The warm Arctic-cold north american pattern in CanESM5 large ensemble simulations: Eurasian influence and uncertainty due to internal variability
Bibliographic record
Abstract
Abstract This study examines the warm Arctic-cold North American pattern (WACNA) and its connection with the warm Arctic-cold Eurasia pattern (WACE) using ERA5 reanalysis and a 50-member ensemble of historical climate simulations produced by CanESM5, the Canadian model participated in CMIP6. The results indicate that a negative WACE-like pattern typically precedes a positive WACNA pattern by one month, and the presence of a negative Asian-Bering-North American (ABNA)-like circulation pattern connects Eurasia and North America, along with the Pacific-North American (PNA)-like pattern. The negative ABNA-like pattern can be attributed to anomalous heating in southern Siberia, which is associated with the negative WACE pattern and its featured Eurasian warming. The negative PNA-like pattern is influenced by negative SST anomalies in the tropical Pacific, resembling tropical ENSO variability. Anomalous temperature advection in the lower troposphere follows the circulation anomaly, which supports the formation of WACNA. Conversely, processes with circulation anomalies of opposite sign result in a negative WACNA pattern. The tropical ENSO variability does not significantly impact the WACNA pattern and its linkage with WACE. CanESM5 simulates the WACNA pattern and WACE-WACNA connection well, with some discrepancies in the magnitude of anomalies compared to ERA5 reanalysis. The uncertainty in the simulated WACNA pattern due to internal climate variability is dominated by two modes of inter-member variability: a southeast-northwest phase shift and a local variation in amplitude.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".