Distinct Impacts of Increased Atlantic and Pacific Ocean Heat Transport on Arctic Ocean Warming and Sea Ice Decline
Bibliographic record
Abstract
Abstract Increased ocean heat transport (OHT) to the Arctic Ocean from the Atlantic and Pacific oceans contributes to Arctic Ocean warming and sea ice decline in a warming climate, processes known as Atlantification and Pacification, respectively. However, the separate impacts of these OHTs and their magnitudes remain unclear. This study uses a fully coupled climate model (FIO‐ESM v2.1) to investigate the specific impacts of increased Atlantic and Pacific OHTs on Arctic Ocean temperature, sea ice extent, and sea ice concentration. Our sensitivity experiments reveal that increased Atlantic OHT affects the temperature of the entire Arctic Ocean with the greatest impacts found in the Barents Sea and Eurasian Basin and at intermediate depths of the Arctic basin. The warming extent and efficiency from increased Atlantic OHT is considerably greater than that from Pacific OHT. Without warming of the Atlantic Water inflow, the rate of Arctic Ocean warming would decrease by approximately 50%. Increased Pacific OHT mainly affects the upper ocean in the Pacific sector, including the Chukchi Sea, East Siberian Sea, and Canada Basin. Increased OHT from both the Atlantic and Pacific oceans leads to notable sea ice decline with distinct regional and seasonal variations. Increased Atlantic OHT contributes to sea ice decline across most of the Arctic Ocean, particularly in the Barents Sea, the Kara Sea, and the central Arctic. In contrast, increased Pacific OHT leads to sea ice loss dominantly in the Pacific sector, including the Chukchi, the East Siberian, and the Beaufort seas.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".