Guest column by Charles Rotter

For years, climate scientists have assured us that NOAA’s homogenized temperature datasets—particularly the Global Historical Climatology Network (GHCN)—are the gold standard for tracking global warming. But what if the “corrections” applied to these datasets are introducing more noise than the signal? A recent study published in Atmosphere has uncovered shocking inconsistencies in NOAA’s adjustments, raising serious concerns about the reliability of homogenized temperature records.

https://www.mdpi.com/2073-4433/13/2/285

The study, conducted by a team of independent climate researchers led by Peter O’Neill, Ronan Connolly, Michael Connolly, and Willie Soon [Editor’s note: Connolly, Connolly, and Soon are co-authors of a chapter in Cornwall Alliance’s book Climate and Energy: The Case for Realism—E. Calvin Beisner], offers a meticulous examination of NOAA’s homogenization techniques. These researchers, known for their expertise in climate data analysis and critical evaluation of mainstream climate methodologies, gathered an extensive archive of NOAA’s GHCN dataset over more than a decade. Their research involved tracking over 1800 daily updates to analyze how NOAA’s adjustments to historical temperature records changed over time.

Their findings reveal a deeply concerning pattern of inconsistencies and unexplained changes in temperature adjustments, prompting renewed scrutiny of how NOAA processes climate data.

The study analyzed NOAA’s GHCN dataset over a decade and found that:

• The same temperature records were being adjusted differently on different days—sometimes dramatically.
• 64% of the breakpoints identified by NOAA’s Pairwise Homogenization Algorithm (PHA) were highly inconsistent, appearing in less than 25% of NOAA’s dataset runs.
• Only 16% of the adjustments were consistently applied in more than 75% of cases, meaning the majority of “corrections” are shifting unpredictably.
• Less than 20% of NOAA’s breakpoints corresponded to actual documented station changes, suggesting that many adjustments were made without supporting metadata.

In layman’s terms: NOAA is repeatedly changing historical temperature records in ways that are inconsistent, poorly documented, and prone to error.

What Is Homogenization Supposed to Do?

Homogenization is a statistical process meant to remove non-climatic biases from temperature records, such as changes in station location, instrument type, or observation time. NOAA’s PHA algorithm adjusts temperature records based on statistical comparisons with neighboring stations—without needing actual metadata to confirm whether an adjustment is necessary.

This method has been defended by NOAA researchers, who claim it effectively removes bias. However, the new study suggests it might be introducing arbitrary and inconsistent changes that could distort temperature trends.

If NOAA’s adjustments are inconsistent, how can we trust the long-term climate trends derived from them? Here’s why this matters:

• Garbage In, Garbage Out: Climate models and policy decisions rely on adjusted temperature data. If those adjustments are unreliable, the conclusions based on them are questionable.
• Artificial Warming or Cooling? The study did not specifically analyze whether these inconsistencies bias the data towards warming or cooling, but past research has shown that homogenization tends to amplify warming trends.
• Lack of Transparency: NOAA’s daily homogenization updates mean that the past is constantly being rewritten, with little accountability or external validation.

The study’s authors argue that homogenization should not be done blindly without using actual station metadata. Instead, adjustments should be:

1. Ground-truthed with station metadata whenever possible—not just assumed based on statistical models.
2. Made transparent—users of temperature data should be informed about exactly when and why adjustments are made.
3. Re-evaluated for bias—does homogenization systematically increase warming trends?

If NOAA’s temperature records are truly the best we have, they should be robust, reproducible, and verifiable. Instead, this study suggests they are a moving target, adjusted differently depending on the day, and often without a clear reason.

The question we must ask is this: Is the global temperature record a reliable dataset, or just a statistical house of cards?

We need transparency, accountability, and scientific rigor in climate science. Until then, every NOAA temperature dataset should be taken with a grain of salt.

This column is reprinted by permission from WattsUpWithThat.com and was written by WUWT editor Charles Rotter.