Experts can produce very different dollar estimates for climate change’s contribution to one flood, hurricane or heat wave without either calculation being wrong. The reason is that attribution studies answer different counterfactual questions: one may ask how much warming changed the event’s probability, while another asks how much it increased the event’s severity. Those methods can be theoretically consistent, yet yield sharply different costs.
What an attribution estimate is actually measuring
Extreme-event impact attribution compares the real event with a counterfactual world without human-caused warming. Researchers estimate the event’s total observed damage, then calculate how the event might have differed in that alternative climate.
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The counterfactual is not directly observable. A study must define which aspect of the event is being changed and which is being held constant. That choice determines what “climate-change damage” means in the resulting estimate.
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A frequency-based analysis estimates how much warming increased the probability of an event reaching a particular threshold. If climate change made a flood twice as likely, the analysis may assign a substantial share of the event’s damage to the altered odds, even if the flood’s physical intensity changed only modestly.
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Intensity-change attribution
An intensity-based analysis keeps the occurrence of the event in view but estimates how much warmer conditions increased its severity—for example, rainfall totals, wind speed, storm surge or the duration of dangerous heat. It then attributes the additional damage associated with that added intensity.
Because probability and severity are different dimensions of risk, the two approaches need not produce the same percentage. The Eos report by Rebecca Owen, published 16 October 2026, states: “Because both methods can be consistent, and therefore valid, the costs of climate change from an individual event are inherently ambiguous.”
Hurricane Harvey shows why the numbers diverge
Hurricane Harvey caused an estimated $90 billion in damage, according to the American Geophysical Union’s Eos coverage in 2026. A frequency-change analysis attributed 75% of those costs to climate change. An intensity-change analysis attributed 15%.
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| Question being asked | Method | Harvey result |
|---|---|---|
| How much did warming alter the event’s probability? | Frequency-change attribution | 75% of estimated costs |
| How much did warming increase the event’s severity? | Intensity-change attribution | 15% of estimated costs |
| What was the observed loss? | Total recorded damage estimate | $90 billion |
The percentages do not mean that one study found $67.5 billion of “real” climate damage and the other found $13.5 billion. They are outputs from different counterfactual constructions applied to the same event. The $90 billion figure is the total estimated loss; it is not itself a measure of damage caused by climate change.
Why perfect information would not create one definitive answer
Attribution uncertainty is not only a measurement problem. Owen’s Eos report says that no technical breakthrough—or even perfect knowledge of climate and society—could eliminate this ambiguity, because the frequency and intensity questions remain distinct. That limits how directly event attribution can guide adaptation, recovery decisions or litigation.
Researchers and decision-makers should therefore report the method, counterfactual, damage definition and uncertainty range alongside any percentage. A single point estimate without those assumptions can imply a level of precision the analysis does not provide.
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Event attribution is different from the social cost of greenhouse gases
Policy agencies use another kind of monetary estimate: the social cost of greenhouse gases. The U.S. Federal Register defines it as the monetary value of the net harm from a marginal increase in greenhouse-gas emissions.
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That accounting is broader than the cost of one hurricane or flood. Depending on the framework, it can include:
- agricultural productivity losses;
- human-health effects;
- property damage from floods and other disasters;
- energy-system disruption;
- conflict risk and environmental migration; and
- ecosystem services and other nonmarket effects.
A marginal-emissions estimate is designed for comparing an additional unit of emissions or evaluating policy, not for assigning a final bill to one named event. It may combine impacts across regions, sectors and decades, while an event study focuses on a particular place and time.
Why social-cost estimates also vary
Integrated assessment models do not fully represent every consequence of warming. The Federal Register’s discussion identifies several sources of uncertainty:
- catastrophic and non-catastrophic impacts that are incompletely modeled;
- assumptions about adaptation and technological change;
- links between regions and economic sectors;
- extrapolation of damages at very high temperatures;
- the discount rate used to compare future losses with present costs; and
- uncertainty about long-run economic growth.
Changing any of these assumptions can alter the estimated value of avoided emissions. Two analyses may use the same broad climate science yet reach different monetary results because they include different harms or place different weight on future generations.
Ambiguity aversion can change what people are willing to pay
Some climate risks have probabilities that are uncertain, not merely low. Olijslagers and van Wijnbergen’s peer-reviewed 2024 study examines this “ambiguity aversion”—the tendency to dislike unknown probabilities as well as bad outcomes.
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The study identifies two opposing channels in the social cost of carbon. Ambiguity aversion can have a positive direct effect, increasing willingness to pay to avoid climate risk. But it can also have a negative effect through discounting. The final valuation depends on how those channels interact, so treating an uncertain probability as if it were known can materially change the result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Planning estimates carry a separate layer of uncertainty
Long-term transition plans are not forecasts of one event. The UK Climate Change Committee lists population and GDP growth, emissions inventories, fuel and technology costs, weather variability and climate impacts as major uncertainties in its net-zero pathway.
In its Seventh Carbon Budget, quantified relative uncertainty rises from 11% in 2030 to 28% in 2040. Those figures describe uncertainty around the pathway, not a probability that a particular storm will occur. They illustrate why infrastructure and climate plans should be stress-tested across several plausible futures rather than built around one central estimate.
How to read a climate-damage number responsibly
- Identify the object being priced. Is it one event, an additional amount of emissions, a national policy, or a global stream of damages?
- Check the counterfactual. For an event, determine whether the study changes frequency, intensity, or both.
- Separate total loss from attributed loss. The observed damage includes exposure, vulnerability, development patterns and non-climatic drivers as well as climate influence.
- Review scope. Note whether health, ecosystems, migration, conflict and other nonmarket effects are included.
- Look for adaptation assumptions. A result can change depending on whether households, cities or industries are assumed to adjust.
- Check the discount rate and time horizon. These choices strongly affect comparisons between present costs and future harms.
- Prefer ranges and scenarios. A range with explicit assumptions is more informative than an apparently exact number detached from its method.
What the estimates are good for—and what they cannot do
These valuations remain useful. They can compare risks, reveal which assumptions drive a decision, support investment in resilience and help policymakers weigh emissions reductions against other priorities. They can also show how much conclusions change when health, ecosystems or catastrophic outcomes are added.
They cannot provide a single, final, universally correct price for climate change’s share of every disaster. For event attribution, frequency and intensity methods answer different but defensible questions. For social-cost calculations, model structure, discounting, scope and uncertain probabilities produce additional variation. The practical standard is transparency: state the question, assumptions, method and range before presenting the number.
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