Simona Meiler

Tropical cyclones cause widespread damage to housing and infrastructure, and recovery is often slow and uneven. Climate risk assessments quantify the likelihood and direct impacts of extreme events, but post-disaster recovery remains largely absent from these models, limiting our ability to assess long-term resilience. We therefore integrate simplified recovery modeling into probabilistic tropical cyclone risk assessment for all counties along the US Atlantic and Gulf coasts. We quantify county-level repair demand for residential buildings using a large synthetic set of tropical cyclone events and compare it with baseline local construction capacity, proxied by residential building permits. The resulting recovery burden expresses the modeled repair workload in months of baseline construction activity. The relative importance of repair demand and construction capacity depends on how recovery burden is summarized. Expected annual recovery burden is dominated by rare, high-impact events and closely tracks the expected annual repair demand. For the median damaging event in each county, construction capacity becomes more important, and counties with comparable repair demands can face substantially different recovery burdens. A joint screening identifies 60 high-risk, low-capacity counties that are particularly susceptible to post-disaster construction bottlenecks. Incorporating baseline construction capacity into risk assessment can therefore inform where reconstruction support would complement efforts to reduce tropical cyclone risk.

North Atlantic hurricanes rank among the costliest natural hazards. Their impacts have increased over recent decades and are projected to rise further with climate change. Yet catastrophe models used in (re)insurance remain largely static, reflecting historical climate with limited integration of near-term climate signals. We present a simplified tropical cyclone model to estimate climate-connected landfall rate changes for North Atlantic hurricanes. Using reanalysis and climate model data, we quantify hazard changes for near-present (+1.2°C) and future (+2°C) climates relative to the historical baseline (1980–2025). These changes are used to adjust a year event loss table with a static view of risk by resampling. We find the average annual loss increases by 15% in the near-present (+1.2°C) climate, and the 1-in-10-year loss increases by 11%. Results show substantially higher landfall and loss increases under future (+2°C) climate than previous studies, highlighting the critical but often overlooked role of frequency changes in hurricane risk assessment.

Climate risks are increasing globally due to climate change and socio-economic development. Societies must implement adaptation measures today despite deep uncertainty regarding future climate trajectories, socio-economic pathways, and intervention effectiveness. Because no single strategy performs equally well across all impacts, for instance, protecting infrastructure versus saving lives, decisions depend on which outcomes are prioritized.Most assessments focus on a single criterion, most often the cost to benefit ratio of measures, overlooking other trade-offs and risking maladaptation. Multi-criteria decision analysis (MCDA) addresses this by explicitly evaluating and weighting multiple objectives. When coupled with probabilistic risk modeling and uncertainty quantification, MCDA can identify strategies that are robust across various futures and stakeholder priorities.In this project, we develop and test an integrated framework by coupling the new MCDA module of the open-source platform CLIMADA with its uncertainty and sensitivity quantification engine. Using a stylized case study from the Economics of Climate Adaptation (ECA), we explore how methodological and normative choices shape adaptation outcomes through three primary research questions:How do different impact units influence the prioritization of adaptation measures? We systematically compare rankings derived from multiple types of impact (e.g., population affected, economic losses, infrastructure exposure) to identify measures that perform consistently well across criteria versus those that are context-specific.How does the choice of risk metric affect the evaluation of adaptation measures? We quantify how rankings vary when using expected annual impact versus tail-risk metrics (high-impact, low-likelihood events), clarifying the normative implications of how "risk" is formulated.How sensitive and robust are MCDA-derived rankings to the weighting of decision criteria? We explore how results shift when assigning equal weights versus emphasizing specific priorities, making explicit how the assignment of preferences affects evaluations.Across these questions, we perform an uncertainty and sensitivity analysis that propagates uncertainty through all model components. This allows for a quantitative assessment of decision robustness and identifies the assumptions to which results are most sensitive.The key contributions of this work include the integration of MCDA with uncertainty analysis in a global modeling platform (CLIMADA); a systematic exploration of how normative modeling choices affect adaptation prioritizations; and a transparent, reproducible workflow for more integrated and value-aware climate-adaptation assessments.

Extreme weather events such as hurricanes exert increasing pressure on communities in hazard-prone areas and on the systems designed to protect them. Insurance serves as a primary risk-transfer mechanism, providing financial security for homeowners and supporting community resilience. Yet, behind this first layer of protection lies a complex web of reinsurers, capital markets, and public institutions that collectively absorb and redistribute disaster risk. Intensifying climate hazards, continued coastal development, and evolving market dynamics threaten the stability of this network.In this study, we develop a risk-propagation model to assess whether single or sequential hurricanes striking Florida could generate systemic financial stress across the property insurance system. The model links physics-based, probabilistic simulations of hurricane wind and flood losses with detailed data on the Florida residential insurance market, its backstop mechanisms, and regulatory frameworks. We examine how losses cascade through interconnected entities under the present-day status quo, under future climate conditions, and when accounting for evolving market dynamics and adaptation measures, revealing who ultimately bears the bulk of catastrophe risk.

Extreme weather events such as tropical cyclones increasingly threaten societies as climate change amplifies their impacts. While climate risk assessments have traditionally focused on direct impacts, such as economic losses, population exposure, or mortality, post-disaster recovery remains largely absent from these frameworks, limiting our ability to assess long-term resilience.This talk presents an approach to integrating recovery modeling into climate risk assessment using open-source, regional disaster recovery simulations that capture key dynamics such as resource constraints and interdependencies across systems.Results reveal spatial disparities in rebuilding capacity relative to climate risks, highlighting where targeted policy and planning interventions could accelerate recovery and strengthen long-term resilience.

