Atmospheric Inputs of Iron and Manganese to Coastal Waters of the Southern California Current System: Seasonality, Santa Ana Winds, and Biogeochemical Implications
Bibliographic record
Abstract
Abstract The magnitude and temporal variability of mineral dust deposition and its associated Fe and Mn inputs to coastal waters of the California Current System (CCS) has been scarcely investigated. Here we report a 5 year time series (April 2010 to December 2014) of mineral dust (Fdust), Fe (FFe), and Mn (FMn) fluxes to the coastal zone of the southern CCS. Atmospheric deposition displayed a strong seasonal trend, with lowest Fdust, FFe, and FMn during the warm season (May–October), a period dominated by strong moisture‐laden winds of oceanic origin. In contrast, the highest Fdust, FFe, and FMn were recorded during the cool season (November–April), a period characterized by strong winds devoid of moisture coming from the mainland. Our analysis suggests that Santa Ana Wind events could contribute with ∼15%, 20%, and 24%, respectively, to the total annual input of dust, Fe and Mn to the region. Besides, atmospheric soluble Fe inputs are equivalent to between 11% (warm season) and 35% (cool season) of the dissolved Fe supplied by upwelling. Our calculations indicate that atmospheric Fe deposition could explain between ∼5% (warm season) and 15% (cool season) of primary production reported for the southern CCS, suggesting that this route could also be an important input of Fe for primary producers in this region. Finally, the average Fdust, FFe, and FMn for the cool seasons showed a positive interannual trend that was significantly correlated with an intensification of drought conditions over the period 2010–2014 in northwest of Mexico and southwest of the United States.
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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 | 0.000 |
| 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 | 0.000 | 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".