by C. Rotter, Apr 1, 2026 in WUWT
A persistent assumption underlies modern global temperature reconstructions: that individual station errors, even when large, are diluted through spatial averaging and homogenization. That assumption deserves closer inspection. Recent analysis of station-level data suggests that under certain conditions—specifically when extreme outliers evade quality control and are subsequently incorporated into homogenization routines—localized anomalies can propagate nonlinearly through the global record.
The present investigation began with a routine audit of tropical station residuals within the GHCN (Global Historical Climatology Network) dataset. The initial objective was unremarkable: quantify the distribution of post-homogenization adjustments across low-latitude stations. What emerged instead was a persistent and statistically anomalous signal centered on a single station in Costa Rica, hereafter designated CR-VOLC-EL-INFIERNO-01.
The anomaly first appears in the late 1970s, coinciding with documented volcanic activity in the Talamanca Range. At face value, elevated temperatures in proximity to geothermal activity are not unexpected. What is unexpected is the magnitude, persistence, and downstream influence of those readings once introduced into the global processing pipeline.
Raw observations from CR-VOLC-EL-INFIERNO-01 indicate sustained daily maximum temperatures exceeding 300°C over multiple reporting intervals. Such values would ordinarily trigger immediate exclusion under standard quality control thresholds. Yet archival flags associated with this station indicate no such exclusion occurred. Instead, the readings were retained and subjected to standard homogenization procedures.
To understand how such values could persist, it is necessary to examine the homogenization framework itself. Modern temperature datasets rely on relative homogenization techniques, wherein each station is adjusted based on comparisons with neighboring stations. The fundamental assumption is that neighboring stations share a common climate signal, allowing discontinuities (instrument changes, relocations) to be corrected through statistical alignment.
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