Land cover change impacts on atmospheric chemistry: simulating projected large-scale tree mortality in the United States
Bibliographic record
Abstract
Land use and land cover changes impact climate and air quality by altering the exchange of trace gases between the Earth's surface and atmosphere. Large-scale tree mortality that is projected to occur across the United States as a result of insect and disease may therefore have unexplored consequences for tropospheric chemistry. We develop a land use module for the GEOS-Chem global chemical transport model to facilitate simulations involving changes to the land surface, and to improve consistency across land–atmosphere exchange processes. The model is used to test the impact of projected national-scale tree mortality risk through 2027 estimated by the 2012 USDA Forest Service National Insect and Disease Risk Assessment. Changes in biogenic emissions alone decrease monthly mean O 3 by up to 0.4 ppb, but reductions in deposition velocity compensate or exceed the effects of emissions yielding a net increase in O 3 of more than 1 ppb in some areas. The O 3 response to the projected change in emissions is affected by the ratio of baseline NO x : VOC concentrations, suggesting that in addition to the degree of land cover change, tree mortality impacts depend on whether a region is NO x -limited or NO x -saturated. Consequently, air quality (as diagnosed by the number of days that 8 h average O 3 exceeds 70 ppb) improves in polluted environments where changes in emissions are more important than changes to dry deposition, but worsens in clean environments where changes to dry deposition are the more important term. The influence of changes in dry deposition demonstrated here underscores the need to evaluate treatments of this physical process in models. Biogenic secondary organic aerosol loadings are significantly affected across the US, decreasing by 5–10 % across many regions, and by more than 25 % locally. Tree mortality could therefore impact background aerosol loadings by between 0.5 and 2 µg m −3 . Changes to reactive nitrogen oxide abundance and partitioning are also locally important. The regional effects simulated here are similar in magnitude to other scenarios that consider future biofuel cropping or natural succession, further demonstrating that biosphere–atmosphere exchange should be considered when predicting future air quality and climate. We point to important uncertainties and further development that should be addressed for a more robust understanding of land cover change feedbacks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".