{"id":395617,"date":"2025-08-16T20:57:38","date_gmt":"2025-08-16T18:57:38","guid":{"rendered":"https:\/\/climatescience.press\/?p=395617"},"modified":"2025-08-16T20:57:45","modified_gmt":"2025-08-16T18:57:45","slug":"no-tribune-short-term-ai-weather-analysis-provides-no-insight-concerning-climate-change","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=395617","title":{"rendered":"No, Tribune, Short Term AI Weather Analysis Provides No Insight Concerning Climate Change"},"content":{"rendered":"\n
\"A<\/figure>\n\n\n\n

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

From ClimateRealism<\/a><\/p>\n\n\n\n

By Anthony Watts<\/a><\/p>\n\n\n\n

\"A<\/figure>\n\n\n\n

The Tribune News Service article, \u201cAI is fast-tracking climate research<\/a><\/em>,\u201d embraces a misleading central premise. Although AI can speed up certain weather<\/em> data processing tasks, it is unable to magically make long-term climate forecasts more accurate. The piece blurs the line between short-term weather model gains and the far more uncertain, decades-scale climate projections \u2014 a fundamental error that misleads both policymakers and the public.<\/p>\n\n\n\n

The article quotes AZTI marine biologist \u00c1ngel Borja saying, \u201cIt will allow us to process data and get results much faster, so people that make decisions can act faster, too.\u201d<\/em> This might be true for fisheries management or local ocean data sets, but when applied to climate, speed does not equal accuracy. Acting \u201cfaster\u201d on flawed or incomplete climate projections risks imposing and enshrining harmful and costly policies for decades, based on noise or computer model errors, not an accurate signal.<\/p>\n\n\n\n

Here is a breakdown of each of the claims in the article.<\/p>\n\n\n\n

Claim:<\/strong> \u201cSome AI-powered models are already outperforming conventional forecasting systems.\u201d<\/em><\/p>\n\n\n\n

Yes \u2014 but that\u2019s in weather forecasting<\/strong>, which operates on hours-to-weeks timescales. Even Microsoft\u2019s Aurora<\/em> and Google DeepMind\u2019s GraphCast<\/em>, cited in the story, focus on short-term atmospheric prediction<\/em>. This has almost nothing to do with the 30-year climate averages that define climate science. See Climate at a Glance: Climate Model Fallibility<\/a>.<\/p>\n\n\n\n

Claim:<\/strong> \u201cAI models\u2026 can enable climate scientists to explore hundreds of times more scenarios than they can today.\u201d<\/em><\/p>\n\n\n\n

Quantity isn\u2019t quality. Exploring \u201chundreds\u201d of flawed scenarios faster doesn\u2019t change the fact that climate models \u2014 AI or not \u2014 still have massive uncertainty ranges and are built upon unverified assumptions<\/strong>. Observations show CMIP6 climate models overstate warming trends by nearly double<\/strong> actual measurements as told in Climate Models vs. Measured Data<\/a>. If your inputs and physics are wrong, multiplying the number of runs just multiplies the wrongness.<\/p>\n\n\n\n

Claim:<\/strong> \u201cHigh-quality weather information is the first step to setting warning systems.\u201d<\/em><\/p>\n\n\n\n

True, for short-term weather events \u2014 but irrelevant to long-term accurate forecasts of changes in a regions climate<\/em>. Weather quality doesn\u2019t fix deep uncertainties in equilibrium climate sensitivity (ECS)<\/a><\/strong>, which still ranges from 0.8\u00b0C to almost 6\u00b0C by 2100. Nor does it solve the known \u00b14 Watts per square meter radiative cloud forcing error \u2014 more than 4,000 times the estimated signal from a year\u2019s worth of CO\u2082 emissions according to the Hoover Institution<\/a>.<\/p>\n\n\n\n

Claim:<\/strong> \u201cAI is set to turbocharge what the center can offer policymakers\u2026 allowing them to make more-informed decisions.\u201d<\/em><\/p>\n\n\n\n

This is the most dangerous overreach. Climate is by definition a 30-year statistical average<\/strong>. The World Meteorological Organization (WMO) defines<\/a> climate as \u201c\u2026the average weather conditions for a particular location and over a long period of time. \u201cFast-tracking\u201d such projections is meaningless \u2014 the data horizon can\u2019t be shortened without destroying the definition of climate itself. Worse, rushing policy on the back of still-uncertain models risks trillion-dollar mistakes.<\/p>\n\n\n\n

Tribune News Service: please stop confusing flash<\/em> with substance<\/em>. AI is not a magical oracle of climate truth \u2014 it\u2019s a faster, sometimes cheaper way to crunch the same flawed models that have consistently overshot observed warming. Until climate models can reconcile with reality, narrow their error bars, resolve cloud physics, and stop producing 200% exaggerations of observed warming, your framing of AI as a \u201cclimate game-changer\u201d is not journalism \u2014 it\u2019s marketing copy. And marketing copy dressed as science is worse than ignorance; it\u2019s an invitation to policy disaster.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

The Tribune News Service article, \u201cAI is fast-tracking climate research,\u201d embraces a misleading central premise. Although AI can speed up certain\u00a0weather\u00a0data processing tasks, it is unable to magically make long-term climate forecasts more accurate. The piece blurs the line between short-term weather model gains and the far more uncertain, decades-scale climate projections \u2014 a fundamental error that misleads both policymakers and the public.<\/p>\n","protected":false},"author":121246920,"featured_media":395622,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_crdt_document":"","advanced_seo_description":"Discover why AI's role in climate research may mislead, blurring the lines between short-term weather and long-term climate accuracy.","jetpack_seo_html_title":"AI and Climate Forecasting: Misleading Short-Term Gains Unveiled","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"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":[691823433,691818056,691818153,691837224,691831875],"class_list":{"0":"post-395617","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-uncategorized","8":"tag-ai-artificial-intelligence-2","9":"tag-climate-change","10":"tag-climate-models","11":"tag-tribune-news-service","12":"tag-weather-models","14":"fallback-thumbnail"},"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/08\/AQMsRSmOF4Hl_yhVXriLK4wUoXGMLd_dBu-gPh5EKIY6ovjVMS0lSZI3gy-Tmh_7tWTSciCVU-seXv8Rm2xYC2GFFcdVkIVqdHu5pxs2hBlxM8_kFI-CtHDxCjxSR3dy3m2Trwnh0ySQuGb-nqExBmPiKdqgJg-1.jpeg?fit=1280%2C1280&ssl=1","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-1EUV","jetpack-related-posts":[{"id":394718,"url":"https:\/\/climatescience.press\/?p=394718","url_meta":{"origin":395617,"position":0},"title":"From Weather to Climate: Why Tribune\u2019s News Service AI Leap is a Logic Fail","author":"uwe.roland.gross","date":"12\/08\/2025","format":false,"excerpt":"The Tribune News Service article, \u201cAI is fast-tracking climate research,\u201d is misleading in its central premise. 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