Multimodel simulations of Arctic Ocean sea surface height variability in the period 1970–2009
Bibliographic record
Abstract
Abstract The performance of several numerical ocean models is assessed with respect to their simulation of sea surface height (SSH) in the Arctic Ocean, and the main patterns of SSH variability and their causes over the past 40 years (1970–2009) are analyzed. In comparison to observations, all tested models broadly reproduce the mean SSH in the Arctic and reveal a good correlation with both tide gauge data and SSH anomalies derived from satellite observations. Although the models do not represent the positive Arctic SSH trend observed over the last two decades, their interannual‐to‐decadal SSH variability is in reasonable agreement with available measurements. Focusing on results from one of the models for a detailed analysis, it is shown that the decadal‐scale SSH variability over shelf areas and deep parts of the Arctic Ocean have pronounced differences that are determined mostly by salinity variations. A further analysis of the three time periods 1987–1992, 1993–2002, and 2003–2009, corresponding to the transition times between cyclonic and anticyclonic regimes of the atmospheric circulation over the Arctic, revealed an unusual increase of SSH in the Amerasian basin during 2003–2009. Results from this model support the recent finding that the increase is caused mainly by changes in freshwater content brought about by the freshwater export through the Canadian Arctic Archiplago and increased Ekman pumping in the Amerasian basin and partly by lateral freshwater transport changes, leading to a redistribution of low‐salinity shelf water. Overall, we show that present‐day models can be used for investigating the reasons for low‐frequency SSH variability in the region.
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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 | 0.001 | 0.000 |
| 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".