Model Catalog
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climate
NOAA GFDL Physical Risk Lens
Climate
Current
active
Process-focused climate lens with strong physical-system grounding.
Horizon 2025–2100
Geography Global (high-resolution ocean-atmosphere coupling)
Resolution Process-level earth system; sector via hazard overlay
Projection years 2030, 2040, 2050, 2060, 2080, 2100
Tropical cyclone intensity
Compound multi-hazard events
Hydrological cycle
Sea-level rise (ice dynamics)
Ocean heat content
Coastal flooding
Drought intensity
Methodology
The NOAA Geophysical Fluid Dynamics Laboratory (GFDL) produces process-based earth system models (CM4 and ESM4) with particular strength in hydrology, ocean heat content, and coupled atmosphere-land dynamics. GFDL models are considered the most physically credible for compound events involving multiple interacting systems — tropical cyclones, storm surge, riverine flooding, and drought — that cause the highest insurance and infrastructure losses. CE uses GFDL for physical plausibility validation: when CMIP6 ensemble outputs produce extreme tails, GFDL process fidelity provides a sanity check. GFDL hazard signals are generally more conservative than CMIP6 ensemble tails but more credible for hydrology-sensitive sectors.
Key Mechanisms
- Coupled ocean-atmosphere dynamics: GFDL explicitly models ocean heat uptake and release, creating more credible tropical cyclone intensity projections
- High-resolution hydrology: GFDL's land surface model produces river discharge and groundwater recharge projections at finer resolution than most CMIP6 models
- Sea level rise from ice dynamics: GFDL's ice sheet model provides physically grounded sea level rise projections beyond 2050
- Compound event simulation: the model captures co-occurring multi-hazard events (wind + storm surge + precipitation) that produce tail losses
- Process plausibility check: GFDL outputs are used to constrain the CMIP6 ensemble tail by eliminating physically implausible outliers
Best For
hydrology, coupled earth-system dynamics, and process credibility
Strengths
- Highest physical process credibility for hydrology, ocean-driven hazards, and compound events — particularly relevant for insurance tail calibration
- Sea level rise and coastal flooding projections are the most rigorous available for long-duration asset risk (Prologis, Brookfield coastal portfolios)
- Compound event modelling captures correlated multi-hazard scenarios that single-hazard models systematically underestimate
Limitations
- Computationally expensive: fewer scenario runs than CMIP6 ensemble, creating less coverage of SSP pathway diversity
- Less suited to rapid policy scenario iteration — designed for physical process analysis rather than flexible policy scenario exploration
- Transition pressure signals are derived indirectly from implied carbon price pathways rather than from the model's physical outputs directly
Industry Signal Dashboard
— projected signals from this model across all tracked industries
Physical Hazard Pressure by Industry
Physical hazard index (0–1) indicating asset and operational exposure to climate-related physical risks.
Transition Pressure by Industry
Regulatory and market pressure from the low-carbon transition — 0 (low) to 1 (high).
Adaptive Resilience by Industry
Resilience index (0–1) — the industry's estimated capacity to adapt to physical and transition risk.
Industry Context
Energy
GFDL's coupled ocean-atmosphere dynamics provide the highest-fidelity projections of tropical storm intensification (offshore rig exposure for Aramco and ExxonMobil) and sea surface temperature trends (hurricane tracks affecting Gulf Coast energy infrastructure). GFDL's coastal flooding and storm surge projections are the most credible for NextEra's Florida coastal solar and wind assets.
Agriculture
GFDL provides the most physically credible projections of hydrological change for agriculture — river discharge, groundwater recharge, and drought intensity for irrigation-dependent regions. These projections are critical for JBS's Brazilian cattle operations, Cargill's corn belt sourcing, and Bunge's South American grain origins. GFDL's agricultural hazard signal is typically the most extreme of the three CE climate models for water-stressed regions.
Manufacturing
GFDL's physical process credibility benefits industrial facilities requiring water-intensive cooling or processing. Its superior land surface model captures water availability risk for BASF's Rhine-dependent Verbund system and Rio Tinto's water-stressed Pilbara operations. GFDL's soil erosion and geotechnical risk projections are also relevant for ArcelorMittal's mine-to-port infrastructure.
Transport
GFDL provides the most credible projections of tropical cyclone track and intensity changes affecting shipping routes (Maersk's trans-Pacific and Atlantic corridors) and aviation disruption (Delta's Gulf Coast hub exposure). Its sea level rise projections for port infrastructure (Maersk terminal network) are the reference for long-duration asset risk. GFDL's transport hazard signals reflect physical reality of increased extreme weather on route scheduling.
Insurance
GFDL produces the most process-grounded probability distributions for extreme weather events that drive insurance loss scenarios. Its coupled model architecture captures co-occurring multi-hazard events (compound flooding + wind) that cause the highest insurance losses, making it the tail-risk calibration anchor for Allianz, Munich Re, and Swiss Re's catastrophe models in CE's insurance sector calibration.
Real Estate
GFDL's coastal flooding and sea level rise projections are the most physically grounded of the three CE climate models. For Prologis's logistics hubs, Brookfield's diversified global portfolio, and British Land's London flood risk, GFDL provides the highest-confidence assessment of chronic inundation risk for long-duration real estate holdings. Its urban pluvial flood hydrology also calibrates Vonovia's German residential flood exposure.