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Abstract Tropical cyclones (TCs) displace millions every year. While TCs pose hardships and threaten lives, their negative impacts can be reduced by anticipatory actions like evacuation and humanitarian aid coordination. In addition to weather forecasts, impact forecast enables more effective response by providing richer information on the numbers and locations of people at risk of displacement. We introduce a fully open-source implementation of a globally consistent and regionally calibrated TC-related displacement forecast at low computational costs, combining meteorological forecast with population exposure and respective vulnerability. We present a case study of TC Yasa which hit Fiji in December 2020. We emphasise the importance of considering the uncertainties associated with hazard, exposure, and vulnerability in a global uncertainty analysis, which reveals a considerable spread of possible outcomes. Additionally, we perform a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to understand how the forecast outcomes depend on these uncertain inputs. Our findings suggest that for longer forecast lead times, decision-making should focus more on meteorological uncertainty, while greater emphasis should be placed on the vulnerability of the local community shortly before TC landfall. Our open-source codes and implementations are readily transferable to other users, hazards, and impact types.

Hail represents a major threat to agriculture in Switzerland, and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damage is challenging. Here, we build and systematically assess an open-source model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. In contrast, for grapevine, decreasing model resolution below 1 km tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about twice as often as observed (high frequency bias and false alarm ratio), but it also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %), while overall model skill, as measured by the Heidke skill score (HSS), remains largely unchanged (0.41–0.44). We argue that, ultimately, the best threshold therefore depends on the relative costs of a false alarm versus a missed event. Finally, the frequency of false alarms is substantially reduced and skill is improved (HSS = 0.54) when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skilful and is best done at 8 km resolution for field crops and 1 km for grapevine.

Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. For the example of Pakistan, we use this flood model to compute flood depths and extents and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting.

Winter windstorms are among the most significant natural hazards in Europe linked to fatalities and substantial damage. However, projections of windstorm impact in Europe under climate change are highly uncertain. This study combines climate projections from 30 general circulation models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the climate risk assessment model CLIMADA to obtain projections of windstorm-induced damage over Europe in a changing climate. We conduct an uncertainty–sensitivity analysis and find large uncertainties in the projected changes in the damage, with climate model uncertainty being the dominant factor of uncertainty in the projections. We investigate the spatial patterns of the climate change-induced modifications in windstorm damage and find an increase in the damage in northwestern and northern central Europe and a decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 30 available climate models in an ensemble-of-opportunity approach and find evidence for an intensification of future climate windstorm damage, in which damage with return periods of 100 years under current climate conditions becomes damage with return periods of 28 years under future SSP585 climate scenarios. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of windstorms in Europe and emphasize the increasing need for risk mitigation due to extreme weather in the future.

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.

Abstract Natural hazards pose significant risks to people and assets in many regions of the world. Quantifying associated risks is crucial for many applications such as adaptation option appraisal and insurance pricing. However, traditional risk assessment approaches have focused on the impacts of single hazards, ignoring the effects of multi-hazard risks and potentially leading to underestimations or overestimations of risks. In this work, we present a framework for modelling multi-hazard risks globally in a consistent way, considering hazards, exposures, vulnerabilities, and assumptions on recovery. We illustrate the approach using river floods and tropical cyclones impacting people and physical assets on a global scale in a changing climate. To ensure physical consistency, we combine single hazard models that were driven by the same climate model realizations. Our results show that incorporating common physical drivers and recovery considerably alters the multi-hazard risk. We finally demonstrate how our framework can accommodate more than two hazards and integrate diverse assumptions about recovery processes based on a national case study. This framework is implemented in the open-source climate risk assessment platform CLIMADA and can be applied to various hazards and exposures, providing a more comprehensive approach to risk management than conventional methods.

Severe hailstorms result in substantial damage to buildings and vehicles, necessitating the quantification of associated risks. Here, we present a novel open-source hail damage model for buildings and cars based on single-polarization radar data and 250 000 geolocated hail damage reports in Switzerland from 2002 to 2021. To this end, we conduct a detailed evaluation of different radar-based hail intensity measures at 1 km resolution and find that the maximum expected severe hail size (MESHS) outperforms the other measures, despite a considerable false-alarm ratio. Asset-specific hail damage impact functions for buildings and cars are calibrated based on MESHS and incorporated into the open-source risk modelling platform CLIMADA. The model successfully estimates the correct order of magnitude for the number of damaged building in 91 %, their total cost in 77 %, the number of damaged vehicles in 74 %, and their total cost in 60 % of over 100 considered large hail events. We found considerable uncertainties in hail damage estimates, which are largely attributable to limitations of radar-based hail detection. Therefore, we explore the usage of crowdsourced hail reports and find substantially improved spatial representation of severe hail for individual events. By highlighting the potential and limitations of radar-based hail size estimates, particularly MESHS, and the utilization of an open-source risk modelling platform, this study represents a significant step towards addressing the gap in risk quantification associated with severe hail events in Switzerland.

