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On Peace Deals...

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Two wars (Russian invasion of Ukraine, Iran and proxies versus Israel and the U.S.) are of macroeconomic importance due to their effect on global energy prices. The Trump administration has been floating the idea of “peace deals” to distract from failure in the midterms. Peace deals are possible, but they are going to conform to the facts on the ground. This should result in lower energy prices — but it is very much unclear whether they will happen.

Before World War I, nationalism either did not exist, or was suppressed (the nineteenth century). In the pre-nationalism world, monarchs were happy to trade territory back and forth. (My home province of Quebec was handed over to the British as part of a wide-ranging peace deal between France and England.) Now, countries are loathe to hand over their nationals to foreigners. So, modern peace deals are either the result of one side being incapable of fighting, or the peace deal freezes the border pretty much exactly where the front lines are (which will naturally be defensible boundaries).

Russia/Ukraine

On the Russian/Ukrainian side, neither side has reached the point where it is unwilling or incapable of fighting. (The Ukrainians do not need to march on Moscow. Since World War II, great powers have a fairly poor track record in fighting conflicts that are existential to the smaller powers, and just give up and bring their troops home.) The result is that the borders would freeze where they are. Based on reports of the negotiations, this has not happened yet because the Russians have imposed delusional extra conditions, like Ukraine giving up territory they control, and impositions on Ukraine’s domestic politics and external affairs. (A report today alleges that Russia is making demands on NATO countries.)

The only reason that a peace deal would happen is if Russia gives up those extra demands. This could plausibly happen if the oil refinery campaign has done enough damage that they accept that a period of ceasefire is in their interest. However, this does not seem to be what is happening. Instead, it seems more likely that the Trump administration is attempting to force the Ukrainians to capitulate.

This is unlikely to work in that the Russians currently do not have any momentum on the front line, and the ability of the White House to dictate American foreign policy may soon be hamstrung by the new Congress. As such, the latest talks are probably just a desperate last gasp attempt to pressure Ukraine in the lame duck session.

Iran Conflict

The White House can easily walk away from the conflict in Ukraine, but it is stuck in a mess that it created when it started the bombing campaign against Iran. The status quo can continue somewhat indefinitely, although ability of the United States to project force in the region is being strained.

The problem appears to be that Iran is content with the status quo — the current situation is more damaging to the White House politically than it is to them. Even if the economic damage to Iran is greater, that is not necessarily a major sensitive point to the Iranian regime. They recently violently repressed an uprising that started in December 2025.

The White House complained that the Iranians were making unreasonable demands. That is exactly what one should expect if one’s enemy views the current situation favouring themselves. A frozen conflict with periodic bursts of violence seems to be the likely outcome, as both sides attempt to outwait the other. Oil is flowing, and it appears that the global economy has adapted to lowered flows.

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sarcozona
29 minutes ago
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‘A new age of climate extremes’: Earth accumulating heat at accelerating rate | Cop31 | The Guardian

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Heat is accumulating in Earth’s systems at an accelerating rate, the UN’s weather agency has reported as the Cop31 climate change summit begins in Turkey, with 2026 on track to be one of the four warmest years on record.

The imbalance between incoming energy from the sun and energy leaving Earth reached its highest level since monitoring began, the World Meteorological Organization (WMO) said in its State of the Global Climate update.

That imbalance and the accumulation of heat will supercharge extreme weather events such as heatwaves, droughts and tropical cyclones.

Around the world, the average surface temperature for January to September 2026 was about 1.47C above the preindustrial average, the agency said. Sea surface temperatures hit monthly records and sea level was the highest since records began. The ocean absorbs 90% of the excess heat trapped by greenhouse gases and 2026 is on track to be a 10th successive year of record ocean heat.

A centre for people experiencing respiratory difficulties in Palangkaraya, Indonesia. More than 11,000 people have been treated during a months-long wildfire crisis. Photograph: REINA/AFP/Getty Images

“We have entered a new age of climate extremes,” said António Guterres, the UN secretary general. “A supersized El Niño is colliding with a planet already superheated by manmade climate change. Climate records keep falling. The human costs keep rising. And those least responsible are suffering first and worst.”

