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As climate writers, my colleagues and I spend a lot of time telling readers that places are hot. The Arabian Peninsula? It’s hot. The Atlantic Ocean? It’s hot. The southern U.S. and northern Mexico? Hot and getting hotter.
But here’s a little secret: “Hot” doesn’t really mean … anything. The word is, of course, of critical importance when it comes to communicating that global temperatures are the highest they’ve been in 125,000 years because of greenhouse gases in the atmosphere, or for public health officials to anticipate and prevent deaths when the environment reaches the point where human bodies start malfunctioning. But when you hear it’s “100 degrees out,” what does that really tell you?
Beyond that you’re a fellow member of the Fahrenheit cult, the answer is: not a lot. Humans can “probably avoid overheating” in temperatures of 115 degrees — but only if they’re in a dry room with 10 percent relative humidity, wearing “minimal” clothing, and not moving, The New York Times reports. On the other hand, you have a high chance of life-threatening heat stroke when it’s a mere 90 degrees out … if the humidity is at 95%. Then there are all the variables in between: if there’s a breeze, if you’re pregnant, if you’re standing in the shade or the sun, if you’re a child, if you’re running a 10K or if you’re napping on your couch in front of a swamp cooler.
In order to better specify how hot “hot” is, a number of different equations and techniques have been developed around the world. In general, this math takes into account two main variables: temperature (the one we all use, also known as “dry bulb” or “ambient air temperature,” which is typically measured five feet above the ground in the shade) and relative humidity (the percentage of air saturated with water vapor, also known as the ugly cousin of the trendier dew point; notably Canada’s heat index equivalent, the Humidex, is calculated from the dew point rather than the relative humidity).
In events like the already deadly heat dome over the southern United States and northern Mexico this week, you typically hear oohing and ahhing about the “heat index,” which is sometimes also called the “apparent temperature,” “feels like temperature,” “humiture,” or, in AccuWeather-speak, the “RealFeel® temperature.”
But what does that mean and how is it calculated?
The heat index roughly approximates how hot it “actually feels.”
This is different than the given temperature on the thermometer because the amount of humidity in the air affects how efficiently sweat evaporates from our skin and in turn keeps us cool. The more humidity there is, the less efficiently our bodies can cool themselves, and the hotter we feel; in contrast, when the air is dry, it’s easier for our bodies to keep cool. Regrettably, this indeed means that insufferable Arizonans who say “it’s a dry heat!” have a point.
The heat index, then, tells you an estimate of the temperature it would have to be for your body to be similarly stressed in “normal” humidity conditions of around 20%. In New Orleans this week, for example, the temperature on the thermometer isn’t expected to be above 100°F, but because the humidity is so high, the heat toll on the body will be as if it were actually 115°F out in normal humidity.
Importantly, the heat index number is calculated as if you were standing in the shade. If you’re exposed to the sun at all, the “feels like” is, of course, actually higher — potentially as many as 15 degrees higher. Someone standing in the New Orleans sun this week might more realistically feel like they’re in 130-degree heat.

Here’s the catch, though: The heat index is “purely theoretical since the index can’t be measured and is highly subjective,” as meteorologist Chris Robbins explains. The calculations are all made under the assumption that you are a 5’7”, 147-pound healthy white man wearing short sleeves and pants, and walking in the shade at the speed of 3.1 mph while a 6-mph wind gently ruffles your hair.
Wait, what?
I’m glad you asked.
In 1979, a physicist named R. G. Steadman published a two-part paper delightfully titled “The Assessment of Sultriness.” In it, he observed that though many approaches to measuring “sultriness,” or the combined effects of temperature and humidity, can be taken, “it is best assessed in terms of its physiological effect on humans.” He then set out, with obsessive precision, to do so.
Steadman came up with a list of approximately 19 variables that contribute to the overall “feels like” temperature, including the surface area of an average human (who is assumed to be 1.7 meters tall and weigh 67 kilograms); their clothing cover (84%) and those clothes’ resistance to heat transfer (the shirt and pants are assumed to be 20% fiber and 80% air); the person’s core temperature (a healthy 98.6°F) and sweat rate (normal); the effective wind speed (5 knots); the person’s activity level (typical walking speed); and a whole lot more.
Here’s an example of what just one of those many equations looked like:

Needless to say, Steadman’s equations and tables weren’t exactly legible for a normal person — and additionally they made a whole lot of assumptions about who a “normal person” was — but Steadman was clearly onto something. Describing how humidity and temperature affected the human body was, at the very least, interesting and useful. How, then, to make it easier?
