Constraining Physical Gas Exchange Processes and Transient Tracer Saturations in the Arctic Ocean using Noble Gas
Bibliographic record
Abstract
The formation of sea ice in the Arctic Ocean, as well as other physical processes such as injection of air and rapid cooling, plays a crucial role in determining the physical and chemical properties of its waters, which in turn drive the circulation in the Arctic [1]. Such processes can be constrained by conservative tracers which are biologically and chemically nonreactive, such as the noble gases. The full suite of stable noble gases (He, Ne, Ar, Kr and Xe) have been measured for the first time in the Arctic Ocean—along with CFC-12, SF 6 , and other transient tracers—during the Ventilation and Anthropogenic Carbon in the Arctic Ocean (VACAO) project of the wider Synoptic Arctic Survey 2021 (SAS21) [2]. The noble gas profiles indicate a water column strongly influenced by rapid cooling and excess air injection, with a surface signature characteristic of solute rejection by sea ice formation. We have compared multiple Arctic Ocean gas exchange models (based on similar models used in the Antarctic by Loose et al. [3] and in the Labrador Sea by Hamme et al. [4]) to constrain the fractions of Arctic water composed of Pacific, Atlantic and sea ice melt-derived origin waters, as well as the amount of sea ice being formed and air being injected into the water via bubbles. These parameters are estimated using a χ 2 -minimisation procedure, where the misfit between fitted parameters and data is minimised. Preliminary results indicate a non-negligible sea ice term in the equations describing gas saturation anomalies in the Arctic. Another key goal of VACAO is to use transient tracers to study the ventilation timescales of the Arctic Ocean, with application towards the study of the efficacy of its CO 2 solubility pump/storage. CFC-12/SF 6 is one tracer pair with which this is attempted; water dating with this pair requires knowledge of the concentration history of the tracers in the surface water, for which there are no direct measurements. Thus, the physical gas exchange parameters modeled from the noble gas data can be used in conjunction with observed atmospheric histories to more accurately describe the surface water histories of CFC-12 and SF 6 . This in turn can be used to better constrain the Transit Time Distribution parameters used when dating Arctic waters [5]. Comparisons with other VACAO age tracer data ( 39 Ar and 129 I/ 236 U) may act as validation tools to this “correction” to CFC-12/SF 6 dating. References for Abstract [1] Rudels, B., and Carmack, E. 2022. Arctic ocean water mass structure and circulation. Oceanography , 35(3–4), 52–65 pp. [2] Snoeijs-Leijonmalm, P. and the SAS-Oden 2021 Scientific Party (2022). Expedition Report SWEDARCTIC Synoptic Arctic Survey 2021 with icebreaker Oden. Swedish Polar Research Secretariat. 300 pp. [3] Loose, B., Stammerjohn, S., Sedwick, P., & Ackley, S. (2023). Sea ice formation, glacial melt and the solubility pump boundary conditions in the Ross Sea. Journal of Geophysical Research: Oceans , 128, e2022JC019322. [4] Hamme, R. C., Emerson, S. R., Severinghaus, J. P., Long, M. C., & Yashayaev, I. (2017). Using noble gas measurements to derive air-sea process information and predict physical gas saturations. Geophysical Research Letters , 44, 9901–9909 pp. [5] Jeansson, E., Tanhua, T., Olsen, A., Smethie, W. M., Rajasakaren, B., Ólafsdóttir, S. R., & Ólafsson, J. (2023). Decadal changes in ventilation and anthropogenic carbon in the Nordic Seas. Journal of Geophysical Research: Oceans , 128, e2022JC019318.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".