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Matilda V1.0: Integrating parameter uncertainty and ensemble weighting with Hector for probabilistic climate projections

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Abstract

One of the primary advantages when using simple climate models (SCMs) is the ability to quickly simulate large ensembles that can be used to complete probabilistic climate analysis. The ability to use these ensembles for uncertainty exploration is a key component in multisector systems research. Providing simple frameworks for such probabilistic analyses has been a goal for many SCMs. Here, we present Matilda, an open-source R software package that partners with the Hector simple climate model to facilitate parameter uncertainty propagation, ensemble weighting, and probabilistic climate projection analysis. The Matilda workflow simplifies the process of building and analyzing perturbed parameter ensembles without requiring significant programming expertise, thus accommodating for diverse use cases. The development of this package and its seamless integration with Hector, provides a tool for global change research associated with the co-evolution of the human and Earth systems.

Category
Model Uncertainties, Model Biases, and Fit-for-Purpose
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