Trajectories of land and ocean primary productivity across the Arctic coastal margin and sensitivity to coastal sea ice decline
Bibliographic record
Abstract
The rapidly warming Arctic and its effects on sea ice extent, hydrology, and nutrient availability influence terrestrial and marine carbon cycles in a number of interrelated ways. While these changes likely have shared effect on adjacent land and ocean systems, we often study them in isolation, making it difficult to understand response patterns and trajectories in these carbon cycle hotspots. Using almost two decades of remotely-sensed Gross Primary Productivity (GPP) in Arctic coastal margins, we test how the magnitude and direction of change in productivity covary. We observed that coastal marine productivity is four times that of coastal tundra productivity in the pan-Arctic. From 2003-2020, GPP in both the coastal land and ocean increased by approximately 12%. This common trajectory seems to be a product of increasing open water conditions, increased terrestrial water balance, and nutrient availability as driven by the regional warming. On a sectoral scale, we proposed a Coastal Synchrony Index (CSI) to compare the rate of change of ocean productivity relative to land productivity and show that ocean productivity is increasing faster than land in inflow margins of Barents, Bering, and Okhotsk, outflow margins of Canadian Arctic Archipelago (CAA) and Greenland/Iceland, and in interior margin of Eurasia. Additionally, we see strong coherence between land and ocean GPP on 4–5-year cycles illustrating that coastal synchrony observed over decadal timescales is mirrored over interannual timescales. These cycles align with variations in open water duration, emphasizing the pivotal role of reducing shorefast ice on terrestrial and marine productivity trajectories.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".