Introduction

In the context of climate risk, transition risk encompasses the economic and social risks associated with a shift towards a low-carbon economy. Such an effort would fundamentally reshape our world and create critical financial uncertainty for assets and industries tied to the old, carbon-intensive system.

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Net zero

Reaching “net zero” is the ultimate goal of most climate policy. This means reducing greenhouse gas emissions as much as possible, with any remaining emissions that are too difficult or costly to eliminate are canceled out by an equivalent amount of “negative emissions” — processes that actively pull carbon dioxide out of the atmosphere. These negative emissions are the “net” part of net zero and it acknowledges the practical reality that some sectors, like long-distance air travel or ocean shipping, may be incredibly difficult to decarbonize in the near future.

What are these negative emissions technologies? The two primary methods discussed are Direct Air Capture (DAC), which uses machines to filter carbon dioxide directly from the air, and Bioenergy with Carbon Capture and Sequestration (BECCS), which involves growing crops, burning them for energy, and capturing and burying the resulting carbon dioxide. However, both technologies face significant hurdles, including high costs, large energy requirements, and, in the case of BECCS, immense land use needs that could compete with food production and biodiversity.

Once we reach net zero, global temperatures will stabilize — although they won’t recover to pre-industrial levels for tens of thousands of years. Getting the climate to actually cool on time scales we care about (decades to centuries) would would require pulling even more carbon dioxide out of the atmosphere, or deploying some type of climate engineering approach like injecting aerosols into the stratosphere.

The scale of the net zero transformation means that reaching net zero will fundamentally overhaul vast parts of the global economy. Many big sectors of our economy — energy, transportation, industry, agriculture — must be reshaped, and that reshaping will create enormous opportunities as well as painful dislocations. The transition to a low-carbon economy is not simply a matter of swapping one energy source for another; it requires rebuilding infrastructure, retraining workers, and redirecting trillions of dollars in investment.

Some industries are poised to prosper. Renewable energy is the most obvious example: in 2025, the world added over 700 GW of new capacity, and sustaining that pace for decades will require ongoing investment in manufacturing, installation, and maintenance of wind turbines and solar panels. The profits for those well positioned will be enormous.

The electric vehicle industry and its supply chains — from battery manufacturers to mining operations for lithium and cobalt — also stand to grow dramatically. Companies that build and manage electrical grid infrastructure, including new transmission lines and energy storage systems, will see surging demand. So too will firms specializing in energy efficiency, building retrofits, and emerging technologies like green hydrogen and sustainable aviation fuels. Even agriculture could see new revenue streams as farmers are paid to adopt practices that sequester carbon in soil.

Other industries, however, face serious decline. Fossil fuel producers (coal, oil, and natural gas) confront the prospect of their core product becoming obsolete, stranding assets worth trillions of dollars. Workers in these industries, from coal miners to oil rig operators, risk losing their livelihoods.

The effects extend well beyond extraction: refineries, pipelines, and petrochemical plants all face an uncertain future. The automotive sector will also see significant disruption, as the shift to electric vehicles renders the internal combustion engine and its complex supply chain of transmissions, exhaust systems, and fuel injection components irrelevant. Communities built around these industries may face economic devastation if the transition is not carefully managed.

This uneven distribution of winners and losers will create difficult economic and political challenges, particularly during the transition period. The enormous capital investment required — in renewable generation, grid modernization, EV charging infrastructure, industrial retooling, and carbon removal — must be mobilized quickly, creating the risk of supply chain bottlenecks, inflation in key materials, and financial instability. Managing this transition in a way that is both fast enough to meet climate targets and equitable enough to maintain broad public support is one of the defining policy challenges of our time.

Stranded assets

A core concept in transition risk is the “stranded asset”. A stranded asset is defined as an asset that loses significant value well before the end of its expected economic life. This loss is often sudden and unexpected, driven by changes in market conditions, technology, or policy. While this can happen for many reasons, it is a particularly potent risk in the context of climate change, arising from both direct physical impacts and the economic shifts of the energy transition.

For example, here is a house that literally fell into the ocean in North Carolina in Sept. 2025:

From Zillow.com, this was a pricey house:

This house could have stood for another few decades, but it collapsed into the ocean due to coastal erosion that was certainly made worse by sea level rise. When that happened, its value instantly dropped to zero, a stark, nonlinear impact that produced a stranded asset.

While physical risks can strand assets, the concept first gained prominence in discussions about transition risk and the fossil fuel industry. Oil and gas companies are valued in the trillions of dollars, with much of that valuation based on their proven reserves—oil and gas that is in the ground and ready to be produced. The transition to a net-zero economy, however, requires that a significant portion of these reserves be “left in the ground” and never burned. Once the market fully accepts that these assets cannot be produced due to climate policies, their value could drop to zero rapidly.

The danger of these fossil fuel assets becoming stranded extends far beyond the energy companies themselves. It poses a systemic risk to the broader economy because large swaths of the general public have financial exposure to these companies through their investments, including 401k programs, pensions, and mutual funds. The sudden devaluation of these energy assets could negatively affect many people’s investment and retirement funds, which in turn could have a widespread and devastating impact on the financial security of the general public.

This same principle applies to the real estate sector. Consider a commercial office building with a low energy efficiency rating located in a city that passes a new ordinance mandating high-performance standards for all buildings. The owner is suddenly faced with a difficult choice: either undertake a costly, large-scale retrofit to meet the new legal requirements or risk being unable to legally rent the space. If the retrofit is too expensive, the building’s value is stranded, as its primary function — generating rental income — has been eliminated by a policy change aimed at reducing emissions.

Another often-overlooked category of risk lies in intangible assets. For companies in the S&P 500, these assets — such as brand value, reputation, and intellectual property (IP) — can represent up to 90% of their total market value. Their non-physical nature makes them vulnerable to rapid devaluation. For example, imagine a company that holds a highly valuable portfolio of patents for a new, efficient diesel engine technology. If a major country or region, aiming to meet climate targets, decides to ban the sale of all new diesel cars, the market for that technology disappears. The intellectual property, once a significant asset, has its value evaporate almost overnight. This is a direct parallel to the risk facing fossil fuel companies, whose reserves — a tangible asset on paper — could become worthless if they cannot be produced.

A final critical category that is often overlooked is human capital. Human capital represents the skills, knowledge, and expertise that workers have developed over their careers — assets that can suddenly lose their value in the transition to a low-carbon economy.

Consider a mechanic who has spent 30 years perfecting the art of repairing internal combustion engines. This individual has accumulated expertise in diagnosing problems, understanding the mechanical systems, and maintaining gasoline-powered vehicles. As the world shifts to electric vehicles — which require fundamentally different maintenance skills — this expertise becomes obsolete. The mechanic’s human capital, built over decades, is stranded.

The scale of this challenge is enormous. Huge numbers of workers have built their careers in fossil fuel industries. Coal miners possess specialized knowledge about underground operations, safety protocols, and extraction techniques. Oil field workers understand drilling technologies, reservoir management, and petroleum systems. Pipeline operators and refinery technicians have invested years developing skills specific to a carbon-intensive economy. As these industries contract or disappear entirely, these workers face the prospect of their expertise becoming rapidly becoming worthless.

This creates both an economic and social crisis. Unlike a stranded power plant that can be written off a company’s books, stranded human capital represents real people with families, mortgages, and communities that depend on their income. A 50-year-old coal miner cannot simply retrain as a software developer overnight. The geographical concentration of these industries compounds the problem — entire regions have been built around fossil fuel extraction, creating communities where the primary source of skilled employment may disappear.

The human dimension of stranded assets also creates political risk for the climate transition itself. Workers facing the loss of their livelihoods can become powerful opponents of climate action, slowing the transition for everyone. The fear and anger generated by the transition can translate into political movements that resist or reverse climate policies, as workers vote to protect their immediate economic interests over longer-term economic reality.

The TCFD Framework: Four Key Drivers of Transition Risk

To better understand and manage transition risks, the Task Force on Climate-related Financial Disclosures (TCFD) developed a framework that organizes these risks into four distinct categories. This framework has become the global standard for how companies and investors think about and report climate-related financial risks.

1. Policy and Legal Risks

Policy and legal risks emerge when governments and courts take action to address climate change. These interventions can fundamentally alter the economic landscape, often with little warning.

Carbon pricing represents one of the most direct policy tools. When governments implement a carbon tax or cap-and-trade system, they make it more expensive to emit CO₂. For instance, a carbon price of $50 per ton of carbon dioxide would add around $20 to the cost of a barrel of oil, fundamentally changing the economics of oil production and consumption. Companies that built their business models around cheap fossil fuels suddenly face dramatically higher operating costs.

Efficiency standards create another layer of policy risk. The UK’s Minimum Energy Efficiency Standard (MEES) provides a clear example: it prohibits landlords from renting properties with poor energy efficiency ratings. A landlord who owns an older, inefficient building faces a stark choice — invest heavily in retrofits or watch the property become unrentable, thereby creating a stranded asset.

The legal dimension adds another layer of risk through climate litigation. There are many lawsuits winding through the courts where people are taking fossil fuel companies to court because they have been or expect to be harmed by climate-change-driven extreme weather. This potential climate liability could expose fossil fuel companies to enormous financial risk, much like tobacco companies faced when the health impacts of their products became legally actionable.

2. Technology Risks

Technology risk represents the classic story of disruption — when a new, cheaper, or better technology makes existing technologies obsolete. In the climate context, this risk is accelerating as clean technologies have reached critical tipping points.

The most dramatic example is the drop in renewable energy costs. Solar power costs have fallen nearly 90% over the past 15 years. In most parts of the world, building a new solar or wind farm is now cheaper and faster than building a new coal or gas plant — even without subsidies. This is rapidly reordering energy economics and energy markets. Coal plants that were expected to operate profitably for 40 years are being shut down early not because of regulation, but because they simply can’t compete economically with cheaper energy sources. Natural gas plants will be next.

Electric vehicles present another technological disruption. As battery costs decline and performance improves, EVs are becoming not just environmentally preferable but superior products — they accelerate faster, require less maintenance, and increasingly cost less to own and operate than internal combustion engines. This technological shift threatens not just automakers who are slow to adapt, but entire ecosystems built around gasoline vehicles: gas stations, oil change shops, parts suppliers, and even dealerships whose business models depend heavily on service revenue from complex internal combustion engines.

3. Market Risks

Market risks encompass the shifts in supply, demand, and investor sentiment that can rapidly revalue assets and companies.

As an example, demand for transition minerals like lithium, cobalt, and copper is soaring as the world builds batteries and renewable energy infrastructure. Companies that secured supply chains for these materials early have gained significant competitive advantages, while those arriving late face production bottlenecks and inflated costs. Conversely, demand for thermal coal is collapsing in many regions, leaving coal mining companies with reserves that may never be extracted.

Perhaps more significant is the shift in investor perceptions. For decades, oil companies were valued based on their proven reserves — the oil and gas they had rights to extract. Now, many investors view these same reserves as worthless, unburnable carbon that will never generate revenue. This shift in perception led BP to write down its assets by $17.5 billion in 2020, with Shell following with a $22 billion write down. These companies acknowledged that much of their oil would likely remain in the ground forever.

The power of changing investor sentiment was dramatically demonstrated in 2021 when Engine No. 1, a tiny activist hedge fund, successfully won three board seats at ExxonMobil. Their argument wasn’t environmental but purely financial: Exxon’s failure to plan for the energy transition was destroying long-term shareholder value. This showed that transition risk has moved from the margins to the center of corporate governance.

4. Reputational Risks

Reputational risk reflects the changing expectations of consumers, employees, and society at large. As public concern about climate change grows, companies associated with high emissions face damage to their brands and their social license to operate.

The financial sector illustrates how reputational concerns translate into business decisions. In 2019, Goldman Sachs announced it would no longer finance new thermal coal mines or Arctic oil exploration. While framed partly in risk management terms, the bank explicitly cited reputational considerations and changing client expectations as key drivers. They recognized that being associated with these projects was becoming bad for business, potentially costing them clients and talented employees who increasingly consider environmental factors in their career choices.

Consumer pressure is also reshaping entire industries. The rapid growth of plant-based milk alternatives like Oatly directly responds to, among other things, consumer concerns about dairy’s environmental impact. Traditional dairy companies, seeing their market share erode, are scrambling to launch their own non-dairy alternatives. This shift isn’t driven by regulation or technology costs but by changing consumer preferences that make high-emission products less desirable, regardless of price or quality.

5. Putting it together

These four categories of risk — policy and legal, technology, market, and reputation — don’t operate in isolation. They interact and amplify each other, creating feedback loops that can accelerate the transition and magnify risks for unprepared economies.

Consider how technological advances in renewable energy trigger cascading effects across all risk categories. As solar and wind become cheaper than fossil fuels (technology risk), governments gain political cover to implement stricter emissions standards and carbon pricing (policy risk), knowing these policies won’t dramatically increase energy costs for voters. These policies, in turn, shift investor capital away from fossil fuels and toward renewables (market risk), further driving down clean energy costs through economies of scale. Companies slow to adapt find themselves not just technologically obsolete but facing reputational damage for clinging to outdated, polluting technologies (reputational risk), which makes it even harder to attract capital, customers, and talent.

The automotive industry provides another vivid example of these interconnected risks. As electric vehicles improve and battery costs fall (technology risk), governments implement EV mandates and phase out internal combustion engines — Norway by 2025, the UK by 2030 (policy risk). These policies signal to investors that traditional automakers without credible EV strategies are poor long-term investments, triggering capital flight (market risk). Meanwhile, young consumers increasingly view gas-powered vehicles as environmentally irresponsible, especially luxury gas vehicles (reputational risk). Each risk reinforces the others: technological improvements justify stricter policies, which shift market dynamics, which shape public perception, which in turn creates pressure for even more aggressive policies and faster technological development.

Understanding these interconnections is essential for understanding transition risk. A company cannot address one type of transition risk while ignoring the others — they must recognize that these risks compound and prepare for the systemic changes that result from their interaction.

The “Just Transition”

The recognition that the shift to a low-carbon economy will create winners and losers, particularly among workers and communities reliant on fossil fuel industries, has given rise to the concept of a just transition. A just transition is an effort to ensure that the benefits of a green economy are shared broadly and that the costs do not fall unfairly on those who can least afford them.

The core idea is to provide support, retraining, and new economic opportunities for workers and communities whose livelihoods are threatened by the phase-out of carbon-intensive industries. This is not merely an ethical consideration; it is a pragmatic one. The threat of widespread job losses can create powerful political opposition to climate action, potentially slowing down or even derailing the transition for everyone. Therefore, managing the human side of the transition is critical to its success.

In a just transition, we would repurpose skills: For example, the skills required to build an offshore oil rig are similar to those needed for constructing an offshore wind platform. A just transition would facilitate this shift through targeted programs.

The private market is unlikely to manage this process efficiently or equitably. Government action is therefore needed to fund retraining programs and help workers seamlessly switch to new jobs in the growing green economy.

Germany’s approach to phasing out coal mining in its Lausitz region serves as a prominent example. The German government is investing €40 billion to manage the process by funding new infrastructure, research institutes, and extensive retraining programs. The goal is not just to compensate for lost jobs but to actively build a new, sustainable economic future for the region.

Conclusion

Transition risk represents a fundamental restructuring of the global financial and social order. As this chapter has detailed, the journey toward a net-zero economy is far more than a simple technological swap. It is a complex, multi-dimensional shift driven by the interplay of policy, technology, and market and social dynamics. While this transition offers immense opportunities for innovation and growth in green sectors, it simultaneously creates the systemic threat of stranded assets — devaluing not just physical infrastructure and fossil fuel reserves, but also intangible intellectual property and the human capital of millions of workers.

Ultimately, the success of this overhaul hinges on the ability to manage these risks. Because the private market is not naturally equipped to solve the social dislocations caused by such rapid change, proactive governance and strategic investment are essential to ensure a just transition, so that the shift to sustainability does not leave vulnerable communities behind. Balancing the urgent need for decarbonization with the economic security of the workforce is not just a moral imperative, but a practical necessity to maintain the political and social stability required to reach our climate goals.

This is a draft of a section of my climate risk textbook (slightly edited & reformatted to make it appropriate for Substack). I’d very much like to identify errors now, so if you see any, please let me know in the comments.

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At this point, just about everyone recognizes that the climate is changing. Even Donald Trump says, “I think something’s happening.” Now, the question being debated is why the climate is changing.

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Though there may be a public debate, there’s no debate among scientists like us — decades of research have demonstrated that human activities, primarily the emission of carbon dioxide from the combustion of fossil fuels, are driving the climate change we are experiencing.

To understand why we are so confident, it’s useful to think about climate change as a whodunit. Climate does not change by itself, so scientists are detectives trying to solve the mystery of what has been warming the Earth for the past century.

Because we know that the climate varied before humans were burning fossil fuels, there are clearly other mechanisms besides humans that can cause change. So, the first thing that scientists do is study these mechanisms to see if they could be the culprit.

One possible non-human mechanism is the brightness of the Sun. If the Sun has been getting brighter, then that could explain the warming. The Sun, however, has an airtight alibi — we have direct measurements of the output of the Sun from satellites, and we observe that the Sun has not gotten any brighter. One suspect down.

