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Current climate models rely on complex mathematical parameterisations to represent sub-grid scale "> <meta name="twitter:image:src" content="https://authorea.com//d197for5662m48.cloudfront.net/assets/blog/default_image-be9182bca7b6523bb6c72e32e5c12398f36b92d81f6ee7882327b6145744bf1f.jpg">  <meta property="og:title" content="RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.authorea.com/doi/full/10.22541/essoar.173238717.76577266/v1?commit=c5014c508e922df889274c86acfe10e71bb45d69" /> <meta property="og:image" content="https://authorea.com//d197for5662m48.cloudfront.net/assets/blog/default_image-be9182bca7b6523bb6c72e32e5c12398f36b92d81f6ee7882327b6145744bf1f.jpg" /> <meta property="og:description" content="This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale " /> <meta property="og:site_name" content="Authorea" /> <meta property="article:modified_time" content="2024-11-24 05:08:56 UTC" /> <link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/mathquill/0.10.1/mathquill.min.css"/> <meta id='mathquillmin' class='mq-editable-field'> <script type="text/javascript"> var tag = document.querySelector('meta#mathquillmin') var tagStyle = window.getComputedStyle(tag).getPropertyValue('display') if (!tagStyle || tagStyle == 'auto' ){ var message = "mathquill.min.0.10.1.css failed to load, falling back to local copy" localLink = document.createElement('link'); localLink.rel = "stylesheet"; localLink.type = "text/css"; localLink.href = "/assets/mathquill.min.0.10.1.css"; document.head.appendChild(localLink); delete localLink; } </script> <meta property="article:publisher" content="https://www.authorea.com" /> <link rel="stylesheet" type="text/css" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.18.1/styles/default.min.css"/> <meta id='defaultmin' class='hljs-strong'> <script type="text/javascript"> var tag = document.querySelector('meta#defaultmin') var tagStyle = window.getComputedStyle(tag).getPropertyValue('font-weight') if (!tagStyle || tagStyle == 'auto' ){ var message = "default.min.css failed to load, falling back to local copy" localLink = document.createElement('link'); localLink.rel = "stylesheet"; localLink.type = "text/css"; localLink.href = "/assets/default.min.css"; document.head.appendChild(localLink); delete localLink; } </script>  <meta name="citation_publisher" content="Authorea"/> <meta name="citation_title" content="RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models"/> <meta name="citation_abstract" content="This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. 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class="preview-avatar"><div class="preview-avatar-extra">+1</div></li></ul><ul class="preview-author-list"><li class="preview-author"><span class="preview-author-name" data-collab-id="1825954">Pritthijit Nath<i class="fas fa-envelope preview-author-icon"></i></span><a class="preview-author-orcid" href="https://orcid.org/0009-0002-1648-4444" rel="noopener noreferrer" target="_blank"><img src="https://d197for5662m48.cloudfront.net/assets/icons/omniauth/orcid-ca37582cd91a671ed5cec57770e0e96c01b4b79b8b5b75d8598fc1ddac18c974.svg" /></a>, </li><li class="preview-author"><span class="preview-author-name" data-collab-id="1825955">Henry Moss</span>, </li><li class="preview-author"><span class="preview-author-name" data-collab-id="1825956">Emily Shuckburgh</span>, </li><li class="preview-author"><span class="preview-author-name" data-collab-id="1825957">Mark Webb</span><a class="preview-author-orcid" href="https://orcid.org/0000-0002-7709-7088" rel="noopener noreferrer" target="_blank"><img src="https://d197for5662m48.cloudfront.net/assets/icons/omniauth/orcid-ca37582cd91a671ed5cec57770e0e96c01b4b79b8b5b75d8598fc1ddac18c974.svg" /></a></li></ul></div><div class="author-popover" data-collab-id="1825954"><div class="author-popover-info"><div class="author-popover-name">Pritthijit Nath</div><a class="author-popover-orcid" href="https://orcid.org/0009-0002-1648-4444" rel="noopener noreferrer" target="_blank"><img src="https://d197for5662m48.cloudfront.net/assets/icons/omniauth/orcid-ca37582cd91a671ed5cec57770e0e96c01b4b79b8b5b75d8598fc1ddac18c974.svg" /></a><div class="author-popover-affiliations">University of Cambridge</div><p>Corresponding Author:<a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="2f5f411c1b1e6f4c4e42014e4c015a44">[email protected]</a></p><a class="author-popover-profile" href="/users/858299">Author Profile</a></div><div class="author-popover-avatar"><img 