Tree-ring oxygen isotope patterns from Siberian and Canadian subarctic to test usability of local versus gridded climate data
Bibliographic record
Abstract
<p>Rapid temperature and vapor pressure deficit (VPD) increase along with precipitation decrease over the past decades lead to massive wildfires and permafrost degradation in boreal forests. Conifer trees growing in subarctic regions are highly sensitive to these climatic changes due to their location at the high latitudes, where air temperature is the limiting factor, but also water relations have a strong impact on tree growth.</p><p>In this study, we aimed (i) to assess the usability of local vs. gridded climate data; (ii) to reveal how conifer trees capture temperature and moisture signals based on the local weather station data vs. gridded data from the two Siberian sites in northeastern Yakutia and eastern Taimyr, and one site from northwestern Canada in Mackenzie Delta; (iii) to perform trend analysis of climatic data and δ<sup>18</sup>O in tree-ring cellulose; and (iv) to carry out spatial correlation analysis of oxygen isotope patterns and determine the distribution of climatic signal over broad geographical scales in the Siberian and Canadian subarctic.</p><p>Comparative analysis of the local and gridded climatic data (air temperature, precipitation and VPD) for the three study sites showed that mainly temperatures are highly correlated between each other. Subarctic trees grow in a temperature-limited environment; therefore, the large spatial coherence of temperature signals is not surprising. Conversely, insignificant correlations between local and gridded for precipitation and rather low correlations for VPD is attributed to the more heterogeneous nature of moisture variables at larger spatial scales.<em> </em>Therefore, analyzing moisture changes in the subarctic using local weather station data is advantageous compared to gridded data.</p><p>Trend analysis of the climate data showed that drastic changes in climate variability occurred from the 1980s in the investigated subarctic regions and were even more pronounced from 2000 to 2021. Recent warming and development of drought conditions were stronger in the Canadian subarctic than the Siberian subarctic sites. Drastic precipitation changes, temperature and VPD increase mainly occurred during winter, spring and autumn in the studied subarctic regions. New updated stable isotope chronologies from remote subarctic regions allowed us to accurately reconstruct moisture changes using precipitation and VPD data from the local weather stations while reconstructing air temperature using gridded data.</p><p>This research was funded by the Russian Science Foundation (RSF) grant number 21-17-00006.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".