Quantification of subsurface heat storage in a GCM simulation
Bibliographic record
Abstract
Shallow bottom boundary conditions (BBCs) in the soil components of general circulation models (GCMs) impose artificial limits on subsurface heat storage. To assess this problem we estimate the subsurface heat content from two future climate simulations and compare to that obtained from an offline soil model (FDLSM) driven by GCM skin temperatures. FDLSM is then used as an offline substitute for the subsurface of the GCM ECHO‐G. With a 600‐m BBC and driven by ECHO‐G future temperatures, the FDLSM subsurface absorbs 6.2 (7.5) times more heat than the ECHO‐G soil model (10 m deep) under the Intergovernmental Panel on Climate Change (IPCC) A2 (B2) emission scenario. This suggests that shallow BBCs in GCM simulations may underestimate the heat stored in the subsurface, particularly for northern high latitudes. This effect could be relevant in assessing the energy balance and climate change in the next century.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".