The growing inadequacy of an open-ended Saffir-Simpson hurricane wind scale in a warming world.

Global warming increases available sensible and latent heat energy, increasing the thermodynamic potential wind intensity of tropical cyclones (TCs). Supported by theory, observations, and modeling, this causes a shift in mean TC intensity, which tends to manifest most clearly at the greatest intensities. The Saffir-Simpson scale for categorizing damage based on the wind intensity of TCs was introduced in the early 1970s and remains the most commonly used metric for public communication of the level of wind hazard that a TC poses. Because the scale is open-ended and does not extend beyond category 5 (70 m/s windspeed or greater), the level of wind hazard conveyed by the scale remains constant regardless of how far the intensity extends beyond 70 m/s. This may be considered a weakness of the scale, particularly considering that the destructive potential of the wind increases exponentially. Here, we consider how this weakness becomes amplified in a warming world by elucidating the past and future increases of peak wind speeds in the most intense TCs. A simple extrapolation of the Saffir-Simpson scale is used to define a hypothetical category 6, and we describe the frequency of TCs, both past and projected under global warming, that would fall under this category. We find that a number of recent storms have already achieved this hypothetical category 6 intensity and based on multiple independent lines of evidence examining the highest simulated and potential peak wind speeds, more such storms are projected as the climate continues to warm.

Anthropogenic aerosols mask increases in US rainfall by greenhouse gases.

A comprehensive understanding of human-induced changes to rainfall is essential for water resource management and infrastructure design. However, at regional scales, existing detection and attribution studies are rarely able to conclusively identify human influence on precipitation. Here we show that anthropogenic aerosol and greenhouse gas (GHG) emissions are the primary drivers of precipitation change over the United States. GHG emissions increase mean and extreme precipitation from rain gauge measurements across all seasons, while the decadal-scale effect of global aerosol emissions decreases precipitation. Local aerosol emissions further offset GHG increases in the winter and spring but enhance rainfall during the summer and fall. Our results show that the conflicting literature on historical precipitation trends can be explained by offsetting aerosol and greenhouse gas signals. At the scale of the United States, individual climate models reproduce observed changes but cannot confidently determine whether a given anthropogenic agent has increased or decreased rainfall.

Role of atmospheric resonance and land-atmosphere feedbacks as a precursor to the June 2021 Pacific Northwest Heat Dome event.

We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest "Heat Dome" event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event.

Increased U.S. coastal hurricane risk under climate change.

Several pathways for how climate change may influence the U.S. coastal hurricane risk have been proposed, but the physical mechanisms and possible connections between various pathways remain unclear. Here, future projections of hurricane activity (1980-2100), downscaled from multiple climate models using a synthetic hurricane model, show an enhanced hurricane frequency for the Gulf and lower East coast regions. The increase in coastal hurricane frequency is driven primarily by changes in steering flow, which can be attributed to the development of an upper-level cyclonic circulation over the western Atlantic. The latter is part of the baroclinic stationary Rossby waves forced mainly by increased diabatic heating in the eastern tropical Pacific, a robust signal across the multimodel ensemble. Last, these heating changes also play a key role in decreasing wind shear near the U.S. coast, further aggravating coastal hurricane risk enhanced by the physically connected steering flow changes.

Bayesian weighting of climate models based on climate sensitivity.

Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this 'hot model' problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth's climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 oC for a low emissions scenario (SSP1-2.6) and 5 oC for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.

Identifying Facilitators, Barriers, and Potential Solutions of Adopting Exoskeletons and Exosuits in Construction Workplaces.

Exoskeletons and exosuits (collectively termed EXOs) have the potential to reduce the risk of work-related musculoskeletal disorders (WMSDs) by protecting workers from exertion and muscle fatigue due to physically demanding, repetitive, and prolonged work in construction workplaces. However, the use of EXOs in construction is in its infancy, and much of the knowledge required to drive the acceptance, adoption, and application of this technology is still lacking. The objective of this research is to identify the facilitators, barriers, and corresponding solutions to foster the adoption of EXOs in construction workplaces through a sequential, multistage Delphi approach. Eighteen experts from academia, industry, and government gathered in a workshop to provide insights and exchange opinions regarding facilitators, barriers, and potential solutions from a holistic perspective with respect to business, technology, organization, policy/regulation, ergonomics/safety, and end users (construction-trade professionals). Consensus was reached regarding all these perspectives, including top barriers and potential solution strategies. The outcomes of this study will help the community gain a comprehensive understanding of the potential for EXO use in the construction industry, which may enable the development of a viable roadmap for the evolution of EXO technology and the future of EXO-enabled workers and work in construction workplaces.

Social inequalities in climate change-attributed impacts of Hurricane Harvey.

Climate change is already increasing the severity of extreme weather events such as with rainfall during hurricanes. But little research to date investigates if, and to what extent, there are social inequalities in climate change-attributed extreme weather event impacts. Here, we use climate change attribution science paired with hydrological flood models to estimate climate change-attributed flood depths and damages during Hurricane Harvey in Harris County, Texas. Using detailed land-parcel and census tract socio-economic data, we then describe the socio-spatial characteristics associated with these climate change-induced impacts. We show that 30 to 50% of the flooded properties would not have flooded without climate change. Climate change-attributed impacts were particularly felt in Latina/x/o neighborhoods, and especially so in Latina/x/o neighborhoods that were low-income and among those located outside of FEMA's 100-year floodplain. Our focus is thus on climate justice challenges that not only concern future climate change-induced risks, but are already affecting vulnerable populations disproportionately now.

Attribution of 2020 hurricane season extreme rainfall to human-induced climate change.

