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Short answer: A March 2026 preprint found that satellite-measured land-surface temperatures near analyzed AI data centers rose by an average of 2.07°C after operations began, with observed increases ranging from 0.3°C to 9.1°C. The 9.1°C figure is the maximum at one analyzed location—not a claim that the surrounding air everywhere became 9.1°C hotter.
What the 9.1°C figure measures
The study by Andrea Marinoni and coauthors examined reconstructed NASA MODIS land-surface temperature records. Land-surface temperature describes the temperature of the ground, roofs, pavement and other surfaces detected by a satellite. It is different from near-surface air temperature measured by a weather station or thermometer.
| Reported result | Qualification |
|---|---|
| 2.07°C | Average post-operation increase in satellite-derived land-surface temperature at analyzed AI data-center locations. |
| 0.3°C to 9.1°C | Observed range across analyzed facilities; 9.1°C was the maximum, not the typical result. |
| 1.5°C to 2.4°C | Range containing the study’s 95th percentile of post-start increases. |
Therefore, “up to 9.1°C hotter” should be read as a localized surface-temperature extreme in this dataset. It should not be converted directly into a 9.1°C increase in outdoor air temperature or assumed to apply to every data center.
How the analysis was conducted
Data sources
The researchers combined a 500-meter-resolution NASA MODIS reconstructed land-surface-temperature grid, an International Energy Agency database containing more than 11,000 data-center locations, and population maps. The historical comparison covered 2004 through 2024.
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Filtering and timing
Highly dense urban areas were filtered out. After preprocessing, the analysis used 6,733 cleaned data points, removed seasonal effects and outliers, and aligned monthly temperature trends with each facility’s reported start of operations. This design looks for a temperature change associated with the time a facility began operating; it is not a controlled experiment in which waste heat was independently measured.
What facilities were represented
The paper focuses on AI hyperscalers. Its results should not automatically be generalized to a small server room, an older conventional data center, or every facility classified as a data center.
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How far the signal appears to extend
The preprint reports a spatial influence extending as far as 10 kilometers in its analysis. The average monthly increase was about 1°C out to 4.5 kilometers, while the reported intensity fell to roughly 30% of its near-site level by 7 kilometers.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThese distances describe a declining statistical signal, not a sharp boundary around each facility. Local land cover, building geometry, wind, terrain and other development can change how a surface-temperature pattern appears in satellite data.
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How many people may be affected?
Using the paper’s exposure definition, the authors estimated that as many as 343 million people live within 10 kilometers of the analyzed hyperscalers and could be exposed to increases reaching about 9°C. That is a modeled population count within a study radius; it does not mean all of those people experience a 9°C air-temperature increase, or that each person is exposed to the maximum value.
Why the finding is not proof of waste heat alone
Data centers release heat, but the observed association can also reflect changes that happen alongside construction and operation. Possible influences include new roads and buildings, altered vegetation or land cover, facility location choices and broader development trends. The authors applied filtering and preprocessing, yet the observational design cannot by itself establish that waste heat caused every measured change.
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The result is also a preprint posted on March 21, 2026, rather than an established peer-reviewed consensus. Independent replication and peer review could change the estimated size, geographic pattern or interpretation.
What the wider U.S. infrastructure picture shows
A 2026 U.S. Geological Survey spatial analysis identified 771 existing AI data centers in Alaska and 11 Western U.S. states. The same report described U.S. data-center electricity use as rising from about 2% of national consumption during 2014–2018 to a projected 6.7%–12% by 2028. Those figures concern electricity demand, not a direct measurement of local temperature, but they explain why cooling, grid capacity and siting are increasingly linked.
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The USGS emphasizes that high ambient temperatures increase cooling requirements and that water availability, transmission capacity and permitting schedules need to be considered together. It specifically describes water availability as a core natural-resource consideration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which cooling approaches change the trade-off?
No cooling design eliminates heat; it changes how efficiently the facility moves heat away and which resources it consumes. The appropriate choice depends on climate, humidity, power prices, available water, rack density and whether an existing building can be modified.
| Approach | Electricity demand | Water demand | Climate suitability | Heat rejection and retrofit issues | Monitoring needs |
|---|---|---|---|---|---|
| Air-cooled or dry heat rejection | Can rise during hot weather because fans and mechanical cooling work harder. | Generally avoids routine evaporative water use at the heat-rejection stage. | Most challenging in persistently hot conditions; humidity affects the usefulness of outdoor-air strategies. | Familiar for many facilities, but high-density AI equipment can require substantial airflow and equipment changes. | Continuous temperature, humidity, airflow and electrical-load monitoring. |
| Evaporative or water-assisted cooling | Can reduce electricity use compared with purely mechanical air cooling in suitable conditions. | Higher water consumption is the central trade-off identified by the USGS; availability and drought risk matter. | Performance depends on ambient temperature and humidity; humid air leaves less evaporative capacity. | Effective heat rejection where water systems and treatment infrastructure are available, but adds plumbing and permitting requirements. | Water flow and quality, leaks, blowdown, temperature, humidity and power use. |
| Direct-to-chip liquid cooling or immersion | May reduce the airflow and fan burden for very high heat densities; total facility power still depends on the entire cooling loop. | A closed equipment loop does not automatically mean zero site water use; final heat rejection determines consumption. | Can be paired with dry, evaporative or hybrid heat rejection selected for the local climate. | Often requires compatible servers, distribution hardware and careful retrofit planning; leak and fluid-management controls are essential. | Fluid temperature, flow, pressure, leak detection, equipment condition and heat-exchanger performance. |
| Hybrid systems | Can switch between modes to balance power demand and operating conditions. | Water use can be limited during scarcity or increased when it saves electricity, depending on controls. | Useful where temperatures, humidity or water restrictions vary seasonally. | Offers flexibility but increases controls, equipment and integration complexity. | Unified monitoring of weather, water, power, heat rejection and operating mode. |
The key planning question is not simply whether a system uses less electricity. A water-based design may save electricity while increasing water use, so operators and communities need both energy and water accounting.
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- Is the reported temperature a satellite land-surface measurement or a direct air-temperature measurement?
- What is the facility’s actual cooling-water demand by season, including treatment and blowdown?
- How will the design perform during heat waves and high-humidity periods?
- What electrical capacity, transmission upgrades and backup generation are required?
- Which nearby land-cover or construction changes could affect future satellite observations?
- What sensors and public reporting will verify temperature, electricity, water use and heat-rejection performance after commissioning?
How to interpret the headline responsibly
The strongest defensible reading is that a 2026 preprint detected a localized, distance-decaying association between AI hyperscaler operations and higher satellite-derived land-surface temperatures. The average reported increase was about 2°C, while 9.1°C was the largest observation in the analyzed distribution. The study does not establish a universal 9.1°C air-temperature rise, a fixed 10-kilometer impact zone for every facility, or waste heat as the sole cause of each change.
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