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The PlioMIP2 Mid-Pliocene Climate Simulations Using the NASA-GISS ModelE

2022· preprint· en· W4285491877 on OpenAlexaboutno aff
Mark A. Chandler, Linda E. Sohl, Jeffrey A. Jonas, Larissa Nazarenko

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal warmingLatitudeClimate modelClimatologyCoupled model intercomparison projectEnvironmental scienceClimate changeGeneral Circulation ModelArcticPeriod (music)Atmospheric sciencesOceanographyGeologyPhysics

Abstract

fetched live from OpenAlex

The Mid-Pliocene Warm Period (MPWP) ca. 3.2 Ma provides an opportunity to explore an equilibrium climate state under pCO2 and solar insolation conditions similar to those that may govern near-term future climate as per the IPCC’s SSP1 “Sustainability” scenario, which limits warming to +3.0 °C by the year 2100. Here we discuss mid-Pliocene simulations using the NASA-GISS-E2.1 coupled ocean-atmosphere model conducted for the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). We include our results using both modern and mid-Pliocene paleogeography, in accordance with PlioMIP2 protocols and provide comparisons to the results from PlioMIP1. The PlioMIP2 simulations do not result in a significant change in global mean surface air temperature or global mean sea surface temperature compared to PlioMIP1. Global mean warming was +2.2 °C in PlioMIP1 and is slightly lower at +2.1 °C in PlioMIP2. However, there are notable contrasts in how temperatures respond regionally and the northern and southern high latitude temperature changes in PlioMIP2 show a marked contrast with PlioMIP2 producing less warming in the Arctic and more warming in the Southern Ocean compared to PlioMIP1. There is significant improvement in the simulation of Pliocene SSTs in PlioMIP2 in the region south of the Labrador Sea, where the PlioMIP1 simulation was anomalously cool. However, the region of greatest warming in the North Atlantic moves too far north, thus the SST warming in the Greenland Sea is only half what was seen in PlioMIP1 simulations weakening correlations to proxy data at key core sites in that region. The GISS GCM has the strongest AMOC amongst the PlioMIP2 GCMs at 33 Sv, as was the case in the PlioMIP1 study, but the GISS model also has the largest increase in Pliocene AMOC strength compared to its preindustrial control run indicating that the intense overturning in GISS PlioMIP2 Pliocene simulations is, at least in part, due to changes in the response of the CMIP5 vs CMIP6 versions of the GCM. Similarly strong AMOC changes are found in the preindustrial control run comparisons of the GISS ModelE CMIP5 vs CMIP6 runs. As with other models in the PlioMIP2 study, the effect of AMOC on ocean heat transports is limited. Regardless, the increased sensitivity in the Pliocene in our model is impacted by changes in the freshwater flux and salinity distribution resulting from the PlioMIP2 paleogeographic changes, specifically the effects of closing the Bering Strait and the straits of the Canadian Archipelago connecting the Arctic Ocean to the Labrador Sea. Overall it is difficult to evaluate the impact of these substantial boundary condition changes in the Arctic gateways versus the effects of changes in model physics between CMIP5 and CMIP6, so additional simulations may be required to separate these factors. As with nearly all the PlioMIP models, the GISS ModelE continues to point to the potentially large impacts of CO2 on climate – and generally shows that the long-term sensitivity to increasing CO2 could be world altering.

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)0.002
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 score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.070
GPT teacher head0.322
Teacher spread0.253 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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