In 1990, the National Weather Service’s Lans P. Rothfusz used multiple regression analysis to simplify Steadman’s equations into a single handy formula while at the same time acknowledging that to do so required relying on assumptions about the kind of body that was experiencing the heat and the conditions surrounding him. Rothfusz, for example, used Steadman’s now-outdated calculations for the build of an average American man, who as of 2023 is 5’9” and weighs 198 pounds. This is important because, as math educator Stan Brown notes in a blog post, if you’re heavier than the 147 pounds assumed in the traditional heat index equation, then your “personal heat index” will technically be slightly hotter.
Rothfusz’s new equation looked like this:
Heat index = -42.379 + 2.04901523T + 10.14333127R - 0.22475541TR - 6.83783x10-3T 2 - 5.481717x10-2R 2 + 1.22874x10-3T 2R + 8.5282x10-4TR2 - 1.99x10-6T 2R 2
So much easier, right?
If your eyes didn’t totally glaze over, it actually sort of is — in the equation, T stands for the dry bulb temperature (in degrees Fahrenheit) and R stands for the relative humidity, and all you have to do is plug those puppies into the formula to get your heat index number. Or not: There are lots of online calculators that make doing this math as straightforward as just typing in the two numbers.
Because Rothfusz used multiple regression analysis, the heat index that is regularly cited by the government and media has a margin of error of +/- 1.3°F relative to a slightly more accurate, albeit hypothetical, heat index. Also of note: There are a bunch of different methods of calculating the heat index, but Rothfusz’s is the one used by the NWS and the basis for its extreme heat alerts. The AccuWeather “RealFeel,” meanwhile, has its own variables that it takes into account and that give it slightly different numbers.
Midday Wednesday in New Orleans, for example, when the ambient air temperature was 98°F, the relative humidity was 47%, and the heat index hovered around 108.9°F, AccuWeather recorded a RealFeel of 111°F and a RealFeel Shade of 104°F.
You might also be wondering at this point, as I did, that if Steadman at one time factored out all these variables individually, wouldn’t it be possible to write a simple computer program that is capable of personalizing the “feel like” temperature so they are closer to your own physical specifications? The answer is yes, although as Randy Au writes in his excellent Substack post on the heat index equation, no one has seemingly actually done this yet. Math nerds, your moment is now.
Because we’re Americans, it is important that we use the weirdest possible measurements at all times. This is probably why the heat index is commonly cited by our government, media, and meteorologists when communicating how hot it is outside.
But it gets weirder. Unlike the heat index, though, the “wet-bulb globe temperature” (sometimes abbreviated “WBGT”) is specifically designed to understand “heat-related stress on the human body at work (or play) in direct sunlight,” NWS explains. In a sense, the wet-bulb globe temperature measures what we experience after we’ve been cooled by sweat.

The “bulb” we’re referring to here is the end of a mercury thermometer (not to be confused with a lightbulb or juvenile tulip). Natural wet-bulb temperature (which is slightly different from the WBGT, as I’ll explain in a moment) is measured by wrapping the bottom of a thermometer in a wet cloth and passing air over it. When the air is dry, it is by definition less saturated with water and therefore has more capacity for moisture. That means that under dry conditions, more water from the cloth around the bulb evaporates, which pulls more heat away from the bulb, dropping the temperature. This is the same reason why you feel cold when you get out of a shower or swimming pool. The drier the air, the colder the reading on the wet-bulb thermometer will be compared to the actual air temperature.
Wet bulb temperature - why & when is it used?www.youtube.com
If the air is humid, however, less water is able to evaporate from the wet cloth. When the relative humidity is at 100% — that is, the air is fully saturated with water — then the wet-bulb temperature and the normal dry-bulb temperature will be the same.
Because of this, the wet-bulb temperature is usually lower than the relative air temperature, which makes it a bit confusing when presented without context (a comfortable wet-bulb temperature at rest is around 70°F). Wet-bulb temperatures over just 80, though, can be very dangerous, especially for active people.
The WBGT is, like the heat index, an apparent temperature, or “feels like,” calculation; generally when you see wet-bulb temperatures being referred to, it is actually the WBGT that is being discussed. This is also the measurement that is preferred by the military, athletic organizations, road-race organizers, and the Occupational Safety and Health Administration because it helps you understand how, well, survivable the weather is, especially if you are moving.
