Abstract
AbstractOceanic 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.
Highlights
Satellite altimetry and the global array of Argo floats have vastly increased the observational coverage of the world's oceans since the early 1990s (Fu et al, 2018; Riser et al, 2016)
We have introduced the concept of dynamical proxy potential (DPP), a dynamics‐based alternative to statistical correlation analysis and conventional observing system simulation experiments (OSSEs)
(1) In contrast to statistical proxy potential, DPP only accounts for covariability that has a dynamical underpinning, by tracing variability back to common causal forcings
Summary
Satellite altimetry and the global array of Argo floats have vastly increased the observational coverage of the world's oceans since the early 1990s (Fu et al, 2018; Riser et al, 2016). Many oceanographic quantities of interest (QoIs) are not directly or continuously measured. Additional QoIs may be ocean heat and freshwater content in regions not well sampled by Argo floats, for example, near the margins or the polar ice sheets. Other examples are strongly related to future societal concerns and key targets for climate predictions. These include future Arctic sea ice cover or regional sea level anomalies. That is, it is desirable—and an active part of climate research—to employ observed quantities as proxies for QoIs that are undersampled or unobserved
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