Exploring ice-ocean boundary layer dynamics in climate models, idealized simulations, and outdoor lab experiments
Bibliographic record
Abstract
Everything that happens in the Arctic Ocean, be it of physical, biological, or chemical nature, is constrained by the vertical distribution of heat and salt. In this talk, I will share recent results and on-going work aimed at examining questions directly related to vertical mixing below sea ice: (1) How accurately are the physical properties of the Canada Basin simulated in climate models? (2) How do observed changes to the size and speed of a sea ice floe and ocean stratification impact ocean mixing in 2D numerical simulations? (3) Can we, for the first time, examine seasonal ice-ocean boundary layer dynamics in a 20 m × 10 m × 3 m outdoor saltwater pool?Our results indicate that the majority of climate models do not accurately simulate the surface freshening trend observed in the Canada Basin between 1975 and 2006-2012, nor do they simulate heat from Pacific Water in the same region. We suggest that both of these biases can be partly attributed to unrealistically deep vertical mixing in the models. We next explore one possible source of this model bias related to decadal changes to the underside of ice floes, called ice keels. Results from idealized numerical simulations highlight the importance of ice keel depth, which controls the range over which ocean mixing occurs, as well as ice keel speed and ocean stratification. Further, we estimate that observational uncertainties related to ice keel depth may translate into uncertainties in the sign of current and future changes to below-ice momentum transfer into the ocean. Lastly, we present the instrument setup for our 2022-2023 pilot experiment and on-going outreach work at the Sea-ice Environmental Research Facility (SERF) in Canada. This is a unique facility centres around an outdoor saltwater pool where sea ice evolves under natural atmospheric conditions in a semi-idealized and well-instrumented setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".