Another possibility is the orbit of the Earth. We know that ice ages are paced by small wobbles in the Earth’s orbit, so one might wonder whether this could be causing the present warming. However, Earth’s orbit changes too slowly and is now in a phase that should be slowly cooling temperatures. Another suspect down.

Volcanoes can cool the atmosphere for a year or two. But that can’t explain decades of warming. Another suspect down.

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There is an entire list of suspects that scientists have looked at, and they have not identified a single viable one. With one exception — greenhouse gases.

Police shows sometimes feature the “world’s dumbest criminal” — who doesn’t wear gloves, leaves fingerprints all over the house, drops his wallet at the crime scene, is caught on videotape exiting the crime scene, brags to his friends that he committed the crime — and when he is finally arrested has evidence of the crime in his pockets.

Carbon dioxide is like the world’s dumbest criminal — it leaves evidence all over the place that it’s guilty. First, the laws of physics tell us that adding carbon dioxide, or any other gas that absorbs infrared radiation, to the atmosphere should warm the planet.

Second, we are 100 percent sure that humans are adding carbon dioxide to the atmosphere. Just based on that, Nobel Prize-winning scientist Svante Arrhenius predicted in the late 19th century that humans would warm the climate. And, sure enough, the climate is warming.

The timing of warming, beginning just after the industrial revolution, and the magnitude of the warming, match our theories almost exactly. The figure below shows that the rapid warming of last few decades was accurately predicted in 1975.

Such predictions are the gold standard of science — if you can make a non-obvious prediction about some physical system, then it means that you understand something fundamental about it. This prediction shows that we really understand the warming of the climate system.

Finally, the geologic record is filled with evidence that greenhouse gases impact the climate. For example, during an event about 55 million years ago known as the Paleocene-Eocene Thermal Maximum, a huge amount of greenhouse gases was released into the atmosphere. At the same time, temperatures spiked. Then, as the greenhouse gases were removed from the atmosphere during the following 100,000 years, temperatures slowly returned to what they were before.

This is why scientists are so confident that human emissions of carbon dioxide are warming the climate — there is a mountain of evidence supporting that explanation and no plausible alternative suspects. In this whodunit, you would have no choice but to arrest carbon dioxide for warming the planet.

related stuff

Gavin Schmidt published an oped (gift link) in the New York Times the same day ours came out, which is also worth reading. He took a similar approach, investigating the warming as if it were a crime and evaluating the suspects.

In the years since this oped was written, the evidence connecting increasing carbon dioxide to a warming climate has gotten a lot stronger. In particular, a lot of terrific work has been done on the paleoclimate record. As Zeke wrote a while back on The Climate Brink, it confirms that carbon dioxide is indeed a primary controller of the climate.

If you’re interested in more details about this, I would point you to a talk by Richard Alley entitled “The biggest control knob”. It’s a really terrific explanation of how the paleoclimate record supports the primacy of carbon dioxide as THE most important regulator of our climate.

I was in the room at the 2009 AGU meeting for this and watched it live. It remains one of the best scientific talks I’ve ever seen.

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It’s a striking number. It’s also not clear what it means. Is this the number of people killed by extreme heat? Or climate change’s contributions to the extreme heat? Or the number of deaths above what we would expect in a normal summer? Or something else.

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This matters a lot. If we want to accurately communicate the impact of climate change on human mortality, we need to be precise about what we’re actually counting.

A graduate student and I just published a paper on this in GeoHealth (link), using heat-related mortality in Texas to demonstrate the issue. Here’s what we found.

the basic picture: a u-shaped curve

The relationship between daily average temperature and daily mortality is a U-shaped curve. The temperature at which the minimum number of deaths occur, often called the optimal temperature (abbreviated OT)1, is around 20°C (70°F) in most places. Mortality goes up as the temperature departs from the OT towards either hotter or colder temperatures.

This temperature-related mortality curve is calculated statistically by looking at how total (non-accidental) deaths vary with temperature. This produces curves like the one above.

By convention, the number of deaths occurring at the OT provides an estimate of the baseline (non-heat-related) deaths. At any other temperature, deaths above this baseline are assumed to be heat related.

For example, if there are 50 deaths on a day at the OT and 75 deaths at 10°C above the OT, we attribute the difference — 25 deaths — to heat.

Now that’s out of the way, let’s go over the different ways of quantifying heat-related mortality.

method 1: the optimal temperature method (OTM)

The most common approach in the scientific literature counts all deaths above the OT. In other words, for all days where the daily average temperature was above the OT, we calculate the heat-related deaths on those days and sum them. This gives us an estimate of the total number of heat-related deaths. The red shaded region in the plot below shows this graphically.

We will refer to this as the optimal temperature method (OTM).

That European headline of 62,000 deaths? That’s this method. The problem is that a lot of these heat-related deaths are occurring at temperatures like 75°F, 80°F, 85°F — temperatures that nobody would consider extreme. While the number of deaths on these days is small, those temperatures occur often, so they dominate the total number of heat-related deaths.

So most of what this method counts isn’t really about heatwaves or record-breaking temperatures. It’s just... summer. It also means that the CNN headline was wrong: most of those 62,000 deaths were not due to extreme temperatures and many of them would have occurred even if the summer had been mild.

For Texas, we estimate roughly 1,130 deaths per year (over 2010-2023) using this method — about 2.2% of all summer deaths.

method 2: the extreme heat method (XHM)

A more intuitive approach is to sum heat-related mortality occurring on days that are extremely hot — say, days above the 95th percentile daily average temperature threshold (the red shaded area in the plot below). This is a more direct metric for what the warmest temperatures are doing.

We will refer to this as the extreme heat method (XHM). Using this method for Texas, we estimate that extreme heat caused an average of 248 summertime deaths per year or about 0.5% of summertime deaths. This is much lower than the OTM because we’re not counting the large number of deaths that occur at moderately hot temperatures.

When we compare these numbers to the official death certificate numbers provided by the Texas Department of State Health Services — which counts cases where a medical examiner determined heat was the cause or a contributor to death — the agreement is good, at least in normal years. In extremely hot years like 2011 or 2023, the official death numbers appear to significantly undercount the true number.

The overall agreement between the extreme heat method and the official count makes sense. A medical professional will only attribute a death to heat when the connection is unambiguous and extreme (e.g., a patient comes into the emergency room with core body temperature of 106°F). Such deaths will mainly occur on very hot days.

On the other hand, if someone has a heart attack when it’s 85°F outside, no medical examiner is going to attribute that to heat. The only way to see the impact of heat on such deaths is with a statistical analysis, so you don’t expect these to show up in the official count.

method 3: the excess death method — what climate change actually did

Neither of the first two methods answers the question most people actually want the answer to: how many people did climate change kill?

For that, we use what we refer to as the Excess Death Method (EDM). Our approach is to take today’s mortality risk curve (based on today’s population, today’s demographics, today’s level of adaptation to heat), but plug in the temperatures from a past period — in our analysis, we used 1950-1963.

This gives us an estimate of what today’s mortality would have been had we had temperatures of the mid-20th century. Then we subtract that from the same calculation using the present-day (2010-2023) temperatures. The difference is a measure of the deaths attributable to global warming.

For Texas, this comes out to roughly 900 additional deaths per year due to climate change that occurred since the 1950s, equal to 1.7% of summertime deaths. Using a typical value of a statistical life of $10 million, this corresponds to a value of $9 billion per year due to climate change, or about $300 per Texas resident.

why this matters

The optimal temperature method counts all deaths above the optimal temperature. It’s the most common method in the literature and produces the largest numbers. It’s not wrong, but you should remember that most of these deaths are occurring at mild temperatures that happen every year, so it’s not measuring the impact of “extreme heat” in any intuitive sense2.

The extreme heat method counts only deaths on genuinely hot days. It produces smaller numbers that align well with official death counts from the medical examiners. It’s the better proxy if you want to understand the impact of acute heatwaves.

The excess death method compares mortality in two periods with different climates, holding everything else constant. It’s the best answer to the question “how many people did global warming kill?” For Texas, it’s about 900 people per year or about 1.7% of summertime deaths.

The official numbers from death certificates are almost always lower than all three modeled estimates because it is genuinely hard to establish heat as a cause of death except in the clearest cases. They should be treated in most cases as a lower bound.

The different ways of counting mortality from heat are fundamentally answering different things. Using them interchangeably, or reporting one without specifying which method, creates confusion about the impacts of climate change on mortality.

Because of this, the field would benefit enormously from agreeing on standard metrics. Right now, if you read ten papers on heat mortality, you may be seeing estimates from ten different methods. Getting them standardized and clearly defined matters for accurately reporting the impacts of heat to the public and policymakers.

I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.

Our paper: “Quantifying Heat-Related Mortality in Texas: A Comparison of Methods,” published in GeoHealth. Read it here.

You can also watch a talk I gave at NCAR over this material.

If you’re a reporter who wants to do a story on this, email me.

related posts

I’ve written a bunch of other posts about mortality related to extreme heat & cold:

• How extreme heat kills you

• Unraveling the debate: Does heat or cold cause more deaths? Part 1

• Unraveling the debate: Does heat or cold cause more deaths? Part 2

• Adaptation to climate change will be an absolute nightmare

Chapter 2: Climate risk

We saw in Chapter 1 that today’s college students will experience around 2°C of warming during their lifetime, unless more strenuous action is taken to reduce emissions of greenhouse gases. But I have not explained how this climate change affects humans and natural ecosystems that we care about. In this chapter, I will begin answering that question by introducing the concept of climate risk.

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What is climate risk?

The IPCC defines risk in a somewhat elliptical way as:

The potential for adverse consequences for human or ecological systems, recognising the diversity of values and objectives associated with such systems. In the context of climate change, risks can arise from potential impacts of climate change as well as human responses to climate change. Relevant adverse consequences include those on lives, livelihoods, health and well-being, economic, social and cultural assets and investments, infrastructure, services (including ecosystem services), ecosystems and species.

Thus, the term climate risk refers to the range of challenges and potential harms that are created or intensified by climate change. This includes changes in the physical climate system (e.g., more intense rain), but it also reflects the extent to which these changes impact things we care about. A rain event by itself is not a climate risk, but one that falls on a city that is vulnerable to flooding is. In a future chapter, we’ll talk about transition risk, which is the economic and social risks of transitioning to climate-safe technology.

Climate risk is therefore best understood as the combination of three interconnected components: hazard, exposure, and vulnerability:

Hazard refers to climate-related physical events that may cause loss of life, injury, or damage to property, infrastructure, livelihoods, service provision, or natural ecosystems. These hazards include sudden events like hurricanes, floods, and wildfires, as well as slow-moving processes such as sea-level rise, drought, and shifts in precipitation patterns. The frequency, intensity, duration, and spatial extent of these hazards are all influenced by climate change, with many becoming more severe and destructive as global temperatures rise.

Exposure represents the presence of things we care about (e.g., people, infrastructure, natural ecosystems) in places that could be adversely affected by climate hazards. For example, coastal cities have exposure to hurricanes and sea-level rise, while Western U.S. cities will be exposed to combination of extreme heat, wildfires, and drought. Most places have exposure to some type of climate risk.

Vulnerability is a measure of how easily an exposed system can be harmed by climate hazards, and this is heavily influenced by social and economic factors. For example, poorer communities have fewer financial resources to develop robust infrastructure (like storm drainage or building sturdier structures) and fewer options to evacuate or relocate, less insurance coverage, and reduced access to healthcare after disasters. They are also often sited in worse places, like areas with poor drainage that richer communities don’t want to build on.

This creates an unfair situation where two neighborhoods might face the same hazard (e.g., a hurricane) but the wealthier area is less vulnerable and therefore sustains less damage and recovers quickly while the poorer, high vulnerability neighborhood suffers lasting harm. Vulnerability, in other words, is mostly a social issue.

Climate risk emerges from the interaction of these three components, often conceptualized as Risk = Hazard × Exposure × Vulnerability. This multiplicative relationship emphasizes that risk factors combine and amplify each other. When all three elements intensify simultaneously, the resulting risk can increase exponentially rather than linearly.

This formula also highlights that interventions addressing any single component can significantly reduce overall climate risk. Communities may have limited ability to directly reduce hazards in the short term but can effectively lower risk by reducing exposure and vulnerability through zoning restrictions or improved building standards, early warning systems, and social safety nets.

Let’s now examine each term individually.

Hazards

Temperature: With 100% confidence, we know that the Earth’s average temperature is rising. But most impacts do not come from shifts in the average temperatures; they come from changes in the extremes. This is bad news because we expect to see much larger (fractional) changes in extreme temperatures than in average temperatures.

To understand this, let me show you a probability distribution of daily average temperature for a fictional place:

The y-axis value of each temperature shows the probability of that temperature occurring. For example, a daily average temperature of 25°C has a probability of about 5%1, while the probability of a 30°C day is about 1%.

Climate change shifts the probability distribution:

The blue line is the baseline climate, while the orange line is a climate that’s 2°C warmer in this location. The probability of the middle of the distribution — average days — doesn’t change much. The plot above shows that the probability of a 25°C day doesn’t change much.

But at the tails, where the extreme days lie, the probabilities change dramatically. A 30°C day will occur twice as frequently, from 1.1% of days to 2.2% of days. A 35°C day will occur three times as frequently. As the climate warms, the chance of an extremely hot day rises rapidly. With 4°C of local warming, the chance of a 35°C day is eight times larger.

Cold days will become less frequent. Days with an average temperature of 10°C, which occurred about 1% of the time in the base climate, essentially disappear with 4°C of local warming.

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Precipitation: Warming also increases precipitation. The reason is straightforward physics: warmer air can hold more water vapor. The Clausius–Clapeyron relation tells us that for every 1°C of warming, the atmosphere’s water-holding capacity increases by about 7%. If the convergence of air into a storm remains the same, then the water vapor converging into a storm also increases as the climate warms. As the air rises and cools in the storm’s updraft, that extra water vapor condenses, producing more rain. In other words, with the same dynamics but a warmer background climate, storms wring out larger amounts of precipitation.

You can think of this as a probability shifting exercise, just as it was with temperature. As a result, moderate rainfall days change little, but the heaviest downpours grow much more intense. For example, here is a plot of how the amount of rain in the heaviest 1% rain days has increased:

Because a larger share of rainfall will occur during the heaviest downpours, the impacts will often be negative. In an intense storm, soil can only absorb water so fast, so much of the rain runs off instead of soaking in. This runoff can overwhelm both natural drainage and built infrastructure, causing flooding.

If the most intense rain events increase faster than the mean — which we expect — then this also means longer dry intervals between storms. Combined with warmer temperatures — which increase the rate at which soils lose water through evaporation — this increases the likelihood of drought.

The result is what scientists call hydrologic whiplash: both wet and dry extremes become more common. Wet extremes bring flooding, erosion, and landslides. Dry extremes bring water shortages, stressed ecosystems, and crop losses. Dry periods also intensify heat, creating feedbacks that link these hazards together.

There are several other important changes in precipitation. Although global total rainfall is expected to increase, the increase will not be distributed evenly. Scientists often describe changes in precipitation as “wet gets wetter, dry gets drier,” meaning that regions that get a lot of rain today will tend to get more in the future, while places that are dry today will get drier in the future.

There will also be shifts in the form of precipitation. Less wintertime precipitation will fall as snow and more will fall as rain. This is more important than it might sound: When snow falls in winter, the water does not run off until the snow melts in spring. Rain, on the other hand, runs off immediately, so changing the form of precipitation changes the timing of runoff, increasing the availability of water in winter and early spring and decreasing it in late spring and summer. Given that humans have adapted to the present timing of runoff, changes will negatively impact human welfare.

Floods rank among the most damaging climate hazards. The fact that floods are also affected by human systems (e.g., development, dams, levees) makes global flood trends difficult to detect. Observations show no clear worldwide signal, though certain regions have experienced increases tied to both climate change and development choices. Even so, our confidence in how precipitation will change gives us good reasons to believe that floods will become more frequent and intense as the climate warms.

Sea-level rise is a 100%-certain impact of climate change for two reasons. First, as grounded ice melts, the meltwater runs into the ocean, increasing the total amount of water in the ocean and, therefore, sea level. Second, like most things, water expands when it warms, which also tends to raise sea level. Measurements confirm that sea levels have been rising as temperatures have gone up, and we can be certain that the seas will continue to rise for a very long time into the future.

The latest IPCC report predicts that sea level will rise 47 to 73 cm (19 to 29 inches) above 1995-2014 levels by 2100. While this might sound manageable, the danger lies in how it amplifies extreme sea-level events. Think of sea level rise as raising the baseline water level upon which storm surges, king tides, and other storm-driven extreme sea level events ride.

A coastal defense system designed to handle a 5-meter extreme sea level event works fine when a 4.9-m storm surge comes along, but it is inadequate when that same surge occurs on top of a 0.5-meter higher sea level. In other words, sea level rise can push flooding beyond critical thresholds, overwhelming drainage systems, breaching seawalls, and inundating areas previously considered safe.

