Strong links between teleconnections and ecosystem exchange found at a Pacific Northwest old‐growth forest from flux tower and MODIS EVI data
Bibliographic record
Abstract
Abstract Variability in three Pacific teleconnection patterns are examined to see if net carbon exchange at a low‐elevation, old‐growth forest is affected by climatic changes associated with these periodicities. Examined are the Pacific Decadal Oscillation (PDO), Pacific/North American Oscillation (PNA) and El Niño‐Southern Oscillation (ENSO). We use 9 years of eddy covariance CO2, H2O and energy fluxes measured at the Wind River AmeriFlux site, Washington, USA and 8 years of tower‐pixel remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to address this question. We compute a new Composite Climate Index (CCI) based on the three Pacific Oscillations to divide the measurement period into positive‐ (2003 and 2005), negative‐ (1999 and 2000) and neutral‐phase climate years (2001, 2002, 2004, 2006 and 2007). The forest transitioned from an annual net carbon sink (NEP=+217 g C m−2 yr−1, 1999) to a source (NEP=−100 g C m−2 yr−1, 2003) during two dominant teleconnection patterns. Net ecosystem productivity (NEP), water use efficiency (WUE) and light use efficiency (LUE) were significantly different (P<0.01) during positive (NEP=−0.27 g C m−2 day−1, WUE=4.1 mg C g−1 H2O, LUE=0.94 g C MJ−1) and negative (NEP=+0.37 g C m−2 day−1, WUE=3.4 mg C g−1 H2O, LUE=0.83 g C MJ−1) climate phases. The CCI was linked to variability in the MODIS Enhanced Vegetation Index (EVI) but not to MODIS Fraction of absorbed Photosynthetically Active Radiation (FPAR). EVI was highest during negative climate phases (1999 and 2000) and was positively correlated with NEP and showed potential for using MODIS to estimate teleconnection‐driven anomalies in ecosystem CO2 exchange in old‐growth forests. This work suggests that any increase in the strength or frequency of ENSO coinciding with in‐phase, low frequency Pacific oscillations (PDO and PNA) will likely increase CO2 uptake variability in Pacific Northwest conifer forests.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".