This is a real problem. In their rush to power data centers are building the least efficient fossil gas generation, often with high equipment gas leakage, many of which are located in gas production regions that are in turn characterized by high leakage rates.

But its also a problem with a reasonable achievable near-term solution: if we are going to build data centers, we should ensure that they are connected to the grid rather than building dirty off grid gas generators. But to make this case, its important to understand just how much worse off-grid gas turbines are from an emissions standpoint, as shown in the figure below:

Here we examine four potential gas combustion technologies that off grid data centers could use. According to Cleanview’s tracker, simple-cycle turbines are planned for around 46% of off-grid data centers, reciprocating engines ~34%, fuel cells ~14%, unattributed generation around 6%, and on-site combined cycle plants effectively 0% today. These values are weighted by planned datacenter size, but only ~30% of planned capacity has identified equipment, so take these numbers as directional rather than precise.

The calculations here are also a bit generous to off-grid gas, as they use a nation-wide average 2.3% of methane production leaked upstream of generation based on Alvarez et al. 2018 (though more on that assumption later!). The grid numbers are 2023 averages from EPA’s eGRID for ERCOT, PJM and the US as a whole. They include the grid’s own upstream gas leaks, coal-mine methane and 3% transmission losses, all of which push the grid number up. Engine slip comes from EPA’s reporting factors for lean-burn engines, about 2.8% of the methane going in.1 Heat rates come from manufacturer data sheets for the equipment showing up in Cleanview’s tracker of behind-the-meter projects.

Reciprocating engines come out at about 740 kg of CO2-equivalent per megawatt-hour (MWh) and simple-cycle turbines at about 705. That is 63 to 80% more than average grid power in ERCOT, PJM or the US. Methane leakage (including engine slop) adds between 70 and 190 kg per MWh to that gap. But even without methane leakage the gap would be large; on CO2 alone, engines and turbines are 31% to 63% above the grid, because they are quite inefficient at turning gas into electricity.

There are more efficient options that are closer to grid emissions, but these are much more rarely installed today given longer build times or lower equipment availability. Fuel cells (mostly Bloom’s, which represent about 14% of identified behind-the-meter capacity in Cleanview’s data) and an on-site combined-cycle plants only emit 7% to 10% more than the US grid average, and are similar to what a utility would build to serve the same load.

That being said, US electricity grids are decarbonizing over time with the rapid deployment of clean energy. Existing gas that is grid connected can reduce capacity factors when low-cost renewable generation is available in a way that off-grid generators cannot (at least unless they also build on-site renewables or grid connect in the future). There is a real risk that off-grid gas generators become locked in over long periods of time while the rest of the electricity generation system decarbonizes.

Emissions also depend on where the data center is

Off-grid projects are clustered in a few specific regions, and both the local grid and the local gas supply differ a lot in terms of generation mix and leakage rates. So I reran my comparison for seven different datacenter hotspots in Cleanview’s tracker. For each region I used the 2025 grid average from EIA’s hourly grid data (counted on a consumption basis so that imported power is included). Each region also gets an upstream leakage rate for both on-grid and off-grid gas generation measured in the gas basin most likely to supply it, which are measured from aircraft and satellite surveys (e.g. MacKay et al. 2026, Varon et al. 2026, and others).2

The figure below shows how each option compares with the local grid average:

Engines and simple-cycle turbines emit more than the local grid in six of the seven regions, by between 36% and 95%. The exception is Cheyenne, Wyoming, where coal still generates nearly 60% of the grid’s power. There, engines and turbines are comparable to the grid average, but they still emit 55% to 64% more than a new gas combined cycle plant on the grid would. Large new loads can also delay coal plant retirements, which would make plugging into that grid worse than its average suggests.

So what is the path forward here?

Engines and turbines off-grid are the most polluting way to power new load in six of the seven hotspots where data centers are being built today. Fuel cells and efficient on-site plants are roughly as bad as building new gas on the grid, but relatively few data centers are planning on building them near-term.

Data center developers would generally prefer to connect to the grid than build their own off-grid systems. Off-grid systems require more redundancy and higher costs; low-efficiency gas generators or turbines run constantly are very expensive (and high emissions) form of generation. But in many cases its the only option they have, as the timelines associated with getting grid connections are too long or there simply is not sufficient generation available on-grid.

Ultimately we need to make our grids more robust – with new generation met by low-carbon energy sources, a modernized transmission grid, and lots of grid-scale storage – if we want to avoid a world where massive amounts of islanded gas generation gets built in the coming decade. In the meantime we should ensure that any off-grid data centers that are built meet existing air pollution regulations, encourage them to supplement gas with on-site solar and storage, and pursue broader efforts to reduce gas leakage (many of which, unfortunately, have been recently rolled back by the Trump administration).

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

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A new paper by Jin and Pu in the journal npj Climate and Atmospheric Science argues that the data through 2025 now show a statistically significant doubling of the warming rate. I broadly agree with them that warming has sped up. But the method they use is problematic, and would find a significant acceleration in many simulated worlds where none exists.

To start with, let’s look at the data. The figure below fits a single continuous trend line to annual global temperatures since 1970, allowing the slope to change at whichever year fits the data best (2012, in this case). The rate of warming between 1970 and 2012 was 0.18C per decade (with an uncertainty range of 0.15C to 0.20C), while warming between 2012 and 2025 was 0.35C per decade (0.26C to 0.45C).1

Whether that change is “statistically significant” depends on how you test it. Beaulieu et al. (2024) used a demanding test that allows the change to happen in any year, and found that a surge in warming was “not detectable yet” with data through 2023. Foster and Rahmstorf (2026) applied the same test to data through 2024 and also found no significant acceleration in the raw data, but argued that acceleration was detectable (with 98% confidence) after removing the estimated effects of El Niño, volcanic eruptions and solar variability. Running their test on the raw data through 2025, I get a p-value of 0.06 for the average of six global temperature datasets (just shy of the conventional 0.05 threshold). Across the individual datasets I’ve used in this analysis it ranges from 0.02 (NOAA) to 0.13 (JRA-3Q), with only NOAA and NASA’s GISTEMP below 0.05. If El Niño, volcanoes and solar are included in the same statistical model, it drops below 0.001.

What Jin and Pu did

Jin and Pu report warming of 0.40C per decade over 2011-2025 compared to 0.20C per decade over 1971-2025, and find the difference statistically significant in all five datasets they examine.2 If you fit a separate trend line to 2011-2025 you do get a number close to theirs (0.41C per decade on the six-dataset average I use here).

The problem lies in how the comparison is constructed. Back in 2017, Rahmstorf, Foster and Cahill described “two important but underappreciated pitfalls waiting to trap those testing for a change in the trend of global temperature time series.” These are “the multiple testing problem, and failure to account for the additional degree of freedom when one uses a model with a jump discontinuity (a broken trend).” At the time that paper was published, these pitfalls were producing claims of a significant slowdown in warming (during the so-called “hiatus” period from 1998-2012). A decade later Jin and Pu’s approach runs into both of these problems, albeit in search of an acceleration rather than a slowdown.

The first is the broken trend. Jin and Pu fit two independent lines, one to 1971-2010 and another to 2011-2025, with nothing forcing the second line to start where the first left off. In the figure below the recent line starts 0.11C below where the earlier trend ended, which steepens it. A single continuous line with a change in slope at 2010 gives 0.32C per decade since then, still well above the 0.18C per decade of earlier decades but not as high as their approach would imply.

The second is how the split year was chosen. Jin and Pu tried every dividing year from 1980 to 2015 and found that “the largest and most statistically significant contrast in warming rates occurs when the dividing year is near 2010–2011. Accordingly, 2011 is adopted as the start of the recent warming period.” As far as the paper describes, the significance test at 2011 makes no adjustment for having searched across all those split years. Beaulieu and colleagues warned about exactly this: if the year of the change is “selected visually among the many possibilities where it can occur without accounting for this, statistical mistakes can ensue.”

Testing the approach on synthetic data

One way to see how much this matters is to apply the same procedure to simulated temperature records where we know there is no acceleration. To do this, I generated 5,000 synthetic records that each warm at a steady 0.20C per decade from 1971 to 2025. Each has year-to-year noise with the same size and persistence as the real temperature record. For each one I tried every split year from 1980 to 2015, kept the one with the biggest contrast between the before and after trends, and tested whether the recent trend was significantly faster using the standard 95% threshold.3

The figure below walks through the process. The first three panels show one of these synthetic worlds that the procedure flagged. Its true trend is a constant 0.20C per decade, but splitting at 2012 gives 0.19C per decade before and 0.36C after, a “doubling” that looks almost exactly like the real record. Across all 5,000 worlds, 22% to 34% are flagged as having a significant acceleration, depending on exactly how the two trends are compared. A test for significance at the 95% level should do this at most 5% of the time.

By contrast, the changepoint test used by Beaulieu et al. and by Foster and Rahmstorf, which builds the search over break years into its significance threshold, only shows a false positive rate of 5%. That extra rigor is why it is so much harder for that test to reach significance, and why its result through 2025 is just shy of the threshold despite a near doubling of the warming rate.

So what are the takeaways here?

A decade ago the same two shortcuts (broken trend lines and a split year chosen after looking at the data) made a temporary slowdown in warming look like a significant “pause.” The statistical tools developed in response to that debate (many of them by the same researchers now finding acceleration) are a big part of why a later review of the pause literature found “little or no statistical evidence” for a slowdown, while describing broken trends as a “common (but questionable) practice.” Applying them consistently means accepting that they will be slow to confirm an acceleration too.

But as I’ve noted, there is a consilience of evidence that leads us to conclude that this time acceleration is likely real, even if it remains just short of statistically significant in surface temperature records. Today the physics and the data point in the same direction, and the raw record is now sitting right at the edge of formal detection with the strictest available test.4 With 2026 on track to be among the warmest years on record, that may not remain the case for long. I’ll return to this question early next year once the full 2026 data are in.

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This year’s El Niño event is already the strongest in the instrumental record based on Niño 3.4 region sea surface temperature anomalies. I’ve written in more detail about this year’s forecast here, and breaking the prior record over at Carbon Brief.

Daily Niño 3.4 anomalies passed the prior record (set in November 2015) on September 20th, and reached 3.4C as of this week. September as a whole had the highest Niño 3.4 temperature anomalies on record. And this year’s El Niño still has a few more months to grow; the latest runs in (from 14 seasonal forecast models) show this year’s El Niño peaking around 4C (with 80% of model runs ending up somewhere between 3.3C and 4.6C).

That being said, while the models underestimated the magnitude of the event earlier in the year, they have overestimated it over the past two months. However, with the first week of October already above 3C, the October forecast of 3.6C doesn’t seem outside the realm of possibility. But there remains a wide range of possible outcomes for this event across different modeling groups and ensemble members.

How far back can we look?

The figure below looks at three different lines of evidence for historical El Niño strength. The top panel shows the thermometer record, where this event has already set a record for events since 1877 and is forecast to smash it. The two lower panels show different lines of evidence from tree rings and corals as to what happened during the millennium before.

Before 1877 there are also written records to draw from. Quinn et al. (1987) compiled El Niño occurrences off Peru back to 1525 from ship logs, mission records and chronicles, and Gergis and Fowler (2008) combined these with tree ring, coral and ice core records into an event chronology back to the same year. Five of the nine events in their most extreme tier are from the 20th century.

But these chronologies sort events into coarse classes and miss a lot (they rate the 1997-98 event as only “weak”, for example), so are only modest evidence for how a 4C event this year would rank. The two records shown in the figure above, from tree rings and coral, reach back to the 1100s and can give us a more solid estimate of how this year’s event will stack up.

Tree rings back to 1100

Cook and Cane (2024) (middle panel of the figure above) used 544 tree-ring chronologies from places whose rainfall responds to El Niño to reconstruct winter (December to February) sea surface temperatures across the equatorial Pacific. However, only 81 of those chronologies reach all the way back to 1100s. If we look at the data since 1500 (when more tree rings are available), it tracks observed winter Niño 3.4 reasonably well, with a typical error of about 0.5C in any one winter. The one starting in 1100 is noisier, with a typical error of about 0.6C.

The largest winter either version reconstructs before 1871 is 2.1C (1746-47 in one, 1791-92 in the other), and the largest before 1500 is 2.0C (1121-22). The biggest instrumental winters (1877-78, 1982-83, 1997-98 and 2015-16) all come in between 2.3C and 2.5C on the same measure. The forecast for the coming winter is 3.9C (with 80% of model members between 2.9C and 4.6C).1

To account for the uncertainty in the tree ring dataset, I simulated the range of true values each winter before 1871 could have had, given how far off the reconstruction was in the years it was tested on.2 If models of this year’s El Niño prove accurate, the chance that this winter beats every winter since 1100 is between 91% and 95% on the standard Niño 3.4 index. On NOAA’s relative index (RONI), which subtracts the warming of the wider tropics, it falls to between 60% and 72%. Over the past 500 years, where the reconstruction is better, the odds rise to between 94% and 98% and between 69% and 85% for ONI and RONI, respectively. Cook and Cane similarly note that “The positive excursions (i.e., El Niño events) in recent decades are generally higher than at any time since 1100.”

What the corals say

The second long record came out in August of this year, when Julia Cole and colleagues published a study in Science using 28 coral records from five islands in the Galápagos. The Galápagos sit in the eastern Pacific, where El Niño events (including this year’s) are centered. Corals lay down annual bands whose chemistry tracks the temperature of the water they grew in, so a fossil coral can be used as a rough thermometer for whatever decades it was alive. The oldest of these corals grew around 1130, and together they sample about half the years of the past millennium.

Corals cannot tell you the peak of a single El Niño as reliably as a thermometer (or a tree ring) can. What they measure well is how much the sea surface temperature swung from year to year over a 20-year window. Through 1850, the year-to-year swings stay inside a relatively narrow band. They begin to rise in the late 19th century, and the corals that grew after 1984 sit mostly above the pre-industrial range. Cole and colleagues find that variability before 1850 was about 37% lower than in recent decades.3 They find the increase comes from stronger El Niño events rather than stronger La Niñas.

