Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems. For instance, increased greenhouse gas concentrations are predicted to lead to drastic decreases in low cloud cover, referred to as a climatological tipping point. In this paper, we learn the evolution of such non-stationary dynamical systems using a novel recurrent neural operator (RNO), which learns mappings between function spaces. After training RNO on only the pre-tipping dynamics, we employ it to detect future tipping points using an uncertainty-based approach. In particular, we propose a conformal prediction framework to forecast tipping points by monitoring deviations from physics constraints (such as conserved quantities and partial differential equations), enabling forecasting of these abrupt changes along with a rigorous measure of uncertainty. We illustrate our proposed methodology on non-stationary ordinary and partial differential equations, such as the Lorenz-63 and Kuramoto-Sivashinsky equations. We also apply our methods to forecast a climate tipping point in stratocumulus cloud cover. In our experiments, we demonstrate that even partial or approximate physics constraints can be used to accurately forecast future tipping points.

Male fruit flies reared in a lab are more successful at mating after an encounter with a robotic dummy designed to look like a rival male. The finding could boost efforts to control populations of the flies, which are a major crop pest. The Mediterranean fruit fly (Ceratitis capitata) is one of the most destructive fruit pests in the world, found on every continent except Antarctica.

Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.

A Bayes network based classifier for distinguishing terrestrial rocks from meteorites is implemented onboard the Nomad robot. Equipped with a camera, spectrometer and eddy current sensor, this robot searched the ice sheets of Antarctica and autonomously made the first robotic identification of a meteorite, in January 2000 at the Elephant Moraine. This paper discusses rock classification from a robotic platform, and describes the system onboard Nomad.

Scientists studying Antarctica's vast Thwaites Glacier – nicknamed the "Doomsday Glacier" – say warm water is seeping into its weak spots, threatening its demise and a massive sea rise. Thwaites, which is roughly the size of Florida, represents more than half a metre (1.6 feet) of global sea level rise potential, and could destabilise neighbouring glaciers that could cause a further 3-metre (9.8-foot) rise. As part of the International Thwaites Glacier Collaboration – the biggest field campaign ever attempted in Antarctica – a team of 13 scientists from the United States and United Kingdom spent about six weeks on the glacier in late 2019 and early 2020. Using an underwater robot vehicle known as Icefin, mooring data and sensors, they monitored the glacier's grounding line, where ice slides off the glacier and meets the ocean for the first time. In one of two papers published on Wednesday in the journal Nature, led by Cornell University-based scientist Britney Schmidt, researchers found warmer water was making its way into crevasses and other openings known as terraces, causing sideways melt of 30 metres (98 feet) or more per year.

Icefin the robot is designed to go where no human can, swimming off the coast of Antarctica under 2,000 feet of ice. Lowered through a borehole drilled with hot water, the torpedo-shaped machine takes readings and--most strikingly--video of Thwaites Glacier's vulnerable underbelly. This Florida-sized chunk of ice is also known as the Doomsday Glacier, and for good reason: It's rapidly deteriorating, and if it collapses, global sea levels could rise over a foot. It could also tug on surrounding glaciers as it dies, which would add another 10 feet to rising seas. In a pair of papers published today in the journal Nature, scientists describe what Icefin and other instruments have discovered underneath all that ice.

It's a simple mission, raise Omega-3 levels for all Americans. Because Omega-3s are an essential nutrient our bodies need to thrive but cannot make on its own and one in which over 70% of Americans are sorely deficient in. Unfortunately, the leading supplement brands have stopped investing to keeping Omega-3s top of mind for consumers. However, in Antarctica there is a brilliant resource that is a powerful source of Omega-3 nutrients called krill. Krill, a small but mighty multi-nutrient source of Omega-3 EPA & DHA and essential choline (known to support brain and nervous system health) which has superior absorption over traditional fish oils is relatively unknown to most consumers.

Antarctica's Thwaites Glacier is'holding on by its fingernails', experts say, after discovering that it has retreated twice as fast as previously thought over the past 200 years. The West Antarctica glacier – which is about the size of Florida – has been an important consideration for scientists trying to make predictions about global sea level rise. The potential impact of its retreat is huge because a total loss of Thwaites and its surrounding icy basins could raise global sea levels by up to 10 feet. That is why it is widely nicknamed the'Doomsday Glacier.' For the first time, scientists mapped in high-resolution a critical area of the seafloor in front of Thwaites that gives them a window into how fast the glacier has retreated and moved in the past.

… enabling planetary scale analysis through artificial intelligence and machine learning. The company also supports a diverse set of federal …

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