On the other hand global CO2 emissions – the very precursors to those concentrations – have largely plateaued. After increasing by more than 20% in the 2000s, CO2 emissions today are a mere 3% higher than they were in 2013. This plateau has been driven in part by a rapid expansion of clean energy globally, with spending on clean energy rising from around $600 billion in 2020 to $2.3 trillion in 2025. At the same time we’ve seen notable reductions in land use emissions associated with reduced rates of deforestation in countries like Brazil.

So if global CO2 emissions are flattening, why do atmospheric concentrations appear to be growing unabated? The answer is in the persistent nature of atmospheric CO2.

About half of the CO2 humans emit into the atmosphere remains there for at least a century (and about 20% for more than 10,000 years), with the remainder being absorbed by land (mostly vegetation) and ocean (mostly geochemical) carbon sinks. This means that even with flat CO2 emissions we would expect atmospheric CO2 concentrations to increase – that concentrations are approximately the integral of annual emissions.

This means that, generally speaking, if emissions remain flat concentrations would linearly increase. If emissions increase, concentration growth accelerates, while if emissions fall, concentration growth slows down. Its a bit more complicated in practice – unlike for temperatures we can get atmospheric CO2 concentrations to fall if emissions are reduced enough, where sinks take up more CO2 than we emit. But broadly speaking we expect atmospheric CO2 to keep growing until we cut emissions pretty substantially (e.g. to <50% of current levels).

Either way, atmospheric CO2 is better seen as a lagging rather than leading indicator of changes in emissions, as it is harder to see the effects of emissions reductions on concentrations over shorter time periods.

What we can do, however, is use reduced-complexity carbon-cycle models to examine how different atmospheric CO2 concentrations would have been if global emissions had not plateaued. To start with, lets assess what would have happened to global CO2 emissions if they had continued increasing at the ~2.2% per year that we saw in the 2000s. This is shown in the figure below.

Next lets use a reduced complexity carbon cycle model to convert these additional emissions into atmospheric concentrations. Here I am using the Joos et al (2013) impulse response function which describes the fraction of a one-year pulse of CO₂ that stays in the atmosphere as the ocean and land sinks gradually draw it down. These pulses are then convolved into changes in atmospheric concentrations over time.

Here we see that atmospheric CO2 concentrations would have been approximately 8 ppm higher if global emissions had not plateaued over the past 13 years.

Finally, lets add in annual variability in atmospheric CO2, both observed (blue line) and modeled (red line).

We can also extend this all the way back to the start of the record. As expected, a plateauing of global CO2 emissions transitioned us from an accelerated growth rate to a more linear growth rate. Its not a dramatic swing – global CO2 emissions remain at above 40 billion tons per year! – but its at least some detectable progress away from a much worse emissions future.

What are the takeaways here? Atmospheric CO2 concentrations are still climbing despite some success in flattening global emissions. But this is generally what we’d expect; if emissions had continued to increase concentrations would be noticeably higher and accelerating rather than exhibiting a more linear increase. Observed increases in atmospheric CO2 are, if anything, a bit on the low end (though still in the uncertainty range) of what the model expects based on observed emissions.1 I’ve included a more detailed writeup and code to reproduce this analysis on my GitHub here.

So next time someone shows you a graph of CO2 concentrations and argues that nothing is changing, you can show them how much worse it would have been had we really done nothing to change our emissions trajectory.

Update

I got a number of questions from folks about the role of slower growth in fossil emissions vs falling land use emissions in driving these changes. It turns out that around 78% of the avoided increase in atmospheric CO2 is attributable to fossil emissions, and 22% to land use. The GitHub repo has more details on this sensitivity test.

Last year’s event was a great success, drawing terrific press coverage including a piece in the NY Times. This year features a strong lineup of talks aimed at a general audience — I highly recommend tuning in.

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I gave a talk yesterday about How Science Works in Three Steps. You can see the short presentation here (I’ve appended a transcript below):

And please tune into the livestream here. It runs through tomorrow at 6 pm ET.

Cleaned up transcript:

Thank you, and thanks to everyone tuning in. I want to talk about something I think a lot of people don’t really understand: how science actually works.

Arguments over any scientific topic — climate change, vaccines, you name it — revolve around competing claims. For climate change, the claims are: Is the Earth warming? Are humans to blame? What will the impacts be? Over the last 150 years, the scientific community has developed a remarkably robust method for sorting out which claims are true and which aren’t. That method is what I want to walk through.

