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Record W4200194985 · doi:10.5194/gmd-14-7605-2021

Application of CCM SOCOL-AERv2-BE to cosmogenic beryllium isotopes: description and validation for polar regions

2021· article· en· W4200194985 on OpenAlexfundaboutno aff
Kseniia Golubenko, Eugene Rozanov, G. A. Kovaltsov, Ari‐Pekka Leppänen, Timofei Sukhodolov, Ilya Usoskin

Bibliographic record

VenueGeoscientific model development · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersHealth CanadaSuomalainen TiedeakatemiaRussian Science FoundationAcademy of FinlandSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsCosmic rayBerylliumEnvironmental scienceAtmosphere (unit)Atmospheric sciencesDeposition (geology)IsotopePolarTRACERMeteorologyGeologyPhysicsNuclear physics

Abstract

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Abstract. The short-living cosmogenic isotope 7Be, which is produced by cosmic rays in the atmosphere, is often used as a tracer for atmospheric dynamics, with precise and high-resolution measurements covering the recent decades. The long-living isotope 10Be, as measured in polar ice cores with an annual resolution, is a proxy for long-term cosmic-ray variability, whose signal can, however, be distorted by atmospheric transport and deposition that need to be properly modeled to be accounted for. While transport of 7Be can be modeled with high accuracy using the known meteorological fields, atmospheric transport of 10Be was typically modeled using case-study-specific simulations or simplified box models based on parameterizations. Thus, there is a need for a realistic model able to simulate atmospheric transport and deposition of beryllium with a focus on polar regions and (inter)annual timescales that is potentially able to operate in a self-consistent mode without the prescribed meteorology. Since measurements of 10Be are extremely laborious and hence scarce, it is difficult to compare model results directly with measurement data. On the other hand, the two beryllium isotopes are believed to have similar transport and deposition properties, being different only in production and lifetime, and thus the results of 7Be transport can be generally applied to 10Be. Here we present a new model, called CCM SOCOL-AERv2-BE, to trace isotopes of 7Be and 10Be in the atmosphere based on the chemistry–climate model (CCM) SOCOL (SOlar Climate Ozone Links), which has been improved by including modules for the production, deposition, and transport of 7Be and 10Be. Production of the isotopes was modeled for both galactic and solar cosmic rays by applying the CRAC (Cosmic Ray Atmospheric Cascade) model. Transport of 7Be was modeled without additional gravitational settling due to the submicron size of the background aerosol particles. An interactive deposition scheme was applied including both wet and dry deposition. Modeling was performed using a full nudging to the meteorological fields for the period of 2002–2008 with a spin-up period of 1996–2001. The modeled concentrations of 7Be in near-ground air were compared with the measured ones at a weekly time resolution in four nearly antipodal high-latitude locations: two in the Northern (Finland and Canada) and two in the Southern (Chile and the Kerguelen Islands) Hemisphere. The model results agree with the measurements in the absolute level within error bars, implying that the production, decay, and lateral deposition are correctly reproduced. The model also correctly reproduces the temporal variability of 7Be concentrations on annual and sub-annual scales, including the presence and absence of the annual cycle in the Northern and Southern Hemisphere, respectively. We also modeled the production and transport of 7Be for a major solar energetic particle event (SPE) on 20 January 2005, which appears insufficient to produce a measurable signal but may serve as a reference event for historically known extreme SPEs. Thus, a new full 3D time-dependent model, based on CCM SOCOL, of 7Be and 10Be atmospheric production, transport, and deposition has been developed. Comparison with real data on the 7Be concentration in the near-ground air validates the model and its accuracy.

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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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

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 head0.034
GPT teacher head0.234
Teacher spread0.200 · 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.

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

Citations15
Published2021
Admission routes2
Has abstractyes

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