Diplomats and world leaders are meeting on Monday in the Turkish resort city of Antalya for the 31st conference of the parties (Cop) to the UN framework convention on climate change. In 1992, when the convention was signed, the atmospheric concentration of carbon dioxide, the primary greenhouse gas, was 356.42 parts per million (ppm). In 2025 it was 426.8 ppm, the WMO said. Overall, since 1750, the onset of the Industrial Revolution, the concentration has increased 53%.

The WMO reported that ocean heat content estimates indicate Earth’s energy imbalance rose at a rate of 0.3W per sq metre each decade for the period 2001–2025, compared with 0.13W per sq metre between 1960 and 2025.

Accordingly, 2026 is on track to be one of the four warmest years on record globally, making 2023 to 2026 the four warmest years since records began.

The Cop31 climate summit begins in Antalya, Turkey, on Monday. Photograph: Murad Sezer/Reuters

The year started with La Niña conditions in the Pacific, which typically act as a brake on global temperatures, lowering the average by about 0.1-0.2C. A brewing El Niño, a phenomenon that warms global temperatures, and whose latest iteration is forecast to be stronger than any to have taken place in the past 1,000 years, makes it likely that the final mean temperature for 2026 will be higher than 1.47C above the pre-industrial average.

Heat in the upper 2,000 metres (6,500ft) of the ocean in 2025 was the highest in the observed record and was accelerating, the WMO said. “This warming is projected to continue, representing a change that is irreversible on centennial to millennial timescales,” according to the report.

At the poles, Antarctic sea ice reached its third-lowest annual maximum ever in 2026, while the Arctic maximum was the lowest or second lowest. Elsewhere in the cryosphere, glaciers lost more than 400 gigatonnes of mass. It was the fourth consecutive year in which all 19 glacierised regions around the world experienced mass loss, the WMO said.

Bill McGuire, a professor emeritus of geophysical and climate hazards at University College London, said there were “no surprises” from the report, “to the extent that every measure reported, whether it be global temperature, sea level rise or sea ice extent, points to the fact that we are in deep shit”.

He said: “Of most concern is the growing imbalance between the incoming energy from the sun and that leaving the planet. Increasing greenhouse gas concentrations in the atmosphere mean that while the heat coming in stays the same, that going out is steadily decreasing as our world hangs on to more and more of it.

“It is this growing imbalance – rather than just emissions – that most accurately tracks global heating and how quickly our planet will continue to heat up in the decades to come. This is because natural feedbacks are now coming into play that are driving up the temperature more rapidly than 20 years ago.

“The latest research suggests we will cross the 1.5C limit permanently in 2028, and with the planet now heating at an extraordinary rate of 0.4C a decade, we can expect to smash through 2C in the late 2030s.

“The widely publicised idea that we are still on track for 2.6-2.8C by the end of the century now seems wildly optimistic, if not downright impossible.”

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sarcozona
3 days ago
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Looks like we’re going to do degrowth the hard way
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Canada's budget fuels new nuclear power with billions but bypasses faster renewables

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Budget 2026 earmarks more than $6.5 billion for nuclear sector investment, but critics flag wind, solar and batteries as a quicker, cheaper path to meet surging electricity demand.
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sarcozona
4 days ago
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California Just Voted for Single-Payer Wildfire Insurance

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Few political wonks outside of California were likely paying much attention to the Golden State on Tuesday night, as Democrats’ anticipated blue wave crested. But the state did have one close, consequential race on the ballot: insurance commissioner. On Friday, Bernie Sanders-endorsed progressive Jane Kim won the race to lead the California Department of Insurance, which oversees the state’s $452 billion insurance market.

Kim declared victory over the more moderate State Senator Ben Allen, who was backed by the California Democratic Party, after reaching a nearly six-point lead on Friday. In a brief speech before supporters, Kim said Allen called to congratulate her that morning. (A spokesman for the Allen campaign confirmed the call to The Orange County Register.) Kim ran on a proposal for a state-run, single-payer insurance program that would cover homeowners against wildfire damage — though the plan would ultimately need approval from both the California legislature and Governor-elect Xavier Becerra.

The idea is that homeowners would automatically receive public coverage for wildfire-related losses, with payouts capped at roughly what it costs to rebuild a typical California home. Policyholders would keep their regular private insurance for other risks such as theft, burst pipes, or an ordinary kitchen fire. Kim says Californians wouldn’t pay more overall, since the money for the program would come from the share of premiums that already goes toward wildfire coverage, though each household’s bill would partly depend on its own wildfire risk.