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src="https://d197for5662m48.cloudfront.net/assets/icons/omniauth/orcid-ca37582cd91a671ed5cec57770e0e96c01b4b79b8b5b75d8598fc1ddac18c974.svg" /></a><div class="author-popover-affiliations">Met Office Hadley Centre</div><a class="author-popover-profile" href="/users/552232">Author Profile</a></div><div class="author-popover-avatar"><img src="data:image/jpg;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAACWCAYAAAA8AXHiAAAABGdBTUEAALGP C/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3Cc ulE8AAAABmJLR0QAAAAAAAD5Q7t/AAAAB3RJTUUH6AsVEBcZWxoq1QAAGZxJ REFUeAHtwW2spvldH/bP93/d58w587A7u16HiBSvjYFClSZtgNo0kXhRIRIg hFatkGibViGNidTmTdK8wIgUTCNVqZImUooqUuoCbdMAAUKhEUVyU0hLSCoe bR4N1JCEeHe9szuzc+acc9//X+/runb27Hgedp7usV9cn0/6r14ui8Vj1iwW O9AsFjvQLBY70CwWO9AsFjvQLBY70CwWO9AsFjvQLBY70CwWO9AsFjvQLBY7 0CwWO9AsFjvQLBY70CwWO9AsFjvQLBY70CwWO9AsFjvQLBY70CwWO9AsFjvQ LBY70CwWO9AsFjvQLBY70CwWO9AsFjvQLBY70CwWO9AsFjvQLBY70CwWO9As FjvQLBY70CwWO9AsFjvQLBY70CwWO9AsFjvQLBY70CwWO9AsFjvQLBY7sPK4 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document.querySelector('.author-popover[data-show]') if (!el) return el.removeAttribute('data-show') popperInstance.destroy() popperInstance = null } authors.forEach(author => { const id = author.getAttribute('data-collab-id') const popover = document.querySelector(`.author-popover[data-collab-id="${id}"]`) author.addEventListener('click', () => { if (popperInstance) { hidePopover() } showPopover(author, popover) }) }) document.addEventListener('click', (e) => { if (!popperInstance) return const container = document.querySelector('.preview-author-list') if (container.contains(e.target)) return hidePopover() }) })</script><div class="article-paragraph preview-abstract"><h2 class="preview-abstract-heading">Abstract</h2><div> This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at <a href="https://github.com/p3jitnath/climate-rl">https://github.com/p3jitnath/climate-rl</a>. </div></div><div class="review-timeline"><div class="dashboard-container"><div class="publication-status-footer"><h4 class="header">Preprint timeline</h4></div><div class="events-container"><div class="version-container"><div class="entry"><span class="bar"></span><span class="indicator"></span><span class="time-box"><span>22 Nov 2024</span></span><span class="message">Submitted to <b><i>ESS Open Archive </i></b> <a target="_blank" rel="noopener noreferrer" href="/inst/20904"><i class="fas fa-external-link-alt"></i></a></span></div><div class="entry"><span class="bar"></span><span class="indicator"></span><span class="time-box"><span>23 Nov 2024</span></span><span class="message">Published in <b><i>ESS Open Archive </i></b></span></div></div></div></div></div><div class="preview-publication-status"></div></div></div> <div id="article-content"> </div> </div>  <div style="clear: both"></div>  <div id='article-sidebar-portal'></div> <script> initialServerStore = {"article":{"titleBlock":{"block_content":null,"type":"htmlBlock","html":"\u003cdiv\u003eRAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models\u003c/div\u003e","render_report":null,"raw":"\u003cdiv\u003eRAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models\u003c/div\u003e","name":"title","format":"html","url":"/users/858299/articles/1241947/master/file/title.html","folder_url":"/users/858299/articles/1241947/master/folder/.","path":"title.html","old_path":null,"collapsed":false,"sha":"add261bee66037a98c74550219bf2ca25769abc7","captionBlock":null,"has_data":false,"has_ipython_notebook":false,"config":null},"id":1241947,"sha":"c5014c508e922df889274c86acfe10e71bb45d69","blocks":[],"oldBlocks":[],"oldLayout":null,"online":true,"layout":"abstract.html\ndata/tccml-neurips-poster/tccml-neurips-poster.pdf","references":[],"referenceSorting":{"key":"default","ascending":true},"citationHash":{},"commits":null,"version":0},"commitToView":"c5014c508e922df889274c86acfe10e71bb45d69","commentToView":null,"onboarding":false} initialFlashMessagesForReact = [] </script> <script src="https://d197for5662m48.cloudfront.net/assets/editable_article_manifest_react-ddeeb8194703aca81267c69ccb1330a3afea795f89456883e9fb9d4f9047c520.js" defer="defer"></script> <link rel="stylesheet" type="text/css" href="//cdnjs.cloudflare.com/ajax/libs/KaTeX/0.11.1/katex.min.css"/> <meta id='katexmin' class='katex'> <script type="text/javascript"> var tag = document.querySelector('meta#katexmin') var tagStyle = window.getComputedStyle(tag).getPropertyValue('font-family') if (!tagStyle || tagStyle == 'auto' ){ var message = "katex.min.0.11.1.css failed to load, falling back to local copy" localLink = document.createElement('link'); 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