Temporal Decoupling Between Total Organic Carbon and Iron in Lakes Linked to Interannual Changes in Precipitation
Bibliographic record
Abstract
Abstract Widespread increases in lake browning, which affects primary production, have been observed in northern lakes. While lake browning is attributed to increases in terrestrially derived total organic carbon (TOC) and total iron (Fe), Fe does not consistently correlate with increasing TOC over time. This temporal mismatch between TOC and Fe indicates that we still do not fully understand the causes of lake browning, especially in the context of gradually changing climatic conditions. In this study, we utilized Fennoscandian 30‐year (1990–2020) time series data for 102 lakes to describe possible reasons for the temporal decoupling between TOC and Fe. Using Bayesian mixed‐effects models and wavelet coherence analysis, we found evidence for differential responses of TOC and Fe concentrations to changes in precipitation, temperature, and sulfur deposition. While TOC appeared more sensitive to the effects of precipitation, temperature and sulfur deposition in individual lakes, Fe concentrations were impacted by complex interactions among these environmental variables. Although TOC and Fe increased in most lakes in response to increased temperature and precipitation, 41% of the lakes—typically with larger catchment‐to‐lake area ratios and shorter water residence times—exhibited a declining trend in Fe. This analysis encompasses lakes of both significant and non‐significant changes over time. This decline in Fe was associated with short‐timescale (2–4 years) increases in precipitation, leading to a temporal decoupling between Fe and TOC. Our findings suggest that Fe concentrations do not increase uniformly with rising temperatures and increased precipitation, especially in regions where sulfur deposition has declined due to atmospheric recovery policies.
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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 | 0.000 |
| 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) | 0.000 | 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".