Over thousands of years, predicted sea level rise is very large. Paleoclimate data tells us that we can (roughly) expect a few meters to a few tens of meters of sea-level rise for every 1°C of warming. The only good news is that this will take a very long time. Thus, the 1.3°C of warming we have already experienced means that we are committed to something in the neighborhood of 6-13 m (20-40 feet) of sea-level rise over the next few thousand years. This will irreversibly alter the shoreline of the planet, for a very long time.

For example, such sea level rise will erase Florida from the map. I know it’s tempting to consider that a net benefit of climate change, but realize that a lot of places we like will also flood. In the end, people alive in 5,000 years will almost certainly be living in a world with much higher sea levels and very different coastlines due entirely to decisions we make in the next few decades.

Ocean acidification is another 100% certain consequence of emissions of carbon dioxide to the atmosphere. This is the result of a simple chain of logic: 1) humans are dumping carbon dioxide into the atmosphere, 2) a significant fraction of this carbon dioxide is absorbed by the oceans where, in the liquid environment of the ocean, carbon dioxide is converted into carbonic acid.

Thus, as humans emit carbon dioxide, the oceans become more acidic. There’s no mystery or uncertainty in this process—we can observe that today’s surface waters are more acidic than they have been in the last two million years. This trend will persist as long as humans continue burning fossil fuels.

This can greatly affect ocean ecosystems. Making oceans more acidic reduces carbonate ions, which makes it harder for animals with shells and skeletons to build and maintain them. Eventually, the increased acidity can become deadly for them. While the acidity of the ocean has varied over geologic times and ecosystems have adjusted, the changes occurring now are extremely fast, so it is far from certain that natural evolutionary processes can buffer the impacts.

These changes in the climate will conspire to make many types of severe weather more hazardous. As an example, let’s consider tropical cyclones. These are called hurricanes if they form in the Atlantic, typhoons in the Northwest Pacific, and cyclones in Southwest Pacific or Indian Ocean. I’ll refer to them in this section as tropical cyclones, abbreviated TCs.

Let me explain why we are 100% certain that changes in the climate system are increasing the hazard from TCs:

1. Sea level

As described above, we are absolutely certain that sea level is rising because humans are heating the planet. As Prof. Adam Sobel said in Congressional testimony a few years ago: “The most certain way in which hurricane risk is increasing due to climate is that, because of sea level rise, coastal flooding due to hurricane storm surge is becoming worse. Storm surge occurs when the winds from a storm push the ocean onto the land. The total flooding is determined by the surge (the part produced by the wind), the tide, and the background average sea level. As sea level has risen … the flooding is exacerbated by that amount.”

2. Rainfall

Climate change will increase TC rainfall due to the same logic that tells us that rainfall is going to get more intense. It’s just simple physics.

3. Intensity

Basic physics tells us that TCs should get more intense as the climate warms. We cannot yet see a clear climate-related trend in the historical record, which is not that surprising given the fact that hurricanes are relatively rare events and high-quality data doesn’t go back that far. In fact, there is a trend in hurricanes getting more intense. And we can have high confidence that this trend will continue into the future.

4. Number of tropical cyclones

There may be some good news: possibly fewer TCs! Some climate models predict fewer TCs as the climate warms, although a larger fraction of those that occur will be intense category 3, 4, or 5 storms.

However, there’s a very large “but” associated with this prediction. The scientific community still lacks a comprehensive theory explaining what determines the annual global frequency of TCs. Each year, approximately 80 of these storms form across the world’s oceans, but we don’t know why nature settles on this number rather than 8 or 800.

This theoretical gap means our projections of TC numbers rely almost entirely on computer simulations of the climate, which struggle to reliably simulate small-scale, complex phenomena like TC formation. This should give us pause when considering their forecasts about future TC frequency. Regardless of how the number of storms change, though, please remember that, when TCs do form, they will be more hazardous than TCs that are forming today.

This discussion has only touched on a few of the ways that TCs are getting more hazardous. There are other possible changes, such as the tracks of TCs moving to higher latitudes, more frequent rapid intensification, or the slowdown of TC translation speed, all of which can also increase TC hazard.

A second example is wildfire. Wildfires are a natural part of many ecosystems, but climate change is altering both their frequency and severity. The connection to hydrologic whiplash is especially important. Wet periods encourage rapid plant growth. When those wet conditions are followed by long dry periods, the accumulated biomass dries out and becomes highly flammable. Climate change is therefore loading landscapes with more fuel and then priming it to burn.

Once fires ignite, the fires spread more quickly and burn more intensely. Large, severe fires not only destroy forests and homes, they also leave soils bare and water-repellent. This makes post-fire flooding and landslides far more likely when rains return, compounding the impacts.

A clear example of this cycle comes from California. In some years, unusually wet winters produce lush vegetation across hillsides. When those plants dry out in the state’s long, hot summers, they provide abundant fuel for fires. In turn, megafires such as those in 2017, 2018, and 2020 have left burn scars that later flooded during heavy rains. This sequence — rapid growth, severe fire, then destructive flooding — shows how warming-driven extremes can reinforce one another.

Wildfire smoke is another hazard. It can spread thousands of miles, reducing air quality and causing respiratory and cardiovascular health problems. In many regions, smoke exposure already represents one of the largest climate-related health burdens, killing 24,000 people per year in the U.S.

Exposure

After hazard, the next factor in climate risk is exposure. Over the past century, humanity has dramatically increased its exposure to climate-related hazards through population growth and increasing wealth, leading to massive infrastructure development in vulnerable locations. This expansion into hazardous regions represents the biggest driver of our growing climate risk in many regions.

Coastal zones illustrate this problem most vividly. The allure of oceanfront property, maritime commerce, and tourism has driven a concentration of people and infrastructure on the coast, precisely where severe storms and sea level rise pose significant threats. For example, here is a photo of Miami Beach from early in the 20^th and 21^st centuries:

This shows the enormous build-out in areas that are susceptible to many different types of hazard.

Similarly concerning is the expansion of housing into what ecologists call the “wildland-urban interface” (WUI)—the transition zone between wilderness and human development. Throughout the American West, Australia, Mediterranean Europe, and other fire-prone regions, residential development has pushed deeper into forested areas that naturally experience periodic wildfires.

The attraction of these woodland settings is understandable. They offer privacy, natural beauty, and escape from urban congestion. However, this migration pattern has effectively moved millions of people directly into the bullseye of the worsening climate hazard of wildfire, causing exposure to skyrocket.

Vulnerability

Vulnerability is a measure of the susceptibility of communities, infrastructure, and other human and natural systems to be damaged when exposed to climate hazards. While hurricanes, floods, and wildfires might seem like equal-opportunity disasters, their impacts are dramatically uneven, often following existing social inequalities. The most vulnerable segments of society — the elderly, the poor, those with chronic health conditions, and marginalized communities — frequently bear a disproportionate burden of climate impacts.

As an example, here is a photo of a city in Florida after a hurricane came ashore:

Most of the houses were wiped off their foundations, but the house in the foreground is basically unaffected. From the NY Times article that the photo came from:

Dr. Lackey said he and Mr. King, who jointly own the Mexico Beach house, did not even refer to the minimum wind resistance required in Bay County. They built the sand palace to withstand 250 mile-an-hour winds.

The house was fashioned from poured concrete, reinforced by steel cables and rebar, with additional concrete bolstering the corners of the house. The space under the roof was minimized so that wind could not sneak in underneath and lift it off. The home’s elevation, on high pilings, was meant to keep it above the surge of seawater that usually accompanies powerful hurricanes.

This demonstrates one of the key truths in climate risk: With enough money, you can engineer yourself out of most climate impacts.

Hurricane Katrina’s devastating impacts on New Orleans’ lower-income neighborhoods compared to wealthier areas shows this clearly. The Lower Ninth Ward, a predominantly Black, lower-income community, suffered catastrophic flooding, with water depths reaching 12 feet in some areas, destroying nearly every structure. Many of the residents who perished in the storm lacked the means to evacuate (e.g., private vehicles, limited financial resources) and had nowhere to go even if they could evacuate. Those who survived often had inadequate insurance coverage to fund recovery.

By contrast, wealthier neighborhoods like the French Quarter experienced significantly less flooding due to their higher elevation, stronger housing construction, and better flood infrastructure. Their residents typically had greater access to transportation, financial resources to evacuate preemptively, and robust insurance policies to facilitate recovery.

The disparity extended into the recovery phase: several years after Katrina, affluent areas had largely been rebuilt, while the Lower Ninth Ward still contained vacant lots and abandoned buildings. This pattern exemplifies how socioeconomic factors — including income, race, housing quality, transportation access, and political influence — transform the same hazard and exposure into vastly different lived experiences, with these differences persisting for generations.

This uneven distribution of climate impacts across society reveals something profound about our climate crisis: it’s not merely an environmental challenge but a complex socio-ecological problem where social structures determine who suffers first and worst.

Non-linearity

Beyond wealth, another crucial factor shaping vulnerability is non-linearity. If climate impacts were linear, each additional increment of warming — say, another 0.1°C — would produce the same increment of damage. Things would slowly get worse, and over several decades, the accumulated damages would become enormous. This linear model has dominated much of our thinking about climate change: a gradual, predictable process where impacts scale proportionally with warming.

But that’s not how it works. Rather, things are fine until they’re not. For example, this figure shows a schematic of how increasing rainfall can impact an individual:

At low and moderate rates, rainfall does no damage because existing infrastructure is built to handle it. The point where damage begins (beyond “heavy” rainfall) is a threshold, and it is largely an engineered one. Society has chosen how much rainfall its drainage systems, levees, and buildings can handle, often based on the climate of the past. Beyond that threshold, damage rises rapidly: You go from zero damage if floodwater stops half an inch below your front door to tens of thousands of dollars if the water rises one more inch and flows inside.

To put everything together, here are vulnerability curves for extreme winds for two regions in the Philippines:

The National Capital Region (NCR) contains Manila and is the most populated, developed, and urbanized area in the Philippines. It has strong infrastructure and high socioeconomic resilience. Eastern Visayas is a less affluent, predominantly rural region characterized by weaker building construction, greater vulnerability to high winds, and significantly lower capacity to cope with disaster-related losses. As a result, at every wind speed, the richer region (NCR) shows less damage.

The plot also shows the non-linearity of vulnerability: zero damage at low wind speeds, then rapidly increasing when wind speeds pass a threshold.

Climate change and impacts

You may have seen a graphic of billion-dollar disasters in the U.S. over time:

Given that climate risk is the combination of these three factors: hazard, exposure, and vulnerability, we would like to decompose the trend in billion dollar disasters into trends in these underlying factors.

We know that most of this trend is driven by exposure: more people, who are richer, living in places where hazards hit (e.g., near the water, in the wildland-urban interface). This by itself is responsible for a large part of the increase in climate-related disasters.

We can also be confident that climate hazards are increasing in frequency and intensity and this is contributing to the trend. But, at the same time, vulnerability is declining. For example, after Hurricane Andrew devastated South Florida in 1992, the state adopted hurricane safety standards that mandated more resilient construction.

This included requiring impact-resistant windows or shutters, banning particle board in coastal areas, and reinforcing roofs and walls with stronger connections and concrete supports. Miami-Dade and Broward counties were designated as High-Velocity Hurricane Zones, with even stricter wind-load requirements. The building code has been updated regularly to reflect advances in engineering and storm science, and research after subsequent hurricanes has shown that homes built under the post-Andrew code sustained significantly less damage than older structures.

In the aftermath of Hurricane Harvey in 2017, Houston and Harris County moved to tighten flood-related building regulations: they shifted from using the 100-year floodplain to the 500-year standard and required new structures in those zones to be built at least two feet above that. Alongside code changes, a $2.5 billion bond referendum passed in 2018 funded flood control projects such as detention basins and bayou improvements.

There are limits to vulnerability reductions, however. For example, after Hurricane Ike made a near-miss on Houston in 2008, Texas began planning for a seawall to protect Galveston Bay and the Houston Ship Channel. Such a structure would be extremely expensive ($30-$60B) and no one seems willing to pay for it. So the project has been in a holding pattern for nearly two decades. We’ll talk about this in more detail in chapter X.

Putting this all together, the rising toll of climate-related disasters can be clearly understood through the changes in its core components. Exposure is increasing as people move to high-risk areas. Simultaneously, climate change is intensifying the frequency and severity of hazards. Counteracting these trends is reduced vulnerability, such as the updated building codes.

This is a draft of a section of my climate risk textbook (slightly edited & reformatted to make it appropriate for Substack). I’d very much like to identify errors now, so if you see any, please let me know in the comments.

I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.

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But a somewhat obscure climate policy choice of how to value methane emissions compared to CO2 is doing just that – and unfortunately a number of climate scientists who should know better are defending it.

The broader context is a big fight at the moment over proposed revisions to New York’s state climate law. Governor Hochul is proposing both delaying the implementation of the law (which in my opinion is not a good thing), and changing the way that methane is treated by using a 100-year timeframe rather than a 20-year one. It is my opinion as a climate scientist that using a 20-year timeframe is deeply problematic – and I’m far from the only one in the community with that view. In this piece I’ll try and explain why.

Stocks and flows

To understand why the timeline over which methane is compared to CO2 matters, we first need to understand the different climate effects of the two gases. I’ve written about this at some length in the past, but here is a short summary.

Methane is relatively short-lived, with a lifetime of around 10 years. But while it is in the atmosphere it has a very strong climate effect, trapping on the order of 100x more heat than CO2 for every ton. Methane is short-lived because it oxidizes in the atmosphere, breaking down into CO2 and H2O through a long chain of chemical reactions catalyzed by interactions with OH radicals.

CO2, by contrast, is extremely long-lived. The mean atmospheric lifetime is on the order of 10,000 years, though this is dominated by a very long (~400k year) tail associated with silicate weathering. On shorter timescales around 60% of a pulse of emissions is absorbed by land and ocean carbon sinks within a century of emissions (though the strength of these may change as a result of our changing climate).

These different lifetimes mean that methane does not accumulate in the atmosphere over longer timeframes, while CO2 does. Methane is a “flow pollutant”, in that its climate effect is a function of the rate of emissions, while CO2 is a “stock pollutant” whose impacts are a function of cumulative emissions.

If emissions of methane increase, we get warming. If they stay flat, their temperature effect is constant, while if they decline we get cooling. CO2 emissions, on the other hand, always warm the planet. Increasing CO2 emissions cause warming to speed up, flat CO2 emissions cause steady warming, and decreasing CO2 emissions slow (but do not stop) warming. The only way to get cooling with CO2 is to actively remove past emissions from the atmosphere.

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Short-term benefits and long-term harms

Global warming potentials (GWPs) are a useful metric to convert different types of greenhouse gas emissions into a single unit, generally expressed as a CO2-equivalent (or CO2e). They are defined as the amount of a gas needed to trap the same amount of heat in the climate system as CO2 over a specified timeframe – commonly 20 years, 100 years, or 500 years.

However, while convenient, they are not necessarily physically meaningful. I cannot tell you how much warmer the world will be in 20, 100, or 500 years based on a certain amount of CO2e.

To illustrate this, let’s look at the climate effects of reducing emissions by one gigaton of CO2e each year for 20 years. The blue line shows the effects of reducing CO2. Here global temperatures are reduced by around 0.01C, and this cooling benefit persists more or less indefinitely (at least on timescales of millennia).

If we cut the same amount of “CO2e emissions” in the form of methane using a 100-year period for GWP calculations, we get the red line. We get much more short-term cooling, reflecting the more powerful heat-trapping effects of methane. But the climate effects fade away over time, and after 100 years or so only a fraction of the cooling remains.1 This is often justified by the fact that short-term cooling is so much larger, so the “area under the curve” between methane (GWP100) and CO2 mitigation is quite similar, for at least a century or so.

If we use a 20-year GWP period to compare methane and CO2, we get the worst of both worlds. Not only does the cooling benefit of methane mitigation not persist, but it’s not that much greater than CO2 during the period of emissions. This is because we need to cut a lot less methane (only around 12.1 megatons per gigaton of CO2) when using GWP20 compared to a much larger reduction (33.6 megatons) when using GWP100.

We can also more directly look at the difference in global temperatures over time if we chose to mitigate methane emissions instead of CO2 emissions. In the figure below, values above zero reflect periods when we get more cooling from methane reductions than from CO2, while values below zero show periods where CO2 reductions give us a bigger climate benefit. Here the GWP100 metric provides at least some period of climate benefits, while GWP20 only gives a very short term boost.

Similarly, if we look at the effect on global temperatures in 2050, 2100, and 2200 we find a large benefit (3.6x more cooling than CO2 mitigation) of methane in 2050 using GWP100, and a small benefit (1.3x) when using GWP20. By 2100, however, methane using GWP100 only gives us 40% of the cooling as CO2 reductions, while methane using GWP20 is a measly 15%.

Even if we sustain methane and CO2 reductions for longer than 20 years, methane with either GWP20 and GWP100 eventually ends up with higher warming than CO2. For GWP20 this occurs after around 40 years, and after 135 years for GWP100.

Discounting the future

How we choose to value the short-term benefits of methane reductions compared to the long-term harm of CO2 is ultimately a question of how much we value the future compared to the present. This is not a new question; the economics literature has long explored how to answer this question using the concept of “discount rates”.

