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Record W7132946012

Short-lived Climate Forcers and Age of Air: Diagnostics for the Performance of Atmospheric Models

2024· dissertation· W7132946012 on OpenAlexaboutno aff
Laura Noelle Saunders

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldEnvironmental Science
TopicScience and Climate Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAtmospheric chemistryAtmosphere (unit)Trace gasSatelliteAtmospheric modelAtmospheric modelsClimate modelAtmospheric compositionTropopause
DOInot available

Abstract

fetched live from OpenAlex

This thesis evaluates the ability of atmospheric models to simulate the chemistry and dynamics of short-lived climate forcers and other trace gases. Modelling is a critical tool for studying the impacts of these trace gases on the climate and making predictions that can inform policy decisions. It is also challenging; the atmosphere involves a large number of physical and chemical processes that must be approximated to varying degrees depending on the specific goals of the model in question. A critical component of climate modelling is therefore determining how well models match measurements of the atmosphere in order to understand their limitations. The work in this thesis contributes to this effort through investigations in three key areas: ozone-related chemistry, the implications of methane distributions, and changes in atmospheric transport. This was mainly done through comparisons of the specified dynamics run of the Canadian Middle Atmosphere Model (CMAM39) with satellite measurements from the Atmospheric Chemistry Exper- iment Fourier Transform Spectrometer (ACE-FTS). In addition, these comparisons were extended to other atmospheric models to gain perspective on the current state of atmospheric modelling and how CMAM39 performs relative to similar models. Measurements from additional satellite instruments were also incorporated to complement the conclusions drawn from the comparisons with ACE-FTS. In the first two studies, ozone-related chemistry in CMAM39 was explored through the parti- tioning of total inorganic chlorine gases, which are primarily responsible for ozone depletion. For this purpose, a suite of climatologies was developed using measurements of stratospheric chlorine gases from ACE-FTS and three other satellite instruments. It was found that CMAM39 generally underestimates the activation of chlorine gases that lead to ozone depletion. The following two studies involved a comprehensive examination of trace gas concentrations in CMAM39 and multiple other models. This began with a larger multi-model assessment as part of an Arctic Monitoring and Assessment Programme project, which focused on comparing ten models with each other. The second study was a more specific investigation of CMAM39 and three other models, focusing on diagnosing issues in atmospheric transport using the distributions of methane and other trace gases such as OH, N2O, and CO. As a global model with detailed stratospheric chemistry, CMAM39 was found to be one of the best-performing models. In the final study, an “age of air” product was developed using ACE-FTS measurements of SF6in order to test model predictions that the general circulation of the atmosphere is accelerating. This was a significant contribution to the collection of observation-based estimates of age of air due to its global coverage, vertical resolution, and relatively long 17-year time series. The dataset was used to detect a significant decrease in age of air in the lower stratosphere between 2004 and 2020, which indicates that part of the Brewer-Dobson Circulation is accelerating as predicted by models.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.316
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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