“It would be nonprofit, and so any excess capital would stay in the state of California and would be used to drive down risk,” Kim told my colleague Matthew Zeitlin a few weeks ago. “That’s the most important piece about this actually, is that it would create another revenue stream for mitigation.”

The insurance commissioner job has taken on an increasing sense of urgency as wildfires — intensified by climate change — continue ripping through communities in California. Mounting losses from hotter, drier conditions combined with decades of new construction in the wildland-urban interface have squeezed insurers’ profits, leading many to drop policyholders in high-risk areas or simply stop writing new policies in the state altogether.

California’s homeowners insurance premiums climbed 84% between December 2020 and March of this year, according to Stanford researchers. Meanwhile, a growing number of residents have been pushed onto the state’s FAIR Plan, which my colleague Matthew described as “the barebones insurer of last resort.” The share of California’s single-family homes covered by the plan has nearly tripled over that same period, from under 2% to about 5%, as homeowners in even low- and moderate-risk wildfire areas increasingly find themselves with few alternatives.

The insurance crunch has become yet another front in California’s broader affordability crisis, alongside persistently high housing, electricity, and gasoline costs. Kim’s victory suggests frustrated voters are increasingly willing to support more radical solutions that once seemed politically out of reach. And while Kim doesn’t have a background in the insurance industry, she’s no stranger to public office. The former civil rights attorney served on the San Francisco Board of Supervisors from 2011 to 2019 before becoming a regional director for Sanders’ 2020 presidential campaign.

But throughout her run for the Insurance office, critics have continually questioned how, exactly, her plan would work. No state has attempted a public disaster insurance program like this before; critics argue there’s no relevant model to work off of, no white paper laying out the details, and no price tag attached to the idea. Kim herself acknowledges that there’s plenty of uncertainty surrounding the plan. “This is a concept and a proposal, and could look quite different by the time it works its way through the process,” she told Matthew. In other words, the program that eventually emerges from negotiations with lawmakers — if one does — could end up looking considerably different from what Kim campaigned on.

Her opponent, Allen, who chairs the state senate’s Energy, Utilities and Communications Committee, argued that creating a public wildfire insurance program would impose a significant financial burden on the state. The California Earthquake Authority, an agency that sells earthquake insurance and administers the state’s Wildfire Fund, examined the potential costs of such a program this spring, estimating it would require roughly $25 billion upfront to have a 75% chance of staying solvent for 20 years. But Kim points out in her policy brief that buying reinsurance — essentially insurance for the insurer — could cut that to about $6 billion.

Allen, on the other hand, pitched a series of reforms to the existing market, including speeding up rate reviews, rewarding homeowners and communities for wildfire mitigation, expanding financing for home-hardening projects, and discouraging development in high-risk areas.

Voters opted instead for the insurance revolution. But Kim argues that her plan isn’t totally without precedent. On the campaign trail, she frequently pointed to New Zealand, where the government’s Natural Hazards Commission provides homeowners with public coverage for disasters including earthquakes, landslides, volcanic eruptions, and tsunamis — though notably not ordinary wildfires. And while France and Spain also operate state-backed disaster insurance programs, these don’t cover wildfires either, a far more common occurrence in California than the comparably rare disasters these programs were built around abroad.

Ultimately, Kim’s ambitious plan will require a champion in the legislature to go anywhere. But in the meantime, there are other issues she could get to work on. On the campaign trail, she said she wants to push insurers to fund a disaster mitigation program run by the Department of Insurance, require companies selling auto policies in a region to also offer homeowners coverage there, and impose minimum loss ratios compelling home insurers to pay at least 65% of premiums in claims and auto insurers at least 70%. It’s unclear, however, whether Kim can implement either of the latter two proposals without new legislation.

Kim will also immediately face the question of what to do with outgoing Insurance Commissioner Ricardo Lara’s Sustainable Insurance Strategy reform package. Designed to entice insurers back to California, these new rules allow companies to set premiums using forward-looking risk models rather than relying on historical losses, and to pass a portion of their rising reinsurance costs onto policyholders. In exchange, insurers are required to write more policies in wildfire-prone areas.

Kim has criticized this approach, arguing that insurers have exploited loopholes to raise premiums on lower-risk customers while continuing to underserve high-risk communities, all without doing enough to address the underlying factors driving wildfire risk.

