Climate Analytics

Developing climate-resilient pathways requires an integrated view of risk that combines physical hazards with socio-economic vulnerability and adaptive capacity under future uncertainty. Within the SPARCCLE project, this is achieved by developing a probabilistic climate risk assessment framework for Europe that highlights the highest and recurrent impact patterns of climate extremes, as well as the challenges these pose for adaptation planning.Building on the core components of risk, namely hazard, exposure, and vulnerability, we integrate the MESMER climate emulator with the CLIMADA risk assessment platform to generate large ensembles of spatially explicit hazard realizations for extreme temperatures under custom emission pathways and global mean temperature trajectories. These hazards are combined with detailed exposure data, focusing on population exposure to extreme heat and accounting for future demographic change. Location-specific socio-economic vulnerabilities, including age structure, gender, and income inequality, are incorporated through hazard-specific impact functions.The use of a climate emulator enables exploration of a wide range of plausible futures, capturing dominant and recurrent spatiotemporal risk patterns as well as low-probability, high-impact outcomes that are often missed by limited climate model ensembles. This probabilistic framework allows us to identify regional hotspots of risk, assess where adaptation needs are greatest, and explore where adaptation constraints and limits may emerge under different climate and socio-economic pathways, reflecting alternative future challenges for Europe.Using heat-related impacts as a detailed application, we assess whether projected adaptation efforts are sufficient to close future adaptation gaps across regions and scenarios. The framework is designed to be scalable to multisectoral analyses and to feed into integrated assessment models and decision-support tools. By linking physical hazards, socio-economic vulnerability, and adaptive capacity in a unified probabilistic approach, this work supports forward-looking climate risk management and strengthens Europe’s preparedness for diverse future climate and socioeconomic challenges.

Climate adaptation is essential to reduce the risks of climate change and to ensure long-term resilience. As climate risks increase, so does the need for climate adaptation, supported by risk-informed decision-making and policy. Modelled projections of future climate and socioeconomic scenarios increasingly guide climate policy and decision-making, however, current modelling frameworks often lack a nuanced representation of adaptation. At the same time, adaptation planning and decision-making requires approaches able to develop flexible and adaptive management strategies that account for uncertainties and reflect specific adaptation objectives, such as adaptation pathways. These are flexible and robust sequences of adaptation options that span the adaptation solution space which is a multidimensional space within which adaptation is enabled and implemented. By projecting constraints on the adaptation solution space under future climate and socioeconomic scenarios, our research explores the link between global climate modelling and adaptation pathways approaches. Projecting dimensions of adaptive capacity allows for the identification and anticipationof possible barriers to adaptation, and establishing of enabling conditions that lead to a wider adaptation solution space. As biophysical and socio-economic changes constrain the range of adaptation options available in the future, we assess the potential adaptation uptake along a set of different climate and socioeconomic scenarios for European regions. We use a range of socioeconomic indicators as proxies for the potential for the uptake of specific adaptation options. Based on the statistical analysis of the observed implementation levels, we project and map the potential uptake of several adaptation options into future along the Shared Socioeconomic Scenarios, and additional stress-testing scenarios. Our study focuses on Europe using a variety of risks, including heat-related health impacts, and wildfire risk to forestry. This research bridges top‑down climate modelling with bottom‑up adaptation planning to assess how socioeconomic conditions can shape the future adaptation solution space. This approach helps to assess socioeconomic limits to adaptation and the future adaptation solution space, and further enables a more nuanced representation of adaptation in climate impact studies. The results can inform local adaptation planning in terms of outlining the availability of individual adaptation options as part of adaptation pathways development, as well as identifying key socioeconomic factors in constraining adaptation uptake potential. Addressing such constraints in adaptation policy on different levels can widen the solution space, and ultimately, inform climate-resilient planning and decision-making.

