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Abstract Oceanic quantities of interest (QoIs), for example, ocean heat content or transports, are often inaccessible to direct observation, due to the high cost of instrument deployment and logistical challenges. Therefore, oceanographers seek proxies for undersampled or unobserved QoIs. Conventionally, proxy potential is assessed via statistical correlations, which measure covariability without establishing causality. This paper introduces an alternative method: quantifying dynamical proxy potential. Using an adjoint model, this method unambiguously identifies the physical origins of covariability. A North Atlantic case study illustrates our method within the ECCO (Estimating the Circulation and Climate of the Ocean) state estimation framework. We find that wind forcing along the eastern and northern boundaries of the Atlantic drives a basin‐wide response in North Atlantic circulation and temperature. Due to these large‐scale teleconnections, a single subsurface temperature observation in the Irminger Sea informs heat transport across the remote Iceland‐Scotland ridge (ISR), with a dynamical proxy potential of 19%. Dynamical proxy potential allows two equivalent interpretations: Irminger Sea subsurface temperature (i) shares 19% of its adjustment physics with ISR heat transport and (ii) reduces the uncertainty in ISR heat transport by 19% (independent of the measured temperature value), if the Irminger Sea observation is added without noise to the ECCO state estimate. With its two interpretations, dynamical proxy potential is simultaneously rooted in (i) ocean dynamics and (ii) uncertainty quantification and optimal observing system design, the latter being an emerging branch in computational science. The new method may therefore foster dynamics‐based, quantitative ocean observing system design in the coming years.
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Leveraging Uncertainty Quantification to Design Ocean Climate Observing Systems
Abstract 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 members of the observing system. (3) The Hessian eigenvalues determine the overall effectiveness of the observing system, and are controlled by the sensitivity‐to‐noise ratio of the observational assets (analogous to the statistical signal‐to‐noise ratio). We illustrate Hessian UQ and its three underlying concepts in a North Atlantic case study. Sea surface temperature observations inform mainly local air‐sea fluxes. In contrast, subsurface temperature observations reduce uncertainty over basin‐wide scales, and can therefore inform transport QoIs at great distances. This research provides insight into the design of effective observing systems that maximally inform the target QoIs, while being complementary to the existing observational database.
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- PAR ID:
- 10451999
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 13
- Issue:
- 4
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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