Effects of Ocean Alkalinity Enhancement in deep and bottom water formation regions on the 21st century CO2 uptake under low and high emission pathways.
Bibliographic record
Abstract
Subduction regions play an important role in transferring carbon from the surface to the deep ocean and sequestering it on a multi-decadal to centennial timescale. Hence, we test the hypothesis that a Carbon Dioxide Removal (CDR) method, namely Ocean Alkalinity Enhancement (OAE) based on olivine addition, is more efficient in deep and bottom water formation region in terms of enhancing the ocean CO2 uptake.Using an ocean-only setup of the physical-biogeochemical model FESOM2.1-REcoM3, we quantify the responses to the spatially uniform and continuous addition of olivine (alkalinity, silicic acid and iron) over the period of 2030-2100 under the SSP1-2.6 and SSP3-7.0 emission scenarios in a global (3 Pg olivine/yr) and a regional application (0.22 Pg olivine/yr). For the regional case, we deposit olivine in the major deep and bottom water formation areas of the Southern Ocean, in the Labrador Sea and the Norwegian Sea.Under the SSP1-2.6 (SSP3-7.0) scenarios, CO2 uptake increases by 1.2 (1.3) Pg C/yr by the end of the 21st century in the global case, whereas it increases by 0.2 (0.2) Pg C/yr in the regional case. The area of uniform olivine deposition is significantly smaller in the regional case compared to the global case, yet the regional OAE has a 2.3-fold higher CDR potential compared to the global OAE in both emission scenarios. The high CDR potential in the regional case is largely (80%) attributed to enhanced biological activity resulting from nutrient fertilization in the Southern Ocean, while only 20% is due to enhanced alkalinity. However, the nutrient effect decreases over time. Furthermore, nutrient addition promotes small-phytoplankton calcification in global and regional OAE cases, leading to lower surface alkalinity by the end of the century. Interestingly, CDR potential of adding alkalinity only (without nutrients) is also 4% and 6.6% higher in the subduction regions than in global OAE under the SSP1-2.6 and SSP3-7.0 scenarios. Overall, the subduction regions, hence show higher CDR efficiency in both cases (alkalinity+nutrients, only alkalinity addition). This effect is two orders of magnitude larger when nutrients are included as this essentially includes Southern Ocean iron fertilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".