Improving a Biogeochemical Model to Simulate Surface Energy, Greenhouse Gas Fluxes, and Radiative Forcing for Different Land Use Types in Northeastern United States
Bibliographic record
Abstract
Abstract Land use changes exert important impacts on climate, primarily through altering greenhouse gas (GHG) and surface energy fluxes. Biogeochemical models have incorporated a relatively complete suite of biogeochemical processes to simulate GHG fluxes. However, these models often lack detailed processes of surface energy exchange, limiting their ability to assess the impacts of land use change on climate. In this study, we incorporated processes of surface energy exchange into a widely used biogeochemistry model, DeNitrification‐DeComposition (DNDC), so that it can quantify both GHG and energy fluxes between the biosphere and the atmosphere. When tested against field observations for the three dominant land use types (forest, hayfield, and cornfield) in the northeastern United States, the improved DNDC successfully captured the observed fluxes of outgoing shortwave radiation, latent heat, sensible heat, net ecosystem exchange of CO 2 , and their differences among the three land use types. To evaluate the differences in radiative forcing among these land use types, we conducted 100‐year simulations and converted the modeled GHG fluxes to radiative forcing using an atmospheric impulse response model. Our results show that the 100‐year cumulative differences in net radiative forcing are 3.35 nW m −2 between the hayfield and forest (slight warming) and 43.2 nW m −2 between the cornfield and forest (warming) per hectare land use difference. The cooling effects of increased albedo after the conversion of forest to hayfield or cornfield (observed and modeled in recent years) are gradually offset by the warming effects of the increasing release of GHG as the forest becomes older.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".