Evaluation of a regional climate model for paleoclimate applications in the Arctic
Bibliographic record
Abstract
The Paleoclimates From Arctic Lakes and Estuaries (PALE) project has been investigating methods of doing high‐resolution model‐data comparisons for the Arctic. As a prelude to a paleosimulation of the North Atlantic region, a modern simulation using observationally driven reanalysis data has been completed. The ARCSyM mesoscale model has been configured for the North Atlantic region, including Labrador, Ungava, Baffin Island, Ellesmere Island, Greenland, and Iceland, with a resolution of 70 km. This high resolution is necessary to predict sub‐GCM grid‐scale climate processes, such as precipitation and storm patterns that depend upon the detailed topography and coastlines of the region. Experiments were performed for the time period of September 1987 to March 1990, driven by observational analyses. The model accurately captures the major summer and winter circulation systems in the North Atlantic region. Comparisons with meteorological station data show high correlations for winter and summer surface temperatures, with a cold bias in winter and a warm bias in summer. Winter precipitation is well simulated by the model because it is driven by the large‐scale circulation. The orographically driven summer precipitation is overrepresented and does not correlate well with observations, although the overall pattern is correct. These results show that the model is capable of capturing the correct temperature and precipitation patterns, although grid‐to‐grid comparisons are not possible. The mesoscale model is therefore useful for regionally based data‐model comparisons, but should not be used to compare individual cores with specific model grids.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".