We particularly welcome contributions on modelling and downscaling (from global to local), ensembles and probabilistic methods, data assimilation and remote sensing, model evaluation, uncertainty quantification under climate change, and storylines or event attribution. The scope is global and includes unprecedented or record-breaking TCs, Medicanes or post-tropical transitions. Submissions addressing multi-hazard interactions, cascading and compounding effects, social vulnerability and exposure, as well as decision-support—such as early warning systems, risk communication, risk assessment, and appraisal of structural, nature-based measures and public policies—are encouraged.
Orals: Tue, 5 May, 08:30–10:15 | Room 1.15/16
TROPICANA (TROPIcal Cyclones in ANthropocene: physics, simulations & Attribution) was a one-month programme gathering 60 scientists at the Institut Pascal (University Paris-Saclay), where we reflected on how to advance knowledge on the impact of Climate Change on Tropical Cyclones, and how to produce information relevant for mitigation, future risk assessment and adaptation. I will present several collective outcomes from the programme, including two white papers on defining Tropical Cyclones seeds and defining CYCLOPS (surface flux-driven cyclones outside the tropics), two reviews on African Easterly Waves and Tropical Cyclones features driving impacts, as well as a new Tropical Cyclones attribution methodology.
How to cite: Bourdin, S., Faranda, D., Camargo, S., Lee, C.-Y., Fromang, S., Wang, Z., Emanuel, K., Nuñez Ocasio, K., and Scussolini, P.: Outcomes of the TROPICANA programme, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19749, https://doi.org/10.5194/egusphere-egu26-19749, 2026.
A first regional configuration of the ICON-Ocean model in Limited Area Mode (ICON-O-LAM) is now available within the ICON Earth System Model framework, enabling fully coupled regional ocean–atmosphere simulations with ICON-NWP over the Mediterranean Sea. This configuration can be used to investigate Mediterranean high-impact weather and provides a flexible framework that can be systematically applied to multiple medicane events occurring in the basin.
Medicanes are characterized by intense small-scale dynamics and a strong dependence on air-sea interactions, requiring a modelling framework capable of resolving key feedback processes such as sea surface temperature cooling, surface heat fluxes, and wind-driven ocean responses. Uncoupled atmospheric simulations with prescribed or static SSTs, typical of many operational setups, are unable to represent these interactions and may therefore misrepresent storm intensity, structure, and evolution.
As a first application, the coupled ICON-O-LAM/ICON-NWP system has been applied at 2.5 km resolution to simulate Medicane IANOS (September 2020), one of the strongest Mediterranean tropical-like cyclones on record, and is used here as a benchmark case to assess the performance of the coupled regional ICON system. The coupled simulations show clear improvements over uncoupled experiments, reproducing IANOS intensity more realistically, capturing SST cooling effects, and providing a better representation of precipitation patterns.
Beyond this initial application, the modelling framework is designed to be extended to other recent Mediterranean medicanes, such as Zorbas (September 2018), Apollo (October 2021), and Daniel (September 2023), enabling systematic, high-resolution analyses across multiple events. This configuration offers new opportunities to investigate medicane intensification, air–sea coupling mechanisms, and event-to-event variability, providing a valuable platform for both research applications and future operational forecasting of Mediterranean tropical-like cyclones.
How to cite: Campanale, A., Bevrnja, A., Raffa, M., Potthast, R., Mercogliano, P., and Schulz, J.-P.: Improving medicanes representation with a high-resolution regional coupled atmosphere-ocean configuration of the ICON Earth System Modelling framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11272, https://doi.org/10.5194/egusphere-egu26-11272, 2026.
Tropical cyclones (TCs) and their post-tropical (PTC) counterparts show contrasting structural and extreme precipitation responses to surface warming. Using a dynamically derived wind-based radius (r6) from ERA5 near-surface winds, we quantify storm size and extreme precipitation characteristics for North Atlantic cyclones from 2001-2024. TCs are compact systems that contract under warmer and moister conditions, with extreme precipitation metrics increasing by more than 20% K⁻¹ for 2-m air temperature and dewpoint. In contrast, PTCs expand after extratropical transition and show weak thermodynamic sensitivity, consist with baroclinic control and more diffuse precipitation. Translational speed and latitude further modulate these patterns: slower, low-latitude TCs sustain intense, localized precipitation under warming, whereas faster, higher-latitude PTCs produce broader, asymmetric precipitation fields. These findings highlight the combined thermodynamic and dynamical controls on cyclone precipitation and structure, demonstrating that TCs and PTCs respond differently to surface warming. The r6 metric offers a physically consistent approach to assessing how cyclone precipitation extremes evolve in a warming climate.
How to cite: Ali, H., Fowler, H., Prien, A., and Reed, K.: Temperature Scaling of Tropical Cyclone Precipitation in the North Atlantic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6883, https://doi.org/10.5194/egusphere-egu26-6883, 2026.
