What are the challenges for modelling isoprene and monoterpene emission dynamics of subarctic plants?
Bibliographic record
Abstract
Abstract. The Arctic is warming at twice the global average speed, and the warming-induced increases in biogenic volatile organic compounds (BVOC) emissions from arctic plants are expected to be drastic. The current global models' estimations of minimal BVOC emissions from the Arctic are based on very few observations and have been challenged by increasing field data. This study applied a dynamic ecosystem model, LPJ-GUESS, as a platform to investigate short-term and long-term BVOC emission responses to climate warming. Field observations in a subarctic heath tundra with long-term (13 years) warming treatments were extensively used for parameterizing and evaluating BVOC related processes. We proposed an adjusted temperature (T) response curve for arctic plants with much stronger T sensitivity than the commonly-used algorithms for large-scale modelling. The simulated emission responses to 2 °C warming between the adjusted and original T response curves in the model were evaluated against the observed warming responses (WR) at short-term scales. Moreover, the model's responses to higher levels' warming (4 °C and 8 °C) were also investigated as a sensitivity test. The model was able to reproduce vegetation CO2 fluxes as well as day-to-day variability of isoprene and monoterpene emissions. The modelled BVOC WR, especially for isoprene, were better captured by using the adjusted T response curve, comparing with using the original one. A few days' underestimation of leaf T led to the underestimated emission rates as well as WR. During 1999–2012, the modelled annual mean isoprene and monoterpene emissions were 20 and 8 mg C m−2 yr−1, with an increase in emission by 55 % and 57 % for 2 °C summertime warming, respectively. Warming by 4 °C and 8 °C further elevated isoprene emission for all years compared with 2 °C warming, but the impacts on monoterpene emissions levelled off because of a decreased coverage of monoterpene-emitters among the evergreen prostrate dwarf shrubs. The high WR captured by the adjusted T response curve highlight the strong T sensitivity of arctic plants. At short-term scale, the WR seem to be strongly impacted by leaf T; while at long-term scale, the WR are a combined effect of plant functional type (PFT) dynamics as well as instantaneous BVOC responses to warming. The identified essential issues associated with estimating arctic BVOC emissions are: (1) leaf T estimation/extrapolation based on air T; (2) PFT parameterization accounting for BVOC emission features as well as PFT's responses to warming; and (3) representation of vegetation dynamics in the past and 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".