Synthetic tropical cyclone (TC) models have become essential tools for assessing TC hazard and risk, due to both the shortness of observational records and, now, the influence of climate change. However, the direct output from these models, TC track sets, requires additional processing to become useful for impact assessments beyond traditional climate science and meteorological research. Converting TC tracks into hazard maps condenses complex information on TC intensity and frequency into widely accessible formats, facilitating diverse impact analyses. Here, we present global coastal wind risk maps generated using the Columbia TC hazard model (CHAZ) for both present-day and future climate conditions. Climate scenario-driven data are provided for a recent historical period (1995–2014) and two future periods, mid-century (2041–2060) and end-of-century (2081–2100); a reference data set downscaled from an observation-based reanalysis for 1981–2019 is also provided. These hazard maps depict TC intensity exceedance probabilities and return periods, providing valuable insights to policymakers, planners, and researchers focused on disaster preparedness.

Extreme weather is increasingly driving human displacement worldwide, a trend expected to worsen with climate change. Quantifying global displacement risk is thus crucial for assessing potential impacts and informing long-term strategies to build more resilient societies, and reducing this risk. One approach involves leveraging classic probabilistic risk modelling methods that hinge on the interplay of hazard, exposure, and vulnerability. Here, we present a methodological stocktaking of these natural-hazard risk models as applied to human displacement. Specifically, we present a globally consistent displacement risk model from multiple hazards under present-day and future conditions. We model population displacement from tropical cyclone winds, coastal floods, river floods, and droughts under present, optimistic, and pessimistic future climate conditions for the middle and end of the century, assuming constant exposure and vulnerability. Our results reveal that current displacement risk is on the order of 30 million annual average displacements (AAD). By 2100, global displacement risk could increase by 75% (157%) under optimistic (pessimistic) climate scenarios. While our risk model makes methodological advances through its global setup, utilisation of two risk frameworks and state-of-the-art datasets, we also highlight current challenges in displacement risk modelling. For instance, our approach primarily models displacement as the direct result of loss of homes from sudden-onset hazards. While we begin to incorporate indirect drivers, such as livelihood loss in river floods and droughts, the model still omits important social, political, and economic dimensions. Nevertheless, as our model adopts a modular design, continuous updates enable the inclusion of additional hazards, improved data, and integration of these broader dimensions. This stocktaking represents a concerted research effort, and our modelling framework may help inform global discussions in international climate negotiations, including those related to Loss and Damage, national action plans, policy development, and other climate adaptation strategies, provided appropriate data and context are applied.

An increasing number of countries and stakeholders now require large companies to disclose physical climate-related risks. However, the lack of standardization has led to the use of varied risk metrics from different data sources, making it difficult to compare physical risks across companies and preventing investors from aggregating risks at the portfolio level. To address this, we develop an approach using standardized, event-based probabilistic natural catastrophe risk assessments to improve global risk data aggregation and enable customized risk metrics. Integrating these methods into climate physical risk reporting would help investors align portfolios with a risk-return efficient frontier that accounts for physical risks, directing capital toward companies better equipped to manage climate impacts and promoting strategic adaptation. We propose a transparent, replicable, open-source methodology using event-loss tables to present per-event impact data. This approach is illustrated with two fictional companies exposed to tropical cyclone risk under current and future climate scenarios, highlighting the potential for company comparison, and portfolio risk assessment and optimization. Finally, we call on the scientific community and regulators to establish standardized hazard sets and financial loss functions to support transparent comparison and risk aggregation.

Extreme weather events are becoming more frequent and intense due to climate change. Yet, little is known about the relationship between exposure to extreme events, subjective attribution of these events to climate change, and climate policy support, especially in the Global South. Combining large-scale natural and social science data from 68 countries (N = 71,922), we develop a measure of exposed population to extreme weather events and investigate whether exposure to extreme weather and subjective attribution of extreme weather to climate change predict climate policy support. We find that most people support climate policies and link extreme weather events to climate change. Subjective attribution of extreme weather was positively associated with policy support for five widely discussed climate policies. However, exposure to most types of extreme weather event did not predict policy support. Overall, these results suggest that subjective attribution could facilitate climate policy support.

An increasing number of countries and stakeholders now require large companies to disclose physical climate-related risks. However, the lack of standardization has led to the use of varied risk metrics from different data sources, making it difficult to compare physical risks across companies and preventing investors from aggregating risks at the portfolio level. To address this, we develop an approach using standardized, event-based probabilistic natural catastrophe risk assessments to improve global risk data aggregation and enable customized risk metrics. Integrating these methods into climate physical risk reporting would help investors align portfolios with a risk-return efficient frontier that accounts for physical risks, directing capital toward companies better equipped to manage climate impacts and promoting strategic adaptation. We propose a transparent, replicable, open-source methodology using event-loss tables to present per-event impact data. This approach is illustrated with two fictional companies exposed to tropical cyclone risk under current and future climate scenarios, highlighting the potential for company comparison, and portfolio risk assessment and optimization. Finally, we call on the scientific community and regulators to establish standardized hazard sets and financial loss functions to support transparent comparison and risk aggregation.