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Aerosols influence fog properties such as visibility and lifetime by affecting fog droplet number concentrations (Nd). Numerical weather prediction (NWP) models often represent aerosol–fog interactions using highly simplified approaches. Incorporating prognostic size-resolved aerosol microphysics from climate models could allow them to simulate Nd and aerosol–fog interactions without incurring excessive computational expense. However, microphysics code designed for coarse spatial resolution may struggle with sub-kilometer-scale grid spacings. Here, we test the ability of the UK Met Office Unified Model to simulate aerosol and fog properties during case studies from the ParisFog field campaign in 2011. We examine the sensitivity of fog properties to variations in Nd caused by modifications to simulated aerosol activation. Our model, with a 500 m horizontal resolution and interactive aerosol and cloud microphysics, significantly underpredicts Nd, although it only slightly underestimates the cloud condensation nuclei concentration. With an updated version of the Abdul-Razzak and Ghan (2000) activation scheme, we produce Nd that are more consistent with those predicted by a cloud parcel model under fog-like conditions. We activate droplets only by adiabatic cooling. We incorporate more realistic hygroscopicities for sulfate and organic aerosols and explore the sensitivity of simulated Nd to unresolved updrafts. We find that both Nd and simulated fog liquid water content are very sensitive to the updated activation scheme but remain less affected by the update to hygroscopicities. Our improvements offer insights into the physical processes regulating Nd in stable conditions, potentially laying foundations for improved operational fog forecasts that incorporate interactive aerosol simulations or aerosol climatologies.

Aerosol–fog interactions affect the visibility in, and life cycle of, fog and are difficult to represent in weather and climate models. Here we explore processes that impact the simulation of fog droplet number concentrations (Nd) at sub-kilometer scale horizontal grid resolutions in the UK Met Office Unified Model. We modify the parameterization of aerosol activation to include droplet activation by radiative cooling in addition to adiabatic cooling and determine the relative importance of the two cooling mechanisms. We further test the sensitivity of simulated Nd to: (a) interception of droplets by trees and buildings, (b) overestimation of updrafts in temperature inversions (which leads to artificially high Nd values), and (c) potential mechanisms for droplet deactivation due to downward fluctuations in supersaturation. We evaluate our model against observations from the ParisFog and LANFEX field campaigns, building on evaluation described in the companion paper. Including radiative cooling in the activation mechanism improves how accurately we represent the liquid water path and the vertical structure of the fog in our LANFEX case study. However, with radiative cooling, the Nd are overestimated for most of the ParisFog cases and for the LANFEX case. The time-averaged overestimate exceeds a factor of three (the normalized mean bias factor exceeds 2.0) in 4 out of 11 ParisFog cases. Our sensitivity studies demonstrate how these overestimates can be mitigated. Assuming the overestimate affects both radiative and adiabatic cooling, we find that although radiative cooling is more often the dominant source, both cooling sources can sometimes dominate activation.

We employ a Lagrangian based moisture back trajectory method on an ensemble of four reanalysis datasets to provide a comprehensive understanding of moisture sources over the Mediterranean land region (30° N–49.5° N and 9.75° W–61.5° E) at seasonal timescales for 1980–2013 period. Using a source region between 10° S–71.35° N along the latitude and 80° W–84.88° E along the longitude that is subdivided into ten complimentary sub-regions, our analyses is able to backtrack up to > 90% of seasonal precipitation at each grid point within the target region. Our results indicate a significant role of moisture advected from the North Atlantic and Mediterranean Sea, and locally recycled moisture over the target region in shaping the spatial organization of seasonal precipitation. However, a clear east–west contrast is witnessed in determining the relative importance of each of these major moisture sources where the North Atlantic dictates the moisture supply over the western Mediterranean while moisture from Mediterranean Sea and local recycling play a key role over the eastern Mediterranean. Our analyses also demonstrate a major footprint of the North Atlantic Oscillation (NAO) on precipitation variability over the Mediterranean land as dynamic and thermodynamic anomalies during the negative phase of NAO match with those during wet years and vice versa. The findings reported here are generally consistent across the four reanalysis datasets. Overall, this study establishes the relative roles of adjacent and far-off oceanic and terrestrial evaporative sources over the Mediterranean land and should help in understanding the drivers of precipitation variability and change at varying timescales.

Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10–15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.

We quantify historical and projected trends in the population exposure to climate extremes as measured by the United States National Center for Environmental Information Climate Extremes Index (CEI). Based on the analyses of the historical observations, we find that the U.S. has already experienced a rise in the occurrence of aggregated extremes in recent decades, consistent with the climate response to historical increases in radiative forcing. Additionally, we find that exposure can be expected to intensify under the Representative Concentration Pathway 8.5, with all counties permanently exceeding the baseline variability in the occurrence of extreme hot days, warm nights, and drought conditions by 2050. As a result, every county in the U.S. is projected to permanently exceed the historical CEI variability (as measured by one standard deviation during the 1981–2005 period). Based on the current population distribution, this unprecedented change implies a yearly exposure to extreme conditions for one in every three people. We find that the increasing trend in exposure to the aggregated extremes is already detectable over much of the U.S., and particularly in the central and eastern U.S. The high correspondence between the pattern of trends in our simulations and observations increases confidence in the projected amplification of population exposure to unprecedented combinations of extreme climate conditions, should greenhouse gas concentrations continue to escalate along their current trajectory. Key Points Many U.S. counties have experienced a rise in extremes in recent decades, consistent with the climate response to historical radiative forcing Every county in the U.S. is projected to permanently exceed the historical CEI variability The increasing trend in exposure to the aggregated climate extremes is already detectable over much of the U.S.

Understanding the physical and chemical response of materials to impulsive deformation is crucial for applications ranging from soft robotic locomotion to space exploration to seismology. However, investigating material properties at extreme strain rates remains challenging due to temporal and spatial resolution limitations. Combining high-strain-rate testing with mechanochemistry encodes the molecular-level deformation within the material itself, thus enabling the direct quantification of the material response. Here, we demonstrate a mechanophore-functionalized block copolymer that self-reports energy dissipation mechanisms, such as bond rupture and acoustic wave dissipation, in response to high-strain-rate impacts. A microprojectile accelerated towards the polymer permanently deforms the material at a shallow depth. At intersonic velocities, the polymer reports significant subsurface energy absorption due to shockwave attenuation, a mechanism traditionally considered negligible compared to plasticity and not well explored in polymers. The acoustic wave velocity of the material is directly recovered from the mechanochemically-activated subsurface volume recorded in the material, which is validated by simulations, theory, and acoustic measurements. This integration of mechanochemistry with microballistic testing enables characterization of high-strain-rate mechanical properties and elucidates important insights applicable to nanomaterials, particle-reinforced composites, and biocompatible polymers. Understanding how materials respond to impacts at extreme strain rates is crucial, yet current approaches present significant challenges. Here, the authors report the use of a mechanophore-functionalized block copolymer to encode and report energy dissipation mechanisms in response to impacts.