Enhancing the realism of numerical models is critical for accurately simulating high-impact weather events such as tropical cyclones (TCs), particularly for coastal hazard applications. Model performance is strongly influenced by the accuracy and spatial resolution of the input data. To address the challenges associated with the asymmetric and rapidly evolving structure of TCs, recent studies have increasingly incorporated advanced satellite observations and state-of-the-art machine-learning techniques. One of recent advance is the use of atmospheric motion vectors (AMVs) derived from satellite imagery. In this study, a dedicated preprocessing framework incorporating quality control, outlier removal, and directional alignment, was developed to refine AMVs for TC wind-field reconstruction. Storm surge simulations driven by these AMV-based winds for TCs (i.e., Lingling, Haishen, and Hinnamnor) demonstrated improved accuracy relative to ERA5 reanalysis at several coastal stations, highlighting their effectiveness in data-sparse oceanic regions. In parallel, a random forest (RF) model was developed to estimate TC pressure fields from wind information. Unlike conventional symmetric parametric approaches, the RF model effectively represents spatial asymmetry, land–sea contrasts, and nonlinear wind–pressure relationships. The model achieves low error rates, particularly within the gale-force wind radius, and performs robustly when driven by real-time satellite wind observations. Overall, the integration of satellite-based observations with machine-learning techniques represents a significant advance toward more physically realistic and operationally valuable numerical modeling, helping bridge the gap between limited observations and complex storm dynamics to improve coastal hazard forecasting and emergency response.
How to cite: Son, S. and Im, S.: Improving tropical cyclone wind and pressure field reconstruction using GK-2A atmospheric motion vectors and machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15656, https://doi.org/10.5194/egusphere-egu26-15656, 2026.
Storm surge hazards have intensified under rising mean sea level, increasing the need for realistic atmospheric forcing in coastal surge models. In the Northwestern Pacific, the spatial structure of tropical cyclone winds strongly controls nearshore water level response, particularly in harbors and shallow coastal zones where wind-field heterogeneity can amplify modeling uncertainty. Over regions with steep and complex topography, tropical cyclone circulations often undergo coherent and persistent asymmetric deformation due to topography–cyclone interaction, commonly referred to as the “topographic locking effect”. Such topography-modulated asymmetry is not adequately represented by conventional symmetric parametric wind models, limiting their reliability for nearshore storm surge applications.
Here we develop a realistic parametric wind-field framework (REP) that captures asymmetry associated with topographic locking using patterns extracted from a historical reanalysis library. We constructed a regional wind-field database from ECMWF ERA5 reanalysis, including sea level pressure and 10-m winds, for 282 typhoon events affecting Taiwan during 1980–2023. For a given storm location and scenario, REP quantifies the contribution of each database member through a designed weighting formula and synthesizes a physically self-consistent two-dimensional wind field via weighted blending, without requiring high-resolution dynamical atmospheric downscaling.
We demonstrate the framework using typhoon events traversing the main island of Taiwan. REP-generated nearshore wind structures are compared against ERA5, and REP winds are further coupled to the COMCOT-SS storm surge model to benchmark surge responses against simulations forced by ERA5. Results show improved consistency with ERA5 in nearshore wind patterns and comparable storm surge evolutions relative to ERA5-forced simulations. In addition, we conduct complementary experiments by replacing the underlying wind-field archive with hindcasts from a tropical-cyclone forecasting numerical weather prediction system, providing an initial assessment of REP’s transferability across databases. Overall, REP offers a computationally efficient and transferable approach to generate reanalysis-like asymmetric wind forcing, supporting storm surge modeling and hazard assessment in regions where topography-modulated cyclone structure is important.
How to cite: Yeh, C.-H., Chang, C.-H., and Wu, T.-R.: A data-driven realistic parametric tropical-cyclone wind-field model with terrain-locked asymmetry for improved storm surge simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7924, https://doi.org/10.5194/egusphere-egu26-7924, 2026.
Tropical islands, due to their location, are highly exposed to climate-ocean related hazards. Among these hazards, tropical cyclones (TCs), which generate extreme weather conditions, storm surges and marine submersions, are particularly devastating. Their extreme intensity, relatively rare occurrence, and sparse spatial distribution complicate their observation, making numerical modelling an essential tool for characterising TC events, and improving our understanding of their dynamics. However, accurately modelling TCs remains very challenging, and significant uncertainties persist in the modelled hazard even after the event. Hence, this study aims to quantify uncertainties in the modelled atmospheric hazard arising from three main sources: physical-process uncertainties associated with current limitations in our understanding and representation of key mechanisms (e.g. turbulent fluxes at the air–sea interface, planetary boundary layer physics, convection) ; numerical uncertainties, linked to model design and computational constraints (resolutions, numerical schemes) ; and forcing uncertainties (initial and boundary conditions, land interactions). We focus on the cyclonic hazard in two contrasted tropical territories: the mountainous island of La Réunion located in the south-west Indian Ocean, and the small volcanic islands of the Caribbean arc in the North Atlantic basin, taking into account the specific characteristics of both territories (small islands with steep bathymetry, orographic effects), and the ocean basins (synoptic conditions, availability of observations). Using the Weather Research and Forecasting (WRF) model with three two-way nested domains (9 km, 3 km, 1 km), we conduct an ensemble of high-resolution retrospective simulations of some of the major events impacting these territories since the 1980s to quantify the respective contribution of the different sources of uncertainties. This work will help to design effective TC risk management and adaptation strategies.
How to cite: Zribi, A., Jullien, S., Dodet, G., Bertin, X., Maillard, L., and Krasniqi, Y.: Uncertainties in Cyclonic Hazard in Tropical Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17063, https://doi.org/10.5194/egusphere-egu26-17063, 2026.
