Arctic ocean–sea ice reanalysis for the period 2007–2016 using the adjoint method
Bibliographic record
Abstract
Abstract We present an Arctic ocean–sea ice reanalysis covering the period 2007–2016 based on the adjoint approach of the Estimating the Circulation and Climate of the Ocean (ECCO) consortium. The spatiotemporal variation of Arctic sea surface temperature (SST), sea ice concentration (SIC), and sea ice thickness (SIT) is substantially improved after the assimilation of ocean and sea ice observations. By assimilating additional World Ocean Atlas 2018 (WOA18) hydrographic data, the freshwater content of the Canadian Basin becomes closer to the observations and translates into changes of the ocean circulation and of transports through the Fram and Davis straits. This new reanalysis compares well with previous filter‐based (TOPAZ4) and nudging‐based (PIOMAS) reanalyses regarding SIC and SST. Benefiting from using the adjoint of the sea ice model, our reanalysis is superior to the ECCOv4r4 product considering sea ice parameters. However, the mean state and variability of the freshwater content and the transport properties of our reanalysis remain different from TOPAZ4 and ECCOv4r4, likely because of a lack of hydrographic observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".