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Record W3175551079 · doi:10.5194/egusphere-egu21-6574

North American rainfall patterns during past warm states: A proxy network-model comparison for the Last Interglacial and the mid-Holocene

2021· article· en· W3175551079 on OpenAlexaboutno aff
Cameron de Wet, Jessica Oster, Daniel Ibarra, Bryce Belanger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsInterglacialHolocenePaleoclimatologyNorthern HemisphereClimatologyClimate changeRadiative forcingLatitudeClimate modelEnvironmental scienceGlacial periodPhysical geographyGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

The Last Interglacial (LIG) period (~129,000–116,000 years BP) and the mid-Holocene (MH) (~6,000 years BP) are the two most recent intervals with temperatures comparable to low emissions scenarios for the end of the 21st century. During the LIG and the MH differences in the seasonal and latitudinal distribution of insolation led to enhanced northern hemisphere high-latitude warmth relative to the pre-industrial, despite similar greenhouse gas concentrations, marking these intervals as potentially useful analogs for future change in regions like North America. Further, the inclusion of both LIG (127 ka) and MH (6 ka) experiments in the CMIP6-PMIP4 effort provides an opportunity to better understand the regional hydroclimate responses to radiative forcing during these two intervals. The dense coverage of paleoclimate proxy records for North America during the MH (N=260 sites) reveals a pattern of relative aridity in the Pacific Northwest and Western Canada and wetness in the southern Great Basin and Mexico. However, the seasonality and driving mechanisms of rainfall patterns across the continent remain poorly understood. Our understanding of terrestrial hydroclimate in North America during the LIG is more limited (N=39 sites), largely because the LIG is beyond the range of radiocarbon dating. Here we present spatial comparisons between output from 14 PMIP4 global circulation models and LIG and MH networks of moisture-sensitive proxies compiled for the North American continent. We utilize two statistical measures of agreement – weighted Cohen’s Kappa and Gwet’s AC2 – to assess the degree of categorical agreement between moisture patterns produced by the models and the proxy networks for each time-slice. PMIP4 models produce variable precipitation anomalies relative to the pre-industrial for both the LIG and MH experiments, often disagreeing on both the sign and magnitude of precipitation changes across much of North America. The models showing the best agreement with the proxy network are similar but not identical for the two measures, with Gwet’s AC2 values tending to be larger than Cohen’s Kappa values for all models. This pattern is enhanced for the much larger MH proxy network and is likely related to the fact that Gwet’s AC2 is a more predictable statistic in the presence of high agreement. Overall agreement is lower for the mid-Holocene than for the LIG, reflecting smaller MH rainfall anomalies in the models. The models with the highest agreement scores during the LIG produce aridity in the Rocky Mountains and Pacific Northwest and wetness in Alaska, the Yukon, the Great Basin, and parts of the Mid-West and Eastern US, although spatial coverage of the proxies in these latter two regions is poor. The models with the highest agreement score for the mid-Holocene tend to produce aridity across Canada and the northern US with dry conditions extending down the US Pacific coast and increased wetness in the American Southeast and across the North American Monsoon region. Our analyses help elucidate the driving mechanisms of rainfall patterns during past warm states and can inform which models may be the most useful for predictions of near-future hydroclimate change across North America.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.185
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.256
Teacher spread0.238 · 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
Published2021
Admission routes1
Has abstractyes

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