They also tested whether natural factors alone could produce a jump this large, using last-millennium simulations from 12 climate models driven only by volcanoes, the sun and changes in Earth’s orbit (as well as control runs with no changes). They find that the increase in variability in the modern era come close to 1-in-100 odds in climate model control runs. Other proxy work finds similar results, including studies of central Pacific corals (Grothe et al., 2020) and an earlier tree-ring reconstruction covering seven centuries (Li et al., 2013).

Putting the two records together

The tree rings and the corals are about as independent as two paleoclimate records can be. One reads El Niño’s effect on rainfall over land through tree growth, and the other reads ocean temperature directly in the eastern Pacific. They agree that the El Niños of recent decades are at or near the top of everything since the 1100s, and the 2026 forecast sits more than a degree above those recent events.

However, climate proxies are necessarily imperfect thermometers, and both types have real limits. Tree rings underestimated the 1877-78 El Niño event and come with uncertainties of more than 0.5C before 1500 (though I account for this uncertainty in my calculation of the odds!). The corals record variability over decades rather than the peak of any one event, they measure local Galápagos temperature rather than the Niño 3.4 index, and the gaps between them leave about half the years unsampled.

Even taking all those limitations into account, I think that this year’s El Niño event is likely to be the strongest in 1,000 years if the latest models of its peak prove accurate. The tree rings alone give it 91% to 95% odds on the standard index (ONI) and 60% to 72% using RONI. The coral records already show that the modern era stands outside the natural range of eastern Pacific variability, and this year will be an unambiguous record in the modern era.

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

That is a remarkable pace of growth from an industry that barely existed two decades ago. But not everyone is impressed; many commenters on social media have responded to these figures by pointing out that despite the growth of renewables, fossil fuel generation continues to grow as well. They aren’t wrong; global coal generation set a record in 2024. Gas generation reached a new high in 2025.

Their point is that clean energy isn’t replacing fossil fuels at all. It’s simply being piled on top of them to feed an ever-growing appetite for electricity, making this an “energy addition” rather than an energy transition. Global fossil electricity generation around 50% higher today than it was in 2005. Coal generation rose by 3,300 TWh and gas by 3,200 TWh. The world burns more coal for power now than it did when wind and solar started taking off.

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This argument has been made in the past both by skeptics of growth and by energy systems modelers. The historian Jean-Baptiste Fressoz argues in More and More and More (2024) that “far from the industrial era passing through a series of transformations, each new phase has in practice remained almost wholly entangled with the previous one.”2 Coal mining created a huge new demand for timber for pit props, while the oil age ran on steel made with coal. Vaclav Smil has made the same point about today’s clean energy boom more bluntly: ”We use more fossil fuels today than at any time in history… Renewables are not replacing fossil fuels; are joining them.”

The question of counterfactuals

The problem with the “addition” argument is that it compares the world today with the world in 2005, rather than with the world as it would have been without clean energy. Electricity demand has not grown because wind turbines and solar panels were built. It has grown because of what the IEA describes as “rising consumption from industry, electric vehicles, air conditioning and data centers”. Demand growth is located overwhelmingly in emerging and developing economies, which accounted for about 80% of global demand growth in 2025 and an average of 95% over the past decade.

Global electricity demand rose by 75% (13,600 TWh) between 2005 and 2025. That demand had to be met by something. Wind and solar supplied 5,400 TWh of new demand, hydro 1,500 TWh, bioenergy and other renewables 530 TWh, nuclear 85 TWh, and fossil fuels the remaining 6,100 TWh. But over the past decade the balance has shifted sharply toward clean energy. Since 2015, wind and solar supplied 57% of demand growth, compared with 29% for fossil fuels. And in 2025, according to Ember’s Global Electricity Review 2026, “wind and solar grew by 841 TWh, meeting 99% of global electricity demand growth”.

A 2024 study by Brantley Liddle found that “intermittent renewables (solar and wind) have unitary displacement effect.” In other words, each unit of wind and solar power displaced about one unit of coal and oil generation. Fossil generation has kept rising in many years because demand has grown faster than clean energy could be added. That is very different from clean energy failing to displace anything.

And electricity demand will continue to rapidly grow, particularly if we succeed in reducing global emissions by electrifying sectors of the economy that currently burn fossil fuels. Most scenarios where we reach net zero emissions involve a doubling or tripling of electricity generation.

So if we want to assess the real world emissions impact of wind and solar buildout over the past 20 years, the right question to ask is not “did fossil use go down?” but rather “how much higher would it have been?”

A world without wind and solar

To answer this, I built a counterfactual for the global power sector in which wind and solar (and geothermal, which is less than 2% of the total) stopped growing in 2005. In every separate country, their generation is held at 2005 levels, while electricity demand, nuclear, hydro and bioenergy follow their real-world paths.3 The missing clean generation has to come from somewhere, and in this counterfactual it comes from fossil fuels.

The key choice is which fossil fuels are used to fill in the gap. A wind farm in China or India displaces coal, while one in Texas or the UK mostly displaces gas. So rather than assuming a single global fossil mix, the analysis fills each country’s gap with that country’s own coal, gas and oil mix in each year, using data for about 200 countries from Ember. This keeps real-world coal-to-gas switching like what occurred during the US shale gas boom in place, rather than crediting it to renewables.4

Without wind and solar growth since 2005, global power-sector CO2 emissions in 2025 would have been 28% higher, at 16.9 billion tonnes rather than 13.2 billion. This adds up to 3.7 billion tonnes avoided in 2025, which is about three-quarters of the total CO2 US emissions that year (or roughly 1.5x EU emissions).

Between 2006 and 2025, wind and solar avoided about 23 billion tonnes of CO2. The biggest assumption I’ve made is what fossil fuel fills in the gap: if every missing kilowatt-hour had come from gas, avoided emissions would be 15 billion tonnes, while if it was all coal it would have been 30 billion.

This lines up almost exactly with Ember’s own estimate. Using a simpler method (and a 2000 baseline rather than 2005 baseline), Ember’s Global Electricity Review 2026 found that “had wind and solar not grown since 2000, fossil generation would have been 30% higher in 2025, and emissions 28% higher.”

While its true that global electricity emissions have yet to clearly peak, it may well be imminent; emissions went down in 2025 and may be on track to decline in 2026 as well, though rapid increases in demand from vehicle electrification and data centers have made this a bit more challenging.5 But there are many countries in which electricity emissions have already peaked and declined substantially, and where clean energy has played a key role in those declines.

Already replacing fossil fuels in some countries

Globally, clean energy has not yet led to fossil fuel use peaking in the electricity sector. In 2025, fossil generation was essentially flat (down ~0.3%). But that global total hides a lot of country-level variability. In many wealthier economies, where demand has grown more slowly, clean energy has driven real emissions reductions (even when accounting for the outsourcing of some industrial production).

Between 2005 and 2025, power-sector CO2 emissions fell by 75% in the UK, 55% in the European Union and 37% in the United States.

The reasons why differ from place to place:

• In the EU, wind and solar account for about 85% of the decline (512 of 601 million tonnes), even though falling nuclear output pushed emissions up by about 100 million tonnes.

• In the UK, the decline had three main drivers: wind and solar (56 million tonnes), lower domestic generation (58 million tonnes, partly because the UK now imports more power), and the near-elimination of coal, which went from 45% of UK fossil generation to effectively zero (36 million tonnes).

• In the US, switching from coal to cheaper natural gas cut emissions by 682 million tonnes, slightly more than wind and solar (606 million tonnes), while a 12% rise in electricity use added 352 million tonnes. Coal’s share of US fossil generation fell from 69% to 29%.

Without wind and solar growth, EU power emissions would have fallen by only about 16% since 2005 rather than 55%, US emissions by about 17% rather than 37%, and UK emissions by about 53% rather than 75%. In these regions clean energy has not just met new demand, its actually pushed fossil generation down.

What about temperatures?

Avoided emissions translate into avoided warming, but the story here has an important twist. Coal plants don’t just emit CO2. They also emit sulfur dioxide, which forms aerosols that reflect sunlight and cool the planet. A world with more coal would also have had more of this aerosol pollution and the cooling it causes, though exactly how much depends on the use of scrubbers, the sulfur content of the coal, and a number of other factors.

To capture the complex dynamics here, I ran the counterfactual emissions (CO2, sulfur dioxide, nitrogen oxides, black and organic carbon, and methane leaked from coal mines and gas supply chains) through the FaIR climate model, using a 841 parameter ensemble constrained to match the climate sensitivity and carbon cycle feedback values assessed in the IPCC AR6 as well as observed warming.

The CO2 avoided by solar and wind growth between 2005 and 2025 has prevented about 0.01C of warming. Adding the methane that would have leaked from the extra coal mining and gas production, the gross effect is larger still. But the extra sulfur that would have been emitted by fossil fuels in that counterfactual world would have masked most of that for now. Taking everything together, the net avoided warming in 2025 is only about 0.002C (−0.008 to +0.009C).

However, this comparison is a bit apples and oranges. Aerosols wash out of the atmosphere within days to weeks, while CO2 persists for centuries (and the warming from CO2 for millennia). Once the extra aerosols clear, the avoided warming from wind and solar deployment to date settles at about 0.01C by 2035. Cooling from sulfur pollution depends on how much is being emitted in a given year, while the warming from CO2 depends on how much has built up over time. So while a new coal plant (in our counterfactual world) would lead to an immediate spike of cooling sulphate aerosols, that effect would be overtaken by warming from CO2 – which will persist long after the coal plant is closed and the cooling from sulfur emissions is gone. Trading a small, temporary cooling from air pollution for permanent warming is a bad bargain, and it comes with catastrophic public health costs of breathing that pollution.

These warming numbers are modest, but they reflect only 20 years of deployment. Much of it is also very recent: 60% of all the emissions wind and solar avoided came in the last five years. The world has emitted roughly 2,600 billion tonnes of CO2 since 1850, so 23 billion tonnes is just under 1% of that total.

More importantly, the 0.01C is only the effect to-date of what has been built so far, assuming the counterfactual world’s extra emissions stopped at the end of 2025. In reality, the wind and solar fleet will keep running and keep growing. At today’s rate, each additional year of operation avoids roughly 3.7 billion tonnes of CO2, and that rate is rising quickly as deployment accelerates.

Addition and displacement at the same time

Clean energy has been an addition to the global power system, but not in the sense the critique implies. It has been added alongside rapidly growing demand that would have existed anyway, and it has displaced fossil generation that would otherwise have been built and burned. Both things are true at once, which is why fossil use can rise while clean energy is still cutting emissions.

Without the wind and solar built since 2005, the world’s power plants would be emitting 28% more CO2 today. Over the long term, the world would be warmer. The warming avoided so far is small (and partly hidden by the side effects of cleaner air), but it grows with every year that clean energy continue producing power. Many countries have already seen their power sector emissions decline, the world as a whole appears to now be on the cusp of absolute declines in electricity sector emissions.

I’ve also put together an animated explainer video that goes through the points of this post to share on other social media channels:

Methods, code and data for this analysis are available over at my GitHub.

I wrote an op-ed about what happened and why it matters. It appeared in the last week in the San Antonio Express-News and Austin American-Statesman. I reproduce it below.

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I’ve been working on the science of climate change for three decades. In the 2000s, when the existence and cause of climate change were already well established by the scientific community but rejected by some politicians, I strongly believed that in a few decades, climate change would be so undeniable that everyone would demand action to stop it.

We’ve reached that point. Climate change is obvious to every Texan who’s paying attention.

In response, Texas is indeed taking action. But not action that would address the problem. Rather, the action is to prevent students at our public universities from learning about climate change.

Officials are prohibiting climate change from being taught in the core curricula of Texas public universities.

To understand why this is so important, let me explain what the core curriculum is. I teach at Texas A&M University. Every student earning a bachelor’s degree takes 42 hours of core curriculum classes. These classes are divided into different categories, including social and behavioral studies and physical sciences.

This means history majors are required to take science classes and STEM majors need to take social science classes. This ensures A&M graduates are well-rounded.

Traditionally, any class could be in the core curriculum as long as it satisfied a set of requirements ensuring it broadens student education.

The core was a free market of education. Students could take classes on topics that interested them while also broadening their education. If students were interested in climate change, they could take a class as part of their degree plans.

Sadly, students no longer have that ability. The free market of education has been brought down by educational central planners who are now telling students that climate change is simply too dangerous for them to learn about.

Texas is one of the most vulnerable states to extreme weather. We experience extreme heat and extreme rainfall. Coastal communities face hurricanes, storm surge and rising seas. East Texas is vulnerable to wildfires. West Texas faces water availability challenges. Climate change amplifies these hazards.

As a result, today’s students will spend their careers making decisions that would be aided by understanding climate change. Engineers will design infrastructure for a climate different from the one we have today.

Farmers will manage crops, water and heat as temperatures and precipitation patterns shift. Communities will decide how to protect homes, roads and electricity systems from changing risks.

Understanding climate change will help Texas graduates manage those risks and identify new business opportunities for solving the problems that climate change creates.

The decision to remove climate change from the core curriculum is therefore impossible to defend on its merits.

But, of course, everyone understands why climate change was removed: Understanding climate change requires confronting the consequences of burning coal, oil and gas.

Reducing students’ exposure to the subject serves fossil fuel interests even as climate change will wreak havoc on our state throughout the coming century.

This is particularly easy to see when you consider the courses that remain approved for the core curriculum. The core curriculum still includes courses titled “Dinosaur World” and “Molds and Mushrooms.”

I’m not criticizing those courses or their inclusion in the core curriculum. They sound fascinating, and I’m sure they effectively advance the goals of the core curriculum.

But there is no way either of those courses better prepares students for living through the changing climate of the 21st century than a course on climate change.

