climate

evidence-of-“snowball-earth”-found-in-ancient-rocks

Evidence of “snowball Earth” found in ancient rocks

climate, geology, Science, snowball earth / /

On ice —

An outcrop in Scotland has material from when the Earth went into a deep freeze.

–

Image of a white planet with small patches of blue against a black background.

Enlarge / Artist’s conception of the state of the Earth during its global glaciations.

Earth has gone through many geologic phases, but it did have one striking period of stasis: Our planet experienced a tropical environment where algae and single-celled organisms flourished for almost 2 billion years. Then things changed drastically as the planet was plunged into a deep freeze.

It was previously unclear when Earth became a gargantuan freezer. Now, University College London researchers have found evidence in an outcrop of rocks in Scotland, known as the Port Askaig Formation, that show evidence of the transition from a tropical Earth to a frozen one 717 million years ago. This marks the onset of the Sturtian glaciation and would be the first of two “snowball Earth” events during which much of the planet’s surface was covered in ice. It is thought that multicellular life began to emerge after Earth thawed.

Found in the Scottish islands known as the Garvellachs, this outcrop within the Port Askaig Formation is unique because it offers the first conclusive evidence of when a tropical Earth froze over—underlying layers that are a timeline from a warmer era to a frigid one. Other rocks that formed during the same time period in other parts of the world lack this transitional evidence because ancient glaciers most likely scraped it off.

“The Port Askaig preserves a relatively complete record of the global “Sturtian glaciation,” the researchers said in a study recently published in the Journal of the Geological Society.

In my snowball era

Underneath the rocks that formed during the Sturtian glaciation is a deep layer of carbonate rocks known as the Garb Eileach Formation. These were dated to the warm, tropical Tonian period, which started 1 billion years ago and lasted until 717 million years ago, when the cold took over. The youngest rocks in this formation are evidence of the transition to the first “snowball Earth.”

Why did Earth endure such a big chill to begin with? A sudden decrease in solar radiation probably led to an especially long winter that set off a (if you’ll pardon the pun) snowball effect. With less radiation, more ice forms, and more ice makes the planet more reflective, meaning it sends more sunlight back into space and causes the planet to continue cooling, allowing even more ice to form.

To find out when this global chill began, the research team collected 11 sandstone samples from the Garvellach Islands to analyze zircons in the sandstone. Zircons are especially useful in dating rock formations because they are often as old as the rock they are in, making some of them the oldest minerals on Earth. They also resist being chemically degraded. What is especially important about zircons is that they contain uranium, which decays into lead over long periods. The amount of uranium that has changed to lead can tell us the amount of time that has passed since the zircon’s formation.

Just a phase

Using both laser ablation (a type of laser imaging that reveals how elements and isotopes are distributed in a sample) and plasma mass spectrometry, the researchers determined the uranium-lead ratio. The time it had taken for the uranium to become lead was in line with their estimates, which were based on previous studies that had estimated, but not confirmed, the time of onset for the Sturtian glaciation.

Another thing that the outcrop’s zircons told the researchers is that the Sturtian glaciation lasted around 58 million years. It was closely followed by the Marinoan glaciation, thought to have lasted another 16 million years, and both these “snowball Earth” phases make up what is known as the Cryogenic period. The rocks containing these zircons were probably deposited by a moving glacier as the supercontinent Rodinia (which preceded the more famous Pangaea) was breaking apart.

The Port Askaig Formation is now, as the scientists say in the same study, “one of the thickest (up to 1.1 km) and most complete records of Cryogenic glaciation.”

Because glaciers did not scrape this formation away, a record of when Earth started to warm up again is also preserved. The zircon crystals that formed during the Sturtian glaciation gradually disappear in younger rocks until they are replaced by zircons formed after the ice began to melt. So not only is there evidence of the beginning of the deep freeze, but also evidence for the thaw that began around 635 million years ago.

As the ice melted, complex multicellular life began to burst onto the scene during the Ediacaran period. There could be several reasons for this. Hypotheses suggest that the temperature of the seawater rose, an influx of sunlight sparked photosynthesis, and there was a greater availability of nutrients than before.

