Earth system model sea-ice loss experiments are wrong. Are they useful?
Bibliographic record
Abstract
Our poor understanding of how the Arctic’s atmosphere, sea ice, and ocean are coupled limits what we can say about Arctic change from greenhouse warming, and what Arctic change means for global weather and climate. Earth system models that simulate Arctic and global change, while complicated and imperfect, are useful to understand drivers of Arctic change and its global influence. In the virtual world of models, you can remove Arctic sea ice and analyze its local and remote response, without greenhouse warming. Or, you can keep sea ice unchanged and investigate a virtual world of greenhouse warming without sea ice loss. But this virtual exploration can fool us: recent work by Mark England and colleagues has shown that this kind of sea ice removal, when carried out in the setting of coupled ocean-atmosphere models, artificially amplifies Arctic warming, with global implications. The basic problem is that these simulations use Arctic sea ice loss as a stand-in for Arctic warming, but targeted ice loss does not account well for the effect on the Arctic of greenhouse warming. We confirm the England et al. result but argue that sea ice loss experiments can nevertheless provide physically reasonable results, if they are linearly combined with greenhouse warming experiments using scaling suggested by simple energy balance models. This post-processing step, along with refined methods for inducing sea ice loss, allows us to gain value from sea ice loss experiments and avoid some of the difficulties arising from interpreting these experiments at face value.
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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 | 0.007 | 0.024 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".