{"id":416977,"date":"2025-12-11T11:51:56","date_gmt":"2025-12-11T10:51:56","guid":{"rendered":"https:\/\/climatescience.press\/?p=416977"},"modified":"2025-12-11T11:52:00","modified_gmt":"2025-12-11T10:52:00","slug":"costly-models-costing-dearly","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=416977","title":{"rendered":"Costly models costing dearly"},"content":{"rendered":"\n
\"A<\/figure>\n\n\n\n

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

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

By Joe Bastardi<\/a><\/p>\n\n\n\n

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

The reason I don\u2019t pick on the U.S. models is that they are so bad overall. In fact, I hardly even look at them anymore. The GEFS, which is the flagship ensemble model of the U.S. fleet, is last in skill score vs the European, Canadian, and, though not shown here, the UK met.  It\u2019s the black line.<\/p>\n\n\n\n

Day 5 \u2013 500 mb forecast:<\/p>\n\n\n

\n
\"Line<\/figure>\n<\/div>\n\n\n

Day 10:<\/p>\n\n\n

\n
\"Graph<\/figure>\n<\/div>\n\n\n

Day 15:<\/p>\n\n\n

\n
\"Line<\/figure>\n<\/div>\n\n\n

This is like having one of the biggest NIL budgets in football and winding up 6-6.<\/p>\n\n\n\n

What happens to the coaches of teams when that happens?<\/p>\n\n\n\n

On Aug 29, we issued our winter forecast in the face of models forecasting a warm winter with a meteorological consensus that not much was going on.<\/p>\n\n\n\n

Here were the bullet points of our forecast, warning \u2014 three months before \u2014 about the period we are in now!<\/p>\n\n\n\n

August 29, 2025<\/strong><\/p>\n\n\n\n

    \n
  • The threat of a cold winter looms.<\/li>\n\n\n\n
  • Plenty of blocking is expected over the Arctic.<\/li>\n\n\n\n
  • A prominent Alaskan ridge should lead to plenty of cold air.<\/li>\n\n\n\n
  • Another cold period from Thanksgiving to Christmas is on the table, but this time it may not break.<\/li>\n\n\n\n
  • The heart of the winter looks coldest relative to average, but there are questions as to how it starts and ends.<\/li>\n<\/ul>\n\n\n\n

    Given the wet front of the summer in Texas that I missed, and the hurricane season red zone being further east, the merit of the winter forecast and the front cold is a plus.  (Again, I am well aware of my mistakes and will acknowledge them. But our methodology gave anyone who wanted to listen a leg up on what is going on now.)<\/p>\n\n\n\n

    How can I tell there was no urgency about the upcoming winter? Look at the price of natural gas futures on Aug 29 and look what has happened.<\/p>\n\n\n

    \n
    \"Line<\/figure>\n<\/div>\n\n\n

    This is one of the biggest early winter moves on record \u2014 a doubling of the price. It can only occur if there is economic turmoil or adverse weather conditions. The weather has stepped in. For this to happen, it means the market consensus, which is not all meteorologists but a mix of meteorologists, traders, and people who obviously believe they know better, coming up with ideas that every time you see it go up, agree with our ideas from three months before, with counter-punching leading to downturns. The question for the rest of the winter is does this flip to warmer? It\u2019s fascinating, as we have caught the three early-season cold shots now (2022, last year). But the methods I use only work in early winter. Looking at the method and trying to use it in mid-winter, we find an even split between warm and cold. Years when the cold collapsed (1989-90,2005-06). Years when it rolled on into January (1983-84, last year)<\/p>\n\n\n\n

    So that is what we are focused on for clients, but what I am going to show you is a case study that shows if it\u2019s going to be cold, the models won\u2019t see it.<\/p>\n\n\n\n

    I am going to pick on the European, which is the acknowledged king of the models. Picking on the U.S. models is like Scarlett O\u2019Hara continuing to whip her beaten mule.<\/p>\n\n\n\n

    Last winter was a fiasco with models.<\/p>\n\n\n\n

    In December, the month before January, the Euro had this for temperatures in January.<\/p>\n\n\n

