{"id":26310,"date":"2026-01-05T11:51:02","date_gmt":"2026-01-05T16:51:02","guid":{"rendered":"https:\/\/www.realclimate.org\/?p=26310"},"modified":"2026-01-05T11:51:06","modified_gmt":"2026-01-05T16:51:06","slug":"ai-ml-climate-magic","status":"publish","type":"post","link":"https:\/\/www.realclimate.org\/index.php\/archives\/2026\/01\/ai-ml-climate-magic\/","title":{"rendered":"AI\/ML climate magic?"},"content":{"rendered":"<div class=\"kcite-section\" kcite-section-id=\"26310\">\n\n<p>There has been a frenzy around artificial intelligence and deep machine learning (AI\/ML) since the \u201cChatGPT Moment\u201d in 2022, and AI\/ML is for sure going to affect us all. It strikes me that this buzz also looks more like a <a href=\"https:\/\/www.theguardian.com\/technology\/2025\/dec\/30\/ai-pull-plug-pioneer-technology-rights\">science fiction story<\/a> (utopy\/dystopy) than the old-fashion Clondyke goldrush craze.&nbsp;<\/p>\n\n\n\n<!--more-->\n\n\n\n<p>\u201c<a href=\"https:\/\/www.letsrecycle.com\/news\/special-report-artificial-intelligence-waste-industry\/\">AI will not replace you. A person using AI will replace you<\/a>\u201d we have been told, and people from high places have made it clear that we need to adopt AI\/ML. I certainly feel the pressure from those who want to promote more AI\/ML in downscaling global climate models, but they don\u2019t seem to know the history of downscaling climate projections.&nbsp;<\/p>\n\n\n\n<p>Nevertheless, I can understand this urge and desire, because it is not just due to large language models (<a href=\"https:\/\/knowledge-centre-translation-interpretation.ec.europa.eu\/en\/news\/what-large-language-model\">LLMs<\/a>) such as chatGPT. A more relevant motivation is more likely the impressive successes in applying AI\/ML to weather forecasting <span id=\"cite_ITEM-26310-0\" name=\"citation\"><a href=\"#ITEM-26310-0\">(Bi et al., 2023)<\/a><\/span>, such as <a href=\"https:\/\/www.huawei.com\/en\/news\/2023\/8\/pangu-weather-forcast\">Pangu-Weather<\/a>, <a href=\"https:\/\/deepmind.google\/blog\/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting\/\">GraphCast<\/a>, <a href=\"https:\/\/www.ecmwf.int\/en\/newsletter\/178\/news\/aifs-new-ecmwf-forecasting-system\">AIFS<\/a>,<a href=\"https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/earth-2\/\">Earth-2<\/a>, and <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/aurora-forecasting\/\">Aurora<\/a>.<\/p>\n\n\n\n<p>Yet there is a subtle, and profound, difference between downscaling climate model results for weather forecasting and climate change, and I have written a few words of caution in <a href=\"https:\/\/arxiv.org\/abs\/2601.00629\">a paper<\/a> recently posted on arXiv.<\/p>\n\n\n\n<p>I fear that more statistics and mathematics based methods are being ditched, and a metaphor for this is the cuckoo laying eggs in other birds nests. We have developed methods for downscaling salient climate information based on mathematics and statistics, which I believe will give more accurate results than present AI\/ML algorithms and strategies.&nbsp;<\/p>\n\n\n\n<p>I think it\u2019s important not to forget that the recent success of AI\/ML does not diminish the standing of mathematics, statistics and physics, but AI\/ML is useful when there is a lot of data and incomplete knowledge concerning how things interact. However, the quality of data, what it really represents, and its volume has a big impact on the simulations.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p>There are some concerns that AI\/ML give inaccurate results, such as in <a href=\"https:\/\/www.theguardian.com\/technology\/2026\/jan\/02\/google-ai-overviews-risk-harm-misleading-health-information\">Google summaries<\/a>, it being a \u201cblack box\u201d and that it may \u201challlucinate\u201d. When it comes to downscaling climate change, the biggest problem is perhaps that AI\/ML algorithms are trained on data that are not representative in a future warmer climate.<\/p>\n\n\n\n<p>AI\/ML should complement more traditional methods, because they are based on different assumptions and have different strengths and weaknesses, but I fear it may replace them because short-term-thinking accountants and administrators want to cut costs.\u00a0<br>Other concerns are the <a href=\"https:\/\/www.theguardian.com\/technology\/2026\/jan\/03\/just-an-unbelievable-amount-of-pollution-how-big-a-threat-is-ai-to-the-climate\">carbon foot-print from data centres<\/a> needed for AI\/ML and how it facilitates <a href=\"https:\/\/www.theguardian.com\/commentisfree\/2025\/dec\/26\/ai-dark-ages-enlightenment\">dogmatic thinking<\/a>, in addition to the risk that careless or inappropriate use of AI\/ML may lead to maladaptation. More details and references are provided in the arXiv paper <a href=\"https:\/\/arxiv.org\/abs\/2601.00629\" title=\"Benestad (2026)\">Benestad (2026)<\/a>.<\/p>\n<h2>References<\/h2>\n    <ol>\n    <li><a name='ITEM-26310-0'><\/a>\nK. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, \"Accurate medium-range global weather forecasting with 3D neural networks\", <i>Nature<\/i>, vol. 619, pp. 533-538, 2023. <a href=\"http:\/\/dx.doi.org\/10.1038\/s41586-023-06185-3\">http:\/\/dx.doi.org\/10.1038\/s41586-023-06185-3<\/a>\n\n\n<\/li>\n<\/ol>\n\n<\/div> <!-- kcite-section 26310 -->","protected":false},"excerpt":{"rendered":"<p>There has been a frenzy around artificial intelligence and deep machine learning (AI\/ML) since the \u201cChatGPT Moment\u201d in 2022, and AI\/ML is for sure going to affect us all. It strikes me that this buzz also looks more like a science fiction story (utopy\/dystopy) than the old-fashion Clondyke goldrush craze.&nbsp;<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[5,1,48,75,38],"tags":[],"class_list":{"0":"post-26310","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-climate-modelling","7":"category-climate-science","8":"category-downscaling","9":"category-featured-story","10":"category-scientific-practice","11":"entry"},"aioseo_notices":[],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/26310","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/comments?post=26310"}],"version-history":[{"count":5,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/26310\/revisions"}],"predecessor-version":[{"id":26315,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/26310\/revisions\/26315"}],"wp:attachment":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/media?parent=26310"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/categories?post=26310"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/tags?post=26310"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}