Biosphere‐climate interactions in a changing climate over North America
Bibliographic record
Abstract
Abstract This study focuses on projected changes to vegetation characteristics and their interactions with the atmosphere under future climatic conditions over North America, using four transient climate change simulations of the Canadian Regional Climate Model (CRCM5). Here CRCM5 performs dynamical downscaling of the Canadian Earth System Model (CanESM2) simulated data, for Representative Concentration Pathways (RCPs) 4.5 and 8.5. For each RCP, two CRCM5 simulations are performed—one with static vegetation phenology and the other with dynamic vegetation phenology—for the 1950–2100 period over North America. The dynamic vegetation model used here is the Canadian Terrestrial Ecosystem Model. Results show that the extension of the growing season under future climatic conditions in the dynamic phenology simulations leads to higher annual vegetation productivity and biomass. In comparison with projected changes based on CRCM5 with static phenology, CRCM5 with phenology dynamics leads to an albedo‐mediated warming enhancement across most of North America in spring. In summer, results suggest a warming enhancement in the northern latitudes and an attenuation of warming for more southern regions due to hydrological feedbacks. Furthermore, results suggest that vegetation enhances its water‐use efficiency with rising atmospheric CO2 concentrations. Over southeastern United States, in the dynamic phenology simulation corresponding to the RCP8.5 scenario, the adverse effects of the projected increase in temperatures and decrease in precipitation on vegetation dominate the CO2 fertilization effect, leading to decreasing trends in productivity during the 2071–2100 period. This study thus clearly demonstrates that phenology dynamics modulate greenhouse gas‐mediated warming through various biophysical feedbacks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".