    \n
    \"Temperature<\/figure>\n<\/div>\n\n\n

    This is a bearish pattern for energy. Big markets all warm.<\/p>\n\n\n\n

    What actually happened was this \u2014 almost exactly opposite.<\/p>\n\n\n

    \n
    \"Map<\/figure>\n<\/div>\n\n\n

    Five days into the period (released Jan 5), it sees the cold. But that is not a forecast, as it is issued after the start period.<\/p>\n\n\n

    \n
    \"Temperature<\/figure>\n<\/div>\n\n\n

    February was not as bad but still underdone.<\/p>\n\n\n\n

    The January issuance for February.<\/p>\n\n\n

    \n
    \"Forecast<\/figure>\n<\/div>\n\n\n

    What happened:<\/p>\n\n\n

    \n
    \"Map<\/figure>\n<\/div>\n\n\n

    Again, colder.<\/p>\n\n\n\n

    February 5 issuance:<\/p>\n\n\n

    \n
    \"Map<\/figure>\n<\/div>\n\n\n

    Even after the month started, not cold enough.<\/p>\n\n\n\n

    We are going through it again!<\/p>\n\n\n\n

    In November, the model had this for December:<\/p>\n\n\n

    \n
    \"Map<\/figure>\n<\/div>\n\n\n

    It has flipped to this:<\/p>\n\n\n

    \n
    \"Temperature<\/figure>\n<\/div>\n\n\n

    That\u2019s more than 8 degrees below normal around Chicago. Chicago, NYC, and Boston all have shots at the top five coldest Decembers in the last 50 years. Simply thinking you can use models is folly. We had seven major physical drivers and analogs we were looking at, none of which the modeling apparently understood mattered.<\/p>\n\n\n\n

    But here is another aspect, the NWS probability scheme, as I call it, and the fact that most people have no idea what it really means. The Media takes it and simply says they are forecasting this or that, when a probability of an occurrence IS NOT A FORECAST. It\u2019s akin to saying if you have six teams in a division, and you give one of them a one in four chance of winning, you are forecasting them to win. And how much are they going to win by? Imagine trying to collect money from a bookie because you gave him a chance a team would win. He won\u2019t even lay the bet down BECAUSE YOU ARE NOT ACTUALLY SAYING SOMEONE WILL WIN.<\/p>\n\n\n\n

    If you are doing it in the wrong neighborhood, it could be hazardous to your health.<\/p>\n\n\n\n

    A forecast has to predict a measurable metric before the start of the period. These probabilities of an occurrence are not truly forecasts, but guidance. There is no such thing as a \u201cprobability forecast\u201d as it does not tell you how much above or below it actually is. It is guidance, that is true, but .1 below normal in Chicago, 1 below normal in Chicago, or 10 below normal in Chicago cannot be scored as a probability. And it makes a huge difference. Chicago this month could have a top five coldest December over the past 50 years. They are off to a heck of a start, as is much of the nation, where heating is important.<\/p>\n\n\n\n

    Here is the December guidance from November 30:<\/p>\n\n\n

    \n
    \"Map<\/figure>\n<\/div>\n\n\n

    It has Chicago, Boston and NYC leaning below \u2014 one day before the start of a month that might be top five coldest in the past 50 years.<\/p>\n\n\n

    \n
    \"Graphic<\/figure>\n<\/div>\n\n\n

    Yet, if it were a top five coldest, that would be scored as a hit. That\u2019s crazy. It tells you nothing about the real weather. How low is that bar?<\/p>\n\n\n\n

    The question is why?<\/p>\n\n\n\n

    Here is what it means.<\/p>\n\n\n\n

    How to Read the Probability Maps Temperature Example:<\/p>\n\n\n\n

      \n
    • If a location is shaded with \u201c60% chance of above average temperature\u201d:\n
        \n
      • The historical (climatological) chance of above-average temperature in any given winter is ~33% (because the three categories \u2014 above, near, and below \u2014 are equal in a third of the time with no climate signals).<\/li>\n\n\n\n
      • A 60% probability means the odds have been shifted upward by about 27 percentage points due to La Ni\u00f1a, ENSO, trends, etc.<\/li>\n\n\n\n
      • This is considered a modest to moderate lean toward warmer-than-average.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      • Common probability thresholds you\u2019ll see:\n
          \n
        • 33%\u201340% \u2192 Very weak signal (often shown as \u201cEC\u201d or Equal Chances on some maps)<\/li>\n\n\n\n
        • 40%\u201350% \u2192 Slight lean<\/li>\n\n\n\n
        • 50%\u201360% \u2192 Moderate lean<\/li>\n\n\n\n
        • 60%\u201370% \u2192 Strong lean<\/li>\n\n\n\n
        • 70% \u2192 Very strong signal (rare in seasonal outlooks)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n

