Effect of sea ice loss on Earth's energy budget depends on its spatial pattern
Bibliographic record
Abstract
Abstract The global mean sea ice concentration (SIC) is decreasing under global warming, but the effect of SIC reduction on Earth’s energy budget remains uncertain. Here we show that SIC-induced radiation anomalies at the top of the atmosphere are sensitive to the location of SIC reduction in each season, and therefore the impact of SIC reductions on Earth’s energy balance depends on their spatial pattern. SIC-induced radiation anomalies warm the Earth system under CO2-induced long-term global warming, but the SIC-induced radiation anomalies during specific historical periods could counterintuitively even cool the Earth system if the SIC reduction occurs with certain spatial patterns. Idealized experiments indicate that SIC-induced surface warming is greater in the Arctic regions, resulting in a more negative Planck feedback. Global low cloud fraction responses to Arctic and Antarctic SIC reduction are also distinct, leading to more negative SIC-cloud feedback in some Arctic regions. As a result, SIC reduction in some Arctic regions induces negative Planck and cloud feedbacks that overwhelm the positive sea ice albedo feedback, resulting in a net cooling radiative effect on the planet, while the radiative effect of SIC reduction over most Antarctic regions warms the earth.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".