A matter of divergence: Tracking recent warming at hemispheric scales using tree ring data
Bibliographic record
Abstract
No current tree ring (TR) based reconstruction of extratropical Northern Hemisphere (ENH) temperatures that extends into the 1990s captures the full range of late 20th century warming observed in the instrumental record. Over recent decades, a divergence between cooler reconstructed and warmer instrumental large‐scale temperatures is observed. We hypothesize that this problem is partly related to the fact that some of the constituent chronologies used for previous reconstructions show divergence against local temperatures in the recent period. In this study, we compiled TR data and published local/regional reconstructions that show no divergence against local temperatures. These data have not been included in other large‐scale temperature reconstructions. Utilizing this data set, we developed a new, completely independent reconstruction of ENH annual temperatures (1750–2000). This record is not meant to replace existing reconstructions but allows some degree of independent validation of these earlier studies as well as demonstrating that TR data can better model recent warming at large scales when careful selection of constituent chronologies is made at the local scale. Although the new series tracks the increase in ENH annual temperatures over the last few decades better than any existing reconstruction, it still slightly under predicts values in the post‐1988 period. We finally discuss possible reasons why it is so difficult to model post‐mid‐1980s warming, provide some possible alternative approaches with regards to the instrumental target and detail several recommendations that should be followed in future large‐scale reconstruction attempts that may result in more robust temperature estimates.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".