Regions that experience frequent extreme weather events are assumed to have better monitoring, assessment and preparedness for future events. This is particularly true in the case of tropical cyclones (TC) that have a degree of predictability in their genesis, trajectory and intensity, especially in the Caribbean and the Pacific. However, in basins such as the Northern Indian Ocean, especially the Arabian Sea sub-basin, where cyclone occurrences are few, assessing and attributing cyclone risks to climate change suffers from a lack of observational data and low confidence in any detectable change. For low-lying coral atolls with dense human habitation, any climate change management and adaptation plan would likely underestimate risks from TC, potentially leading to increased vulnerability in the future. We focus our study on the densely populated Lakshadweep archipelago, situated in the Arabian Sea and address three main gaps. Using renalysed weather variables from ERA5 datasets, we first assess how two main abiotic drivers of cyclonic activity i.e., SST and wind shear have changed since 1940 across the Northern Indian Ocean and relate these with cyclones that have impacted the archipelago. Second, we examine wave heights and rainfall intensity related to TC and assess their joint probability of occurrence across 10 inhabited islands of the Lakshadweep since 1940. Finally, we use a storylines approach to attribute anthropogenic climate change to the occurrence of the most impactful TC in Lakshadweep, compared to a counterfactual scenario of no anthropogenic climate change. Our results highlight that i) seas around the Lakshadweep are becoming conducive for cyclone formation and propagation with an increase in SST and decrease in wind shear; ii) co-occurring extreme wave and rainfall events are associated with cyclonic conditions with larger return-periods (1-500+ year events) compared to their univariate distributions, and iii) based on our storylines approach, intense cyclones in Lakshadweep can overwhelmingly be attributed to anthropogenic climate change, while weaker storms have limited evidence. We conclude by highlighting how climate change mitigation plans in the global south require long-term analysis and attribution studies of rare events to climate change, without which, any plans may underestimate and potentially increase vulnerability of an already vulnerable population.
How to cite: Dey, M., Perry, C., and Arthur, R.: Climate change increases risks from tropical cyclone compound events to densely populated coral atolls in the Northern Indian Ocean., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8830, https://doi.org/10.5194/egusphere-egu26-8830, 2026.
Tropical cyclone precipitation (TCP) and associated floods have caused widespread damage globally. Despite growing evidence of significant changes in the activity of tropical cyclones (TCs) in recent decades, the influence of TCs on regional flooding remains poorly understood. Here, we distinguish the role of TCs in fluvial discharge by explicitly simulating discharge with and without observed TCP in the Lancang‒Mekong River Basin, a vulnerable TC hotspot. Our results show that TCs typically contributed approximately 30% of annual maximum discharge during 1967–2015. However, for rare and high‐magnitude floods (long return periods), TCs are the dominant driver of extreme discharge events. Moreover, spatial changes in Tcinduced discharge are closely related to changes in TCP and TC tracks, showing increasing trends upstream but decreasing trends downstream. This study reveals significant spatiotemporal differences in TC‐induced discharges and provides a methodology for quantifying the role of TCs in fluvial discharge.
How to cite: Chen, A., Wang, J., Toumi, R., Huang, H., Yang, L., Chen, D., He, B., and Liu, J.: Impact of Tropical Cyclone Precipitation on Fluvial Discharge in the Lancang‒Mekong River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10281, https://doi.org/10.5194/egusphere-egu26-10281, 2026.
Posters on site: Tue, 5 May, 10:45–12:30 | Hall X3
Huracán (HUrricane Risk Amplification & Changing north Atlantic Natural disasters) is a UK–US partnership to deliver a new, physical understanding of tropical cyclone risk across the British Isles, Western Europe, and the Northeast US in a changing climate. In this talk, I will present some of the preliminary results from the project. I will present our strategy to improve the Cyclone Phase Space in order to better distinguish tropical cyclones from warm seclusion cyclones, a new dataset merging observations and reanalyses together in order to create a catalogue of past Cyclones of Tropical Origin reaching Europe, as well as a few case studies including wind, precipitation and storm surge impacts.
How to cite: Bourdin, S., Hodges, K., Han, Y., Saffin, L., Baker, A., Vidale, P.-L., Methven, J., Ali, H., and Wood, M.: Huracán: Investigating Atlantic Cyclones of Tropical Origin reaching Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19417, https://doi.org/10.5194/egusphere-egu26-19417, 2026.
Cyclone tracks are an important diagnostic in model evaluation when looking at long-term historical statistics and are also of interest in future scenario projections. In addition to modelling, generating tracks from observational or reanalysis data is important in aiding comparisons and assessing trends.
Multiple approaches to generating cyclone tracks from model and observational data exist, each with their own opinion as to which variables are important, how candidate storms are identified, and how these candidates are stitched together to form tracks. Each method comes in its own codebase with varying degrees of user documentation meaning that those interested in tracking often select just one approach and stick to it. Further, all tracking codes produce different, often customised, output formats with minimal metadata that do not come close to meeting FAIR data standards.
This makes any downstream data use or tracking intercomparison a challenge.
We present TCTrack, an open-source Python-based package that provides a common interface to popular tropical cyclone tracking codes. Extensive documentation ensures accessible usage and manipulation of existing codes, as well as providing guidance on input requirements and preprocessing of data. Perhaps most notable is that all output data provided by TCTrack is in a common data format, regardless of the tracking algorithm used, and conforms to the Climate and Forecast (CF) metadata conventions (specifically H4:Trajectory data) preserving variable metadata information from the input files. This metadata-rich output aids in reproducibility and reusability and makes downstream analysis with a variety of other tools straightforward. Finally, TCTrack is built on an easily extendable framework meaning that addition of other tracking approaches from, and for use by, the community is both straightforward and encouraged.
