Snow thermal conductivity controls future winter carbon emissions in shrub-tundra
Bibliographic record
Abstract
Abstract. The Arctic winter is disproportionately vulnerable to climate warming and approximately 1700 Gt of carbon stored in high latitude permafrost ecosystems is at risk of degradation in the future due to enhanced microbial activity. Few studies have been directed at high-latitude cold season land-atmosphere processes and it is suggested that the contribution of winter season greenhouse gas (GHG) fluxes to the annual carbon budget may have been underestimated. Snow, acting as a thermal blanket, influences Arctic soil temperatures during winter and parameters such as snow effective thermal conductivity (Keff) are not well constrained in land surface models which impacts our ability to accurately simulate wintertime soil carbon emissions. A point-model version of the Community Land Model (CLM5.0) forced by an ensemble of NA-CORDEX (North American Coordinated Regional Downscaling Experiment) future climate realisations (RCP 4.5 and 8.5) indicates that median winter CO2 emissions will have more than tripled by the end of the century (2066–2096) under RCP 8.5 and using a Keff parameterisation which is more representative of Arctic snowpack. Implementing this Keff parameterisation increases simulated winter CO2 in the latter half of the century (2066–2096) by 130 % and CH4 flux by 50 % under RCP 8.5 compared to the widely used default Keff parameterisation. The influence of snow Keff parameterisation within CLM5.0 on future simulated CO2 and CH4 is at least as significant, if not more so, than climate variability from a range of NA-CORDEX projections to 2100. Furthermore, CLM5.0 simulations show that enhanced future air and soil temperatures increases the duration of the early winter (Sept–Oct) zero-curtain, a crucial period of soil carbon emissions, by up to a month and recent increases in both zero-curtain and winter CO2 emissions appear set to continue to 2100. Modelled winter soil temperatures and carbon emissions demonstrate the importance of climate mitigation in preventing a significant increase in winter carbon emissions from the Arctic in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".