry-breaking leads to unexpected flow bifurcation phenomena, which result in enhanced concurrent precipitation in both downstream channels. Finally, we extend our findings to rough channel networks and demonstrate that the identified inertial effects on precipitation at the intersection scale are also present and even more dramatic at the network scale. This study sheds light on the fundamental mechanisms underlying mixing-induced mineral precipitation and provides a framework for designing and optimizing processes involving mineral precipitation.

Understanding the Asian water tower requires a redesigned precipitation observation strategy.

The Asian water tower (AWT) serves as the source of 10 major Asian river systems and supports the lives of ~2 billion people. Obtaining reliable precipitation data over the AWT is a prerequisite for understanding the water cycle within this pivotal region. Here, we quantitatively reveal that the "observed" precipitation over the AWT is considerably underestimated in view of observational evidence from three water cycle components, namely, evapotranspiration, runoff, and accumulated snow. We found that three paradoxes appear if the so-called observed precipitation is corrected, namely, actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent exceeding contemporaneous precipitation. We then explain the cause of precipitation underestimation from instrumental error caused by wind-induced gauge undercatch and the representativeness error caused by sparse-uneven gauge density and the complexity of local surface conditions. These findings require us to rethink previous results concerning the water cycle, prompting the study to discuss potential solutions.

Constraints on regional projections of mean and extreme precipitation under warming.

The projected changes in the hydrological cycle under global warming remain highly uncertain across current climate models. Here, we demonstrate that the observational past warming trend can be utilized to effectively co1nstrain future projections in mean and extreme precipitation on both global and regional scales. The physical basis for such constraints relies on the relatively constant climate sensitivity in individual models and the reasonable consistency of regional hydrological sensitivity among the models, which is dominated and regulated by the increases in atmospheric moisture. For the high-emission scenario, on the global average, the projected changes in mean precipitation are lowered from 6.9 to 5.2% and those in extreme precipitation from 24.5 to 18.1%, with the inter-model variances reduced by 31.0 and 22.7%, respectively. Moreover, the constraint can be applied to regions in middle-to-high latitudes, particularly over land. These constraints result in spatially resolved corrections that deviate substantially and inhomogeneously from the global mean corrections. This study provides regionally constrained hydrological responses over the globe, with direct implications for climate adaptation in specific areas.

Weaker land-atmosphere coupling in global storm-resolving simulation.

The debate on the sign of the soil moisture-precipitation feedback remains open. On the one hand, studies using global coarse-resolution climate models have found strong positive feedback. However, such models cannot represent convection explicitly. On the other hand, studies using km-scale regional climate models and explicit convection have reported negative feedback. Yet, the large-scale circulation is prescribed in such models. This study revisits the soil moisture-precipitation feedback using global, coupled simulations conducted for 1 y with explicit convection and compares the results to coarse-resolution simulations with parameterized convection. We find significant differences in a majority of points with feedback that is weaker and dominantly negative with explicit convection. The model with explicit convection is more often in a wet regime and prefers the triggering of convection over dry soil in the presence of soil moisture heterogeneity, in contrast to the coarse-resolution model. Further analysis indicates that the feedback not only between soil moisture and evapotranspiration but also between evapotranspiration and precipitation is weaker, in better agreement with observations. Our findings suggest that coarse-resolution models may not be well suited to study aspects of climate change over land such as changes in droughts and heatwaves.

Implicit learning of convective organization explains precipitation stochasticity.

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R2 ∼ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R2 ∼ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

Iontronic analog of synaptic plasticity: Hydrogel-based ionic diode with chemical precipitation and dissolution.

In this study, an aqueous nonlinear synaptic element showing plasticity behavior is developed, which is based on the chemical processes in an ionic diode. The device is simple, fully ionic, and easily configurable, requiring only two terminals-for input and output-similar to biological synapses. The key processes realizing the plasticity features are chemical precipitation and dissolution, which occur at forward- or reverse-biased ionic diode junctions in appropriate reservoir electrolytes. Given that the precipitate acts as a physical barrier in the circuit, the above processes change the diode conductivity, which can be interpreted as adjusting "synaptic weight" of the system. By varying the operating conditions, we first demonstrate the four types of plasticity that can be found in biological system: long-term potentiation/depression and short-term potentiation/depression. The plasticity of the proposed iontronic device has characteristics similar to those of neural synapses. To demonstrate its potential use in comparatively complex information processing, we develop a precipitation-based iontronic synapse (PIS) capable of both potentiation and depression. Finally, we show that the postsynaptic signals from the multiple excitatory or inhibitory PISs can be integrated into the total "dendritic" current, which is a function of time and input history, as in actual hippocampal neural circuits.