In fact, the choice of time horizon for determining the equivalence between CO2 and methane can be directly translated into an effective discount rate based on how much near-term climate damages you avoid at the cost of greater longer-term harm. A 2018 paper by Marcus Sarofim and Michael Giordano found that GWP100 is equivalent to a 3% discount rate – similar to what governments use for long-term infrastructure investments. GWP20, by contrast, translates into a discount rate of 12% per year, which is much higher than almost any discount rate used for policy decisions today.2

A 12% discount rate means that the same level of climate impact to a person alive 100 years from now is only worth 2% as much as it is today. It represents a deep discount of the welfare of future generations, and is fundamentally at odds with our goal of stabilizing the climate under the Paris Agreement.

So why do people advocate for GWP20?

If heavily discounting the future is so deeply inconsistent with the concern for the welfare of future generations usually espoused by climate advocates, why do we see people making the case for GWP20?

There are a few reasons. One major one is a concern over near-term climate harms, particularly the possibility of tipping points in the climate system. If the world is likely to cross a critical threshold in the coming decades, then wouldn’t it be critical to prioritize short-term cooling of the climate, even if it might come at the expense of longer-term warming?

This argument misses the mark for a number of reasons. First, most of what we call “tipping points” are not instantaneous changes between climate states, but rather reflect feedbacks with hysteresis. That is to say that it does not just matter if we pass a particular temperature level, but rather how long we pass that level is critical. The temporary cooling associated with substituting methane reductions for CO2 reductions will only avoid these impacts if it’s followed by some additional measure to keep global temperatures down. If we end up with more warming long-term by prioritizing methane mitigation over CO2, the risks of tipping points will increase rather than decrease.

Second, the risk of tipping point exceedance is linked to the peak level of warming, which will not occur until the latter half of the 21st century even under ambitious mitigation scenarios. Methane mitigated today will have a minor impact on temperatures in a world where warming peaks in 2070, while CO2 will have a much larger impact.

The other argument for using GWP20 is to ensure that we get more methane mitigation than we otherwise would, and specifically reduce the use of natural gas (which involves both CO2 emissions when combusted and methane from leaks). But the use of GWPs by definition introduces a tradeoff between methane and CO2 when used in the context of a climate target measured in CO2e. If New York wants to cut its emissions by 40% by 2030, using GWP20 will make it a lot cheaper to achieve that goal by cutting methane than by cutting CO2 because it means that methane counts a lot more than CO2 toward that goal. While we might ultimately end up with (net) zero CO2 and methane emissions under a net-zero target, the path we take to get there matters a lot for temperature outcomes.

A better path forward

It is important to take measures to cut methane emissions and it provides real, tangible benefits in the near-term. But there are ways to design systems that do not come at the cost of the welfare of future generations.

If we want to stick with traditional GWPs as a framework (and ideally we should not), using GWP100 is much more justified than GWP20 as it aligns with the level of future discounting (~3% per year) that we currently apply to a lot of other long-term investments in public welfare. However, GWP100 still is not well aligned to the goal of temperature stabilization given the long-term differences in climate outcomes.

There are alternative metrics like GWP* that compare flows of methane to stocks of CO2 rather than treating the two as directly equivalent, as well as simple to use climate models that can allow policymakers to determine the actual temperature effects of their decisions over time.

But the simplest approach – and one increasingly espoused by the climate science community – is to avoid conversions altogether by setting separate targets for CO2 and short-lived climate pollutants like methane. Rather than having a goal of reducing the (not-physically-meaningful) “CO2e” emissions 40% by 2030, set separate goals for both CO2 and methane. That way we know what we are getting, and we don’t create the problem of having to trade off between the two.

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A runaway greenhouse occurs when humans add enough carbon dioxide to the atmosphere to push it past a threshold beyond which warming becomes self-sustaining and unstoppable1.

Venus is the canonical example of what that looks like. At some point in the past, it probably looked a lot like Earth. But then the planet heated up and boiled the oceans. Over time, ultraviolet radiation from the Sun split the water molecules into constituent hydrogen and oxygen atoms and the hydrogen escaped to space. In this way, the planet lost its water.

The loss of the ocean also caused the carbon cycle to shut down. This means that, unlike on Earth, where carbon dioxide is continuously removed from the atmosphere by dissolving into the ocean, there’s nothing removing carbon dioxide from the Venusian atmosphere. As a result, carbon dioxide accumulated in the atmosphere.

Today, Venus has an atmosphere that’s 90 times more massive than the Earth’s and it’s almost entirely carbon dioxide. The resulting greenhouse effect leads to surface temperatures around 450°C (850°F) — basically the temperature of your oven on self-clean cycle — and under pressures comparable to those at the bottom of the ocean.

In this hellish environment, spacecraft engineered specifically to survive Venusian conditions are still only able to last one or two hours before they are destroyed by the harsh environment.

So let’s take a step back and consider what it takes for the climate to run away.

what determines how much warming you get?

If you add carbon dioxide to our atmosphere, you get an initial amount of warming due to increased trapping of heat. If that were all there was to climate change, it would be a pretty simple scientific problem. But lots of things change in response to that warming and some of these changes can lead to additional warming. This is known as a feedback.

An example is the ice-albedo feedback. As the climate warms, you melt ice. This NASA figure shows sea ice is bright white, meaning that it is reflecting most of the sunlight that hits it. Open ocean is dark blue, meaning that it’s absorbing most of the sunlight that hits it.

When sea ice melts due to warmer temperatures, a bright white surface (ice) is replaced with a dark surface (ocean). The net result of this change in surface reflectivity is that the Earth absorbs more sunlight.

This causes more warming, which melts more ice, which leads to more absorbed sunlight and more warming, which drives more ice melt, etc. Lather, rinse, repeat.

feedback math

To get more quantitative about feedbacks, let us first go over some basic feedback math. For any feedback, we can express its strength as f, which is the warming produced by one additional trip through this feedback loop per degree of warming from the previous trip through the loop.

Thus, in response to an initial warming ∆T_i, the first trip through the feedback loop produces additional warming of f∆T_i due to melting ice and increased absorption of sunlight.

But the feedback operates on this additional warming, producing an additional warming of f × f∆T_i = f²∆T_i. The feedback then operates on this additional warming too, leading to an additional warming of f × f²∆T_i = f³∆T_i . This goes on forever, so the final warming ∆T_f is:

The math ninjas among you will recognize this infinite series can be rewritten simply as:

the runaway condition

As long as f remains below 1, each pass through the feedback loop contributes less warming than the last, and the infinite series converges to a finite total. But when f reaches 1, every pass contributes the same warming as the one before — the series never converges, and the total warming becomes unbounded. This is a runaway climate.

The temperature won’t actually reach infinity, of course. Every feedback has a physical limit at which it shuts off. The ice-albedo feedback, for instance, stops amplifying warming once all ice has melted. The water vapor feedback stops once the oceans have boiled away and no liquid water remains. These are natural endpoints that have all occurred on Venus.

But the essential question stands: can warming on Earth ever take off on its own? Can Earth reach a runaway climate?

We know our current climate is stable — we add warming every year, and the climate hasn’t run away. This tells us that Earth’s f is well below 1. Detailed analyses of all the major feedbacks — water vapor, clouds, ice albedo, and the lapse rate — confirm this, placing the combined f somewhere between 0.5 and 0.75. A stable climate, just as we’d expect.

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how it could happen

There are a few ways that have been proposed whereby the Earth’s atmosphere runs away. They mostly revolve around the behavior of water vapor in the atmosphere.

warning: you are entering the nerd zone

If the surface warms enough, the atmospheric temperature profile asymptotes to the saturation vapor pressure curve, known as a pseudoadiabat. In this state, the temperature at a given optical depth becomes fixed, regardless of the surface temperature.

Consequently, the energy out for the Earth reaches an asymptotic upper bound, known as the Simpson-Nakajima limit. If energy in for the Earth exceeds this maximum energy out, the energy imbalance cannot be corrected by a rise in surface temperature, leading to a runaway greenhouse effect2.

People have estimated the limit of energy in to be around 280-290 W/m². Right now, energy in from the Sun is about 240 W/m², so this suggests that it would take at least +40 W/m² of radiative forcing to push Earth into a runaway climate state. Given that doubled carbon dioxide has a radiative forcing of +4 W/m², +40 W/m² corresponds to 10 doublings of carbon dioxide, a factor of 1000x above preindustrial concentrations.

It seems quite unlikely that there is anywhere near enough fossil fuels to reach this runaway threshold.

A far more plausible way for our climate to run away is a brightening Sun. Solar physicists think that the Sun is getting brighter at a rate of about 1% per 100,000,000 years. At this rate, energy in for the planet might reach the runaway threshold in one or two billion years.

so should you worry?

Not about Venus. A true runaway greenhouse — boiling oceans, hellish surface temperatures — is simply not in the cards on any time horizon we care about.

But “not Venus” is a remarkably low bar to set for ourselves. There’s an enormous amount of territory between our current climate and a Venusian apocalypse, and we’d strongly prefer not to explore it. As Jim Hansen has argued, what we should really care about is warming sufficient to break the things that matter to us — ecosystems, infrastructure, food and water security, human lives.

We’re already seeing bad things happen to those — with only 1.5°C of warming. Climate change is affecting us right now, without any runaway greenhouse in sight. That alone should be more than enough motivation to limit future warming as much as we can.

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related “runaway” information

Another possible scenario for very large warming is the emergence of a strong carbon-cycle feedback: A truly worst-case climate scenario: Losing control of the carbon cycle

And, of course, clouds could do something weird.

Ray Pierrehumbert’s excellent book Principles of Planetary Climate contains several illuminating discussions of a runaway greenhouse (note: this book is at the grad school level).

other stuff

I wrote recently about the cost of fossil fuels to our national and economic security. Then came reports that the Pentagon has asked for approval for a $200 billion request to Congress to pay for the war in Iran. This is a lot of money — around $600 per U.S. resident.

This war, as well as the war in the Ukraine, are fundamentally enabled by fossil fuels. In a world of climate-safe renewable energy, it’s hard to see that these wars are being waged.

This really undercuts the “solar and wind are expensive” argument.

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El Niño also has a major effect on global temperatures, reducing the rate of ocean heat uptake and increasing atmospheric temperatures. Global mean temperature can temporarily increase as much as 0.2C during a very strong El Niño event, with the maximum temperature increase in global mean temperature occurring around 3 to 4 months after El Niño conditions peak in the tropical Pacific.

In the past week, a number of modeling groups that try to forecast future ENSO conditions have released forecasts that suggest that a very strong El Niño may be in the works for late 2026. This is a notable revision upwards from earlier forecasts in January and February that suggested that an El Niño might develop, but that it would likely be more modest. Historically it has been hard to precisely predict ENSO development early in the year – hence the famed spring predictability barrier – 

I’ve collected 11 different models that have been updated since the beginning of March. Each of these in turn features a number of ensemble members, so that we end up with 433 total ENSO forecasts. A subset of these obtained from Copernicus’ C3S (from Australia’s BOM, CMCC, DWD, ECMWF, and Meteo-France) only extend through August, while the remainder (CFSv2, ECC-CanESM5, ECC-GEM5.2, NASA-GEOS, NCAR-CCSM4, and NCAR-CESM1) extend all the way through November.

The figure below shows a combined super plume of all the ensemble members of all the models, with the mean of each model shown as a bold colored line and the average of all the models (the multi-model mean) shown as a black dashed line.

We can also look at the distribution of temperatures in each month across all ensemble members. The figure below shows the median, interquartile range (25th to 75th percentile) and whiskers (1.5x the interquartile range) for each month, with the statistics weighted based on the number of ensemble members for each model so that models with more ensemble members do not get more weight.

These clearly show that a strong El Niño is indeed likely to develop later in the year. While I’d probably discount some of the higher values (much above 3C) as outliers here, the median and mean across all the models still gives an estimate around 2.5C, which would put it notably stronger than the 2023/2024 El Niño and close to if not matching what we saw back in 2015/2016.1

So what does this mean for global temperatures this year and in 2027? All things being equal, the lag between peak El Niño conditions and the global surface temperature response would result in the largest impacts on 2027 temperatures (as El Niño events generally peak between November and January). It would still boost 2026, but probably not enough to set a new record this year.

However, I have to be a bit cautious here. Long time readers may remember my post in May 2023 where I deemed it unlikely that 2023 would set a new record (given this historical lag in global temperature response to El Niño) and argued that 2024 would instead. I was partially wrong – 2023 was weird, and the heat came much earlier than expected. We think the extended triple-dip La Niña event between 2020 and 2023 may have primed the system for more rapid heating, something absent this time around. But we don’t know for sure. Fool me once, and all that.

Either way, this means that 2027 looks increasingly likely to set a new record, perhaps by a sizable margin if we end up on the high end of the range of El Niño forecasts.

Also, as a bonus, I’ve also added a new permanent tab to our Climate Dashboard where you can see the latest El Nino / La Nina forecasts, and play around with interactive versions of all these charts.

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We had invested $2.3 trillion on clean energy in 2025 – renewables, nuclear, electric vehicles, heat pumps, and other decarbonization technologies. Global military spending when last I had checked (prior to this post) was $2.4 trillion in 2023 (as good data lags a bit behind for that measure). We had already reached a point where global clean energy spending this past year exceeded global military spending in 2022.

Unfortunately, I might have been a bit premature in my prediction of a crossover point, both because there has been some slowdown in the growth of clean energy spending after 2023 (though it is still increasing!), and because the advent of a more conflict-ridden era is causing the most rapid growth in global military spending since at least the Cold War.

Global energy transition investment increased by 8% from 2024, reaching a record $2.3 trillion, though this was slower growth than the ~20% year-over-year increase that has characterized the post-2020 period. However, after the start of the wars in Ukraine, Gaza, and other regional conflicts, global military spending has increased sharply, up 9.4% from 2023 to 2024 (the latest year with firm data available).

The figure below shows historical global clean energy spending (blue) and military spending (red), as well as projections for how they might grow in the future. These are of course rough guesses; it is possible for global military spending to slow down and clean energy spending to speed up (or vis versa).

The central scenario (clean energy at 12% compound annual growth rate – CAGR – and military at 5.5%) puts the crossover when annual clean energy investment flows would exceed annual global military expenditure for the first time at around 2029. The sensitivity case (clean energy at a more conservative 8%, matching the 2024 to 2025 observed rate) pushes that out to around 2036, while high clean energy grown and high military spending growth (8% CAGR) would cross over in 2032.

The 12% central case is reasonable: BNEF’s observed 2020–2025 CAGR was ~20%, and their base-case Economic Transition Scenario implies ~$2.9T/yr average over the next five years. The 5.5% military growth reflects the current rearmament cycle (NATO’s 3.5% GDP target, Asia-Pacific buildup, Middle East tensions), slower than 2024’s exceptional 9.4% but faster than the 3% to 4% that characterized the 2015–2022 period.

So while I’m hopeful that we will soon be investing more in clean energy technologies than we are spending on all the world’s militaries combined, we will probably have to wait a few more years to see this realized.

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The rest of the story are subsidies. In most discussions, it’s laser-focused on subsidies for renewable energy, not fossil fuels. But fossil fuels get enormous subsidies. Those are deeply hidden, though, spread across government budgets, healthcare systems, and military spending in ways most people can’t connect back to their energy choices.

To the extent that they do get attention, most of it goes to the implicit subsidy for fossil fuels from climate change and air pollution, which economists have valued at trillions of dollars per year.

But there’s another hidden subsidy that few talk about: national security. And right now, as oil prices surge in response to U.S. strikes on Iran, that cost is impossible to ignore.

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the subsidy nobody talks about

The United States spends more than $81 billion every single year just to protect the global supply of oil.

That figure comes from Securing America’s Future Energy (SAFE), a nonpartisan national security organization led by retired senior military officers. They calculated that about one fifth of the entire Department of Defense base budget exists, at least in part, to keep oil flowing through vulnerable choke points like the Strait of Hormuz, the Suez Canal, shipping lanes in the South China Sea.

That’s $81 billion — every year — and it doesn’t appear on your gas station receipt. It’s a subsidy, paid by taxpayers, that makes oil look far cheaper than it actually is. Spread across U.S. oil consumption, it works out to roughly $11 per barrel, or about 28 cents per gallon, hidden in the defense budget. For a typical fill up, that subsidy amounts to $5 per tank.

This subsidy is just to be ready to fight. Then there are the actual wars we’ve fought over the oil supply. For example, the 2003 Iraq War’s cost was estimated to be $3 trillion — nearly $10,000 per American.

When you add it all up, fossil fuels are not cheap. They’ve never been cheap. We’ve just been brilliant at hiding the costs — in the defense budget, in emergency rooms, in FEMA disaster relief, etc.

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fossil fuels are our pimp

But despite spending trillions protecting global oil routes, we remain economically vulnerable to disruption in oil supplies.

Why? Because oil is a globally priced commodity: everyone pays the same price. When something disrupts supply anywhere in the world, prices go up everywhere — including in the U.S.

This occurs despite the United States being the largest oil producer in the world.

