Low-frequency Sea Level Variability and Impact of Recent Sea Ice Decline on the Sea Level Trend in the Arctic Ocean from a High-Resolution Simulation
Bibliographic record
Abstract
The Arctic Ocean is undergoing significant changes, with rapid sea ice decline, unprecedented freshwater accumulation and pronounced regional sea level rise. In this work we analyzed the sea level variation in the Arctic Ocean based on a global simulation with 4.5 km resolution in the Arctic Ocean using the multi-resolution Finite Element Sea ice-Ocean Model (FESOM). The simulation reasonably reproduces both the main spatial features of the sea surface height (SSH) and its temporal evolution in the Arctic Ocean in comparison to tide gauge and satellite data. Using the model results we investigated the low-frequency variability of the Arctic SSH. The decadal variability is the dominant mode of the annual-mean SSH evolution in the Arctic Ocean, which can be mainly attributed to the variability of the halosteric height. The atmospheric circulation associated with the Arctic Oscillation drives the accumulation and release of freshwater in the Arctic deep basin, thus leading to the decadal variability of the SSH. The associated redistribution of water mass changes the ocean mass over the continental shelf, so the change in SSH is opposite between the shelf seas and the deep basin. By using a dedicated sensitivity simulation in which the recent sea ice decline is eliminated, we find that the sea ice decline contributed considerably to the observed sea level rise in the Amerasian Basin in the recent decades. Although the sea ice decline did not change the mean SSH averaged over the Arctic Ocean, it significantly changed the spatial pattern of the SSH trend. Our finding indicates that both the wind regime and on-going sea ice decline should be considered to better understand and predict the changes in regional sea level in the Arctic Ocean.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".