Plant cell-like tip-growing polymer precipitate with structurally embedded multistimuli sensing ability.

Soft systems that respond to external stimuli, such as heat, magnetic field, and light, find applications in a range of fields including soft robotics, energy harvesting, and biomedicine. However, most of the existing systems exhibit nondirectional, nastic movement as they can neither grow nor sense the direction of stimuli. In this regard, artificial systems are outperformed by organisms capable of directional growth in response to the sense of stimuli or tropic growth. Inspired by tropic growth schemes of plant cells and fungal hyphae, here we report an artificial multistimuli-responsive tropic tip-growing system based on nonsolvent-induced phase separation of polymer solution, where polymer precipitates as its solvent dissolves into surrounding nonsolvent. We provide a theoretical framework to predict the size and velocity of growing precipitates and demonstrate its capability of sensing the directions of gravity, mechanical contact, and light and adjusting its growing direction in response. Exploiting the embedded physical intelligence of sensing and responding to external stimuli, our soft material system achieves multiple tasks including printing 3D structures in a confined space, bypassing mechanical obstacles, and shielded transport of liquids within water.

Uncovering social and environmental factors that increase the burden of climate-sensitive diarrheal infections on children.

Climate-sensitive infectious diseases are an issue of growing concern due to global warming and the related increase in the incidence of extreme weather and climate events. Diarrhea, which is strongly associated with climatic factors, remains among the leading causes of child death globally, disproportionately affecting populations in low- and middle-income countries (LMICs). We use survey data for 51 LMICs between 2000 and 2019 in combination with gridded climate data to estimate the association between precipitation shocks and reported symptoms of diarrheal illness in young children. We account for differences in exposure risk by climate type and explore the modifying role of various social factors. We find that droughts are positively associated with diarrhea in the tropical savanna regions, particularly during the dry season and dry-to-wet and wet-to-dry transition seasons. In the humid subtropical regions, we find that heavy precipitation events are associated with increased risk of diarrhea during the dry season and the transition from dry-to-wet season. Our analysis of effect modifiers highlights certain social vulnerabilities that exacerbate these associations in the two climate zones and present opportunities for public health intervention. For example, we show that stool disposal practices, child feeding practices, and immunizing against the rotavirus modify the association between drought and diarrhea in the tropical savanna regions. In the humid subtropical regions, household's source of water and water disinfection practices modify the association between heavy precipitation and diarrhea. The evidence of effect modification varies depending on the type and duration of the precipitation shock.

Warming may offset impact of precipitation changes on riverine nitrogen loading.

Climate change, especially in the form of precipitation and temperature changes, can alter the transformation and delivery of nitrogen on the land surface and to aquatic systems, impacting the trophic states of downstream water bodies. While the expected impacts of changes in precipitation have been explored, a quantitative understanding of the impact of temperature on nitrogen loading is lacking at landscape scales. Here, using several decades of nitrogen loading observations, we quantify how individual and combined future changes in precipitation and temperature will affect riverine nitrogen loading. We find that, contrary to recent decades, rising temperatures are likely to offset or even reverse previously reported impacts of future increases in total and extreme precipitation on nitrogen runoff across the majority of the contiguous United States. These findings highlight the multifaceted impacts of climate change on the global nitrogen cycle.

Atmospheric heat transport is governed by meridional gradients in surface evaporation in modern-day earth-like climates.

Evaporation adds moisture to the atmosphere, while condensation removes it. Condensation also adds thermal energy to the atmosphere, which must be removed from the atmosphere by radiative cooling. As a result of these two processes, there is a net flow of energy driven by surface evaporation adding energy and radiative cooling removing energy from the atmosphere. Here, we calculate the implied heat transport of this process to find the atmospheric heat transport in balance with the surface evaporation. In modern-day Earth-like climates, evaporation varies strongly between the equator and the poles, while the net radiative cooling in the atmosphere is nearly meridionally uniform, and as a consequence, the heat transport governed by evaporation is similar to the total poleward heat transport of the atmosphere. This analysis is free from cancellations between moist and dry static energy transports, which greatly simplifies the interpretation of atmospheric heat transport and its relationship to the diabatic heating and cooling that governs the atmospheric heat transport. We further demonstrate, using a hierarchy of models, that much of the response of atmospheric heat transport to perturbations, including increasing CO2 concentrations, can be understood from the distribution of evaporation changes. These findings suggest that meridional gradients in surface evaporation govern atmospheric heat transport and its changes.

