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Scenario Analyses
05 / 10 Acute physical full-depth

Tropical cyclone

Cyclone wind exposure from historical tracks, scaled to a warming world.

The question it answers

Which assets sit in cyclone-exposed locations, and how does that wind exposure change as the planet warms?

What it does

Screening tropical cyclone across a portfolio

  • Screens assets for tropical-cyclone wind exposure using the historical track record.
  • Translates tracks into site wind through a parametric wind-decay treatment and annual exceedance probability.
  • Scales exposure to global warming levels so results read across scenarios and horizons.
How it works

Inputs in, banded result out

The governed model combines the inputs below into a single banded, scenario-aware result, under formal version and change control.

Inputs

  • Historical cyclone tracks (from 1980)
  • Parametric wind field & decay
  • Global warming level scaling

Governed model

Tropical cyclone

Anchored to public, peer-reviewed climate science.

Version and change controlled

Outputs

  • Asset wind-exposure band
  • Exceedance curve
  • Scenario–horizon trajectory
Inputs & sources

What goes in, and where it comes from

  • Historical cyclone tracks (from 1980)

    Best-track records

    The observed basis for where and how often cyclones pass.

  • Parametric wind field & decay

    Established wind-decay treatment

    Turns a track into site-level wind, including overland decay.

  • Global warming level scaling

    IPCC AR6 WG1 pattern-scaling

    Anchors forward change in cyclone intensity to warming levels.

Scenario assumptions

The futures we test

  • Baseline exposure is built from observed tracks since 1980 via annual exceedance probability.
  • Forward change is applied by pattern-scaling to global warming levels, anchored to IPCC AR6 WG1.
  • A v2 pathway scales CHAZ synthetic tracks by cyclone category to the relevant warming level.
Method

How the result is built

  1. Assemble historical tracks and derive an annual exceedance probability of damaging wind at each asset.
  2. Apply a parametric wind field with overland decay to get site wind speeds.
  3. Scale the exposure to the relevant global warming level using AR6-anchored pattern-scaling.
  4. Band the result on the shared physical-risk axis, per scenario and horizon.
Outputs

What you get back

  • Asset wind-exposure band

    Cyclone wind exposure at screening resolution.

  • Exceedance curve

    Annual probability of exceeding wind thresholds at the asset.

  • Scenario–horizon trajectory

    How exposure scales with warming levels.

  • Low
  • Medium
  • High

Annual exceedance probability

The chance per year of exceeding a given wind level at the asset — flatter tails mean rarer, stronger events.

0.0 0.1 0.2 0.3 0.4 0.5 Wind-intensity index →
Annual probability of exceeding a given wind level (illustrative). The flat tail at higher intensities marks rarer, more severe events.
Interpretation

How to read the band

  • A High band reflects frequent or intense modelled cyclone wind at the location — it does not predict a specific landfall.
  • Read the exceedance curve for the return-period view: the same band can hide very different tail shapes.
  • Warming-level scaling shifts intensity more than frequency in most basins — expect trajectory change concentrated in the strongest events.

Disclosure relevance

  • IFRS S2 Acute physical risk identification for wind-exposed assets.
  • IFRS S2 Resilience under warming-level scenarios.
  • TCFD Physical hazard metrics and exposure.

Limitations

  • Screening scope: a wind-exposure screen, not an engineering wind-load or cat-loss model.
  • Cyclone-driven surge and rainfall are handled in the coastal and flood models, not here.
  • The historical record since 1980 under-samples the rarest tracks; pattern-scaling carries structural uncertainty.

Screening-level analysis; not investment or engineering advice.

Bring tropical cyclone screening to your portfolio

Screened, banded and framed for IFRS S2 and TCFD disclosure.