Global and regional effects of land-use change on climate in 21st century simulations with interactive carbon cycle
Bibliographic record
Abstract
Abstract. Biogeophysical (BGP) and biogeochemical (BGC) effects of land-use and land cover change (LULCC) are separated at the global and regional scales in new interactive CO2 simulations for the 21st century. Results from four earth system models (ESMs) are analyzed for the future RCP8.5 scenario from simulations with and without land-use and land cover change (LULCC), contributing to the Land-Use and Climate, IDentification of robust impacts (LUCID) project. Over the period 2006–2100, LULCC causes the atmospheric CO2 concentration to increase by 12, 22, and 66 ppm in CanESM2, MIROC-ESM, and MPI-ESM-LR, respectively. Statistically significant changes in global near-surface temperature are found in three models with a BGC-induced global mean annual warming between 0.07 and 0.23 K. BGP-induced responses are simulated by three models in areas of intense LULCC of varying sign and magnitude (between −0.47 and 0.10 K). Modifications of the land carbon pool by LULCC are disentangled in accordance with processes that can lead to increases and decreases in this carbon pool. Global land carbon losses due to LULCC are simulated by all models: 218, 57, 35 and 34 Gt C by MPI-ESM-LR, MIROC-ESM, IPSL-CM5A-LR and CanESM2, respectively. On the contrary, the CO2-fertilization effect caused by elevated atmospheric CO2 concentrations due to LULCC leads to a land carbon gain of 39 Gt C in MPI-ESM-LR and is almost negligible in the other models. A substantial part of the spread in models' responses to LULCC is attributed to the differences in implementation of LULCC (e.g., whether pastures or crops are simulated explicitly) and the simulation of specific processes. Simple idealized experiments with clear protocols for implementing LULCC in ESMs are needed to increase the understanding of model responses and the statistical significance of results, especially when analyzing the regional-scale impacts of LULCC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".