So how does science generate knowledge? If you ask a high schooler, they’ll give you the version they learned in ninth grade: an individual scientist forms a hypothesis, runs an experiment, and draws a conclusion. That’s more or less what happens at the individual level, though in practice it’s more iterative — you design, test, and refine all at once, landing on a slightly different idea each time until you reach a conclusion about whatever hypothesis you’ve ended up with.

But this isn’t science. It’s only step one. Any individual scientist can make mistakes — scientists are human. Maybe there’s a bug in the code, maybe there’s an error in the experiment. Just because someone ran an experiment and reached a conclusion doesn’t mean it’s right, and scientists don’t treat a single result as settled truth.

Step two is peer review, the first layer of quality control. The scientist writes up what they did and submits it to a journal, which sends the paper to other experts in the field. Those experts judge whether the methods are sound, whether it properly references prior work, and whether the conclusions actually follow from the data. Ask any scientist and they’ll tell you peer review is miserable — the comments come back brutal, often harsh, and you have to revise and resubmit, sometimes multiple times, before an editor finally accepts the paper.

Peer review catches an immense amount of bad science, but it can’t catch all of it. Reviewers get sloppy or overworked, they miss obvious mistakes, and they can’t catch hidden errors — a bug in the code, a misread instrument. So a peer-reviewed paper making it into a journal doesn’t mean the paper is right. Nobody treats a single paper as the final word.

That’s where step three comes in: the crucible of science. Important results get retested by the broader community. If you publish something interesting, it gets dissected. Sometimes people try to reproduce your work directly but more often they test its implications. If someone claims X is true, and that implies that Y is also true, then scientists will go check Y. If a claim survives this gauntlet long enough, it becomes accepted. That’s how we generate knowledge.

Let me give an example: the ozone hole. In 1985 — and I doubt many people watching were around then, but I remember it well — Joe Farman and his colleagues published observations from Antarctica showing that a huge chunk of ozone was disappearing every year. This was shocking because most atmospheric chemists had expected ozone loss to occur at mid-latitudes and high altitudes, not over the South Pole.

When scientists see something they don’t understand, they rush to explain it, and several competing hypotheses quickly appeared in the peer-reviewed literature. One pointed to chlorine chemistry from chlorofluorocarbons. A second blamed nitrogen chemistry, possibly driven by solar proton events — meaning the sun was responsible. A third argued it wasn’t chemistry at all but dynamics: winds were carrying naturally low-ozone air from the lower atmosphere up into the stratosphere.

Then the community went to work testing which theory could survive the crucible. Aircraft flew into the ozone hole and found no nitrogen, ruling out the nitrogen theory. Measurements showed the air in the polar vortex was descending, not rising — which would bring high-ozone air down, eliminating the dynamics theory. Meanwhile, those same flights measured enormous amounts of chlorine, strongly correlated with exactly the regions where ozone was being destroyed. That became known as the “smoking gun” paper, and it established that chlorine chemistry was the cause.

Through this process, a consensus emerged. And it’s worth being clear that this consensus is organic. There’s no vote, no poll, no meeting where it gets decided. Scientists go to conferences, talk to one another, and independently arrived at the same answer. It’s the ultimate free market of ideas. After the consensus was established, the peer-reviewed literature doesn’t even mention the rival theories — it simply takes chlorine chemistry as given. That’s what a consensus looks like. It doesn’t mean every single scientist agrees, but it does mean there’s no longer any legitimate debate.

So where can you see what scientists actually think? It’s all in the peer-reviewed literature, which contains the entire historical record of the argument: the discovery, the competing theories, the debate, the testing, and finally the community coalescing around one answer. But there’s a lot of room for misrepresentation. You can dig up an old paper claiming the ozone hole was caused by dynamics and wave it around as if there still was a debate about the cause of the ozone hole — without mentioning that the idea was disproven long ago and that even its own authors no longer believe it.

There’s a bigger problem, though: the peer-reviewed literature is written in a language I call “nerd.” It’s extremely hard to read — honestly, some of it is hard for me to follow. It takes graduate students years to work through the literature in a single field, so we can’t expect policymakers or ordinary people to do it.

