Why climate modeling needs continuous updates

“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 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

91% of climate tech AI projects fail in production. Not because of bad models. Because of bad infrastructure.   After analyzing 847 production failures at Net0link and through The Fix Loop community, I've found the same pattern:   👉 Everyone obsesses over model accuracy during development. 👉 Nobody thinks about model decay in production.   Your 99.6% accurate energy prediction model? It'll lose 2-3% accuracy every week once deployed.   Why? 👉 Building occupancy patterns shift 👉 Weather patterns change seasonally 👉 Equipment degrades over time 👉 Energy prices fluctuate 👉 Your training data becomes stale   The fix isn't better models. It's better infrastructure.   That's why its critical to have Climate infrastructure layer. Why? 👉 Continuous retraining pipelines 👉 Drift detection systems 👉 Automated rollback mechanisms 👉 Provenance tracking for reproducibility   Because the climate crisis doesn't wait for you to debug your model at 3 AM.   What's your experience with model decay in production?   #MakAIlessons #MachineLearning #ClimateAI #MLOps #ProductionAI

Is ‘historical data’ still a reliable guide for climate risk models? Imagine winning a million-dollar lottery, followed by months of travel, big spending, and generous giving. Now, would you make a purchase today based on the bank balance from the day that money first hit your account? Of course not! Climate risk works the same way. Most models assume climate variables fluctuate around a stable baseline. But that baseline is no longer stable - it’s continuously shifting. For businesses, this shows up as: •⁠ ⁠Assets priced on outdated risk •⁠ ⁠Insurance underwriting yesterday’s exposure •⁠ ⁠Capex decisions built for climate conditions that no longer exist •⁠ ⁠“Once-in-a-century” events arriving every few years The Network for Greening the Financial Sector (NGFS - https://www.ngfs.net/en) notes that “Continued emissions of greenhouse gases since the industrial revolution have led to about 1.2 °C of global warming. While this change is seemingly small, current temperatures are unprecedented in at least the past 12,000 years”. In summary, while historical data still matters, treating it as a forecast is like trying to spend money you don’t have anymore. For asset-owners, investors and insurers, it’s time to use real-time & continuously evolving data from advanced AI tools like #DEFINE to accurately estimate and predict your exposure while protecting asset value! #ClimateRisk #NonStationarity #Data #RiskManagement #CapitalAllocation #ClimateChange