Landslides are among the deadliest natural hazards. Their impact is the highest in urban areas, where human exposure peaks. However, in addition to increasing exposure, multiple human-induced landscape alterations may also increase the probability of landslide occurrence. Hence, when we consider landslide risk in urban areas, not only does elevated exposure increase risk, but the exposed elements may also alter the frequency and intensity of the hazard.This relationship between exposure and hazard has already been demonstrated in physics-based hazard models that explore landslide potential in informal neighbourhoods. There is also ample evidence of landslides along intercity roads, linking hillslope modification to landslide occurrences. However, the number of observations is limited in urban areas. Here, we discuss the Granizal Landslide that occurred in June 2025, killing 27 people in the Metropolitan Area of the Aburrá Valley, Colombia. The rainfall-induced Granizal Landslide occurred in the steepest section of the urban zone, as could be expected. However, the landslide’s source area coincides with a road and a potentially malfunctioning sewage system, indicating that the exposed elements may have contributed to the landslide[1].The Granizal Landslide is alarming, as such incidents may increase as climate change intensifies. Especially in the tropical urban centres, more extreme rainfall events may overburden water infrastructure, not only informal infrastructure but also infrastructure that complies with the design standards. The statistical thresholds used to design the infrastructure may become inadequate due to shifts in rainfall patterns. Hence, we could broadly argue that climate change may be eroding the knowledge base that we used to design infrastructure. Perhaps not in the Aburrá Valley, but in other places we have already observed landslides in locations with little or no prior experience and risk awareness. This poses an additional risk due to the lack of knowledge among the newly exposed population about effective behavioural responses[2].[1] Ozturk, U., Braun, A., Gómez-Zapata, J. C., and Aristizábal, E.: Urban poor are the most endangered by socio-natural hazards, but not exclusively: the 2025 Granizal Landslide case, Landslides, https://doi.org/10.1007/s10346-025-02680-y, 2025.[2] Bubeck, P., Ozturk, U., Aristizabal, E., Thieken, A. H., and Wagener, T.: Mortality reduction despite changing climate extremes requires better understanding of human behavioral response to warnings, Environmental Research Letters, 20, 101004, https://doi.org/10.1088/1748-9326/ae034f, 2025.

In a probabilistic analysis, epistemic uncertainties associated with modeling choices and limited data are accounted for, and their effect on the resulting risk metrics is quantified. In this paper, we address the challenge of identifying, classifying, quantifying, and comparing different sources of uncertainty and their influence on the losses and risk metrics of spatially distributed systems under sequential hazards, such as an earthquake event followed by a tsunami or a tropical cyclone followed by a storm surge. Through the example of earthquake and tsunami risk of the residential-building stock of the communes of Valparaíso and Viña del Mar in Chile, we investigate and discuss the relation between aleatory and epistemic uncertainties and their effects on different risk metrics. We identify epistemic uncertainty factors and perform a sensitivity analysis to assess their influence on the annual average loss, value at risk, and expected shortfall. Additionally, we investigate the influence of the considered time period on the uncertainty of the risk metrics and demonstrate that epistemic uncertainty dominates the total variance of the risk for long time periods. In the specific application, we observe that the risk metrics are most sensitive to the uncertainty of the earthquake fragility parameters and the ground motion model.

Abstract. Many tasks in climate science, including climate impact assessment, scenario analysis, and end-to-end attribution, require efficient methods to translate a wide range of emissions scenarios into regional-scale climate indicators while explicitly accounting for uncertainty. Climate and impact model emulators are statistical models that approximate selected outputs of comprehensive models and can perform this translation. The Rapid Impact Model Emulator (RIME) uses individual simulations from climate or impact models to empirically relate global mean surface air temperature (GMT) levels to regional-scale indicators, enabling the conversion of GMT trajectories, commonly derived from Simple Climate Models (SCMs), into time series of regional climate impacts. Here, we present the Rapid Impact Model Emulator Extended (RIME-X), an extension of the RIME framework that replaces deterministic emulation of individual models along single GMT trajectories with a probabilistic approach. RIME-X combines ensemble simulations of GMT derived from SCMs with warming-level-dependent regional indicator distributions estimated from weighted Model Intercomparison Project (MIP) data. This results in scenario-dependent, time-evolving probability distributions of regional indicators. By jointly quantifying global and regional sources of uncertainty from the start, RIME-X enables systematic exploration of the full space of plausible regional climate impact trajectories under different emissions scenarios. The method is applicable to regional indicators whose distributions are predominantly determined by warming level and provides a computationally efficient framework for uncertainty-aware regional indicator emulation. We provide an open-source Python implementation of RIME-X, including preprocessing workflows for data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and support for user-defined indicators.