This is where scientific assessments come in. The most famous is the IPCC — the Intergovernmental Panel on Climate Change. The job of an assessment is to read and summarize the peer-reviewed literature, synthesizing what’s settled, what’s been disproven, and what remains genuinely debated.

To ensure the assessments accurately summarize the peer-reviewed literature, assessments are written by large teams, which are far less prone to bias than individuals and which dilute the influence of any one scientist with an axe to grind. The teams are deliberately assembled to avoid stacking the deck, and the reports themselves go through multiple rounds of peer review. That rigorous process is exactly why you can trust them and why people consider the IPCC to be the gold standard of what we know about the climate system. An assessment produced through a different process may well not be credible.

You can even watch the consensus evolve across assessments. Take the attribution of warming. The 1990 IPCC report said the observed warming was broadly consistent with model predictions but also comparable to natural variability — in other words, we don’t know whether humans are responsible. By 1995, it concluded that the balance of evidence suggested a discernible human influence — a sentence only a committee could write, and one that barely commits to anything. By 2001, most of the warming over the previous 50 years was judged “likely” due to greenhouse gases; by 2007, “very likely”; by 2013, “extremely likely.” The 2023 report says essentially the same as 2013 — there’s not much stronger language available.

So, to wrap up: science is our ultimate tool for deciphering the universe — the most successful method we’ve ever developed for determining what’s true and what isn’t. Our understanding is written into the peer-reviewed literature, so if you want to know what scientists think, that’s where to look. And for those of us who don’t speak nerd, scientific assessments are the Rosetta Stone — they translate nerd into English.

Let me open the floor to questions. Thank you.

I’ve long been struck by how hard it is to predict the evolution of our energy system, even a few years in advance, never mind 25 or 30 years. I still remember the “peak oil” craze in the mid 2000s, when people were telling me the end of oil was nigh. It sounded convincing right up until it turned out to be wrong.

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In this post, let me show you how bad previous predictions have been for the electricity sector.

evolution of our energy system in 6 charts

Each plot below shows annual predictions of how a particular source of electricity will evolve as well as what actually happened. The data come from the Energy Information Administration and cover the U.S. electricity sector.

We’ll start with coal. In the first plot, the black line shows actual U.S. coal-fired electricity generation. The colored lines are predictions made each year since 2008.

In 2008, coal was expected to produce increasing amounts of electricity into the future. Instead, it immediately started to decline and it took until 2023 before the EIA began to predict a long-term decline in coal, despite the fact that coal had been declining for 15 years.

Natural gas, by contrast, has generated an increasing share of U.S. electricity. This is largely due to the tidal wave of cheap natural gas from fracking. The predictions, on the other hand, did not anticipate this.

The takeaway here is that predicting the evolution of our energy system is not just hard in the long run, e.g., thirty years from now, but it’s hard even in the short run.

If we combine coal and gas, the forecasts look better. This reflects the fact that natural gas was replacing coal, so that the overestimate for coal was cancelled to some extent by the underestimate for natural gas.

But even for the combined category, the forecasts vary widely.

Here’s solar (including both utility and residential solar):

And here’s wind:

For both energy sources, predictions before 2015 were really bad.

Across all energy sources, the 2023 and 2025 forecasts differ sharply from the 2026 forecast. The predictions made for 2023/2025 assume Biden’s Inflation Reduction Act, while 2026 predictions assume the reversal of those policies.

The difference between 2023/2025 and 2026 is an estimate of the role that politics plays in the future evolution of our electricity sector. Because we cannot confidently predict who will win future elections or what their policies will be, this is a very good reason why it’s so hard to predict the future of our energy system.

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the cost of energy

Why is it so hard to predict the energy mix in our electricity system? One big reason is that it is hard to predict the future rate of innovation. We can see this in a plot of the cost of energy1:

You can see that the price of wind and solar plummeted in the early 2010s, reflecting enormous innovation in the production of renewable energy. That was not predicted by most mainstream forecasts (as confirmed by predictions of wind and solar above).

There has also been a lot of innovation in fossil fuel production, most importantly hydraulic fracturing and horizontal drilling. These technologies drove down the cost of natural gas in the late 2000s and changed the economics of electricity generation almost overnight. Coal plants that had looked like safe long-term investments suddenly faced a cheaper competitor. Yet this, too, was largely missed. In the late 2000s, many utilities were still trying to build coal plants, unable to see that coal was entering a precipitous decline.

