There are many similar plots that make the rounds; some focus on the frequency of US heatwaves, others on the number of records set over time. All suffer from similar problems when changes in measurement techniques are not taken into account.

One criticism we got on our prior article that we used the Berkeley Earth data, a different dataset than was used in the construction of the original plot. In this post, we look at a different plot – the number of records over time – and use the same dataset used in the original plot, the Global Historical Climatology Network-daily (GHCNd) data, to analyze it.

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The plot

If you’re on social media, you’ve probably seen a plot that look like this:

Let’s focus on the top panel, which shows that more high-temperature records were set in the 1930s than today by a huge margin. It’s not even close. And there’s been no increase over time since then, which seems to confound expectations for a warming planet.

This would be great news, if it were true.

Reproducing the result

Let’s see if it is. The data in plots like this almost always come from one dataset: the Global Historical Climatology Network daily (GHCNd), which covers the continental U.S. (CONUS), or a closely related variant.

In this post, we’ll use a version analyzed in this paper. Using those data, we can accurately reproduce the top panel of the plot in the figure above.

It shows the number of high-temperature records in each year in the 125-year record of 1,266 stations located in the CONUS. It looks an awful lot like the plot above (although the values are larger because we’re looking at more stations), confirming the methodology used in the plot.

Biases in the data

But, as discussed in our last post, these daily GHCN data are raw or unhomogenized, meaning they have not accounted for known biases in the data. These include time-of-observation changes, biases in the conversion from liquid-in-glass to electronic thermometers, biases due to stations moves, urbanization, etc.

So let’s estimate how wrong the plot is. To quantify the impact of the biases, we created an adjusted or homogenized daily dataset by applying the adjustments NOAA used in the production of NOAA’s homogenized GHCN monthly data. See the appendix for details about how that was done.

Here are plots of temperature records using the raw and the homogenized daily dataset:

We see that correcting the biases reduces the number of records in the 1930s and increases the number of records in the present period, leading these two periods to be have roughly equal numbers of records. There’s also now an upward trend in record-setting temperatures starting around 1970s, which makes sense given the overall warming of the planet since then.

Biases in space

But we’re not done. Here is a plot of the location of stations we’re analyzing along with the summertime average daily high temperature anomaly for the 1930s:

As you can see, the stations are not evenly distributed — there are a lot more stations in the middle of the continent — the exact location where the heat of the 1930s was focused. This is the Dust Bowl region, whose exceptional heat was due to drought exacerbated by poor land management by humans.

Without adjusting for this, the result will be biased towards the hottest part of the continent, thereby overestimating the heat of the 1930s.

To fix that, we area-weight the data by gridding and then averaging the gridded values. We’ve done that here:

As expected, area weighting further reduces the number of record highs in the 1930s and increases it in the recent decades. By the late 2000s, the number of high-temperature records clearly exceeds the 1930s.

We also did this calculation using the gridded Berkeley Earth daily homogenized dataset, which we used in our rebuttal to the DOE Climate Working Group report (see Fig. 2 on page 186) and which was used in our last post. It shows a similar result to the one above. The agreement with our analysis here provides independent confirmation of the result.

But … but … but … adjustments!

Adjustments are a source of controversy among Twitter Ph.D.’s. For example, here’s a comment on our last post:

To scientists, adjustments to account for known biases are entirely uncontroversial. For example, James Webb Space Telescope images are heavily adjusted. Because the telescope captures a vast dynamic range of light across the universe, scientists mathematically shift pixel values, boosting the brightness of the darkest regions while compressing the brightness of the brightest areas.

The data are then further adjusted by removing various digital and physical artifacts. This includes digitally filling in black star cores — areas where bright light oversaturated the detector — as well as filtering out faint vertical striations caused by the camera’s readout noise. The team must also erase ghost stars, which are leftover light signatures from previous exposures, and delete bright spots caused by energetic cosmic rays physically striking the camera’s detectors.

The UAH (University of Alabama in Huntsville) satellite temperature dataset provides another example of the importance of adjustments. Since 1978, it has relied on more than fifteen satellites with different instruments and degradation rates. The satellites do not agree with each other for various reasons, so they use overlapping observations to adjust them into a consistent long-term record.

In addition, satellites gradually lose altitude, altering sensor viewing angles, and their observation times can drift, creating false temperature trends. UAH adjusts for these effects to remove artificial warming or cooling. In the end, the UAH data are heavily adjusted:

The bottom line is this: If you do not adjust the data for known biases, you’re getting the wrong answer.

Thus, adjustments and bias correction are not a conspiracy, they’re good science. In fact, the NCEI group has documented the algorithm and published the code used to bias correct these data (this is what we use to adjust the daily data). If you want to claim that the adjustments are wrong/bad science/fraudulent, please be specific about which adjustments should not be made.

Ultimately, the opposition to adjustments is entirely instrumental: climate skeptics like the results that the raw, biased data give them for the US, and they don’t like the results that bias-corrected data give them. So they oppose adjustments to the data.

It’s worth noting that globally the picture is reversed, with the raw data actually showing more warming than the adjusted data.

Cherry picking

Another issue with the plot is the focus on the U.S. As we wrote last week, the heat of the 1930s was a regional event, due to a combination of unforced variability and human activities (poor agricultural land management). The continental U.S. was indeed hot, while the rest of the globe was cool:

Today, the continental U.S. is just about as hot as the 1930s, but everywhere else is also very hot.

What this means is that, had they picked any other region, they would not have gotten this result — even using raw, unadjusted data (something discussed in the last post). Thus, the focus on the U.S. in the 1930s is a classic cherry pick.

So: next time you see a plot focusing on:

1. The U.S.

2. In the 1930s

3. Using raw, unadjusted GHCNd data

The argument is most likely a scam. Please hit the “share” button below and send them a link and use the hashtag #1930sScam.

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Appendix: how to create the daily adjusted GHCN data

To adjust the raw GHCNd data, we started with the monthly GHCN dataset (GHCNm), from which we can estimate the monthly biases as the difference between the monthly raw (unadjusted) and monthly adjusted to come up with an adjustment for each station for each month and year. The code to do that is here. To create the daily homogenized GHCN data, we take the daily raw data and add each month’s and station’s adjustment to all days in that month. The code to do that and create most of the plots in the post is here.

Other stuff

If you’re looking for a way to fight the power, check out Marcus Sarofim’s latest substack post. He identifies places where you can send in comments on proposed regulations: 1) a rollback of diesel pollution rules, 2) a rollback of the process for setting appliance efficiency standards, 3) a rollback of PFAS drinking water standards, 4) the elimination of public notice and comment for Forest Service actions. If you’re unhappy with what’s going on, take action! These things really do make a difference.

If you're teaching a programming course, I recommend having students use cocalc.ai as the programming platform. I used the last time I taught Python programming and it worked well. You don’t have to worry about students installing software (or whatever you use) and you can also control how much AI they can use (unlike google colab). More on how to use it for teaching is here.

I also use this platform for spinning up google VMs to do very large data analysis projects that require downloading TBs of, e.g., CMIP6 data. The people that run it are very very helpful and, if you have a problem, they'll fix it quickly.

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It is a compelling figure. The 1930s Dust Bowl really was an extraordinary period of extreme heat in the US, and no amount of correction for changes in measurement techniques over time makes it go away. But the graph is also a case study in why you cannot naively count threshold exceedances in raw daily station data and call it a climate record. Its results rest on two well-documented thermometer problems that artificially depress modern hot day counts, plus a station network that happens to be oversampled where the Dust Bowl happened.

Reproducing the viral chart

To start with, let’s reproduce the figure properly. Rather than averaging whatever stations happen to be reporting in a given year (the station network grew from a few hundred stations in 1895 to many thousands today, with big shifts in where they are located), I selected the 543 GHCNd stations in the contiguous US with long, near-continuous maximum temperature records over the full 1895-2025 period,1 gridded them to 2x2 degree cells, and computed an area-weighted national average.