Climate change and the nonlinear impact of precipitation anomalies on income inequality.

Climate anomalies, such as floods and droughts, as well as gradual temperature changes have been shown to adversely affect economies and societies. Although studies find that climate change might increase global inequality by widening disparities across countries, its effects on within-country income distribution have been little investigated, as has the role of rainfall anomalies. Here, we show that extreme levels of precipitation exacerbate within-country income inequality. The strength and direction of the effect depends on the agricultural intensity of an economy. In high-agricultural-intensity countries, climate anomalies that negatively impact the agricultural sector lower incomes at the bottom end of the distribution and generate greater income inequality. Our results indicate that a 1.5-SD increase in precipitation from average values has a 35-times-stronger impact on the bottom income shares for countries with high employment in agriculture compared to countries with low employment in the agricultural sector. Projections with modeled future precipitation and temperature reveal highly heterogeneous patterns on a global scale, with income inequality worsening in high-agricultural-intensity economies, particularly in Africa. Our findings suggest that rainfall anomalies and the degree of dependence on agriculture are crucial factors in assessing the negative impacts of climate change on the bottom of the income distribution.

Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau.

Glaciers are key components of the mountain water towers of Asia and are vital for downstream domestic, agricultural, and industrial uses. The glacier mass loss rate over the southeastern Tibetan Plateau is among the highest in Asia and has accelerated in recent decades. This acceleration has been attributed to increased warming, but the mechanisms behind these glaciers' high sensitivity to warming remain unclear, while the influence of changes in precipitation over the past decades is poorly quantified. Here, we reconstruct glacier mass changes and catchment runoff since 1975 at a benchmark glacier, Parlung No. 4, to shed light on the drivers of recent mass losses for the monsoonal, spring-accumulation glaciers of the Tibetan Plateau. Our modeling demonstrates how a temperature increase (mean of 0.39 ∘C ⋅dec-1 since 1990) has accelerated mass loss rates by altering both the ablation and accumulation regimes in a complex manner. The majority of the post-2000 mass loss occurred during the monsoon months, caused by simultaneous decreases in the solid precipitation ratio (from 0.70 to 0.56) and precipitation amount (-10%), leading to reduced monsoon accumulation (-26%). Higher solid precipitation in spring (+18%) during the last two decades was increasingly important in mitigating glacier mass loss by providing mass to the glacier and protecting it from melting in the early monsoon. With bare ice exposed to warmer temperatures for longer periods, icemelt and catchment discharge have unsustainably intensified since the start of the 21st century, raising concerns for long-term water supply and hazard occurrence in the region.

Long-range dependence and extreme values of precipitation, phosphorus load, and Cyanobacteria.

Extreme daily values of precipitation (1939-2021), discharge (1991-2021), phosphorus (P) load (1994-2021), and phycocyanin, a pigment of Cyanobacteria (June 1-September 15 of 2008-2021) are clustered as multi-day events for Lake Mendota, Wisconsin. Long-range dependence, or memory, is the shortest for precipitation and the longest for phycocyanin. Extremes are clustered for all variates and those of P load and phycocyanin are most strongly clustered. Extremes of P load are predictable from extremes of precipitation, and precipitation and P load are correlated with later concentrations of phycocyanin. However, time delays from 1 to 60 d were found between P load extremes and the next extreme phycocyanin event within the same year of observation. Although most of the lake's P enters in extreme events, blooms of Cyanobacteria may be sustained by recycling and food web processes.

Notable impact of wildfires in the western United States on weather hazards in the central United States.