And, as wind and solar costs fell, renewables began taking market share too. Coal was not beaten by a single technology; it was beaten by a sequence of technologies that forecasters failed to anticipate.

Based on economics, coal is now a stone-cold loser. Its remaining advantage is not cost, speed of construction, or flexibility. It is politics. The Trump Administration is forcing coal-fired plants to stay open and some recent reporting suggests these interventions are raising costs for consumers.

In the competition between solar, wind, and natural gas, solar and wind are the cheapest. The combination of low costs, short construction times, and natural gas’ price volatility gives wind and solar a huge market advantage, explaining their exponential growth. Yes, solar and wind are coming for natural gas.

The plot also shows the profound disadvantage nuclear faces. Nuclear energy costs nearly $200/MWh, around four times the cost of wind and solar. And it takes a decade or two to get it online. Without government mandates or heavy policy support, I believe there is little likelihood that we will see a nuclear renaissance.

what are the implications of this?

Much of the debate in climate policy centers on the cost, difficulty, and timeline for phasing out fossil fuels in order to achieve net zero. You constantly hear pundits and analysts throwing around eye-popping numbers, confidently claiming, e.g., that “it will cost XXX trillions of dollars to reach net zero in our economy by 2050.”

But if the forecasting failures of the last twenty years have taught us anything, it’s this: we simply have no idea how much decarbonization will cost.

You should treat numbers like McKinsey’s estimate above as guesses. They could be right, but historically speaking, they probably aren’t. To summarize, here are the reasons why the true cost of reaching net zero remains so uncertain:

• We can’t predict the foundational energy mix: As the charts above show, our ability to forecast the trajectory of the electricity sector even a few years out is abysmal. If forecasters cannot accurately predict the baseline scenario (how much wind, solar, or natural gas will be on the grid), it seems unlikely they will be able to make accurate predictions of how much additional solar and wind will be needed in 2050 to reach net zero.

• Innovation shatters financial models: Long-term cost forecasts rely heavily on estimates of how fast innovation will occur. Such predictions are incredibly hard to make. Almost no one foresaw the exponential drop in the price of solar energy since the late 2000s, nor did experts predict the current plummeting costs of battery storage. Falling battery costs could reshape the electricity system.

• Geopolitics rewrites the math: External shocks can alter energy economics overnight. Few energy forecasts anticipated wars in Ukraine and Iran, both of which are going to have an enormous impact on our energy mix going forward.

Overall, the uncertainty in these long-term forecasts is enormous. And if history is any guide, the errors are not random. They usually point in the same direction: they overestimate the cost of the energy transition.

One reason is that traditional forecasting models tend to assume slow, steady technological progress. But energy technologies do not always improve that way. Solar, wind, batteries, and fracking all show that costs can change fast when conditions line up. Most models, which assume gradual change, will miss these breaks.

Another problem is that fossil fuels are often treated as stable, low-risk alternatives. They are not. Their prices can swing wildly, and their supply chains are exposed to wars, political instability, and global market shocks. Those costs are real and hard to predict, so they are left out of these estimates.

That is the central point: estimates of the cost of the energy transition should be treated as conditional guesses built on assumptions about technology, fuel prices, politics, and geopolitics, all of which have repeatedly surprised us.

The lesson of the last twenty years is not that the energy transition will be easy or hard — we really don’t know. Anyone claiming to know the cost decades in advance should be treated with skepticism.

Code to reproduce the plots can be found here.

related things

Is nuclear energy the answer? Nope.

Is renewable energy cheaper than fossil fuels? Yup.

An explanation of how renewable energy saves you money. It’s not that complicated unless you’re being paid to push fossil fuels. Then it’s very complicated.

If you’re looking for a new Substack on energy, check out Bright Spots by Jan Rosenow. This recent post discusses how renewables change the price of energy.

Do you want to see how each U.S. state’s mix of electricity has changed? Brendan Pierpont has you covered here.

van Vuuren et al justify this move by noting that “the CMIP6 high emission levels (quantified by SSP5-8.5) have become implausible, based on trends in the costs of renewables, the emergence of climate policy and recent emission trends,” citing the paper that we published in Nature back in 2020.

Others have pointed out that RCP8.5 was never particularly plausible, and have criticized claims that the move away from using these scenarios reflects actual progress on reducing emissions.