The lack of transparency surrounding the decision is equally troubling: We don’t know who ordered climate change removed from the core curriculum. A faculty committee supposedly was in charge of these decisions, but members of the committee told me that they did not make the decision to remove the course.

Texas A&M should restore climate change to the core curriculum. If university administrators want to remove it, they should do so publicly, justified by a set of consistent standards.

If this decision is not reversed, it will hurt Texas. Climate change is real, and Texans will have to deal with it. Making it harder for students to learn about that reality will leave them and the state less prepared for what we know is coming.

Andrew Dessler is a professor of atmospheric sciences at Texas A&M University. Follow him on Bluesky or Substack.

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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I’ve previously written about how unprecedenteted this event will be. The question I get asked most often now is a more practical one: what does it actually mean for the weather where I live?

I recently published a piece in Nature on the impacts of El Niño and what countries should do to prepare. But I can only fit so much in a short journal article, and there is vast scientific literature on regional El Niño impacts that has accumulated over the past few decades.

This post walks presents a map of higher-confidence El Niño impacts, with an in-depth discussion of each different region. Some of the canonical El Niño impacts show up in nearly every strong event on record, while others turn out to be closer to a coin flip when looking at the record of past events.

If you want to dig into a particular region, I’ve put togehter an interactive version of this map over at the Climate Dashboard. Clicking on a region brings up how it fared in each of the eight past strong El Niños, what each of this year’s forecast models shows, and a short summary of the relevant research. You can also replay any past strong event across the whole map, step through the forecast season by season, or click anywhere to see the raw model forecast for that spot. In addition, I’ve put together an animated explainer video below:

Likelihoods but not guarantees

El Niño events shift the odds of different climate outcomes, but not every El Niño has the exact same impacts (Mason and Goddard 2001). As NOAA’s Climate Prediction Center puts it in their current ENSO discussion: “With an event of this magnitude, the chances of experiencing impacts consistent with El Niño are larger, but they are not guaranteed.”

To get at how much the odds shift, I’ve leaned on three separate lines of evidence for each region. The first is the published literature on El Niño teleconnections (the physical pathways by which a warm tropical Pacific changes rainfall and temperature elsewhere). I’ve assessed these with IPCC-style confidence levels like high, medium-high, and medium confidence. You can find links to the individual studies I’ve relied on below, with more details shown over at the Climate Dashboard when clicking on a particular region.

The second is the current generation of dynamical seasonal forecasts: 13 independent modeling systems (six from NOAA’s North American Multi-Model Ensemble and seven from the European Copernicus Climate Change Service), which let us see where the models agree and disagree with the teleconnection literature during this particular event.

The third is the past performance during strong El Niño events. For every region on the map I went back to the eight strong events since 1957 (1957-58, 1965-66, 1972-73, 1982-83, 1997-98, 2009-10, 2015-16 and 2023-24) and checked how often that region was drier or wetter than a typical ENSO-neutral year.2 The figure below shows the results. Each cell is one past event, colored by how dry or wet it was relative to neutral years, with a check mark wherever it landed on the expected side.3

A few things jump out here. Across the 26 regions on the map, 86% of region-events went the expected way (176 of 205). The two biggest east Pacific events, 1982-83 and 1997-98, gave the expected impacts in every single region. The two earliest events delivered in only 68% of regions, which likely reflects both sparser rain gauge networks in the 1950s and 60s and genuine differences between events.

That being said, the teleconnection literature draws in part from these same events, particularly 1982-83 and 1997-98, so these hit rates partly restate the evidence the literature was built on. It’s also worth noting that no past event looks like the one forecast for this winter. All eight of these are “strong” events, and 2026-27 is forecast to be well beyond any of them. In the regions with the most reliable signals that probably means larger impacts. But it also means we are extrapolating well outside the training data here.

Asia, Australia and the Pacific

The western tropical Pacific is where El Niño’s fingerprint is clearest, because it is where the atmosphere’s rising branch of the Walker circulation normally sits. When that rising air shifts east toward the central Pacific, the islands and archipelagos that normally sit under it dry out.

Indonesia and the Maritime Continent are the single most reliable El Niño impact on the planet. September through December was drier than the median neutral year in all eight past strong events, and in six of them it was drier than any neutral year in the record. Past strong events resulted in around 73% of normal rainfall, and all 13 models agree on drought this autumn. The main risk for the region is fire. Burning on drained peatlands responds very nonlinearly to drought, and the fires during the 1997 El Niño released an estimated 3 to 9 billion tonnes of CO2, equivalent to 13% to 40% of global fossil fuel emissions that year. The haze from the 2015 fires has been linked to around 100,300 excess deaths across Indonesia, Malaysia and Singapore.

The Philippines has an equally consistent record, dry from December through April in all eight strong events (with a median of 58% of normal rainfall). This is driven by an anomalous high pressure system that sets up over the western North Pacific during El Niño winters (Lyon and Camargo 2009). Every one of the 13 models shows it this year.

The western Pacific islands, from Palau and Guam through Micronesia, the Solomon Islands, Vanuatu and Fiji, were drier than normal (i.e. than in a typical neutral year) in all eight events. These islands also see sea level drop as the warm water sloshes east, which can expose and kill shallow reef flats (Widlansky et al. 2015).

On the other hand, the central Pacific islands of Kiribati and Tuvalu get deluged. Satellite-era data show September to May rainfall there wetter than in any neutral year in each of the five strong events since 1982, with a median of around double normal.

Southern China tends to be wetter from winter through spring, fed by moisture flowing around the same western Pacific high pressure system. The observed record is weaker than for the Philippines, with 5 of 8 strong events wetter than normal, though the big events were very wet (1982-83 brought around 200% of normal rainfall and 2015-16 164%). The models are in strong agreement (12 of 13) on heavier than normal rainfall this year.

The Yangtze basin is generally expected to experience heavy summer rain in the year after a strong El Niño, when the Indian Ocean warms up and acts as a “capacitor” that sustains the western Pacific high pressure system into summer. That signal was real in 1998, the year of catastrophic Yangtze floods. In June and July, when the East Asian monsoon rain band normally sits over the basin, it was wetter than normal during 6 of 8 past strong events, including all six since 1972. However, next summer is beyond the range of any current seasonal forecast, so no model-based odds are available.

The southern Mekong basin (Cambodia, southern Laos and southern Vietnam) was drier than normal in March to May of the year after the El Niño peak in 7 of 8 strong events, with a median of around 71% of normal rainfall. The teleconnections literature suggests that the ENSO drought signal in mainland Southeast Asia is “most consistent and expressed over the largest areal extents during March-May of the year when the ENSO events decay.” The models show a bit less agreement this year (only 4 of 6 models that extend through May show drying) and the literature notes the relationship varies between events, so it shows up on the map as only medium confidence.

In India, the the textbook example of El Niño is a weaker than normal Indian monsoon. North and central India were drier than normal in all eight years during which strong El Niño events developed, and the 2026 monsoon was running around 12% below normal by mid-August. But that season is already behind us; the monsoon in the year after a strong El Niño was drier than normal in only 1 of 8 cases (1966), with the other seven running between 102% and 115% of normal. Part of this is that strong El Niños often flip into La Niña by the following summer, and part of it is that eastern Pacific events like this one appear less effective at suppressing Indian rainfall in the first place (Kumar et al. 2006). Either way, the data do not support calling for a weak 2027 monsoon.4

Sri Lanka’s northeast monsoon (October to December) was wetter than normal in all eight strong events, with a median of 126% of normal (Zubair and Ropelewski 2006). However, Sri Lanka was also on the wet side in 8 of 12 La Niña years, so the island’s autumn rain is less tied to El Niño specifically than the count might suggests. Southern India shows a similar observed record (7 of 8), but it is not on the map because this year’s models disagree about it (only 5 of 13 show a wet signal).

Southeast Australia (Victoria and Tasmania) was drier than normal between September and November in 7 of 8 strong events, with a median of 81% of normal and a severe 44% in 2015. The El Niño dry signal is concentrated in spring, when it is reinforced by the positive Indian Ocean Dipole that often accompanies El Niño (Cai et al. 2009), and it has a habit of reversing in summer. In 2023-24, national rainfall ran 20% below average in spring before summer came in 19% above average. And for eastern Australia more broadly, this year’s models show little signal at all (only 10 of 13 lean dry, which is not significant). It’s worth noting that the 2019-20 Black Summer fires, often associated with El Niño in the public mind, happened during a record positive Indian Ocean Dipole with only borderline El Niño conditions.

Africa

El Niño’s effects on Africa run through the Indian Ocean. This year a positive Indian Ocean Dipole (warm water off East Africa, cool water off Sumatra) has been developing alongside it. That combination points in opposite directions for the two halves of the continent.

Southern Africa probably the most potentially disasterious impact on the map, because summer rain there feeds the maize crop that tens of millions of people depend on. December to February was drier than normal in 6 of 8 strong events, consistent with Pomposi et al. (2018), who estimate an around 80% chance of below-normal rain in a strong El Niño. The two clear misses were early in the record (1957-58 and 1965-66). The largest recent failure was 1997-98, when the expected regional drought failed to materialize. That has been attributed to regional circulation features (the Angola Low and Botswana High) that vary independently of ENSO, and it isn’t something we can rule out this year. But the most comparable recent case suggests we will see a bigger impact this year: in 2023-24, with an Indian Ocean Dipole almost as strong as 1997’s, southern Africa had the driest season of all eight events (69% of normal), prompting a $5.5 billion regional humanitarian appeal for more than 61 million people. All 13 models show drought this coming summer.

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The Horn of Africa (southern Ethiopia, Kenya and Somalia) was wetter than normal between October and December in all eight strong events, with 1997 at 241% of normal and 2023 at 175%. Here the Indian Ocean Dipole matters more than El Niño itself. In model studies the influence of the dipole on the short rains is “overwhelming as compared to that of ENSO,” and World Weather Attribution’s analysis of the catastrophic 2023 floods found “no significant role of ENSO but a significant influence of the IOD.” Because strong eastern Pacific El Niños tend to come with positive dipole events, the hit rate here is really a record of the two acting together. That is also what it looks like will occur this year, as all 13 models show a wet season.

The Sahel is not on the map. Its El Niño signal mostly falls on the summer rainy season of the developing year (which for this event was 2026), and even then it was drier than normal in only 3 of 8 strong events. The literature agrees this is one of the weakest teleconnections, with El Niño explaining only a small share of Sahel rainfall variability (Pomposi et al. 2020).

South America

The Amazon was drier than normal between October and May in 7 of 8 strong events, and in each of the last five (from 1982-83 onward) it was drier than any ENSO-neutral year in the record. All 13 models agree for this season. But the Amazon is also strongly impacted by longer-term climate changes. A World Weather Attribution study found that “the severity of the [2023-24] drought… is largely driven by climate change,” with El Niño and warming contributing about equally to the rainfall deficit but the drying trend almost entirely due to warming. Fire risk also depends heavily on land management practices, as the large majority of Amazon fires are set by people.

Northern South America (Colombia, Venezuela and the Guianas) was drier than normal in December to February in all eight strong events, with a median of 71% of normal (Poveda et al. 2011). All 13 models agree this year. Northeast Brazil’s March to May rainy season was drier than normal in 6 of 8 events (Hastenrath 2011). That signal depends on tropical Atlantic temperatures as well as the Pacific, which is why it shown as only medium-high confidence. It also has a dashed outline on the map, because after the last El Niño the 2024 rainy season came in right at normal (100%).

The coast of northern Peru and Ecuador is where El Niño got its name, and where its impacts can be most violent. Normally a desert, the coast floods when the ocean just offshore warms enough to support deep convection. Only 1982-83 and 1997-98 brought truly extreme rains, with the coastal grid cells averaging more than double normal rainfall from December to April. Other strong events ranged from somewhat below to somewhat above normal, and in 2023-24 the expected coastal rains largely failed after Peru had set aside around $1.2 billion for flood preparation. What distinguished 1982-83 and 1997-98 was very warm water right along the coast. This year looks more like those two events than the others; in mid-September the far eastern Pacific (the Niño 1+2 region) was running 4.6C above normal, compared to 3.5C in 1997, 2.1C in 2015 and 2.6C in 2023 at the same point. I’ve rated this high confidence provided that coastal warmth persists as predicted by current models.

Southeastern South America (Uruguay, northeastern Argentina, southern Brazil and Paraguay) was wetter than normal between September and February in 7 of 8 strong events (Grimm and Tedeschi 2009), while the 2023-24 season was close to normal. The devastating Rio Grande do Sul floods of May 2024 came much later – in the autumn after the El Niño’s peak – though one attribution study found the El Niño approximately doubled their likelihood (albeit with a very wide uncertainty range).

The Altiplano, the high plateau shared by southern Peru, western Bolivia and northern Chile, gets most of its rain from December to March, and El Niño tends to suppress it. A warmer tropical atmosphere strengthens the upper-level westerly winds over the Andes, which cuts off the easterly flow that normally carries Amazon moisture up onto the plateau (Garreaud and Aceituno 2001). All 13 models show a dry summer this year, one of the strongest model signals anywhere on the map. The observed record is weaker as only 5 of 8 past strong events were drier than normal. The link is closest over the drier western part of the plateau (Vuille et al. 2000) and weaker toward Lake Titicaca in the north, so I’ve rated the region medium confidence.

In Chile, Montecinos and Aceituno (2003) find that during El Niño “there is a tendency for the occurrence of above-average precipitation between 30° and 35°S in winter [June–July–August (JJA)] and from 35° to 38°S in late spring [October–November (ON)].” October and November were wetter than normal in 7 of 8 strong events (with a median of 170% of normal) and in all six moderate El Niños, and all 13 models agree this year.

Further south, the same literature finds relatively dry summers between about 38°S and 41°S during El Niño, and the observed record agrees (7 of 8 strong events drier than normal from January to March), but this year’s models are split (only 8 of 13 show a drier January and February), so I’ve left it off the map. The Atacama Desert does get rare heavy rains in El Niño years, but historically these have come in the winter of the developing year (Vargas et al. 2000), which for this event has already passed.