Increased wildfire events constitute a significant threat to life and property in the United States. Wildfire impact on severe storms and weather hazards is another pathway that threatens society, and our understanding of which is very limited. Here, we use unique modeling developments to explore the effects of wildfires in the western US (mainly California and Oregon) on precipitation and hail in the central US. We find that the western US wildfires notably increase the occurrences of heavy precipitation rates by 38% and significant severe hail (≥2 in.) by 34% in the central United States. Both heat and aerosols from wildfires play an important role. By enhancing surface high pressure and increasing westerly and southwesterly winds, wildfires in the western United States produce (1) stronger moisture and aerosol transport to the central United States and (2) larger wind shear and storm-relative helicity in the central United States. Both the meteorological environment more conducive to severe convective storms and increased aerosols contribute to the enhancements of heavy precipitation rates and large hail. Moreover, the local wildfires in the central US also enhance the severity of storms, but their impact is notably smaller than the impact of remote wildfires in California and Oregon because of the lessened severity of the local wildfires. As wildfires are projected to be more frequent and severe in a warmer climate, the influence of wildfires on severe weather in downwind regions may become increasingly important.

Rapid formation of uniformly layered materials by coupling reaction-diffusion processes with mechanical responsiveness.

Straightforward manufacturing pathways toward large-scale, uniformly layered composites may enable the next generation of materials with advanced optical, thermal, and mechanical properties. Reaction-diffusion systems are attractive candidates to this aim, but while layered composites theoretically could spontaneously arise from reaction-diffusion, in practice randomly oriented patches separated by defects form, yielding nonuniformly patterned materials. A propagating reaction front can prevent such nonuniform patterning, as is the case for Liesegang processes, in which diffusion drives a reaction front to produce layered precipitation patterns. However, while diffusion is crucial to control patterning, it slows down transport of reactants to the front and results in a steady increase of the band spacing as the front advances. Here, we circumvent these diffusive limitations by embedding the Liesegang process in mechanically responsive hydrogels. The coupling between a moving reaction front and hydrogel contraction induces the formation of a self-regulated transport channel that ballistically carries reactants toward the area where patterning occurs. This ensures rapid and uniform patterning. Specifically, large-scale ([Formula: see text]5-cm) uniform banding patterns are produced with tunable band distance (d = 60 to 160 µm) of silver dichromate crystals inside responsive gelatin-alginate hydrogels. The generality and applicability of our mechanoreaction-diffusion strategy are demonstrated by forming patterns of precipitates in significantly smaller microscopic banding patterns (d = 10 to 30 µm) that act as self-organized diffraction gratings. By circumventing the inherent limitations of diffusion, our strategy unlocks the potential of reaction-diffusion processes for the manufacturing of uniformly layered materials.

Assessing the precision of a detergent-assisted cartridge precipitation workflow for non-targeted quantitative proteomics.

Detergent-based workflows incorporating sodium dodecyl sulfate (SDS) necessitate additional steps for detergent removal ahead of mass spectrometry (MS). These steps may lead to variable protein recovery, inconsistent enzyme digestion efficiency, and unreliable MS signals. To validate a detergent-based workflow for quantitative proteomics, we herein evaluate the precision of a bottom-up sample preparation strategy incorporating cartridge-based protein precipitation with organic solvent to deplete SDS. The variance of data-independent acquisition (SWATH-MS) data was isolated from sample preparation error by modelling the variance as a function of peptide signal intensity. Our SDS-assisted cartridge workflow yield a coefficient of variance (CV) of 13%-14%. By comparison, conventional (detergent-free) in-solution digestion increased the CV to 50%; in-gel digestion provided lower CVs between 14% and 20%. By filtering peptides predicting to display lower precision, we further enhance the validity of data in global comparative proteomics. These results demonstrate the detergent-based precipitation workflow is a reliable approach for in depth, label-free quantitative proteome analysis.

Differences in Protein Capture by SP3 and SP4 Demonstrate Mechanistic Insights of Proteomics Cleanup Techniques.

