A datacenter may operate for 20 years, yet its cooling plant is commonly sized from the previous 30 years of weather. The two intervals no longer describe the same climate. During the United Kingdom’s 2022 heatwave, temperatures reached 40°C and Google observed elevated server errors and degraded operation for 35 hours[1]. The standard design point of 37.7°C had historically represented an event expected about once in 200 years. Forward projections make it closer to once in 50.
Prometheus, presented by Google and the University of Pennsylvania at ISCA 2026, treats that mismatch as a computer-systems problem rather than a footnote in facility design[1]. It combines 25 years of observations with 20 years of climate projections, estimates how each site will cross its dry-bulb or wet-bulb limit, then attaches an action to three horizons: infrastructure planning over 20 years, upgrades over two years and workload response over two weeks. Across 30 production datacenters, the method calls for 11% more cooling capacity on average. The largest site-level requirement reaches 48%.
A design temperature is a probability statement
Cooling capacity depends on two different descriptions of heat. Dry-bulb temperature is the familiar ambient reading and matters for systems that reject sensible heat to outside air. Wet-bulb temperature also includes humidity and bounds what evaporative cooling can achieve. A site can therefore be limited by a hot dry afternoon or by a cooler but humid day. Prometheus models both instead of applying one global margin.
Its forecasting pipeline uses support-vector machines and random forests as base models, with a neural network combining their outputs. The training data joins site observations to CMIP6 climate projections[3]. Against analytic baselines, the learned model reduces wet-bulb error by 40% to 60%. One representative root-mean-square error falls from 1.7°C to 0.7°C, while error at the 99.5th percentile declines by more than 60%. Accuracy at the tail matters because the cooling plant is sized for the few hours the average conceals.
The change is not uniform. Compared with ASHRAE weather files[2], Prometheus finds an average 4.4°C difference in dry-bulb design conditions and 1.4°C in wet-bulb conditions. For 2044, the SSP5-8.5 high-emissions case produces dry-bulb increases whose smallest and largest values are 2.0°C and 10.7°C. Its wet-bulb counterpart rises between 2.7°C and 6.8°C. London illustrates the sequence: the design file says 37.7°C, the 2022 observation reached 40.2°C, and the 2044 projection reaches 41.2°C. The old margin did not merely become smaller. It was spent before the building reached the middle of its life.
Thirty sites do not need the same margin
Applying the model to Google’s fleet produces a distribution rather than a corporate rule. The average site needs an 11% increase in cooling capacity. The most exposed dry-bulb location needs 39%, and the most exposed wet-bulb location needs 48%. Meanwhile, 12% of sites already face an annual probability above 2% of exceeding their design condition. An event described as exceptional in a building standard has become an operational scenario.
Workload flexibility does not remove the infrastructure gap. In the production sample, 30% of datacenters lack enough load that can be shed or migrated during extreme heat. Most sites can move less than 20%, and each 10% reduction in compute lowers the cold-aisle requirement by about 1°C. The resulting 1°C to 2°C benefit is useful around the edge of the operating range, but cannot cover a projection that moved by 6°C or 10°C.
The paper translates this boundary into a concrete wet-bulb example. Below 30°C, control tuning may maintain service with less than 3% load shedding. At 32°C, the requirement rises to about 12%. At 35°C, it reaches 30%, which the authors treat as operationally infeasible and therefore an infrastructure-upgrade case. Cooling equipment also comes in discrete increments, commonly around 15 MW, so the final decision is not a smooth percentage slider.
One forecast, three clocks
Prometheus is most interesting where it connects facility and software time scales. The 20-year forecast informs site selection and the installed cooling plant. A rolling two-year view decides where modular units, pumps or controls should be upgraded. A two-week forecast initiates the operating runbook before the temperature arrives.
That runbook begins about 14 days ahead with a risk assessment. Eight to ten days out, operators select migrations and shedding plans. Four to seven days out, they execute data movement and service changes, leaving the final day for last adjustments. The lead time is not generous at hyperscale. Moving 10 MW of compute can mean roughly 200,000 virtual machines and 3.2 PB of memory. Even a continuous 50 Gb/s transfer takes about a week to move that memory volume, before application dependencies are counted.
The economics support selective upgrades instead of a universal overbuild. A hyperscale facility costs roughly $7 to $12 per watt, of which cooling represents 15% to 25%. Adding 20% to cooling capacity therefore costs an estimated $0.20 to $0.60 per watt. The paper’s lower-bound estimate for the 35-hour London event is $0.44 per watt in service impact. With a typical 10% capacity addition and the software value protected by availability targets, the authors estimate that an upgrade can be about four times more favorable than absorbing the disruption. This is a planning comparison, not a universal tariff, but it places climate risk in the same units as datacenter capital.
The missing input to AI capacity planning
AI infrastructure discussions often count megawatts as if the value were available in every hour of the facility’s life. Prometheus shows that cooling can make electrical capacity conditional on local weather, humidity and movable workload. A 100 MW campus with insufficient heat rejection does not own 100 MW during the event that defined its design.
The practical lesson is to preserve the distribution. Fleet averages cannot size individual sites, and a dry-bulb margin cannot stand in for wet-bulb exposure. Operators need site-specific projections, calibrated tail error and an explicit boundary between control tuning, workload response and physical expansion. The same forecast should reach the facilities team early enough to buy equipment and the scheduler early enough to move state.
The paper also has limits. Climate projections depend on emissions scenarios, and the study reports an anonymized production fleet rather than full site-by-site designs. Its cost model uses lower-bound interruption estimates and cannot capture every service contract. Nevertheless, the systems contribution is clear. Weather data has a version, and a datacenter whose design input is never updated is running critical infrastructure on an expired dependency.
Source and attribution
This article is an editorial summary prepared for Silicon & Systems. It restates the cited paper in our own words. No text, tables or figures from the paper are reproduced; both figures were created for this article from reported results. The version of record was published by IEEE in the proceedings of ISCA 2026. Copyright (c) 2026 IEEE. The paper is available through its DOI record.