We saw this play out in real time just last week. Following U.S. and Israeli strikes on Iran, oil prices surged. Gas prices are following. And this was before the conflict escalated to directly threaten the Strait of Hormuz — a waterway through which roughly 20% of the world’s petroleum flows every day.

real energy security

Fossil fuel pushers don’t want you to understand this. And they particularly don’t want you to recognize that the price of solar energy and wind energy is not affected by events in the Middle East. A missile strike on Iranian oil infrastructure has zero effect on the cost of generating electricity from a solar panel in Texas or a wind turbine in Iowa. The “fuel” — sunlight and wind — is free, domestic, and geopolitically inert.

This is one of the key reasons China is pushing electric vehicles with government industrial policy so strongly. They import most of their oil and this has created an enormous geopolitical risk. Electric vehicles, charged by rapidly growing solar energy, is an important way China is addressing the national security problems of fossil fuels.

In many parts of the country and world, solar and wind are now the cheapest sources of new electricity generation — full stop. Not because of subsidies (though those exist but are likely smaller than those for fossil fuels), but because the underlying economics have shifted irreversibly.

Renewable energy is the only credible path to energy and economic security. “Drill, baby, drill” is a fantasy — and an expensive one. More drilling means more pain, not more security.

time to move on

It’s worth pausing to appreciate what fossil fuels made possible. They powered our civilization for the last 200 years. But the transition to renewable energy isn’t a rejection of that legacy — it’s the next chapter. And it’s a better one.

related posts

Have renewables decreased electricity prices?

How renewables are saving Texans billions

Is renewable energy cheaper than fossil fuels?

From the indispensable Earthview substack: Trump’s War on Solar Is Failing

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It is with heavy hearts that we announce the passing of the DOE Climate Working Group (CWG) Report.

The CWG Report’s death was announced in footnote 8 of the EPA’s Endangerment Finding rollback:

The CWG Report was, in the end, a footnote in a footnote.

The Report was born in July 2025 amid great fanfare. Its authors proclaimed it to be the opening salvo of a new debate over climate science—a bold reassessment that would finally challenge the so-called consensus.

That did not last long.

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The report was mortally wounded almost immediately when the scientific community and general public submitted tens of thousands of comments documenting how the report was not a scientific assessment but pseudo-scientific propaganda.

Critics noted that the report violated nearly every conceivable norm of scientific practice—in its cherry-picking of evidence, its misrepresentation of the literature, and its abandonment of the standards of scientific assessment. These wounds proved too much for the CWG Report to overcome.

The Report was preceded in death by the Climate Working Group itself, which the DOE disbanded in September 2025 after losing a lawsuit over the unlawful process used to produce the Report.

The report is survived by grieving former members of the Climate Working Group, who remain defiant. They have vowed to respond to the scientific criticisms of their report but have so far done nothing. Instead, they simply continue to repeat the discredited claims of their deceased report as though critiques were never made.

The report was also preceded in death by its close companion, the idea of a “red team-blue team debate” over climate science. This concept had long claimed that climate science had never been truly stress-tested and that, if only the right skeptics were given the opportunity to make their best case, the edifice would crack.

The DOE gave them that opportunity. The team the DOE assembled was, by any reasonable measure, the cream of the crop of climate skepticism. But the resulting critique of climate science was so thin, so reliant on misrepresentation, and so quickly dismantled that it actually proves how robust climate science is.

After all, if there were a legitimate case against climate science, these authors would make it. That they can’t is deeply revealing.

But even though the CWG Report is gone, it will never truly leave us. It will continue to be cited in venues where a discredited report is welcome: blogs, Twitter, op-eds, and the occasional congressional hearing.

Its only appearance in the peer-reviewed literature will be in papers documenting its errors:

And it will never see the inside of a courtroom—which is precisely why the EPA pulled the plug in footnote eight rather than stake a legal defense on it.

In lieu of flowers, the scientific community requests that you forget the Report ever existed.

Wow, that was beautiful.

climategate 2

As part of the litigation over the actions of the Climate Working Group, the emails between the CWG group members have been released (get them here). I will refer to them as “climategate 2”. If you do too, we can make climategate 2 a thing.

There’s a lot of important things in the climategate 2 email dump. For example, here’s a graphic that I put together for a “spot the difference” contest:

If you answered “they’re using a different font but are otherwise the same”, you are correct!

As I read the climategate 2 emails, one thing really stood out to me. Science works because scientists talk to each other all the time. They attend conferences, email each other, serve on committees, and have informal conversations with colleagues down the hall.

Reading through the CWG emails, it is clear that the CWG members don’t talk enough (or at all) to the scientific community. In fact, I haven’t seen any of them at a scientific meeting in at least 15 years. If they did, they could have headed off a large number of errors in the Report.

Consider what happens at a typical scientific meeting. At the coffee break, you tell a colleague your latest idea. If it’s good, they’ll tell you so and may suggest ways to strengthen it. But equally valuable is when they tell you your idea is dumb (happens to me more often that you’d think). You might not believe the first person who says so. But when a second person says the same thing, and then a third, you start to listen.

This informal feedback process is a key one in science. It tells you what the rest of the field thinks and what you’d need to do to convince them. It also stops a lot of bad ideas from wasting the community’s time.

But the CWG authors are not getting this key feedback. As a result, they seem genuinely unaware of what the scientific community thinks. It sometimes feels like the report was written in 2013 and then frozen, only to be unthawed in 2025.

In particular, they appear to believe that public concern about climate change is manufactured by a small number of extremists—and that the broader scientific community is quietly sympathetic to their views.

You can see this in their email discussion of whether to get their report peer reviewed1. At one point, they list people they think would be open to their arguments:

I know most of these people and talked to several of them about the CWG report. None thought the Report had any merit. Some even went on the record against the Report—e.g., Kerry Emanuel was an author on our rebuttal of the CWG Report and also wrote a realclimate post dissecting their arguments over climate’s effects on hurricanes. Graeme Stephens was an author of the NASEM report that disputed the central claims of the Report.

People were legitimately shocked that the CWG thought this list would support their sloppy and half-baked science2.

The former CWG members need to start talking to the rest of the scientific community. They should go to a scientific conference and present their work there. Then they should publish their work in the peer-reviewed literature. After all, that’s what the scientists debunking the CWG’s work are doing. And it’s what you’d do if you legitimately wanted to debate the science.

The fact that they will never never never do those things shows that, in their heart, they have zero interest in climate science.

other things to read

A very nice post from the Earthview substack about why libertarians should love solar energy. That they don’t suggests they’re actually more motivated by getting $$$ from fossil fuel interests.

One of our frequent commenters, Just Dean, has a new substack that looks good. I particularly like this post about what paleoclimate variations tell us about future warming.

You should subscribe to both of these!

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I’m something of an unusual climate scientist as, rather than working in academia, my main role is as the climate research lead at Stripe, a financial technology company in Silicon Valley. As such, I’ve probably used AI far more than most other folks in the scientific community, given that we are strongly encouraged to use it extensively for work. I’ve also worked directly with AI labs on projects to evaluate the performance of LLMs in answering climate science questions, and to help enable AI tools to support scientific collaboration.

I started using GPT3.5 back in 2022 when it first came out. Initially it was a novelty but not particularly useful for scientific applications. It was quite prone to hallucinations, would get into endless spirals of errors it would then try and fix, and would often grossly misinterpret instructions. But it had decent skill at coding, and could (sometimes) help solve bugs in my code much faster than trying to search Stack Overflow or old Reddit posts.

This changed with the release of GPT4 in 2023, and particularly with the release of Code Interpreter that could automate data analysis and visualization capabilities. It still hallucinated, was not great at writing, but could arguably code better than the typical scientist. One of the earlier projects I did was to ask it to help visualize how unusual the summer of 2023 was in terms of global temperatures, which helped generate both the ideas of and code for this somewhat viral Climate Brink post (which I referred to as “gobsmackingly bananas” at the time).

Today the tools are much better than they were in 2023. Hallucinations still exist, but they are much less frequent. As someone who has used these tools more than most in the scientific community, I have a good sense of what they work for and what they do not do well today. The tools I primarily use now are Claude Code (Opus 4.6, via my terminal) and the web-app for Gemini (3.1) for projects where integration with my email, Drive, and other parts of the Google ecosystem is helpful.

AI, what is it good for?

So how can scientists best use AI tools to be more productive or expand their ability to do impactful science? What, in my experience, works well with current AI tools?

First and foremost is coding. Scientists are, by and large, not software engineers. Much of their coding is self-taught or from long-ago college classes, and many scientists struggle with writing code quickly, producing well-documented reproducible code, and fixing coding errors. The ability of AI tools to write high quality code has grown by leaps and bounds over the past three years, to the point where its comparable to (or even better than) professional software engineers for many applications.

As such, English (or any plain text language) has increasingly become the new code. Specifying the details about your project, creating a concise plan, and planning tests to ensure that it is working properly has largely replaced writing python code in my workflow. There is a risk here – as with any muscle, coding skill will atrophy without use. It may well be that a decade from now that I’m less able to effectively review what has been produced by AI coding tools. But this can be mitigated by both good tests and an deep understanding of the topic matter, with the latter allowing a critical assessment of results to ensure that they are reasonable.

Another area where I’ve found AI tools increasingly useful is for data cleanup and analysis. In many past projects I’ve spent as much time wrangling the data – merging different files with inconsistent naming conventions, identifying and correcting data input errors – than doing the actual analysis. AI tools have gotten quite good at cleaning, merging, and analyzing large datasets. They can seamlessly operate with more niche filetypes like NetCDF, as well as help put together a clean and well-documented file management system.

Data visualization is another domain where I’ve seen a lot of progress in recent years. As someone who has spent my share of hours banging my head against the proverbial wall trying to get matplotlib (python’s main plotting library) to do what should be fairly simple things, the ability to just write what I want a graph to do in plain English and see it happen is nothing short of magical. After being able to move a color bar scale from the horizontal to vertical axis just by telling the tool to do it, it would be hard to ever go back to the endless browsing of arcane feature documentation.

I’ve also found AI tools increasingly good at coming up with ideas for visualizing data. Some of my recent experiments with new ways to visualize global temperatures (e.g. the tree ring plot) came from prompting these tools to come up with new novel visualizations that are both compelling and different than anything that has been previously published. Similarly, translating static plots that I’ve previously developed into interactive versions (such as those on the new Climate Dashboard) is quite straightforward.

Finally, there are skills that I’ve never developed that AI makes easy. “Vibe coding” with tools like Claude Code lets me build and host websites in a day (e.g. the Climate Dashboard), two things I’ve never done before in my life. I’ve created fun math leaning games for my 8-year-old daughter to help with her school work, and I’m also working on some more complex interactive data visualizations using large geospatial datasets that hopefully should be live on the dashboard soon.

What is AI not as good for (at least not yet)

AI is good at the more technical, quantitative stuff. Where it falls short, at least for me, is when producing the type of content (like this blog post) that is more of a personal expression of experience or views. You can write a reasonably tight spec (or plan) for a bit of data analysis. Its harder to do the same with an essay. Despite trying lots of difference experiments over the years – trying to have the AI analyze past writings to learn my style – I still find AI writing a poor simulacra of my own.1 There is a certain style of AI writing that is distinct and a bit soulless, and while this may improve in future I suspect this will remain a domain where humans continue to play the key role.

That being said, there are a few use cases where AI can be helpful for writing. It can serve as a reasonably good editor, suggesting tweaks to make a first draft more readable before publication. It can also help out with tone when trying to write something that is outside your normal style. For example, I gave a commencement speech last year to a graduate school program where I wrote an initial draft and used AI to help make the tone more inspirational and fit for the occasion.

I also often use it help draft internal team memos or other non-public-facing writing where the style is less important than the substance. But my personal rule is that anything I publish thats public-facing under my name remains solely written by me.

Research using AI is in my experience good but not great. I find tools like Deep Research in Gemini helpful to provide a good overview for topics where I personally do not have deep expertise; for example, I used it last week to write up a quick report comparing and contrasting different lifecycle assessment standards for using industrial wastes like steel slag for carbon removal projects. But these tools lack full access to the peer-reviewed academic literature (much of which remains behind journal paywalls), and often will provide a less thorough (albeit still useful) assessment of topics that I personally know well.

Similarly, AI still has some shortcomings when used for synthesis or assessment (rather than a simple review) of scientific findings. Exercises like the writing an IPCC report or a thorough review of a climate tipping points requires both familiarity with the literature and the ability to make expert judgements on how to weight the credibility and importance of different sources, something that I find lacking in most AI-based assessments.

One way to potentially improve it is to use AI to help provide the scaffolding for scientific assessments but with scientists ultimately controlling them. I recently collaborated with Google Deepmind on piloting a tool to this end, and you can find a preprint we wrote up about the results (as well as a draft assessment of the risk of AMOC tipping points) here.

AI has a bit of a conservative bias (small-c, not in the political sense) where it will tend to go with prevailing conventions represented in its training data and discount newer studies. For example, when helping develop an AI scoring rubric for climate science knowledge, one of the questions I developed was around what would happen to global temperatures if CO2 emissions ceased. The AI answers often insistented that there was significant warming in the pipeline, despite more recent findings of near-zero zero emissions commitment.

Finally, while I’ve had some good experiences using AI for idea generation – prompting it for creative data visualization options, for example – it will generally not do a good job of coming up with original research ideas. The context that a specialist in a field has about what is important and how it fits into the broader field’s needs is one of the most important reasons that, at least in science, humans will remain the key driver of scientific innovations for the foreseeable future.

The energy elephant in the room

AI is a useful tool for science, but it is important to emphasize some of the macro challenges involved. The current data center buildout driven by AI has (along with other factors) led to a sharp increase in US electricity demand. Some estimates have data centers alone consuming up to 12% of US electricity by 2030 – up from about 2% prior to 2025. Much (but hopefully not all) of this is going to be powered by new natural gas generation, at least near-term.

At the same time, its easy to overemphasize how large an impact this would have on US emissions. A ~10% increase in overall US electricity use driven by data centers would increase US electricity sector emissions by around 10% if powered entirely by gas, resulting in an overall increase in US emissions of 2.5%. To put this in perspective, total US emissions have fallen by around 20% since 2005. A gas-driven AI boom would make it harder to reach our climate goals, but by itself would not move the needle all that much on 2100 temperature outcomes.

Actual energy use per AI query is actually surprisingly modest, coming in at only around 0.3 watt-hours per typical query.2

To put this in perspective, showering for 10 minutes (which is rather frugal) uses as much energy as around 6,000 AI queries,3 while a 10 mile round trip commute to the office (also relatively short) in a gas car uses as much energy as over 30,000 AI queries.4 Now, these statistics can be a bit misleading as models run on reasoning mode can involve a lot of individual queries – and tools like Deep Research could involve hundreds or even thousands. But even power users of AI tools are very unlikely to be responsible for more emissions than a short commute to work.

Thats not to say that the AI industry should be let off the hook here. They should be more ambitious in procuring clean energy to power data centers, including exploring the possibility of large-scale behind-the-meter solar and storage systems. I coauthored a report published in December 2024 that found that in sunny parts pf the country 90% of the annual energy use of a datacenter could be powered by solar and storage (with the remaining 10% from gas backup generations) at only a modest premium over powering them with 100% gas.

But many of the companies building AI models like Google and Microsoft are also the largest buyers of clean energy today. There is a world where spending on AI data centers ends up kickstarting a lot of next-generation energy technologies – things like large-scale solar and storage microgrids, enhanced geothermal, next-generation nuclear, and the like. That will only happen at scale if state and local governments and consumers keep the pressure on for these companies to meet their climate goals – and not let the AI boom become an excuse to let clean energy commitments slide by the wayside.

A (semi) real-time experiment

As I was writing this post I came up with a good idea for an analysis to illustrate how my workflow in Claude Code goes in practice. A few years back I did an analysis looking at how much uncertainty in future warming comes from climate sensitivity vs carbon cycle feedbacks, and I thought it would be interesting to see of Claude could do the climate model runs needed to reproduce the results. As you will see, this provides both an example of what AI tools can do, but also the pitfalls of not clearly specifying precisely what analysis should be undertaken.

I started with a prompt describing the analysis I wanted:

Lets try a new analysis. I want to figure out how much of the range of future warming in 2100 is associated with carbon cycle feedbacks and how much is associated with climate sensitivity. To do this lets focus on the medium scenario, and do three experiments.

First, a full normal run that contains carbon cycle feedbacks and climate sensitivity.

Second a run that use the median concentration/forcings scenario from that initial run and runs the model in concentration/forcing driven rather than emissions driven mode, to give us the range of results holding carbon cycle feedbacks constant.

Finally, a run fixing climate sensitivity at the median value in the FaIR model and only letting carbon cycle feedbacks determine the variance in 2100 temperatures.

Lets create a composite figure with two side by side top plots showing the time evolution of carbon cycle feedback only and climate sensitivity only runs, and the bottom plot showing box plots indicating the 2100 temperature results for the three experiments.

Claude took about 7 minutes of thinking to come up with the plan, as it involved Claude spinning up multiple sub-agents to read through the FaIR model code and figure out the best way to implement the analysis (particularly Experiment 3, which FaIR is not natively configured to do).5

Plan: Decompose Warming Uncertainty — Climate Sensitivity vs Carbon Cycle                                                                                    

Context

We want to understand how much of the range of future warming in 2100 under the CMIP7 "medium" scenario comes from uncertainty in climate sensitivity (how temperature responds to forcing) versus carbon cycle feedbacks (how emissions translate to concentrations and forcing). We'll create three FaIR experiments and a composite figure.

