Summer albedo variations in the Arctic Sea ice region from 1982 to 2015
Bibliographic record
Abstract
Abstract The spatiotemporal changes in the sea ice region albedo over the entire Arctic region and in eight subregions (the Central Arctic Ocean [CAO], the Beaufort and Chukchi Seas [BC], the East Siberian and Laptev Seas [ESL], Baffin Bay and Labrador Sea [BL], the Canadian Archipelago [CA], the Greenland Sea [GS], Hudson Bay [HB] and the Kara and Barents Seas [KB]) in the summer of 1982–2015 are analysed with CLARA‐A2‐SAL data. The results indicate that in the summer of 1982–2015, the Arctic Sea ice region albedo fluctuated with a downward trend of −1.6% per decade (significance level of 99%). The BC had the largest decline in the albedo trend of −2.7% per decade (significance level of 99%), and most other subregions had downward trends except the GS, which exhibited a slight upward trend. The mean Arctic Sea ice region albedo was 44%. The high albedo areas were mainly concentrated in the CAO and the vicinity of Greenland. The albedo decreased with decreasing latitude, while the low‐value areas were mainly concentrated in the outer sea ice area. In the Arctic region, both the sea ice concentration (SIC) and the sea ice extent (SIE) showed a decreasing trend, while the near‐surface air temperature (NSAT) and the summer Central Arctic Index (CAI) showed an increasing trend. The Arctic Sea ice region albedo was positively correlated with the SIC and the SIE (0.84, 0.78) and negatively correlated with the NSAT (−0.72), all with a statistical significance level of 99%. The correlation between sea ice region albedo and the summer CAI revealed different relationships in these regions. The BC and ESL had a significant negative correlation, and the GS showed a significant positive correlation. These findings indicated that the decrease in albedo is closely related to the reduction in Arctic Sea ice and the increase in air temperature. In addition, the sea ice region albedo variations in the BC, ESL and GS are also greatly influenced by atmospheric circulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".