This poster showcases key aspects of the TCTrack software, a discussion of tracking data format using the CF-Conventions, and some results from deployment in an intercomparison study of different tracking methods applied to CMIP data.
References:
- Atkinson, J.W. & Avis, S.J. (2025). TCTrack. https://github.com/Cambridge-ICCS/TCTrack
- Eaton, B., et al. (2025). NetCDF Climate and Forecast (CF) Metadata Conventions v1.13. https://cfconventions.org/
- Hodges, K., Cobb, A., & Vidale, PL. (2017). How Well Are Tropical Cyclones Represented in Reanalysis Datasets? Journal of Climate 30, 14: 5243-5264
- Ullrich, P.A., et al. (2021) TempestExtremes v2.1: A Community Framework for Feature Detection, Tracking, and Analysis in Large Datasets. Geoscientific Model Development 14, no. 8: 5023–48.
- Vitart, F., & Stockdale T.N. (2001) Seasonal Forecasting of Tropical Storms Using Coupled GCM Integrations. Monthly Weather Review 129, 10: 2521-2537
How to cite: Atkinson, J., Avis, S., Ming, A., and Powell, C.: TCTrack: Facilitating FAIR tropical cyclone tracking software and data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5825, https://doi.org/10.5194/egusphere-egu26-5825, 2026.
Tropical cyclones (TCs) in Southeast Africa pose severe flood risks, yet understanding these risks is hindered by a sparse observational network. While reanalysis products like ERA5 are standard tools for risk assessment, their coarse resolution often smooths out the intense convective features that drive flash flooding. This study challenges the sufficiency of current reanalysis data by evaluating km-scale convection-permitting simulations using the Conformal Cubic Atmospheric Model (CCAM).
ERA5 data were dynamically downscaled from 2014 to 2023, identifying six high-impact TCs that affected Malawi, Madagascar, and Mozambique, including the record-breaking TCs Idai and Freddy. These simulations were compared against reanalysis, satellite products, and gauge observations.
Results demonstrate that CCAM significantly corrects the underestimation bias found in ERA5 and satellite datasets. Crucially, the km-scale simulations reveal detailed structural features missed by coarser models, including complex inner-core structures and distinct asymmetric outer spiral bands. These structural details are not merely meteorological curiosities; they determine the spatial footprint of the hydrological hazard.
It is concluded that relying on standard reanalysis products underestimates the true flood potential of TCs in the region. By resolving these fine-scale storm features, CCAM provides a more realistic baseline for understanding present-day flood risk and assessing future climate risk. This work highlights the critical need for convection-permitting approaches to support effective climate resilience in vulnerable communities.
How to cite: Hooker, H., Steinkopf, J., Vanya, C., Maure, G., Nhantumbo, B., Engelbrecht, F., Cloke, H., and Stephens, E.: Unmasking the hidden hazard: Convection-permitting modelling reveals extreme tropical cyclone rainfall structures in Southeast Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2570, https://doi.org/10.5194/egusphere-egu26-2570, 2026.
Tropical cyclones represent one of the major natural hazards for coastal regions worldwide, primarily due to the extreme storm surges they generate. Reliable estimates of storm surge associated with return periods are essential for coastal risk assessment, infrastructure design, and climate adaptation planning. These estimates, however, are highly sensitive to the characteristics of the accuracy of the input forcing and the underlying datasets used for the extreme value analysis, including their temporal length, time resolution and the representation of rare but high-impact events.
The goal of this study is to analyze this sensitivity. To this end, several tropical cyclone–induced storm surge datasets are considered for the Caribbean Sea and Gulf of Mexico region, differing in duration, structure, input forcing, and underlying assumptions. The datasets are derived using commonly adopted approaches, including a 32-year (1993–2024) continuous storm surge hindcast forced by ERA5 reanalysis wind and pressure fields, as well as event-based storm surge simulations generated using a parametric Holland wind model for both historical and synthetic tropical cyclones. The historical hurricane dataset is analysed for the full period of available records (1851–2024) and separately for the period overlapping with the ERA5 hindcast (1993–2024), enabling a consistent comparison across datasets. Return level curves obtained from these datasets are compared to evaluate the sensitivity of extreme storm surge estimates to dataset length, input forcing, and the inclusion of synthetic events. The results provide valuable insights into the uncertainties affecting storm surge return period estimates and emphasize the importance of carefully selecting datasets when assessing tropical cyclone–induced coastal hazards.
How to cite: Ramírez-Pérez, M., Menéndez, M., and Chaigneau, A. A.: Sensitivity of extreme storm surge estimates induced by tropical cyclones to different widely used approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20493, https://doi.org/10.5194/egusphere-egu26-20493, 2026.
