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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.

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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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

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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.

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

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

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

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

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

Decomposing the drivers

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

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

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

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

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

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

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

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

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

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

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

So what are the takeaways?

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

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

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

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

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

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

Here is a reprint of our Carbon Brief piece:

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

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

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

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

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

Outpacing projections

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

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

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

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

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

Counting the death toll of climate change

The downstream impacts of these extreme temperatures are lethal.

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

There are several ways to do this.

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

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

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

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

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

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

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

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

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

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

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

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

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

Calculating climate risk

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

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

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

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

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

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

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

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

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

Substantial uncertainty persists at every stage of this sequence.

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

Managing the future of heat risk

Almost all heat-related deaths are preventable.

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

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

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

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

other stuff

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2. How much did climate change warm this day?

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

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

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

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

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Determining the counterfactual world

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

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

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

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

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

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

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

Traditional detection and attribution

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

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

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

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

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

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

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

Here are the results:

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

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

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

This is the basic logic of detection and attribution.

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

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

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

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

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

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

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

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

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

Confidence in attribution studies

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

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

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

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

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

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

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

The new Merchants of Doubt

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

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

The key quote is:

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

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

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

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

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

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

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The new new Merchants of Doubt

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

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

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

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

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

A note to the press: Fix your frame

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

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

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

That frame is a trap.

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

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

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

other stuff

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Update

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

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

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

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

Cleaned up transcript:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Let me open the floor to questions. Thank you.

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

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

evolution of our energy system in 6 charts

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

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

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

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

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

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

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

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

And here’s wind:

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

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

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

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the cost of energy

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

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

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

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

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

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

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

what are the implications of this?

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

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

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

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

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

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

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

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

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

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

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

Code to reproduce the plots can be found here.

related things

Is nuclear energy the answer? Nope.

Is renewable energy cheaper than fossil fuels? Yup.

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

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

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