Predictability of Chilean Coastal El Niño: Insights From A Low-Order Modeling Approach

Predictability of Chilean Coastal El Niño: Insights From A Low-Order Modeling Approach | 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 Predictability of Chilean Coastal El Niño: Insights From A Low-Order Modeling Approach Eduardo Martínez, Cristian Martinez-Villalobos, Boris Dewitte This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8484917/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 Chile Niño is a seasonally modulated coastal warm mode that affects northern--central Chile and challenges early-warning systems because events are short-lived and geographically confined. Here we assess the predictability of the Chile Niño Index (CNI) using a hierarchy of data-driven inverse models, ranging from a baseline Linear Inverse Model to Seasonally Varying and nonlinear extensions, and a lightweight hybrid scheme in which a Long Short-Term Memory network is trained to correct systematic forecast residuals. We first show that the Linear Inverse-Model framework reproduces key characteristics of coastal sea-surface temperature variability associated with Chile Niño, supporting its suitability for predictability assessment. Deterministic and probabilistic verification identifies a clear window of forecast skill for austral-autumn initializations (April--May) at short lead times (1--3 months), together with a secondary but shorter-lived enhancement for early-winter initializations (June--July) that is largely confined to 1--2-month leads. Within these windows, the Seasonally Varying Extended Configuration yields the strongest correlations, the lowest normalized errors, and the most reliable probabilities of warm and cold coastal conditions. The Hybrid correction does not substantially increase the short-lead maximum, but it consistently improves performance at intermediate lead times (4--8 months), with the largest gains when warm eastern Pacific conditions precede the target period. A case study of the 2017 event illustrates this added value: the inverse model captures the onset of coastal warming but underestimates its magnitude and delays the phase transition, whereas the hybrid scheme better maintains warm persistence and improves the timing of the reversal. Overall, our results indicate that a seasonally explicit stochastic Inverse-Model framework, augmented with a parsimonious data-driven correction, provides a physically interpretable and computationally efficient basis for coastal warming prediction in the southeast Pacific. Full Text Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revision 12 Mar, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 08 Jan, 2026 First submitted to journal 02 Jan, 2026 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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