Estimating present temperature climate in a warming world: probabilistic verification of a model-based approach for years 2008-2023

Estimating present temperature climate in a warming world: probabilistic verification of a model-based approach for years 2008-2023 | 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 Research Article Estimating present temperature climate in a warming world: probabilistic verification of a model-based approach for years 2008-2023 Jouni Räisänen, Mika Rantanen, Antti Toropainen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5602233/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract When climate changes, statistics derived from past observations become unrepresentative of the true present climate. In 2008, Räisänen and Ruokolainen proposed a model-based method for alleviating this bias. Here, the fidelity of this method in predicting the probability distributions of monthly mean temperatures is evaluated, focusing on the 16 years 2008–2023 that post-date the original study. Compared with the traditional approach in which the distributions are estimated directly from observations, a major improvement is found in both the continuous ranked probability score ( CRPS ) and the logarithmic score ( L ). The rank histograms that describe the positions of the verifying observations within the predicted distributions also become far more balanced. In addition, the optimal length of the baseline period from which observations are used increases when the observed temperatures are adjusted for climate change. The verification statistics are further improved when augmenting the model-based climate change estimates with information from local observed temperature trends. Conversely, both CRPS and L are degraded when the t -distributions fitted to the climate-change-adjusted observations are replaced with more flexible Stochastically Generated Skewed distributions. However, a blend between the two distributions using the Akaike information criterion yields statistics nearly identical to those for the t -distribution. climate change temperature present climate probability distribution verification Full Text Supplementary Files Supplementarymaterial081224.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revision 28 Apr, 2026 Reviewers agreed at journal 13 Jan, 2025 Reviewers invited by journal 06 Jan, 2025 Editor assigned by journal 09 Dec, 2024 First submitted to journal 07 Dec, 2024 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. 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