Increase in Near-Surface Temperature Simulation Skill due to Predictive Soil Moisture in a Numerical Seasonal Simulation under Observed SST Forcing
Bibliographic record
Abstract
Abstract Two sets of 12-yr eight-member ensemble integrations were run with the Experimental Climate Prediction Center’s (ECPC) seasonal forecast model (SFM) to investigate the sensitivity of near-surface temperature skill to evolving soil moisture. The first ensemble had evolving soil moisture, which was fully interactive with the atmospheric component of the model. The second ensemble had soil moisture fixed to the monthly climatological value. Several regions showed an increase in skill in the evolving soil moisture ensemble, including northeastern Australia, southeastern Africa, Europe, northern Brazil, Western Australia, northwestern Russia, Argentina, western Canada, and Indo-China. A survey of these regions showed that most had sensitivity to soil moisture following a peak rainy season, and of those, most also had a high soil moisture time-lag correlation (soil moisture memory) at that time. In a few of the regions high year-to-year soil moisture variability was an additional potential source of soil moisture sensitivity. The sensitivity to soil moisture is considered both in terms of actual predictability (anomaly correlation with observations) and theoretical potential predictability. It was found that the regions listed above, with evolving soil moisture, have anomaly correlations that are close to the potential predictability, suggesting that the model estimates near-surface temperature in these regions as well as could be expected. However, it was also found that when comparing the two sets of integrations, improvements in potential predictability of one ensemble over the other did not necessarily give a reasonable estimate to improvements in anomaly correlation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".