North American forest dieback simulated in response to warm mid-Holocene summers
Bibliographic record
Abstract
Vegetation plays a critical role in regulating climate, not least as a sink of atmospheric carbon. How will anthropogenic warming affect the future distribution and behaviour of vegetation? The study of past warm intervals can contextualise biosphere responses to changes in temperature and precipitation. Pollen archives from central North America, in the Great Plains region, suggest that mid-Holocene (10-4 ka) warming was characterized by an abrupt expansion of grasslands and reduced forest cover. It has been suggested that these changes were a response to drying triggered by an increase in insolation and the abrupt collapse of the Laurentide Ice Sheet but evidence in support of this explanation is lacking. Here we report results from a new dynamic vegetation simulation of the mid-Holocene (6 ka) using the United Kingdom Earth System Model version 1.1 (UKESM1.1), in an atmosphere-land-only configuration. Our simulation is forced by sea-surface temperatures and sea-ice concentrations derived from the PMIP4 HadGEM3-GC3.1 midHolocene experiment and the orbit and greenhouse gas concentrations follow the PMIP4 protocol. In response to summer warming of between 0.5 and 1.5 °C, the model simulates a drying of up to 200 mm yr-1 in the North American continental interior and a substantial decrease in soil moisture. These land surface changes drive shifts in the distribution of plant functional types (PFTs) with a widespread decline in the fractional coverage of forests and a concurrent expansion of grasslands. The forest dieback is most intense in the north and central US and Canadian Great Plains where coverage falls by an area roughly equivalent to half the size of Texas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".