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Record W4220756229 · doi:10.5194/egusphere-egu22-6562

Towards spatio-temporal comparison of transient simulations and temperature reconstructions for the last deglaciation

2022· preprint· en· W4220756229 on OpenAlexaff
Nils Weitzel, Heather Andres, Jean‐Philippe Baudouin, Oliver Bothe, Andrew M. Dolman, Lukas Jonkers, Marie Kapsch, Thomas Kleinen, Uwe Mikolajewicz, André Paul, Kira Rehfeld

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDeglaciationProxy (statistics)Probabilistic logicStandard deviationAlgorithmClimatologyGlacial periodComputer scienceGeologyEnvironmental scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

An increasing number of Earth System Models has been used to simulate the climatic transition from the Last Glacial Maximum to the Holocene. This creates a demand for benchmarking against environmental proxy records, which have been synthesized for the same time period. Comparing these two data sources in space and time over a period with changing background conditions requires new methods. We employ proxy system modeling for probabilistic quantification of the deviation between temperature reconstructions and transient simulations. Regional and global scores quantify the mismatch in the pattern and magnitude of orbital- as well as millennial-scale temperature variations. In pseudo-proxy experiments, we test the ability of our algorithm to accurately rank an ensemble of simulations according to their deviation from a prescribed temperature history, dependent upon the amount of added non-climatic noise. To this purpose, noisy pseudo-proxies are constructed by perturbing a reference simulation. We show that the algorithm detects the main features separating the ensemble members. When scores are aggregated spatially, the algorithm ranks simulations robustly and accurately in the presence of uncertainties. In contrast, erroneous rankings occur more often if only a single location is assessed. Having established the effectiveness of the algorithm in idealized experiments, we apply our method to quantify the deviation between data from the PalMod project: an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. No simulation performs consistently well across different regions and components of the temperature evolution which we attribute to the larger spatial heterogeneity in reconstructions. Our work provides a basis for a standardized model-data comparison workflow, which can be extended subsequently with additional proxy data, new simulations, and improved representations of uncertainties.

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)0.012
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 score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.044
GPT teacher head0.308
Teacher spread0.264 · 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
Published2022
Admission routes1
Has abstractyes

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