Snowpack signals in North American tree rings
Bibliographic record
Abstract
Abstract Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn have intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually resolved snowpack reconstructions from regional to high ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo><</mml:mo> </mml:math> 12 km) spatial scales in western North America and, by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".