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Yes, a 2026 field study found a measurable downwind air-temperature increase around four data centers in the Phoenix area. Across five vehicle traverses, researchers recorded an average difference of 0.7–0.9 °C between downwind and corresponding upwind areas, with a maximum of 2.2 °C and detectable thermal signatures up to 500 meters from facility boundaries. Those results establish a local signal at the sampled sites—not a fixed effect for every data center, neighborhood, or climate.
Why data centers can add heat to their surroundings
Data centers use electricity to run servers and supporting equipment. That electricity ultimately becomes heat, which cooling systems must remove and reject outdoors. Because large facilities concentrate substantial electrical loads in one place, their waste heat can affect nearby air, particularly downwind of the site.
Whether that added heat is noticeable beyond the facility depends on conditions such as wind, weather, surrounding land cover, cooling design, and the location and timing of measurements. A measured temperature difference near one facility is therefore not a universal “data center heat” multiplier.
What the Phoenix field study measured
In a peer-reviewed study published May 1, 2026, David J. Sailor, Soroush Samareh Abolhassani, and Eli P. Martin reported the first field measurements of neighborhood-scale air-temperature impacts from data-center waste heat. The team made five vehicle-based traverses at four Phoenix-area facilities, from a 36 MW single-building site in Mesa to a 169 MW colocation campus in Chandler. The ASU-indexed article record identifies the paper as peer reviewed and lists DOI 10.1115/1.4071922.
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- Average: Downwind air temperatures were 0.7–0.9 °C higher than corresponding upwind areas.
- Maximum: The largest reported downwind difference was 2.2 °C.
- Extent: Thermal signatures were detectable as far as 500 meters from facility perimeters.
These are results from the sampled Phoenix sites and measurement conditions, not a prediction for every data center. The study’s abstract also gives facility-specific estimates of rejected heat: the 36 MW Mesa site was equivalent to the electricity consumption of approximately 40,000 households, and the 169 MW Chandler campus to more than 180,000 households. Those comparisons describe the two facilities in the study; they are not general household-equivalence figures for data centers.
How the measurements were collected
According to Arizona State University’s account, researchers mounted high-accuracy, fast-response temperature sensors on vehicles and used deployable weather stations to measure local wind. Measurements ran from June 18 through October 25, 2025. ASU describes the sampled sites as reflecting a common hyperscaler design, with many thousands of servers and primarily air-based cooling.
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ASU reports rounded Fahrenheit versions of the findings: an average difference of 1.3–1.6 °F and a maximum of 4 °F, detectable about a third of a mile from facility boundaries. These are rounded conversions of the study’s Celsius results, not separate measurements.
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Not every temperature claim measures the same thing. Air temperature recorded near the ground, the temperature of a land surface inferred from satellites, and temperatures produced by a climate model are distinct quantities. They also answer different questions, so their numbers should not be compared as if they were interchangeable.
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Satellite analysis: broader coverage, different measurement
A 2026 arXiv preprint by Andrea Marinoni and coauthors analyzed reconstructed NASA MODIS land-surface-temperature data from 2004–2024 alongside a database of AI hyperscaler locations. Comparing monthly land-surface temperatures around facility start dates with preceding years, the authors report a mean increase of 2.07 °C across analyzed locations, with a reported range of 0.3–9.1 °C. The preprint describes satellite-derived land-surface-temperature analysis, not vehicle-level observations of near-ground air temperature. Its observational method also does not, by itself, establish that facilities caused all of the measured change.
Modeling: useful for scenarios, not a substitute for reported results
A June 24, 2026 Zenodo archive documents code and data for a separate California modeling study, including facility-level energy data, WRF-ready anthropogenic-heat inputs, processed model outputs, and population data. The archive metadata establishes that analysis artifacts exist; it does not provide a basis for claiming particular modeled temperature results.
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What the evidence does—and does not—settle
The Phoenix study is important because it reports direct, localized air-temperature measurements rather than relying only on remote sensing or modeling. Its results support the conclusion that waste heat from the sampled facilities was associated with warmer air downwind under the observed conditions.
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The broader controversy is about how to weigh this local signal as data-center capacity grows. A difference that may look modest in isolation could matter in an already hot neighborhood, while the actual exposure depends on where and when heat is released and how local air moves. David Sailor, the study’s lead researcher, told ASU: “Even if these data centers only contribute to an additional heat island magnitude of 1 degree or 2 degrees, that can still have a very significant impact on our lives.”
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ASU’s discussion points to possible responses such as changes to facility design and cooling equipment, microclimate modeling, greenbelts or parks, and siting or permitting conditions. These are options for evaluation, not remedies whose effectiveness the Phoenix field study tested. Decisions should be based on site-specific conditions and evidence rather than assuming one buffer distance or design change will work everywhere.
For local monitoring, a single thermometer reading near a facility cannot show that the facility caused a temperature difference. A credible comparison needs attention to wind direction, shade, timing, and an appropriate upwind reference. Household weather devices also do not replicate the study’s calibrated, fast-response vehicle sensors and local wind measurements.
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