Abstract Engineered carbon dioxide removal and other point source carbon capture and storage (CCS) projects typically are capital intensive and reliant on incomes from voluntary carbon markets. This perspective article explores the financial implications of conventional versus digital monitoring, reporting and verification (dMRV) for engineered removal and CCS project investments. 
To ensure high carbon credit quality, conventional MRV requires substantial manual work and is therefore often costly and time-intensive. dMRV may cut costs and time as well as increase transparency by automating data collection, streamlining reporting processes, and enabling transparent real-time verification. Based on a real world example, we show how dMRV can foster a financial investment decision by reducing the time lag between physical carbon capture and credit issuance. The earlier sale of credits (compared to conventional MRV) leads to earlier break-even and enables investment into projects that otherwise would be unattractive. 
We conclude that dMRV is an important building block for icreased investment into capital intensive mitigation measures like engineered removal and CCS projects.

Meeting the Paris Agreement's temperature goals requires limiting future carbon emissions, yet current policies make temporarily overshooting the 1.5°C target likely. The potential climate feedback from destabilizing peatlands, storing large amounts of carbon, remains poorly quantified. Using the reduced-complexity Earth System Model OSCAR with an integrated peat carbon module, we found that across various overshoot pathways that temporarily exceed 1.5°C-2.5°C, northern peatlands exhibit net positive feedback, amplifying the overshoot challenge. Warming increases peatlands' net carbon uptake, but this is largely offset by higher methane emissions. We estimated that for each 1°C increase in peak warming, the positive feedback from peatlands decreases the remaining carbon budget by 37 GtCO2 (22-48 GtCO2). If the 1.5°C temperature target is exceeded, peatlands would increase carbon removal requirement by about 40 GtCO2 (16-60 GtCO2) (8.6%). Our findings highlight the importance of properly accounting for northern peatlands for estimating climate feedbacks, especially under overshoot scenarios.

ABSTRACT Introduction Integration of Carbon Dioxide Removal (CDR) into existing climate change mitigation policy frameworks has begun. A broad set of policy instruments is available for the development and deployment of CDR technologies. While current discussions emphasize carbon markets to scale CDR, other non-market based policy instruments could play an important role. This article examines several non-market policy instruments through a new CDR-specific assessment framework across four dimensions: feasibility, climate effectiveness, impacts on individuals and society, and the ratio and distribution of impacts. Four theoretical scenarios, heterogeneous in assumed time horizon, spatial coverage, and utilized non-market policy instrument, are evaluated: (1) a technology-specific Carbon Contract for Difference in the EU; (2) an internationally funded capacity-building campaign for Brazilian farmers to utilize voluntary carbon markets; (3) a global carbon takeback obligation for fossil fuel producers; and (4) a national corporate tax exemption for Direct Air Capture (DAC) plant setup and operation. Discussion The assessment finds that no single policy instrument performs strongly across all four dimensions. This suggests that the prioritization of policy instruments for CDR depends on how decision-makers weigh the assessed dimensions. Different stakeholder objectives will lead to different weighing of dimensions and, thus, rankings of policy instruments. Conclusion Careful and transparent policy design process that reflects local contexts and national conditions is essential to ensure robust and stable decisions supported by a critical mass of stakeholders. A nuanced, inclusive approach can facilitate the implementation of a complementary suite of policy instruments that underpin responsible and effective global collaboration on CDR.