- Climate Proof
- Posts
- Climate Proofers: Thomas Mortlock On Cat Model Misunderstandings
Climate Proofers: Thomas Mortlock On Cat Model Misunderstandings
Hear from experts on the cutting-edge of adaptation & resilience
Support Climate Proofers and get full access to the twice-a-week Climate Proof newsletter by becoming a premium subscriber👇
Catastrophe (or ‘cat’) models are essential tools for estimating losses from natural disasters. With the rise of climate risk management as a discipline in its own right, efforts are underway to harness these tools to guess the effects of human-induced warming on hurricanes, floods, storms, and other catastrophes.
However, in the rush to develop effective climate risk analytics, we may be trying to force cat models to do things they weren’t designed to — and promoting a flawed way of thinking about the link between climate change and financial loss in the bargain.
Thomas Mortlock, from reinsurance broker and risk solutions company Aon, joins Climate Proofers to explain what cat models can and can’t do, unpack the relationship between climate risk and insured losses, and get into the knotty problem of slow-onset events, like sea-level rise.
Listen below, download from the Podcasts page on Climate Proof, or tune in via Spotify or Apple Podcasts.
We talk about:
👉 Why insured losses from natural catastrophes have increased in recent years, and why climate change is just part of the answer
👉 Why insured loss records aren't the place to look for evidence of escalating climate risks
👉 How cat models are being used to gauge climate risks, and why coupling them with climate models for future projections remains a challenge
👉 The challenges of insurance and housing affordability, and how climate change is exacerbating them
👉 The high-end uncertainties surrounding rapid sea-level rise
Future guests include Michael Jacobs of IBM and Alan Leung of Macquarie Group.
Like what you hear? Then make sure to follow the pod and consider leaving a review!
Thanks for listening!
Louie Woodall
Editor