So what actually happened here? It turns out that two things can be true at the same time:

• RCP8.5 (and its successor SSP5-8.5) were designed to be a worst case emissions scenario, not the most likely outcome even in a world that did nothing to address climate change. We were probably never headed to a tripling of global emissions by 2100 (to say nothing of a five-fold increase in coal use), even in the absence of climate policy.

• Rapid declines in clean energy costs have bent the curve of future emissions downward, with new scenarios designed to reflect current policies notably lower than most baseline scenarios in the literature. The 21st century is now unlikely to see a continued expansion of fossil fuel use globally, with current policy scenarios reflecting relatively flat global emissions going forward.

Beyond business as usual

Emissions scenarios can broadly be categorized as “baseline” scenarios or “mitigation” scenarios. Baselines represent worlds where there are no additional efforts to address climate change (or in some cases turn back the clock to some earlier period and assume no policy after that point), while mitigation scenarios explore concerted efforts to reduce global emissions.

When RCP8.5 was first published global emissions were skyrocketing, having increased 30% in just the past decade. Clean energy sources were quite expensive, electric vehicles largely non-existent, and the idea that we would continue to increase our use of coal, oil, and gas through the end of the century was not seen as far-fetched.

Even in that context, RCP8.5 was chosen to represent the high end of the baseline scenario range available to the researchers at the time – around the 90th percentile. It was never a likely outcome even in a world that did not address climate change; rather it was always intended to represent a worst case scenario that pushed fossil fuel expansion to the max.

There are many other baseline scenarios with lower emissions, which were equally plausible as RCP8.5, even in the absence of climate policy. The original article that published the baselines, shows total CO2 emissions can be as low as RCP4.5 depending on the socioeconomic assumptions and the model used.

However, in part due to a breakdown in communication between the energy modeling community that develops the scenarios and the climate science community that uses them, RCP8.5 came to be incorrectly portrayed by many as the most likely “business as usual” scenario.

Around a decade ago the scenario started to garner more criticism. Justin Ritchie and Hadi Dowlatabadi published a paper in 2017 questioning whether the extremely high use of coal in RCP8.5 – which envisioned things like turning coal to oil for vehicles when oil reserves ran dry later in the century – was even possible given the world’s recoverable coal reserves. Ritchie and I (Zeke) wrote in 2019 that a 3C world was now “business as usual”, reflecting that:

Our business-as-usual projection of 3C of warming – rather than 4 or 5C – is a testament to the progress in global decarbonization over the last few decades. It also reflects the fact that rapid growth in coal use during the 2000s was not necessarily characteristic of longer-term energy use trends. The world has taken concrete steps to move away from coal in the past decade, and this progress should be reflected in our assessment of likely emissions pathways – and their resulting climate impacts – going forward.

In 2020 we published our Nature piece, arguing that we should “stop using the worst-case scenario for climate warming as the most likely outcome”, and that outcomes like RCP8.5 had become increasingly implausible with every passing year as clean energy costs fell and coal use plateaued.

Identifying real progress

So if we were likely never heading for a world of RCP8.5, with its tripling of global CO2 emissions by 2100 (and five-fold increase in coal use), where were we actually headed? How much has the energy transition to-date (which has grown to over $2 trillion annual global spending) actually changed our future trajectories?

This is an impossible question to precisely answer given that it relies on an inherently unknowable counterfactual scenario. It is not correct to state or assume that RCP8.5 was the baseline; we simply do not know the baseline and can only estimate it. But one way to approach the question is to look at where the scenario literature thought we were headed – what the actual range of baseline scenarios were.

The figure below show the CO2 emissions between 2000 and 2100 in the old RCP8.5 scenario, the full range of baseline scenarios published in the IPCC AR6 WG3 scenario database,1 and the new CMIP7 medium illustrative scenario published by van Vuuren et al 2026.2

We don’t actually know where in the baseline range (or outside of it!) we might have been heading in this counterfactual world. But the average of the baseline range – with its approximately 3.5C warming by 2100 – is a much more justifiable counterfactual than the high-end RCP8.5 baseline. This suggests that progress on policy and technology has reduced expected 2100 warming by around 0.7C, rather than the full 1.7C difference between RCP8.5 and the new medium scenario. A plausible reduction of ~0.7C warming represents enormous progress and a large reduction in future damages, even as a 2.8C world under current policies remains far from acceptable.

