On the formation of high‐latitude soil carbon stocks: Effects of cryoturbation and insulation by organic matter in a land surface model
Bibliographic record
Abstract
We modify the soil component of the ORCHIDEE terrestrial carbon cycle model to include vertically‐discretized soil carbon. With this model, we investigate the feedback of considering thermal insulation by soil carbon, which modifies the soil thermal regime by lowering the thermal conductivity and increasing the heat capacity of a carbon‐rich soil, on the total carbon stocks the model builds up. In addition, we demonstrate the effects of diffusive vertical mixing of soil organic matter by cryoturbation on the total carbon stocks that the model builds up in mineral soils in equilibrium with a steady climate. We show that including these two effects together leads to up to 30% higher soil carbon stocks in the top meter of permafrost soils, as well as large stocks of carbon below 1m in the upper permafrost soil layers. The vertical profile of partitioning of carbon between different lability pools is also affected, as the slower pools are more deeply mixed; also the time to reach equilibrium lengthens considerably. These effects are largest in the coldest regions such as Eastern Siberia. The inclusion of cryoturbative mixing and insulation by soil carbon leads to better agreement with estimates of high‐latitude soil carbon stocks, where substantial amounts of carbon are found in permafrost regions, to depths of three meters; however we do not include peat, Yedoma, or alluvial deposition processes here, so the total carbon stocks are still lower than observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".