Photochemical processes drive thermal responses of dissolved organic matter in the dark ocean
Bibliographic record
Abstract
Abstract How dissolved organic matter (DOM) responds to climate warming is critical for understanding its role in future ocean carbon cycling. Here, we use a highly resolved dataset of over 800 DOM samples covering the surface waters to the deep Atlantic, Southern, and Pacific oceans to examine the changes in DOM molecular composition in response to warming water temperatures, referred to as DOM thermal responses. Towards the deep waters, the strength (i.e., overall magnitude) and diversity (i.e., variation among molecules) of these thermal responses both decline. However, these responses show opposite trends with the concentration of more recalcitrant molecules, decreasing and increasing, respectively. These contrasting trends concur with the observation that, compared to the strength of thermal responses, their diversity is more strongly explained by photochemical processes of DOM. By projecting global ocean thermal responses from 1950 to 2020 using environmental temperature, salinity and radiation, we predict that increases in the diversity of thermal responses are unexpectedly largest at deeper depths (> 1,000 m). Such increases could elevate the recalcitrant deep-ocean carbon sink by approximately 10 Tg C yr -1 , which accounts for > 5% of the carbon flux reaching and persisting in the deep ocean. Our findings highlight the role of photochemical processes in imprinting DOM thermal responses, offering new insights into the future capacity of the oceanic carbon sink under global climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".