Correcting for artificial heat in coupled sea ice perturbation experiments
Bibliographic record
Abstract
A common approach to assessing how polar amplification affects lower latitude climate is to perform coupled ocean-atmosphere experiments in which sea ice is perturbed to a future state. A recent critique by M. England and others uses a simple 1-dimensional energy balance model (EBM) to show that sea ice perturbation experiments add artificial heat to the climate system. We explore this effect in a broader range of models and suggest a technique to correct for the artificial heat post-hoc. Our technique successfully corrects for artificial heat in the EBM and a possible generalization of this approach is developed to correct for artificial heat in an albedo modification experiment in a comprehensive earth system model. However, this technique can not be directly generalized to sea ice perturbation methodologies that employ a "ghost flux" seen only by the sea ice model. Applying the correction to the comprehensive albedo modification experiment, we find stronger artificial warming than in the EBM. Failing to account for the artificial heat also leads to overestimation of the climate response to sea ice loss, and can suggest false or artificially strong "tugs-of-war" between 19 low latitude warming and sea ice loss over some fields, for example Arctic surface temperature and 20 zonal wind.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".