Here we see the same basic story as the viral version: a huge spike in the 1930s (1936 alone averaged 33 days at or above 95F across these stations), elevated values through the mid-1950s, and nothing since that comes close. So the Martz figure is not fabricated, and its shape is not an artifact of the changing station network. To be fair to its author, counting hot days in raw data really does produce this picture.

The problem is what “raw” means here.

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Two thermometer problems, both pointing the same way

Raw sounds virtuous, like unfiltered honesty. But the US cooperative observer network has changed in two important ways over the past century, and both changes bias hot day counts downward in recent decades relative to earlier ones.

The first is time of observation bias. Volunteer observers read and reset their max/min thermometers once a day. In the early 20th century most did so in the late afternoon, near the hottest part of the day. An afternoon reset means a very hot afternoon can get counted twice: once for the day it happened, and again the next day if the following afternoon is cooler, since the thermometer still holds yesterday’s peak. Over the 20th century the network gradually shifted to morning observations (better for measuring precipitation), which does not double count heat. Vose et al (2003) documented how this shift alone imparts a spurious cooling trend of a few tenths of a degree in US records, and the double counting directly inflates hot day counts at afternoon-observing stations.

The second is the thermometer switch. In the mid-1980s NOAA replaced liquid-in-glass thermometers in wooden Cotton Region Shelters with electronic maximum-minimum temperature sensors (MMTS) at most cooperative stations. Quayle et al (1991) showed the new sensors read maximum temperatures around 0.4C (0.7F) cooler than the old shelters. This produced a one-time step change at thousands of stations that landed right at the start of the modern warming era. When your threshold is a hard cutoff like 95F, a step down of nearly half a degree C removes a lot of days.

Homogenization algorithms (like NOAA’s pairwise method, Menne and Williams 2009, or the Berkeley Earth approach, Rohde et al 2013) detect and correct these breakpoints by comparing each station to its neighbors. Our 2016 paper validated these adjustments against the pristine, purpose-built US Climate Reference Network and found they perform well. While NOAA does not have daily homogenized data (they only provide monthly homogenized data), Berkeley Earth does. So let’s compare the raw hot day count to the same metric computed from Berkeley Earth’s homogenized daily maximum temperature fields.

The two datasets agree that the 1930s were exceptional. Where they disagree is the modern era: in the homogenized data, recent decades rival the Dust Bowl years CONUS-wide, with 2011 (16.1 days) actually edging out 1936 (14.0 days) as the biggest year in the Berkeley Earth series.

We can make the comparison cleaner by putting each series relative to its own 1951-1980 average:

The raw and homogenized series track each other closely for the first 85 years, through the Dust Bowl peak and the cool 1960s and 70s. Then, right around 1980 (just when the MMTS transition began), they split. The homogenized data rises to around 1.4 times its mid-century baseline while the raw data stays flat at roughly 1.0. The raw data does not exaggerate the 1930s, but rather erases the last 40 years of increases in extreme heat.

The dashed and solid blue lines in the figure are also worth a closer look. The dashed line averages the Berkeley Earth data over only the 130 grid cells where our long-record stations actually sit; comparing it to the raw series is the fair like-for-like test, since the places are the same and data adjustments are the only difference. The solid line averages over the whole country, and the gap between the two exposes a sampling problem rather than a data problem. Century-old stations cluster in the Midwest and East, which is precisely where the 1930s heat was centered and where extreme daytime heat has increased the least since. Averaged over the long-lived station locations, even in homogenized data, puts the 1930s roughly 45% above the last two decades. If we average over the full contiguous US, however, that gap shrinks to about 10%.

A Dust Bowl story, not a national one

There is a second, subtler issue with interpreting the viral graph: geography. Long-record stations are heavily concentrated in the Midwest and East (only 116 of our 543, around a fifth, sit west of 100W), which happens to be exactly where the 1930s heat was centered. Let’s break the country into NOAA’s nine US climate regions and look at each one separately, using the spatially complete Berkeley Earth data.

The Dust Bowl turns out to be a story about three regions. In the Upper Midwest the 1930s averaged around 15 times as many 95F days as the last two decades (3.4 vs 0.2 per year), in the Northern Rockies and Plains around 9 times (2.8 vs 0.3), and in the Ohio Valley around 4 times (8.7 vs 2.1), with 1936 the record year in all three.

Everywhere else the present rivals or beats the past: the South is essentially tied (22.4 days in the 1930s vs 22.7 over 2000-2023, with 2011 the biggest year in the record), while the Southeast (14.7 vs 11.1 days), Southwest (4.9 vs 3.9), and West (4.6 vs 3.6) all see more 95F days now than in the 1930s, with the two western regions peaking in 2020. (The remaining two regions, the Northeast and Northwest, average less than one 95F day per year throughout the record, too few for meaningful comparisons.)

The mid-century spike in that average comes almost entirely from three regions in the middle of the country. This makes physical sense: the Dust Bowl heat was tied to a specific regional catastrophe, a multi-year drought amplified by human-induced land degradation (Cook et al 2009), with bare, desiccated soils driving daytime temperatures to levels those same fields have not approached since. A record set during an ecological disaster in one part of the country is not evidence that the whole country, much less the planet, was hotter. The national chart is really being driven by a distinct regional anomaly.2

Meanwhile, the thermometers all agree it is warming

Finally, it is worth stepping back from the hottest afternoons of the year, which are a noisy, bias-sensitive sliver of the temperature record, and looking at what US temperatures as a whole are doing. The figure below shows annual average maximum, minimum, and mean temperatures for the contiguous US from NOAA’s homogenized nClimDiv dataset.

All three are unambiguous. Since 1970, maximum temperatures have warmed at 0.52F per decade, minimums at 0.51F per decade, and the average at 0.51F per decade (all p < 0.0001), with the last decade roughly 2F above the 20th century baseline. The 1930s show up here too, but as a modest bump in maximum temperatures far below present (as the dust bowl event was largely limited to summer TMax temperatures, with a much smaller effect on the remainder of the year). Extreme daytime heat in summer is one of the places where the US warming signal is weakest (a real and interesting scientific result, related in part to agricultural intensification and irrigation in the Midwest (Mueller et al 2016), but it is not representative of the climate system as a whole.

Zooming all the way out

One last piece of context. The contiguous US covers less than 2% of the Earth’s surface, and as we saw above, even within the US the Dust Bowl signal is regional. So what does the very same chart look like for the planet as a whole? The figure below reproduces the design of the viral graph (days at or above 95F, 100F, and 105F) using the Berkeley Earth daily data over global land. To avoid mixing climate changes with changes in the locations we measure (global station coverage grew from under 40% of land area in the 1890s to essentially complete today), I restrict the average to the grid cells with continuous century-long records, covering 42% of global land.3

Globally there is no 1930s spike at all: 1936, the year that towers over the US record, comes in at 15.1 days at or above 95F, less than a day above the surrounding years. The Dust Bowl, extraordinary as it was in Kansas, barely registers when averaged over the world’s land. Instead, hot days hold roughly steady until around 1980 and then climb: days at or above 95F are up around 70% between the early 20th century (1895-1924) and the last decade (12.8 to 22.1 per year), days at or above 100F have more than doubled (3.0 to 7.5), and days at or above 105F have nearly quintupled (0.3 to 1.6). The hotter the threshold, the faster the rise, which is exactly what you expect when a whole temperature distribution shifts upward. All ten of the warmest years by the 95F metric have occurred since 1998, and the six most recent years in the series (2018-2023) are all among them.

The US Midwest is one of the few places on Earth where the hottest days of the mid-20th century still stand; picking it as your yardstick for global warming is, to put it charitably, a choice.

So what are the takeaways here?

First, the Dust Bowl was real, and it remains the benchmark for multi-year extreme daytime heat in the central US, in adjusted and unadjusted data alike. Anyone claiming the 1930s heat is purely an artifact of bad data is simply wrong.

