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Record W4388717154 · doi:10.5194/esd-2023-36

Impact of volcanic eruptions on CMIP6 decadal predictions: A multi-model analysis

2023· preprint· en· W4388717154 on OpenAlexaff
Roberto Bilbao, Pablo Ortega, Didier Swingedouw, Leon Hermanson, Panos Athanasiadis, Rosie Eade, Marion Devilliers, Francisco J. Doblas‐Reyes, Nick Dunstone, An-Chi Ho, William J. Merryfield, Emmanuel Mignot, Dario Nicolì, Margarida Samsó, Reinel Sospedra‐Alfonso, Xian Wu, Stephen Yeager

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersH2020 European Research CouncilBiological and Environmental Research
KeywordsVolcanoClimatologyForcing (mathematics)Northern HemisphereGeologyClimate modelRadiative forcingAtmospheric sciencesEnvironmental scienceSouthern HemisphereClimate changeOceanographySeismology

Abstract

fetched live from OpenAlex

Abstract. In recent decades three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal-to-decadal timescales that could have been potentially predictable with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of such volcanic eruptions on decadal prediction. It consists in repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of the volcanic forcing, which are then compared with the baseline predictions to disentangle their effect. We present the results from six CMIP6 decadal prediction systems. These systems show a strong agreement in predicting the radiative response to the volcanic eruptions, simulating a reduction in global mean top-of-atmosphere radiation fluxes, surface temperature and ocean heat content. The characteristic geographical patterns of the response are consistent across the models and share similarities across the volcanic eruptions, however some differences across models and eruptions arise due to the varying magnitude and spatiotemporal structure of the volcanic forcing. Taking advantage of the large multi-model ensemble we additionally analyse the dynamical responses in the Northern Hemisphere atmospheric circulation, in the tropical Pacific Ocean and the North Atlantic Ocean. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that including the volcanic forcing results in overall better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed SST variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo, however in the tropical Pacific Ocean the predicted SST anomalies are degraded.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.322
Teacher spread0.258 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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