Regional variability of acidification in the Arctic: a sea of contrasts
Bibliographic record
Abstract
Abstract. The Arctic Ocean is a region that is particularly vulnerable to the impact of ocean acidification driven by rising atmospheric CO2, negatively impacting calcifying organisms such as coccolithophorids and foraminiferans. In this study, we use an ocean general circulation model, with embedded biogeochemistry and a full description of the carbon cycle, to study the response of pH and saturation states of calcite and aragonite to changing climate in the Arctic Ocean. Particular attention is paid to the strong regional variability within the Arctic and, for comparison, simulation results are contrasted with those for the global ocean. Simulations were run to year 2099 using the RCP 8.5 (the highest IPCC AR5 CO2 emission scenario). The separate impacts of the direct increase in atmospheric CO2 and indirect effects via climate feedbacks (changing temperature, stratification, primary production and fresh water fluxes) were examined by undertaking two simulations, one with the full system and the other in which ocean-atmosphera exchange of CO2 was prevented from increasing beyond the flux calculated for year 2000. Results indicate that climate feedbacks, and spatial heterogeneity thereof, play a strong role in the declines in pH and carbonate saturation (Ω) seen in the Arctic. The central Arctic, Canadian Arctic Archipelago and Baffin Bay show greatest rates of acidification and Ω decline as a result of melting sea ice. In contrast, areas affected by Atlantic inflow including the Greenland Sea and outer shelves of the Barents, Kara and Laptev seas, had minimal decreases in pH and Ω because weakening stratification associated with diminishing ice cover led to greater mixing and primary production. As a consequence, the predicted onset of undersaturation is highly variable regionally within the Arctic, occurring during the decade of 2000–2010 in the Siberian shelves and Canadian Arctic Archipelago, but as late as the 2080s in the Barents and Norwegian Seas. We conclude that, in order to make future projections of acidification and carbon saturation state in the Arctic, regional variability needs to be adequately resolved, with particular emphasis on reliable predictions of the rates of retreat of the sea-ice which are a major source of uncertainty.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".