A critical manifestation of anthropogenic climate change is the intensification of extreme weather events, particularly storm surges. Driven primarily by strong winds and atmospheric pressure fluctuations associated with severe storm systems, storm surges can devastate coastlines through a rapid rise in sea level. Much of the current research using altimetry to monitor storm surges focuses on localized case studies and utilizes a combination of in-situ, model-generated, and remote sensing data. The overarching objective of this research is to develop a global approach for monitoring extreme sea level events at the coast using altimetry-derived sea level anomaly (SLA) and wave parameters. This study focuses on assessing the capability of altimetry-derived products to detect storm surge events through validation against in-situ observations and reanalysis data. The data used in this project are 20 Hz, along-track altimetry data sourced from the ESA Climate Change Initiative (CCI)1 Sea State and Sea Level products. Supplementary datasets used for validation purposes are tide gauges sourced from the GESLA-42 dataset and reanalysis data from the hydrodynamic model, GTSMv3.03. The analysis approach entails performing extreme value statistics on tide-gauge and GTSM data to flag potential surge days, then evaluating individual altimetry tracks on flagged days for signatures of storm surges, then computing wave parameters to improve surge identification. Initial results demonstrate that 20 Hz coastal SLA data can successfully detect storm surge events. However, the intensity and appearance of the surge signature are contingent on the temporal alignment between the surge peak and the satellite pass. The evaluation framework supports the integration of altimetry products into digital flood models, enhancing the ability to quantify coastal risk and predict the impacts of extreme sea-level events. The work contributes to the broader climate adaptation strategies within the European Space Agency (ESA) FRACCEO4 project in collaboration with Deltares in Delft, the Netherlands, TU Delft in Delft, the Netherlands, and the Nansen Environmental and Remote Sensing Center in Bergen, Norway.
1https://climate.esa.int/en/projects/
2https://gesla787883612.wordpress.com/
3https://www.deltares.nl/en/expertise/projects/global-modelling-of-tides-and-storm-surges
4https://climate.esa.int/en/supporting-the-paris-agreement/fracceo/
How to cite: Johnson, J., Passaro, M., Hart-Davis, M., Backeberg, B., and Connors, S.: Validation of Satellite Altimetry for Coastal Storm Surge Detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14366, https://doi.org/10.5194/egusphere-egu26-14366, 2026.
Coastal flood risk assessments traditionally treat storm surges as instantaneous responses to wind and pressure, assuming stationary physical drivers. However, under a warming climate, the thermodynamic processes preconditioning coastal catchments for extremes are evolving. This study quantifies how the influence of antecedent environmental precursors in modulating coastal surges has shifted over the last four decades. A physics-aware Convolutional LSTM framework analyzes 40 years (1984–2024) of ERA5 reanalysis data across six diverse global hotspots, including the U.S. Gulf Coast, Bay of Bengal, and East Asia. The model integrates lagged anomalies of soil moisture and integrated water vapor transport (IVT) to capture multi-day preconditioning. Explainable AI diagnostics—specifically temporal sensitivity analysis—are employed to assess changes in the relative importance of drivers between early (1984–2000) and late (2004–2024) epochs. Results indicate that while instantaneous wind stress remains the dominant control on surge peaks, the predictive weight of antecedent conditions has shifted. In the U.S. Gulf Coast and East Asia, the influence of 7–14-day soil moisture and IVT anomalies increased significantly in the recent period, particularly regarding flood duration and compound surge–precipitation likelihood. These findings reveal a strengthening coupling between terrestrial hydrologic memory and coastal extremes. Consequently, the results challenge "snapshot-based" hydrodynamic approaches, suggesting that effective early-warning horizons in a non-stationary climate must extend from days to weeks to account for these evolving precursor regimes.
How to cite: Alizadeh, M. R.: Evolving Land–Atmosphere Preconditioning of Coastal Storm Surges: A Multi-Basin Analysis of Shifting Drivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22095, https://doi.org/10.5194/egusphere-egu26-22095, 2026.
Accurate sea level prediction is crucial for coastal communities and infrastructure. Unfortunately, classical approaches can be computationally expensive and inaccurate, especially in data-sparse regions. Recently, scientists have started to explore to which degree deep learning models could help address these problems. Combined with long historical tidal gauge records, these data-driven approaches offer the potential to improve the accuracy and spatial resolution compared to classical modeling schemes, while at the same time being computationally far cheaper to operate.
Yet, deep learning for sea level modeling is still in its infancy: existing studies typically struggle to accurately predict extreme events, differ in their strategies of (pre-)processing input and output data, and focus on individual gauges or small regions. In this contribution, we take inspiration from recent successes in global riverine hydrologic modeling, where deep learning models greatly improved the accuracy of predictions in ungauged regions. Translating these findings to a coastal floods context, we present our work towards globally applicable deep learning models of sea level and storm surge. Specifically, we focus on these models’ ability to make predictions in places that lack historical gauge measurements.
Our framework utilizes state-of-the-art deep learning architectures to capture complex spatial dependencies between atmospheric drivers and the ocean state. To ensure robust performance, we integrate a diverse set of input features, including ERA5 atmospheric reanalysis (wind and pressure), FES tidal predictions, and high-resolution static geospatial data such as bathymetry and land-sea masks. Furthermore, we explore the utility of pre-trained geospatial embedding data to encode local station properties.
We compare the data-driven predictions with established hydrodynamic model baselines, such as the ocean model run by Environment and Climate Change Canada (ECCC) and the Global Tide and Surge Model (GTSM). Our findings indicate that deep learning approaches can exceed the performance of physics-based models across standard metrics. Furthermore, validation against real-world extreme events confirms the model's superior ability to identify high-impact storm surges.
