Affiliation
Department of Geosciences, University of ArizonaLaboratory of Tree-Ring Research, University of Arizona
School of Geography Development, and Environment, University of Arizona
Issue Date
2023-10-12
Metadata
Show full item recordPublisher
Copernicus PublicationsCitation
King, J., Tierney, J., Osman, M., Judd, E. J., and Anchukaitis, K. J.: DASH: a MATLAB toolbox for paleoclimate data assimilation, Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, 2023.Journal
Geoscientific Model DevelopmentRights
© Author(s) 2023. This work is distributed underthe Creative Commons Attribution 4.0 License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Paleoclimate data assimilation (DA) is a tool for reconstructing past climates that directly integrates proxy records with climate model output. Despite the potential for DA to expand the scope of quantitative paleoclimatology, these methods remain difficult to implement in practice due to the multi-faceted requirements and data handling necessary for DA reconstructions, the diversity of DA methods, and the need for computationally efficient algorithms. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. DASH provides command line and scripting tools that implement common tasks in DA workflows. The toolbox is highly modular and is not built around any specific analysis, and thus DASH supports paleoclimate DA for a wide variety of time periods, spatial regions, proxy networks, and algorithms. DASH includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications. © 2023 Copernicus GmbH. All rights reserved.Note
Open access journalISSN
1991-959XVersion
Final Published Versionae974a485f413a2113503eed53cd6c53
10.5194/gmd-16-5653-2023
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Except where otherwise noted, this item's license is described as © Author(s) 2023. This work is distributed underthe Creative Commons Attribution 4.0 License.