Climate intervention data modeling challenges

Can climate models reproduce observed trends? The answer can be challenging. Our new review paper in Science Advances led by Isla Simpson and Tiffany Shaw discusses challenges and ways forward in confronting climate models and observations. It's tricky. Climate models and observations may disagree (1) by chance, due to unforced internal variability, (2) due to error in the model response, (3) due to inaccurate prescribed external forcings, (4) due to incomplete or uncertain observations or (5) due to inappropriate comparison methods. The paper discusses ways forward in disentangling the reasons for potential mismatches between observed and simulated trends. It provides a long catalogue of examples of success, discrepancies and unclear situations that require further attention. https://lnkd.in/dHrEJfDh Let by Isla Simpson and Tiffany Shaw with Paulo Ceppi, Amy Clement, Erich Fischer, Kevin Grise, Angeline Pendergrass, James Screen, Robert Jinglin Wills, Tim Woollings, Russell Blackport, Joonsuk Kang, and Stephen Po-Chedley supported by US CLIVAR

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

“All models are imperfect,” says Sankar Arumugam, corresponding author of the paper and a professor of civil, construction and environmental engineering at NC State. “Sometimes a model may underestimate rainfall, and/or overestimate temperature, or whatever. Model developers have a suite of tools that they can use to correct these so-called biases, improving a model’s accuracy. “However, the existing suite of tools has a key limitation: they are very good at correcting a flaw in a single parameter (like rainfall), but not very good at correcting flaws in multiple parameters (like rainfall and temperature),” Arumugam says. “This is important, because compound events can pose serious threats and – by definition – involve societal impacts from two physical variables, temperature and humidity. This is where our new method comes in.” The new method takes a novel approach to the problem and makes use of machine learning techniques to modify a climate model’s outputs in a way that moves the model’s projections closer to the patterns that can be observed in real-world data.