An inter-comparison of the mass budget of the Arctic sea icein CMIP6 models
Bibliographic record
Abstract
Abstract. We compare the mass budget of the Arctic sea ice for 14 models submitted to the latest Climate Model Inter-comparison Project (CMIP6), using new diagnostics that have not been available for previous model inter-comparisons. Using these diagnostics allows us to look beyond the standard metrics of ice cover and thickness, to compare the processes of sea ice growth and loss in climate models in a more detailed way than has previously been possible. For the 1960–89 multi-model mean, the dominant processes causing annual ice growth are basal growth and frazil ice formation, which both occur during the winter. The main processes by which ice is lost are basal melting, top melting and advection of ice out of the Arctic. The first two processes occur in summer, while the latter process is present all year. The sea-ice budgets for individual models are strikingly similar overall in terms of the major processes causing ice growth and loss, and in terms of the time of year during which each process is important. However, there are also some key differences between the models. The relative amounts of frazil and basal ice formation varies between the models. This is, to some extent at least, attributable to exactly how the frazil growth is formulated within each model. There are also differences in the relative amounts of top and basal melting. As the ice cover and mass decline during the 21st century, we see a shift in the timing of the top and basal melting in the multi-model mean, with more melt occurring earlier in the year, and less melt later in the summer. The amount of basal growth in the autumn reduces, but the amount of basal growth later in the winter increases due to the ice being thinner. Overall, extra ice loss in May–June and reduced ice growth in October-November is partially offset by reduced ice melt in August and increased ice growth in January–February. For the individual models, changes in the budget components vary considerably in terms of magnitude and timing of change. However, when the evolving budget terms are considered as a function of the changing ice state itself, behaviours common to all the models emerge, suggesting that the sea ice components of the models are fundamentally responding in a broadly consistent way to the warming climate. Additional results from a forced ocean-ice model show that although atmospheric forcing is crucial for the sea ice mass budget, the sea ice physics also plays an important role.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".