The mid-range of the baseline scenarios back in the late 2010s when most of those scenarios were developed is roughly analogous with the CMIP7 high illustrative scenario today (~3.3C), which envisions a roll back of policies that have been enacted over the past decade as well as slower technological progress on clean energy going forward.

The mid-range baseline scenario warming is also consistent with baseline warming estimates published by both the IEA (3.5C) and Climate Action Tracker (3.6C) prior to the signing of the Paris Agreement. Climate Action Tracker has also tracked the changes in its “policies and action” scenario over time, which has declined from 3.6C in 2015 to 2.6C in 2026, finding a similar ~1C decline in expected future warming.

It is hard to gauge the “impact of the Paris Agreement” or any other specific climate policy intervention in isolation. In a world without Paris we would still likely have seen a reduction of future emissions projections associated with continued cost declines of clean energy technologies. Similarly, even if coal use did continue to grow, it is not at all obvious it would have grown at the scale and rate as in RCP8.5. But this does not mean that these declines are divorced from other policy decisions made by countries over the past few decades.

Technology is, after all, not exogenous; it does not arise solely from spontaneous innovations. Rather, it reflects an interplay between the government and the private sector over cycles of research and development, early stage deployment, and eventual economies of scale.

Let’s take the example of solar energy, which is covered in depth in Greg Nemet’s excellent book “How Solar Energy Became Cheap”. Here formative R&D work done by Bell Labs in the 1950s and by the US DOE in the 1970s helped develop and commercialize the technology, while subsidies in Germany and Japan helped drive down costs with larger scale deployments in the 1990s and 2000s. More recently, dramatic cost declines have been driven at least in part by enormous investments in both domestic and export markets by China.

High warming outcomes can still occur

When we try to estimate how much the world will warm this century and beyond, we run into three fundamental uncertainties: our future emissions, the sensitivity of the climate to increasing forcings, and the carbon cycle feedbacks that determine the portion of our emissions that remain in the atmosphere.

While we tend to give the central estimate of future warming in 2100 associated with a given emissions scenario (e.g. 2.8C), this single number hides a pretty wide range of actual possible climate responses. For example, the figure below shows probability of reaching different temperature outcomes under the CMIP7 medium illustrative scenario. While the median is 2.8C, the 5th to 95th percentiles span 2.1C to 3.7C, and there is even a small (~2%) chance of 4C or more warming.

The new medium scenario is designed to be in-line to current policy scenarios in the literature. But current policies represent neither a ceiling nor a floor on future emissions. Future emissions are in society’s hands. Indeed, it is ironic to see President Trump criticizing climate science for its past use of high emissions scenarios when his administration actively supports a roll back of existing climate policy, the restriction of new clean energy development, and mandating that coal plants remain operating despite their high costs.

The new CMIP7 scenarios include a “High” emissions scenario that explores a more Trumpian future where current policy is rolled back and clean energy deployment slows. The high illustrative scenario finds 2100 warming of closer to 3.3C (with a range of 2.5C to 4.4C).

It is important to emphasize that the world doesn’t end in 2100, even if many of our past emissions scenarios and climate model simulations did. One of the major advances in the IPCC AR7 is a plan to extend scenarios through 2150, since 2100 is not nearly as far away as it used to be.

The brutal math of climate change is this: as long as CO2 emissions remain above zero, the world will continue to warm. The medium scenario ends up closer to 3.7C by 2150, while the high scenario ends up more or less matching the warming in the old RCP8.5 scenario despite an assumption of flattening or modestly declining emissions after 2100.

It is also wrong to say that the worst predictions of climate impacts this century can now be ruled out by this revision. High-end temperature projections for the end of the century are reduced compared to earlier IPCC assessments. Yet, the IPCC WGII report found that risks across the five “reasons for concern” it examines have all risen for a given level of global warming. So, even if the high-end emissions in RCP8.5 won’t materialize, the damages projected in these earlier climate simulations remain very much in play.

A tripling of global CO2 emissions by 2100 may never have been particularly plausible even back in 2011 when RCP8.5 was originally published. But a 21st century of increasing fossil fuel use leading to a doubling of emissions was within the realm of the possible. The fact that we are no longer heading toward that is a sign of progress, rather than somehow undermining the edifice of all of climate science as both President Trump and some overly excited internet pundits claim. And of course, we still have a long way to go to get emissions down to (net) zero and stabilize global temperatures.

