Climate change impacts on CO2 and CH4 exchange in an Arctic polygonal tundra depend on changes in vegetation and drainage
Bibliographic record
Abstract
Model projections of CO2 and CH4 exchange in Arctic tundra during the next century diverge widely. In this modelling study, we used ecosys to examine how climate change will affect CO2 and CH4 exchange through its effects on net primary productivity (NPP), heterotrophic respiration (Rh) and thereby on net ecosystem productivity (NEP) in landform features (troughs, rims, centers) of a coastal polygonal tundra landscape at Barrow AK. The model was shown to simulate diurnal and seasonal variation in CO2 and CH4 fluxes associated with those in air and soil temperatures (Ta and Ts) and soil water contents (q) under current climate in 2014 and 2015. During RCP 8.5 climate change from 2015 to 2085, rising Ta, atmospheric CO2 concentrations (Ca) and precipitation (P) increased NPP from 50 – 150 g C m-2 y-1, consistent with current biometric estimates, to 200 – 250 g C m-2 y-1, depending on feature elevation. Concurrent increases in Rh were slightly smaller, so that net CO2 exchange rose from values of -25 (net emission) to +50 (net uptake) g C m-2 y-1 to ones of -10 to +65 g C m-2 y-1, again depending on feature elevation. Large increases in Rh with thawing permafrost were not modelled. Increases in net CO2 uptake were largely offset by increases in CH4 emissions from 0 – 6 g C m-2 y-1 to 1 – 20 g C m-2 y-1, depending on feature elevation, reducing gains in NEP. Increases in CH4 emissions with climate change were mostly attributed to increases in Ta, but also to increases in Ca and P. These increases in net CO2 uptake and CH4 emissions were modelled with hydrological boundary conditions that were assumed not to change with climate. Both these increases were smaller if boundary conditions were gradually altered to increase landscape drainage during model runs with climate change. The model was then applied to the entire permafrost zone of North America to project RCP 8.5 climate change effects on active layer depth and ecosystem productivity by 2100.
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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 | 0.001 |
| 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 | 0.001 | 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".