Initialization and Potential Predictability of Soil Moisture in the Canadian Seasonal to Interannual Prediction System
Bibliographic record
Abstract
The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".