Nanomaterials must be systematically designed to be technologically viable 1 – 5 . Driven by optimizing intermolecular interactions, current designs are too rigid to plug in new chemical functionalities and cannot mitigate condition differences during integration 6 , 7 . Despite extensive optimization of building blocks and treatments, accessing nanostructures with the required feature sizes and chemistries is difficult. Programming their growth across the nano-to-macro hierarchy also remains challenging, if not impossible 8 – 13 . To address these limitations, we should shift to entropy-driven assemblies to gain design flexibility, as seen in high-entropy alloys, and program nanomaterial growth to kinetically match target feature sizes to the mobility of the system during processing 14 – 17 . Here, following a micro-then-nano growth sequence in ternary composite blends composed of block-copolymer-based supramolecules, small molecules and nanoparticles, we successfully fabricate high-performance barrier materials composed of more than 200 stacked nanosheets (125 nm sheet thickness) with a defect density less than 0.056 µm −2 and about 98% efficiency in controlling the defect type. Contrary to common perception, polymer-chain entanglements are advantageous to realize long-range order, accelerate the fabrication process (<30 min) and satisfy specific requirements to advance multilayered film technology 3 , 4 , 18 . This study showcases the feasibility, necessity and unlimited opportunities to transform laboratory nanoscience into nanotechnology through systems engineering of self-assembly. Following a micro-then-nano growth sequence to fabricate composites that are blends of block-copolymer-based supramolecules, small molecules and nanoparticles shows that high-performance barrier materials can be manufactured by means of entropy-driven assembly.

Co-occurrence of high relative humidity levels and high temperatures can increase human discomfort, thereby affecting electricity requirements for space cooling. While relative humidity is generally projected to decrease over land in a warming climate, the combined impact of warming and changes in humidity on heat stress, and thus electricity demand, are less clear. To evaluate the role of relative humidity in determining future electricity demand, we first develop predictive models based, separately, on temperature (T) and a heat stress index (apparent temperature (AT)) at an hourly scale using meteorological reanalysis data and electricity load from the United States Energy Information Administration over the four electricity regions in the southeastern United States. The AT model performs better than the T model in the historical period. We then apply the predictive models to a set of high-resolution climate projections to understand the role of relative humidity in determining the electricity demand in a warmer climate. Due to the nonlinear behavior of heat stress with warming, future electricity demand is substantially larger when estimated from AT than from T. The increase in demand projected by AT ranges between 16%–29%, 20%–33%, 14%–32% and 13%–26% and that by T model ranges between 12%–19%, 15%–19%, 14%–22% and 12%–20% over Southeast, Florida, Carolina, and Tennessee respectively. This amplification of electricity demand by humidity is strongest for the highest temperature quantiles, but also occurs at moderate future temperatures that coincide with elevated relative humidity episodes, emphasizing the importance of considering humidity in future heat stress and electricity demand assessments.

The ethics of autonomous vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. Starting from the assumption that human harm is unavoidable, many authors have developed differing accounts of what morality requires in these situations. In this article, a strategy for AV decision-making is proposed, the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation: different road users hold different moral claims on the vehicle’s behavior, and the vehicle must mitigate these claims as it makes decisions about its environment. Using the context of autonomous vehicles, the harm produced by an action and the uncertainties connected to it are quantified and accounted for through deliberation, resulting in an ethical implementation coherent with reality. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of ‘moral positions’ concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an autonomous vehicle’s ethical decision making.

Force Sensitive Resistors (FSRs) are commercially available thin film polymer sensors commonly employed in a multitude of biomechanical measurement environments. Reasons for such wide spread usage lie in the versatility, small profile, and low cost of these sensors. Yet FSRs have limitations. It is commonly accepted that temperature, curvature and biological tissue compliance may impact sensor conductance and resulting force readings. The effect of these variables and degree to which they interact has yet to be comprehensively investigated and quantified. This work systematically assesses varying levels of temperature, sensor curvature and surface compliance using a full factorial design-of-experiments approach. Three models of Interlink FSRs were evaluated. Calibration equations under 12 unique combinations of temperature, curvature and compliance were determined for each sensor. Root mean squared error, mean absolute error, and maximum error were quantified as measures of the impact these thermo/mechanical factors have on sensor performance. It was found that all three variables have the potential to affect FSR calibration curves. The FSR model and corresponding sensor geometry are sensitive to these three mechanical factors at varying levels. Experimental results suggest that reducing sensor error requires calibration of each sensor in an environment as close to its intended use as possible and if multiple FSRs are used in a system, they must be calibrated independently.