Modelling the response of Arctic and Subarctic marine systems to climate warming
Bibliographic record
Abstract
The Arctic and the North Atlantic Oceans are experiencing multiple stresses such as loss of sea ice, changing atmospheric patterns, increasing wind energy at the ocean surface and larger freshwater discharge to coastal regions. To address how the marine system may respond to these stresses I designed and analysed a suite of simulations using a state of the art ocean circulation- sea ice - biogeochemical coupled model. By combining physical oceanography, biogeochemistry and general ecology, this thesis attempts to give an interdisciplinary perspective on the simulated regional changes. The approach was to use sensitivity experiments to isolate the stimuli and the response, and to study the underlying mechanisms leading to the response. I was able to address the impacts of three stresses: large scale atmospheric forcing, stormy wind events, and increasing freshwater discharge. (1) In regard to the sensitivity of deep ocean ventilation to changes in large scale atmospheric patterns like the North Atlantic Oscillation (NAO), I find that ventilation within the deep Labrador Sea is sensitive to the NAO, but unlike previously suggested, the lateral oxygen fluxes dominate the ventilation process over air-sea oxygen fluxes. (2) Windy conditions, which are predicted to increase in the Arctic Ocean, are indeed responsible for a large part of the primary production and biogenic carbon export in the Arctic and Subarctic. The importance of stormy winds is highest in seasonal and ice free regions and lowest in light limited perennial ice regions. (3) The hosing experiments performed to measure the effect of increasing meltwater run-off from the Greenland Ice Sheet revealed that a positive feedback may develop within Baffin Bay with the potential to accelerate melting by bringing warm waters closer to marine terminating glaciers.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".