Random Analog Prediction of Arctic Sea Ice Extent: a Benchmark for Seasonal Models

Random Analog Prediction of Arctic Sea Ice Extent: a Benchmark for Seasonal Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Random Analog Prediction of Arctic Sea Ice Extent: a Benchmark for Seasonal Models Faiq Raees, Francesco Paparella This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7868093/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract We develop a Random Analogue Predictor (RAP) algorithm for the forecasting of Arctic Sea Ice Extent (SIE) on seasonal timescales. This is a stochastic variant of the celebrated method of the analogues that only uses the historical SIE record to produce ensemble forecasts. When comparing the observations with the most representative forecast of the ensemble (as identified through the band–depth, a centrality measure for functional data) the algorithm shows negligible bias and RMSE no larger than 0.6 · 10 6 km 2. We argue that simplicity, interpretability, independence on physical hypothesis, and the ability to attach an uncertainty estimate to its own forecasts, should make RAP the benchmark of choice for physics–based and AI–based models alike. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Ocean sciences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 19 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 25 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers invited by journal 04 Nov, 2025 Editor invited by journal 24 Oct, 2025 Editor assigned by journal 17 Oct, 2025 Submission checks completed at journal 17 Oct, 2025 First submitted to journal 15 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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