Three Experiments

Experiment 1 — Full run (baseline)

Standard emissions-driven run for "medium" scenario only, all 841 configs. Reuse setup_and_run() pattern from run_cmip7_fair.py:62-113 but with f.define_scenarios(["medium"]). The 5-95% spread at 2100 captures all sources of uncertainty.

Experiment 2 — Climate sensitivity only

1. Extract median forcing from Exp 1: np.median(f.forcing_sum.sel(scenario="medium").values, axis=1) → shape (351,)
2. Create forcing-driven FAIR with "bulk" species (input_mode="forcing") — same pattern as run_cmip7_attribution.py:205-266
3. Fill all 841 configs with the same median forcing
4. Load all 841 climate configs via override_defaults() — these still vary
5. Run → spread comes only from variation in climate response parameters (ECS/TCR)

Experiment 3 — Carbon cycle & forcing only

1. Set up standard emissions-driven FAIR (same as Exp 1)
2. After override_defaults(), overwrite the 11 climate response parameters in f.climate_configs with their median values across all 841 members:
   - gamma_autocorrelation, sigma_eta, sigma_xi
   - ocean_heat_capacity[0,1,2], ocean_heat_transfer[0,1,2]
   - deep_ocean_efficacy, forcing_4co2
3. Leave seed, use_seed, stochastic_run untouched (boolean flags, not climate sensitivity)
4. Run → spread comes only from variation in carbon cycle parameters (iirf_*, partition_fraction*), forcing scale parameters (forcing_scale[*], erfari_*, aci_*, ozone_*), and baseline concentrations

Key confirmation: forcing_4co2 is purely a climate response parameter — it does NOT appear in any GHG forcing function. CO2 forcing uses forcing_scale[CO2] instead. So fixing it in Exp 3 is correct.

Verification

1. Exp 1 median at 2100 should be ~2.8°C (consistent with existing medium scenario results)
2. Exp 2 + Exp 3 medians should be similar to Exp 1 median (same central estimate)
3. Exp 2 + Exp 3 spreads should sum to roughly (not exactly) Exp 1 spread
4. Climate sensitivity should account for the majority of the spread (typically ~60-70%)

The actual analysis was much faster once the plan was created. It took around 2 minutes and 30 seconds to write the code, run the climate model simulations, and produce the results. The simulations here are using a placeholder “medium” CMIP7 emissions scenario provided by Chris Smith, as the official versions of the emissions scenarios will not be publicly released until later this summer.

However, these results immediately looked a bit odd, with the spread in the climate sensitivity only runs exceeding the full uncertainty. Claude noted an unusual aspect of the results upon concluding the analysis:

The figure looks good. The results reveal an interesting finding — the climate sensitivity spread (122% of full) exceeds the full spread, while carbon cycle & forcing accounts for 51%. The sum exceeds 100% because there are negative correlations in the calibrated ensemble between climate sensitivity and forcing parameters, which partially offset each other in the full run.

Now, when you find an unexpected result like this in an analysis, your first thought is that it might be a bug, or if not perhaps an unintended result of the approach you took. I tend to find that Claude is quite helpful at diagnosing issues like this – and helping determine if they were an error in it’s implementation of the code or in my instructions for the analysis. I prompted it to dig a bit deeper:

Lets dig further into why the climate sensitivity only result has larger uncertainty than the full uncertainty analysis. Lets compare each member of the ensemble between the two analyses, focusing on how the temperature and CO2 concentrations differ.

After an additional 3 minutes and 8 seconds of writing and running code, it produced the following diagnostic figure exploring why the results appeared counterintuitive:

Interestingly, this appears to be due in part to the calibrated ensemble approach that FaIR takes to ensure that model runs are constrained to match historical observations. As a result, higher sensitivity models end up with lower associated radiative forcing per a quadrupling of atmospheric CO2 (which is a commonly used diagnostic model output and a proxy for ECS) to avoid having too divergent historical results, while lower sensitivity models end up with higher forcing. Specifically, higher sensitivity models tend to have correspondingly larger negative aerosol forcing, while lower sensitivity models have lower negative aerosol forcing. Holding forcing constant in Experiment 2, as we did, breaks this relationship.

Now, this is where having solid background knowledge of the processes involved are useful, and where relying too much on AI can potentially lead you astray. My initial prompt was not precise; I asked the Claude to “use the median concentration/forcings scenario from that initial run and runs the model in concentration/forcing driven rather than emissions driven mode, to give us the range of results holding carbon cycle feedbacks constant.” FaIR supports three different types of runs: emissions-driven, concentration-driven, and forcing-driven. I meant (but did not specify) to use concentration-based runs when possible, and use forcing-based runs when not (e.g. for solar and volcanic forcings). Claude interpreted this as creating forcing driven run for all the variables in its Experiment 2. To properly assess the spread of results when carbon cycle feedbacks are excluded, I need to use concentration-driven runs here (and should have probably caught that in Claude’s research plan if I had taken more time to review it!).

Indeed, rerunning the experiment using concentration-driven runs (with forcing-driven runs for solar and volcanic forcings) produced much more reasonable results in-line with my prior expectations:

It also largely eliminates the odd artifacts in the diagnostics when comparing the same ensemble members across the two runs:

If I were planning to use this analysis in a proper research paper, I’d have spent quite a bit more time at the beginning getting the plan right (including prompting Claude to ask me questions when writing it to resolve any uncertainties). I’d also spend some more time digging into why the climate sensitivity only spread remains so large (albeit slightly smaller than the total spread) and why the medians of experiments two and three are slightly off from the median of the full uncertainty.

But I think this broadly illustrates the power of these tools. Being able to create a detailed plan for an experiment and have AI agents write the code and kick off the needed model runs is an immense productivity booster. It will not be perfect every time – diligence is needed to make sure that the instructions were followed accurately and the results do not include bugs. But as I’ve learned the hard way, human written code has no guarantee of being bug-free either.

I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.

I often use ECMWF’s ERA5 reanalysis (which is arguably the most state-of-the-art of the bunch) to look at current daily global temperature anomalies, to forecast where the current month might end up, or to use as inputs to a model (along with El Nino / La Nina predictions) to estimate what temperatures for the year will be.

But rather than manually making these charts every week or so, I (admittedly with the help of Claude Code) have put together an interactive climate dashboard that will live here at The Climate Brink: https://dashboard.theclimatebrink.com/

It currently includes interactive graphics for daily surface temperatures (you can mouse over each day to see the anomaly, and drag your curser to zoom in on any particular period of interest).

As well as a view showing the current year compared to the past three years (colored lines) and all prior years since 1940 (when ERA5 currently begins).

There are also predictions for the current monthly temperature based on the month-to-date; this will update with each day of new data, and the error bars will shrink accordingly.

Similarly, I’ve adapted the model I use to predict annual temperatures for the current year, pulling in the latest El Nino and La Nina plume of model predictions from IRI (using the average of the dynamic models) to inform the estimate.

This next one is a bit in the technical weeds, but I find it fascinating to track how the annual prediction changes as each new day of data (and updated El Nino/La Nina forecasts) become available. It shows a history of each day’s 2026 projection (along with uncertainties) as well as the year-to-date temperature.

Finally, some pretty (and interactive) Hawkins-style climate stripe plots showing global mean surface temperatures (and anomalies) over time.

As well as a plot of the distribution of temperature anomalies for each year (inspired by the cover of Joy Division’s 1979 album Unknown Pleasures), with 2026 to-date shown with a dashed line.

There are some additional options including a dark (default) and light mode, as well as the option to turn off interactive graphs for faster page loads (as its a lot of data for folks with slow internet).

If you have other suggestions for datasets that we could include in the dashboard (ideally regularly updated ones) let me know! I’m also planning on having a tab for the full set of monthly surface temperature products as well as up-to-date climate model observation comparisons in the near future. Go check it out here!

The term “global warming” is typically used to describe increasing global temperatures as a result of human emissions of carbon dioxide (CO2) and other greenhouse gases.

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However, unusually cold events are often portrayed as being made worse by human activity, as a result of increased variability or a disruption of the “polar vortex” in a fast-warming world.

There is significant debate in the scientific community about whether rapid Arctic warming and sea ice loss could disrupt atmospheric circulation patterns and lead to cold-air outbreaks in the northern hemisphere mid-latitude regions.

In this analysis, I find that few places in the world have seen an increase in extreme cold days over the past 55 years.

If climate change is influencing atmospheric circulation, any effects on extreme cold appear to be more than compensated by the rapid winter warming the world has experienced.

A controversial hypothesis

In an episode of Marketplace’s How We Survive podcast last year, the host – US radio journalist Kai Ryssdal – noted that climate change is expected to cause “hotter hots [and] colder colds in unexpected parts of the world”.

This is not an uncommon sentiment, with the media commonly attributing extreme cold events to human activity, particularly during episodes of bitter cold or polar-vortex events.

Some researchers have argued that these extreme cold snaps might be becoming more frequent due to reduced sea ice and Arctic amplification – the phenomenon where the Arctic warms more quickly than the global average.

At the core of the hypothesis that Arctic warming could influence mid-latitude cold extremes is the fact that a rapidly warming Arctic changes the temperature difference between the poles and the equator.

Normally, the strong contrast in temperature between these regions drives key patterns of atmospheric circulation, including the jet stream. According to proponents of this theory, when Arctic temperatures rise faster than those farther south, atmospheric circulation can weaken, meander, or buckle more frequently. As a result, cold polar air can spill down into areas that are usually less frigid.

A related argument focuses on how diminished sea ice – particularly in the Barents and Kara seas – might disrupt the atmosphere. Less sea ice means more heat and moisture escapes from the ocean surface into the air, which can potentially alter weather patterns downstream. This can boost the odds of “blocking high” weather systems, or unusual circulation patterns that pull cold air into mid-latitudes.

However, there are relatively few studies that attribute an increase in cold extremes – or an individual extreme cold event – to human activity.

Carbon Brief’s attribution map – which charts extreme weather events around the world and their links to human-caused warming – includes 33 studies that examine extreme cold events specifically.

Of these, 24 studies found that the extreme cold was made less likely due to climate change, six found no discernible human influence and three found insufficient data to conclude either way. Only one extreme event attribution study in the database found that a cold extreme – severe frosts in Western Australia in 2016 – was made more likely due to climate change. However, even that study noted that “warmer temperatures may have offset or countered this effect of the circulation driver”.

Many climate scientists disagree with the hypothesis that warming could lead to increased cold outbreaks, arguing that cold extremes are decreasing overall in a warming world.

Others contend that causality cannot yet be proved and that both models and observations provide limited support for a significant role of climate change in mid-latitude cold events.

In addition, observed data reveals that, although cold spells still occur, they have become less frequent and less intense over recent decades. Most modeling studies do not consistently reproduce more frequent or severe cold outbreaks. Instead, they often show that the overall warming trend dominates, making cold extremes rarer over time.

This suggests that, if there were a connection between climate change and extreme cold events, the long-term warming trend will still likely lead to fewer, milder cold outbreaks – and that any effect from Arctic amplification would be relatively small.

Have any regions experienced increased cold events?

To help assess the effects of climate change on extreme cold events, I used gridded daily minimum global temperature data from Berkeley Earth to calculate how the temperatures of the coldest 5% of the days of the year have changed since the 1970s.

Minimum daily temperatures reflect the single coldest measurement taken over the course of the day. (While the coldest 5% of days is a somewhat arbitrary number, the results are largely similar for the coldest 10%, 5%, 2%, 1%, or the single coldest day of the year.)

The figure below shows the results for every one-by-one degree latitude-longitude grid cell on the Earth’s land (a size approximately equivalent to 100 km by 100 km). Grid cells coloured red experienced a decrease in extreme cold days, while those coloured blue had more extreme cold days.

The vast majority of the planet has seen a strong decrease in extreme cold events, with the largest declines seen in high-latitude northern hemisphere regions (which have also experienced the fastest rate of warming overall).

The few regions that have seen an increase in extreme cold events tend to be those with the slowest average rates of warming, including India, South Africa and Antarctica.

In India, this has likely been influenced by rapid increases in air pollution – particularly cooling sulfate aerosols – over this period. Causes of more extreme cold events in Antarctica are less clear, though it is possible that they could be linked to the seasonal loss of ozone layer over the past 50 years.

Another way to assess changes in cold events is to look at the change in the number of days where any hour of the day – that is, the daily minimum temperature – is below freezing (0C, 32F). The map below shows the change in average annual freezing days between 1970 and 2024.

The map shows dramatic shifts in the number of freezing days in much of the high-latitude northern hemisphere, with nearly a month fewer freezing days over the Himalayas, eastern Europe and parts of Canada and the western US over the past 50 years.

(White areas on the graph – such as much of the tropics and subtropics – represent regions of the world where temperatures below freezing almost never occur and thus changes over time cannot be calculated.)

Fewer cold outbreaks in the US

A sizable portion of the academic research on cold outbreaks has been focused on the contiguous US, as it is a region prone to occasional extreme cold conditions caused by intrusions of Arctic air.

The map below shows there has been a warming trend in the coldest days of the year across virtually the entire US over the past 50 years.

This suggests that the effect of Arctic amplification on cold-air patterns is smaller than the strong winter warming trend.

Almost no regions of the US have seen a cooling trend in the 5% coldest days of the year – and higher latitude regions have tended to experience the fastest winter warming.

There has been a slight cooling trend in the coldest days of the year in northern Mexico and slower warming in California, Arizona, southern New Mexico and southern Texas.

Similarly, the figure below examines the change in days where minimum temperatures are below freezing in the US.

It shows that there are, on average, 13 fewer days below freezing each year compared to the 1970s. Or, to put it another way, there is half a month where daily minimum temperatures are no longer cold enough for icy and snowy conditions to occur.

Increasingly rare cold spells

The latest climate models overwhelmingly project that cold extremes will continue to diminish as greenhouse gas concentrations rise.

This means that, even if certain patterns occasionally transport freezing polar air southward, winters on the whole are likely to be milder than in the past.

However, the scientific understanding of precisely how Arctic warming might – or might not – influence mid-latitude weather is still evolving.

Researchers continue to refine models, incorporate better reanalysis data and examine how changes in atmospheric circulation dynamics might play out under different scenarios. Additional data – especially over multiple decades – will help clarify whether the Arctic’s role in mid-latitude cold extremes is significant or overstated.

For now, observations over the past 50 years generally show a world with fewer cold extremes – and projections point toward increasingly rare cold spells in the future.

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Matt Yglesias had a provocative piece in the NYT arguing that liberals should be less opposed to oil and gas, arguing that it might help make energy more affordable and win more conservative states and labor (without which there would be no climate policy at all). He also noted that US oil and gas is generally lower carbon than foreign alternatives in a world that is still using vast amounts of the stuff. Policies, in his view, should focus on making production cleaner by more strictly regulating methane emissions, in-sector electrification, and other best practices rather than restricting supply. Other mitigation advocates like Jesse Jenkins and Ramez Naam chimed in to support the broad thrust of his argument.

This is, it is worth pointing out, not too far from the policies pursued by both the Obama and Biden1 administrations, where both clean energy and domestic oil and gas production boomed (while the dirtiest fossil fuel, coal, saw a dramatic decline).

Representative Sean Casten (D-IL) posted a long rebuttal on BlueSky arguing that we’ve already overshot our climate goals, and the only way to turn things around is to keep fossil fuels in the ground. He noted that what is politically popular is not always what is right, and that sometimes politicians need to do what is necessary to meet the moment. He also notes that leakage from US gas “makes natural gas worse than coal from a global warming perspective.”

These responses broadly reflect two different schools of thought on how to best practically (and politically) achieve decarbonization goals: by reducing fossil fuel supplies, or by reducing fossil fuel demands.

The physical science is absolutely clear that to stop the world from warming we need to get global emissions of CO2 and other long-lived greenhouse gases to (net) zero. Every 220 gigatons (billion tons) of CO2 we emit warms the surface by around 0.1C, and the world is already at 1.4C above preindustrial levels today. But the specific path to limit warming – how much we focus on the reducing the supply of fossil fuels vs reducing their demand by accelerating the adoption of cleaner alternatives is very much an active debate. My personal view is that demand side policies are considerably more achievable at the moment – particularly given the new focus on affordability on the left.

I’d also note that this post is about the politics of mitigation rather than the physical science. There is no clear right answer to how to best reduce emissions, and there are many reasonable folks with differing views on the topic. We should generally try and extend grace to those we disagree with, as when it comes to policy there is no real arbiter of truth.

Demand vs supply-side decarbonization

Broadly speaking, there are two different schools of thought on how to best achieve decarbonization: supply side vs demand side policy.

Supply side policy focuses on making fossil fuels more expensive by restricting the supply or directly taxing the their sale. Examples include restrictions on fossil resource leasing and development, blocking transmission systems like pipelines, cap and trade systems, and carbon taxes.

