Toward a Better Surface Radiation Budget Analysis Over Sea Ice in the High Arctic Ocean: A Comparative Study Between Satellite, Reanalysis, and local‐scale Observations
Bibliographic record
Abstract
Abstract Reanalysis datasets from atmospheric models and satellite products are often used for Arctic surface shortwave (SW) and longwave (LW) radiative budget analyses, but they suffer from limitations and require validation against local‐scale observations. These are rare in the high Arctic, especially for longer periods that include seasonal transitions. In this study, radiation and meteorological observations acquired during the Norwegian Young Sea Ice Cruise (N‐ICE2015) campaign over sea ice north of Svalbard (80–83°N, 5–25°E) from January–June 2015, cloud lidar observations from the Ice‐Atmosphere‐Ocean Observing System and the Cloud and Aerosol Lidar with Orthogonal Polarization are compared to daily and monthly satellite retrievals from the Clouds and the Earth's Radiant Energy System (CERES) and ERA‐Interim and ERA5 reanalysis. Results indicate that surface temperature is a significant driver for winter LW radiation biases in both satellite and reanalysis data, along with cloud optical depth in CERES. In May, the SW and LW downwelling irradiances are close to observations and cloud properties are well captured (except for ERA‐Interim), while SW upward irradiances are biased low due to surface albedo biases in all datasets. Net SW and LW radiation biases are comparable (∼20–30 Wm−2) but opposite in sign for ERA‐Interim and CERES in May, which allows for error compensation. Biases reduce to ±10 Wm−2 in ERA5. In June downward LW remains biased low (8–10 Wm−2) in all datasets suggesting unsettled cloud representation issues. Surface albedo always differs by more than 0.1 between datasets, leading to significant SW and total flux differences.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".