IceCube’s cosmic-ray leftovers could become an Antarctic climate record
A neutrino detector buried in Antarctic ice may be able to track the temperature of the stratosphere above the South Pole—not by measuring air, but by counting particles made when cosmic rays hit it. The catch is exacting: a tiny, unnoticed drift in the detector’s rate could look like a climate trend. To use the instrument as an independent atmospheric thermometer, its long-term stability must be held to about 0.3% per decade.
That is the central finding of Tanvi Krishnan, Gabriela Barenboim and Carlos A. Argüelles’ study. IceCube sees a steady rain of muons, energetic particles born in cosmic-ray showers high above Antarctica. The team’s calculations suggest that changes in those muon counts can reveal changes in the atmosphere’s effective temperature, a weighted measure that is especially responsive to the stratosphere. They estimate that a 14-year record could distinguish a temperature trend of 1.6 kelvin per decade at a one-sided 95% confidence level. That is not yet a measurement of Antarctic cooling: the researchers used simulated IceCube rates, not the detector’s long-term observed rate series.
The promise is consequential because the Antarctic interior is one of the least directly measured parts of the atmosphere. An independent record could help test the reanalyses—model-based reconstructions that combine weather observations with physics—that scientists rely on there. It could also turn a background signal in a neutrino observatory into a climate-monitoring instrument. But the work is a feasibility study, and detector stability, not a shortage of particle counts, sets the hard limit.
The Science
IceCube was built to detect neutrinos, elusive particles that can pass through matter with little interaction. Its more than 5,000 optical modules are embedded between roughly 1,450 and 2,450 metres beneath the South Pole. The detector also records a much larger stream of downward-moving atmospheric muons. These are not neutrinos: they are produced when cosmic rays collide with air molecules, then travel through Antarctic ice to the detector.
The connection to weather begins in the stratosphere. Cosmic-ray collisions create short-lived particles called pions and kaons, which can either collide again or decay into muons. When the atmosphere warms, it expands and becomes less dense at a given altitude. Pions and kaons then have more room—and time—to decay before another collision. More muons reach the detector. In this way, the muon rate acts as an indirect sensor of the air above IceCube.
The researchers describe the relevant air temperature with the effective atmospheric temperature, written . It is not the temperature at one altitude, but a weighted average of temperatures through the atmosphere. For IceCube, the weighting is concentrated in the stratosphere: about 94% of the sensitivity comes from atmospheric pressures below 300 hectopascals, around eight kilometres above sea level, and the weighting peaks near 120 hectopascals, roughly 14 kilometres up.
A response coefficient, , connects a fractional change in effective temperature to the fractional change in the muon rate. IceCube’s published seasonal coefficient is : a 1% shift in effective temperature corresponds to about a 0.75% change in detected muons. Seasonal temperature swings already produce a large, roughly 8% variation in the rate. The question is whether the same relationship can reveal a much smaller, slower change across decades.
That is not automatic. A winter-to-summer temperature profile differs from the pattern of long-term climate change. The seasonal profile in the study warms at every atmospheric level, while the 2011–2025 trend in the South Pole reanalysis indicates cooling in the stratosphere and warming in the troposphere. If muon production weights those layers differently, the seasonal calibration might translate a decadal signal incorrectly.
To test the idea, the authors combine atmospheric reanalysis, solar and geomagnetic records, a calculation of cosmic-ray particle showers, and an analytic model of muon production. They compare their calculated seasonal response with IceCube’s published response, then simulate what a long-term muon record would look like. They use the NCEP/NCAR reanalysis for historical temperature profiles and compare its effective-temperature estimates with 217 months of radiosonde measurements—balloon-borne observations taken at the South Pole. The match in month-to-month variability is very close, with a correlation of 0.9975; the reanalysis averages 0.44 K warmer, with a 1.21 K root-mean-square difference.
What They Found
First, the seasonal calibration works well enough to make the longer-term test plausible. Their simulated seasonal muon response has a fitted coefficient of 0.745, close to IceCube’s measured 0.750. The simulated seasonal rate changes by 15.9% peak to peak, compared with approximately 16% in the published IceCube measurement. The paper’s calculation is simplified—it uses vertical muons and a single energy threshold, rather than the detector’s full angular and energy acceptance—but it reproduces the main observed seasonal behavior.
The long-term temperature pattern changes the coupling only modestly. After normalizing different atmospheric profiles to the same change in effective temperature, the authors find that the decadal trend profile produces a muon-rate response about 3% larger than the seasonal profile. Their estimate for the ratio is 1.03, with uncertainties tied to the baseline atmosphere and to how they extrapolate the temperature trend above the reanalysis’s highest level. For a future precision measurement, that small correction matters; using the seasonal coefficient unchanged would overstate the inferred long-term temperature change by about 3%.
The temperature response depends slightly on the atmospheric profile
Effective coupling values reported for January and July baseline atmospheres. The trend-profile entries show the ranges from the paper’s two high-altitude extrapolations.
| Label | Value |
|---|---|
| Seasonal profile, January | 0.724 |
| Seasonal profile, July | 0.745 |
| Trend profile, January | 0.747 |
| Trend profile, July | 0.76 |
| Uniform offset, January | 0.816 |
| Uniform offset, July | 0.823 |
The study also asks whether solar activity could masquerade as warming or cooling. The Sun’s magnetic environment modulates the cosmic rays reaching Earth, and those cosmic rays produce the muons IceCube sees. But IceCube’s deep muons come from much higher-energy parent cosmic rays than the particles most strongly registered by neutron monitors. Using a standard solar-modulation model, the researchers estimate peak-to-peak changes of 0.01% to 0.13% in the integrated primary cosmic-ray flux for the energy thresholds they consider. This is a bound on the solar contribution, not a full prediction of the muon-rate change: it does not include the detailed yield of muons from each primary particle.
