Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models; Part 2: Eurasian watersheds
Bibliographic record
Abstract
Abstract A synoptic climatology of daily sea level pressure patterns over Eurasia has been created and used to assess simulations of regional circulation and hydroclimatology from fourteen general circulation models (GCMs). The quality of simulated circulation varies considerably among both individual models and seasons, with summer and winter showing the best agreement with reanalysis data. Close examination of five select models reveals a tendency for GCMs to either over‐ or under‐emphasize the strength and persistence of the winter storm track over Eurasia, with an overemphasized track resulting in better agreement with reanalyses on annual time‐scales. Although pronounced, these biases have relatively little impact on the hydroclimatology of the Ob, Yenisey, and Lena watersheds. Instead, large‐scale precipitation biases appear to be primarily the result of systematic errors in simulated precipitation processes, perhaps related to relevant parameterization schemes. Finally, an effort was made to identify and characterize 20th century precipitation trends related to greenhouse gas induced climate change, by decomposing precipitation time series on the basis of variability in circulation and the mean intensity of precipitation events. Results suggest the influence of anthropogenic climate change over the past forty years is masked by natural variability. Copyright © 2008 Royal Meteorological Society
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".