CAT models can express climate change in financial terms
While knowing the percentage change in the frequency of extreme rainfall by 2050 may be useful for some applications, it tells us nothing about the financial implications of this change. For example, how does this translate to property damage, insurance affordability or the probability of default on a home loan?
CAT models provide a vast amount of information on the probability of a certain level of dollar loss from extreme weather events being exceeded. Fortunately, this can be condensed down into a very powerful physical climate risk metric – the Average Annual Loss (AAL).
The AAL describes the dollar loss that can be expected in any given year, under a set of stable climate conditions. The AAL is the basis for the technical cost of insurance (or the break-even cost for an insurer) and is therefore a directly relevant metric for insurance and banking, where much of the climate risk is related to insurance affordability.
A CAT model can be ‘climate-conditioned’ in a number of ways, so that the event set in the model reflects projected future climate states, rather than present day conditions.
The result is an AAL for today, and an AAL for a range of climate futures. The difference between the present and future AAL is typically expressed as a median percentage change and is called ‘delta AAL’.
Delta AAL includes what we know about the relative contribution of each peril to a portfolio today, the climate-conditioning of each peril-specific CAT model, and the aggregation of these results into a single, financially actionable climate risk metric.
CAT models can distinguish between volatility and climate change
The bottom line for most boards when presented with a climate risk assessment is whether any change is material to their business. In other words, could the magnitude of the projected change be expected year-on-year due to natural variability (in the climate, or financial markets), irrespective of underlying climate change?
This is a difficult question to answer and is often left in the too-hard basket. However, by leveraging the probabilities in the CAT model event set, the climate change signal (delta AAL) can be presented in the context of expected year-on-year variability.
In a recent study3, Aon developed a method to determine whether a delta AAL was in fact significant or not, for a range of climate futures. This provides some realism around the materiality of projected physical climate risk and allows the client’s risk appetite to be considered in what is deemed as material.
Gaps and opportunities for CAT models
While CAT models have a lot to offer for assessing climate risk, there are still several gaps and opportunities for improvements. To provide a more complete TCFD solution, CAT modellers need to also consider how we assess chronic climate hazards, supply chain risk, and peril correlations.
Physical climate risk does not stop at extreme weather perils. Chronic climate hazards, such as drought, heatwaves, and water availability, can also have significant impacts on business operations but for which there is no dedicated CAT model solution.
In addition, CAT models assess risk on an in-situ basis, but physical climate risk can also be inherited upstream, and propagated downstream, through a supply chain. The interconnectivity of assets is not considered in the traditional CAT modelling approach.
Finally, the reactionary nature of CAT model development means the coverage of available models – across perils and regions – remains incomplete. This means the frequency correlation between different perils modelled across the same region is often not considered, and the loss correlation between global regions is difficult to quantify. This is a problem for those with a global portfolio today and compounded by the fact that these correlations are likely to change in the future with climate change.
For the future
CAT models can put a dollar loss value on extreme weather risk and have been doing so for the past several decades. Their holistic view of risk, financial applications and reasonable handling of tail risk and uncertainty, makes them well placed to measure the impacts of future climate risk for a large range of industries beyond insurance. However, they do not (yet) offer a seamless global coverage, do not include climate events such as heatwaves or water availability, and struggle with supply chain factors. If these gaps can be supplemented by other data sources, or incorporated by CAT model vendors, this would represent a truly holistic physical climate risk solution.
 IPCC (1990). Climate Change: The IPCC 1990 and 1992 Assessment.
 McAneney et al. (2019). Normalised insurance losses from Australian natural disasters: 1966–2017. Environ. Haz., 18(5), 414-433.
 Mortlock, T., Knight, J. (2022). Measuring climate change impacts using CAT models. 2022 All Actuaries Summit, Melbourne, Australia.