Climate risk has become one of the most frequently discussed topics in insurance, but from where I sit as a climate scientist who has spent two decades studying the performance and use of climate models, including their ability to represent weather extremes, climate risk is still widely misunderstood.
In the recent Send Infuse webinar, I joined the conversation to help bridge the gap between climate science and underwriting practice. What became clear is that climate risk is no longer a distant scenario or a theoretical modeling exercise. It is already shaping losses, challenging long‑held assumptions, and exposing structural weaknesses in how insurers make decisions.
The industry, from a scientific perspective, is being asked to do something incredibly difficult, which is to try and understand climate change impacts in financial material terms. This translation of climate to financial risk involves the use of tools designed to represent past relationships between hazard and loss. These tools are now applied to explore a future that may look very different from today. Can these tools be informative about future losses? This tension sits at the heart of today's climate-risk challenge.
Climate Losses Are Surpassing Expectations, and It's Not Just Exposure Growth
Recent events illustrate the scale of the shift. Italy's 2023 floods, France's 2022 hailstorms, and the Los Angeles wildfires of early 2025 all produced losses far beyond what models anticipated. These aren't isolated anomalies, and they reflect a pattern we see across multiple regions and perils.
The science tells us hazard behavior is changing. In many places, we are observing nonlinear shifts — including threshold effects, intensification patterns, and clustering of extremes — that traditional catastrophe models were never designed to capture. In a transient climate, it is reasonable to regularly reassess models to assure users that they are still fit for purpose.
This is why I often emphasize that climate risk cannot be reduced to a new data layer or a simple model adjustment. It requires a deeper understanding of how physical systems behave under stress, and how those behaviors diverge from historical norms.
Why Traditional Models Struggle with Climate Scenarios
One of the most common misconceptions I encounter is the belief that we can simply "climate-adjust" existing catastrophe models and use them to explore future scenarios. But catastrophe models were built for a specific purpose: to price financial loss. They are essentially statistical models calibrated to a current reality — in terms of hazard but also socio-economic conditions that shape building standards, defense infrastructure, disaster response readiness, and ability to afford insurance, etc.
Climate models, or ensembles of climate models, by contrast, are designed to explore a plausible uncertainty space (socio-economically, and physical model response). Importantly, the socio-economic scenarios are not assigned probabilities or likelihoods. When regulators ask insurers to assess long-term climate scenarios, there is a clash of scientific perspectives. The mindset of insurance is focused on estimating a likely loss using predictive tools. From a climate science perspective, approaches and tools are designed for understanding system response. By exploring the plausible, firms can assess their ability to meet different types of challenges should they eventuate. If deriving a single estimate of future loss, how meaningful is this value for risk management when in fact many others are plausible?
This is why I have said that "the tools are not developed to be applied in a climate scenario approach where we don't understand how losses might occur." It is reasonable to assume that near-term projections are serviceable, but the further we look into the future, the more cautious we must be about the assumptions implicitly embedded in our tools.
The insurance sector is now at the stage where many other industries have found themselves. They are using familiar tools to answer unfamiliar questions. Eventually, organizations realize that the questions themselves need to change to seek information that is, if not predictable, at least actionable. This implies a shift from a predictive mode to an anticipatory mode of management for the far-forward time horizons.
The Real Challenge Is Governance, Not Just Analytics
While the scientific limitations are real, the operational challenges inside insurance organizations are equally significant. Climate-relevant insights often fail to travel through the organization in a meaningful way. Underwriters may be doing sophisticated work such as running local flood maps, tracking wildfire accumulation, and monitoring aggregates, but that insight rarely reaches reserving, capital, or the board. This is not a failure of effort. It is a failure of structure.
Insurance organizations are highly optimized for efficiency. Information flows sequentially, not collaboratively. Each function performs its role well, but climate risk requires a different kind of conversation — one that cuts across silos and acknowledges uncertainty rather than seeking to reduce it. We need to connect the chain links together to talk about how we do things in a more meaningful way. If we don't, climate risk becomes fragmented, and the organization loses the ability to see the full picture.
Climate Risk Is About Resilience, Not Just Pricing
One of the most important shifts insurers must make is recognizing that climate risk is not solely a pricing problem. It is a resilience problem. And resilience operates across multiple time horizons.
Near-term risks may be manageable with existing tools, but long-term risks require different approaches to monitoring climate drivers, understanding adaptation pathways, and then recognizing that exposure, hazard, and vulnerability will all evolve. It is unrealistic to assume that building standards, land-use planning, or national adaptation strategies will remain static. Yet many scenario exercises implicitly make that assumption.
This is why I encourage firms to think beyond model outputs and ask: What aspects of risk are we capturing? What aspects are we missing? And what decisions are we trying to inform?
A Path Forward: More Cross-Fertilization, More Curiosity
The insurance sector is early in its climate-risk journey, but it is not alone. Sectors such as water management, infrastructure planning, and emergency response have been grappling with deep uncertainty for years. There is much to learn from their approaches, particularly around decision-making under uncertainty.
We need more cross-fertilization between climate adaptation communities and the insurance sector. We need more conversations that bring together scientists, underwriters, actuaries, and risk managers. And we need leadership that recognizes climate risk as a strategic issue, not a compliance burden.
Climate risk is more than a model output. It is a shift in how we understand the world, how we interpret uncertainty, and how we make decisions in the face of change.
The Bottom Line
Climate risk now lives inside the underwriting workflow because the physical world is changing faster than our tools and structures were designed to handle. The challenge is not simply to refine models, but to rethink how information flows, how decisions are made, and how uncertainty is embraced rather than ignored.
As I said during the Infuse webinar, “climate risk is more than data layers and output from catastrophe models. It’s about understanding what risks might look like at different time horizons.” That understanding requires curiosity, collaboration, and a willingness to evolve.
The insurance sector has the expertise and the ingenuity to meet this challenge. It now requires new conversations and new ways of listening.
