Parametrization schemes of incident radiation in the North Water polynya
Bibliographic record
Abstract
Abstract Surface incident radiation is a critical component of the Arctic surface energy balance making it important for sea‐ice model parametrizations to properly account for these fluxes. In this article, we test the performance of various incident short‐wave (K?) and long‐wave (L?) flux parametrizations using unique observations from the 1998 International North Water (NOW) Polynya Project between March and July. The dataset includes hourly observations over terrestrial, fast‐ice and full marine polynya environments allowing for parametrization comparisons between each environment and determination of any seasonal biases. Performance testing is highly dependent on observed input parameters that contain relative errors, however, significant differences between the marine and fast‐ice fluxes are evident. Results are very similar between the terrestrial and fast‐ice sites. The best K? clear‐sky schemes underestimate fluxes in the colder season and overestimate them in the warm season, with greater biases in the marine setting. The K? cloudy‐sky results suggest a similar cold and warm season bias but with greater magnitudes, especially in the marine environment. The K? cloudy‐sky schemes require seasonal improvements, especially in the marine atmosphere. The L? clear‐sky fluxes were generally overestimated during the colder season. Accounting for a less emissive atmosphere resulted in better flux approximations in all environments. L? cloudy‐sky fluxes were generally underestimated. Adjusting the cloudy‐sky emissivity improved the estimated fluxes, however, results were very different in the marine setting. The L? cloudy‐sky parametrizations may require re‐evaluation due to a consistent negative bias as the observed flux increases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".