Second, it was a regional phenomenon. Break the country into NOAA’s nine climate regions and the 1930s is only exceptional in only three of them (the Upper Midwest, the Northern Rockies and Plains, and the Ohio Valley, at roughly 4 to 15 times recent levels). The four regions where hot days are the most common (the South, Southeast, Southwest, and West) all match or exceed the Dust Bowl today, with record years of 2011 and 2020, not 1936.

Third, raw daily data is the wrong tool for this question. Time of observation changes and the 1980s switch to MMTS sensors both suppress modern hot day counts relative to the past, and the raw and homogenized series diverge almost exactly when the instrument transition happened. In homogenized data, recent decades rival the 1930s even averaged nationally.

Fourth, hot days above a fixed threshold are a narrow and noisy way to look at the data. The overall US warming trend (around 0.5F per decade since 1970 in max, min, and mean temperatures) is robust in every dataset, raw or adjusted, satellite or surface. And globally, days above 95F have been climbing steadily for a century, with no Dust Bowl bump at all: the central US is one of the few spots on the planet where the mid-20th century still holds the record for extreme daytime heat.

The viral chart is built from real measurements, and the heat it shows was real too. But it takes a regional catastrophe, fails to account for changes in instruments and observation times, and presents the result as a national climate verdict. Accounting for the thermometers and the geography, and the US looks a lot like the rest of the planet: the hottest days on record are increasingly the ones we are living through now.

I’ve included a more detailed writeup of the methods and code to reproduce this analysis on my GitHub here.

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This smoke-pocalypse has renewed a long-standing debate online that these fires aren’t really about climate change at all, but about forest mismanagement. Decades of aggressive fire suppression, the argument goes, have let fuel pile up. We don’t log enough, thin enough, or do enough prescribed burning The forests are overgrown tinderboxes and we have only ourselves to blame. Climate is a distraction from a problem we created by listening to Smokey Bear’s insidious propaganda.

I want to take this argument seriously, because it has a real kernel of truth. The fire-deficit story is a genuine phenomenon in the dry conifer forests of the western United States. But most of what’s burning in Canada is boreal forest, and the boreal is a fundamentally different beast. So in this piece I’ll try to explain why the management argument, largely valid in a California pine stand, mostly falls apart when you move it a couple of thousand kilometers north, and explore what the fire data actually shows.

Here’s the short version. Canada’s area burned has surged, and it has surged in step with warming: hot, dry fire seasons burn far more forest, with area burned rises roughly 80% for each 1C increase in fire-season temperature (with a correlation coefficient of 0.61). This relationship is robust to a variety of statistical tests and controlling for confounding variables. The boreal burns in rare, high-intensity crown fires on a natural cycle measured in a century or more, across enormous remote areas that have never been logged, thinned, or effectively suppressed. Only about a fifth of Canada’s burned area over the past four decades was even inside forest regions that have ever been actively managed. You cannot have a fuel-buildup from forest mismanagement in a forest you were never managing.

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Forest mis-management?

The forest-mismanagement argument was, in fairness, based on real evidence from some regions. In the frequent-fire dry forests of the western US that are dominated by ponderosa pine and mixed conifers the natural fire regime is low-to-moderate intensity surface fire returning every 5 to 30 years, historically stoked in part by Indigenous burning. A century of fire exclusion in those forests really did remove that frequent fire, let stands grow denser and more continuous, and build up a “fire deficit” that raises the odds of severe fire (Hagmann et al. 2021). And in that setting, fuel treatments work: Prichard et al. (2021) find wide agreement that mechanical thinning combined with prescribed burning measurably reduces subsequent fire severity. If you’re arguing about the Sierra Nevada, the management story is as important if not more important than the climate one.

The problem is that the Canadian boreal is not the Sierra Nevada. It burns in infrequent, high-intensity, stand-replacing crown fires that kill the whole stand on natural fire cycles measured in many decades to centuries (commonly a century or more, and two centuries or longer in the east), not 5 to 30 years. It is, ecologically speaking, supposed to burn this way; black spruce is practically built for it. Fire in this system was never the gentle recurring ground-clearing that suppression interrupted in California. And the burning is astonishingly concentrated: across the boreal, something like 3% of fires account for about 97% of the area burned. This is not a landscape full of many small fires that “used to clean out the fuel.” Rather, its a landscape that waits, and then burns catastrophically under extreme weather.

Why do we know that Canadian boreal forest fires are not being driven by mismanagement?

First, and most importantly, most of the boreal was never being managed in the first place. As University of Alberta fire scientist Jen Beverly has pointed out, only about one-fifth of Canada’s total burned area from 1986 to 2023 occurred within long-term forest tenure (the land that’s actually logged and managed). The vast majority burns in remote forest with no timber operations. In Canada’s so-called “extensive” fire-management zones there has been no serious suppression efforts historically. Fires there are monitored and largely left to burn unless they threaten people or infrastructure. You can’t blame overgrown, over-suppressed forests for fires in places nobody was suppressing.

Second, the fuel-buildup mechanism doesn’t fit the recent fires. If decades of suppression had loaded the forest with excess old fuel, you’d expect the big fire years to preferentially consume the oldest, most fuel-laden stands. But in Alberta’s brutal 2023 season, fires burned stands of essentially all ages in proportion to how much of each was on the landscape. That’s the signature of fire driven by weather, which doesn’t care how old the trees are, not by fuel accumulation.

Third, the proposed fixes don’t scale to the boreal even if you wanted them. This is the conclusion of the very scientists who documented Canada’s fire deficit. Coogan, Parisien and colleagues (2020) state it flatly: mechanical fuel treatments “require continued maintenance over time, are too expensive to apply across large boreal landscapes, and are usually not designed to halt extreme wildfires.” The boreal is on the order of three million square kilometers. You are not going to thin or prescribe-burn your way across it, and the crown fires that produce the smoke wouldn’t stop at a fuel break anyway.

So if it’s not mismanagement, what is it? Let’s look at the data.

The Canadian fire record

To start with, here is the long-term record of area burned, combing the satellite-mapped NBAC composite from 1972 on with the less-complete point-based records before that shown in grey (note that this is primarily for illustration; I don’t compute any statistics across the 1972 splice to avoid potential bias from changing measurement approaches).

2023 stands out like a sore thumb with 14.8 million hectares in the satellite-mapped data (Canadian agency tallies actually run higher at 17–18 Mha as different products count differently),1 roughly 2.5 times the previous record. 2025 came in second at ~7.3 Mha in the satellite data. And 2026 so far is about 2.8 Mha by mid-July, a bit above the typical pace for this date, but far below the last few extreme years.

One caveat around how unprecedented this actually is. A recent tree-ring reconstruction back to 1800 (Danneyrolles et al. 2025) found that while 2023 itself was off the charts in most regions they studied, the decadal burn rate for 2014–2023 still sits within the range of the past two centuries in several zones, especially in the eastern boreal. We are not necessarily seeing more fire than ever everywhere, but rather a rapid rate of increase with the increasing prevalence of fire weather driving it. In addition, parts of the northwestern boreal are now burning at rates that do appear to exceed anything in thousands of years.

One important driver: higher temperatures

Canadian fire seasons (May through September) have warmed about 2.2C on average since 1959. Canada warms at roughly twice the global rate, and its north at roughly three times. That warming matters for fire through a well-understood mechanism: warmer air is exponentially “thirstier” (saturation vapor pressure rises ~7% per degree), so it pulls moisture out of live vegetation and dead fuels alike, priming the landscape to burn. It’s the same fuel-drying pathway that Abatzoglou and Williams (2016) found had roughly doubled cumulative forest area burned in the western US.

Hanes et al. (2019), examining the Canadian record from 1959 to 2015, found fire seasons starting earlier and ending later, more days with conditions suitable for fire spread, and increases in both annual area burned and the frequency of large fires. They found that these trends were consistent with human-caused warming, not with changes in forest management (which have been broadly stable since the 1980s even as the fire seasons have gotten dramatically worse).

Hot and dry years burn

A few years ago my Berkeley Earth colleague Robert Rohde made a lovely figure plotting each California fire season by its temperature and precipitation. Here is my Canadian version: every year from 1959 to 2025 placed in climate space, with dot size proportional to national area burned and the ten largest fire years outlined in black.