That leaves Kim in a tricky situation. Until she is able to secure legislative backing for some version of her proposed public insurance program, she’ll have to decide whether to scrap or simply build on the existing reforms. She hasn’t yet laid out any plan for doing so. But whatever direction she takes could ultimately reshape California’s insurance market well before a new state insurer gets off the ground.

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sarcozona
4 days ago
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This really needs to be paired with land use policy
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AI and climate modeling update

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Things are moving very fast.

I wrote two years ago about the role that AI/ML was playing in helping accelerate useful developments in climate models. In a field where literally new possibilities are emerging on a week-by-week basis, that might as well be prehistory.

So it’s worth reviewing what directions have been fruitful, which ones are going slower than expected, which are struggling, and what is going gangbusters that I didn’t even know about back then. This update is in part due to a workshop I attended last week at the Allen Institute in Seattle, but since a lot of what was discussed there is not yet published, I apologize if I’m a little coy about some details.

Emulation for the win (mostly)

The anticipated success of emulation (of whole models or major components) has been continuing – for model calibration/tuning, for scenario filling, attribution, forecasting, and bigger ensembles. This draws in no small measure in the ability to evaluate the results from the ML version directly using the original physics-based model, and so the appropriate degree of confidence that the emulation is doing what it’s supposed to be doing can be assigned. This issue of ‘trust’ comes up a lot in conversations around this topic.

The highest profile emulators are those being used for weather forecasting (e.g. AIFS from ECMWF, and Google WeatherNext 3) which are doing well and sometimes better (on some short-term forecast metrics) than the physics-based models they are emulating. Emulators for climate and longer-time scales (such as Samudra (Dheeshjith et al. (2025)) or ACE or HiRO-ACEv2) are also appearing to do well in limited situations.

There is an effort (AIMIP) that is attempting to benchmark these efforts (at this point only for atmospheric emulators based on ERA5) and interestingly, is finding that the emulators work well in the domain for which they were designed (i.e. short term forecasts), but have very diverse and mostly unreliable responses in out-of-sample tests such as what happens if you increase the ocean temperatures uniformly. This is perhaps unsurprising – the emulators are not learning physics that they then apply, they are learning correlations which they assume persist. Indeed, there are now many examples of ML-models that have good in-sample validation failing to maintain conservation of energy (for instance) when given a forcing of some sort.

This seems a generic enough problem that their might be a generic reason why this is so, and there is some very clear work by Elizabeth Barnes (in prep) that diagnoses this (something to look forward to!). There is a broader discussion about how to optimally train these emulators if we want them to respond well to out-of-sample tests. Some results were presented at the workshop suggested that spectrally-complex near-random forcings (for instance in ocean temperatures), despite not being physical, might be better at getting the atmospheric emulators to distinguish what is really causal from what is merely correlation.

To give an example, weather emulators trained on ERA5 will generally recognise that persistence is a pretty good forecast most of the time (as have humans!). That correlation gets baked into the machine learning. But if something sudden happens (say you’d like to know what happens when the ocean temperatures are higher), the emulator will follow persistence rather than warm up as a physics-based model would. But if the training data included a lot more sudden and uncorrelated changes in SST then that might lead the emulator to pay more attention to a physically consistent response as opposed to the time-step to time-step inertia. This would need the physics-based models to be run in very different ways than they currently are, but that is not insurmountable.

However, to be clear, none of these emulators are real climate models yet: they don’t have a climate sensitivity to CO2 (or most any other forcing except SST), they can’t be used for paleo-climate out-of-sample tests (like the last glacial maximum), and don’t provide any understanding for why things change. Thus the need for physics-based models to continue to be developed and used has not gone away (phew!).

Hybrid models

Physics-based models that successfully include ML-parameterizations remain a little thin on the ground. Despite the promise of faster execution the combination of legacy code issues (tying fortran-based models to python-based ML components is trickier than you might expect) and reliability concerns (ML components sometimes blow up or give nonsense results more often than standard parameterizations), these kinds of models are still mostly in an experimental phase and are (as far as I can tell) not going as fast as expected (feel free to disagree with examples in the comments!).

The new kid on the block: Agentic AI

This is the crazy thing. Two years ago, I did not know what Agentic AI was, and even six months ago, I was not fully aware of what it could do. But there is a real use case in climate modeling that this is more or less perfect for.