North and Central America

The US Gulf Coast and Southeast was wetter than normal between December and February in all eight strong events, with 1982-83 at 151% and 1997-98 at 168% of normal (Ropelewski and Halpert 1986). The strengthened subtropical jet stream also brings wetter winters to Cuba and the Bahamas, which were wetter than normal in all eight strong events, with a median of 163% of normal (Giannini et al. 2000). This year all 13 models agree on wetter than normal conditions in both regions.

Southern California, the US Southwest and northern Mexico were wetter than normal between December and March in 7 of 8 strong events, with 1982-83 and 1997-98 both around double normal. The exception was 2015-16 when coastal southern California ended up at only 60% to 68% of normal despite 9 of 10 seasonal models predicting a wet winter that year. Rerun for 1982-83 and 1997-98, those same models called for wet winters (9 of 9 and 10 of 11), which is what happened. El Niño explains at most around a third of West Coast winter rainfall variability even in strong years, so a lot comes down to where individual storms go.

Northern California is a bit more ambigious. It was wetter than normal between December and March in 6 of 8 strong events, including 2015-16 (120% of normal) when Southern California missed, and 1982-83 and 1997-98 were both well above normal (173% and 156%). All 13 models lean wet this year. The misses were 1965-66 (75%) and 2009-10 (90%). However, the signal is not very specific to El Niño. Northern California was also on the wet side in 7 of 12 La Niña winters, only 2 of 6 moderate El Niños were wet, and the El Niño relationship across all winters since 1950 is weak. Past work has generally found the El Niño rainfall signal is concentrated in Southern California, and I’ve added it to the map as only medium confidence for this year.

Further north, El Niño winters tend to be drier in interior British Columbia and the inland Northwest, with lower snowpack (Shabbar et al. 1997). This was observed in 7 of 8 past events, though the typical dry anomaly is small (~95% of normal), so I’ve assigned it only medium confidence.

Central Canada tends to have mild winters during El Niño, with the strongest warmth just west of Hudson Bay (Shabbar and Khandekar 1996). All eight strong El Niño winters were warmer than the typical neutral winter once long-term warming is removed, and the four since 1997 were strikingly warm (2023-24 was Canada’s warmest winter on record). But La Niña winters were also on the warm side in 7 of 12 cases, and it is sensitive to how the warming trend is removed so I’ve assigned it medium confidence.

Hawaii is usually dry during El Niño winters, particularly in eastern Pacific events like this one (Lu et al. 2020). A composite of long-running Hawaiian rain gauges shows November to March was drier than normal in every strong event since 1972 (six of six, with a median of 69% of normal), though 1957-58 and 1965-66 were both wet. All 13 models show a dry winter this year.

Central America and the southern Caribbean (Costa Rica, Panama and the Caribbean coast of Colombia) tend to be drier during El Niño winters. During the 2023 El Niño, drought restricted traffic through the Panama Canal. But December to February is the dry season across much of this region anyway, and in the places where it normally rains during those months drier conditions only occurred in 5 of 8 past events. The more robust Central American signal is midsummer drought during the developing year (e.g. July and August 2026), which has already passed.

While some of the literature on teleconnections suggests that El Niño drives drier conditions in the Ohio Valley, this year’s models lean wet there (only 3 of 13 show drying), and the observed record is a coin flip as it was only drier in 4 of 8 past strong events.

Europe

Europe is conspicuously absent from the map, which has surprised some people given the summer heat and drought across the continent this year. For most of Europe we simply don’t know what El Niño will do this winter.

The textbook late-winter pattern (cold northern Europe, wet south) mostly shows up when a sudden stratospheric warming occurs, which can’t be forecast months in advance (Ineson and Scaife 2009). The link may also have weakened since the 1970s. Past strong El Niño winters split 4 to 4 on the North Atlantic Oscillation, 5 of 8 showed a wet UK and central Europe in autumn, and 5 of 8 had a cold Scandinavian late winter. The models (12 of 13) lean wet for the UK and central Europe from October to December this year, but the shifts are small.

The global picture

Because global temperature lags the tropical Pacific by a few months, 2027 is almost certain to be the warmest year on record. My latest forecast, which combines six global temperature datasets with the 14-model ENSO forecast, gives it a 95% to 96% chance, up from around 85% in April. El Niño-driven drought and fire in the tropics also weaken the land carbon sink, which is why the growth rate of CO2 at Mauna Loa hit a record 3.33 ppm in 2024. A similar spike in 2027 is likely. The fourth global coral bleaching event was driven by the 2023-24 El Niño on top of longer-term warming and affected 84% of the world’s reef area. A stronger event arriving before those reefs have recovered raises the risk of widespread mortality next year.

The most reliable El Niño impacts are clustered around the tropical Pacific and in the Americas. Drought in Indonesia, the Philippines, the western Pacific islands and northern South America, and wet winters from the Gulf Coast to Cuba, have shown up in essentially every strong event since the 1950s, and this year’s models agree. Southern Africa, the Amazon and the Horn of Africa are nearly as consistent. Those are the places where preparation (water storage, food reserves, fire management, flood defenses) is urgently needed now before the rains or droughts arrive.

The impact I’d flag most for policymakers is on food. Of the major climate modes of variability, “only El Niño can force globally synchronous crop failures,” because it simultaneously affects breadbaskets on multiple continents. The map shows drought risk for maize in southern Africa, rice in the Philippines and Southeast Asia and a range of crops in northern South America during the first half of 2027.

Its also worth remembering that this event is forecast to be well beyond anything in the observed record, and the two most similar past events (1982-83 and 1997-98) showed the expected impacts in every region on the map.

I’ll update the map when the October forecasts come in, and I’ve put the code and data to reproduce this analysis on my GitHub here.

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We hear a lot about central outcomes of climate change. For example, under the new "medium" scenario (e.g. current policies continuing), the world warms around 2.9C by 2100. But these numbers mask some massive uncertainties in how the climate actually responds to our emissions.

These arise from two factors: carbon cycle feedbacks – how the biosphere and ocean’s ability to absorb carbon changes as we emit more and the world warms, and climate sensitivity – how the interplay of feedbacks like water vapor, clouds, snow and ice, etc. respond to warming.

So while we think we are heading toward just below 3C today, we could really end up at 4C if we role 6s on the proverbial climate dice. Its more useful to treat climate outcomes as a set of risks – the odds of the world warming by 2C, 3C, or 4C over time – rather than a deterministic outcome.

And on the path we are on today those odds are deeply worrying. Its virtually certain we reach 2C by 2100. By 2150 we have a 77% chance of being above 3C, a ~30% chance of being above 4C, and a 6% of more than 5C warming.

As the climate scientist Wally Broecker memorably put it, “the climate is an angry beast and we are poking it with sticks”. The more we push the climate system past where its been for the past few million years, the bigger the risks of larger changes becomes.

These numbers might sound small (particular to US audiences who are used to thinking in Fahrenheit). But global average numbers mask much larger regional changes. The middle of the past ice age was only around 6C cooler in global average temperatures.

Parts of the world are warming much faster. The global land (where, its worth noting, we all happen to live) is warming around 50% faster than the world as a whole, and has already passed 2C.

There is also no guarantee that we will stay in a current policy world. While I hope that countries will strengthen climate action over time, as we’ve seen in the US its also possible to directly subsidize fossil fuels and put up barriers to clean energy deployment.

In a world of policy reversal and active support for fossil fuel burning (the CMIP7 “High” scenario), we could end up well above 3C by 2100, have a ~70% chance of >4C by 2150, and a 30% chance of over 5C.

The progress we made in moving away from higher emissions futures should tell us that progress is possible. But it shouldn’t breed complacency; the risks remain far too high and grow with each molecule of carbon emitted. The world still isn’t nearly concerned enough.

What does “precipitable water” mean?

Precipitable water (abbreviated PWAT) is the water in the atmosphere that’s available to condense and fall as rain or snow. We quantify it by calculating how deep a puddle it would make if you condensed all of the water vapor in the atmosphere and rained it to the surface. The value is typically reported in millimeters.

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Here is the average PWAT over the 1991–2020 period. The global average is about 24 mm, enough to make a puddle about an inch deep.

The distribution of PWAT follows the distribution of temperature. The reason is thermodynamics: the amount of water vapor that air can hold increases by roughly 7% for every degree Celsius of warming, following the Clausius–Clapeyron relation. The result is high PWAT in warm tropics and low PWAT in cold polar regions.

It is increasing

The planet has been warming, so you might expect that the PWAT should also be rising. Indeed, this is what we see. Here is global-mean PWAT for every August since 1979.

Also plotted is the August global average temperature; it’s also going up. In fact, we see the expected correlation (r = 0.86) between PWAT and temperature.

August 2026’s PWAT was 1.5 mm above the average PWAT in August of the 1980s, a difference of 5.8%. This is in the ballpark of what we would expect given the ~1°C of global average warming over this period.

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We’re #1

A few days ago, news came out that August 2026 was the hottest month in the last 170 years. Yup, climate change strikes again (with an assist from El Niño).

Therefore, it might not be too surprising that August 2026 also set a record for PWAT, 27.35 mm, beating the previous record (July 2024) by 0.04 mm1.

The data I analyzed only goes back to 1979, but given the very strong connection between PWAT and temperature, it is unimaginable that any month in the last few thousand years, maybe 100,000 years, has more PWAT in the atmosphere than August 2026. We are, as the proverb says, living in interesting times.

Why this matters: extreme rain

PWAT is one of the most important factors that controls the intensity of rainfall. In particular, we expect increases in PWAT to increase the frequency or intensity of extreme precipitation events. This has been confirmed in data. For example, Kunkel et al. (2020) concluded:

An analysis of 3,104 stations in the United States shows virtually every station exhibits a positive correlation between precipitable water (PW) and extreme daily precipitation (EP) with over one-third statistically significant.

In Trenberth et al. (2003), the authors state:

We have argued that because heavy rainfall rates greatly exceed evaporation rates and thus depend on low-level moisture convergence, then the rainfall intensity should also increase at about the same rate as the moisture increase, namely 7% per K with warming.

The IPCC AR6 report says:

In summary, precipitation extremes are controlled by both thermodynamic and dynamic processes. Warming-induced thermodynamic change results in an increase in extreme precipitation, at a rate that closely follows the C-C [Clausius–Clapeyron] relationship at the global scale (high confidence).

So the increase in PWAT is not just a weirdo statistic. It is a statement about the ceiling on the worst rain events we can face.

July 4 flooding

As an example of a real-life case, let’s consider the flooding in Texas on July 3-4, 2025. This tragic rain event caused flooding that claimed the lives of more than 130 people, including 27 campers and counselors at Camp Mystic.

The PWAT over Texas for that event was incredibly high. Here is a plot of the percentile rank of PWAT at 2 am CDT on July 4, 2025.

Most of the state has PWAT above the 95th percentile, with a large plume of moisture in the middle of the state exceeding the 99.9th percentile. Here is a histogram of PWAT for the grid point closest to Kerrville:

PWAT over Kerrville was 55.6 mm, about 60% higher than a typical early-July value of 35 mm. Out of 23,688 late-June/early-July hours over 47 years of data, only 23 hours were moister.

It is inarguable that the high PWAT contributed to the extreme rain of this event. Given that we can connect increases in PWAT to global warming, we can confidently conclude that climate change made this rain event more intense than it otherwise would have been.

The bottom line

The atmosphere holds more water vapor than it used to because it is warmer than it used to be. August 2026 saw the highest water vapor levels in the modern record due to the extreme warmth from climate change and the El Niño. This trend is not going to stop as long as we’re dumping carbon into the atmosphere and it’s going to contribute to more and more intense rain events.

Code for the plots in this post can be found here.

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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The NCA is required at least every four years under the 1990 Global Change Research Act and it serves as the evidentiary basis for a lot of government climate planning in the U.S. Because of this, it’s frequently attacked by the Merchants of Doubt and weakening it is a clear goal of this administration.

There was some chatter that the administration would try to write an entire new version of the NCA that would deny well-established climate science, but my guess is that they have abandoned this. The NCA is a “Highly Influential Scientific Assessment” and therefore must follow specific rules. These include rules about how to select authors, peer review, etc. Following those rules would make it impossible to produce the biased, climate-denying report that they require. And if they don’t follow the rules, they’d lose the inevitable court challenges.

So they instead, they are proposing to amend the Fifth National Climate Assessment (NCA5) by (basically) removing references to the highest emissions scenarios, RCP8.5/SSP5-8.5, which produces the most warming:

This is justified on two grounds: 1) RCP8.5/SSP5-8.5 is not an expected emissions scenario and 2) it is not policy relevant. Point 1 is irrelevant since the NCA5 does not refer to it as business as usual, baseline, etc. And Point 2 is just wrong. My comment below expands on these points.

The USGCRP is presently soliciting comments. You can tell USGCRP understands their arguments are weak since they go out of their way to make it as hard as they can for people to comment on it: They’re limiting the comment window to 15 days and comment length to 3,500 words.

Below is my comment on this. If you see any errors, please leave a comment below or email me.

Public Comment on the Proposed Amendment to the Fifth National Climate Assessment

Summary

I write to oppose the proposed amendment. It should be rejected in its entirety.

The amendment rests on two false premises: 1) that NCA5 presents RCP8.5 and SSP5-8.5 results as expected, baseline, or business-as-usual futures, and 2) that RCP8.5/SSP5-8.5 scenario runs are not useful for policy deliberations. As described here, neither of these premises is true.

First, NCA5 accurately describes RCP8.5/SSP5-8.5 as very high emissions scenarios throughout the report. Second, because of uncertainty in many climate parameters (e.g., climate sensitivity), the temperatures produced under RCP8.5/SSP5-8.5 are a reasonable upper bound on what the future climate might be. These scenarios are therefore worth examining not as expected outcomes but as plausible worst cases. Eliminating RCP8.5/SSP5-8.5 therefore commits one of the cardinal sins of risk assessment: assuming the worst-case will not occur.