The scientists involved in the Port Askaig study think that any life that survived the Cryogenian period faced an immense challenge once the ice began to melt. These organisms had been used to perpetual cold for millions of years, and now they faced the struggle of adapting as soon as possible—or perishing.

What about those that survived? They ended up being the ancestors of all animals that ever existed, and that includes us.

Journal of the Geological Society, 2024.  DOI: 10.1144/jgs2024-02

Evidence of “snowball Earth” found in ancient rocks Read More »

model-mixes-ai-and-physics-to-do-global-forecasts

Model mixes AI and physics to do global forecasts

atmospheric science, climate, Earth science, forecasting, meteorology, Science, Weather / /

Cloudy with a chance of accuracy —

Google/academic project is great with weather, has some limits for climate.

–

Image of a dark blue flattened projection of the Earth, with lighter blue areas showing the circulation of the atmosphere.

Enlarge / Image of some of the atmospheric circulation seen during NeuralGCM runs.

Google

Right now, the world’s best weather forecast model is a General Circulation Model, or GCM, put together by the European Center for Medium-Range Weather Forecasts. A GCM is in part based on code that calculates the physics of various atmospheric processes that we understand well. For a lot of the rest, GCMs rely on what’s termed “parameterization,” which attempts to use empirically determined relationships to approximate what’s going on with processes where we don’t fully understand the physics.

Lately, GCMs have faced some competition from machine-learning techniques, which train AI systems to recognize patterns in meteorological data and use those to predict the conditions that will result over the next few days. Their forecasts, however, tend to get a bit vague after more than a few days and can’t deal with the sort of long-term factors that need to be considered when GCMs are used to study climate change.

On Monday, a team from Google’s AI group and the European Centre for Medium-Range Weather Forecasts are announcing NeuralGCM, a system that mixes physics-based atmospheric circulation with AI parameterization of other meteorological influences. Neural GCM is computationally efficient and performs very well in weather forecast benchmarks. Strikingly, it can also produce reasonable-looking output for runs that cover decades, potentially allowing it to address some climate-relevant questions. While it can’t handle a lot of what we use climate models for, there are some obvious routes for potential improvements.

Meet NeuralGCM

NeuralGCM is a two-part system. There’s what the researchers term a “dynamical core,” which handles the physics of large-scale atmospheric convection and takes into account basic physics like gravity and thermodynamics. Everything else is handled by the AI portion. “It’s everything that’s not in the equations of fluid dynamics,” said Google’s Stephan Hoyer. “So that means clouds, rainfall, solar radiation, drag across the surface of the Earth—also all the residual terms in the equations that happen below the grid scale of about roughly 100 kilometers or so.” It’s what you might call a monolithic AI. Rather than training individual modules that handle a single process, such as cloud formation, the AI portion is trained to deal with everything at once.

Critically, the whole system is trained concurrently rather than training the AI separately from the physics core. Initially, performance evaluations and updates to the neural network were performed at six-hour intervals since the system isn’t very stable until at least partially trained. Over time, those are stretched out to five days.

The result is a system that’s competitive with the best available for forecasts running out to 10 days, often exceeding the competition depending on the precise measure used (in addition to weather forecasting benchmarks, the researchers looked at features like tropical cyclones, atmospheric rivers, and the Intertropical Convergence Zone). On the longer forecasts, it tended to produce features that were less blurry than those made by pure AI forecasters, even though it was operating at a lower resolution than they were. This lower resolution means larger grid squares—the surface of the Earth is divided up into individual squares for computational purposes—than most other models, which cuts down significantly on its computing requirements.

Despite its success with weather, there were a couple of major caveats. One is that NeuralGCM tended to underestimate extreme events occurring in the tropics. The second is that it doesn’t actually model precipitation; instead, it calculates the balance between evaporation and precipitation.

But it also comes with some specific advantages over some other short-term forecast models, key among them being that it isn’t actually limited to running over the short term. The researchers let it run for up to two years, and it successfully reproduced a reasonable-looking seasonal cycle, including large-scale features of the atmospheric circulation. Other long-duration runs show that it can produce appropriate counts of tropical cyclones, which go on to follow trajectories that reflect patterns seen in the real world.

Model mixes AI and physics to do global forecasts Read More »