Ocean dinitrogen fixation and its potential effects on ocean primary production in Earth system model simulations of anthropogenic warming
Bibliographic record
Abstract
Dinitrogen fixation (DNF) provides a large fraction of the ‘new’ nitrogen supporting upper ocean productivity, and is associated with environmental conditions likely to show substantial change under anthropogenic warming. For example, surface warming induces stronger stratification, weaker nutrient supply and more rapid nutrient depletion. Using six Earth System Models, we have examined spatial patterns and trends of DNF in the CMIP5 historical and RCP 8.5 experiments. Four models (CanESM2, CESM1-BGC, IPSL-CM5R-LR, and UVicESCM) show high DNF rates in warm, stratified waters mostly associated with the western parts of the ocean basins, while GFDL-ESM2M and MPI-ESM-LR show elevated rates near the eastern boundaries because of coupling of DNF and denitrification. Despite a growing body of data, the spatial pattern of DNF is still insufficiently resolved by available observations, and none of the models agrees well with the observations. Modelled and observed rates are mostly in the same general range except for UVicESCM, and frequency distributions are similar, but spatial pattern correlations are weak and in most cases not statistically significant. Only a few models show strong trends in DNF and primary production in a warming climate, and the signs of the trends are inconsistent. Observations of primary production at the benchmark subtropical station ALOHA (22.75°N, 158°W) and proxies for historical DNF from the same region appear to corroborate trends in CanESM2 that are not present in other models. However, the CanESM DNF parameterization does not include any limitation by P or Fe, so modelled future trends may not materialize due to nutrient limitation. Analysis of available models and observations suggests that our understanding of environmental controls on ocean DNF remains limited and future trends are highly uncertain. Long-term global simulations of DNF will only be meaningful if we maintain long-term observations and extend coverage to undersampled regions.
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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) | 0.000 | 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".