In addition, NCA5 frequently uses these scenarios in global-warming-level analysis, which evaluates impacts at different levels of warming but makes no claim about how likely any level of warming is.

Overall, removing RCP8.5/SSP5-8.5 analyses would strip sound science from the assessment and make it harder for U.S. policymakers to evaluate the risks of climate change.

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1. NCA5 does not present results with RCP8.5 or SSP5-8.5 as business-as-usual

The term “RCP8.5” and “SSP5-8.5” appear 113 times in NCA5 — 74 mentions of “RCP8.5” and 39 of “SSP5-8.5”. I have inventoried all of them. They appear in 98 passages. Eighty-four of those describe the pathway as a very high scenario and six as a high scenario; the remaining eight carry no descriptor in the sentence itself. NCA5 never refers to RCP8.5 or SSP5-8.5 as business-as-usual or anything similar. Thus, NCA5 already labels the scenario correctly.

2. RCP8.5/SSP5-8.5 emissions may be implausible, but temperatures are not

Much of the RCP8.5 discourse misses a key point: in trying to understand future climate changes, our focus is not primarily on emissions. Rather, it is on temperature, which is what drives most climate impacts. So the key question is: are the temperatures predicted by today’s climate models driven by RCP8.5 implausible?

To quantify this, I ran the FaIR climate emulator (v2.2.4, Leach et al., 2021) between 1750 and 2100 under SSP2-4.5 and SSP5-8.5 emissions from RCMIP v5.1.0. Climate-response uncertainty was represented by the 841-member calibrated and constrained parameter ensemble of fair-calibrate v1.4.1. This analysis shows that the warmest of the SSP2-4.5 runs reach the median SSP5-8.5 run (Figure 1).

Let me be precise about what this result means. The spread within each scenario in Figure 1 is not noise. Each of the 841 parameter defines a plausible set of uncertain parameters that determine how the climate system responds to emissions: how sensitive it is to carbon dioxide, how quickly the ocean takes up heat, how strongly aerosols cool, how the carbon cycle responds, etc. Because the ensemble was constrained against observations, every one of these descriptions is consistent with the warming observed to date.

The real climate system, of course, has one true set of these properties. This analysis shows that we cannot rule out that the real climate system driven by a SSP2-4.5 emissions delivers warming at the end of the century equal to what a typical model produces under SSP5-8.5 (around 4°C).

The median temperatures in the RCP8.5/SSP5-8.5 ensemble are therefore plausible, even if the emissions that produced them are not. As discussed in the next section, this is important for risk assessment.

One might reasonably wonder why we need SSP5-8.5 to estimate the upper limit of SSP2-4.5 runs. Why can’t we just use SSP2-4.5 runs to evaluate the upper limit? You certainly could specifically analyze high-sensitivity models driven by SSP2-4.5 to determine the worst-case scenario, but the available literature doing that is sparse compared to the literature describing RCP8.5/SSP5-8.5 results and doing that would require a wholesale revision of the NCA5 that is not being considered here.

Keeping the RCP8.5/SSP5-8.5 runs as a plausible upper limit is particularly important given that recent research has emphasized the possibility that models are underestimating future warming. Gyuleva et al. (2026), for example, use the Earth’s energy imbalance to suggest that models with higher transient climate response more accurately fit the planetary energy balance observations of the last few decades. Armour et al. (2024) show further that the strongest evidence against high-sensitivity models, their poor reproduction of the warming since the 1970s, does not actually rule those models out.

In addition, the SSP2-4.5 scenario contains enormous amounts of carbon removal (Anderson & Peters, 2016). In 2100, for example, total carbon capture & sequestration (CCS) in that scenario is 32 GtCO2^[1]. Our willingness and ability to implement CCS at this level (about 75% of today’s total CO2 emissions) is entirely hypothetical (Daigle & Ravikumar, 2026). It is therefore possible that we will not do so, meaning that SSP2-4.5 will be understating emissions. This would push SSP2-4.5 temperatures even further into the range of temperatures simulated by RCP8.5/SSP5-8.5.

Thus, the median global temperature predicted by today’s models driven by RCP8.5/SSP5-8.5 is a plausible upper limit of warming this century. To the best of my knowledge, there is no analysis that opposes this conclusion and I do not believe the case can be made on current evidence. The proposed amendment does not attempt it.

3. Risk assessment should not confine itself to the most likely outcome

Reaching RCP8.5-level temperatures with SSP2-4.5 emissions, while plausible, is not likely. But that does not mean we should ignore it. In fact, conventional risk management tells us that, when consequences are severe, low-probability extreme outcomes can determine the response.

Levees are built to withstand the hundred-year flood, not the average year. Nuclear power plants’ safety systems are designed against worst-case scenarios, not average operations. Insurance companies maintain solvency by accounting for the worst-case claims scenario, not typical scenarios. In the national defense arena, Vice President Cheney famously said that, if there is a one percent chance that Pakistani scientists are helping al-Qaeda build a nuclear weapon, “we have to treat it as a certainty in terms of our response.”^[2]

It is in this role that RCP8.5/SSP5-8.5 is valuable: as a plausible upper bound of the temperature distribution given SSP2-4.5 emissions. This contradicts the claim in the amendment that RCP8.5/SSP5-8.5 is not policy relevant. In fact, adopting this amendment would mean that our risk assessment would ignore tail risk. This would go against best practices of risk assessment. I also note that the amendment presents this as a correction of a scientific error, when in reality it’s a value judgement about what role upper limit risks should play in deciding how to respond to climate change.

4. Global warming levels

The amendment also ignores other ways the NCA5 uses RCP8.5/SSP5-8.5. In many places, NCA5 does not use RCP8.5/SSP5-8.5 as a possible emissions trajectory but to predict impacts at various global warming levels (GWLs). In other words, the analysis estimates the climate impacts when the planet is 2°C, 3°C, or 4°C warmer than preindustrial.

For example, Figures 2.9, 2.10, and 2.13 map U.S. temperature, precipitation, and sea surface temperature at 1.5°, 2°, 3°, and 4°C of global warming; Figures 2.11 and 2.12 map extreme heat and extreme precipitation at 2°C; Figures 1.14 and 1.15 in the Overview and Figure 30.3 for the Pacific Islands do the same. The report’s findings are read off these figures: e.g., at 2°C of global warming, U.S. average temperature very likely rises 4.4°–5.6°F (p. 2-21), tropical cyclone winds are 5% faster (p. 2-26), and Midwest extreme precipitation intensifies by 10%–15% (p. 24-19).

None of these are predictions that these temperatures will occur. Rather, they answer a different question: what happens when the climate reaches that temperature.

The amendment’s plausibility argument does not address this alternate use and it performs none of the analysis needed to separate warming-level results from genuinely emissions-dependent ones. It simply declares RCP8.5/SSP5-8.5 off-limits, which could be used to strike the GWL analysis from the NCA5. It offers no justification for discarding RCP8.5/SSP5-8.5 scenarios in GWL analyses, which is of value even if reaching the top of the temperatures range is unlikely.

Recommendation

Reject the proposed amendment in its entirety.

The amendment fails to correct any errors in the NCA5 and is inconsistent with the underlying science. First, NCA5 accurately describes results with RCP8.5. Second, the amendment declares RCP8.5/SSP5-8.5 off-limits to federal decision-making but doesn’t address the fact that the median temperatures predicted from these scenarios is plausible. Third, it presents an institutional judgment about risk tolerance (that we should ignore worst-case scenarios) as though it were a scientific correction. Fourth, it proposes to jettison research about impacts at global warming levels. Thus, adopting the amendment would leave the country less prepared for future climate impacts, including lower probability but high severity outcomes.

Anderson, K., & Peters, G. (2016). The trouble with negative emissions. Science, 354(6309), 182-183, doi:10.1126/science.aah4567

Armour, K. C., Proistosescu, C., Dong, Y., Hahn, L. C., Blanchard-Wrigglesworth, E., Pauling, A. G., Jnglin Wills, R. C., Andrews, T., Stuecker, M. F., Po-Chedley, S., Mitevski, I., Forster, P. M., & Gregory, J. M. (2024). Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity. Proceedings of the National Academy of Sciences, 121(12), e2312093121, doi:10.1073/pnas.2312093121

Daigle, H., & Ravikumar, A. (2026). Can we sequester carbon fast enough? Growing disconnect between models and reality. Environmental Research: Energy, 3(3), 031001, doi:10.1088/2753-3751/ae987b

Gyuleva, G., Fischer, E., Knutti, R., & Sippel, S. (2026). Recent Temperature and Energy Imbalance Trends Point to Higher Estimates of Future Warming. Earth’s Future, 14(8), e2026EF008356, doi:10.1029/2026EF008356

Leach, N. J., Jenkins, S., Nicholls, Z., Smith, C. J., Lynch, J., Cain, M., Walsh, T., Wu, B., Tsutsui, J., & Allen, M. R. (2021). FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration. Geosci. Model Dev., 14(5), 3007-3036, doi:10.5194/gmd-14-3007-2021

Related posts

Marcus Sarofim has posted his comment on this amendment. It’s good.

Here at TCB, we’ve written a lot about RCP8.5/SSP5-8.5. Here are a few of them:

• RCP8.5 asks: why can’t we just get along?

• On the death of RCP8.5

• Moving away from high-end emissions scenarios

^[1] Retrieved from the IIASA SSP database.

^[2] Quoted in Ron Suskind’s book, The One Percent Doctrine, published 2006.

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The tragedy is unfortunately familiar for the Langtang valley. In 2015, shaking from a magnitude 7.8 earthquake released an avalanche of ice and rock from this same mountain that buried Langtang village and killed around 300 people. But there is an important difference between the two events: in 2015 an earthquake triggered the collapse. This time there was no tectonic trigger. The mountain simply failed, and the collapse itself produced a seismic event that was initially mistaken for an earthquake.

Within hours, some people on social media were confidently asserting that climate change caused the disaster, while others were just as confidently asserting it had nothing to do with it. In this piece I’ll try and lay out what the data and the scientific literature actually supports.

The short version is this: any formal attribution of this specific collapse to climate change is premature, and single events like this are genuinely hard to attribute even after careful study. At the same time, the collapse happened on a mountain that has warmed rapidly for decades, immediately after four consecutive summers that all rank among the five warmest in at least 86 years, and in a valley that has been losing ice at an accelerating rate. A rapidly warming climate and retreating glaciers are increasing risks of these sorts of events in the region, whether or not we can ultimately attribute this particular event.

A rapidly warming mountain

Let’s take a look at how temperatures have changed in the region where the glacier collapsed. The figure below shows ERA5 surface air temperatures for the ~25 km grid cell containing the glacier, both for monthly anomalies since 1940 and the summer (June–August) average for each year.1

Summers at the site have warmed at around 0.29C per decade since 1940, with the most recent 30 years (1996-2025) averaging about 1C warmer than the 1961-1990 baseline. The four most recent summers (2022 through 2025) all rank among the five warmest on record. A newly published study of the Langtang Catchment (Silwal et al 2026) also provides a separate check on my numbers. Using the higher-resolution ERA5-Land product, they find the glacial areas of the catchment (above 4000 m) warmed at 0.31C per decade over 1960-2023.

The glacial collapse came at the end of an exceptionally warm monsoon season. July 2026 was the second warmest July in the 86-year record (at 9.4C, behind only 2024’s 9.8C). The first three weeks of August (the latest daily data available when I ran this analysis) averaged 9.3C, running above the warmest complete August on record (9.1C in 2022). The collapse came on August 26th.2

Warm summers do not by themselves knock a mountain down. But sustained warmth at a glacial cliff increases risks by melting the ice that buttresses steep slopes, sending meltwater into cracks in the rock, and degrading the permafrost that acts as a frozen glue holding fractured high-altitude rock faces together (Gruber and Haeberli 2007). As the glaciologist Jakob Steiner told Scientific American, “you basically had the lower part of a glacier tongue that sheared off because the rock below failed.”

A valley losing its ice

Next let’s look at whats been happening to the region’s glaciers. The figure below shows glacier mass balance for the surrounding region (the Randolph Glacier Inventory’s South Asia East region, which spans the Himalaya east of ~81E, including Nepal) from the WGMS annual mass change estimates, along with in-situ measurements from two Nepali glaciers where long term measurements are available. This includes Yala Glacier in the Langtang valley itself, around 10 km from the collapse site.

Regional glaciers have lost a cumulative 26 meters of water equivalent since 1957 – equivalent to shaving roughly 30 meters of ice thickness off the average glacier surface. More than half of that loss (about 14 m w.e.) has occurred since the year 2000, with 2024 and 1997 as the two biggest loss years on record. Yala Glacier, the closest measured glacier to the collapse site, lost an average of 0.92 m w.e. per year over 2012–2025, with its worst year (-1.90 m w.e.) in 2024.3

Yala has become something of a symbol of Himalayan ice loss: this past May, ICIMOD and local communities held a tribute ceremony at the glacier, which has shrunk by 66% and retreated 784 meters since it was first surveyed in the 1970s. In their new paper, Silwal and colleagues find Yala’s equilibrium line (the altitude above which a glacier gains mass) has risen roughly 200–250 m since the 1980s and now sits above 5600 m. This is above the top of the glacier itself, meaning the entire glacier now sits in the melt zone.

Studies of the Langtang catchment specifically have found glacier thinning nearly doubled between 1974-2006 and 2006-2015 Ragettli et al 2016, mirroring the Himalaya-wide doubling of ice loss between 1975-2000 and 2000-2016 found by Maurer et al (2019) and the global acceleration documented by Hugonnet et al (2021).

The Langtang catchment has lost around 42% of its glacier area since the Little Ice Age (~1815, Silwal et al 2026), with the rate of loss more than quadrupling from 0.11% per year over 1815-1964 to 0.49% per year over 1964-2023, and the fastest loss coming after 2000. The number of glaciers nearly doubled (58 to 115) over that period as retreating glaciers have fragmented and disconnected into pieces.