Our bodies regulate temperature by sweating to shed heat, but sweat stops working “once the wet-bulb temperature passes 95°F,” explains Popular Science. “That’s because, in order to maintain a normal internal temperature, your skin has to stay at 95°F degrees or below.” Exposure to wet-bulb temperatures over 95°F can be fatal within just six hours. On Wednesday, when I was doing my readings of New Orleans, the wet-bulb temperature was around 88.5°F.
The WBGT is helpful because it takes the natural wet-bulb temperature reading a step further by factoring in considerations not only of temperature and humidity, but also wind speed, sun angle, and solar radiation (basically cloud cover). Calculating the WBGT involves taking a weighted average of the ambient, wet-bulb, and globe temperature readings, which together cover all these variables.
That formula looks like:
Wet-bulb globe temperature = 0.7Tw + 0.2Tg + 0.1Td
Tw is the natural wet-bulb temperature, Tg is the globe thermometer temperature (which measures solar radiation), and Td is the dry bulb temperature. By taking into account the sun angle, cloud cover, and wind, the WBGT gives a more nuanced read of how it feels to be a body outside — but without getting into the weeds with 19 different difficult-to-calculate variables like, ahem, someone we won’t further call out here.
Thankfully, there’s a calculator for the WBGT formula, although don’t bother entering all the info if you don’t have to — the NWS reports it nationally, too.
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1. Marion County, Indiana — State legislators made a U-turn this week in Indiana.
2. Baldwin County, Alabama — Alabamians are fighting a solar project they say was dropped into their laps without adequate warning.
3. Orleans Parish, Louisiana — The Crescent City has closed its doors to data centers, at least until next year.
A conversation with Emily Pritzkow of Wisconsin Building Trades
This week’s conversation is with Emily Pritzkow, executive director for the Wisconsin Building Trades, which represents over 40,000 workers at 15 unions, including the International Brotherhood of Electrical Workers, the International Union of Operating Engineers, and the Wisconsin Pipe Trades Association. I wanted to speak with her about the kinds of jobs needed to build and maintain data centers and whether they have a big impact on how communities view a project. Our conversation was edited for length and clarity.
So first of all, how do data centers actually drive employment for your members?
From an infrastructure perspective, these are massive hyperscale projects. They require extensive electrical infrastructure and really sophisticated cooling systems, work that will sustain our building trades workforce for years – and beyond, because as you probably see, these facilities often expand. Within the building trades, we see the most work on these projects. Our electricians and almost every other skilled trade you can think of, they’re on site not only building facilities but maintaining them after the fact.
We also view it through the lens of requiring our skilled trades to be there for ongoing maintenance, system upgrades, and emergency repairs.
What’s the access level for these jobs?
If you have a union signatory employer and you work for them, you will need to complete an apprenticeship to get the skills you need, or it can be through the union directly. It’s folks from all ranges of life, whether they’re just graduating from high school or, well, I was recently talking to an office manager who had a 50-year-old apprentice.
These apprenticeship programs are done at our training centers. They’re funded through contributions from our journey workers and from our signatory contractors. We have programs without taxpayer dollars and use our existing workforce to bring on the next generation.
Where’s the interest in these jobs at the moment? I’m trying to understand the extent to which potential employment benefits are welcomed by communities with data center development.
This is a hot topic right now. And it’s a complicated topic and an issue that’s evolving – technology is evolving. But what we do find is engagement from the trades is a huge benefit to these projects when they come to a community because we are the community. We have operated in Wisconsin for 130 years. Our partnership with our building trades unions is often viewed by local stakeholders as the first step of building trust, frankly; they know that when we’re on a project, it’s their neighbors getting good jobs and their kids being able to perhaps train in their own backyard. And local officials know our track record. We’re accountable to stakeholders.
We are a valuable player when we are engaged and involved in these sting decisions.
When do you get engaged and to what extent?
Everyone operates differently but we often get engaged pretty early on because, obviously, our workforce is necessary to build the project. They need the manpower, they need to talk to us early on about what pipeline we have for the work. We need to talk about build-out expectations and timelines and apprenticeship recruitment, so we’re involved early on. We’ve had notable partnerships, like Microsoft in southeast Wisconsin. They’re now the single largest taxpayer in Racine County. That project is now looking to expand.
When we are involved early on, it really shows what can happen. And there are incredible stories coming out of that job site every day about what that work has meant for our union members.
To what extent are some of these communities taking in the labor piece when it comes to data centers?
