Challenges of imperfect information in climate science

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

Not the day after tomorrow: Why we can't predict the timing of climate tipping points. Uncertainties are currently too large to accurately predict exact tipping times for critical Earth system components like the Atlantic Meridional Overturning Circulation (AMOC), polar ice sheets, or tropical rainforests. These tipping events, which might unfold in response to human-caused global warming, are characterized by rapid, irreversible climate changes with potentially catastrophic consequences. However, predicting when these events will occur is more difficult than previously thought. Climate scientists have identified three primary sources of uncertainty. -First, predictions rely on assumptions regarding underlying physical mechanisms, as well as regarding future human actions to extrapolate past data into the future. These assumptions can be overly simplistic and lead to significant errors. -Second, long-term, direct observations of the climate system are rare and Earth system components in question may not be suitably represented by the data. -Third, historical climate data is incomplete. Huge data gaps, especially for the longer past, and methods used to fill these gaps can introduce errors in statistics used to predict possible tipping times. To illustrate their findings, researchers examined AMOC, a crucial ocean current system. Previous predictions from historical data suggested a collapse could occur between 2025-2095. However, a new study revealed that uncertainties are so large these predictions are not reliable. Using different fingerprints=data sets, predicted tipping times for AMOC ranged from 2050-8065 even if underlying mechanistic assumptions were true. Knowing that AMOC might tip somewhere within a 6000-yr window isn't practically useful, and this large range highlights complexity/uncertainty involved in such predictions. Researchers conclude that while idea of predicting climate tipping points is appealing, reality is fraught with uncertainties. Current methods and data are not up to the task. Research is both a wake-up call and a cautionary tale. There are things we still can't predict, and we need to invest in better data and a more in-depth understanding of systems in question. The stakes are too high to rely on shaky predictions. While study shows we can't reliably predict tipping events, possibility of such events can't be ruled out either. Statistical methods are still very good at telling us which parts of the climate have become more unstable. This incl not only AMOC, but also Amazon rainforest and ice sheets. The large uncertainties imply we need to be even more cautious than if we were able to precisely estimate a tipping time. We still need to do everything we can to reduce our impact on the climate, first and foremost by cutting greenhouse gas emissions and greed of people! Even if we can't predict tipping times, probability for key Earth system components to tip still increases with every tenth of a degree of warming!

Statistical early warning signals for tipping points need rigorous testing before we can rely on them to predict catastrophic climate shifts. Current methods claiming to detect approaching climate tipping points may produce false alarms due to fundamental ambiguities in how we interpret statistical changes in climate data. While critical slowing down occurs near tipping points, it also appears in many systems experiencing gradual transitions without catastrophic shifts. Researchers found that identical statistical changes can occur both in climate systems approaching irreversible tipping points AND those undergoing smooth, reversible transitions, challenging our ability to distinguish actual danger signals from normal climate variability. Additionally, data quality issues create misleading trends in up to 30% of analyzed climate datasets. Scientists propose a Bayesian framework that integrates prior knowledge with detected signals to better quantify tipping probabilities, preventing overconfidence in ambiguous warnings. By Max Rietkerk, Vanessa Skiba, Els Weinans, Raphaël Hébert, and Thomas Laepple.

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