          Fact:  The NWS seasonal outlook probabilities do not tell you how far above or below normal the temperatures (or precipitation) will be.<\/p>\n\n\n\n

          So, does that mean a temperature of 1 below normal would get the same skill score in a probability scheme as 10 below normal?<\/p>\n\n\n\n

          Grok chimes in: Yes \u2014 exactly. In the official NWS\/CPC probabilistic seasonal outlook and in the way they compute verification skill scores (like the Heidke Skill Score or Ranked Probability Skill Score), a winter that ends up 1 \u00b0F below normal is treated identically to a winter that ends up 10 \u00b0F below normal, as long as both finish in the below-normal tercile (the coldest third of the 30-year historical distribution).<\/p>\n\n\n\n

          An actual temperature forecast (a specific anomaly like \u201c+2.4 \u00b0F above normal\u201d or \u201cseasonal average of 44.1 \u00b0F\u201d) always gives you far more information than a tercile probability map (\u201c60 % chance of above-normal\u201d).<\/p>\n\n\n\n

          So basically, you are running away from accountability since the magnitude of the above or below and the amount it takes to be above and below are trying to avoid an exact number, rather than trying to nail it<\/p>\n\n\n\n

          The official NWS\/CPC seasonal outlook is deliberately structured to minimize accountability for magnitude and exact outcomes. By using only tercile probabilities and refusing to put a specific \u00b0F or inch number on the public map, the agency protects itself in two big ways:<\/p>\n\n\n\n

            \n
          1. No magnitude accountability
            +5 \u00b0F above normal or +0.5 \u00b0F above normal \u2192 both count as an identical \u201chit\u201d if the map said \u201cabove-normal.\u201d
            The forecast can be technically correct while being practically useless (or wildly off in intensity).<\/li>\n\n\n\n
          2. Very low bar for \u201csuccess\u201d
            Because the baseline (climatology) is 33 %, any forecast between 34 % and 100 % will be right more often than random guessing.
            A map full of 40\u201350 % probabilities is almost impossible to beat with a coin flip, so the Heidke skill score is almost always slightly positive, even in years when the winter is a total bust in magnitude<\/li>\n<\/ol>\n\n\n\n

            By the way, I am not Monday morning forecasting. We have been very loud about this cold, and you saw me blog on it a couple of weeks ago \u2014 in case you were not aware of what was coming.<\/p>\n\n\n\n

            So, what is the conclusion as heating bills skyrocket?<\/p>\n\n\n\n

            1. The U.S. models for all the money spent come up woefully short.<\/p>\n\n\n\n

            2. Even the emperor (European) had no clothes last winter and to start this winter. As cold as it is, not a very good idea.<\/p>\n\n\n\n

