Modeling Climate Change Impacts on an Arctic Polygonal Tundra: 1. Rates of Permafrost Thaw Depend on Changes in Vegetation and Drainage
Bibliographic record
Abstract
Abstract Model projections of permafrost thaw during the next century diverge widely. Here we used ecosys to examine how climate change will affect permafrost thaw in a polygonal tundra at Barrow AK. The model was tested against diurnal and seasonal variation in energy exchange, soil heat flux, soil temperature (Ts), and active layer depth (ALD) measured during 2014 and 2015, and interannual variation in ALD measured from 1991 to 2015. During RCP 8.5 climate change from 2015 to 2085, increases in Ta and precipitation (P) to 6.2 °C and 27% above current values, and in atmospheric CO2 concentrations (Ca) to763 μmol mol−1, altered energy exchange by increasing leaf area index of dominant sedge relative to that of moss. Increased Ca and sedge leaf area index imposed greater stomatal control of transpiration and reduced soil heat fluxes, slowing soil warming, limiting increases in evapotranspiration, and thereby causing gradual soil wetting. Consequently, increases in surface Ts and ALD of 2.4–4.7 °C and 21–24 cm above current values were modeled after 70 years. ALD increase was slowed if model boundary conditions were altered to improve landscape drainage. These rates were smaller than those of earlier modeling studies, some of which did not account for changes in vegetation, but are closer to those derived from current studies of warming impacts in the region. Therefore, accounting for climate change effects on vegetation density and composition, and consequent effects on surface energy budgets, will cause slower increases in Ts and ALD to be modeled during climate change simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".