The Possible Reasons for the Misrepresented Long-Term Climate Trends in the Seasonal Forecasts of HFP2
Bibliographic record
Abstract
Abstract The climate trend in a dynamical seasonal forecasting system is examined using 33-yr multimodel ensemble (MME) forecasts from the second phase of the Canadian Historical Forecasting Project (HFP2). It is found that the warming trend of the seasonal forecast in March–May (MAM) over the Eurasian continent is in a good agreement with that in the observations. However, the seasonal forecast failed to reproduce the observed pronounced surface air temperature (SAT) trend in December–February (DJF). The possible reasons responsible for the different behaviors of the HFP2 models in MAM and DJF are investigated. Results show that the initial conditions used for the HFP2 forecast system in MAM have a warming trend over the Eurasian continent, which may come from high-frequency weather systems, whereas the initial conditions for the DJF seasonal forecast do not have such a trend. This trend in the initial condition contributes to the trend of the seasonal forecast in the first month. On the other hand, an examination of the lower boundary SST anomaly forcing shows that the SST trend in MAM has a negative SST anomaly along the central equatorial Pacific, which is favorable for a positive phase of the North Atlantic Oscillation atmospheric response and a warming over the Eurasian continent. The long-term SST trend used for the seasonal forecast in DJF, however, has a negative trend in the tropical eastern Pacific, which is associated with a Pacific–North American pattern–like atmospheric response that has little contribution to a warming in the Eurasian continent.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".