Demand side policy aims to make cleaner alternatives more affordable and, in turn, reduce demand for fossil fuels. This generally involves subsidizing both the research, development, and deployment of clean energy technologies (renewable energy, nuclear, electric vehicles, heat pumps, etc.) and its supporting infrastructure (transmission, vehicle charging infrastructure)

These are not necessarily mutually exclusive; for example, some advocates of demand side interventions also support carbon taxes. But generally speaking, supply side policies impose more direct costs on consumers, while demand side policies involve more indirect costs (e.g. through higher government spending).2 There are also ways to make supply side policies less costly – such as the revenue neutral carbon taxes that Canada and Washington have experimented with – but even these have been hard to muster political support for.3

Most of the policies pursued by the Biden administration (and the Obama administration before it) have focused on demand-side mitigation policy. While we may wish otherwise, most of the general public in the US does not prioritize climate change above economic issues, and in the past when gasoline prices have spiked some politicians have quickly pivoted from “keep it in the ground” to “drill baby drill”. To be effective in changing consumer and commercial purchasing behavior at the scale needed to avoid dangerous levels of warming this century, supply side policies will necessarily need to impose a level of cost that will be politically challenging (to say the least).

Demand side mitigation can effectively reduce fossil fuel emissions. The large-scale phase down of coal in the US was driven by a combination of stricter conventional pollutant restrictions and – primarily – low cost fossil gas4 and renewable generation that made it not economically competitive.5 More broadly, the Global Carbon Project finds that emissions have declined over the past decade (2015-24) in 35 nations, which collectively account for 27% of global emissions, driven by faster installation of clean energy technologies and the retirement or curtailment of existing fossil fuel resources. This remains true even when emissions embodied in international trade are taken into account.

The challenge with demand side policies is that they are arguably a poor fit for meeting our most ambitious global climate targets. If countries want to limit warming to 1.5C by the end of the century (and minimize overshoot on the way there) it leaves us with a vanishingly small carbon budget. To meet these goals would require wartime style mobilization of the world to rapidly decarbonize, prematurely retiring vast amounts of infrastructure and rapidly curtailing fossil fuel production. In short, it would cost a lot of money – and in turn require significant political and public buy-in.

Is 1.5C increasingly a trap?

The 2015 Paris Agreement set an aspirational target of limiting warming to 1.5C. This came as a bit of a surprise to the climate science community; prior to Paris the most ambitious mitigation scenario that was widely modeled was RCP2.6, which limited warming to around 1.8C by 2100 (and had a ~66% chance of avoiding 2C warming). Following the Paris Agreement the UNFCCC requested that the IPCC write a special report on 1.5C as a climate target, which was published in 2018.

By the time that report came out, it was increasingly clear that actually limiting warming to 1.5C would be quite unlikely, with scenarios that avoided overshoot requiring all global emissions to start declining immediately and reach (net) zero emissions by 2050. By the time the IPCC 5th Assessment Report was published in 2021, the 1.5C target has been quietly redefined as an “overshoot” target where the world passed 1.5C before bringing global temperatures back down through large-scale deployments of net-negative emissions. In that report 206 of 230 the integrated assessment model (IAM) scenarios exploring 1.5C warming outcomes – a full 96% – involved overshoot on the way there, often reaching 1.7C or 1.8C by mid-to-late century.

As 2026 dawns, the world has already seen a year above 1.5C, and global emissions have yet to decline (even if they have plateaued), scenarios that limit warming to 1.5C by 2100 have become increasingly implausible. They are forced to rely on ever more rapid near-term emissions reductions – and ever larger amounts of carbon removal later in the century – to make the brutal math of the 1.5C target work.

For this reason many of us in the scientific community have been trying to make it clear that these targets never reflected a climate threshold – a point in the system where something particularly bad happens. Rather, they reflect an attempt to balance minimizing climate damages (and risks) given what is plausibly achievable. Climate targets are always inherently probabilistic given uncertainties in climate sensitivity and carbon cycle feedbacks. Its quite possible that we could end up closer to 3C even though we thought we were heading to 2C if we roll 6s on the proverbial climate dice. So these targets more about minimizing tail risks than ending up at a particular round number.

Similarly, there are no particular climate feedbacks or tipping points that occur at 1.5C that would not occur at 1.45C or 1.55C; rather, we try to emphasize that every tenth of a degree matters. 1.5C is lower risk than 1.7C, which is lower risk than 2C, etc. Its less “1.5C to stay alive” and more that we should try to mitigate as rapidly as practically possible to reduce the future damages to society and the natural world.

While targets can be useful as a focus for pushing countries to adopt more ambitious mitigation policy, they can also become counterproductive if they are unachievable. For example, a narrow focus on 1.5C today could lead folks to advocate to oppose policies that might be consistent with (or even help enable) a 2C world but would not be consistent with the vanishingly small remaining carbon budget under 1.5C. In other words, pursuing our most ambitious targets can lead us to make the perfect the enemy of the good.

The gas debate

In the US context the distinction between policies that would be inconsistent with a 1.5C world but might help us get close to 2C often involve fossil gas. The climate impacts of gas (and how it compares to coal) is a topic I’ve long been interested in; I published my first analysis back in 2011, and ultimately wrote two peer reviewed papers on the topic in 2015 and 2016 looking at the impacts of replacing coal with gas in the US.

This is relevant because in the US fossil gas has been the single largest driver of CO2 reductions, particularly in the power sector. Along with renewables it has led to a reduction of coal use by 60%. This progress remains fragile, as may coal plants have lowered their operation time (their capacity factor) rather than fully shutting down, and we see an uptick in US coal generation whenever gas prices rise.

From a carbon standpoint the comparison is relatively straightforward. Fossil gas is results in about 60% lower CO2 emissions per kWh generated, as shown in the figure below:

However, there has been quite a bit of controversy around the broader climate impacts of fossil gas, given that a portion (likely 2% to 3%)6 of fossil gas leaks as methane. Methane is a short-lived greenhouse gas with a lifetime of around 12 years in the atmosphere, but while it is in the atmosphere it has approximately 120x more warming impact than CO2. The different lifetime of the gases makes it difficult to directly compare the two, given that the results depend on the timeframe considered.

The figure below (from my 2015 paper) attempts to compare gas and coal climate impacts over time, using the change in radiative forcing of both (which is a reasonably proxy for temperature change). It compares a new gas plant in the US with 2% leakage (green line) to both new coal (black solid line) and existing coal (black dashed line). The grey area represents the uncertainty for gas across leakage rates from 1% to 6% and generation efficiencies (HHV) of 42% to 50%. This plot assumes that both the gas and coal plant would be operated for 30 years before being retired.

This implies that a leakage rate of 5.1% would be required to make gas worse than coal over a 20-year period (assuming new gas displacing existing coal – the most common occurrence) and 13.1% over a 100-year period. If we assume the gas and coal plants would both be operated for 100 years rather than just 30, the leakage rate required for 100-year forcing parity falls to 9.9%.

Some folks argue that a 20-year time horizon should be preferred given the near-term impacts of climate change and the risk of passing tipping points. But in my view such a short time horizon is misguided. Its equivalent to discounting the future by around 13% per year – saddling future generations with higher warming from CO2 in exchange for minimizing near-term warming today. The case for 20-year GWPs to avoid tipping points is also fairly weak; most proposed tipping points depend on the level of peak warming and the timeframe over which it is sustained. It is quite unlikely that the world will reach peak warming before 2070 or 2080 even in ambitious mitigation scenarios, which means that little of the methane emitted today will be warming the planet by then.

Of course, the argument that gas is“worse than coal” is an unnecessarily high bar in a world that needs to get to net zero emissions. But gas displacing coal is generally still a good thing today, provided there are no other viable cheaper options. Gas also is something of a Swiss army knife of the electricity system, able to be built quite cheaply but expensive to operate (e.g. low cap-ex and high op-ex). This makes it quite well suited to be a dispatchable resource; cost effective to shut down during periods when cheap renewable or battery storage is available, and able to run when extended low resource outputs makes it necessary. That is one of the reasons why deep decarbonization scenarios for the US generally have considerable amounts of fossil gas capacity remaining in a net-zero system in 2050, even if utilization rates are quite low (~5%).

More broadly, integrated assessment models used by the IPCC to assess mitigation options have a pretty clear “merit order” for phasing out fossil fuels: coal first, than oil, than gas. For example, here is unabated (without CCS) coal use in 2C scenarios (SSP1-2.6) in the SSP Database used in the IPCC 6th Assessment Report, showing rapid declines in global coal use after 2020 (something that, unfortunately, we have yet to see globally in the real world).

Global unabated gas use is more of a mixed picture, with some models (WITCH) showing rapid declines but others showing flat (AIM) or even increasing (GCAM, MESSAGE, REMIND) global gas use through 2030 or 2040.

As clean energy alternatives – particular solar and batteries – get cheaper they should undermine the economic case for fossil gas and help drive down usage and emissions. But in my view policies to restrict supplies today will be less effective than accelerating the deployment of more cost-effective alternatives to gas.

Its the (political) economy

Effectively mitigating climate change will depend on a combination of technology and policy.7 But we live in a world where policy and politics are the art of the possible, and given the increasing focus on affordability it seems like a focus on making clean energy cheap will be considerably more viable than making fossil fuels more expensive. As Yglesias noted in the Times, for the Democrats to retake power and control the Senate requires winning places like North Carolina, Ohio, or even places like Texas, Alaska, and Kansas.

I do disagree with Yglesias that the left should actively “support” the oil and gas industry, but there are a wide range of effective mitigation policies that we can pursue that make clean energy cheaper without supporting or directly penalizing the oil and gas industry. I also think we need to double down on phasing out coal as rapidly as possible, both domestically and overseas.

This means that there is likely still some role for supply side mitigation, particularly in a world where demand side mitigation is succeeding. As demand for oil and gas peaks and declines, the cost of these fuels will fall, potentially undercutting the economics of cleaner alternatives. One option would be to effectively backstop the price of oil and gas with a carbon tax – at least up to an estimated social cost of carbon. This would have the advantage of helping stabilize fuel prices, and consumers would not experience any cost increases (though they would not benefit from declines).

Similarly, there are other non-climate damages associated with fossil fuel use – air pollution and water pollution – that also merit regulation, and restrictions on mercury, sulfur, and other conventional pollutants have played an important role in retiring coal plants in the US by making coal more expensive to operate. There is an important role for the government in regulating these – as well as implementing stricter restrictions on methane leakage. And it should go without saying that we should oppose the types of direct subsidies or mandates around fossil fuels or restrictions on clean energy production that tilt the playing field toward dirtier energy that the current US administration is pursuing.

Ultimately the costs of fossil fuel use need to be reflected in the costs of energy for decarbonization to succeed, and this can occur either through artificially reducing the cost of clean energy or increasing the cost of dirty energy. At the current political moment, the former seems far more likely to work.

I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.

Let us know in the comments what you think the biggest climate news in 2025 was and your predictions for 2026.

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Not to be outdone, James Hansen released his estimate that 2026 temperatures will also be around 1.47C in the GISTEMP dataset (albeit using a somewhat different 1880-1920 baseline)3, with the 12 month average dipping down to around 1.4C in the coming months before rising back up by year’s end.

Hansen also adds a prediction for 2027 at 1.7C (1.65C to 1.75C), albeit with the caveat that this refers to the peak 12-month warming during the year rather than the annual average. The prediction is based on an assumed El Nino developing in late 2026 – something that models have suggested is increasingly likely in recent weeks.

I’ve long done year-ahead predictions of global mean surface temperatures (included in the Carbon Brief annual state of the climate report). I base it on a linear regression model that uses a year count, the prior year’s temperature, the latest monthly temperature, and the predicted ENSO (El Nino / La Nina) conditions of the first three months of the coming year, as these factors tend to be the most predictive historically.

The model is fit on historical data since 19704 using the WMO average of six datasets,5 and I’ve slightly tweaked the model this year to include a squared term for the year count to ensure it is not forced to be too linear (though the effects of this change are minor).

For 2026 I expect global temperatures to be around around 1.41C, with a 95% confidence interval of 1.27C to 1.55C. This means that it is almost certain to be one of the top-4 warmest years, but quite unlikely to exceed 2024’s record. Global temperatures in 2026 will be slightly suppressed by modest La Nina conditions in the tropical Pacific early in the year, while a late-developing El Nino (if it occurs) will primarily affect 2027 temperatures.

This model generally does a good job at predicting past calendar years using the same set of predictors (e.g. no actual data from the year except for Jan-March ENSO state). We can see this if we extend the hindcast back to 1970, where all years are within the model envelope with the exception of 1992 (when Pinatubo’s eruption resulted in significant unexpected cooling) and 2023 (which remains a bit of a mystery).

Predicting temperatures two years ahead is a bit more of a challenge. Here I’ve adopted a somewhat different approach of using the current warming level and the rate of warming (0.27C/decade) that we estimated in the Forster et al 2025 paper as a starting point. I’ve further assumed a boost of 0.1C in global mean surface temperatures from an assumed moderately strong El Nino event in the central estimate, though the error bars encompass no El Nino (0C boost) to a very strong El Nino (0.15C boost).

This gives a central estimate of 1.57C for 2027, albeit with error bars wide enough to ride an elephant though (1.30C to 1.76C!) given the difficulty of accurately predicting ENSO state that far into the future.

My central estimate of 1.57C is a bit lower than Hansen’s 1.7C, in part because I’m assuming a moderately strong El Nino and somewhat lower warming rates (0.27C/decade in Forster et al) then the 0.31C/decade that Hansen assumes. But his estimates remain within my (admittedly large) prediction error bars.

The fun part about making these short term forecasts is that we won’t have to wait that long to see how well they play out. The less fun part is that we are all forecasting a future rate of warming well above the ~0.2C per decade that has characterized the post-1970 period.

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If you guessed climate change was playing a role in this, you’d be right. Climate change isn’t just making storms “wetter” in a simple sense; it is fundamentally breaking the region’s natural plumbing system.

Here is why:

1. The atmosphere is wetter

The first mechanism is the one you hear about most often: basic thermodynamics. The rule of thumb (the Clausius-Clapeyron relation for the science geeks) is that for every degree Celsius the atmosphere warms, it can hold about 7% more water vapor.

If the atmosphere is a sponge, then a warmer atmosphere is a bigger sponge. This allows the atmospheric river that carries water from the Pacific to soak up more moisture from oceans that are also warmer than average. When that “sponge” hits the Cascades or Olympics and gets wrung out, there is simply more water available to fall than there was in the past.

Because of this, the IPCC says: “Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are more abundant (high confidence) (North America, Europe and Asia).” [IPCC AR6 WG1, Section 11.4.4].

2. The Phase Shift: Rain vs. Snow

This is another critical factor for the PNW. In the cooler 20th-century, much of the precipitation hitting the Cascades or Olympics would fall as snow. Snow is safe. Snow sits there. It accumulates without drama, effectively “banking” water for the spring and summer months when it is needed most.

However, as freezing levels rise due to warming temperatures, precipitation is increasingly falling as rain. Unlike snow, rain does not sit quietly in the mountains and wait for spring. It runs off immediately.

So when a storm dumps 10 inches of precipitation and half of it falls as snow, the rivers only have to handle 5 inches of water immediately. But if all of it falls as rain because it’s 45°F in the mountains, the rivers have to handle the full 10 inches right now. This climate-enhanced runoff can overwhelm the river system, leading to the widespread flooding we see now.

Climate misinformers

I have no doubt that climate misinformers will be quick to argue whatever they can to downplay the clear physical connections between climate change and the disastrous flooding the PNW.

One technique they use is a laser focus on observational trends rather than the physics of the situation. They do this because observational trends are harder to interpret and more likely to be ambiguous than the physics.

The reason is that extreme events are, by definition, rare. Thus, even with a century of data, it can be hard to statistically identify a trend.

To demonstrate this, I performed a Monte Carlo simulation to evaluate the likelihood of statistically confirming a flood trend in a “flashy” Pacific Northwest river. For each of 1000 iterations, I generated a 100-year synthetic time series where the median flow linearly increased by 20%—representing a strong underlying climate signal. This was added to random year-to-year variability simulated by a log-normal distribution with a logarithmic standard deviation of 0.5.

I then ran a standard linear regression1 on each of the 1,000 synthetic datasets to test for a trend. The histogram displays the resulting distribution of P-values; crucially, it shows that a statistically significant trend is detected in about one sixth of these simulations, even though the trend was in all of them.

Another one sixth of the slopes are negative, which would give you the opposite of the acruel underlying climate trend.

This calculation demonstrates that, in high-variability rivers like those found in the PNW, a clear climate signal can be largely undetectable in the observed records. Thus, absence of a trend should not be taken as evidence that the trend does not exist.

But the physics tells us that a trend is almost certainly there. So please don’t trust anyone that only talks about the observed trends and then, mysteriously, fails to talk about the physics.

Note that this simple physical argument won’t tell you how much of the trend is due to climate change—that will require additional analysis. But for the question “Is climate change having an effect,” we can answer, “Almost certainly yes.”

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To answer that, we need to look at how well climate models reproduce observed global mean surface temperatures. Here I will look at the last three generations of climate models (CMIP3, CMIP5, and CMIP6) as well as a version of the latest generation of models (CMIP6) that excludes the so-called “hot models” whose climate sensitivity is higher than the range deemed likely in the most recent IPCC report.