The pattern is hard to miss. The biggest fire years pile up in the hot-and-dry corner with 2023 really standing out, and the recent era (red dots, 2011–2025) has shifted visibly toward that corner relative to the black dots of the 1960s. Canada’s fire seasons are migrating into the part of weather-space where the big burns happen.

We can also try and more directly quantify the relationship between area burned and temperature. Nationally, fire-season temperature correlates with log area burned at r = 0.61 (1972–2025), which means that a fire season 1C warmer sees ~80% more area burned. I want to be careful about what this does and doesn’t show, so I stress-tested it: the correlation survives detrending (r = 0.57), first-differencing (r = 0.46), and partialling out precipitation (r = 0.58). It isn’t just two things drifting upward together, and it isn’t a rebranded precipitation effect.2

The relationship is strongest in the western and northern boreal – Yukon, BC, the Northwest Territories, the Prairie provinces, Ontario – and weak to non-significant in Nunavet, Quebec, Newfoundland, and New Brunswick, matching the literature’s finding that eastern boreal fire is generally less temperature-limited.

Lightning in the middle of nowhere

About 71% of Canada’s area burned over 1990–2023 came from lightning-ignited fires, and in the record 2023 season it was ~93%. These are remote boreal megafires, ignited by lightning far from any road, timber lease, or fuel-treatment crew. This is worth considering in the context of the management debate: the fires driving the smoke are not escaped campfires or mismanaged plantations. They are lightning strikes into a drying landscape, often burning in exactly the places no one was managing. And because warming is expected to increase high-latitude lightning, climate change can contribute to both the fuel dryness and the ignition side of the equation.

What attribution science actually says

This week the National Academies released a major report on the attribution of extreme weather events and their impacts, updating their influential 2016 assessment. Its wildfire findings are careful, and they land almost exactly on the distinction I’ve been trying to make in this piece.

On the general question, the report concludes it is very likely that climate change has increased the likelihood and severity of extreme fire weather: the hot, dry, windy conditions that let fires ignite and spread. At the same time it assigns low confidence to attributing any specific individual wildfire, because fires are irreducibly multivariate: ignition, fuels, land management, and suppression all mediate the on-the-ground relationship between fire weather and hectares burned. Notably, the report also flags that no attribution study has yet isolated the role of fuel or ignition changes, precisely because those human factors are so entangled. The science is much stronger on “climate change made conditions like these more likely” than on “climate change, specifically, caused this fire.”

For Canada’s exceptional 2023 fire year the evidence is unusually strong, and the report devotes a whole box to it. Kirchmeier-Young et al. (2024) found the record burned area was 2–5 times more likely thanks to human influence in Canada’s eastern and western ecozones, and the extraordinarily long fire season more than 5 times more likely. World Weather Attribution found the cumulative fire-weather severity at least 7 times more likely and ~50% more intense. Jones et al. (2024) put the fire-weather likelihood increase at ~2.9–3.6x and burned area ~10% higher (95% CI: 3-40%) than without warming. And Barnes et al. (2025) found the James Bay severity rating at least 32% more intense, while noting the season was amplified by an extreme run of atmospheric blocking (~50 blocking days versus an average of 15), a reminder that in any single year the weather still matter enormously.

Where management does matter

The prior sections are about the physical science of what’s driving the fire trend. What we should do about it is a policy question, and here the management crowd is not wrong so much as aiming at the wrong target.

There genuinely is a fire deficit in parts of boreal Canada — but it’s local, and it’s about people, not country or regional fire totals. Parisien, Coogan and colleagues (2020) found that of 160 boreal communities they studied, 54% were surrounded by less recently-burned forest than their fire regime would predict reflecting a suppression legacy that leaves older, more flammable forest ringing towns. This is a place where thinning, fuel breaks, and prescribed burning could genuinely help.

Fuel management is valuable around communities, but is essentially useless across the remote boreal. But in Canada it does not explain and cannot reverse the rise in area burned that’s filling the sky with smoke. Treating the wildland-urban interface and cutting the emissions that are drying the forest should be seen as complementary rather than as competitors.

Why the smokey skies

One last point on the thing thats actually dominating the news a the moment. Even a year like 2026 that appears not to be headed for a top-5 record for area burned in Canada can still have catastrophic air pollution impacts.

Whether a given city chokes on smoke depends on where fires burn, how high their plumes loft, which way the wind blows, and how many people live downwind, not on total hectares burned nationwide. This July, fires across northwestern Ontario, the Prairies, and Quebec have sat upwind of the Great Lakes population corridor under a persistent transporting flow. A modest fire season in the wrong place can choke tens of millions, while a record season in the remote north can have much smaller impacts on populated regions. While overall area burned is the climate-linked trend, who breathes the smoke on a given week in July is mostly driven by the weather.

A few takeaways

So what are the takeaways here?

First, the forest-mismanagement explanation is borrowed from the wrong forest. Its a real problem in the frequent-fire dry forests of the western US, but the Canadian boreal is a rare-crown-fire system, mostly unmanaged and unsuppressed, where only about a fifth of the burned area is even on managed land and where the fire scientists themselves say landscape-scale fuel treatments can’t scale or stop the megafires.

Second, the surge in Canadian burning tracks temperature with striking consistency, and hotter, drier fire seasons burn far more forest (+80% per 1C), the biggest fire years are almost universally hotter and drier, over 90% of the record 2023 burn was remote lightning fire, and the attribution studies tie these extremes to a warmer atmosphere without needing to invoke forest management at all.

And third, where management does matter like for the forests ringing communities it’s a genuine and worthy fix. But here it just protects towns; it doesn’t solve the underlying factors driving area burned or wildfire smoke pollution.

So next time someone tells you Canada’s fires can be solved by raking the forests, you can point out that the thing filling their sky with smoke is a lightning-struck, drought-primed boreal forest doing what a warming climate is making it do more and more often.

In case its helpful, I’ve put the code and data to reproduce this analysis on my GitHub here.

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The multi-model median for the event’s peak (measured as detrended sea surface temperature anomalies in the Niño 3.4 region of the tropical Pacific) currently stands at 3.6C, roughly 0.8C hotter than the prior record of 2.75C set in 2015-16. For context, the gap between the strongest and the fifth strongest El Niño of the past 150 years is only about 0.5C. The models are forecasting something outside the envelope of anything we have ever observed.

A few things stand out in this figure. First, no event in a century and a half of observations has ever pushed meaningfully past 2.75C. The legendary 1877-78 event comes closest, in a statistical dead heat with 2015-16 (2.73C vs 2.75C, well within the uncertainty of 19th-century ship data). Second, the middle 80% of this year’s forecast ensemble sits entirely at or above that all-time record: even the low end of the plume (2.8C) grazes it. Around 91% of ensemble members exceed the 2015-16 record at their peak.

To see what this would look like as the event unfolds, we can compare the forecast trajectory against the five strongest events ever observed, month by month through the development year and into the following spring.

What is remarkable here is not just the level but the trajectory. The 2026 event is developing faster than 1997-98, the previous gold standard for explosive El Niño onsets. And unlike 2015 which started its year already warm from a precursor event, this one launched from genuinely La Niña-ish conditions in January.

Of course, a multi-model median can hide a lot of disagreement, so it is worth looking at where each individual model puts the peak. The figure below shows the distribution of member peaks across all 14 models.

Every single model’s median peak lands at very strong (”super”) El Niño intensity, and all but one (JAMSTEC’s SINTEX-F, at 2.2C) put their median above the 2015-16 record. Model agreement this strong is unusual, though I’d note that agreement is not the same thing as skill as I discuss later on.

Long-time readers may recall that in a warming world, the raw Niño 3.4 anomaly risks conflating El Niño with the broader ocean warming trend. NOAA’s answer is the relative ONI (RONI), which subtracts the tropical-mean SST anomaly to isolate the ENSO signal. In RONI terms the record holder is actually 1982-83 (a peak monthly value of 2.69C), not 2015-16. However, even using RONI the multi-model median in 11 of the 14 models shows a record event.

