Impacts of extreme land-use change on wind profiles and wind energy according to regional climate models  
Bibliographic record
Abstract
Humans change the climate in many ways, for example, by emitting greenhouse gases or by changing land-use. While studies typically investigate the joint effects of human activity, we here isolate the impact of afforestation and deforestation on winds in the lowermost 350 m of the atmosphere to better understand the role of forests in large-scale wind energy assessments. We use vertically resolved sub-daily output from two regional climate models and compare two extreme scenarios from the LUCAS simulations (Davin et al., 2020). Our results show that afforestation lowers wind speeds by more than 1 m/s in many locations across Europe even 300 m above ground and thus matters at wind turbine hub heights. While adapting the parameters in standard extrapolation allows to capture long-term mean winds well, it remains insufficient to compute wind energy potentials as it fails to capture essential spatio-temporal details, such as changes in the daily cycle. We therefore follow an alternative approach that leverages the vertical resolution of the regional climate models to account for wind profile complexity. Doing so, we report strong changes in wind energy capacity factors due to afforestation and deforestation: they change by up to 50 % in relative terms. Our results confirm earlier studies that land use change impacts on wind energy can be severe and that they are generally misrepresented with common extrapolation techniques. References:Davin, E. L., Rechid, D., Breil, M., Cardoso, R. M., Coppola, E., Hoffmann, P., Jach, L. L., Katragkou, E., de Noblet-Ducoudré, N., Radtke, K., Raffa, M., Soares, P. M. M., Sofiadis, G., Strada, S., Strandberg, G., Tölle, M. H., Warrach-Sagi, K., and Wulfmeyer, V: Biogeophysical impacts of forestation in Europe: First results from the LUCAS Regional Climate Model intercomparison, Earth Syst. Dynam., 11, 183–200, 2020, https://doi.org/10.5194/esd-11-183-2020, 2020Preprint:Wohland, J., Hoffmann, P., Lima, D. C. A., Breil, M., Asselin, O., and Rechid, D.: Extrapolation is not enough: Impacts of extreme land-use change on wind profiles and wind energy according to regional climate models, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2533, 2023
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".