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A conversation with Zeke Hausfather, the climate scientist and lead researcher at Frontier, on why last month was so appallingly warm.

Congrats! You just lived through the hottest September ever recorded.
“This month was — in my professional opinion as a climate scientist — absolutely gobsmackingly bananas,” said Zeke Hausfather, who leads research at the carbon-removal initiative Frontier.
In many parts of the world, last month saw temperatures that would not have been out of place in July. It smashed the previous record for hottest September ever by nearly 1 degree Fahrenheit (or half a degree Celsius). And it was a gobsmacking 3 degrees Fahrenheit — that is, 1.8 degrees Celsius — warmer than what would have been historically normal.
Earlier today, I called up Zeke to ask him why September saw such appalling warmth. Our conversation has been edited for length and clarity.
The chart looks crazy. Does it seem as crazy to you as it would seem to us?
It feels kind of crazy. I mean, people are going to write dissertations about 2023, and just how unusual this year has been for the climate.
Where has this year been worse?
The North Atlantic is the place that really stands out. It’s just so far above anything we’ve seen in that region — it’s hard to know what’s going on there. But we’ve also seen the warmest year to date over China. We’ve seen South America be exceptionally warm. We’ve seen some winter temperatures in Brazil that rival the hottest summer temperatures ever seen there — although it’s in the tropics, so there’s less seasonal variability there, generally. Australia’s been unusually warm.
Why has this year, in particular, been so hot?
Part of the reason that these summer charts look so crazy is that the most recent big El Niño events that we’ve had have primarily boosted winter temperatures. 1998 and 2016 both had really high December, January, and February temperatures. And we’re probably on track for that as well this year — El Nino is still growing.
But this year, we saw a very dramatic shift from a moderate La Niña — a very unusually long, “triple dip” moderate La Niña that lasted from late 2020 to the start of this year — to strong El Niño conditions over the course of a few months. And so it’s not just the transition from neutral to El Niño that affects temperatures, it’s the swap from La Niña to El Niño. And that’s been part of the story this year, and one of the reasons why you’ve seen such high temperatures this summer.
There’s a bunch of other contributing factors that we’re still in the early stages of precisely quantifying. Those include an uptick in the solar cycle that happens every 11 years — that has a small effect, 0.05 degrees Celsius maybe. There’s the phase-out of sulfate shipping fuels by 2020, which shouldn’t suddenly affect the summer of this year but which certainly has contributed to more recent warming. And that’s on top of the broad decline in forcing from aerosols — and sulfur dioxide, in particular, that’s fallen about 30% since the year 2000.
And then there’s a bit of a wild card with this Tonga volcano that erupted last year that put a huge amount of water vapor in the atmosphere. Again, most of the early modeling of that shows somewhere in the range of an increase of 0.05 to 0.08 C — a boost to warming, but not the main cause. But I think if you combine the rapid switch from La Niña to El Niño and all these smaller contributors on top of the 1.3 degrees Celsius or so of human-driven warming that we’ve had to date, you can get temperatures this extreme.
So if I understand correctly, the last big El Niño that we had — in 2016, I think — developed during the big Northern Hemisphere summer. Is that right?
It developed a little later than this one developed. But more importantly, it switched more gradually from neutral conditions to El Niño conditions. What is a bit unusual this year is just how rapidly we’ve transitioned from La Niña to El Niño.
Why would making the leap from La Niña to El Niño make the temperature leap worse than switching from neutral to El Niño?
The last three years have been slightly cooler than we would normally expect because of La Niña conditions. And so the jump we see this year is somewhat relative to what we’ve seen in previous years — if the previous summers had been much warmer, than this summer’s jump on the chart would seem less extraordinary.
I see. So part of what we’re seeing is the past three years of warming sort of getting unmasked, so to speak?
Yeah, on top of a big El Niño and those other factors.
I assume August sealed it, but 2023 will definitely be the hottest year on record, right?
It would be extremely unlikely to not be the hottest year on record. Barring an asteroid hitting the planet or maybe a Pinatubo-sized volcano erupting tomorrow, 2023 is definitely going to be the hottest year on record.
Do we have any sense of how this compares to baselines after this El Niño ends? Is this a new normal?
I think that 2024 will probably be fairly similar. The El Niño that’s evolving now will — should, actually — have its bigger effects next year. But this year has been so weird, it’s hard to say what’s going on. We do expect temperatures to fall down below 2023-2024 levels in 2025 or 2026.
One way I like to think about this is we have this long-term human-driven warming. And then on top of that, there’s plus or minus two tenths of a degree Celsius in any given year due to internal climate variability, primarily due to La Niña or El Niño. So when we have all the stars align, as we do this year, we get a peek of what the new normal is going to be a decade from now.
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The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation.What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.