How to cite: Ebel, P., Sicherman, A., Gauch, M., and Cohen, D.: Towards Deep Learning Models for Global Coastal Sea Level Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10045, https://doi.org/10.5194/egusphere-egu26-10045, 2026.
Tropical-cyclone (TC) impacts often cascade when storms arrive in sequence or simultaneously, amplifying risk and recovery demands. We introduce an operational, threshold-based classification of TC-multi-hazards that is explicitly tailored to sequential TCs within a single hurricane season, was applied to basin, country and local. Using IBTrACS for the North Atlantic basin (1980–2023), we define four temporal dependencies—concurrent (<1 day), overlapping (1–7 days), consecutive (8–30 days), and within-season (>30 days within the same season)—and couple them with spatial dependencies based on landfall, tracks that intercept a 100-km buffer around countries or localities, and multiple landfalls. This framework is used to identify TC-multi-hazards hotspots and to characterize sequential intensity patterns in where the second TC is stronger than the first.
At the basin scale, 11% of the events are multiple-landfall-concurrent types, of which 88% is concentrated in the Greater Antilles, and 38% each overlapping and consecutive types. Though hotspots of the overlapping, consecutive and within-season types are mainly concentrated in the western Atlantic basin, no clear hotspot patterns were identified between types that include landfalls involving one TC compared to those involving multiple TCs.
At the country scale, 49% of the events are buffer-consecutive types, which are found across the basin, with a high density in the Lesser Antilles. At the locality scale, buffer-consecutive events (the Lesser Antilles, Florida, Bahamas, Nicaragua) and buffer-within-season events (Gulf of Mexico, Cuba, and Mexican Caribbean) dominate this scale, representing 74% and 22% of the events, respectively.
This classification supports time-dependent recovery planning, enhances the design of early warning systems, and provides a crucial methodological link between generic multi-hazard types and practical TC risk management and insurance applications.
How to cite: Henry, M. D., Odériz, I., Toimil, A., and de Ruiter, M.: Spatiotemporal hotspots of sequential tropical-cyclone multi-hazards in the North Atlantic Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17820, https://doi.org/10.5194/egusphere-egu26-17820, 2026.
Tropical cyclones (TCs) pose significant threats to coastal communities, primarily through hazardous wind speeds and intense rainfall that drive storm surge and coastal flooding. These wind and water related hazards often occur simultaneously, amplifying impacts on both the built environment and socially vulnerable populations. Despite extensive prior research on individual TC hazards, limited attention has been given to their joint occurrence and evolving risk characteristics over time in relation to changes in social vulnerability.
This study proposes a comprehensive, time-evolving TC risk assessment framework that explicitly accounts for the likelihood of coinciding wind and rainfall hazards. The analysis covers the period from 1979 to 2022, incorporating long-term hydroclimatic records to characterize TC-related multi-hazard exposure. In parallel, social vulnerability was evaluated using multiple combinations of vulnerability indicators for the period 2000–2022, allowing temporal changes in population sensitivity and adaptive capacity to be captured. By progressively incorporating newly available data and historical records as time advances, this study reflects how TC risk assessments would have evolved under real-world knowledge constraints in past decades.
A total of 29 major TC events impacting the southeastern U.S. coast were examined, and statistical correlations were evaluated between estimated TC risks and observed economic damages. The results indicate that, prior to 2017, fewer than 6% of the cases exhibited stronger correlations when TC risk was quantified using a multi-hazard hurricane index that jointly considers wind and rainfall. In contrast, more recent events demonstrate a growing dominance of wind-based risk metrics in explaining observed damages.
These findings suggest a shifting risk regime in which coastal communities are becoming increasingly vulnerable to wind-related TC impacts, including extreme winds, storm surge, and coastal flooding, rather than rainfall-driven hazards alone. The proposed framework highlights the importance of dynamic, multi-hazard risk assessments that integrate evolving social vulnerability, providing critical insights for future coastal resilience planning and disaster risk reduction strategies.
Acknowledgments: This work was supported by National Research Foundation of Korea funded by the Ministry of Education (RS-2023-00249547).
How to cite: Song, J. Y., Lee, J. H., and Chung, E.-S.: A Time-Evolving Multi-Hazard Risk Assessment of Tropical Cyclones Incorporating Wind, Rainfall, and Social Vulnerability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11028, https://doi.org/10.5194/egusphere-egu26-11028, 2026.
Tropical cyclones generate hazards such as extreme winds coastal flooding from surge and rainfall driven inundation that drive severe impacts on Islands territories. Losses quantification is a key factor for the implementation of risk-reduction and adaptation strategies. In this context, the objective of this work is to evaluate a hazard-to-damage workflow to estimate losses from hazards generated by tropical cyclones using different hazard and damage simulation approaches.
This work presents a modular workflow allowing to estimate multi-hazard damages originated by tropical cyclones focused on French island territories (Reunion, French Antilles and Mayotte). Hazard simulations generate wind speed, coastal water-level and inundation depths due to rainfall using separate components. These hazard layers are harmonised for the damage estimation using a reinsurance data base of historical losses in French territories exposed to cyclones.
Wind speed simulation is based on three complementary approaches to reflect different data availability and use cases.
- Dynamical way: near-surface wind from WRF (Weather Research and Forecasting) simulations, providing spatially continuous fields that can support event reconstruction and forecast oriented application
- Observation-driven option relies on Météo-France wind stations and applies terrain-related adjustments base on land-surface roughness and topography to represent local exposure heterogeneity.