The cause of this ice loss is clear. The IPCC’s Sixth Assessment Report concluded that “human influence is very likely the main driver of the global retreat of glaciers since the 1990s.” When researchers have done formal attribution studies of individual glacier hazards (e.g. Stuart-Smith et al (2021) on the glacial lake threatening Huaraz, Peru), they have found glacier retreat that is “virtually certain” to be outside natural variability.

Why attribution of the collapse itself is premature

So why not simply connect the dots, and say that a warming mountain, retreating glaciers, and a record-warm August led to the collapse? The challenge is that the chain of causation for rock-ice avalanches is much messier than for glacier retreat, and the scientific literature on these events is consistently, and appropriately, cautious.

The Himalayan mountains have always shed ice and rock, as skeptics of any climate link will point out. The 2015 Langtang avalanche in the region was caused by a magnitude 7.8 earthquake rather than a string of warm summers. Steep glaciated faces fail for reasons of geometry, geology, and bad luck that have nothing to do with human emissions. Attribution of a specific collapse requires detailed forensic reconstructions on specific drivers before causes can be known with any certainty, and that work will take months to years.

The best precedent here is the February 2021 Chamoli disaster in the Indian Himalaya, where a rock-ice avalanche killed more than 200 people. The definitive study of that event (Shugar et al 2021 in Science) reconstructed the failure in detail but stopped short of attributing it to climate change, while noting that warming increases the probability of such events. Studies of other large glacier collapses, like the 2016 twin glacier detachments in Tibet (Kääb et al 2018), reach similarly hedged conclusions. A 2021 survey found an apparent increase in large glacier-related landslides in High Mountain Asia over 1998–2018, but its authors flagged the small sample size and cautioned that the association with warming “requires further research.” We simply do not have the kind of long, well-observed event record for high-mountain collapses that we have for heatwaves or heavy rainfall, where rapid attribution is now routine.

There is a related argument I’ve seen circulating online that events like this happened long before modern warming, and if anything a world with more glaciers should have more ice available to fall. The first part is clearly true. The largest ice avalanche ever observed in the Alps came off the Altels glacier in 1895 (around 4 million cubic meters), and a 1962 rock-ice avalanche from Huascarán in Peru killed around 4,000 people with no apparent trigger at all. Mountains were shedding ice catastrophically well before we started burning fossil fuels in earnest. But the existence of old events tells us nothing about whether the odds are changing, and heat waves happened in 1900 too (just less frequently). It is worth noting that the the best modern reanalysis of the Altels collapse, Faillettaz et al 2011, suspects hot summers in the preceding years warmed the glacier’s frozen bed toward the melting point.

Hazard from these slopes are not solely determined by how much ice sits on them. Rather, what drives risks is how fast the ice and the frozen ground beneath the ice are changing. A cold glacier frozen to its bed in a stable climate will be comparatively secure, but one in transition will be more at risk. Thinning glaciers withdraw support from the steep rock faces they once buttressed (called the “paraglacial response”, Ballantyne 2002), meltwater reaches previously dry failure planes, and warming permafrost loses the ice cement that held fractured rock together.

Its also worth listening to what experts have been saying about the event. Kristen Cook of Grenoble Alpes University told Scientific American that “it’s difficult to conclusively link a single event directly to climate change. We know it’s getting warmer in the Himalaya.” Dan Shugar, who led the Chamoli study, was cautious on this event’s specific cause while noting he is “100 percent” certain climate change will make such events more likely, “by some combination of glacier melt, permafrost thaw, and changes to precipitation and temperature patterns.”

Confident claims that climate change caused this disaster are getting ahead of the evidence. But at the same time, confident claims that it played no role ignore the rapid changes that we’ve observed in the area around where the mountain failed.

What are the takeaways here?

This is a humanitarian catastrophe, and the immediate causes of the enormous death toll include things like villages and infrastructure concentrated along narrow river corridors, a debris flow that covered 100 km, and limited warning systems that have little to do with climate change. As with other extreme events, we shouldn’t let debates about attribution distract from the urgent need to reduce vulnerability.

It is clearly premature to attribute the collapse itself to climate change. Given the messy causality of rock-ice avalanches, a definitive single-event attribution may never be possible. We will likely see careful forensic studies to be published over the next year or two (as happened after Chamoli), and I’d treat any confident causal claim made this week (in either direction) with a lot of skepticism.

But the broader context is not in doubt. The collapse happened on a mountain warming at around 0.29C per decade in summer, immediately after four consecutive summers that all rank among the five warmest in an 86-year record, during an August that was running warmer than any complete August on record, in a valley that has lost ice at an accelerating pace, and in a region where human influence is very likely the main driver of glacier retreat.

High Mountain Asia already has more people exposed to glacier flood hazards than anywhere else on Earth, and projections suggest the Hindu Kush Himalaya could lose a large fraction of its remaining ice this century, with losses scaling with emissions (Rounce et al 2023). The ice that holds these mountains together is being lost, and every tenth of a degree of additional warming pushes these systems further outside the conditions in which the region’s settlements, trails, and hydropower plants were built.

So when people ask whether climate change caused the disaster in Nepal, the answer is that we don’t know, and we may never know for this specific event. But if the question is whether a warming climate is making disasters like this one more likely in the Himalayas, the literature and essentially every scientist working on these hazards point in the same direction.

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

To what extent does this upcoming El Niño give us a preview of what the climate will be like a decade or two from now?

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What is El Niño?

Here’s what one of us (AD) wrote in his climate textbook:

The best-known example of unforced variability in our climate is the El Niño/Southern Oscillation (referred to by scientists as ENSO). El Niño events, which make up the warm phase of ENSO, occur every few years and last a year or so, and alternate with cooler La Niña events. These ENSO events are associated with large-scale shifts of rainfall and temperature patterns around the globe, and these have enormous consequences for humanity. Some regions see more rainfall during an El Niño, and some see less; some regions experience warmer temperatures than normal, while others are cooler.

Here’s something Kevin Trenberth wrote about ENSO (from a not-yet-published article):

ENSO is the dominant mode of interannual climate variability. It is a natural phenomenon arising from coupled interactions between the atmosphere and ocean in the tropical Pacific Ocean. El Niño events occur roughly every two to seven years and each event has its own character. Surface tropical winds create changes in sea temperatures that determine where the main tropical convection and storm activity occur, and thus further change the winds.

ENSO redistributes heat and moisture around the globe and regulates temperatures in the tropical Pacific Ocean, which cools mainly from increased evaporation. The added atmospheric moisture is realized in rainfall and as latent heat in the atmosphere, which contributes to a general warming of the global atmosphere and global mean surface temperature that peaks a few months after a strong El Niño event. ENSO underpins seasonal climate prediction, and has major impacts and strongly influences ecosystems, economies, and society.

For a more detailed explanation of ENSO, watch this video from the UK Met Office:

The global picture

This figure from AD’s textbook shows global average temperature measured by satellite and color coded by phase of ENSO. It is clear that, when you are in an El Niño, the Earth’s global average temperature rises dramatically:

Thus, from a global average perspective, the framing of “El Niño is a time machine” is correct. Indeed, one of us (ZH) has been using this analogy to try and explain 2027’s El Niño-fueled global average temperatures, which may equal what the long-term trend predicts the temperature of 2037 will be.

The catch

The catch is that the future world that has been warmed by greenhouse gases and a present-day world that has been temporarily warmed by El Niño can have the same global-average temperature while having significant differences in the impacts of this warming.

The reason is that the two kinds of warming come from different physical processes. Greenhouse-gas-driven global warming (hereafter, GHG warming) results from greenhouse gases trapping heat that would otherwise escape to space, leading to an accumulation of energy in the Earth system, which in turn leads to increasing temperatures.

El Niño is different. To a first approximation, it is better thought of as a redistribution of heat already in the climate system. During El Niño, changes in winds in the tropical Pacific allow warm water that had been stored below the surface in the West Pacific to spread eastward and warm the surface. That warming then propagates to the rest of the planet.

Those different mechanisms leave very different fingerprints.

Temperature

Here is the pattern of long-term warming, calculated from Berkeley Earth data:

The first thing to notice is that greenhouse warming really is global warming. Almost every spot on the planet is experiencing long-term warming. There are well-understood spatial variations in the rate of warming: e.g., land has warmed faster than the oceans because of water’s high heat capacity.

Any individual year, of course, will not look like this because a single year’s pattern is the combination of GHG warming and random variability from ENSO and an alphabet of other modes of variability (PDO, NAO, etc.).

The strength of an El Niño is measured by the average temperature in the tropical Pacific (the black box in the figures below), which we refer to as the ENSO index, with units of degrees Celsius.

We can regress the global average temperature against the ENSO index, which tells us that the globe warms about 0.08°C for every degree increase in the ENSO index. The forecast is for the ENSO index to reach +3.9°C in November 2026, which implies approximately +0.3°C of (temporary) global warming in the following year1. This additional ENSO warming is the reason that 2027 will be a record breaker and a preview of a greenhouse-warmed world of the late 2030s.

We can also estimate the spatial pattern of the global warming due to El Niño by regressing the ENSO index against the temperature at each grid point (at two lags, zero and 6 months):

As you can see, the El Niño warming pattern is very different than the GHG warming pattern. It is definitely not global; instead, a large fraction of the warming occurs in the tropical Pacific. And some regions actually cool during an El Niño.

So while the global average temperature in 2037 may equal that of 2027, the warming will be distributed differently. You should therefore not expect your local conditions in 2027 to equal your local conditions in 2037.

Precipitation

As temperatures shift in response to an El Niño, the distribution of rainfall also shifts.

Observational records of precipitation aren’t as good for this type of calculation, so for these plots I am using ten members of the CESM1 Large Ensemble. Here is the long-term change in precipitation between the early 20th century and the late 21st century:

There is a broad increase in global precipitation, which makes physical sense. Surface energy balance requires evaporation to increase as the globe warms, so precipitation, which must balance evaporation, must also increase.

But, as with temperature, the increase is not distributed evenly. Some regions will see very large increases in precipitation while others get drier. Many of the drying regions are in the subtropics, including parts of the Mediterranean, southern Africa, Australia, and the Americas.

Now compare that with the rainfall response to El Niño, calculated the same way as the temperature response above:

There are similarities between the responses to GHG warming and ENSO — but also important differences, particularly at high latitudes and in the tropics.

Other differences

The differences extend beyond temperature and precipitation. For example, consider hurricanes. During an El Niño, the distribution of hurricanes changes in significant ways:

Thus, a future greenhouse-warmed climate of 2037 with exactly the same global-average temperature as 2027 but in an ENSO-neutral state (neither El Niño nor La Niña) would be expected to have more Atlantic hurricanes than El Niño-enhanced 2027 would.

The same logic applies to droughts, floods, heat waves, and many other climate impacts.

So is El Niño a preview of the future?

Yes, in a limited sense.

The coming El Niño may temporarily push global-average temperatures to a level that will not occur during (ENSO-neutral) conditions for another decade or two. In that sense, it really is a glimpse of the future.

But it is not a true time machine. It does not provide a full picture of what the Earth will actually look like a decade hence.

The future greenhouse-warmed Earth of 2037 will contain more energy than today’s Earth. Its oceans will be warmer, its land will be warmer, and the underlying changes in temperature and precipitation will be driven by persistent greenhouse forcing rather than the temporary rearrangement of heat associated with ENSO.

So while the 2027 El Niño may well help us gain some insight into what a hotter planet looks like, we should avoid thinking this is the climate of the future.

We thank Kevin Trenberth for his comments on a draft of this post.

Code to reproduce the figures is here.

Thankfully, most of you will never come face-to-face with one of these massive, climate-enhanced beasts. But a lot of you will breathe the smoke they produce.

In some respects, this is worse than the fires themselves: wildfire smoke kills 24,000 Americans per year. And it negatively impacts our lives: it forces people to stay indoors, ruins vacations, drives people to spend money on air purifiers, destroys ice cream bars, and so on.

I was wondering if we could see a trend in wildfire smoke, so I decided to look at the data.

Smoke data

I downloaded wildfire aerosol data from NASA1 and plotted how smoke frequency has changed over the past two decades across the continental U.S. (CONUS):

As you can see, starting around 2018, smoke over the U.S. has gotten worse (positive trend is statistically significant). Before 2018, no year averaged more than 2%, while five of the next seven (complete) years did.

This is consistent with research that showed that U.S. air quality, which had been improving for decades, started getting worse around 2016 due to wildfire smoke.

Here are maps of the NASA smoke frequency over the past 25 years:

The “eye test” confirms that something changed in the late 2010s.

We can also make a difference plot showing the distribution of the increase in days per year covered by smoke:

The data show that smoke occurrence increased over most of the U.S., with the upper west quadrant of the U.S. dominating the overall increase. In that region, there were 5-10 more days of smoke per year in the last few years than in the early 2000s.

Why is this worrisome?

Beyond the obvious — smoke kills people! — wildfire smoke shows how climate change is changing how we live. Robinson Meyer described this well in Heatmap News:

[S]tate and local governments across the eastern U.S. will now need to reckon with a new form of extreme weather. You grew up with snow days, but now we’ll have smoke days — and schools and sports leagues and concert venues will need rules about how to deal with them. When should games be canceled, tickets refunded? … Hotels and office buildings will need to review their ventilation policies and possibly upgrade their equipment; municipal emergency response plans will be revised and printed in triplicate.

But I think there’s an even bigger lesson here. Ten years ago, virtually no one talked about wildfire smoke as a serious climate impact. According to climate economists I surveyed on Bluesky, it is not really included in economic damage estimates of climate change. It turns out to be pretty costly, as Meyer described:

How costly? “When monetized,” a group of Stanford and Princeton economists wrote in Nature last year, in the first major study on the topic, “the climate-driven smoke deaths result in economic damages that exceed existing estimates of climate-driven damages from all other causes combined in the U.S.A.”

