Impacts of atmospheric variability on a coupled upper-ocean/ecosystem model of the subarctic northeast Pacific
Bibliographic record
Abstract
[1] The biologically-mediated flux of carbon from the upper ocean to below the permanent thermocline (the biological pump) is estimated to be ∼10 PgC/yr [Houghton et al., 2001], and plays an important role in the global carbon cycle. A detailed quantitative understanding of the dynamics of the biological pump is therefore important, particularly in terms of its potential sensitivity to climate change and its role in this change via feedback processes. Previous studies of coupled upper-ocean/planktonic ecosystem dynamics have considered models forced by observed atmospheric variability or by smooth annual and diurnal cycles. The second approach has the drawback that environmental variability is ubiquitous in the climate system, and may have a nontrivial impact on the (nonlinear) dynamics of the system, while the first approach is limited by the fact that observed time series are generally too short to obtain statistically robust characterizations of variability in the system. In the present study, an empirical stochastic model of high-frequency atmospheric variability (with a decorrelation timescale of less than a week) is estimated from long-term observations at Ocean Station Papa in the northeast subarctic Pacific. This empirical model, the second-order statistics of which resemble those of the observations to a good approximation, is used to produce very long (1000-year) realizations of atmospheric variability which are used to drive a coupled upper-ocean/ecosystem model. It is found that fluctuations in atmospheric forcing do not have an essential qualitative impact on most aspects of the dynamics of the ecosystem when primary production is limited by the availability of iron, although pronounced interannual variability in diatom abundance is simulated (even in the absence of episodic iron fertilization). In contrast, the impacts of atmospheric variability are considerably more significant when phytoplankton growth is limited in the summer by nitrogen availability, as observed closer to the North American coast. Furthermore, the high-frequency variability in atmospheric forcing is associated with regions in parameter space in which the system alternates between iron and nitrogen limitation on interannual to interdecadal timescales. Both the mean and variability of export production are found to be significantly larger in the nitrogen-limited regime than in the iron-limited regime.
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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 | 0.001 |
| 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) | 0.000 | 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".