Background: Generalized myasthenia gravis (gMG) is a chronic, unpredictable disease associated with high treatment and disease burdens, with a need for more effective and well-tolerated treatments. Objectives: To evaluate the long-term safety, tolerability, and efficacy of zilucoplan in a mild-to-severe, acetylcholine receptor autoantibody-positive (AChR+) gMG population. Design: Ongoing, multicenter, phase III open-label extension (OLE) study. Methods: Eligible patients had completed a qualifying randomized, placebo-controlled phase II or phase III zilucoplan study and received daily, self-administered subcutaneous 0.3 mg/kg zilucoplan. The primary endpoint was incidence of treatment-emergent adverse events (TEAEs). Secondary efficacy endpoints included change from baseline in Myasthenia Gravis Activities of Daily Living (MG-ADL) score. Results: In total, 200 patients enrolled. At the cut-off date (8 September 2022), median (range) exposure to zilucoplan in RAISE-XT was 1.2 (0.11–4.45) years. Mean age at OLE baseline was 53.3 years. A total of 188 (94%) patients experienced a TEAE, with the most common being MG worsening ( n = 52, 26%) and COVID-19 ( n = 49, 25%). In patients who received zilucoplan 0.3 mg/kg in the parent study, further improvements in MG-ADL score continued through to Week 24 (least squares mean change [95% confidence interval] from double-blind baseline −6.06 [−7.09, −5.03]) and were sustained through to Week 60 (−6.04 [−7.21, −4.87]). In patients who switched from placebo in the parent study, rapid improvements in MG-ADL score were observed at the first week after switching to zilucoplan; further improvements were observed at Week 24, 12 weeks after switching (−6.46 [−8.19, −4.72]), and were sustained through to Week 60 (−6.51 [−8.37, −4.65]). Consistent results were observed in other efficacy endpoints. Conclusion: Zilucoplan demonstrated a favorable long-term safety profile, good tolerability, and sustained efficacy through to Week 60 with consistent benefits in a broad AChR+ gMG population. Additional long-term data will be available in future analyses. Trial registration: ClinicalTrials.gov identifier: NCT04225871 ( https://clinicaltrials.gov/ct2/show/NCT04225871 )

Climate change will result in more intense and more frequent weather and climate events that will continue to cause fatalities, economic damages and other adverse societal impacts worldwide. To mitigate these consequences and to support better informed decisions and improved actions and responses, many National Meteorological and Hydrological Services (NMHSs) are discussing how to provide services on weather and climate impacts as part of their operational routines. The authors outline how a risk framework can support the development of these services by NMHSs. In addition to the hazard information, a risk perspective considers the propensity for a given hazard to inflict adverse consequences on society and environment, and attempts to quantify the uncertainties involved. The relevant strategic, methodological and technical steps are summarized and recommendations for the development of impact-related services are provided. Specifically, we propose that NMHSs adopt an integrated risk framework that incorporates a hazard-exposure-vulnerability model into operational services. Such a framework integrates all existing forecast and impact services, including the underlying impact models, and allows for flexible future extensions driven by the evolving collaboration with partners, stakeholders and users. Thereby, this paper attempts to unify existing work streams on impact-related services from different spatial and temporal scales (weather, climate) and disciplines (hydrology, meteorology, economics, social sciences) and to propose a harmonized approach that can create synergies within and across NMHSs to further develop and enhance risk-based services.

Abstract Thermoelectric generators are very attractive devices for waste heat energy harvesting as they transform a temperature difference into electrical power. However, commercially available generators show poor power density and limited operation temperatures. Research focuses on high‐temperature materials and innovative generator designs. Finding the optimal design for a given material system is challenging. Here, a theoretical framework is provided that allows appropriate generator design selection based on the particular material properties. For high‐temperature thermoelectric oxides, it can be clearly deduced that unileg multilayer generators have the highest potential for effective energy harvesting. Based on these considerations, prototype unileg multilayer generators from the currently best thermoelectric oxide Ca3Co4O9 are manufactured for the first time by industrially established ceramic multilayer technology. These generators exhibit a power density of 2.2 mW cm−2 at a temperature difference of 260 K, matching simulated values and confirming the suitability of the technology. Further design improvements increase the power density by a factor of 22 to facilitate practicable power output at temperature differences as low as 7 K. This work demonstrates that reasonable energy harvesting at elevated temperatures is possible with oxide materials and appropriate multilayer design.

In this article, we describe a multi-year writing intervention in a highenrollment professional pharmacy course, implemented by a multidisciplinary team of pharmacy and writing instructors. Built around one capstone writing assignment, the "drug information question" paper, the intervention was designed to specifically improve students' writing and health science reasoning skills and their overall scores in the course, since historically students scored low on this assignment. We provide a background of our pharmacy program and an overview of writing in pharmacy, describe the history of the intervention and collaboration between pharmacy and writing faculty, and explain the design and principles of the intervention, the results, and the implications of the study for STEM writing pedagogy. Over the course of four years, starting with a peer-review model, we have gradually added lectures, workshops, and optional and mandatory Writing Center sessions in an effort to improve students' learning and health science reasoning skills. Over the same period of time, student scores on their written capstone in the course improved significantly, and survey results indicated that the students viewed the peer review process and the writing program interventions favorably.

Abstract Tropical cyclone risks are expected to increase with climate change and socio-economic development and are subject to substantial uncertainties. We thus assess future global tropical cyclone risk drivers and perform a systematic uncertainty and sensitivity analysis. We combine synthetic tropical cyclones downscaled from CMIP6 global climate models for several emission scenarios with economic growth factors derived from the Shared Socioeconomic Pathways and a wide range of vulnerability functions. We highlight non-trivial effects between climate change and socio-economic development that drive future tropical cyclone risk. Furthermore, we show that the choice of climate model affects the output uncertainty most among all varied model input factors. Finally, we discover a positive correlation between climate sensitivity and tropical cyclone risk increase. We assert that quantitative estimates of uncertainty and sensitivity to model parameters greatly enhance the value of climate risk assessments, enabling more robust decision-making and offering a richer context for model improvement.

Abstract Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5 °C and 2 °C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives.

Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.