Excessive positive response of model‐simulated land net primary production to climate changes over circumboreal forests
Bibliographic record
Abstract
Land carbon cycle components in an Earth system model (ESM) play a crucial role in the projections of forest ecosystem responses to climate/environmental changes. Evaluating models from the viewpoint of observations is essential for an improved understanding of model performance and for identifying uncertainties in their outputs. Herein, we evaluated the land net primary production (NPP) for circumboreal forests simulated with 10 ESMs in Phase 5 of the Coupled Model Intercomparison Project by comparisons with observation-based indexes for forest productivity, namely, the composite version 3G of the normalized difference vegetation index (NDVI3g) and tree-ring width index (RWI). These indexes show similar patterns in response to past climate change over the forests, i.e., a one-year time lag response and smaller positive responses to past climate changes in comparison with the land NPP simulated by the ESMs. The latter showed overly positive responses to past temperature and/or precipitation changes in comparison with the NDVI3g and RWI. These results indicate that ESMs may overestimate the future forest NPP of circumboreal forests (particularly for inland dry regions, such as inner Alaska and Canada, and eastern Siberia, and for hotter, southern regions, such as central Europe) under the expected increases in both average global temperature and precipitation, which are common to all current ESMs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".