Effects of specifying bottom boundary conditions in an ensemble of atmospheric GCM simulations
Bibliographic record
Abstract
Interannual variability in an ensemble of six 47‐year climate simulations with specified time‐varying sea surface temperature (SST) and sea ice extent is analyzed. Variability is compared with that in a 250‐year control simulation with climatological SST and sea ice extent. The simulations were performed with the Canadian Climate Centre second generation General Circulation Model. An analysis of variance approach was combined with rotated Empirical Orthogonal Function analysis to assess the influence of the prescribed boundary conditions on the simulated atmospheric circulation structure and variability and to identify potentially predictable spatially coherent modes of variation. The quantities analyzed are seasonal mean 500 hPa geopotential (Z500) and mean sea level pressure (Pmsl). The prescribed boundary conditions increase the variability of the simulated atmosphere and modulate its large‐scale circulation structure. The effects on Z500 are found to be strongest in DJF and MAM and weakest in SON. The prescribed boundary conditions have a significant effect over the extratropical land areas, especially over northern North America, in El Niño‐Southern Oscillation (ENSO) years, but have little influence in non‐ENSO years. The variability of several of the leading spatial modes is significantly affected by the prescribed boundary conditions. In contrast with some previous studies, we find that the simulated North Atlantic Oscillation is not affected by the prescribed boundary forcing.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".