Sources of Subseasonal Predictability of Atmospheric Rivers

Sources of Subseasonal Predictability of Atmospheric Rivers | 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 Sources of Subseasonal Predictability of Atmospheric Rivers Wei Zhang, baoqiang xiang, Kai-Chih Tseng, Nathaniel Jonhson, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4077755/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2024 Read the published version in npj Climate and Atmospheric Science → Version 1 posted You are reading this latest preprint version Abstract Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs on subseasonal timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on hindcast experiments from the Seamless System for Prediction and Earth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory, we evaluate the global subseasonal prediction skill of wintertime AR statistics. Overall, the results from SPEAR are comparable to the European Centre for Medium-Range Weather Forecasts (ECMWF). Higher forecast skill is detected for strong AR activities than weak AR activities, despite that the occurrence frequency for weak ARs exceeds that of strong ARs. Importantly, we assess the sources of predictability and find that three most predictable modes of ARs in the North Pacific sector can be interpreted as arising from the influence of the El Niño–Southern Oscillation, the Pacific North American and the Arctic Oscillation patterns. Subseasonal AR forecast skill in western North America is modulated by different phases of these modes of large-scale seasonal variability highlighting the potential windows of opportunity for subseasonal AR forecasting. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Atmospheric science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2024 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4077755","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":279984555,"identity":"9964e2a6-9d47-4f10-baa2-cd061e23c688","order_by":0,"name":"Wei 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