The 2020 North Atlantic hurricane season was one of the most active on record, causing heavy rains, strong storm surges, and high winds. Human activities continue to increase the amount of greenhouse gases in the atmosphere, resulting in an increase of more than 1 °C in the global average surface temperature in 2020 compared to 1850. This increase in temperature led to increases in sea surface temperature in the North Atlantic basin of 0.4-0.9 °C during the 2020 hurricane season. Here we show that human-induced climate change increased the extreme 3-hourly storm rainfall rates and extreme 3-day accumulated rainfall amounts during the full 2020 hurricane season for observed storms that are at least tropical storm strength (>18 m/s) by 10 and 5%, respectively. When focusing on hurricane strength storms (>33 m/s), extreme 3-hourly rainfall rates and extreme 3-day accumulated rainfall amounts increase by 11 and 8%, respectively.

Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations.

We examine the resolution dependence of errors in extreme sub-daily precipitation in available high-resolution climate models. We find that simulated extreme precipitation increases as horizontal resolution increases but that appropriately constructed model skill metrics do not significantly change. We find little evidence that simulated extreme winter or summer storm processes significantly improve with the resolution because the model performance changes identified are consistent with expectations from scale dependence arguments alone. We also discuss the implications of these scale-dependent limitations on the interpretation of simulated extreme precipitation. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius-Clapeyron (CC) rate (6-7% K-1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Anthropogenic influences on major tropical cyclone events.

There is no consensus on whether climate change has yet affected the statistics of tropical cyclones, owing to their large natural variability and the limited period of consistent observations. In addition, projections of future tropical cyclone activity are uncertain, because they often rely on coarse-resolution climate models that parameterize convection and hence have difficulty in directly representing tropical cyclones. Here we used convection-permitting regional climate model simulations to investigate whether and how recent destructive tropical cyclones would change if these events had occurred in pre-industrial and in future climates. We found that, relative to pre-industrial conditions, climate change so far has enhanced the average and extreme rainfall of hurricanes Katrina, Irma and Maria, but did not change tropical cyclone wind-speed intensity. In addition, future anthropogenic warming would robustly increase the wind speed and rainfall of 11 of 13 intense tropical cyclones (of 15 events sampled globally). Additional regional climate model simulations suggest that convective parameterization introduces minimal uncertainty into the sign of projected changes in tropical cyclone intensity and rainfall, which allows us to have confidence in projections from global models with parameterized convection and resolution fine enough to include tropical cyclones.

Increasing the Dimensionality of Soft Microstructures through Injection-Induced Self-Folding.

Devices fabricated using soft materials have been a major research focus of late, capturing the attention of scientists and laypersons alike in a wide range of fields, from microfluidics to robotics. The functionality of such devices relies on their structural and material properties; thus, the fabrication method is of utmost importance. Here, multilayer soft lithography, precision laser micromachining, and folding to establish a new paradigm are combined for creating 3D soft microstructures and devices. Phase-changing materials are exploited to transform actuators into structural elements, allowing 2D laminates to evolve into a third spatial dimension. To illustrate the capabilities of this new fabrication paradigm, the first "microfluidic origami for reconfigurable pneumatic/hydraulic" device is designed and manufactured: a 12-layer soft robotic peacock spider with embedded microfluidic circuitry and actuatable features.

Soft Somatosensitive Actuators via Embedded 3D Printing.

Humans possess manual dexterity, motor skills, and other physical abilities that rely on feedback provided by the somatosensory system. Herein, a method is reported for creating soft somatosensitive actuators (SSAs) via embedded 3D printing, which are innervated with multiple conductive features that simultaneously enable haptic, proprioceptive, and thermoceptive sensing. This novel manufacturing approach enables the seamless integration of multiple ionically conductive and fluidic features within elastomeric matrices to produce SSAs with the desired bioinspired sensing and actuation capabilities. Each printed sensor is composed of an ionically conductive gel that exhibits both long-term stability and hysteresis-free performance. As an exemplar, multiple SSAs are combined into a soft robotic gripper that provides proprioceptive and haptic feedback via embedded curvature, inflation, and contact sensors, including deep and fine touch contact sensors. The multimaterial manufacturing platform enables complex sensing motifs to be easily integrated into soft actuating systems, which is a necessary step toward closed-loop feedback control of soft robots, machines, and haptic devices.

An integrated design and fabrication strategy for entirely soft, autonomous robots.

Soft robots possess many attributes that are difficult, if not impossible, to achieve with conventional robots composed of rigid materials. Yet, despite recent advances, soft robots must still be tethered to hard robotic control systems and power sources. New strategies for creating completely soft robots, including soft analogues of these crucial components, are needed to realize their full potential. Here we report the untethered operation of a robot composed solely of soft materials. The robot is controlled with microfluidic logic that autonomously regulates fluid flow and, hence, catalytic decomposition of an on-board monopropellant fuel supply. Gas generated from the fuel decomposition inflates fluidic networks downstream of the reaction sites, resulting in actuation. The body and microfluidic logic of the robot are fabricated using moulding and soft lithography, respectively, and the pneumatic actuator networks, on-board fuel reservoirs and catalytic reaction chambers needed for movement are patterned within the body via a multi-material, embedded 3D printing technique. The fluidic and elastomeric architectures required for function span several orders of magnitude from the microscale to the macroscale. Our integrated design and rapid fabrication approach enables the programmable assembly of multiple materials within this architecture, laying the foundation for completely soft, autonomous robots.

Comparing regional precipitation and temperature extremes in climate model and reanalysis products.

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

Identifying human influences on atmospheric temperature.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.