Increased wintertime CO<sub>2</sub> loss as a result of sustained tundra warming
Bibliographic record
Abstract
Abstract Permafrost soils currently store approximately 1672 Pg of carbon (C), but as high latitudes warm, this temperature‐protected C reservoir will become vulnerable to higher rates of decomposition. In recent decades, air temperatures in the high latitudes have warmed more than any other region globally, particularly during the winter. Over the coming century, the arctic winter is also expected to experience the most warming of any region or season, yet it is notably understudied. Here we present nonsummer season (NSS) CO2 flux data from the Carbon in Permafrost Experimental Heating Research project, an ecosystem warming experiment of moist acidic tussock tundra in interior Alaska. Our goals were to quantify the relationship between environmental variables and winter CO2 production, account for subnivean photosynthesis and late fall plant C uptake in our estimate of NSS CO2 exchange, constrain NSS CO2 loss estimates using multiple methods of measuring winter CO2 flux, and quantify the effect of winter soil warming on total NSS CO2 balance. We measured CO2 flux using four methods: two chamber techniques (the snow pit method and one where a chamber is left under the snow for the entire season), eddy covariance, and soda lime adsorption, and found that NSS CO2 loss varied up to fourfold, depending on the method used. CO2 production was dependent on soil temperature and day of season but atmospheric pressure and air temperature were also important in explaining CO2 diffusion out of the soil. Warming stimulated both ecosystem respiration and productivity during the NSS and increased overall CO2 loss during this period by 14% (this effect varied by year, ranging from 7 to 24%). When combined with the summertime CO2 fluxes from the same site, our results suggest that this subarctic tundra ecosystem is shifting away from its historical function as a C sink to a C source.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".