Assessment of the sea-ice carbon pump: Insights from a three-dimensional ocean-sea-ice-biogeochemical model (MPIOM/HAMOCC)
Bibliographic record
Abstract
Abstract It has been suggested that geochemical processes related to sea-ice growth and melt might be important for the polar carbon cycle via the so called sea-ice carbon pump (SICP). The SICP affects the air-sea CO2 exchange by influencing the composition of dissolved inorganic carbon (DIC) and total alkalinity (TA) in the surface ocean. Here we quantify the strength of the SICP-induced air-sea CO2 flux using the global three-dimensional ocean-sea-ice-biogeochemical model MPIOM/HAMOCC. Simulations prescribing the range of observed DIC and TA concentrations in the sea ice were performed under two idealized climate scenarios for the present-day and the future oceanic and sea-ice state, both forced with a fixed atmospheric CO2 concentration. Model results indicate that the SICP-induced air-sea CO2 uptake increases with higher ratios of TA:DIC prescribed in the sea ice relative to the basic oceanic TA:DIC ratios. Independent of the modeled scenario, the simulated strength of the SICP is larger in the Antarctic than in the Arctic, because of more efficient export of brine-associated DIC from the Antarctic mixed layer. On an annual basis, we generally find an enhanced SICP-induced oceanic CO2 uptake in regions with net sea-ice melt, and enhanced SICP-induced oceanic CO2 out-gassing in regions with net sea-ice growth. These general regional patterns are modified further by the blockage of air-sea gas exchange through sea-ice coverage. Integrated over the sea-ice zones of both hemispheres, the SICP-induced oceanic CO2 uptake ranges from 2 to 14 Tg C yr−1, which is up to 7% of the simulated net CO2 uptake in polar regions, but far less than 1% of the current global oceanic CO2 uptake. Hence, while we find that the SICP plays a minor role in the modern global carbon cycle, it is of importance for the regional carbon cycle at high latitudes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 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".