You read that right: The cost of climate-worsened wildfire smoke alone is larger than what earlier studies said every other estimated cost of climate change would be, combined.

Thus, we have suddenly — in just the last few years — come to understand that it is, in fact, a huge problem that we’re going to have to face as the climate warms. Daniel Swain said it well:

So, while we should definitely worry about wildfire smoke, we should also worry about other surprise catastrophes that are lurking and which we’ll only learn about after they occur. This should turbocharge our resolve to reduce emissions of greenhouse gases.

Appendix: the fire-denial machine

Because I’m sure someone will bring it up, let me address the “but acreage burned/number of fires has gone down” argument by quoting something I wrote in a previous post:

The argument that fewer acres burn today than burned in the early 20th century may be correct (the data are not great, so take that statistic with a grain of salt), but it’s also true that fires today are different from those in the early 20th century.

In the early 20th century, fires were typically of low intensity, easier to manage and extinguish, primarily consuming underbrush while leaving larger trees unharmed. In fact, fire played a crucial role in forest ecology, aiding in the natural cycle of regeneration and maintenance of healthy woodlands.

Today, the fires are absolute monsters. They can’t be stopped by humans. If they roll through your town, all they leave are foundations and chimneys and melted cars.

Fire seasons are longer (and sometimes they simply don’t end) and fires are occurring in places that they just didn’t occur in the past, like the Arctic.

How do we know all this? We can actually measure fires from space. For example, we can see that the heat put out by extreme wildfires has significantly increased over the last few decades:

As you can see, the biggest fires of today are burning much hotter than just 20 years ago.

Thus, it doesn’t matter if more acreage burned in the 1930s if those fires were weak (e.g., grassland) fires that were easily extinguished. Today’s wildfires are just built different.

[update: TCB superfan “Gary” recommends people look at the EPA website AirNow, which shows you the current air quality in your area, or anywhere else in the U.S.]

Code to reproduce the NASA figures is here.

related stuff

People don’t appreciate how terrible air pollution is. It is truly one of the most underappreciated negative impacts of our fossil fuel addiction. For more on this, read David Wallace-Wells’ tour de force “Ten Million a Year”, which documents how air pollution kills, you guessed it, 10 million people per year.

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In 2025 Google published an article calculating that median Gemini text prompt used only 0.24 watt-hours (Wh), less energy than “watching nine seconds of television”. Around the same time, Sam Altman said that an average ChatGPT query uses about 0.34 Wh, and Epoch AI came out with similar numbers. Writers like Andy Masley and Hannah Ritchie have shown that at these rates an individual using chatbots has a pretty negligible impact, with one prompt only amounting to roughly 1/150,000th of an average American’s daily emissions.

Those numbers are basically right. They are also increasingly divorced from how AI is actually being used today.

The fastest-growing way that software engineers and scientists actually use AI is not typing questions into a chat box. Rather, we use AI agents through tools like Claude Code and Codex that plan, write code, run it, read the results, and iterate on their own. These agents make dozens of model calls per human prompt, and engage in complex reasoning chains that involve attempting and evaluating multiple answers to the same question.

I work for a company in Silicon Valley (Stripe) and admittedly use the latest AI tools more than most people. But I thought it would be instructive to take a deep dive into my own AI use over the past 8 weeks and calculate the actual energy use I was responsible for.

Over the past 8 weeks I typed 1,138 prompts into Claude Code. Those prompts triggered more than 14,000 model calls that processed 3.2 billion tokens. My best estimate is that this used around 170 kWh of data center electricity (with an uncertainty range of roughly 70 to 330 kWh across methods and assumptions). That works out to around 150 Wh per prompt (60 to 290 Wh), which is roughly 600 times (250 to 1,200) the energy of a median chat prompt. A “prompt” is ultimately not a unit of AI use any more than “trips” is a measurement of driving; it’s how far you go that matters.

Agents supercharge AI usage

Part of the impetus for this post is the publication of a new white paper from Watershed (Bistline et al. 2026) proposing a standardized framework for corporate AI emissions accounting. It is the most careful treatment I have seen of why published per-query numbers differ by orders of magnitude (system boundaries, mostly), and it contains a figure that should reframe the whole discussion: electricity per AI task spans more than five orders of magnitude, from thousandths of a watt-hour for text classification to 50-500 Wh for an agentic workflow making 5-50 frontier model calls. As they put it, emissions attributed to one “interaction” may understate the compute actually consumed “by an order of magnitude or more.”

Other researchers have found similar results. Bai et al. (2026) measured coding agents on real software tasks and found they consume roughly 1,000 times the tokens of an ordinary chatbot interaction. And these sort of agents tasks represent the most rapid driver of increased AI usage; Anthropic’s Economic Index found that 97% of their API usage now show “automation-dominant” patterns associated with agents.

To put these values in perspective, the figure below compares published per-prompt and task estimates (blue) with what I measured from my Claude Code use (orange) as well as common benchmarks for energy use (running a microwave, a fridge, or a whole home):

My median Claude Code session uses around 0.6 kWh (0.25 to 1.2 kWh), which is at the top end of Watershed’s generic agentic usage estimate, and fifty times the energy used to charge a cellphone. My average day of Claude Code (3.0 kWh, range 1.2 to 5.9 kWh) uses more electricity than running two refrigerators.

Measuring my own footprint

Claude Code keeps complete local transcripts of every session, including the exact token counts the API reports for every model call.1 This lets me precisely know how much AI usage I was responsible for rather than simply extrapolating it from published benchmarks; its only the step to convert tokens used to energy that requires assumptions.

The first thing I found is that the gap between “prompts” and reality is massive: my 1,138 typed prompts resulted in just over 14,000 distinct model calls (12 per prompt), and each prompt consumed on average 2.9 million tokens. For comparison, typical web-based AI chat exchanges with no reasoning or web searches only use around a thousand tokens.

Over the past the 8 weeks, my Claude Code used 3.2 billion tokens. These overwhelmingly came from the agent re-reading its own working memory. Every time an agent takes a step (e.g. runs a command, reads a file, or calls a tool), the model re-processes its entire accumulated context. The figure below shows the breakdown of how tokens were used and their share of total electricity use.

The text I actually see (e.g. the model’s output) is only around 0.4% of total tokens processed. Some 96% of the tokens are cache reads where the agent re-reads its own context at each of those 14,000 steps. This matters enormously for the energy estimate, because a cached token is much cheaper to re-read than a fresh one is to process. AI companies charge about 10% of the price for cache reads compared with fresh content, and I use that ratio as my central energy assumption, with 1% and 25% as bounds.2

Since nobody outside of the labs actually knows the true per-token energy of a frontier model (Anthropic has published no per-prompt or per-token figures, something the Watershed paper politely but firmly flags as the field’s biggest data gap), I ran my measured token counts through three independent published methodologies: Watershed’s activity-tier factors, the per-token factors Simon Couch’s estimates derived from Epoch AI’s work, and the claude-carbon tool’s pricing-inferred coefficients.

Every one of these methodologies gives an answer between roughly 70 and 330 kilowatt-hours over 8 weeks. The estimate is genuinely uncertain, by a factor of ~2 in either direction. But the broader conclusion is not: counting my 1,138 prompts at published per-chat-prompt rates would have suggested about 0.3 kWh, while the reality is 150 to 1,200 times that.

My daily pattern of energy use is shown in the figure below. The day to day variability is huge: my heaviest day (11 kWh central estimate) involved multiple parallel agents churning through a large geospatial analysis, and used more than a third of the total daily electricity of an average US home. This reflects that fact that even within the category of agentic usage, the complexity of the task and the number of simultaneous sub-agents used will greatly influence the resulting energy use.

My numbers are a bit higher than some of the other published estimates of agentic use, and it is worth digging in a bit to determine why. Couch estimated that a median Claude Code session uses around 41 Wh, involving 24 model calls and 592k tokens. Andy Masley’s June 2026 calculator puts a 100k-token Claude Opus agent session at ~459 Wh. My median session is ~600 Wh, involving a hundred-plus calls and around ten million tokens including numerous subagents for large data analyses projects. Hannah Ritchie’s hypothetical heavy user (24 agentic queries a day) came out at 2.4 kWh/day, while I measured a central estimate of 3.0 kWh/day (1.2 to 5.9 kWh) for my actual usage.

None of these estimates are necessarily wrong, they just reflect a wide range of actual usage assumptions. Software engineers, researchers, and data analysts (e.g. folks like me) probably lie pretty far down the tail of the usage distribution. At the same time, usage will likely grow over time as more complex agentic tools increasingly become the norm.

What a year of this looks like

If we assume that these 8 weeks are fairly typical, we can estimate that a full year of my agentic Claude Code use would consume roughly 1.1 MWh of data center electricity (0.4 to 2.2 MWh), which is about a tenth of what an average US household uses. Applying the US-average grid intensity, that is roughly 370 kgCO2e per year (150 to 730 kgCO2e).3

My personal and professional AI usage now emits a bit more per year than running an electric clothes dryer, and about half as much as driving an electric car 11,500 miles in California or taking one San Francisco to New York round-trip flight in economy.4 It is about 8% of the annual emissions of a typical American gasoline car, and roughly 2% of the average American’s ~18-ton annual greenhouse gas footprint.

This is simultaneously a large emissions source and a relatively modest part of my total carbon footprint. I typically take a round trip flight from San Francisco to the East Coast twice a year to visit my aging parents (not to mention work travel), and I generally don’t lose sleep over that choice. It is also fundamentally a much easier-to-decarbonize end-use than aviation (more on that below). But this also represents a net new source of emissions, at a time when global temperatures are skyrocketing and our emissions reduction goals are increasingly off track.

So what do we do about it?

Having spent most of this post arguing that agentic AI use is hundreds of times more energy intensive than the chatbot numbers suggest, let me be clear that I don’t think the answer is guilt or abstinence. But there are real levers here that we can use to shape the trajectory of AI energy use and emissions going forward.

On the personal side we can try and not be frivolous with agentic tools. There is a real difference between pointing five parallel agents at a hard research problem and doing the same to settle a bar bet (or, in my case, making axolotl-themed games with my daughter). What models you use matters too: sending simple tasks to smaller models uses perhaps 5 to 7 times less energy per token than defaulting to a frontier model,5 and it is what I increasingly do for searches and mechanical work. That said, I don’t want to oversell this. My entire annual AI footprint is a few hundred kilograms of CO2; personal restraint by the small population of heavy users is not going to bend any curves.

The technology lever is more powerful, and it is genuinely impressive. The figure below shows the energy efficiency of NVIDIA’s data center chips over the past decade. The amount of math an AI chip can do per joule of energy has grown roughly 150-fold since 2016, doubling about every two years, per Epoch AI. The latest B300 chips running at their lowest supported precision use about a quarter of the energy per operation of the 2022-era H100s that trained today’s frontier models. This represents a 3.8-fold improvement in energy efficiency in three years. Software gains can make this even faster: Google reports the energy of a median Gemini prompt fell 33-fold in a single year through a combination of better models, better tools, and better hardware.

But if 150-fold efficiency gains were going to reduce AI’s energy use, they would have done it by now. This is the Jevons paradox in action: making compute cheaper per token in turn tends to lead to greater levels of AI use. Efficiency is why my agentic habit costs 170 kWh rather than the 950 kWh it would have used with 2020-era hardware. But efficiency only determines how much intelligence we get per unit of energy, but so far it has so far shown no sign of determining AI’s total energy use.

Which is why the lever that actually matters most is the carbon intensity of the electricity. Every number in this post assumed the US-average grid; run the same workload on largely clean power and my footprint falls by roughly 90%. Unlike aviation, this is an end-use we already know how to decarbonize.

The problem is that we are moving in the wrong direction today: a sizable portion of planned US data center capacity intends to build its own behind-the-meter generation, and nearly three quarters of that is natural gas. AI companies with genuine climate commitments need to do better at finding alternatives: solar plus storage (which I helped lead a study about in 2024), next-generation nuclear and restarts of retired reactors, enhanced geothermal, and siting data centers in regions where both the average and the marginal generation is low-carbon).

There is also a silver linings version of this story where AI demand becomes an asset for decarbonization. Getting to net-zero emissions requires roughly tripling electricity generation by mid-century as we replace nearly all the current uses of fossil fuels with clean electricity. The barriers are mostly not technological, but rather things like interconnection queues, permitting, transmission. The AI buildout is a preview of that world of rapidly increasing electricity demand, backed by companies with enormous capital and unusual urgency. If that money and impatience gets spent speed-running the elimination of those barriers (buying firm clean power, funding transmission, absorbing the early costs of advanced nuclear and geothermal the way early corporate buyers did for wind and solar), the AI boom could leave the grid cleaner than it found it. If it gets spent on behind-the-meter gas turbines, it won’t. That choice is being made right now, and it will matter far more than how many prompts any of us type.

In the interest of full disclosure: the python code underlying the analysis and figures in this post were, naturally, built with the help of Claude Code, but the writing is all mine.6

A hall of fame for bad climate plots would definitely include this one:

The plot covers the continental U.S., and it dramatically shows that temperatures in the 1930s were more extreme than those of the present period. The metric plotted, the heat wave index, is constructed as a station‑average count of qualifying 4‑day warm spells (mean temperature exceeds a local one‑in‑ten‑year threshold) in each year, so higher values mean that heat waves were more frequent in that year.

It was originally published in 1999 and has gone through several iterations, with the one above from an EPA website in 2021.

Don’t take this as criticism of the original paper — it was published in 1999, after all. However, the plot has hung around long past its expiration date and has since become a staple of climate denial. I think it’s time to reconsider what the plot actually tells us.

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But is it right?

After reading our previous posts on the heat of the 1930s, you probably already know the answer: this plot is wrong in important ways.

The plot above is constructed from raw, unadjusted station data, which contain significant biases both in the individual station records and in the geographic distribution of the stations.

