Tree-ring width and density data around the Northern Hemisphere: Part 1, local and regional climate signals
Bibliographic record
Abstract
A detailed description is presented of the statistical patterns of climate forcing of tree growth (annual maximum latewood density and ring-width time series), across a network of 387 specially selected conifer sites that circle the extra-tropical Northern Hemisphere. The in‘ uence of summer temperature dominates growth. A mean April–September response is optimum for describing the major forcing signal over the whole densito-metric network, though a shorter June–July season is more relevant in central and eastern Siberia. The ring- width chronologies also have a shorter optimum (June–August) seasonal signal, but this is much weaker than the density signal. The association between tree-ring density and precipitation variability (as measured by partial correlations to account for the correlation between temperature and precipitation) is considerably weaker than with temperature. The ring-width response to precipitation is dominated by ‘noise’ and local site in‘ uences, though a negative response to winter precipitation in northern Siberia is consistent with the suggestion of an in‘ uence of delayed snowmelt. Average correlations with winter temperatures are small for all regions and correlations with annual temperatures are positive only because of the strong link with summer temperatures. Reconstructions of summer temperature based on composite regional density chronologies for nine areas are presented. Five regions (northwestern North America, NWNA; eastern and central Canada, ECCA; northern Europe, NEUR; northern Siberia, NSIB; and eastern Siberia, ESIB) constitute an arbitrary ‘northern’ division of the network, while the four other regions (western North America, WNA; southern Europe, SEUR; central Asia, CAS; and the Tibetan Plateau, TIBP) make up the ‘southern’ part. We also present two larger composite regional reconstructions comprising the data from the five higher-latitude (HILAT) and four lower-latitude (LOLAT) areas respectively; and a single series made up of data from all regions (ALL), which is highly correlated with Northern Hemisphere mean summer temperature. We calculate time-dependent uncertainty ranges for each of these reconstructions, though they are not intended to represent long timescales of tempera ture variability (>100 years) because the technique used to assemble the site chronologies precludes this. Finally, we examine in more detail the reduced sensitivity in the tree-growth data to decadal-timescale summer-temperature trends during the last 50 years, identified in earlier published work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".