Douglas-fir radial growth in interior British Columbia can be linked to long-term oscillations in Pacific and Atlantic sea surface temperatures
Bibliographic record
Abstract
A major problem in modern dendrochronology is that the methods traditionally used for linking tree-ring growth data to climate records are not well suited to reconstructing low-frequency climatic variations. In this study, we explored the alternative ensemble empirical mode decomposition (EEMD) to detrend tree-ring records and extract climate signals without removing low-frequency information. Tree cores of Pseudotsuga menziesii var. glauca (Mayr.) Franco were examined in a semi-arid forest in southern interior British Columbia, western Canada. Ring width data were decomposed into five oscillatory components (intrinsic mode functions, IMFs) of increasingly longer periodicities. IMF 1 was considered white noise, IMF 2 was used to create the first diameter growth index (DGI-1), and IMF 3 and IMF 4 were combined to create the second diameter growth index (DGI-2), whereas IMF 5 and the residual term together were considered as the trend term. The highest significant cross-correlations between DGI-1 and the NAOAugust, NIÑO12May, and PDOJanuary indices were found at 1-year lags. DGI-2 had positive and persistent correlations with NAOJune and PDOMay at 0- to 3-year lags and with NAOMay at 2- and 3-year lags. Our results indicate that periods of slow growth in the tree-ring record matched periods of drought in the North American Pacific Northwest. Such water-limiting conditions are likely caused by oscillatory patterns in the Pacific Ocean sea surface temperatures that influence precipitation in the Pacific Northwest. These drought events are likely exacerbated by changes in winter precipitation (snowpack) related to oscillations of the Atlantic Ocean sea surface temperatures, highlighting the ecological effects of both oceans on terrestrial ecosystems. Such relationships could not be easily found by traditional tree-ring analyses that remove some of the low-frequency signal, and therefore, we suggest EEMD as an additional tool to establishing tree growth – climate relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".