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It turns out that that the resulting picture is complex. Earlier generations of models better reproduce the rate of warming observed since 1970, while the latest generation better captures the rate of warming in the last two decades. Whether this is evidence that warming is occurring faster than earlier generations of climate models expected will depend on how much of the recent warming acceleration is here to stay, and how much is being driven by short-term climate variability. While a bit unsatisfying as an answer, it probably remains too early to tell.

This post represents an update of my 2023 TCB post on model-observation comparisons, albeit with a fair bit of new analysis included (and two more years of observations!).

Comparing warming over time

One classic way to compare models and observations is to look at how temperatures have changed over time, compared to the multi-model mean and 5th to 95th percentile range across individual climate model runs. This is generally done using a single run for each unique climate model (in cases where modeling groups submit more than one modeling run) in order to ensure that each gets equal weight in the analysis.

For example, here are the 23 unique CMIP3 climate models that accompanies the IPCC 4th Assessment Report published in 2007. These models were run around 2004, and use historical data on CO2 concentrations, volcanoes, and other climate forcings through the year 2000 (and the SRES scenarios thereafter, with the middle-of-the-road A1B scenario shown here).

These are compared to observations from the Berkeley Earth surface temperature product on a monthly basis, and generally show quite a good agreement over the past century, with observations close to the multi-model mean except for a brief period in the 1900s and 1940s.

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We can zoom into the more recent post-1970 period when the bulk of warming (>1C) in the instrumental record has occurred due to rapidly increasing human emissions of CO2 and other greenhouse gases. Here I also include the linear trend in the observations (dashed yellow line) to better compare with the multimodel mean.

The next generation of climate models was CMIP5, and accompanies the IPCC 5th Assessment Report which came out in 2013. These models were run around 2011 and use historic forcings (CO2, volcanoes, etc.) up through 2005 and the Representative Concentration Pathways (RCPs) thereafter (with the RCP4.5 scenario shown below):

This similarly shows excellent agreement with observations in the years since 1970. Similar to CMIP3, the multimodel mean passes 1.5C above preindustrial levels around 2030.

The latest generation of models is CMIP6, which accompanied the IPCC 6th Assessment Report which came out in 2021. These models were run around 2020, and used historical forcings through 2015 with the Shared Socioeconomic Pathways (SSPs) used thereafter (with SSP2-4.5 shown below).

Unlike with prior generations of models, CMIP6 shows considerably higher warming compared with the post-1970 observed trend, with the multi-model mean warming around 25% faster than observations over the past 55 years and crossing 1.5C around 2025. This is due to a subset of these models featured a much higher climate sensitivity (>5C per doubling CO2) than seen in prior generations of models, resulting in a higher multi-model mean.

Due to the disproportionate amount of “hot models” in CMIP6, the IPCC AR6 report chose to develop its own Assessed Warming Projections that provided future warming projections more in-line with CMIP5. In a paper in Nature in 2022, we suggested an alternative approach to filter CMIP6 models based on their transient climate response that reproduced the IPCC’s projections while allowing us to still use most of the CMIP6 ensemble.

These TCR-filtered CMIP6 models have a post-1970 warming rate comparable to observations and show the world passing 1.5C above preindustrial levels around 2030.

Now, you might be asking why this post is focusing so much on linear trends since 1970. After all, if global warming is accelerating it would make sense to use a different approach to comparing models and observations that better assesses more recent periods. But this is inherently tricky, as shorter timescales have more influence from internal climate variability – events like El Niño and La Niña – that may not occur at the same time in climate models.

I also tend to find these “spaghetti plots” comparing models and observations useful to provide broader context around the agreement between the two, but less useful to assess changes over time. Our eyeballs are not well tuned to picking out relative changes over different time periods, and other ways to visualize the data can be more illuminating – if a bit more complex and less intuative.

A trendier view of changes

One way around this problem is to look at trends in climate models and observations over multiple time periods. For example, we can examine three different time periods: the 56 years between 1970 and 2025, the 25 years between 2001 and 2025, and the 15 years between 2011 and 2025.

This latter period (15 years) is the shortest period of time over which it is reasonable to look at trends given year-to-year variability in the record, and was the length of the period examined in Fyfe et al 2016 which explored a potential hiatus or slowdown in warming between 1998 and 2012.

The figure below shows a histogram (a frequency density plot) of climate model trends over these three periods compared with observations from four different groups: Berkeley Earth (which was used in the earlier plots), NASA’s GISTEMP, NOAA’s GlobalTemp v6, and Copernicus’ ERA5 reanalysis dataset. HadCRUT5 is not included as it does not have data available through October 2025 at the time this was posted.

This plot reveals a few fascinating things (at least in my opinion):

• While CMIP3 and CMIP5 got trends since 1970 more or less right (with CMIP3 arguably being slightly better), most models in CMIP6 were running a bit too warm compared to observations over that period.

• Observed trends in global mean surface temperatures over the past 25 years (2001-2025) were higher than the majority of CMIP3 models, on the high end of CMIP5 models, but more or less in the middle of the CMIP6 range.

• Over the past 15 years, the rate of warming observed is well outside of the range of any CMIP3 models and all but one CMIP5 model, and on the higher side of the CMIP6 range.

Its illuminating to compare the trend over the past 15 years to that of the “hiatus” period from 1998 to 2012. The divergence between observations and the CMIP5 models featured in Fyfe et al is actually greater in the recent period than it was back in 2012 – albeit with an acceleration rather than a hiatus.

Of course the hiatus should also serve as a cautionary tale here. There were observational biases – e.g. the transition from ships to buoy-based measurements – that were not accounted for at the time. And there was an important role for internal climate variability in driving the apparent 1998-2012 hiatus that largely vanished in subsequent years.

It is quite possible – I’d even say quite likely – that a portion of the 0.4C per decade warming over the past 15 years is attributable to internal climate variability. After all, 2011 featured a strong La Niña event, while 2024 had a moderately strong El Niño. But there is also increasingly strong evidence that the rate of human-driven warming is now closer to 0.3C per decade than the ~0.2C that characterized much of the post-1970 period.

Picking the whole cherry tree

Trends in warming may be more useful for comparing models and observations than spaghetti plots of warming over time, but they are still quite sensitive to the choice of start and end dates. Models may agree with observations over some periods and not others, so the choice of time periods to present can influence your audience’s conclusions.

One way to get around the sensitivity to trend start date is to just pick the whole damn cherry tree – show comparisons between model and observations for all possible trend start dates. This of course comes at the expense of making a figure that is considerably harder to explain, but I nonetheless find quite useful.1

The figure below shows trends for all possible start dates between 1970 and 2011 for CMIP3 models. The first point on the x-axis shows the trend between 1970 and present in models in observations, the second shows trends between 1971 and present, and so on.

Here we can clearly see that CMIP3 models were more or less spot on with trends since 1970, but tend to underestimate the observed rate of warming after the 1990s. Observed warming moves fully outside the model envelope (e.g. the 5th to 95th percentile of model trends) between 2002 and 2004 (depending on the observational dataset).

The CMIP5 multi-model mean shows a tad faster warming (~10%) than observations since 1970, but observations remain well within the model envelope. Observed warming trends start to be on the high end of the model range after 2000 or so, and are outside the 95th percentile of CMIP5 models over the past 18 years.

The CMIP6 multi-model mean shows notably faster warming (~23%) than observations since 1970, and observed warming trends are well on the low side of the model envelope starting years between 1970 and the early 1990s. They are roughly the same as the multi-model mean in the mid-2000s, and slightly above it in recent years.

Finally, the TCR-filtered subset of CMIP6 models is much more similar to the CMIP5 ensemble in terms of warming since 1970, but observed warming trends remain inside the model envelope in more recent years.

Recent high global temperatures (and trends) have raised questions about whether the “hot models” in CMIP6 may have been right after all, and that warming is indeed occurring faster than some climate models projected. I personally think the jury is still very much out on this question; if the observed warming rate of ~0.4C per decade over the past 15 years is driven in part by internal variability, it may not be representative of what we would expect to see in the future.

If the human-driven warming rate is currently around 0.27C per decade – as we suggested in the recent Forster et al 2025 paper – than it would be more in-line with the filtered CMIP6 models than the full CMIP6 ensemble. We will have to wait a few more years to know how much of the recent rate of warming is here to stay.

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At its heart, cooking a turkey is about getting heat from the hot oven into the cold, raw bird, raising its internal temperature to a safe (and delicious) level — a classic problem in heat conduction.

step 1: assume a sphere

A long running joke is that physicists always start out assuming that whatever they’re analyzing is a sphere.

Why? Because spheres are simple shapes that are easy to manipulate mathematically. In this case, while a turkey is a complex, irregularly shaped object with bones, cavities, varying densities of meat, a sphere is not a bad assumption.

Let’s further assume the turkey has the thermal properties of water.

Our spherical turkey starts cold (say, straight from the fridge at 5C). We then put it into a hot oven and assume the surface of the turkey quickly reaches 100C1.

The goal? To get the very center of that “turkey-sphere” to a safe internal temperature (like 80C).

step 2: the physics

The governing equation for transient heat conduction in a sphere with constant thermal properties and no internal heat generation is:

This was originally derived by Joseph Fourier. To solve it, he also had to develop Fourier series. Smart guy!

The general solution is a complicated infinite sum. It’s the kind of problem that you slog through once, when you’re in grad school, and never again. If I need to solve this (like I did for this post), I looked up the solution and used a computer to work it out numerically.

The time it takes to cook is equal to:

where R is the radius, ⍺ is the thermal diffusivity of water, T_init is the initial temperature (5C), T_hot is the temperature of the surface (100C), and T_center is the temperature you want the center (80C).

step 3: the answer

The results are pretty reasonable. For a sphere of water that weighs 10 lbs (radius is about 4 inches), the time to cook until the center reaches 80C is 2.2 hours, or about 13 minutes per pound. That’s very close to the label on our turkey:

Interestingly, the equation above shows that time scales with the square of the radius. Because mass scales as the cube of the radius, the time to cook therefore scales with mass to the 2/3rds power, i.e., time ∝ mass^(2/3).

That’s why a 30-lb turkey doesn’t take three times as long to cook as a 10-lb turkey. To calculate how long it would take, we can scale the 2.2 hours for a 10-lb turkey to 30 lbs:

Again, this agrees well with the label.

When I originally worked this out 35 years ago, I asked my mother how long it took to cook a turkey and she said “12 minutes per pound”. That answer suggested cooking time was linear with mass, which is not what I calculated. But, after a pause, she said, “Unless it’s a really big turkey, in which case it’s 9 minutes per pound”.

“Ah hah,” I thought, “There’s your non-linearity.” Once again, physics FTW.

Happy thanksgiving everyone! Enjoy your approximately spherical turkey.

A few other things:

Today is the day everyone googles “how to cook a turkey”. This plot shows the relative frequency of the google search:

There’s a slight peak in the evening of the 26th and a big peak between 6 and 7 am CT on Thanksgiving day. Never let people tell you Americans don’t procrastinate. By 3 pm CT (when I’m writing this), it’s clearly too late to be googling.

On the Halcyon blog, there’s an interesting analysis of how energy consumption is different on Thanksgiving. People sleep late, so there’s no early peak, and they eat dinner early, so there’s one big mid-afternoon peak and no late afternoon peak:

Read the entire thing, it’s good.

Grok is somewhat of an outlier in the LLM space as its developers have tried to manipulate its outputs to fit their ideological priors (with some unfortunate results). But even Grok is surprisingly consistent at giving scientifically accurate answers to questions about topics like climate change, vaccines, evolution, GMOs, and others where the US public tends to be divided along ideological lines.

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While it remains to be seen how this will change – and if models themselves will fragment over time in their depiction or framing of of factual information – there is a case to be made that the transition toward using LLMs as sources of knowledge might end up inadvertently recentering scientific expertise and creating a more unified information landscape for society.

Models as consensus machines

LLMs are at their core consensus machines. We ask LLMs a question and they give us an answer. But what they’re really doing is picking from a distribution of possible answers that they learned from the sum total of digitized human knowledge: the internet, books, papers, code, etc. Call that distribution the model’s “consensus.” It isn’t a vote of experts; it’s what the data and later training steps make most likely given your prompt. Post-training then teaches the model which of those plausible answers people generally prefer, and tools like retrieval can put fresh evidence into the model’s short-term memory so recent facts get more weight.

Broadly speaking, LLMs involve three layers:

1. Priors from pretraining. The model reads the world and learns patterns that make some answers more likely than others. Transformers do this well because attention lets them pull together relevant bits across long contexts (Vaswani et al., 2017).

2. Preferences from post-training. We teach the model to choose answers people like using supervised examples and preference learning (Ouyang et al., 2022).

3. Evidence at inference. Retrieval and tools give the model updated sources, numbers, and citations in the prompt itself, which can override stale memories (Lewis et al., 2020).

The pre-training process is at its core teaching the model to most effectively predict the next token. In practice, this means that common, consistent answers get high prior probability; rare or newly emerging answers get less by default. This is responsive to user’s choice of framing, as asking for competing explanations or evidence against a hypothesis will tend to give different answers. Under the proverbial hood, transformer attention learns to weight context that historically co-occurs with your query, which is why wording shifts can change outcomes.

Post-training tends to collapse many plausible replies into one good, safe, helpful reply, further pushing models to represent consensus views for a particular topic. Increasingly LLMs also incorporate retrieval-augmented generation adds snippets from a search index (papers, docs, web) into the prompt so the model conditions on current evidence, often with links you can verify. This usually reduces hallucinations and moves the answer toward whatever the retrieved sources support.

The net effect of these is to provide answers that are – generally speaking – pretty clearly in the mainstream for a given topic. You have to actively try to get LLMs to cast doubt on topics like climate change that are broadly accepted by the scientific community. This also means that expert views - articles in leading scientific journals, major newspapers, IPCC and government reports – will potentially reach a wider audience in an LLM world as they are given more weight in model responses.

Defragging the information ecosystem

Over the past few decades the information ecosystem has been rapidly fragmenting. People are increasing choosing information sources that confirm their preconception – Fox News vs MSNBC on television, X vs BlueSky online. Even Google searches, the closest thing we have to a common space on the internet, makes it easy to pick the search result that matches your preconception. If you are skeptical of vaccines, it does not take long to find a Google result to confirm your preconception.

Thats because Google does not have an incentive to give you the “right” answer in a search result, but rather returns those answers that are most popular with users of the internet. When Googling climate science questions, the first results are often from reputable sources like NASA, but you don’t have to scroll down far to find more fringe climate skeptic blogs.

Traditional internet search is optimized to provide a variety of answers to your search query, with the user than selecting which of the results they want to read. LLMs, by contrast, are designed to provide the single “best” answer to a question.

Decades ago our information ecosystem was more shared in part due to the paucity of sources. Large swaths of people tuned in to watch the same nightly news with hosts like Walter Cronkite or Edward R. Murrow. While it is easy to overstate the degree of consensus back then, it was more difficult for people to end up in information bubbles (and more fringe publications trafficking in conspiracies like the National Enquirer were rightly ridiculed).

There is a good outcome for society where increased reliance on LLMs for information pushes us back toward a shared reality, where models can provide an “objective” view on topics without being tied up in debates over the political views of the author.

But there is also a bad outcome where models themselves fragment, and people increasingly seek out models trained to reinforce a particular set of views.

Thumbs on the digital scale?

Earlier this year xAI embarked on an experiment to make its model Grok less “woke”. Buoyed by concerns that model responses on questions about South Africa and other topics did not reflect the preferred views of Elon Musk, xAI changed the models system prompt (its core instructions on how to interact with users) to have it “not shy away from making claims which are politically incorrect, as long as they are well substantiated.”

This relatively minor change had a cascade of side effects. It turns out that content in model training data that self-identifies as politically incorrect is associated with a range of views that are problematically racist, antisemitic, sexist, or otherwise offensive. It was not long before Grok was calling itself “MechaHitler” and making insinuations about Jewish people, among other abhorrent behavior.

There are two potential takeaways from this imbroglio: one is that its easy for model creators to put their thumb on the digital scale and shape model behavior in problematic ways. The other, potentially more optimistic, is that efforts to manipulate model results will have unintended side effects that make the model less useful for general purposes or so problematic that it will lose out in the broader private sector market. It is hard to see many Fortune 500 companies wanting their brand associated with MechaHitler.

That being said, it may well be that motivated individuals figure out a way to make models more malleable on particular topics without being fallible on others. We see this in the latest instantiation of Grok, which generally behaves reasonably until you ask it a question about its owner. There is also the recent development of “Grokipedia” that is intended to replace Wikipedia in the future training of select LLMs.

I suspect we may end up somewhere in the middle. The most powerful LLMs – those from Google, OpenAI, and Anthropic - will continue to be effective consensus machines and will be used by the majority of companies and mainstream users. At the same time, second-tier models will be more ideologically slanted and will lead motivated users to less factual information. But given that we already have strong information bubbles in most of our digital experiences today, I’m at least cautiously hopeful that the increased use of LLMs will help recenter expertise in the public discourse.

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