Investigating the Relative Roles of Dynamics and Thermodynamics in Sea‐Ice Volume Changes in the Canada Basin
Bibliographic record
Abstract
Abstract The Canada Basin (CB) has seen significant sea‐ice loss in recent decades. We use output from the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System to examine the 1979–2023 evolution of seasonal sea‐ice volume (SIV) changes in the CB partitioned into advective and thermodynamic changes. In winter, some years show net convergence into the region that is of comparable magnitude to the SIV change attributed to sea‐ice growth. In summer, melt/ablation dominates the change each year. In both seasons, 44 year trends in seasonal SIV changes are driven primarily by thermodynamic processes. The inferred thermodynamic growth each year is nearly equal to the inferred melt consistent with SIV at the end of the melt season declining more rapidly than SIV at the end of the growth season. Increased melt season atmospheric heating of the ice‐ocean system over 1979–2023, estimated from ERA5 reanalysis, is consistent with the ice‐albedo feedback. In the growth season, net cumulative atmospheric heat release from the ice‐ocean system shows no trend, suggesting increases in inferred thermodynamic ice growth can be attributed to more rapid growth of thinner ice. In each season, cumulative atmospheric heat input exceeds that required for ice melt/growth resulting in a residual that influences ocean heat content (OHC). Seasonal OHC changes, inferred from ocean observations, are equal to approximately one‐third of this residual, although limited ocean observations leave the total heat budget poorly constrained, highlighting a need for more water column 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".