A global climate model tells you the planet is warming. But what does that mean for a specific warehouse in Houston, a vineyard in Provence, or a data center in Singapore? The translation problem sits at the heart of climate risk management—and it's harder than it looks.

Climate science operates at continental scales with multi-decade horizons. Investment decisions operate at asset-specific scales with quarterly pressures. Bridging this gap requires more than just zooming in on a map. It demands understanding how uncertainty compounds, how local geography reshapes global trends, and how physical hazards become financial losses.

The good news: frameworks and methodologies exist to make this translation. The sobering news: they come with significant limitations that practitioners must understand. Getting this right determines whether climate risk assessment produces actionable intelligence or expensive false precision.

Global climate models divide Earth into grid cells typically 50-100 kilometers wide. Useful for understanding planetary dynamics, nearly useless for assessing a specific property. Downscaling attempts to bridge this gap through statistical or dynamical methods—each with distinct trade-offs.

Statistical downscaling uses historical relationships between large-scale climate patterns and local weather to project future conditions. It's computationally cheap but assumes past relationships hold under novel climate states. When atmospheric physics shift fundamentally, historical correlations may break down precisely when you need them most.

Dynamical downscaling nests high-resolution regional models within global models, simulating local physics directly. More physically grounded but computationally expensive and still constrained by boundary conditions from coarser parent models. A regional model can only be as good as the global model feeding it.

The critical insight for practitioners: downscaled projections inherit and often amplify uncertainties from global models. A temperature projection with ±2°C uncertainty at global scale might carry ±4°C uncertainty at asset level. Treating downscaled outputs as precise predictions rather than probability distributions leads to systematically overconfident risk assessments.

Takeaway

Downscaled climate data provides informed approximations, not precise predictions. Build risk assessments around ranges and scenarios rather than single-point estimates, and stress-test decisions against the upper bounds of uncertainty.

Climate science describes physical changes—more intense rainfall, higher temperatures, rising seas. Finance cares about dollars at risk. The Hazard-Exposure-Vulnerability framework provides the conceptual bridge, decomposing risk into three distinct components that multiply together.

Hazard captures the probability and intensity of climate events at a location. Exposure describes the assets present—their value, criticality, and irreplaceability. Vulnerability measures how sensitive those assets are to hazard impacts and their capacity to recover. A flood of identical depth causes vastly different losses depending on whether it hits a parking lot or a semiconductor fab.

The framework's power lies in separating variables that require different expertise. Hazard assessment draws on climate science. Exposure analysis requires asset inventories and valuations. Vulnerability estimation combines engineering knowledge, operational understanding, and historical loss data. Each component can be updated independently as better information emerges.

Implementation challenges cluster around vulnerability estimation, which tends to be the weakest link. Physical damage functions for many asset types remain poorly calibrated. Indirect losses from supply chain disruption, business interruption, and reputational damage often exceed direct physical damage but resist quantification. Conservative practitioners treat modeled losses as floors rather than best estimates.

Takeaway

The Hazard-Exposure-Vulnerability framework structures climate risk analysis into separable components, but vulnerability estimation—particularly for indirect losses—typically drives the largest uncertainties in final impact numbers.

Individual asset risks matter, but strategic decisions require portfolio-level views. How do physical climate risks concentrate, correlate, and compound across an enterprise or investment portfolio? Aggregation introduces its own analytical challenges beyond summing individual exposures.

Geographic concentration creates the most obvious portfolio risk. A real estate portfolio heavy in Florida coastal properties faces correlated hurricane exposure. Climate change intensifies this correlation as warming oceans fuel stronger storms across broader areas simultaneously. Diversification benefits erode when climate shifts affect entire regions.

Temporal correlation presents subtler challenges. A hot summer that damages crops also strains power grids, disrupts transportation, and reduces labor productivity. These cascading impacts hit multiple sectors and asset classes within the same period. Traditional risk models assuming independence between asset classes underestimate these compounding effects.

Leading practitioners integrate physical risk into existing enterprise risk frameworks rather than treating climate as a standalone category. This means translating climate hazards into familiar risk metrics—value-at-risk, earnings volatility, credit rating migration probabilities. The translation isn't perfect, but embedding climate in established decision processes beats building parallel systems that compete for executive attention.

Takeaway

Portfolio climate risk isn't simply the sum of asset-level risks. Geographic and temporal correlations mean extreme climate events can simultaneously impair multiple holdings—model these dependencies explicitly rather than assuming diversification will protect you.

Physical climate risk assessment has matured from theoretical exercise to operational necessity. The tools exist to translate global projections into local decisions—imperfectly, but usefully. The key is matching analytical precision to decision needs while honestly acknowledging uncertainty.

The practitioners getting this right share common habits: they work with scenarios rather than point estimates, stress-test against tail risks, and update assessments as both climate science and local data improve. They treat models as thinking tools rather than oracle outputs.

Climate risk will remain genuinely uncertain for decades. The goal isn't eliminating that uncertainty but making better decisions despite it—allocating capital more wisely, pricing risk more accurately, and building resilience where it matters most.