Leveraging Uncertainty Quantification to Design Ocean Climate Observing Systems.
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Ocean observations are expensive and difficult to collect. Designing effective ocean observing systems therefore warrants deliberate, quantitative strategies. We leverage adjoint modeling and Hessian uncertainty quantification (UQ) within the ECCO (Estimating the Circulation and Climate of the Ocean) framework to explore a new design strategy for ocean climate observing systems. Within this context, an observing system is optimal if it minimizes uncertainty in a set of investigator-defined quantities of interest (QoIs), such as oceanic transports or other key climate indices. We show that Hessian UQ unifies three design concepts. (1) An observing system reduces uncertainty in a target QoI most effectively when it is sensitive to the same dynamical controls as the QoI. The dynamical controls are exposed by the Hessian eigenvector patterns of the model-data misfit function. (2) Orthogonality of the Hessian eigenvectors rigorously accounts for redundancy between distinct.....
JournalJournal of Advances in Modeling Earth Systems
Sustainable Development Goals (SDG)14.a
Essential Ocean Variables (EOV)N/A
CitationLoose, N. and Heimbach, P. (2021) Leveraging uncertainty quantification to design ocean climate observing systems. Journal of Advances in Modeling Earth Systems, 13: e2020MS00238, 25pp. DOI: https://doi. org/10.1029/2020MS002386
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