GloMarGridding: A Python Package for Spatial Interpolation to Support Structural Uncertainty Assessment of Climate Datasets

GloMarGridding: A Python Package for Spatial Interpolation to Support Structural Uncertainty Assessment of Climate Datasets | 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 Method Article GloMarGridding: A Python Package for Spatial Interpolation to Support Structural Uncertainty Assessment of Climate Datasets Richard C. Cornes, Steven. C. Chan, Archie Cable, Duo Chan, Agnieszka Faulkner, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7427869/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Global surface temperature datasets are constructed through processing chains that inherently introduce structural uncertainty. This arises from choices made in both the processing of input observations and in the spatial interpolation methods employed. Because these steps are often tightly integrated, it is difficult to isolate their individual contributions to uncertainty. Here we introduce GloMarGridding, a Python package designed to support the evaluation of structural uncertainty by providing flexible tools for spatial interpolation using Gaussian Process Regression Modelling (GPRM), also known as kriging. It enables the generation of spatially complete temperature fields from grid-box average and point observations, and associated uncertainties. GloMarGridding supports three spatial covariance parameterizations: fixed isotropic variograms, ellipse-based anisotropic model and empirically-derived covariance matrices. It also allows for uncertainty propagation via error covariance matrices and conditional simulation from input ensembles. By decoupling interpolation from earlier stages of dataset development - such as homogenization, quality control, and aggregation - this framework enables independent assessment of upstream processing choices and their impacts on gridded outputs. Climatology Kriging Gaussian Process Regression Model Global Mean Surface Temperature Climate Change Structural Uncertainty Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7427869","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":503784464,"identity":"5731a379-6516-493a-98e8-ef1ced4c2683","order_by":0,"name":"Richard C. 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