- A parametric option based on a Holland-type wind field to generate time evolving wind footprints. These wind options deliver consistent outputs (e.g. wind speed, direction ...) on a common grid for intercomparison.
Flooding is generated by interfacing two flooding-related components: a coastal submersion chain providing water levels from marigrams combined with inland propagation, and an inland flooding component forced by river discharge and precipitation observations.
All generated hazard layers are then compared to the available information on risk and infrastructure in the studies territory and loss estimation can be done with two main options.
How to cite: Stahl, P.-A., Parard, G., Peredo, D., and Pugnet, L.: From tropical cyclone hazard layers to vulnerability and loss estimation: a comprehensive framework , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10176, https://doi.org/10.5194/egusphere-egu26-10176, 2026.
Severe tropical cyclones generate major marine hazards, including large waves and extreme water levels, which can lead to substantial coastal flooding, erosion, and associated socio-economic damages. Climate change—particularly sea-level rise—is expected to exacerbate these impacts by allowing hurricane-induced extreme water levels to penetrate further inland, thereby increasing flood risk in already vulnerable low-lying coastal areas.
This study first aims to accurately reconstruct the hazards, impacts, and risks associated with Hurricane Irma (2017), one of the most intense hurricanes to affect Miami (Florida, USA) in recent decades. Second, it examines how coastal flood risk may evolve if a hurricane with characteristics similar to Irma were to occur under different global warming scenarios. Particular emphasis is placed on identifying and characterizing potential abrupt shifts in future flood risk and their underlying physical and socio-economic drivers.
To achieve this, we adopt an integrated modeling framework that combines components often treated separately. Hydrodynamic processes—including storm surge, tides, and waves—are simulated using meso-scale models. These hydrodynamic outputs then serve as forcing for the 2D surfbeat version of the XBeach model, which simulates coastal flooding, erosion, and their interactions across the entire Miami region. Flood risk is subsequently quantified by coupling hazard outputs with exposure data for population and built capital. Climate change impacts are incorporated through scenario-based projections of sea-level rise and associated long-term shoreline retreat.
Results reveal a nonlinear escalation of coastal flood risk, characterized by two distinct critical thresholds. The first, affecting population exposure, emerges around +1.5 °C of global warming, when sea-level rise exceeds the imposed inundation threshold, allowing storm surge to propagate further inland. The second critical threshold, associated with economic damages, occurs near +5 °C of global warming and is driven by the near-complete permanent inundation of the Miami Beach peninsula.
How to cite: Chaigneau, A. A., Toimil, A., Álvarez Cuesta, M., and Menéndez, M.: Miami’s Coastal Flood Risk Under Climate Change: Abrupt Shifts Informed by Hurricane Irma, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20600, https://doi.org/10.5194/egusphere-egu26-20600, 2026.
Compound flooding involves the interaction of multiple flood processes (e.g., coastal, fluvial, and pluvial) and is modulated by several factors (e.g., weather, climate, topography, morphology, time-lag). In many of the world’s tropical and subtropical regions, Tropical Cyclones (TCs) are a primary cause of compound flooding as they generate substantial rainfall runoff and subsequent elevated river discharge, in combination with strong winds and low-pressure systems that produce large storm surges and waves. In this study, we develop a novel 30m resolution compound flood modeling framework centered around Lisflood-FP, SCHISM-WWIII, SFINCS, FUSE, and MizuRoute to simulate compound coastal-fluvial-pluvial flooding across the continental US. This framework is demonstrated by simulating compound flooding associated with 9 historical TC events in the Greater New Orleans Metropolitan Area and the surrounding Mississippi River Delta. Findings reveal several regions that regularly encounter compound flood interactions during TC events, with the most prominent being Lake Maurepas, Lake Pontchartrain, and surrounding coastal estuary basins. For all TC events, the average flood disturbance across sites of nonlinear compound interactions is found to be underestimated by 60% or more if flood drivers are simulated separately and summed. Preliminary relationships are identified between TC characteristics and the extent and magnitude of compound flood interactions, suggesting that greater compounding correlates with intense (low center pressure, high rainfall rate, and high max wind velocity) but concentrated (small maximum wind radius) storm events. Lastly, skilled performance is observed by the model framework given the complex study area, which can be replicated for future research.
How to cite: Green, J., Neal, J., Haigh, I., Wilkinson, H., Collings, T., Addor, N., Quinn, N., Bruneau, N., Loridan, T., Mani, B., and Pranantyo, I.: A Framework for Modelling Tropical Cyclone-Induced Compound Flooding of the Continental US: Demonstrated in New Orleans, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1582, https://doi.org/10.5194/egusphere-egu26-1582, 2026.
