Challenges in static climate risk modeling

“Fifty years into the project of modeling Earth’s future climate, we still don’t really know what’s coming. Some places are warming with more ferocity than expected. Extreme events are taking scientists by surprise. Right now, as the bald reality of climate change bears down on human life, scientists are seeing more clearly the limits of our ability to predict the exact future we face. The coming decades may be far worse, and far weirder, than the best models anticipated… This is a problem. The world has warmed enough that city planners, public-health officials, insurance companies, farmers, and everyone else in the global economy want to know what’s coming next for their patch of the planet… Today’s climate models very accurately describe the broad strokes of Earth’s future. But warming has also now progressed enough that scientists are noticing unsettling mismatches between some of their predictions and real outcomes… Across places where a third of humanity lives, actual daily temperature records are outpacing model predictions… And a global jump in temperature that lasted from mid-2023 to this past June remains largely unexplained… Trees and land are major sinks for carbon emissions, and that this fact might change is not accounted for in climate models. But it is changing: Trees and land absorbed much less carbon than normal in 2023, according to research published last October… The interactions of the ice sheets with the oceans are also largely missing from models, Schmidt told me, despite the fact that melting ice could change ocean temperatures, which could have significant knock-on effects… The models may be underestimating future climate risks across several regions because of a yet-unclear limitation. And, Rohde said, underestimating risk is far more dangerous than overestimating it.” #ClimateRisk #TransitionRisk https://lnkd.in/eiSRvUeF

The Probability Gap: How the mispricing of Climate Tail Risk threatens financial stability The financial sector relies on a simple yet increasingly risky misunderstanding of risk. We have traditionally regarded catastrophic climate scenarios as tail risks—unlikely events that are only a small part of our models. But what if the mathematical foundation for that perspective is flawed? This isn't a philosophical question; it's a measurable problem of model risk that needs urgent attention from any fiduciary responsible for protecting capital. After thirty years of developing enterprise risk management systems, from founding Algorithmics to creating RiskThinking.AI, I’ve learned that the biggest vulnerabilities come from assumptions we refuse to question. The evidence now shows that our core assumption about the likelihood of climate-related disaster is flawed, and it's time to revisit our understanding of risk from the ground up. The Probability Gap: Where Financial Models Diverge from Reality The gap between climate science and financial practice is evident. Recent analysis from Oxford Economics estimates a 57.5% chance of climate catastrophe scenarios. However, the standard Expected Credit Loss (ECL) models used by banks assign only a 5% likelihood to these same scenarios. This isn't a calibration mistake; it's a fundamental mismatch of scale that risks undermining systemic stability. Climate science indicates a 57.5% chance of catastrophic scenarios, yet traditional bank credit models assign them only a 5% weight. This isn't a calibration error; it's a fundamental "Probability Gap" at the heart of our financial system. We are misjudging highly probable outcomes as unlikely tail risks because our models—intended for a stable world of mean reversion—are not functioning correctly in our new, non-stationary climate reality. The result is a widespread mispricing of risk. When the data suggests that catastrophic outcomes are this probable, failing to consider them properly isn't just poor strategy—it is a breach of fiduciary duty. The only logical response is to update our framework. This requires a new technological infrastructure capable of modelling these complex, multifactor risks stochastically. After decades of building risk systems, from Algorithmics to RiskThinking.AI, I can say with certainty that the tools to do this exist today. The challenge is no longer technical; it's about leadership. Institutions that revise their planning assumptions and acknowledge the real likelihood of these tail events will gain a crucial analytical edge in the coming decades. Is your risk framework designed for the world that is, or the world that was? #ClimateRisk #FinancialRisk #RiskManagement #ESG #Finance #Adaptation #SystemicRisk #Leadership  

The challenges of climate change modeling: "The Earth is an unfathomably complex place, a nesting doll of systems within systems. Feedback loops among temperature, land, air, and water are made even more complicated by the fact that every place on Earth is a little different. Natural variability and human-driven warming further alter the rules that govern each of those fundamental interactions. On every continent except Antarctica, certain regions showed up as mysterious hot spots, suffering repeated heat waves worse than what any model could predict or explain. Across places where a third of humanity lives, actual daily temperature records are outpacing model predictions. And a global jump in temperature that lasted from mid-2023 to this past June remains largely unexplained. Per one researcher: “We have to approximate cloud formation because we don’t have the small scales necessary to resolve individual water droplets coming together." "Similarly, models approximate topography, because the scale at which mountain ranges undulate is smaller than the resolution of global climate models, which tend to represent Earth in, at best, 100-square-kilometer pixels. That resolution is good for understanding phenomena such as Arctic warming over decades. But “you can’t resolve a tornado worth anything.” "Models simply can’t function on the scale at which people live, because assessing the impact of current emissions on the future world requires hundreds of years of simulations. Some variables are missing from climate models entirely. Trees and land have been considered major sinks for carbon emissions. But it is changing: Trees and land absorbed much less carbon than normal in 2023. In Finland, forests have stopped absorbing the majority of the carbon they once did, and recently became a net source of emissions, which swamped all gains the country has made in cutting emissions from all other sectors since the early 1990s. The interactions of the ice sheets with the oceans are also largely missing from models. Changing ocean-temperature patterns are currently making climate modelers at NOAA rethink their models of El Niño and La Niña; the agency initially predicted that La Niña’s cooling powers would kick in much sooner than it now appears they will. "The models may be underestimating future climate risks across several regions because of a yet-unclear limitation. And underestimating risk is far more dangerous than overestimating it. Excerpts from The Atlantic article: Climate Models Can’t Explain What’s Happening to Earth Global warming is moving faster than the best models can keep a handle on. By Zoë Schlanger