As readers are likely aware, until very recently, climate models were written in Fortran and run on CPU-based high performance computers. Some groups have embarked on multi-year multi-million dollar translation efforts (to C++/Kokkos, or CUDA) but most did not because of the time it would take and the extra expense in a time of shrinking budgets. Some scientists had thought about the potential for using LLMs to do this (for instance, Zhou et al (2024) or Lalhou et al (2026)), but while these efforts were interesting, they were limited. However, three months ago, a preprint from Koldunov et al showed that a full ocean model could be quickly and successfully translated to JAX using agents. This provided automatic differentiation (!) and GPU capabilities without compromising the physics. One month ago, the same group used the latest Claude model to translate MITGCM (another ocean model) from Fortran to JAX over a weekend. Software engineers at NASA a few weeks ago took a configuration of the GISS ESM and converted it in 18 hours. This, quite frankly, is fundamentally game-changing.

To be clear, this is not trivial. The agents need to develop there own tests to verify that their translations are correct, they need to be guided in how design the new code base, and in and of itself translation to JAX (or CUDA or Julia) doesn’t guarantee code speed up on GPUs. But once this is in place (and the performance issues put aside for a moment), a task that would have taken multiple people multiple years to complete can be done in under a day. Say 20 person-years to under 1 person-day – a factor of nearly 10,000 times faster (and cheaper).

The questions climate modeling groups are dealing with thus have very dramatically changed from ‘what is your multi-year strategy to move towards hardware agnostic codes’, to ‘what does it even mean to be a climate model center when anyone anywhere can fork a version of their own and run it anywhere’. The broader questions (that are also hitting software engineers in industry or open source projects) about the shift from human to agent-written code are also profound. What does it mean to be scientist who’s main expertise was coding? Can people re-envisage their role as code managers? Should people still want to edit code themselves at all? How do we maintain momentum and community on group projects? These issues will take some time to work through and I’m not at all confident I know where we’ll end up. However, what is clear is that any climate model code development plans that were written more than three months ago, are basically obsolete.

It is conceivable that groups that are currently working on their CMIP7 simulations (for the most part using their legacy codes on standard HPC machines) may be able to augment these simulations with newly-translated code bases very quickly – say in the next few months. We shall see!

Coda

Speaking personally, I am quite critical of the majority of uses for LLMs – in replacing search, plagiarizing creative industries, summarizing your email (FFS!), replacing human decision-making in crucial fields (medical insurance, benefit payments etc.), providing fatal ‘medical’ advice, and creating massive problems in science for journal editors and program managers as they get spammed with hundreds of low-quality AI-generated submissions with hallucinated references. The environmental damage of un-permitted gas-based electricity generation for massively debt-laden data centers is increasingly bad for both local pollution and global climate. And the danger of AI slop overwhelming our ability to even discover what is true or known is very real. However, there clearly are cases (such as described above) where (under controlled conditions) they can dramatically accelerate work that would have been very difficult or impossible to do before. Some of the recent AI results in mathematical proofs may also fall into this category (but some definitely do not!). How we should square this circle is not obvious (though here are some thoughts on how to use Generative AI ethically). It might also be smart to save some research dollars to pick up some second-hand cheap GPUs for science if (when?) the AI industry boom turns to bust.

In the meantime, it’s a brave new world that has such creatures in’t.

References

  1. S. Dheeshjith, A. Subel, A. Adcroft, J. Busecke, C. Fernandez‐Granda, S. Gupta, and L. Zanna, "Samudra: An AI Global Ocean Emulator for Climate", Geophysical Research Letters, vol. 52, 2025. http://dx.doi.org/10.1029/2024GL114318

The post AI and climate modeling update first appeared on RealClimate.

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sarcozona
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B.C. premier-designate’s new chief of staff is a former lobbyist

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B.C. Conservative interim Leader Lorne Doerkson celebrates on stage during provincial election night in Vancouver, on Oct. 24.
B.C. premier-designate Lorne Doerkson has appointed Dimitri Pantazopoulos, a former lobbyist and pollster, as his chief of staff. (The Canadian Press/Ethan Cairns)

The B.C. premier-designate’s new right hand man is a former lobbyist who has worked on behalf of waste management companies, media groups and Uber.

Last week, B.C. Conservative premier-designate Lorne Doerkson tapped political strategist Dimitri Pantazopoulos as his chief of staff. A party news release says Pantazopoulos has more than two decades of experience in government, politics and business and played a “central role” in the Conservative Party’s successful fall election campaign.

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