Challenges and insights in comparing simulated tree cover changes over the last 20,000 years with reconstructions for the Northern Hemisphere
Bibliographic record
Abstract
Pollen records are the most widespread archive for past climate and vegetation changes, offering valuable insights into Earth’s environmental history. These records provide a unique opportunity to evaluate Earth System Models. In recent years, the availability of quantitative plant cover reconstructions on a continental scale has increased, exemplified by the consistent dataset of REVEALS-based reconstructions provided by Schild et al. (2024) for the entire Northern Hemisphere.We use this data set for comparison with the changes in tree cover simulated by the Max Planck Institute Earth System Model (MPI-ESM) for the last 20,000 years. While the overall agreement between model and data is promising, there are significant regional discrepancies. Notable differences emerge in boreal regions such as Alaska/Western Canada and Siberia, where the model predicts a delayed and weaker tree cover increase during the deglaciation. Conversely, in temperate forest-steppe transition zones, the model shows an earlier and stronger tree cover expansion, balancing out the Northern Hemispheric mean change.However, systematic biases complicate the interpretation of this comparison. For instance, the model tends to simulate excessively cold conditions in boreal latitudes, while the reconstructions likely overestimate tree cover in these regions. As a result, the agreement in vegetation history remains uncertain leaving the comparison of absolute values between reconstructions and model results questionable. An EOF analysis highlights common modes of vegetation changes over the last 20,000 years in MPI-ESM and reconstructions, deepening our understanding despite these uncertainties.References: Schild, L., Ewald, P., Li, C., Hébert, R., Laepple, T., and Herzschuh, U.: LegacyVegetation 1.0: Global reconstruction of vegetation composition and forest cover from pollen archives of the last 50 ka, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-486, in review, 2024
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".