The features of MCSs in Canada and the United States using convection-permitting climate models forced by ERA5 and CMIP6
Notice bibliographique
Résumé
Global climate models (GCMs) are tools that help us understand how the climate works and how it might change in the future. They are based on mathematical equations that describe the physical processes of the atmosphere, ocean, land and ice. The Coupled Model Intercomparison Project (CMIP) is a project that compares different GCMs and their results. Regional Climate Models (RCMs) are similar to GCMs, but they focus on a smaller area and have more details. However, RCMs rely on parameterization schemes to represent subgrid-scale processes that cannot be resolved by the model grid. Parameterization schemes introduce uncertainties and errors in the model results, especially for complex and nonlinear processes like convection and cloud formation. Convection-permitting climate models (CPCMs) are a type of RCMs that can capture small-scale weather features like thunderstorms and clouds without using parameterization schemes. By using a finer grid resolution, CPCMs can explicitly resolve these processes and reduce the uncertainties and biases from parameterization schemes. This makes CPCMs more accurate and reliable for simulating the climate and its changes. Mesoscale Convective Systems (MCSs) are large groups of thunderstorms that can produce heavy rain, hail and strong winds. They are important for the water cycle and the climate of the Rocky Mountains in Canada and the United States. The main goals of this study are to use CPCMs to 1) analyze how well they represent the long-term features of MCSs before they occur in the current climate and 2) project how these features may vary in the future under different greenhouse gas emission scenarios. \n\n\nThis study aimed to improve our knowledge of the features of MCSs in current and future climates using CPCMs. However, the study was limited by the data availability of both the input forcing datasets and observational datasets. The study had three main objectives: 1) to understand the climatological characteristics of MCSs in the central United States (2004 - 2018), 2) to examine the regional features of MCSs over the Canadian Prairies (2009 - 2018), and 3) to project the future climate in the central United States (2076 - 2100) for summer seasons. \nDue to the computational resources required to run the CPCMs, the domain size and analysis period were restricted to 24 degrees in longitude and 20 degrees in latitude, respectively, as well as the summer season.\nThis constraint prevented the study from covering longer periods or larger domains for each objective. The study analyzed meteorological parameters that represented both dynamical and thermodynamic conditions. However, it did not consider physical factors, even though the higher resolution CPCMs showed agreement with previous studies. One of the benefits of the study was that it provided detailed information about the conditions preceding convection when identifying daytime and nighttime MCSs. The findings of the study also yielded important insights into the regional features of MCSs but were extremely restricted to statistical results, lacking a comprehensive analysis of the physical aspects. However, it is essential to interpret these findings with caution, as they may not fully capture the entire range of variability and uncertainty of MCSs in different climates and regions. Furthermore, the absence of explicitly simulated convection initiation limits their physical meaning.
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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 | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| 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 ».