Impact of interactive vegetation phenology on the Canadian RCM simulated climate over North America
Bibliographic record
Abstract
Biosphere–atmosphere interactions play a very important role in modulating regional climate. To capture these bi-directional interactions, a dynamic vegetation model, the Canadian Terrestrial Ecosystem Model (CTEM), has been implemented in the fifth generation of the Canadian Regional Climate Model (CRCM5). CTEM can grow vegetation from bare ground and includes processes of photosynthesis, autotrophic and heterotrophic respiration, phenology, turnover, mortality and allocation. This study focuses on assessing the impact of interactive vegetation phenology, i.e. CTEM, on the CRCM5 simulated climate over North America. This is achieved by comparing two CRCM5 simulations—one with interactive phenology and the other with prescribed vegetation, driven by ECMWF reanalysis data (ERA40 and ERA-Interim) at the lateral boundaries, for the 1971–2010 period. Comparison of simulated vegetation attributes, temperature and precipitation in both simulations to those observed indicates that introduction of interactive phenology improves the performance of CRCM5 in some regions, although it introduces new biases in other regions, which are related to the underestimation of leaf area index (LAI). Interactive phenology enhances biosphere–atmosphere interactions, which are reflected in the higher values of correlation between atmosphere and biosphere variables. Interactive phenology also introduces long-term memory in CRCM5, estimated via lagged correlations between precipitation/temperature and LAI. Improved biosphere–atmosphere interactions and long-term memory in the CRCM5 simulation with interactive phenology leads to better interannual variability, particularly noticeable in the biosphere and atmosphere states during anomalously wet and dry years. This study thus provides useful insights related to the added value of interactive phenology in CRCM5 as well as the nature and variability of biosphere–atmosphere interactions over North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".