Putting the two together: models give a ~91% chance of a record peak in Niño 3.4 terms and ~77% in RONI terms this year. Whichever way you slice the index, the forecast says the same thing: this is more likely than not to be the strongest El Niño ever observed.

So how did we get here? The forecast has been building all spring. The figure below shows how each model’s projection evolved from its March run through its July run, against observed monthly conditions.

Nearly every panel shows the same thing: each successive run warmer than the last, across five months and thirteen independent modeling systems.2 This pattern of sustained revision as initialization improves is the classic signature of a real intensifying event rather than model noise. The reason is visible in the black line: observed conditions kept outrunning the forecasts. (Though credit where credit is due: NCAR’s CESM1 was calling ~4C back in March when that looked absurd, and I and others called it out as unrealistic at the time. The ensemble has since converged toward it.)

The combined multi-model picture makes the same point more concisely.

The peak median has climbed from ~2.8C in the March runs to 3.6C in July — though notably the revisions are decelerating (+0.5C, +0.14C, +0.14C over the last three cycles), suggesting the forecast is converging rather than still escalating.

It is also worth looking at what this event looks like spatially. The figure below maps each model’s SST anomaly field at its own forecast peak month.

The classic east-Pacific El Niño tongue is there in every model. I’d flag CMCC as the outlier to discount: its 5.3C peak sits a full 1.3C above the next warmest model. Because we are looking at the multi-model median, even discounting CMCC doesn’t change the overall forecast meaningfully.

Meanwhile, the ocean is not waiting for the models. Daily SSTs in the Niño 3.4 region are already running around 2C above their era-adjusted average – the threshold for a very strong (”super”) El Niño if sustained – and it is only mid-July. The figure below puts this in context, showing the daily Niño 3.4 anomaly for every year in the satellite record, with each year measured against its own era’s climatology so the long-term warming trend doesn’t mess up the comparison.

No prior year in the 45-year record has been anywhere near this warm this early: not 1997 (+1.6C at this date), the previous benchmark for an explosive onset, and not 2015 (+1.3C). And El Niño almost always peaks near the end of the calendar year – typically between November and January, occasionally as early as October – so the physics of ENSO’s seasonal phase-locking says there is likely a good deal of intensification still to come.3

What does all this mean for global temperatures? Because global temperature lags ENSO by around three to five months, most of this event’s warming will land in 2027, which is now shaping up to be a genuinely alarming year and the warmest on record by a sizable margin. But a strengthening El Niño does load the dice for late 2026: our dashboard currently gives this year a non-trivial chance (~28%) of edging out 2024 as the warmest on record, up from ~13% at the start of the month..

I want to end with an important caveat about these numbers: the models have never been verified in this territory. Seasonal forecast systems have real, demonstrated skill at this lead time for ordinary events, but no ensemble has ever forecast (and then verified against) a 3.6C El Niño, because one has never happened. Model agreement is reassuring, but it is not proof. But the uncertainties can cut both ways, and the observed ocean, not just the models, is already in uncharted waters.

As always, for daily updates on the El Niño forecast and global temperatures head over to our Climate Dashboard.

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It’s a fair question, and one that climate scientists have spent a fair amount of effort studying. The early 20th century warming is genuinely interesting: it was fast, it shows up in every surface temperature dataset we have, and no single factor can explain it. But when we actually decompose the drivers, the story that emerges is quite different from the skeptic framing: between a quarter and half of the warming comes from greenhouse gases, with sizable contributions from natural forcings and (likely) internal variability, and is partially offset by human emissions of aerosols.

For this analysis I used the FaIR reduced-complexity climate model driven by historical emissions to attribute 1900–1945 warming to individual forcings, and compare the results against six different observational surface temperature datasets.

A note on the choice of period before we dig in: I am deliberately using 1900–1945 and measuring warming with linear trends to give the early-century warming its strongest case. The period ends right on the mid-1940s warm peak, and if you scan every possible start and end year in the early record, windows ending around 1945 are the ones that maximize the apparent warming.1 This is, in other words, a somewhat cherry-picked assessment of the early 20th century warming.

To start with, let’s put the early 20th century warming in context. The figure below shows global mean surface temperatures since 1850, with linear trends fit to the 1900–1945 period and the modern warming era (1970–2025):

The world warmed at around 0.1C per decade between 1900 and 1945. That was fast for its day, but is only half the 0.2C per decade we have experienced since 1970.

Decomposing the drivers

To attribute the early-century warming to individual forcings (CO2, CH4, other greenhouse gases, aerosols, other anthropogenic forcings, and natural forcings, I ran the FaIR calibrated constrained ensemble in emissions-driven mode using historical emissions through 2025, then re-ran it with each forcing category removed in turn. The difference between the all-forcing run and each removal run gives the warming attributable to that category, with an 841-member ensemble sampling from the parameter space to calculate uncertainties.2

The figure below shows the resulting linear-trend warming over 1900–1945 for each forcing, alongside the total modeled warming and the observed warming across the six datasets:

A few things stand out here. First, human emissions of greenhouse gases were already contributing to a meaningful amount of warming by the 1940s: CO2 contributed around 0.16C (0.12C to 0.23C) of warming over the period, with methane adding another 0.06C. This shouldn’t be too surprising, as atmospheric CO2 had already risen from ~285 ppm in the mid-1800s to ~310 ppm by 1945.

Second, natural forcings gave the early 20th century a substantial helping hand of around 0.12C (0.08C to 0.18C), the second largest positive contribution after CO2. This mostly reflects the recovery from a cluster of volcanic eruptions in the early 1900s (Santa María in 1902, Novarupta/Katmai in 1912), along with a modest increase in solar output. And note that this is partly an artifact of our cherry-picked window: starting the period in the volcanically-cooled 1900s means some of the “warming” is just the climate returning to where it would have been anyway. The stacked attribution below shows how this played out over time — you can clearly see the two volcanic dips followed by three decades with essentially no major eruptions:

Third, aerosols were contributing to a notable amount of cooling. Growing sulfur emissions from coal burning offset around 0.14C (0.05C to 0.29C) warming over the period, masking roughly 60% of the contribution of CO2 and other greenhouse gases. This is the same tug-of-war between GHGs and aerosols that has shaped the entire historical temperature record; even in the early 20th century the greenhouse side was winning, just not by much.

Adding everything up, the total forced response in FaIR is around 0.25C (0.11C to 0.42C) of warming from 1900 to 1945, of which about half is attributable to human forcings and half to natural forcings. That is meaningfully less than the ~0.44C warming we see in the observations. The difference between the estimated externally forced warming and observed warming of ~0.19C is consistent with what more sophisticated detection and attribution studies have found: the early 20th century warming was likely given a substantial boost from internal variability, particularly warmth in the North Atlantic (the Atlantic Multidecadal Oscillation) and an unusual run of El Niño-ish conditions in the late 1930s and early 1940s. Forty-five years is a short enough period that multidecadal ocean variability can still matter a lot.

How well do we actually know the early-century warming?

There is one more wrinkle in the story that we need to explore: it turns out that we are much less certain about how much the world warmed from 1900 to 1945 than we are about modern warming. The figure below compares six surface temperature datasets with full early-century coverage:

The spread is remarkable: DCENT shows around 0.26C of warming over the period while HadCRUT5 shows 0.54C, more than a factor of two. For comparison, the same datasets agree on the 1970–2025 trend to within about 10%.

The culprit is mostly the ocean. Early 20th century sea surface temperatures were measured by ships hauling up buckets of seawater (canvas, wooden, or insulated, each with different cooling biases), which in turn transitioned to engine room intake measurements at different rates in different fleets. There was a particularly messy discontinuity around World War II where both the composition of fleets rapidly changed and the number of available observations dramatically declined. That wartime data problem matters a lot since the period we are examining ends in 1945; it is quite possible that part of the mid-1940s warm peak may be an artifact of the rapid wartime shift in measurement methods. Different groups make different (defensible) choices about how to correct these biases, and those choices matter far more in 1920 than they do in 2020. The new DCENT dataset, which applies a different approach to homogenizing early ship data, notably shows the least early-century warming. If it is closer to the truth, the gap between the forced response and observations largely disappears, leaving less need to invoke internal variability at all.

