Ozone variability induced by synoptic weather patterns in warm seasons of 2014–2018 over the Yangtze River Delta region, China
Notice bibliographique
Résumé
Ozone (O 3 ) pollution is of great concern in the Yangtze River Delta (YRD) region of China, and the regional O 3 pollution is closely associated with dominant weather systems. With a focus on the warm seasons (April–September) from 2014 to 2018, we quantitatively analyze the characteristics of O 3 variations over the YRD, the impacts of large-scale and synoptic-scale circulations on the O 3 variations and the associated meteorological controlling factors, based on observed ground-level O 3 and meteorological data. Our analysis suggests an increasing trend of the regional mean O 3 concentration in the YRD at 1.8 ppb per year over 2014–2018. Spatially, the empirical orthogonal function analysis suggests the dominant mode accounting for 65.7 % variation in O 3 , implying that an increase in O 3 is the dominant tendency in the entire YRD region. Meteorology is estimated to increase the regional mean O 3 concentration by 3.1 ppb at most from 2014 to 2018. In particular, relative humidity (RH) plays the most important role in modulating the inter-annual O 3 variation, followed by solar radiation (SR) and low cloud cover (LCC). As atmospheric circulations can affect local meteorological factors and O 3 levels, we identify five dominant synoptic weather patterns (SWPs) in the warm seasons in the YRD using the t -mode principal component analysis classification. The typical weather systems of SWPs include the western Pacific Subtropical High (WPSH) under SWP1, a continental high and the Aleutian low under SWP2, an extratropical cyclone under SWP3, a southern low pressure and WPSH under SWP4 and the north China anticyclone under SWP5. The variations of the five SWPs are all favorable to the increase in O 3 concentrations over 2014–2018. However, crucial meteorological factors leading to increases in O 3 concentrations are different under different SWPs. These factors are identified as significant decreases in RH and increases in SR under SWP1, 4 and 5, significant decreases in RH, increases in SR and air temperature (T2) under SWP2 and significant decreases in RH under SWP3. Under SWP1, 4 and 5, significant decreases in RH and increases in SR are predominantly caused by the WPSH weakening under SWP1, the southern low pressure weakening under SWP4 and the north China anticyclone weakening under SWP5. Under SWP2, significant decreases in RH, increases in SR and T2 are mainly produced by the Aleutian low extending southward and a continental high weakening. Under SWP3, significant decreases in RH are mainly induced by an extratropical cyclone strengthening. These changes in atmospheric circulations prevent the water vapor in the southern and northern sea from being transported to the YRD and result in RH significantly decreasing under each SWP. In addition, strengthened descending motions (behind the strengthening trough and in front of the strengthening ridge) lead to decreases in LCC and significant increases in SR under SWP1, 2, 4 and 5. The significant increases in T2 would be due to weakening cold flow introduced by a weakening continental high. Most importantly, the changes in the SWP intensity can make large variations in meteorological factors and contribute more to the O 3 inter-annual variation than the changes in the SWP frequency. Finally, we reconstruct an empirical orthogonal function (EOF) mode 1 time series that is highly correlated with the original O 3 time series, and the reconstructed time series performs well in defining the change in SWP intensity according to the unique feature under each of the SWPs.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».