I think that’s a challenging question to answer because it varies on the individual person, on what their priority is as a member of a community. What they know, what they prioritize.
Across the board, again, we’re a known entity. We are not an external player; we live in these communities and often have training centers in them. They know the value that comes from our workers and the careers we provide.
I don’t think I’ve seen anyone who says that is a bad thing. But I do think there are other factors people are weighing when they’re considering these projects and they’re incredibly personal.
How do you reckon with the personal nature of this issue, given the employment of your members is also at stake? How do you grapple with that?
Well, look, we respect, over anything else, local decision-making. That’s how this should work.
We’re not here to push through something that is not embraced by communities. We are there to answer questions and good actors and provide information about our workforce, what it can mean. But these are decisions individual communities need to make together.
What sorts of communities are welcoming these projects, from your perspective?
That’s another challenging question because I think we only have a few to go off of here.
I would say more information earlier on the better. That’s true in any case, but especially with this. For us, when we go about our day-to-day activities, that is how our most successful projects work. Good communication. Time to think things through. It is very early days, so we have some great success stories we can point to but definitely more to come.
The number of data centers opposed in Republican-voting areas has risen 330% over the past six months.
It’s probably an exaggeration to say that there are more alligators than people in Colleton County, South Carolina, but it’s close. A rural swath of the Lowcountry that went for Trump by almost 20%, the “alligator alley” is nearly 10% coastal marshes and wetlands, and is home to one of the largest undeveloped watersheds in the nation. Only 38,600 people — about the population of New York’s Kew Gardens neighborhood — call the county home.
Colleton County could soon have a new landmark, though: South Carolina’s first gigawatt data center project, proposed by Eagle Rock Partners.
That’s if it overcomes mounting local opposition, however. Although the White House has drummed up data centers as the key to beating China in the race for AI dominance, Heatmap Pro data indicate that a backlash is growing from deep within President Donald Trump’s strongholds in rural America.
According to Heatmap Pro data, there are 129 embattled data centers located in Republican-voting areas. The vast majority of these counties are rural; just six occurred in counties with more than 1,000 people per square mile. That’s compared with 93 projects opposed in Democratic areas, which are much more evenly distributed across rural and more urban areas.
Most of this opposition is fairly recent. Six months ago, only 28 data centers proposed in low-density, Trump-friendly countries faced community opposition. In the past six months, that number has jumped by 95 projects. Heatmap’s data “shows there is a split, especially if you look at where data centers have been opposed over the past six months or so,” says Charlie Clynes, a data analyst with Heatmap Pro. “Most of the data centers facing new fights are in Republican places that are relatively sparsely populated, and so you’re seeing more conflict there than in Democratic areas, especially in Democratic areas that are sparsely populated.”
All in all, the number of data centers that have faced opposition in Republican areas has risen 330% over the past six months.
Our polling reflects the breakdown in the GOP: Rural Republicans exhibit greater resistance to hypothetical data center projects in their communities than urban Republicans: only 45% of GOP voters in rural areas support data centers being built nearby, compared with nearly 60% of urban Republicans.

Such a pattern recently played out in Livingston County, Michigan, a farming area that went 61% for President Donald Trump, and “is known for being friendly to businesses.” Like Colleton County, the Michigan county has low population density; last fall, hundreds of the residents of Howell Township attended public meetings to oppose Meta’s proposed 1,000-acre, $1 billion AI training data center in their community. Ultimately, the uprising was successful, and the developer withdrew the Livingston County project.
Across the five case studies I looked at today for The Fight — in addition to Colleton and Livingston Counties, Carson County, Texas; Tucker County, West Virginia; and Columbia County, Georgia, are three other red, rural examples of communities that opposed data centers, albeit without success — opposition tended to be rooted in concerns about water consumption, noise pollution, and environmental degradation. Returning to South Carolina for a moment: One of the two Colleton residents suing the county for its data center-friendly zoning ordinance wrote in a press release that he is doing so because “we cannot allow” a data center “to threaten our star-filled night skies, natural quiet, and enjoyment of landscapes with light, water, and noise pollution.” (In general, our polling has found that people who strongly oppose clean energy are also most likely to oppose data centers.)
Rural Republicans’ recent turn on data centers is significant. Of 222 data centers that have faced or are currently facing opposition, the majority — 55% —are located in red low-population-density areas. Developers take note: Contrary to their sleepy outside appearances, counties like South Carolina’s alligator alley clearly have teeth.