On the Arctic near-surface permafrost and climate sensitivities to soil and snow model formulations in climate models
Bibliographic record
Abstract
This study investigates the sensitivity of the Canadian Regional Climate Model (CRCM5) simulated near surface permafrost and its climate interactions to soil and snow formulations. In particular, sensitivities to the depth of the soil column, inclusion of organic soils and modified snow conductivity formulation are investigated. The impact of these modifications are first assessed in offline simulations performed with the Canadian Land Surface Scheme (CLASS), which is the land surface scheme used in CRCM5, when driven by ERA-40/ERA-Interim for the 1957–2008 period. Analysis of CLASS simulations shows major improvements in the simulated permafrost extent, particularly with a deeper soil column. Inclusion of organic soil decreased the summer ground heat flux and therefore the summer soil temperatures, leading to improvements in the simulated active layer thickness (ALT). The impact of the new snow thermal conductivity formulation is moderate compared to the effect of organic soils, but reduces the cold biases in winter soil temperatures. CRCM5 experiments revealed similar sensitivities to soil depth, organic soil and snow conductivity changes as with the offline simulations. Significant changes are noted in the land–atmosphere interactions, through modified energy and moisture partitioning at the surface resulting from the inclusion of the organic soils. The inter-annual variability of the ALT shows larger sensitivities to summer temperatures for mineral soil while experiments including organic soils show increased sensitivities to annual temperatures. The ALT trends in the CRCM5 are similar to the observed values, despite the overestimation of ALT associated with a warm bias in the CRCM5 climate.
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 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 | 0.001 | 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".