Ocean dynamics amplify non-local warming effects of forestation
Bibliographic record
Abstract
Large-scale forestation, including reforestation, afforestation, and forest restoration, is prevalent in net zero climate strategies due to the carbon sequestration potential of forests. In addition to capturing carbon, forestation has biogeophysical effects, such as changes in albedo, 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 and carbon accounting frameworks. Yet, the role of ocean dynamics, such as ocean circulation, ocean-atmosphere interactions, and ocean-sea ice interactions in driving non-local effects 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 forestation over a multicentury timescale using the University of Victoria Earth System Climate Model (UVic ESCM), an Earth System Model of Intermediate Complexity (EMIC). We conduct multicentury paired global forestation simulations, with the first simulation using a dynamic ocean (Dynamic Ocean Simulation) and the second using prescribed sea surface temperatures (Prescribed SST Simulation). To be able to separate local from non-local effects, we use the checkerboard approach in both simulations, alternating grid cells undergoing forestation (subject to local and non-local effects) with grid cells remaining deforested (subject to non-local effects only), and compare land surface temperature to a control simulation where all grid cells remain deforested. Using the model simulation data, we perform a surface energy balance decomposition for each simulation at multiple points in time. After a 500-year period, we find a non-local warming effect on land surface temperature in both the Dynamic Ocean Simulation and the Prescribed SST Simulation. However, these non-local warming effects are of much greater magnitude and encompass a greater geographic area, particularly at high latitudes, in the Dynamic Ocean Simulation compared to the Prescribed SST Simulation. Moreover, in the Dynamic Ocean Simulation, non-local warming effects on land continue to strengthen for multiple centuries after most of the forest has regrown and local effects have stabilized. This prolonged land surface warming is the result of a gradual increase in sea surface temperature over multiple centuries caused by ocean-atmosphere interactions combined with the ocean’s thermal inertia. Furthermore, the ocean warming is amplified by climate feedback mechanisms, including the water vapor feedback, fueled by ocean evaporation, and the sea ice-albedo feedback. Consequently, forestation has non-local warming effects that develop gradually and intensify over multiple centuries due to interactions within the Earth system. Net zero policies and carbon accounting frameworks must therefore consider the complete Earth system response over a sufficiently long timeframe to include the slow ocean’s response. Without the consideration of the full Earth system response, net zero policies relying heavily on forestation may not deliver on their climate objectives.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".