Exploring the impacts of unprecedented climate extremes on forest ecosystems: hypotheses to guide modeling and experimental studies
Bibliographic record
Abstract
Climatic extreme events are expected to occur more frequently and potentially be stronger in the future, increasing the likelihood of unprecedented climate extremes (UCEs), or record-breaking events such as prolonged droughts, to occur. To prepare for UCEs and their impacts, we need to develop a better understanding of terrestrial ecosystem responses to events such as extreme drought. We know that intense, extreme droughts can substantially affect ecosystem stability and carbon cycling through increased plant mortality and delaying ecosystem recovery. Our ability to predict such effects is limited due to the lack of experiments focusing on climatic excursions beyond the range of historical experience. We explore the response of forest ecosystems to UCEs using two dynamic vegetation demographic models (VDMs), ED2 and LPJ-GUESS, in which the abundances of different plant functional types, as well as tree size- and age-class structure, are emergent properties of resource competition. We investigate the hypothesis that ecosystem responses to UCEs (e.g., unprecedented droughts) cannot be extrapolated from ecosystem responses to milder extremes, as a result of non-linear ecosystem responses (e.g. due to plant plasticity, functional diversity, and trait combinations). We evaluate each model’s mechanisms and state variables prior, during, and after a continuum of drought intensities ultimately reaching very extreme drought scenarios (i.e., 0% to 100% reduction in precipitation for drought durations of 1-year, 2-year, and 4-year scenarios) at two dry forested sites: Palo Verde, Costa Rica (i.e. tropical) and EucFACE, Australia (i.e. temperate). Both models produce nonlinear responses to these UCEs. Due to differences in model structure and process representation, the model’s sensitivity of biomass loss diverged based on either duration or intensity of droughts, as well as different model responses at each site. Biomass losses in ED2 are sensitive to drought duration, while in LPJ-GUESS they are mainly driven by drought intensity. Elevated atmospheric CO2 concentrations alone did not buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight discrepancies in process formulations and uncertainties in models, notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes. This shows that different hypotheses of plant responses to UCEs exist in two similar models, reflecting knowledge gaps, which should be tested with gap-informed field experiments. This iterative modeling-experiment framework would help improve predictions of terrestrial ecosystem responses and climate 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".