Beautiful Days in the Neighborhood: Land–Atmosphere Interactions as Drivers of Forest Expansion
Bibliographic record
Abstract
Abstract We explore the possible role of plant–atmosphere feedbacks in accelerating forest expansion using a simple example of forest establishment. We use an unconventional experimental design to simulate an initial forest establishment and the subsequent response of climate and nearby vegetation. We find that the forest’s existence produces favorable nearby growing-season conditions that would promote forest expansion. Specifically, we consider a hypothetical region of forest expansion in modern Alaska. We find that the forest acts as a source of heat and moisture for plants to the west, leading them to experience earlier springtime temperatures, snowmelt, and growth. Summertime cooling and cloud formation over the forest also drive a circulation change that reduces summertime cloud cover south of the forest, increasing solar radiation reaching plants there and driving warming. By isolating these vegetation–atmosphere interactions as the mechanisms of increased growth, we demonstrate the potential for forest expansion to be accelerated in a way that has not been highlighted before. These simulations illuminate two separate mechanisms that lead to increased plant growth nearby: 1) springtime heat advection and 2) summertime cloud feedbacks and circulation changes; both have implications for our understanding of past changes in forest cover and the predictability of biophysical impacts from afforestation projects and climate change–driven forest-cover changes. By examining these feedbacks, we seek to gain a more comprehensive understanding of past and potential future land–atmosphere interactions. Significance Statement This study investigates whether the emergence of a high-latitude forest could influence the way water and energy are exchanged between the land and atmosphere in a way that impacts nearby growing conditions and subsequent forest expansion. We use a computer model to simulate a climate with and without forest establishment in the high latitudes and test the response of plants surrounding the forest to the two different climates. We find that a forest is indeed able to spur neighboring plant growth by modifying regional climate and producing more favorable growing conditions for surrounding vegetation. Specifically, forest establishment can bring better growing conditions to plants adjacent to it by warming the air and altering nearby circulation and cloud cover.
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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 | 0.000 | 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) | 0.003 | 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".