After all, as we wrote back in 2020, “This admission does not make climate action less urgent. The need to limit warming to [well below 2C]3… does not depend on having a 5C counterpoint.”

A lot has happened since then. We’ve gotten the first three months of data in for 20261 (and have a good sense of where April 2026 will end up in reanalysis data – see our Climate Dashboard for daily updates).

More importantly, models are converging on a doozy of an El Niño event developing in the latter part of 2026, with the latest multi-model median projection of a peak anomaly of 2.7C in the ENSO3.4 region of the tropical Pacific. While the prediction remains uncertain (we remain within the “spring predictability barrier” when its historically hard to predict ENSO2 development), this would put the 2026/2027 roughly on par with the “super” El Niño the world experienced in 2015/2016.

I’ve updated the models I use for both my 2026 and 2027 projections. I’ll go into the gory methodological details shortly, but the headline numbers are in the figure below: the estimate for 2026 has risen from 1.41C (with a range of 1.27C to 1.55C) to 1.46C (1.36C to 1.59C). The 2027 estimate has similarly increased from 1.57C (1.3C to 1.76C) to 1.61C (1.4C to 1.93C).

So what changed? Before the start of the year I was using a pretty simple regression model. It estimate what the annual temperature anomaly would be based on the prior year’s anomaly, the last month of the prior year, the predicted ENSO state over the next three months, as well as a year count (and year count squared) to reflect linear and non-linear aspects of the trend since 1970.

I’ve updated this to use the equation below, which includes the year count, prior year’s temperature anomaly, the anomaly over the year to date for 2026 (currently Jan-Mar), the latest month, the observed ENSO state over the year to date, and the forecasted ENSO state over the remainder of the year.3 The uncertainty in the 2026 prediction also accounts for the uncertainty in the ENSO forecast using a Monte Carlo sampling approach.4

Similarly, the original 2027 calculation was pretty ad hoc; I just took the 2026 estimate and added the current warming trend (0.026C per year) that we calculated in our Forster et al 2025 paper. I then added a range of possible boosts from El Niño ranging from 0C (no El Niño develops) to 0.2C (very strong El Niño), roughly encompassing the range we’ve seen across past events.

Now I’ve converted it into a proper regression model. It calculates the expected year-over-year change in temperatures based on the year count (reflecting the trend) and the ENSO forecast for the latter part of the year (September-December, reflecting the period over which the currently developing El Niño will likely peak).5 This is then added to the 2026 estimates, with their uncertainty (and the uncertainty in future ENSO forecast) propagated through using the same Monte Carlo approach. This actually slightly increases the error bars from the original 2027 estimate, reflecting the uncertainty in both the 2026 estimate and the El Niño forecast.

One way to test how well this approach works is to see how well it predicts year-over-year temperature changes during past strong El Nino events (e.g. with a peak >2C and a Sept-Dec average of >1.5C), as shown in the figure below:

In general the model does pretty well; it slightly underestimates the year-over-year increase in 1973, 1983, and 1998, gets 2016 pretty spot on, and slightly overestimates the increase between 2023 and 2024. This is, of course, contingent on where 2026 annual temperatures end up, so a warmer 2026 in this model would result in a warmer 2027.

We can also look at how well the model “hindcasts” past years by applying the 2026 prediction model to past years using the same year-to-date and temperature and ENSO values:

Overall the development of a strong El Niño event in 2026 (and its effects on 2027 temperatures) have bumped my predictions up a bit from where they were at the start of the year.

But 2026 remains more likely than not to end up as the second warmest year on record (~56% chance), but has a non-trial chance of being the warmest year (~26%) with a somewhat larger change (34%) of being above 1.5C.

2027, by contrast, is likely (~85% chance) to be the warmest year on record and has a 88% chance to be above 1.5C. My updated estimate central estimate (1.61C) remains a bit lower than Hansen’s (1.7C),6 but its consistent with the size of the year-over-year bumps we’ve seen in past strong El Niño events.

Update: A new set of ENSO runs came in this afternoon from CanSIPS, which previously set the lower bound on the ENSO forecast for 2026. The new update has notably higher estimates; these don’t change the central estimate (as the median across all ENSO model remains unchanged), but it reduces the lower end of the uncertainties in the error bars. I’ve updated the figures and text accordingly.

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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