High-performance computing for mechanistic prediction of biome distribution
Notice bibliographique
Résumé
Predictive biome distribution models allow us to investigate how ecosystem dynamics may respond to climate change. A key challenge lies in capturing vegetation’s dynamic response, as plants react individually to climate shifts, forming and dissolving biomes over time. Therefore, models that predict the response of biomes to climate change must adopt a physiology-based approach rather than basing themselves on the apparent climatic distributions of biomes as they exist today. BIOME4, a widely used equilibrium vegetation model developed in 1999, incorporates key components that enhance its ecological realism such as a mechanistic approach driven by climate variables, explicit modeling of plant functional types (PFTs), sensitivity to CO₂ effects and soil-climate interactions, and bioclimatic limits. However, the model has been limited by its computational constraints, running at a coarse resolution of 55 km and relying on legacy Fortran code which leads to compiling challenges and lack of modern GIS compatibility. To address these issues, we implement BIOME4 in Julia, a high-performance and open-source computational language towards which a growing fraction of computational geoscientists are turning. In Julia, just-in-time compilation permits fast development while matching the speed of Fortran, and the use of a modern language allows interfacing with state-of-the-art GIS libraries. Moreover, Julia’s multiple dispatch allows for modularizing the model for future needs and Julia displays high expressivity, which means that it can represent a wide variety of ideas, making models developed in the language highly comprehensive. Thanks to the language improvements, our updated version allows for (1) full parallelization, reducing computation times on HPC systems, (2) improved scalability to handle global datasets at fine resolutions, and (3) enhanced maintainability and modularity for future adaptations. Using the CHELSA global climate dataset, we demonstrate how our novel BIOME4 version enables new applications. We present predictions of biome distribution at fine resolutions, resolving biome belts along ambiguous elevational gradients in coarse-scale applications. By isolating the individual effects of environmental variables such as temperature, precipitation, and CO₂, we show how BIOME4 facilitates attribution studies on the sensitivity of vegetation to drivers of change and the mechanisms underlying biome shifts. We show that the model can be used to explore climate change impacts through CO₂ fertilization effects or to investigate how changes in net primary productivity (NPP) of PFTs translate into shifts in biome distributions. With access to a wide range of climate scenarios, we provide examples of how one can now use BIOME4 to predict how future climate and CO₂ levels might induce shifts in plant functional type and biome distributions.This work underscores the value of BIOME4 and the importance of modernizing legacy models to harness advances in computational capabilities, ensuring their relevance in predicting vegetation dynamic responses to climate change.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».