{"id":367850,"date":"2025-02-27T07:37:30","date_gmt":"2025-02-27T06:37:30","guid":{"rendered":"https:\/\/climatescience.press\/?p=367850"},"modified":"2025-02-27T07:37:32","modified_gmt":"2025-02-27T06:37:32","slug":"noaas-homogenized-temperature-records-a-statistical-house-of-cards","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=367850","title":{"rendered":"NOAA\u2019s Homogenized Temperature Records: A Statistical House of Cards?"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

From Watts Up With That?<\/a><\/p>\n\n\n\n

By Charles Rotter<\/a><\/p>\n\n\n\n

For years, climate scientists have assured us that NOAA\u2019s homogenized temperature datasets\u2014particularly the Global Historical Climatology Network (GHCN)\u2014are the gold standard for tracking global warming. But what if the \u201ccorrections\u201d applied to these datasets are introducing more noise than signal?\u00a0A recent study published in\u00a0Atmosphere<\/em><\/a>\u00a0has uncovered shocking inconsistencies in NOAA\u2019s adjustments, raising serious concerns about the reliability of homogenized temperature records.<\/p>\n\n\n\n

\"\"
https:\/\/www.mdpi.com\/2073-4433\/13\/2\/285<\/a><\/figcaption><\/figure>\n\n\n\n

The study, conducted by a team of independent climate researchers led by Peter O\u2019Neill, Ronan Connolly, Michael Connolly, and Willie Soon, offers a meticulous examination of NOAA\u2019s 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\u2019s GHCN dataset over more than a decade. Their research involved tracking over 1800 daily updates<\/strong> to analyze how NOAA\u2019s adjustments to historical temperature records changed over time.<\/p>\n\n\n\n

Their findings reveal a deeply concerning pattern of inconsistencies and unexplained changes<\/strong> in temperature adjustments, prompting renewed scrutiny of how NOAA processes climate data.<\/p>\n\n\n\n

The study analyzed NOAA\u2019s GHCN dataset over a decade and found that:<\/p>\n\n\n\n

    \n
  • The same temperature records were being adjusted differently on different days<\/strong>\u2014sometimes dramatically.<\/li>\n\n\n\n
  • 64% of the breakpoints identified by NOAA\u2019s Pairwise Homogenization Algorithm (PHA) were highly inconsistent<\/strong>, appearing in less than 25% of NOAA\u2019s dataset runs.<\/li>\n\n\n\n
  • Only 16% of the adjustments were consistently applied in more than 75% of cases<\/strong>, meaning the majority of \u201ccorrections\u201d are shifting unpredictably.<\/li>\n\n\n\n
  • Less than 20% of NOAA\u2019s breakpoints corresponded to actual documented station changes<\/strong>, suggesting that many adjustments were made without supporting metadata.<\/li>\n<\/ul>\n\n\n\n

    In layman\u2019s terms: NOAA is repeatedly changing historical temperature records in ways that are inconsistent, poorly documented, and prone to error.<\/p>\n\n\n\n

    What Is Homogenization Supposed to Do?<\/strong><\/p>\n\n\n\n

    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\u2019s PHA algorithm adjusts temperature records based on statistical comparisons with neighboring stations\u2014without needing actual metadata to confirm whether an adjustment is necessary.<\/p>\n\n\n\n

    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<\/strong> that could distort temperature trends.<\/p>\n\n\n\n

    If NOAA\u2019s adjustments are inconsistent, how can we trust the long-term climate trends derived from them? Here\u2019s why this matters:<\/p>\n\n\n\n

      \n
    • Garbage In, Garbage Out:<\/strong>\u00a0Climate models and policy decisions rely on adjusted temperature data. If those adjustments are unreliable, the conclusions based on them are questionable.<\/li>\n\n\n\n
    • Artificial Warming or Cooling?<\/strong>\u00a0The study did not specifically analyze whether these inconsistencies bias the data towards warming or cooling, but past research has shown that homogenization tends to\u00a0amplify warming trends.<\/strong><\/li>\n\n\n\n
    • Lack of Transparency:<\/strong>\u00a0NOAA\u2019s daily homogenization updates mean that the past is constantly being rewritten, with little accountability or external validation.<\/li>\n<\/ul>\n\n\n\n

      The study\u2019s authors argue that homogenization should not be done blindly<\/strong> without using actual station metadata. Instead, adjustments should be:<\/p>\n\n\n\n

        \n
      1. Ground-truthed with station metadata whenever possible<\/strong>\u2014not just assumed based on statistical models.<\/li>\n\n\n\n
      2. Made transparent<\/strong>\u2014users of temperature data should be informed about exactly when and why adjustments are made.<\/li>\n\n\n\n
      3. Re-evaluated for bias<\/strong>\u2014does homogenization systematically increase warming trends?<\/li>\n<\/ol>\n\n\n\n

        If NOAA\u2019s 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<\/strong>.<\/p>\n\n\n\n

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

        We need transparency, accountability, and scientific rigor in climate science. Until then, every NOAA temperature dataset should be taken with a grain of salt.<\/p>\n","protected":false},"excerpt":{"rendered":"

        For years, climate scientists have assured us that NOAA\u2019s homogenized temperature datasets\u2014particularly the Global Historical Climatology Network (GHCN)\u2014are the gold standard for tracking global warming. But what if the \u201ccorrections\u201d applied to these datasets are introducing more noise than signal?\u00a0A recent study published in\u00a0Atmosphere\u00a0has uncovered shocking inconsistencies in NOAA\u2019s adjustments, raising serious concerns about the reliability of homogenized temperature records.<\/p>\n","protected":false},"author":121246920,"featured_media":367854,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691822949,691833564,691818397,691833565],"class_list":["post-367850","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-climate-data","tag-global-historical-climatology-network-ghcn","tag-noaa","tag-pairwise-homogenization-algorithm-pha","fallback-thumbnail"],"jetpack_publicize_connections":[],"jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-1xH4","jetpack-related-posts":[{"id":378577,"url":"https:\/\/climatescience.press\/?p=378577","url_meta":{"origin":367850,"position":0},"title":"Our 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https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/09\/00glo-urban-heat-island-effect.webp?fit=1200%2C690&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/09\/00glo-urban-heat-island-effect.webp?fit=1200%2C690&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/09\/00glo-urban-heat-island-effect.webp?fit=1200%2C690&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":243950,"url":"https:\/\/climatescience.press\/?p=243950","url_meta":{"origin":367850,"position":1},"title":"Evidence that Statistical Adjustments and Urbanization Spuriously Warm Temperature Trends","author":"uwe.roland.gross","date":"02\/09\/2023","format":false,"excerpt":"Urbanization Effects on GHCN Temperature Trends, Part II: Evidence that Homogenization Spuriously Warms Trends","rel":"","context":"Similar post","block_context":{"text":"Similar 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