Ocean dynamics amplify remote warming effects of reforestation
Bibliographic record
Abstract
Abstract. Forestation, including reforestation, afforestation, and forest restoration, is prevalent in net-zero climate strategies due to the large carbon sequestration potential of forests. In addition to capturing carbon, forestation has biogeophysical effects that can influence surface temperatures locally (local effects), and at distant locations (non-local effects). Biogeophysical effects may offset the cooling benefits of carbon sequestration, hence requiring a robust understanding of their mechanisms to adequately integrate forestation into climate mitigation strategies. Yet, the role of ocean dynamics, such as ocean circulation, ocean-atmosphere interactions, and ocean-sea ice interactions in mediating the non-local effects of forestation remains underexplored. In this study, we investigate the impact of ocean dynamics on the magnitude and geographic patterns of the non-local biogeophysical effects of large-scale reforestation, with the exclusion of cloud feedbacks, over a multi-century timescale using the University of Victoria Earth System Climate Model. We conduct multi-century paired global reforestation simulations, with the first set of simulations using a dynamic ocean and the second set using prescribed sea surface temperatures. We separate local from non-local effects using the checkerboard approach. Our results show that non-local warming effects are of much greater magnitude and encompass a greater geographic area, particularly at high latitudes, when ocean dynamics are considered. Moreover, this study shows that ocean dynamics introduce a lag in the non-local effects, leading to a continued increase in non-local warming even after the local effects have stabilized. This committed non-local warming is driven by the thermal inertia of the ocean, which sustains a gradual long-term increase in sea surface temperatures, combined with amplifying climate feedbacks. Decision-making frameworks must therefore consider the complete Earth system response to forestation over a sufficiently long timeframe to account for the committed non-local warming.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".