So any answer to the question of “what caused the early 20th century warming?” comes with real error bars, on both the model side and the observational side.

So what are the takeaways?

The early 20th century warming is not an unsolved mystery that undermines climate attribution. It was caused by rising greenhouse gases (contributing ~0.23C of forced warming, before aerosol offsets), recovery from early-century volcanic eruptions, a slightly brighter sun, and at least some multi-decadal internal variability (though exactly how much remains unclear given uncertainties in the observations). The modern era is fundamentally different: since 1970, natural forcings have been roughly flat, internal variability averages out over the longer period, and greenhouse gases explain essentially all of the observed warming.

The same aerosol masking that counterbalanced warming from greenhouse gases early in the century is now running in reverse. As air quality regulations cut sulfur emissions, warming from greenhouse gases is being unmasked. The early 20th century is a useful reminder that the temperature record is a story of many different counterbalancing factors rather than CO2 alone.

So next time someone shows you the run-up to the mid-1940s and argues that today’s warming is just more of the same, you can show them this article. Even taking the most generous possible period for the early warming, human emissions an important contributor, and the world warmed at 0.1C per decade. Today they are essentially the whole story, and we are warming at least twice as fast.

One point we wanted to make, but that was downplayed after the elves at Carbon Brief edited the piece, is that this event should make us rethink the victory lap people took after RCP8.5 was judged to be implausible.

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The focus on arguments about emissions scenarios that only affect the climate late in this century takes the spotlight off the fact that the impacts of climate change are already here and they’re significant, as this French heatwave shows.

The reduction in future emissions is good news, but it may be cancelled by the fact that models seem to be underestimating the extreme heat that Europe is experiencing.

Here is a reprint of our Carbon Brief piece:

In June 2026, a record-breaking heatwave swept across Europe, with France among the first and hardest hit countries.

In a new analysis, we estimate that the extreme conditions caused more than 2,700 heat-related deaths in France.

We also show how France’s extreme temperatures in June exceeded projections from climate models.

Our findings illustrate the human toll of extreme weather as the world warms.

We also highlight the challenges in projecting the magnitude of future heatwaves and their impacts on people.

Outpacing projections

For most of this century, Europe has seen summer heat extremes that outpace projections from climate models.

Several different factors likely explain this trend, including reductions in planet-cooling aerosols as nations have cleaned up their air pollution, as well as changes in atmospheric circulation patterns, which models struggle to represent.

In June 2026, daily high temperatures averaged across France reached 36.9°C, shattering the previous June record set in 2022 by 2.4°C1.

The rise in observed temperatures in France has outpaced projections made by climate models, with June maximum temperatures more in line with what was expected for the 2070s.

This is illustrated in the figure below, which shows how France’s average maximum daily high temperature for June recorded in 2026 (black line) compares to climate model projections (blue and orange lines).

Counting the death toll of climate change

The downstream impacts of these extreme temperatures are lethal.

Scientists are able to estimate the death toll of high temperatures in many locations, depending on the availability of mortality and climate data.

There are several ways to do this.

One option is to examine death certificates to see which deaths have been directly recorded by physicians as related to heat. However, there is strong evidence that this method significantly undercounts heat-related deaths, as most death certificates do not consider environmental factors such as heat when diagnosing the cause of death.

Alternatively, it is possible to calculate the rate of total (“all-cause”) mortality in a given time period relative to previous time periods – for example, by comparing the total number of deaths in June 2026 compared to the average of previous Junes. This “excess deaths” figure can be used as an estimate of the deaths from a heat wave.

Using this approach, Public Health France attributed around 2,000 deaths in France to the extreme heat in the week of 22-28 June.

Finally, scientists can use long-term data on overall mortality and correlate changes in mortality with changes in temperature to understand the statistical relationship between the two.

Research published in Proceedings of the National Academy of Sciences in 2025 that used this third approach found that mortality rates in France increase rapidly in cold or hot conditions as daily maximum temperatures depart further from approximately 20C.

This pattern of a U-shaped response of mortality to temperature — shown in the figure below — is very consistent across time periods and regions around the world.

To calculate the death toll of the June 2026 heatwave in France, we compared observed temperatures over 12-29 June to their baseline average over 1980-2025.

The difference between these two temperatures helps us understand how many more people died than they would have in the absence of such extreme conditions.

Over 12-29 June, we found that France has experienced around 2,700 heat-related deaths above the average baseline. Day-to-day heat-related mortality rates rose from less than 100 to almost 300 on the hottest days of 24 and 25 June.

This is shown in the graph below, which illustrates the cumulative total heat-related deaths seen in France over the two-and-a-half week period. The inset shows how heat-related deaths fluctuated on a day-to-day basis during this time.

Recent analysis by World Weather Attribution has already shown that human-caused climate change increased the frequency and intensity of the June heat wave across Europe.

Meanwhile, previous research has shown there is substantial evidence that heat-related mortality in Europe has already been elevated by greenhouse gas emissions.

As a result, we can be confident that at least some of the more than 2,700 deaths already seen in France are directly due to the burning of fossil fuels.

Calculating climate risk

In April, the UN-led body responsible for coordinating the work of climate modelling centres — the Coupled Modelling Intercomparison Project (CMIP) — unveiled a set of seven new emissions scenarios.

These are designed to replace the previous scenarios that have been used by scientists to understand how the climate might change in the future. They will feed into the upcoming seven assessment report (AR7) of the Intergovernmental Panel on Climate Change (IPCC).

The range of future emissions in the new CMIP scenarios is smaller, with scenarios of very high or very low emissions no longer on the table.

The retirement of the very-high emissions scenario — known as “RCP8.5” — led to certain commentators in the media and in politics, including US president Donald Trump, arguing that the risks of climate change had been “overstated”2.

Our analysis of June’s heat-related deaths in France suggests that, even if the most severe emissions pathways are no longer needed, climate impacts are taking a heavy toll on society.

Moreover, the temperatures seen in France show that climate models continue to underpredict the magnitude of heatwaves for a particular level of global warming.

This is because greenhouse gas emissions are only a first step in estimating the impacts of climate change.

The second step is converting emissions to changes in the climate at both the global and local levels — or hazards. This includes heatwaves, flash floods and droughts.

The third step is to determine how changes in the hazards will affect local populations. This can be determined by calculating people’s exposure and vulnerability to hazards.

Substantial uncertainty persists at every stage of this sequence.

For example, scientists do not know exactly how the global climate will react to ever-rising greenhouse gas emissions — nor the extent to which global temperature increases will drive local climate hazards. We also do not know how climate change at a local level impacts human health outcomes.

Managing the future of heat risk

Almost all heat-related deaths are preventable.

Adaptation options, such as air conditioning, heat action plans and social support for isolated people, will be crucial as the climate moves away from the typical conditions that people are used to.

Our previous research showed that France made a lot of progress reducing heat-related mortality after the deadly 2003 summer heatwave by taking many of these actions.

Adaptation can reduce deaths, but it cannot eliminate the risk created by continued warming.

Without a move away from fossil fuels, future heatwaves will keep testing the limits of public health systems and more people will die.

other stuff

My old friend3 Steve Wilson has a new substack. It’s not about climate, it’s about U.S. drug policy. He’s writing a book and is posting chapters and they’re pretty good. Check it out.

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.

Heatwaves can bring on more than just a sweat; they can trigger health crises, damage infrastructure, affect our economy, and even reshape the natural world around us. But what actually causes these periods of unusually hot weather?

In this post, I’ll explain the meteorology that is responsible for these scorching conditions.

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Due to the temperature gradient between the pole and equator, both hemispheres feature jets of wind flowing from west to east in the mid-latitudes (aka the jet stream). These jets are unstable, so ‘ripples’ tend to form on it. Anyone who looks at weather maps knows what they look like. This NASA animation shows the jet as red and yellow colors and you can see how it waves back and forth.

