Representation of the rain shadow effect in Patagonia using an orographic‐derived regional climate model
Bibliographic record
Abstract
Abstract This study focuses on Patagonia, where Foehn events observed in the lee of the Andes mountains are not yet well simulated by state‐of‐the‐art climate models. It has been agreed that one source of this shortcoming is related to the poor relief representation in models. To resolve this need, a common method used is to enhance the spatial resolution of the model to retrieve a more complex surface elevation, at the expense of calculation time or surface area covered. This paper tackles the problem from a different angle by addressing the Digital Elevation Model (DEM) generalization, that is, the altitudes generalization from a high‐resolution DEM to a coarser resolution grid model. Most current climate models use DEM generalization methods that smooth the relief, a key controlling factor in Foehn events modelling. The aim of this study is to compare three original methods of DEM generalization (percentile 90 [P90], envelope maximum [EM], and thalweg and crests [TC]) and to evaluate their impact on simulated precipitation and temperature fields on the eastern part of Patagonia, where warm and dry air masses are expected. Thanks to MAR, a Regional Climate Model, we validate the models at 10 and 5 km resolutions against the Climate Research Unit and perform three sensitivity experiments involving a change in the DEM generalization. Our results show that (a) a finer spatial resolution can slightly improve the temperature biases, however, it cannot resolve the precipitation biases and (b) a more appropriate use of DEM generalization induces a significant decrease in precipitation for the P90 and EM methods and an increase in mean temperature for all three methods in the study area. This study serves as a recommendation for a better use of DEM generalization in climate models performing in Patagonia, but also regions sharing the same orographic features as the Patagonian relief.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".