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Record W2766336747

Roles of sudden stratospheric warming events and energetic particle precipitation in polar middle atmosphere : odd nitrogen and ozone

2017· dissertation· en· W2766336747 on OpenAlexfundno aff
S.‐M. Päivärinta

Bibliographic record

VenueTyöväentutkimus Vuosikirja · 2017
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersFP7 People: Marie-Curie ActionsNational Institute on AgingRussian Science FoundationCanadian Space AgencyDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaDeutsches KlimarechenzentrumUniversität BremenEuropean CommissionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science FoundationEuropean Space AgencyNational Aeronautics and Space Administration
KeywordsAtmosphere (unit)Atmospheric sciencesPolarPrecipitationEnvironmental scienceOzoneStratospherePolar vortexSudden stratospheric warmingClimatologyOzone layerParticle (ecology)Ozone depletionNitrogenMeteorologyGeographyPhysicsGeologyAstronomyOceanography
DOInot available

Abstract

fetched live from OpenAlex

Odd nitrogen (NOx = N + NO + NO2) in the polar regions is mainly produced in the upper atmosphere through ionization processes by solar extreme ultraviolet radiation, soft X-rays and high energy particles originating from the space. During periods of high geomagnetic activity, normally close to the solar maximum, energetic particle precipitation (EPP) provides an in-situ source of NOx also in the middle atmosphere. Understanding the behaviour of NOx in the middle atmosphere is of great importance due to its capability to act as a catalyst in chemical reaction cycles destroying ozone in the stratosphere. This work considers EPP in the form of solar proton events (SPEs). Atmospheric dynamics play an important role in determining the distributions of long-lived trace gases in the middle atmosphere. The main loss mechanism for NOx is photolysis at the upper stratospheric and mesospheric altitudes, leading to long photochemical lifetime of NOx during the dark polar winter. NOx in the middle atmosphere, also if produced in-situ due to SPEs, is therefore affected by atmospheric dynamics, and transported from the mesosphere-lower hermosphere (MLT) region down to the middle atmosphere. This descent phenomenon can be intensified in the aftermath of sudden stratospheric warmings (SSWs), which are dynamical phenomena able to affect a wide range of altitudes in the Northern polar region atmosphere. The enhanced downward transport of NOx can thus strengthen the NOx-ozone connection in the stratosphere. In this work we used both space born observations from several satellite instruments and a chemistry transport model in the examination of the SSW and SPE caused effects in the stratosphere and mesosphere. The scientific objectives of this work were to find out the individual and combined effects of SSWs and SPEs on the NOx and ozone balance in the Northern middle atmosphere, and assess the relative contributions of dynamics (SSWs) and in-situ production of NOx (SPEs) on ozone in the stratosphere. The results showed dramatic increases in NOx in the middle atmosphere, even by a factor of 50, following both periods of enhanced NOx descent in connection with SSWs and in-situ production of NOx due to SPEs. A clear long-term (order of months) decrease in stratospheric ozone (10-90 %), coinciding with the enhanced amounts of NOx, was evident and affected mostly by dynamics in the upper stratosphere. The results of this work emphasize the importance of in-situ production of NOx (SPEs) on the ozone balance in the upper stratosphere, but also the key role of dynamics (SSWs) in transporting the SPE effect to even lower altitudes and its capability to strengthen the effect.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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 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
Published2017
Admission routes1
Has abstractyes

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