Impact of Synthetic Arctic Argo-Type Floats in a Coupled Ocean–Sea Ice State Estimation Framework

Abstract

The lack of continuous spatial and temporal sampling of hydrographic measurements in large parts of the Arctic Ocean remains a major obstacle for quantifying mean state and variability of the Arctic Ocean circulation. This shortcoming motivates an assessment of the utility of Argo-type floats, the challenges of deploying such floats due to the presence of sea ice, and the implications of extended times of no surfacing on hydrographic inferences. Within the framework of an Arctic coupled ocean–sea ice state estimate that is constrained to available satellite and in situ observations, we establish metrics for quantifying the usefulness of such floats. The likelihood of float surfacing strongly correlates with the annual sea ice minimum cover. Within the float lifetime of 4–5 years, surfacing frequency ranges from 10–100 days in seasonally sea ice–covered regions to 1–3 years in multiyear sea ice–covered regions. The longer the float drifts under ice without surfacing, the larger the uncertainty in its position, which translates into larger uncertainties in hydrographic measurements. Below the mixed layer, especially in the western Arctic, normalized errors remain below 1, suggesting that measurements along a path whose only known positions are the beginning and end points can help constrain numerical models and reduce hydrographic uncertainties. The error assessment presented is a first step in the development of quantitative methods for guiding the design of observing networks. These results can and should be used to inform a float network design with suggested locations of float deployment and associated expected hydrographic uncertainties.

The Arctic Subpolar Gyre sTate Estimate: Description and Assessment of a Data‐Constrained, Dynamically Consistent Ocean‐Sea Ice Estimate for 2002–2017

“…The model‐data misfit y ( t )− E ( t ) x ( t ) is weighted by the inverse error covariance matrix R ( t ). This accounts for both observational uncertainty and model representation error, where the latter considers the extent to which real variability cannot be represented at the chosen model resolution (Nguyen, Heimbach et al., 2020). B ( t 0 ) and Q ( t ) are error covariances of x 0 and u ( t ), respectively.…”

“…As is common in data assimilation (Janjić et al., 2017) the ECCO framework utilizes “representation errors”—described above—in the weighting of the model‐data misfits, Equation , to safeguard against over‐fitting and facilitate more meaningful model‐data comparison (Wunsch & Heimbach, 2007). However, these representation errors are themselves highly uncertain, often relying on unconstrained high‐resolution model runs from which they are inferred (see Nguyen, Heimbach et al., 2020, for a more detailed discussion).…”

“…(1), to safeguard against over-fitting and facilitate more meaningful model-data comparison (Wunsch & Heimbach, 2007). However, these representation errors are themselves highly uncertain, often relying on unconstrained high-resolution model runs from which they are inferred (see Nguyen et al, 2020a, for a more detailed discussion).…”

Preparing the New Phase of Argo: Scientific Achievements of the NAOS Project

“…On the other hand, their use under sea ice will require appropriate deployment strategies. In a recent study, Nguyen et al (2020) showed that error in Argo's geo-positioned salinity and temperature measurements below 300 m due to limited surfacing opportunities in the Arctic Ocean is smaller than the uncertainty level across most of this ocean, indicating the usefulness of these measurements. At lower depths, the error is often larger, so specific strategies must be adopted.…”

Assessing the current and future Arctic Ocean observing system with observing system simulating experiments

“…Moreover, a more comprehensive profiling system (e.g. Nguyen et al ., 2020), comprising Ice‐Thread Profilers (ITPs), moorings and repeated vehicle lines, should be designed, coordinated and evaluated.…”