Tootfinder

Court rules countries must protect human right to stable climate, urging urgent action to reduce emissions and adapt to climate change. #climatechange #climatesolutions #climate

Court rules: Countries must protect human right to a stable climate. #climatechange #climatesolutions #climate

So sad. The first #glacier I saw in 2018, during our honeymoon.
Indeed at a time they were telling us the glacier wasn’t really impacted by climate change, I had not truly understood why.
Such a majestic and unique place to visit.

Snowball Bistability Vanishes at Moderate Orbital Eccentricity
Xuan Ji, Dorian S. Abbot
https://arxiv.org/abs/2509.08994 https://arxiv.org/pdf/2509.08994…

Snowball Bistability Vanishes at Moderate Orbital Eccentricity
Snowball episodes are associated with increases in atmospheric oxygen and the complexity of life on Earth, and they may be essential for the development of complex life on exoplanets. Sustained stable Snowball episodes require a Snowball bifurcation and climate bistability between the globally ice-covered Snowball state and a state with at least some open ocean. We find that climate bistability disappears for an aquaplanet with a slab ocean in the Global Climate Model ExoCAM when the orbital ec…

"Breaching the target would ramp up the extreme weather already devastating communities around the world. It would also require carbon dioxide to be sucked from the atmosphere in future to restore the stable climate in which the whole of civilisation developed over the past 10,000 years."
This is bogus. Carbon-neutral #CarbonCaptureAndStorage is the modern perpetual…

Central Asia’s last stable glaciers just started to collapse #CentralAsia

Central Asia’s last stable glaciers just started to collapse
Snowfall shortages are now destabilizing some of the world’s last resilient glaciers, as shown by a new study in Tajikistan’s Pamir Mountains. Using a monitoring station on Kyzylsu Glacier, researchers discovered that stability ended around 2018, when snowfall declined sharply and melt accelerated. The work sheds light on the Pamir-Karakoram Anomaly, where glaciers had resisted climate change longer than expected.

Up until 2016, the sea ice in Antarctica seemed relatively stable.
Then everything started to change.
At first, the decline was mostly in line with climate models.
But suddenly, in 2023, there was an enormous drop.
About 2.5 million sq km of Antarctic sea ice went missing relative to the average before 2023.
The anomaly was of such a magnitude that it's quite hard for scientists to know what to make of it.
It has been described as a five sigma ev…

Ancestral origins of attention to environmental issues
C\'esar Barilla, Palaash Bhargava
https://arxiv.org/abs/2509.09598 https://arxiv.org/pdf/2509.09…

Ancestral origins of attention to environmental issues
How does the climatic experience of previous generations affect today's attention to environmental questions? Using self-reported beliefs and environmental themes in folklore, we show empirically that the realized intensity of deviations from typical climate conditions in ancestral generations influences how much descendants care about the environment. The effect exhibits a U-shape where more stable and more unstable ancestral climates lead to higher attention today, with a dip for intermediate…

The origin of long-range links of air pollution in China
Qiuyue Li, Daqing Li, Yossi Ashkenazy, Shlomo Havlin
https://arxiv.org/abs/2509.05974 https://arxi…

The origin of long-range links of air pollution in China
Weather conditions significantly influence the formation and dispersion of pollution variations. Here we study networks of pollution as well as climate networks and find that pollutants may not only have an impact close to their source but also show a significant correlation with pollutant concentrations thousands of kilometers away. We develop a pollution network model based on cross-correlation between PM2.5 concentration time series in different sites in China to detect stable long-range lin…

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Adding a Storage Pool improves 3-PG Tree-ring Simulations
Yanfang Wang, Liang Wei, Liheng Zhong, Xizi Yu, Pengtao Huang, Fang Wang, John D. Marshall
https://arxiv.org/abs/2508.18327

Adding a Storage Pool improves 3-PG Tree-ring Simulations
Tree rings provide long-term records of tree growth and climate changes, which makes them ideal benchmarks for forest modeling. Tree-ring information has greatly improved the reliability of 3-PG, which is one of the most commonly used process-based forest growth models. Here we strengthen 3-PGs ability to simulate tree-ring width and carbon stable isotopes (δ13C) by enhancing its descriptions of tree physiology. The major upgrade was adding a carbon storage pool for tree-ring formation using s…

The Role of Tectonic Luck in Long-Term Habitability of Abiotic Earth-like Planets
Brandon Park Coy, Edwin S. Kite, R. J. Graham
https://arxiv.org/abs/2507.23124 https://

The Role of Tectonic Luck in Long-Term Habitability of Abiotic Earth-like Planets
Carbonate-silicate weathering feedback is thought to stabilize Earth's climate on geologic timescales. If climate warms, faster mineral dissolution and increased rainfall speed up weathering, increasing CO2 drawdown and opposing the initial warming. Limits to where this feedback might operate on terrestrial exoplanets with N2-O2-CO2-H2O atmospheres are used to define the 'habitable zone'-the range of orbits around a star where liquid water can be stable on a planet's surface. However, the impac…