Meiler et al. (2026) Global coastal wind hazard maps from the CHAZ tropical cyclone model

Identification

• Journal: Scientific Data
• Year: 2026
• Date: 2026-01-17
• Authors: Simona Meiler, C. Y. Lee, Suzana J. Camargo, A. S. Sobel
• DOI: 10.1038/s41597-025-06452-0

Research Groups

• Civil and Environmental Engineering, Stanford University, USA
• Institute for Environmental Decisions, ETH Zurich, Switzerland
• Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
• Columbia Climate School, Columbia University, New York, NY, USA
• Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA

Short Summary

This study presents global coastal wind hazard maps, derived from the CHAZ tropical cyclone model, for present-day and future climate conditions, depicting tropical cyclone intensity exceedance probabilities and return periods to support disaster preparedness and risk assessment. The dataset translates complex synthetic tropical cyclone track data into accessible, spatially explicit hazard layers for broader interdisciplinary use.

Objective

• To generate and provide global coastal wind hazard maps, derived from the Columbia tropical cyclone hazard model (CHAZ), that depict tropical cyclone intensity exceedance probabilities and return periods for present-day and future climate conditions, thereby making complex tropical cyclone information accessible for diverse impact analyses.

Study Configuration

• Spatial Scale: Global coastal regions, with a horizontal resolution of approximately 9.3 kilometers (300 arcseconds) over land and 1 degree (3600 arcseconds) over the ocean.
• Temporal Scale:
  • Historical reference: 1981–2019 (ERA5 reanalysis-based)
  • Present-day baseline: 1995–2014 (GCM-driven)
  • Mid-century future: 2041–2060
  • End-of-century future: 2081–2100 (Future periods are 20-year time windows.)

Methodology and Data

• Models used:
  • Columbia HAZard model (CHAZ): A statistical-dynamical tropical cyclone (TC) model.
  • CLIMADA (CLIMate ADAptation) v6.0.2-dev: An open-source probabilistic climate risk modeling platform (Python 3.11+).
  • Holland (2008) parameterization: Used within CLIMADA for calculating 1-minute sustained winds at 10 meters above ground.
  • Beta-and-advection model: Used for simulating storm trajectories.
  • Autoregressive linear model: Used for modeling TC intensity along tracks.
  • Tropical Cyclone Genesis Index (TCGI): Developed by Camargo et al. (2014) and Tippett et al. (2011), with two alternative moisture variable formulations: Column-Integrated Relative Humidity (CRH) and Saturation Deficit (SD).
• Data sources:
  • ERA5 reanalysis data: European Centre for Medium-Range Weather Forecasting’s fifth-generation climate reanalysis dataset (1981–2019).
  • Coupled Model Intercomparison Project Phase 6 (CMIP6) models: Outputs from six GCMs (CESM2, CNRM-CM6-1, EC-Earth3, IPSL-CM6A-LR, MIROC6, UKESM1-0-LL) for future projections.
  • Shared Socioeconomic Pathways (SSPs): SSP2-4.5, SSP3-7.0, and SSP5-8.5 emission scenarios.
  • IBTrACS (International Best Track Archive for Climate Stewardship) observational dataset: Used for statistical inference of wind model parameters and frequency bias correction.

Main Results

• Global coastal wind hazard maps were generated for present-day and future climate conditions, depicting TC intensity exceedance probabilities and return periods.
• The maps provide local exceedance intensities for return periods of 10, 25, 50, 100, 250, and 1000 years, and local return periods for wind speed thresholds of 33 meters per second (Category 1) and 50 meters per second (Category 3 and higher).
• Global 100-year exceedance intensities derived from ERA5-downscaled wind fields show peak values exceeding 60 meters per second along known TC-prone coastlines, consistent with observational climatologies.
• Historical (1995–2014) exceedance intensities from the multi-model median GCM CHAZ baseline closely match ERA5-derived values, with differences generally within ±5 meters per second.
• Future climate projections demonstrate expected responses: CRH TCGI-based medians remain at or slightly above baseline, while SD TCGI-derived medians fall below baseline, consistent with known TC frequency changes.
• Regional comparisons of Category 1 return periods show median differences generally under ±20% between the GCM baseline and ERA5.
• Mid-century changes in return periods are modest (typically within ±25%), but end-of-century shifts intensify, with most coastlines showing increased return periods (fewer storms meeting the threshold), particularly under SSP5-8.5 and the SD variant.
• The dataset provides transparent uncertainty bounds through multi-model median values and tabulated minimum-maximum ranges across individual GCMs and TCGI variants.

Contributions

• This study provides the first global coastal wind hazard maps derived from the CHAZ tropical cyclone model, offering a valuable counterpart to existing datasets like STORM.
• It translates complex synthetic tropical cyclone track data into readily usable, spatially explicit hazard maps (exceedance intensities and return periods), significantly enhancing accessibility for broader interdisciplinary applications beyond traditional climate science and meteorological research.
• The dataset includes projections for both present-day and future climate conditions under multiple CMIP6 GCMs and Shared Socioeconomic Pathways (SSPs), incorporating two alternative TCGI moisture formulations to transparently bracket uncertainty in future TC genesis.
• The methodology leverages and integrates open-source tools (CHAZ and CLIMADA) through a consistent and reproducible workflow, promoting transparency and reusability.
• The generated dataset has already demonstrated practical value by being integrated into several applied projects, including the Natural Hazards Climate Change Projections platform, the Climate Migration Dashboard, the Greater Caribbean Climate Mobility Initiative, and a World Bank Development Group working paper, highlighting its utility for long-term climate risk assessment, adaptation planning, and regional policy design.

Funding

• Swiss National Science Foundation Postdoc.Mobility Fellowship (P500PN_222189)
• Aon plc

Citation

@article{Meiler2026Global,
  author = {Meiler, Simona and Lee, C. Y. and Camargo, Suzana J. and Sobel, A. S.},
  title = {Global coastal wind hazard maps from the CHAZ tropical cyclone model},
  journal = {Scientific Data},
  year = {2026},
  doi = {10.1038/s41597-025-06452-0},
  url = {https://doi.org/10.1038/s41597-025-06452-0}
}