Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products
Notice bibliographique
Résumé
Abstract A consistent approach to evaluate the annual wettest day (Rx1day) across global and regional gridded observational datasets is presented using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) to define climatological regions. Global daily 1° × 1° latitude/longitude gridded products available from the Frequent Rainfall Observations on Grids (FROGS) database are compared with regional high-resolution (∼1–25 km) daily gridded rainfall datasets using several interpolation methods and order of operation. Climatologies are calculated for each global product and region using the overlapping period 2001–16, with global datasets then organized into in situ, satellite, and reanalysis groupings and compared with each other and the regional reference. Our findings show that reanalyses (especially CFSR and MERRA-2) tend to be among the wetter products for precipitation extremes in most regions and that reanalysis groupings also have the largest spread. Perhaps surprisingly, regional datasets are often among the drier, if not the driest products in many regions (especially Southeast Asia, Eurasia, and the Middle East), and are almost always drier than reanalyses except in a few cases. Rx1day is significantly positively correlated in most regions and products except in a handful of cases where data issues are likely to affect correlations. Rx1day timing deviates substantially between products, but agreement is highest among in situ products (40%–70% of the time) especially in data-dense regions with least agreement among reanalyses (10%–40% of the time). Despite uncertainties, the mean relative long-term trend estimates in Rx1day averaged across global land areas, with respect to increases in global mean temperature, are close to 7% °C −1 . Significance Statement While there have been numerous global and regional assessments of trends and variability of historical rainfall extremes, there has been little coordination or limited ability to compare across studies or to use regional products consistently when evaluating global products. Our results show for the first time that despite large uncertainty in the wettest day of the year [annual wettest day (Rx1day)] estimates, regional (continental scale) gridded precipitation products are consistently drier than their global counterparts. However, the trend in Rx1day averaged across all products is broadly consistent with the global increase of ∼7% °C −1 found in other studies. Given the huge spread in observations of daily precipitation extremes, our findings have implications for their efficacy in informing global monitoring, event attribution, and model evaluation efforts.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».