High-pressure systems are key to heat waves, and they form in “ridges” in the jet stream:

The “H” indicates the location of a high-pressures system while the “L”s indicate low-pressure systems.

To understand why heat waves are associated with high pressure, first realize that surface air flows away from high pressure and towards low pressure. If the air at the surface is flowing away from the high pressure, then by conservation of mass, there must be descent in the column.

Sinking air brings down air with very low water vapor from the cold upper atmosphere down into the mid and lower atmosphere. This very dry air prevents clouds from forming.

In addition, descending air is heated as it is compressed, so the column of air is also quite warm, leading to extremely stable conditions. Stability in this context means vertical motion is suppressed, i.e., thunderstorms or other rain events are strongly disfavored.

But, you might ask, “Why is it so humid outside?” To understand this unpleasant aspect of heat waves, realize that we live in a very thin layer of air near the ground known as the “planetary boundary layer”:

The boundary layer is humid because the surface is a source of water vapor: evaporation from surface water, like lakes or the ocean, transpiration from plants, etc. In high-pressure systems, the strong temperature inversion at the top of the boundary layer traps humidity in this thin layer where we live and it gets very humid there.

The net result of all of this atmospheric physics is an atmosphere with high near-surface humidity that is also devoid of clouds and rainfall. While this makes beautiful blue skies, it also allows a lot of sunlight to reach the Earth's surface. As the ground absorbs heat, temperatures rise.

Normally, these conditions stick around for a day or two and then the high-pressure system moves to the east (you can see this in the video above). However, meteorological conditions can sometimes lead to a high-pressure system parking over a region, a process known as blocking.

In such a situation, a region experiences a long period of clear skies and no rain. This dries out the ground and, once the soil moisture is gone, all of the remaining solar energy goes into warming the surface and the temperatures soar. Along with the humidity, this can produce dangerously hot conditions.

Climate change has an amplifying effect on heatwaves. Just like the adage “a rising tide lifts all boats,” a warming climate raises the baseline temperature from which heatwaves start. This means that our heatwaves become hotter, essentially ‘floating’ on the ‘rising tide’ of our warming world. This increase in the intensity of heatwaves is one of the many impacts we’re already seeing from climate change.

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 am finalizing a textbook on climate risk and am posting chapters as I finish them. I’d previously posted chapters about embedded energy, physical climate risk, and transition risk. This post is a chapter on attribution of extreme weather to climate change. It is an emerging battleground in the public debate over climate change, so this chapter is an opportunity to get up to speed on the science.

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Introduction

In the aftermath of any major weather disaster, the question inevitably arises: “Did climate change cause this disaster?” However, this is a bad way to think about the problem. Climate change does not cause a rainstorm, a heatwave, or a hurricane in a direct sense. These hazards would still occur in a world without human influence on the climate: hurricanes would still form over warm ocean waters, heatwaves would remain a product of stationary high-pressure systems, and low-pressure systems will always generate rain and the occasional flood.

But climate change can make these events more intense. It takes a heatwave and pushes the temperature higher. It adds moisture to a rain event, generating more intense rainfall, enhancing flooding. Warmer oceans make a hurricane’s winds stronger and its rainfall more severe.

Therefore, the scientifically interesting question is not “Did climate change cause this?”, but about influence:

1. How much more likely did climate change make this event?

2. How much more intense did climate change make this event?

Answering these questions is the work of a rapidly advancing field of science called Extreme Event Attribution. This chapter will explore the methods scientists use to disentangle the roles of natural variability and climate change.

To understand the two different ways of looking at the problem, let’s examine a hot 37°C (99°F) day in a hypothetical city (like Houston, Texas) and examine each question in turn.

1. How much more likely has climate change made a 38°C day?

This figure sets up how scientists look at the problem. It shows two hypothetical distributions of daily temperature for the city. The red curve is the present-day distribution: the climate as it actually is today. The distributions tell you how often each temperature occurs in that world. For example, today’s temperature distribution peaks around 35°C with a value of 17%, meaning that 17% of days have temperatures around 35°C.

The blue curve is a counterfactual: the distribution that would occur in a world without climate change. For this hypothetical example, climate change has shifted the climate a few degrees toward warmer temperatures.

Given the two distributions, we can now answer the question of how much more likely a 38°C day is. In the counterfactual no-climate-change world, a 38°C day occurred on roughly 1.5% of days; in the present-day climate, it occurs on about 6% of days.

We define the relative risk to be the risk of the event in today’s climate divided by the risk in the counterfactual climate:

where P_a is the probability of the event in the world with climate change (6%) and P_c is the probability in the counterfactual world (1.5%). Thus, the relative risk in this example is 6%/1.5% = 4. We can therefore say that, “Climate change made this 38°C day four times as likely” or that, “The relative risk of a 38°C day is four.”

Because of the shape of the distributions, the relative risk increases with temperature. Using the same curves, a 42°C day occurs on 0.013% of days in the counterfactual world and 0.19% in the world with climate change, implying a relative risk of 15. This confirms that the hotter the day, the more the relative risk of that day has increased. In fact, we are approaching a world (if we’re not already there) with so much climate change that events occur that could not have occurred in the counterfactual world (P_c ≈ 0). For those events, the risk ratio is infinity.

Temperatures near average have a risk ratio around 1, meaning their probability isn’t changing much due to climate change. Below average temperatures (e.g., 27°C) have risk ratios less than one, indicating that they are occurring less often than in the counterfactual world.

2. How much did climate change warm this day?

This is a different way to ask the same question: how much hotter was the day because of climate change? To answer this, we look at the graph horizontally. We find the probability of a 38°C day on the present-day curve and trace it back to the same probability on the counterfactual curve.

In this example, we find that the same probability for today’s 38°C corresponds to a temperature of 36°C in the counterfactual climate. In this case, we can say, “Climate change made this hot day 2°C warmer.”

While some economic actors (e.g., governments, insurers) may focus on probability, most impact evaluations are clearer when examined through magnitude changes. In other words, it may be of more practical use to know that climate change warmed this day by 2°C or increased total rainfall by 30% than knowing that climate change increased the probability by a factor of four.

Thus, we can express climate change’s contribution to this hypothetical 38°C day in two ways. We can say that 1) climate change made the event four times more likely, or that 2) climate change added 2°C to the temperature.

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Determining the counterfactual world

As discussed in the last section, estimating the impact of climate change on a meteorological event requires assessing the distribution of those events in the present-day world and in a counterfactual world without climate change.

To establish the present-day distribution of climate events (red curves in the plots above), researchers rely primarily on a combination of real-world observational records and numerical climate-model simulations of the present-day climate. To estimate the counterfactual climate (blue curves), the world that would have existed had human activities not altered the climate system, scientists will often run these same climate models but with human influences removed (e.g., carbon dioxide and other climate forcers set at pre-industrial values).

Researchers can also use historical observations to estimate the counterfactual climate. In some cases, researchers can use observations from a time period when human influence on the climate was small, such as the late 19th or early 20th century. As an example, this is a plot of Houston’s daily maximum temperature in the 1970s and 2016-2025:

There is a clear shift in the climate between these two periods, much — but not necessarily all of it — due to climate change. This shift made a day with a maximum temperature of 37°C (99°F) about 6.5 times more likely (i.e., risk ratio = 6.5); alternatively, it increased the temperature of this day by 2.3°C (4.1°F).

Notably, in the 1970s, daily maximum temperatures above 40°C (104°F) never occurred in Houston, but they did occur in the 2016-2025 period. Such days would therefore be considered virtually impossible without climate change.

One thing to look out for: After every extreme event, fossil-fuel interests will take to social media to point out that a more severe event — e.g., more rain, higher winds, and higher temperatures — occurred in, e.g., 18XX (some year in the late 19^th century). They use this to incorrectly imply that climate change therefore could not have influenced the event.