As we discussed in our previous posts, we can adjust for those biases. Here are the plots with biases removed:

In these plots, I’m using the adjusted and area-weighted GHCN-daily data (described in this post) and the daily Berkeley Earth land data (used in this post).

Both plots show something different than the raw, unadjusted data. The GHCN-daily data show that the most extreme years were 1936, 2023, 1934, 1931, and 2011. In the Berkeley Earth dataset, the most extreme years were 2023, 2011, 1936, 2020, and 1931.

Rather, the only fair conclusion is that the last few years have been about as extreme as the hottest years of the 1930s over the continental U.S.

The 1930s were hot, but not because of global warming

Despite this ambiguous answer, we can still learn something from the plot.

First, as we’ve pointed out many times, the heat of the 1930s was regional — it was concentrated in the middle of the U.S. That’s different from today, when extreme temperatures are a global phenomenon.

We understand why this small part of the U.S. was so hot in the 1930s. It was normal climate variability combined with human activities. The relevant human activity was not fossil-fuel combustion but bad farming practices that contributed to the Dust Bowl.

During the early 20th century, aggressive plowing and the replacement of native grasses left Great Plains soils exposed. When drought conditions developed, those degraded soils dried out quickly, reducing evaporation and soil moisture and reinforcing the heat.

This feedback between drought and land degradation amplified temperatures over the U.S. Midwest and helped create the extraordinary heat of the Dust Bowl era seen in the plots above.

A picture is worth 1,000 words, so here’s what Dalhart, Texas looked like in 1938:

Here’s what Dalhart, Texas looks like today from space:

Those circles are the signature of center-pivot irrigation. Irrigation cools the surface, and the expansion of agriculture and irrigation in the central U.S. in the middle of the 20th century played a key role in why heatwaves were low in the middle of the 20th century:

Starting in the 1970s, we see heatwaves trending upward. Unlike the heat of the Dust Bowl in the 1930s, this post-1970 increase is global. This is climate change making heatwaves more extreme.

Conclusions

1. The extreme heat of the 1930s covered a very small part of the planet, the U.S. Midwest. Focusing on just that region is a classic case of cherry-picking.

2. The heat of the 1930s was due to human activity in the form of bad agricultural practices combined with natural variability.

3. Since the 1970s, heatwaves have become more frequent. This differs from the Dust Bowl because it is a global signal, occurring essentially everywhere. This is almost certainly due to greenhouse gas emissions from human activities.

Thus, if you see someone trying to downplay today’s extreme heat by claiming that ‘the 1930s were hotter,’ it’s a scam. Tag it with #1930sScam, hit the share button below, and share the link and the corrected figure.

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Code to reproduce the figures is here.

Other stuff

My colleague, Prof. Adam Sobel, has a nice post on his substack Deep Convection about km-scale modeling and the state of U.S. climate modeling. You should read it.

Based on this scientific evidence, we expect that when the horse race comes down the stretch, in November and December of this year, we will be riding a thoroughbred, a strong young horse, and Zeke will be atop a fading old nag.

For the record, I am the fading old nag in this metaphor. I have been called worse.

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But here is the fun part: when the race comes down the final stretch at the end of 2026, there is a good chance we will both be declared winners. Hansen’s prediction is about NASA’s GISTEMP record specifically. Mine is about the average across the major surface temperature datasets. And when I run my own forecast model separately on each of six datasets, it gives GISTEMP a ~65% chance of a new 2026 record even as the multi-dataset average only has a ~32% chance and is likely to come in second place.

First, where my forecast stands. Back in December I projected 2026 at 1.41C (1.27C to 1.55C) above preindustrial levels, and in early June I revised that up to 1.46C (1.36C to 1.59C) as forecast models converged on a doozy of an El Niño developing in the latter half of the year. Hansen, via the Washington Post, cited my June estimate of a 26.6% chance that 2026 sets a new record.

That number has continued to creep up. With observations through June and the latest El Niño forecast ensemble, my current central estimate for 2026 is 1.50C (1.44C to 1.57C) above the 1850-1900 baseline in the average of six surface temperature datasets.1 That translates into a ~32% chance that 2026 beats 2024’s record, a ~66% chance it comes in second, and almost no chance (<2%) it falls to third or below. The figure below shows where those projections sit against the observational record.

Here we see 2026 sitting just below the 2024 record line, with 2027 well above it. So the horse race (to continue the metaphor) is drifting in Hansen’s direction, though still short of the finish line. In the average of all the groups reporting global surface temperatures, second warmest remains my central call for 2026.

It is worth being clear about why my odds keep rising, because it is not that 2026 has been running unexpectedly hot. Year-to-date temperatures have actually drifted slightly down since March (a January-June mean of 1.39C, versus 1.41C for January-March). What changed is the El Niño forecast. To show this, I reran my forecast as it would have looked with each month’s information: that month’s multi-model El Niño plume plus observations through that month.

The odds of a 2026 record in the composite have risen from ~7% at the March vintage to ~35% now,2 and the decomposition on the right shows that the strengthening El Niño forecast accounts for ~84% of that rise; incoming observations contributed just over 4 points. In other words, my drift toward Hansen’s position is not the 2026 observations through June being particularly extraordinary. It is the ENSO models converging on an unprecedentedly large event. If that El Niño underdelivers, these odds will sag back down. If it holds, the odds will likely hold as well. But it seems unlikely (I hope!) that we see continued strengthening of the El Niño forecast beyond what already would blow past the prior record by a “truly mind-numbing margin”.

But “the warmest year on record” is not a single number that nature hands us; it depends on whose record you check. Hansen’s prediction is specifically about GISTEMP. So a natural question is: what does my model say if I fit it to each dataset individually, using each dataset’s own 2024 record as the bar to clear? The figure below shows the result for six datasets: the four traditional surface station products (GISTEMP, HadCRUT5, NOAA GlobalTemp, and Berkeley Earth) and two reanalysis products (Copernicus/ERA5 and JRA-3Q).

The same model, fed the same El Niño forecast, gives 2026 a ~65% chance of a record in GISTEMP and a ~66% chance in Berkeley Earth,3 but only ~35% in HadCRUT5, ~24% in NOAA, ~13% in ERA5, and ~9% in JRA-3Q. Conveniently, the average across the six (~35%) lands nearly on the blended estimate (32%), which is a reassuring consistency check.

Why the spread? It is not that the datasets disagree much about how warm 2026 will be; the projections are quite similar. They disagree about how high the bar is. The reanalysis products (ERA5 and JRA-3Q) ran exceptionally hot during the 2023-2024 event, so their 2024 records sit further above their long-term trend lines and are harder to beat. GISTEMP’s 2026 median projection sits ~0.02C above its 2024 record, while ERA5’s sits ~0.05C below its own and JRA-3Q’s ~0.07C below. When the margin is a few hundredths of a degree, structural differences between datasets could end up deciding the race.

So Hansen predicting a GISTEMP record and me predicting second warmest in the multi-dataset average are, oddly, compatible bets. If 2026 sets a record in GISTEMP but not in the dataset average (or in ERA5), expect a flurry of confused headlines in January as people try and explain how its the warmest or second warmest year depending on what dataset you look at.

I should concede the larger point plainly: Hansen made this call earlier and more confidently than I did, and the odds have moved steadily his way since. If 2026 ends up warmest across the board, he won outright, and a 32% chance is the kind of thing that happens all the time. I’d also gently note that a probabilistic forecast of “second warmest, with a one-in-three chance of a record” is a difficult thing to lose spectacularly.

What our agreement on 2026 does not settle is the more consequential disagreement about why. Hansen’s forecast rests on a specific physical story: his argument for a climate sensitivity of 4-5C per doubled CO2, a large forcing boost from falling aerosols, and a warming rate that has roughly doubled.4 My forecast is simpler: it just relies on the long-term trend, the state of ENSO, and the year-to-date observations and ends up in more or less the same place. When a statistical model based on the historical trend and an El Niño forecast lands on essentially the same 2027 number as Hansen (more on that in a moment), it tells you that a single warm year, or even two, cannot distinguish between “very rapid acceleration driven by aerosols and high sensitivity” and “the trend and more modest acceleration plus a very strong El Niño.” That debate will be settled by energy balance observations and the post-El-Niño years, not by whether 2026 clears 2024 by 0.03C in one dataset.

And on 2027 there will be no horse race at all: my model puts the odds of a new record next year at ~91%. Here I should give Hansen his due on a second count. Back in December he was already predicting a ~1.7C 2027, at a time when my own central estimate was 1.57C. Seven months and many rounds of strengthening El Niño forecasts later, my regression has drifted up to 1.70C (1.48C to 1.93C): essentially the number he wrote down at the start.

Ultimately both probabilistic forecasts and confident predictions can be validated by the same outcome, and the interesting scientific disagreement (how fast is warming accelerating, and why) will outlive whatever the December photo finish shows. Either way we are in for quite a wild climate ride in both the latter half of 2026 and 2027 due to a combination of accelerating warming and a super El Niño event.

So how do we get from the world of today to a future where forests and other ecosystems occupy different parts of the landscape? The transition will not be smooth. Forests do not simply pack up and slowly walk toward their new locations over a few decades, like the Ents in The Lord of the Rings.

Instead, the transition happens through violent, discrete events — like the massive wildfires burning around the world right now. Old forests burn, and what grows back may be a different kind of forest, or perhaps not a forest at all. At the same time, forests will begin to establish themselves in places that were once too cold or otherwise inhospitable. In other words, the forests are on the move.

That is what makes this moment so striking. We aren’t just watching extreme weather. We are watching the world literally remake itself.

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Commentary: Climate change is remaking the world

By Andrew E. Dessler, For the Express-News, Updated Jan 21, 2020 1:30 p.m.

The brush fires raging across Australia have been cited as evidence of climate change, but they are also the stunning process of the Earth remaking itself as the climate warms.

NASA and NOAA recently announced 2019 was the second-hottest year on record, trailing only 2016.

It’s been 43 years since global temperatures weren’t above average. Fires are scorching broad swaths of Australia, as they have around the world, from California to Siberia. The seas are rising, taking over real estate in South Florida on sunny days and devastating coastal communities when hurricanes strike.

This is not just destruction. It is transformation.

We are seeing the world remade before our eyes.

During the depth of the last ice age 20,000 years ago, the world was a different place. Ice sheets, thousands of feet thick, covered much of North America. Sea levels were 300 feet lower, changing the contours of the continents. Woolly mammoths roamed North America. The environment looked different, and ecosystems were made up of plants and animals that are quite different from today’s critters.

What’s amazing is that the planet was only 10 degrees Fahrenheit colder at that time.

Let me repeat that: If you cool the Earth by 10 degrees Fahrenheit, you end up with a completely different planet.

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If we don’t take action to head off climate change, we could experience 10 degrees Fahrenheit of warming in the next century or two, comparable to the warming since the last ice age. If it occurs, such warming would remake the face of Earth. That future Earth would be as unrecognizable to those of us living today as the last ice age.

Humans are really good at “linear thinking,” so most people imagine that impacts of climate change will be small changes occurring over many years, summing eventually to large changes. But that’s not the way it will happen. The transformation to come will be decidedly nonlinear, meaning it will not occur as small impacts over many years but rather a series of infrequent, huge events of enormous destructive power.

Consider how sea level rise interacts with New York City. As the sea rises, there is little impact until the seas become high enough that storm surges reach the entrance to the subway. At that instant during Hurricane Sandy, for example, the damages skyrocketed, causing billions more in damages than the city would’ve seen in a world that hadn’t warmed. Such nonlinear effects will interact with other problems, such as unwise and inequitable development, to exacerbate the overall impacts.

Hurricane Harvey was a good example of this. So was the 2011 Texas drought, which killed 300 million trees — and went a long way toward changing, potentially permanently, the face of Texas.

The extreme fires in California and Australia are doing the same thing to other parts of the planet.

These tragedies are not one-off events and should not be viewed that way. They need to be viewed as part of a long-term process, the vanguard of fundamental changes in the Earth system we will experience as we transition to a new, much warmer and much different planet.

With great sorrow, we are able to watch as the Earth is rebuilt into a different, warmer planet.

What is particularly worrying is we’re seeing these severe impacts with only about 2 degrees Fahrenheit of warming. If we get all the way to 10 degrees Fahrenheit, the tragedies we’re seeing play out now will seem quaint by comparison.

At this point, we’ve missed the boat at completely preventing climate change. Our atmosphere has been fundamentally altered by carbon pollution. However, that does not mean we should give up. We still have the ability to decide whether we experience the worst case or not. We can still choose to protect those most at risk, those already struggling to survive, and in doing so protect everyone else as well.

As we watch, the climate is changing our planet in ways we are only just beginning to comprehend, giving us a glimpse of our planet’s future that eventually may be unrecognizable. The sooner we replace fossil fuels with renewables, the more likely we are to have a planet in 2100 that still resembles the one we know and love today.

Andrew E. Dessler is a professor of atmospheric sciences at Texas A&M University who studies the science and politics of climate change. Follow him on Twitter at @andrewdessler. [update: Follow him on bluesky.]

Related posts

We’ve written several things about wildfires, so check them out:

• Canadian wildfires and climate change

• Wildfire risk is increasing in a warming world

• Los Angeles burns: What you need to know

• Canada’s boreal wildfires aren’t just bad forest management

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.

Here on The Climate Brink, we’ve written many many posts about whether temperatures were more extreme in the 1930s than today. Just last week, we wrote about one such plot and pointed out one problem with using raw station records that do not account for well-understood biases associated with large changes in the way we measure temperature over the past century.

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.

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.

A graph has been making the rounds on social media showing the average number of days per weather station above 95F, 100F, and 105F across the contiguous US since 1895. It comes from CFACT analyst Chris Martz, drawing on raw data from NOAA’s Global Historical Climatology Network daily dataset (GHCNd), and it shows the 1930s towering over everything since. The implication is that extreme heat in the US is nothing new, and that all the recent fuss about record temperatures is overblown.

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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.