The goal of proteomics experiments is to identify proteins to observe changes in cellular processes and diseases. One challenge in proteomics is the removal of contaminants following protein extraction, which can limit protein identifications. Single-pot, solid-phase-enhanced sample preparation (SP3) is a cleanup technique in which proteins are captured on carboxylate-modified particles through a proposed hydrophilic-interaction-liquid-chromatography (HILIC)-like mechanism. Recent results have suggested that proteins are captured in SP3 due to a protein-aggregation mechanism. Solvent precipitation, single-pot, solid-phase-enhanced sample preparation (SP4) is a newer cleanup technique that employs protein aggregation to capture proteins without modified particles. We hypothesize that differences in capture mechanisms of SP3 and SP4 affect which proteins are identified by each cleanup technique. Herein, we assess the proteins identified and enriched using SP3 versus SP4 for MCF7 subcellular fractions and correlate protein capture in each method to protein hydrophobicity. Our results indicate that SP3 captures more hydrophilic proteins through a combination of HILIC-like and protein-aggregation mechanisms, while SP4 captures more hydrophobic proteins through a protein-aggregation mechanism. Ultimately, we demonstrate that protein-capture mechanisms are distinct, and the selection of a cleanup technique that yields high proteome coverage is dependent on protein-sample hydrophobicity. Data has been deposited into MassIVE (MSV000094130) and ProteomeXchange (PXD049965).

Association of weather variables with pathogens contributing to conjunctivitis worldwide.

PURPOSE: To identify weather variables associated with pathogens contributing to infectious conjunctivitis globally. METHODS: Sample collection and pathogen identification from patients with acute infectious conjunctivitis was performed from 2017 to 2023. We linked pathogens identified from 13 sites across 8 countries with publicly available weather data by geographic coordinates. Mixed effects logistic regression analysis was performed to estimate the associations between temperature, precipitation, and relative humidity exposures, and the prevalence of infection types (RNA virus, DNA virus, bacteria, and fungus). RESULTS: 498 cases from the United States, India, Nepal, Thailand, Burkina Faso, Niger, Vietnam, and Israel were included in the analysis. 8-day average precipitation (mm) was associated with increased odds of RNA virus infection (odds ratio (OR)=1.47, 95% confidence interval (CI): 1.12 to 1.93, P=0.01) and decreased odds of DNA infection (OR=0.62, 95% CI: 0.46 to 0.82, P<0.001). Relative humidity (%) was associated with increased odds of RNA virus infections (OR=2.64, 95% CI: 1.51 to 4.61, P<0.001), and fungal infections (OR=2.35, 95% CI: 1.19 to 4.66, P=0.01), but decreased odds of DNA virus (OR=0.58, 95%CI: 0.37 to 0.90, P=0.02) and bacterial infections (OR=0.42, 95% CI: 0.25 to 0.71, P<0.001). Temperature (°C) was not associated with ocular infections for any pathogen type. CONCLUSIONS: This study suggests that weather factors affect pathogens differently. Particularly, humidity and precipitation were predictors for pathogens contributing to conjunctivitis worldwide. Additional work is needed to clarify the effects of shifts in weather and environmental factors on ocular infectious diseases.

Climate dependence of the macrofaunal effect on litter decomposition-A global meta-regression analysis.

Litter decomposition by microorganisms and animals is influenced by climate and has been found to be higher in warm and wet than in cold and dry biomes. We, however, hypothesized that the macrofaunal effect on decomposition should increase with temperature and aridity since larger animals are more tolerant to aridity than smaller organisms. This hypothesis was supported by our global analysis of macrofauna exclusion studies. Macrofauna increased litter mass loss on average by 40%, twofold higher than the highest previous estimation of macrofaunal effect on decomposition. The strongest effect was found in subtropical deserts where faunal decomposition had not been considered important. Our results highlight the need to consider animal size when exploring climate dependence of faunal decomposition, and the disproportionately large role of macrofauna in regulating litter decomposition in warm drylands. This new realization is critical for understanding element cycling in the face of global warming and aridification.

Crack-Deflecting Lattice Metamaterials Inspired by Precipitation Hardening.

Lattice structures, comprising nodes and struts arranged in an array, are renowned for their lightweight and unique mechanical deformation characteristics. Previous studies on lattice structures have revealed that failure often originates from stress concentration points and spreads throughout the material. This results in collapse failure, similar to the accumulation of damage at defects in metallic crystals. Here the precipitation hardening mechanism found in crystalline materials is employed to deflect the initial failure path, through the strategic placement of strengthening units at stress concentration points using the finite element method. Both the mesostructure, inspired by the arrangement of crystals, and the inherent microstructure of the base materials have played crucial roles in shaping the mechanical properties of the macro-lattices. As a result, a groundbreaking multiscale hierarchical design methodology, offering a spectrum of design concepts for engineering materials with desired properties is introduced.