| 26 Aug 2026
Satellite Data Records as a Tool to Monitor Changes in Air Temperature
Abstract. We analyze satellite-derived lower-tropospheric temperature (TLT) data for the period 1981–2025 and examine their relationship to the pronounced warming observed in in situ measurements during 2023–2024. To reduce uncertainty and improve the robustness of detecting long-term changes in Earth's warming rate, we construct an adjusted TLT record by removing variability associated with the El Niño–Southern Oscillation (ENSO) and atmospheric aerosols. This adjustment reduces the magnitude of annual TLT variability by nearly 50 %, substantially enhancing the robustness and reliability of the observed satellite temperature trends. Using the adjusted TLT record, we identify statistically significant warming trends of up to 0.482 ± 0.113 °C decade⁻¹ after 2015 across all satellite and reanalysis datasets examined in this study. These trends are approximately four to five times larger than those during the pre-2015 period. However, these trend estimates are likely conservative. At the upper end, statistically significant increases in the warming rate of up to 0.48 ± 0.12 °C decade⁻² are inferred near 2024, indicating that the pronounced warming observed during 2023–2024 are part of an ongoing increase in the underlying warming rate that was further amplified by the El Niño event. Projections based on these increased warming rates suggest the potential for an additional 0.5–1.0 °C of lower-tropospheric warming over the next decade. The physical mechanisms responsible for this unusually rapid warming, however, remain unclear. Resolving these mechanisms is therefore essential for improving future climate projections and informing effective mitigation and adaptation strategies.
There are huge issues with this submission, all making accelerated warming appear more likely than it statistically is (right now). While I also suspect that planetary warming has accelerated recently, the authors' analysis here seems rather misguided.
1) First, subtracting/adjusting for the effects of ENSO, solar aerosols, etc. on the temperatures, analyzing the adjusted series, and then claiming that any trend findings for it also pertain to the original temperature series is fallacious. The authors justify this via the references [8] and [40-41], which make similar mistakes to varying degrees. We could also fit and remove additional factors like PDO, AO, CO_2, etc. and obtain even more significant findings. Where does it stop? The point is what you have found does not apply to the original temperatures (the ones we experience on Earth) any more.
2) Second, annual temperature series are usually positively autocorrelated. I do not see where autocorrelation is taken into account in the standard error margins or p-values for the various model fits. If you don't account for correlation, your standard errors and p-values will be too small, making insignificant fluctuations appear significant.
3) Third, the changepoint techniques are naive. At least, one can't fit the model for every potential breakpoint time b, find the p-value assuming this value of b is known, and then report results for the best b. This never accounts for the variability of the changepoint time, leading again to overstated conclusions.
There are other issues, but the above three dominate.
In the end, I am afraid there's nothing salvageable here: 1) above isn't fixable, and while 2) is, doing 3) right is hard (at least, current research papers on joinpin changepoint models are just now appearing in the statistics literature).