Multisite statistical downscaling of daily temperature extremes for climate-related impact assessment studies
Notice bibliographique
Résumé
Global climate change has been considered in many engineering studies due to its drastic impacts on the design and planning of various infrastructures. In order to reduce the risks of those impacts, the present study focuses on accurate prediction of daily temperature extremes for future periods under different climate change scenarios. The main objectives of this study are therefore: (a) to detect the evidences of climate change from the statistical analysis of existing observed daily extreme temperature data; (b) to assess the performance of single-site and multi-site statistical downscaling (SD) approaches in order to identify the best SD model that could describe accurately the linkage between global scale climate variables and the observed statistical properties of daily temperature extremes at a given local site; and (c) to provide a prediction of daily temperature extremes for future periods based on the best SD model identified under different climate change scenarios. Firstly, a detailed statistical analysis of daily extreme temperature data available during the 1973-2009 period from a network of 25 weather stations located in South Korea was carried out to identify the possible trends in 18 different temperature characteristics. Results of this data analysis have indicated significant changes in the characteristics of daily maximum temperature (Tmax) and daily minimum temperature (Tmin) during this period. In particular, the positive trends in annual means of Tmax and Tmin were found statistically significant. In addition, the number of cold events tends to decrease while the number of warm events tends to increase at most of the stations considered. Secondly, statistical downscaling methods were used to describe the linkage between the coarse resolution of General Circulation Model (GCM) climate variables and the daily extreme temperature characteristics at a local site for impact assessments. Most previous studies have been dealing with downscaling of daily temperature extremes at a single site. However, more recent studies have been conducted to develop improved downscaling methods for many sites concurrently. This study was carried out to assess the performance of the multi-site SD method based on the Singular Value Decomposition (SVD) method as compared with the performance of the popular SDSM for single-site downscaling. The application of the multi-site and single-site SD methods was performed using the observed daily Tmax and Tmin data from the 25 stations in South Korea and the corresponding NCEP re-analysis data for the 1973-2001 period. It was found that the multi-site SD method and the single-site SDSM could accurately reproduce basic properties of Tmax and Tmin at each local site. However, the multi-site SD method could describe more accurately the temporal and spatial correlations of daily temperature extremes than the SDSM. Overall, the multi-site SD method was found to be more accurate than the SDSM. Finally, future prediction of daily extreme temperatures was accomplished based on the multi-site SD method under the A1B and A2 climate scenarios provided by the third version of the Canadian Global Climate Model (CGCM3). The increasing trends were found in the monthly means of Tmax and Tmin, the monthly90th percentiles of Tmax, and the monthly10th percentiles of Tmin for the future 2010-2100 period over South Korea.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».