A proxy-year analysis shows reduced soil temperatures with climate warming in boreal forest
Bibliographic record
Abstract
Abstract Scientists unequivocally agree that winter air temperature (TA) in northern high latitudes will increase sharply with anthropogenic climate change, and that such increases are already pervasive. However, contrasting hypotheses and results exist regarding the magnitude and even direction of changes in winter soil temperature (TS). Here we use field and satellite data to examine the ‘cold soil in a warm world’ hypothesis for the first time in the boreal forest using a proxy year approach. In a proxy warm year with a mean annual temperature similar to that predicted for ~2080, average winter TS was reduced relative to the baseline year by 0.43 to 1.22 °C in open to forested sites. Similarly, average minimum and maximum winter TS declined, and the number of freeze-thaw events increased in the proxy warm year, corresponding to a reduction in the number of snow-covered days relative to the baseline year. Our findings indicate that early soil freezing as a result of delayed snowfall and reduced snow insulation from cold winter air are the main drivers of reduced winter active-layer TS (at ~2-cm depth) under warming conditions in boreal forest, and we also show that these drivers interact strongly with forest stand structure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".