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Record W2343736958 · doi:10.5194/acp-2016-352

A long term study of polar ozone loss derived from data assimilation of Odin/SMR observations

2016· article· en· W2343736958 on OpenAlexfundaboutno aff
Kazutoshi Sagi, D. Murtagh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersCanadian Space AgencyCentre National d’Etudes SpatialesTekesUniversity of LeedsEuropean Space Agency
KeywordsOzoneStratospherePolar vortexEnvironmental scienceAtmospheric sciencesOzone layerNorthern HemisphereClimatologyOzone depletionData assimilationSouthern HemisphereMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

Abstract. Odin, a Swedish-led satellite project in collaboration with Canada, France and Finland, was launched on 20 February 2001 and continues to produce profiles of chemical species relevant to understanding the middle and upper atmosphere. Long-term observations of stratospheric ozone are useful for trend analysis of chemical ozone loss. This study concerns ozone loss over both poles utilizing 12 years of ozone data from Odin/Sub-Millimetre Radiometer (SMR). We have applied the data assimilation technique described by Rösevall et al. (2007) with a number of improvements to study the inter-annual variability during the entire Odin period. The chemical ozone losses at potential temperature levels between 425 K and 950 K, (corresponding to an altitude range of 15 to 40 km approximately 90 hPa and 7 hPa in pressure), are derived. Two SMR ozone products retrieved from the emission lines centred at 501 GHz and 544 GHz were used. An internal comparison of the two analyses using 501 GHz and 544 GHz ozone has been carried out by inspecting the vortex mean ozone in March and October during 2002–2013 and 2003–2012 in the Northern and Southern Hemisphere, respectively. Ozone derived from data assimilation using the two data sets match within 10 % at the levels studied, while below 550 K in the Southern Hemispheremore than 50 % of the difference is found. Here, 544 GHz ozone is 0.5 parts per million volume (ppmv) lower than 501 GHz ozone because of better sensitivity in 544 GHz ozone in the lower stratosphere. Comparisons with other studies have been mainly performed against Sonkaew et al. (2013) since Sonkaew et al. (2013) is one of the few studies having consistent estimations of ozone depletion using a SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) from 2002 to 2009. 544 GHz ozone loss in the Arctic winter 2004/2005 is in good agreement with SCIAMACHY loss below 450 K to within 0.2 ppmv, while showing no loss around 550 K where SCIAMACHY detected 0.5 ppmv loss. The comparison of Antarctic ozone depletions with Kuttippurath et al. (2015) shows agreement with MLS ozone loss within 0.1 ppmv, while our results were constantly 0.3 ppmv lower than Mimosa-Chim model calculations. In the Northern Hemisphere, our assimilation analyses show large inter-annual variability. Three classes of chemical ozone losses are found to occur in cold, warm and intermediate winters between cold and warm. The cold type loss maximises in March below 500 K as in the Southern Hemisphere. The maximum loss in the Northern Hemisphere between 2001/2002 and 2012/2013 was during the cold winter, which happened in 2010/2011 with a loss in volume mixing ratio of 2.1 ppmv at 450 K. Losses of 1.5 ppmv took place at 700 K in the warm winters related to the occurrence of mid-winter major sudden stratospheric warming (SSW) events. In the Southern Hemisphere between 2002 and 2012, chemical ozone losses began in mid-August and generally grew to 2.5 ppmv by the end of October. The vertical extent of this loss was 425–550 K. All Antarctic winters except 2002 had approximately 80 DU loss in the stratospheric column. In both hemispheres partial columns in the stratosphere show a small increase over the time period from 2002 to 2013, however the statistical confidence is not high enough to identify ozone recovery.

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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 score0.020
Threshold uncertainty score0.039

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.264
Teacher spread0.189 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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