But that’s a logical error. If you look at the probability distributions from Houston, you’ll see that you could have had a 39°C day in the counterfactual 1970s world (it’s just very unlikely). But the existence of a 39°C day in the 1970s doesn’t contradict the conclusion that the 37°C day you just lived through was enhanced by climate change.

Traditional detection and attribution

There is another way scientists connect climate change to extreme events known as detection and attribution. This approach focuses on trends in some quantity — for example, the trend in the annual maximum 1-day precipitation amount, with units of mm/decade. This is different from extreme event attribution, which focuses on individual extreme weather events.

As a simple example, suppose we have an observational time series of the aforementioned maximum daily rainfall at a station in the U.S. This gives us one number each year: the maximum daily rainfall in 1950, the maximum daily rainfall in 1951, and so on:

We want to know whether the variations in in this observed (synthetic) time series can be attributed to greenhouse gases from human activities, aerosols (basically air pollution and dust, this is partially due to human activities), or some combination of them. These different drivers have different trends over time: greenhouse gases have been steadily increasing, while aerosols in the U.S. increased until around 1970 and have declined since then.

The first step in detection and attribution is to run climate models to estimate the fingerprint of each process. To do this, scientists run climate models with just the increase in greenhouse gases, and then run the models again with just the changes in air pollution. These runs tell us what the observations would look like if just this forcing was present in the atmosphere.

We then compare the observed time series to the model fingerprints. A simple way to do this is with a regression model:

Here, obs(t) is the observed time series. G(t) is the greenhouse gas fingerprint timeseries, and A(t) is the aerosol fingerprint timeseries.

We then use regression techniques to estimate the coefficients b_GHG and b_AER, which tell us how much of each fingerprint is present in the observations. e is the residual, the part of the observations not explained by the fingerprints.

Here are the results:

In the left panel, the blue line is the synthetic observations and the orange line is the fit:

where G(t) and A(t) are the fingerprints above. As you can see, we can closely fit the observations with the fingerprints for greenhouse gases and aerosols.

Next, we examine the estimated coefficients (b_GHG and b_AER) and their uncertainty ranges (right panel). If a coefficient is statistically different from zero, we say that fingerprint has been detected. That’s the case here — the uncertainty bars do not cross zero — so the fingerprints for greenhouse gases and aerosols are detected.

This is the basic logic of detection and attribution.

Importantly, detection and attribution does not allow us to link specific events (e.g., Hurricane Harvey) to climate change. Rather, it allows us to connect the trend in some type of extreme weather (hurricanes) to human activities.

If you want to play around with a simple demonstration that shows how this works, click here.

This is an extremely powerful technique that has laid the foundation for much of our understanding of how climate change is affecting extreme weather. Here are some key results:

• Hot extremes and heatwaves: Humans are clearly making hot extremes more frequent and more intense through human-caused greenhouse gas emissions.

• Heavy precipitation: Human influence, especially greenhouse gas emissions, is likely the main driver of the observed global-scale intensification of heavy precipitation over land.

• Flash flooding due to extreme rainfall: Scientists have high confidence that stronger extreme precipitation leads to more frequent and larger surface-water and flash floods.

• Drought: Scientists have medium confidence that human-caused climate change has increased drought1 in some regions.

• Tropical cyclones: Scientists have medium confidence that human influence has contributed to extreme tropical cyclone rainfall. In addition, the global proportion of the strongest tropical cyclones has likely increased over the past four decades, a change that cannot be explained with natural factors alone.

• Fire weather: Scientists have concluded with medium confidence that compound hot, dry, and windy conditions have become more likely in some regions.

Confidence in attribution studies

In reading the previous section, you saw that scientists have varying levels of confidence in the attribution statements. Some conclusions are termed likely or very likely, and confidence may be medium or high. How is this confidence determined?

The foundation of any attribution study is a solid physical understanding of why climate change would affect a particular type of event. If we can’t provide a mechanism, you really can’t believe an attribution study.

For many extreme events, the physics is well-understood and there is a clear connection between the climate and the severity of the events:

• Heatwaves: Greenhouse gases trap heat, shifting the distribution of temperatures toward warmer values, thereby making extreme heat more probable.

• Rainfall: The connection is the physical fact that a warmer atmosphere can hold more water vapor. This means that when a storm system develops, there is more available moisture to convert into rain, leading to more intense downpours.

• Wildfires: Warmer temperatures produce a drier atmosphere, dry out vegetation and make it more flammable. Thus, wildfires are expected to be bigger and more intense in a warmer climate.

For other phenomena, the physics is murkier. For example, there is no robust theory explaining how the total number of tropical cyclones will change as the climate warms (tropical cyclone is the general term for storms known as hurricanes in the Atlantic, typhoons in the Pacific, and cyclones in the Indian Ocean). Right now, there are around 80 tropical cyclones per year around the planet, and we can’t explain why that’s the number. Why isn’t it 8? Or 800? We don’t know. Without such an understanding, we can’t really trust climate models’ counterfactual world for this.

But this doesn’t mean that we know nothing about tropical cyclones. There are strong physical arguments that tropical cyclones will get more intense (higher wind speeds) and rainier as the climate warms, which will cause more damage. As an example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by ~20%. The increase in wind speed means that we expect the fraction of tropical cyclones reaching the major storm category (cat 3-5) will increase as the climate warms, in general agreement with our observational record.

Once you have a physical mechanism, the other factors that determine confidence are things like:

• How good is the observational record? For some things, like temperature, we have good observations going back to the 19^th century. For others, like tropical cyclones, good observations are only available during the satellite era, i.e., since the 1970s. With a shorter dataset, your confidence in any attribution study will be lower.

• How good are climate models at simulating the weather phenomenon? Most attribution studies require climate models to generate the various counterfactual worlds (the world without climate change). Climate models do a really good job with temperature, which is a large-scale atmospheric phenomenon. But they do a much poorer job with precipitation, which is a very small-scale process that models struggle with.

• How many independent studies have confirmed an attribution statement? In science, the gold standard for a rigorous conclusion is that it has been multiply replicated by independent scientific groups. While any individual study might be flawed, the odds of a major error go down significantly if several groups have independently reached the same conclusion.

It is also worth noting that there may be times when detection and attribution studies fail but extreme event attribution studies can still identify a contribution of climate change to an extreme event. For example, high quality time series of some extreme events (e.g., tropical cyclones) may go back only a few decades, which may be too short for confident detection and attribution. Extreme event attribution approaches, which do not rely on long time series of observations, are nevertheless able to conclude that climate change is making specific hurricanes stronger.

Conclusion

The science of extreme event attribution has transformed our ability to discuss the impacts of climate change. We can now make statements about how climate change affected the likelihood and intensity of individual events as well as the trends in extreme events. This helps demonstrate that climate change is indeed increasing the ‘hazard’ component of climate risk.

As the science of extreme event attribution has developed, it has increasingly moved into the legal and public policy arenas. Fossil fuel interests are terrified that someone will use extreme event attribution science to sue them for their contribution to a natural disaster, or that policymakers will use extreme event attribution science to justify policies to restrict fossil-fuel use.

Because of this, the legitimacy of extreme event attribution is frequently contested by fossil-fuel interests. So don’t be surprised if you hear a lot of misinformation about extreme event attribution in the future. And don’t think that, just because people are criticizing these studies, that criticism is legitimate.

Note that the techniques described in this chapter deal with the effects of climate change on hazards. A new subfield, extreme event impact attribution connects climate change to the actual impacts (e.g., number of houses burned down in a wildfire, dollars of flood damage). This is an emerging area of research, and I expect that this is something we’re going to hear a lot more about in the future.

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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For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%.

One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages.

Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work.

This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt.

The new Merchants of Doubt

We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science.

This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process.

The key quote is:

About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare.

This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”.

This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position:

But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument.

We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.”

The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side.

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The new new Merchants of Doubt

The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process.

Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee:

In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.”

To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report.

When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see.

A note to the press: Fix your frame

To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it.

You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use.

They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC?

That frame is a trap.

Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate?

Ultimately, you need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal.

If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt.

other stuff

The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience.

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.

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.

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.

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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