Understanding Drivers of Stratospheric Ozone Change and Fingerprinting its Recovery
Notice bibliographique
Résumé
Stratospheric ozone serves as Earth’s natural protective layer, shielding the surface from harmful ultraviolet radiation. The discovery of the Antarctic ozone “hole” in the late 1980s raised significant societal and scientific concern, prompting the rapid regulation of ozonedepleting substances (ODSs) under international treaties. While the signs of ozone recovery have begun, new challenges continue to arise. This thesis investigates three critical factors driving stratospheric ozone changes and influencing the detection of ozone recovery: (1) ODS emissions, (2) chemical chlorine processes, and (3) internal climate variability. With ODS emissions being regulated under the Montreal Protocol and studies now focusing on illicit new production on the order of tens of gigagrams per year, the ocean’s role as both a natural source and sink of ODSs becomes increasingly important. However, these processes have often been overlooked or highly simplified in past ozone assessments. Using a hierarchy of models, from simple box models to global ocean general circulation models, I quantified the ocean’s uptake and release of various ODSs. Chapter 2 examines the ocean’s uptake of chlorofluorocarbons (CFCs), particularly emphasizing its influence on recent illicit CFC emissions estimation. Chapter 3 extends this analysis to include ocean uptake and potential microbial degradation processes, evaluating their effects on emission estimates for various hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs), which are chemical constituents that have been used to replace CFCs. Once these man-made ODSs reach the stratosphere, they are photolyzed to chlorine reservoir species (e.g., HCl and ClONO2), which, through heterogeneous reactions, can transform into reactive chlorine that depletes ozone. While heterogeneous chlorine activation on volcanic ash is well understood, the unprecedented 2020 Australian wildfires raised new questions about chemical processes on smoke particles. This knowledge gap existed because only a few wildfires had injected significant amounts of smoke particles into the stratosphere during the satellite era. Leveraging over 30 years of satellite data, I separated chemical and dynamic processes affecting chlorine reservoir species to quantify chemical chlorine activation across different aerosol types. In Chapter 4, I developed a new approach to quantitatively estimate the onset temperature for chemical chlorine activation after the 2020 Australian wildfire using satellite observations. Chapter 5 applies this method to compare the impact of chemical chlorine activation from two independent wildfire events with that from a series of volcanic eruptions of varying magnitudes. Despite emerging challenges such as illicit emissions and recent wildfires and volcanic eruptions, advancements in observational records, our understanding of ozone chemistry, and computational power have significantly enhanced our ability to quantitatively detect and attribute stratospheric ozone changes. In Chapter 6, I applied a pattern-based “fingerprinting” technique to quantitatively separate the contributions of ODS forcing from other external forcings and internal variabilities in satellite observations. This analysis shows that Antarctic ozone increases cannot be explained by climate internal variability alone, providing strong confidence that ozone recovery is underway, primarily driven by human efforts to reduce ODS emissions.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 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 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 ».