Typhoons are among the most destructive natural hazards globally, yet systematic assessments of disaster risks and driving mechanisms in non-traditional typhoon-affected regions remain limited. This study integrates typhoon disaster statistics, historical track data, and socioeconomic indicators from 1978 to 2020 to analyze the spatiotemporal patterns and driving factors of typhoon-induced losses across 23 provincial-level regions in China. Using spatiotemporal statistics and multiple regression analysis, we find that although the population affected by typhoons and the direct economic losses in coastal areas, which are traditionally high incidence regions for typhoons, have continued to rise, this growth has slowed over the past two decades. In contrast, typhoon-induced losses have shown a significant increasing trend in China’s southern inland transition zones and northern regions, which are traditionally low-incidence areas for typhoons. Northeast China has seen a sharp rise in crop losses over the past decade, while housing damage has declined in coastal areas but increased in inland provinces such as Yunnan and Heilongjiang. Compared to 1978–1999, disaster impacts during 2000–2020 have expanded inland and northward, with relative loss metrics displaying a bimodal distribution along the south–north axis. The affected population rate has intensified inland, and while the share of economic loss in Gross Domestic Product (GDP) is declining in coastal areas, the proportion of crop losses is rising nationwide. Regression results suggest meteorological factors (e.g., typhoon frequency and intensity) dominate disaster impacts in coastal regions, whereas socioeconomic factors (e.g., GDP, population) are more influential inland. Urbanization, as indicated by impervious surface area (ISA), may play a mitigating role. These findings highlight the joint effects of climate change and socioeconomic development in shifting typhoon risks toward emerging vulnerable regions, underscoring the urgency of enhancing risk governance and adaptive capacity.
How to cite: Han, X., Shi, Z., Chen, Q., Fu, D., and Shi, H.: Non-prevailing region facing more severe Tropical Cyclone disaster losses in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2190, https://doi.org/10.5194/egusphere-egu26-2190, 2026.
Unprecedented tropical cyclones can result in catastrophic devastation due to their unforeseen impacts. This paper conducts an intercomparison of selected approaches to identify plausible unprecedented tropical cyclone scenarios. We review datasets from statistical tropical cyclone track models, hindcast archives from numerical weather prediction models, and a coupled approach of rainfall and flood modelling, comparing how each represents tropical cyclones that would be unprecedented in the historical record. Whilst highlighting the fundamental and incidental advantages and limitations of each dataset, our results demonstrate that these can and should be used to develop diverse scenarios of unprecedented tropical cyclones. We show that the plausible events that fall outside the observational record in these datasets provide a wealth of opportunities to build scenarios of unprecedented tropical cyclone for humanitarian disaster management in a way that would be both scientifically robust and imaginative, going beyond current practice. We recommend greater access and use of these opportunities by disaster risk managers and call for greater collaboration between scientists and practitioners on these questions.
How to cite: Heinrich, D., Stephens, E., Coughlan de Perez, E., Archer, L., Bloemendaal, N., Hodges, K., Hooker, H., Shepherd, T. G., Sparks, N., and Toumi, R.: Hurricanes that haven’t happened, yet: Towards identifying unprecedented tropical cyclone scenarios , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21047, https://doi.org/10.5194/egusphere-egu26-21047, 2026.
Posters virtual: Fri, 8 May, 14:00–18:00 | vPoster spot 3
EGU26-4650 | ECS | Posters virtual | VPS14
Integrating Climate Models and Coastal Risk Assessment in relation to Tropical Cyclones using an Adaptive Mesh FrameworkFri, 08 May, 14:21–14:24 (CEST) vPoster spot 3
Translating coarse-resolution climate projections into actionable, city-scale hazard information remains a critical challenge for coastal infrastructure planning worldwide. We present a transferable framework that combines adaptive-mesh numerical modeling with a physically consistent pseudo-global warming (PGW) methodology to generate high-resolution, climate-adjusted tropical cyclone (TC) scenarios. Here, we employ the CPAS (ClusterTech Platform for Atmospheric Simulation) model at variable resolutions (96-48-24-12-3 km), coupled with bias-corrected CMIP6 data under SSP5-8.5 forcing. Climate perturbations are applied using a physically consistent approach that also helps reduce model spin-up. The methodology incorporates a scale-aware physics scheme specifically validated for TCs. It bridges the scale gap between global climate models (~100 km) and decision-relevant hazard assessment (~1 km), offering a pathway applicable to coastal megacities globally.
We demonstrate the framework using five representative TCs impacting the South China coast during 2008-2021, spanning a range of intensities, sizes, and approach characteristics. Historical control simulations accurately reproduce observed storm tracks and structures, establishing confidence in the climate-perturbed scenarios. Systematic climate change signals emerge across the event portfolio: (1) variable intensity amplification (3.1-8% °C⁻¹ climate sensitivity), dependent on storm structure, with the strongest storms exhibiting the largest response; (2) nonlinear precipitation enhancement, with median increases of 30-35% and amplification up to 50% at extreme percentiles; and (3) diverse structural responses, with some storms contracting while others expand their damaging wind field.
Event-to-event differences (e.g., initial intensity, storm size, track angle, and rapid intensification) drive diverse climate responses, making uniform adjustment factors potentially misleading. The framework provides physics-based, scenario-specific hazard simulations at 3 km resolution (extendable to < 1 km), directly linkable to exposure databases for “what-if” stress-testing of historical events under future climate conditions. Although demonstrated for TCs, the framework is transferable to other storm types and regions, with adaptive meshing enabling efficient, decision-relevant hazard modeling over complex coastal terrain.
How to cite: Zheng, Y., Tam, C., Cheung, C.-C., and Sze, W.-P.: Integrating Climate Models and Coastal Risk Assessment in relation to Tropical Cyclones using an Adaptive Mesh Framework , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4650, https://doi.org/10.5194/egusphere-egu26-4650, 2026.
