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Record W7112132412

Understanding Drivers of Stratospheric Ozone Change and Fingerprinting its Recovery

2025· dissertation· W7112132412 on OpenAlexaboutno aff

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2025
Typedissertation
Language
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsOzone layerOzoneOzone depletionMontreal ProtocolChlorineBox modelSink (geography)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.249
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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