            3. What the NWS puts out is NOT A FORECAST. It\u2019s guidance with a very low bar for verification.<\/p>\n\n\n\n

            Pretty costly stuff.<\/p>\n\n\n\n

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

            The reason I don\u2019t pick on the U.S. models is that they are so bad overall. In fact, I hardly even look at them anymore. The GEFS, which is the flagship ensemble model of the U.S. fleet, is last in skill score vs the European, Canadian, and, though not shown here, the UK met. It\u2019s the black line.<\/p>\n","protected":false},"author":121246920,"featured_media":417006,"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":"Explore the shortcomings of winter weather models and how improper forecasts impact heating costs in this insightful analysis.","jetpack_seo_html_title":"Navigating Winter Forecasts: U.S. Models Fall Short","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":true,"token":"eyJpbWciOiJodHRwczpcL1wvY2xpbWF0ZS1zY2llbmNlLnByZXNzXC93cC1jb250ZW50XC91cGxvYWRzXC8yMDI1XC8xMlwvQVFQMW93bWxrYjJlbGJaTVlYN1RHaGxXS0ZZeVF1aVdQeFdScmUySW9TNWpZSkd2bWdHbF96WGxPcmpqUEZic19JX0dxY05lUURKVHFRazRhek5XM0dUZkdNTG9LYVpHTlc5TjhwdnIybV9iVHNCTDBUaW9hQ1FkUU9fSlZkLTdFWVR4Y21IQVBnM1RzYjJ0Z1VDTDZEanNHWUhTS3ctMTAyNHgxMDI0LmpwZWciLCJ0eHQiOiJDb3N0bHkgbW9kZWxzIGNvc3RpbmcgZGVhcmx5IiwidGVtcGxhdGUiOiJoaWdod2F5IiwiZm9udCI6IiIsImJsb2dfaWQiOjE1NTgxMjQ0OX0.ZDaZTgcH4X6_fNLrT6mb3nayq75WjFqHY_4SLi0RiLkMQ"},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691818382,691840112,691840111,691818473],"class_list":{"0":"post-416977","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-uncategorized","8":"tag-forecasting-models","9":"tag-gefs","10":"tag-u-s-models","11":"tag-weather","13":"fallback-thumbnail"},"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/AQP1owmlkb2elbZMYX7TGhlWKFYyQuiWPxWRre2IoS5jYJGvmgGl_zXlOrjjPFbs_I_GqcNeQDJTqQk4azNW3GTfGMLoKaZGNW9N8pvr2m_bTsBL0TioaCQdQO_JVd-7EYTxcmHAPg3Tsb2tgUCL6DjsGYHSKw.jpeg?fit=1280%2C1280&ssl=1","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-1Ktr","jetpack-related-posts":[{"id":418046,"url":"https:\/\/climatescience.press\/?p=418046","url_meta":{"origin":416977,"position":0},"title":"NOAA deploys new generation of AI-driven global weather models\u00a0","author":"uwe.roland.gross","date":"21\/12\/2025","format":false,"excerpt":"NOAA has launched a groundbreaking new suite of operational, artificial intelligence (AI)-driven global weather prediction models, marking a significant advancement in forecast speed, efficiency, and accuracy. The models will provide forecasters with faster delivery of more accurate guidance, while using a fraction of computational resources.","rel":"","context":"In \"AIGEFS (Artificial Intelligence Global Ensemble Forecast System)\"","block_context":{"text":"AIGEFS (Artificial Intelligence Global Ensemble Forecast System)","link":"https:\/\/climatescience.press\/?tag=aigefs-artificial-intelligence-global-ensemble-forecast-system"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/AQO6K46xuENC17Rg4g1OXBaskwTJhHjNzIc_o6kDDOH5agHhLlIimiDaZfe0QtUtLYUfdMLbR5Grvz3XLpNGqCmlZErCwBDd2s8vHCT3eRDiMKReMBPt6C9ulMIp7t_QYAMeaHYksOr7gp5ckHWUhjQzC174DA.jpeg?fit=1200%2C1200&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/AQO6K46xuENC17Rg4g1OXBaskwTJhHjNzIc_o6kDDOH5agHhLlIimiDaZfe0QtUtLYUfdMLbR5Grvz3XLpNGqCmlZErCwBDd2s8vHCT3eRDiMKReMBPt6C9ulMIp7t_QYAMeaHYksOr7gp5ckHWUhjQzC174DA.jpeg?fit=1200%2C1200&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/AQO6K46xuENC17Rg4g1OXBaskwTJhHjNzIc_o6kDDOH5agHhLlIimiDaZfe0QtUtLYUfdMLbR5Grvz3XLpNGqCmlZErCwBDd2s8vHCT3eRDiMKReMBPt6C9ulMIp7t_QYAMeaHYksOr7gp5ckHWUhjQzC174DA.jpeg?fit=1200%2C1200&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/AQO6K46xuENC17Rg4g1OXBaskwTJhHjNzIc_o6kDDOH5agHhLlIimiDaZfe0QtUtLYUfdMLbR5Grvz3XLpNGqCmlZErCwBDd2s8vHCT3eRDiMKReMBPt6C9ulMIp7t_QYAMeaHYksOr7gp5ckHWUhjQzC174DA.jpeg?fit=1200%2C1200&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/AQO6K46xuENC17Rg4g1OXBaskwTJhHjNzIc_o6kDDOH5agHhLlIimiDaZfe0QtUtLYUfdMLbR5Grvz3XLpNGqCmlZErCwBDd2s8vHCT3eRDiMKReMBPt6C9ulMIp7t_QYAMeaHYksOr7gp5ckHWUhjQzC174DA.jpeg?fit=1200%2C1200&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":233736,"url":"https:\/\/climatescience.press\/?p=233736","url_meta":{"origin":416977,"position":1},"title":"Christmas Day Forecast: Snowiest and Coldest Since 2000","author":"uwe.roland.gross","date":"13\/12\/2022","format":false,"excerpt":"Joe Bastardi forecast: Christmas Day - snow as far south as Louisana and Houston Texas, temperatures over 20 degrees