Definition differences and internal variability affect the simulated Arctic sea ice melt season
Bibliographic record
Abstract
Satellite observations show that the Arctic sea ice melt season is getting longer. This lengthening has important implications for the Arctic Ocean's radiation budget, marine ecology and accessibility. Here we assess how passive microwave satellite observations of the melt season can be used for climate model evaluation. By using the Community Earth System Model Large Ensemble (CESM LE), we evaluate the effect of multiple possible definitions of melt onset, freeze onset and melt season length on comparisons with passive microwave satellite data, while taking into account the impacts of internal variability. We find that within the CESM LE, melt onset shows a higher sensitivity to definition choices than freeze onset, while freeze onset is more greatly impacted by internal variability. The CESM LE accurately simulates that the trend in freeze onset largely drives the observed pan-Arctic trend in melt season length. Under RCP8.5 forcing, the CESM LE projects that freeze onset dates will continue to shift later, leading to a pan-Arctic average melt season length of 7–9 months by the end of the 21st century. However, none of the available model definitions produce trends in the pan-Arctic melt season length as large as seen in passive microwave observations. This suggests a model bias, which might be a factor in the generally underestimated response of sea ice loss to global warming in the CESM LE. Overall, our results show that the choice of model melt season definition is highly dependent on the question posed, and none of the definitions exactly match the physics underlying the passive microwave observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".