Estimated solar modulation at IceCube-relevant energies
The paper reports a range for peak-to-peak modulation of integrated primary cosmic-ray flux, depending on the minimum primary-energy threshold and observed modulation potential.
| Label | Value |
|---|---|
| Lower end of reported range | 0.01 % |
| Upper end of reported range | 0.13 % |
The main obstacle is not whether IceCube collects enough events. At an average trigger rate of about 2.15 kilohertz, it records a vast number. Random counting fluctuations can be reduced by accumulating more data. A systematic drift in the detector, however, can imitate a real atmospheric signal and does not disappear just because the sample is large.
The conversion is striking: an unmodeled 1% per decade drift in the muon rate would resemble a temperature trend of 2.8 K per decade. The rate therefore needs to be stable to approximately 0.3% per decade to keep detector drift within the study’s error budget. Snow accumulation above the detector can also alter the overburden muons pass through. The authors conclude that this contribution can be constrained within the stability budget, but a real application must monitor such effects rather than assume them away.
For a 14-year record, the study estimates a one-sided 95% statistical sensitivity of 1.6 K per decade from atmospheric variability. In practical terms, a trend smaller than that would be difficult to separate from the atmosphere’s own fluctuations on that timescale, even before all systematic uncertainties are resolved. The reanalysis indicates a cooling of about 0.9 K per decade over 2011–2025, smaller than the 14-year sensitivity. The paper’s main text says a cooling of that size would become distinguishable at 95% confidence after approximately 22 years of measurement. The supplied abstract also gives an estimate of approximately 30 years for distinguishability; the paper presents these two different timelines, so the forecast should be treated as approximate rather than as a single settled number.
![Figure 3: (a) Monthly anomaly in reanalysis Teff at the South Pole from
1979–2025, together with its 24-month running mean. The fitted trend is
−0.58±0.15 K decade-1, with its uncertainty adjusted for
correlation between neighboring monthly residuals. (b) Linear trend estimates
as a function of window length for the reanalysis, radiosonde, and RSS
lower-stratospheric satellite records [11]. All windows end in
2025. The dotted line marks the 47-year window shown in panel (a). The
geophysical trend depends on the window length and can change sign, so a
muon-derived trend must be quoted for a specified window.](https://arxiv.org/html/2611.00918v1/figs/fig_trend.png)
Why This Changes Things
Antarctica’s climate matters far beyond the continent. Changes in the Antarctic stratosphere are connected to ozone recovery and shifts in polar circulation, and those atmospheric changes can influence weather patterns. Yet measurements over the interior are sparse. Reanalysis products fill gaps by combining observations with atmospheric models, but their output is inevitably less directly anchored where few observations exist.
IceCube would offer a different kind of evidence. It would not replace radiosondes or reanalysis, and it would not deliver a full temperature profile. Instead, it would measure a weighted temperature signal through the response of cosmic-ray showers. If that independent signal agreed with reanalysis, confidence in the inferred trend would strengthen. If it diverged, the disagreement could point to a limitation in the observations, the model, or the particle-physics calibration. Independent measurements are especially valuable when the system being measured is also the system used to fill observational gaps.
The paper also clarifies what the detector can and cannot say. Its muons mainly sample stratospheric air, rather than providing a thermometer for the entire atmosphere. And the long-term trend depends on the time window: the South Pole effective-temperature series has a fitted trend of −0.58 ± 0.15 K per decade across 1979–2025, while shorter windows can give different values and even change sign. A claimed climate trend must therefore specify its start and end dates, rather than treating one rate as the timeless Antarctic answer.
The work’s positive implication is not that IceCube has already discovered a new climate trend. It has not. It is that a detector designed for particle physics may carry a useful, independent record of a remote atmosphere—and that its limitations can be stated quantitatively. The analysis identifies a measurable target, a calibration adjustment, and a demanding stability benchmark. That is more useful than a vague promise that “more data” will solve the problem.
What’s Next
The next essential step is to apply the method to IceCube’s actual muon-rate history. The present paper generates simulated rates using atmospheric and cosmic-ray inputs, adds expected counting fluctuations, and tests whether a trend can be recovered. It does not analyze a long-term observed IceCube rate series. Real data could reveal detector changes, operational shifts, or selection effects absent from the simplified simulation.
That analysis will need careful control of the instrument’s long-term response. IceCube’s active optical modules, triggers, event selections, reconstruction, and changing ice overburden all matter when the sought-for signal is smaller than one percent per decade. The study treats snow accumulation as a constrained contribution, but the real record must demonstrate that it is monitored well enough. A successful result will depend on documenting stability, not merely on the detector’s enormous event count.
The atmospheric calibration deserves further refinement too. The authors’ vertical-muon approximation is adequate for comparing relative changes in their model, but a precision measurement should account for the detector’s full range of directions and energies. It should also carry the roughly 3% difference between seasonal and long-term response into its conversion from rate to temperature. Solar activity appears small at the relevant primary energies, but its contribution should still be modeled alongside geomagnetic effects and changes in cosmic-ray flux.
Finally, the measurement should be compared with independent atmospheric records over explicitly stated windows. The reanalysis trend changes with the period selected, and the 2011–2025 cooling estimate is not identical to the multidecadal result. A muon-derived temperature trend would be most informative as part of a comparison among IceCube, radiosondes, satellite records and reanalyses—not as a standalone verdict on Antarctic climate.
If the detector can meet the stability requirement and its observed rate behaves as the simulations predict, IceCube could provide something Antarctica urgently lacks: a long, independent atmospheric measurement from the interior of the continent. The detector already watches particles cross the ice. The next challenge is to make sure that, over decades, changes in their arrival rate tell us about the sky rather than the instrument.