below\u00a0normal.","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/12\/0FjxvcagUUAEHF8S.png?fit=984%2C808&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/12\/0FjxvcagUUAEHF8S.png?fit=984%2C808&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/12\/0FjxvcagUUAEHF8S.png?fit=984%2C808&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/12\/0FjxvcagUUAEHF8S.png?fit=984%2C808&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":419226,"url":"https:\/\/climatescience.press\/?p=419226","url_meta":{"origin":416977,"position":2},"title":"NOAA deploys a new generation of AI-driven global weather models","author":"uwe.roland.gross","date":"30\/12\/2025","format":false,"excerpt":"I am watching them. So far, they are a joke.","rel":"","context":"In \"AI (artificial intelligence\"","block_context":{"text":"AI (artificial intelligence","link":"https:\/\/climatescience.press\/?tag=ai-artificial-intelligence"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/0Earth-globe-from-space-weather-scaled-1.jpg?fit=1200%2C673&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/0Earth-globe-from-space-weather-scaled-1.jpg?fit=1200%2C673&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/0Earth-globe-from-space-weather-scaled-1.jpg?fit=1200%2C673&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/0Earth-globe-from-space-weather-scaled-1.jpg?fit=1200%2C673&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/12\/0Earth-globe-from-space-weather-scaled-1.jpg?fit=1200%2C673&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":262703,"url":"https:\/\/climatescience.press\/?p=262703","url_meta":{"origin":416977,"position":3},"title":"Epic Fail in America\u2019s Heartland: Climate Models Greatly Overestimate Corn Belt Warming","author":"uwe.roland.gross","date":"18\/06\/2023","format":false,"excerpt":"For the last decade I\u2019ve been providing long-range U.S. Corn Belt forecasts to a company that monitors and forecasts global grain production and market forces. My continuing theme has been, \u201cdon\u2019t believe gloom and doom forecasts for the future of the U.S. Corn Belt\u201d.","rel":"","context":"In \"Climate models\"","block_context":{"text":"Climate models","link":"https:\/\/climatescience.press\/?tag=climate-models"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00corn-belt-1.jpg?fit=1024%2C682&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00corn-belt-1.jpg?fit=1024%2C682&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00corn-belt-1.jpg?fit=1024%2C682&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00corn-belt-1.jpg?fit=1024%2C682&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":221268,"url":"https:\/\/climatescience.press\/?p=221268","url_meta":{"origin":416977,"position":4},"title":"European Models Provide Far Better Forecasts than U.S. Models for Hurricane Ian","author":"uwe.roland.gross","date":"29\/09\/2022","format":false,"excerpt":"European weather prediction models provided to be substantially superior to U.S weather prediction systems predicting the track of Hurricane Ian.","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/09\/image-1393.png?fit=1024%2C512&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/09\/image-1393.png?fit=1024%2C512&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/09\/image-1393.png?fit=1024%2C512&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/09\/image-1393.png?fit=1024%2C512&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":224936,"url":"https:\/\/climatescience.press\/?p=224936","url_meta":{"origin":416977,"position":5},"title":"50-Year U.S. Summer Temperature Trends: ALL 36 Climate Models Are Too Warm","author":"uwe.roland.gross","date":"20\/10\/2022","format":false,"excerpt":"Given that U.S. energy policy depends upon the predictions from these models, their tendency to produce too much warming (and likely also warming-associated climate change) should be factored into energy policy planning.\u00a0","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/10\/image-993.png?fit=550%2C550&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/10\/image-993.png?fit=550%2C550&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/10\/image-993.png?fit=550%2C550&ssl=1&resize=525%2C300 1.5x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/416977","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/users\/121246920"}],"replies":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=416977"}],"version-history":[{"count":12,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/416977\/revisions"}],"predecessor-version":[{"id":417008,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/416977\/revisions\/417008"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/media\/417006"}],"wp:attachment":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=416977"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=416977"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=416977"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}