MétaCan
Menu
← Back to cohort
Record W3162191457 · doi:10.5194/egusphere-egu21-8354

Arctic winter warming due to cloud feedbacks in warm climates

2021· article· en· W3162191457 on OpenAlexaboutno aff
Eli Tziperman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsClimatologyArcticEnvironmental scienceClimate modelClimate changeGlobal warmingAtmospheric circulationCloud coverLatitudeAtmospheric sciencesLapse rateNorthern HemisphereOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

The climate of the Cretaceous and Eocene (146-34 Million years ago) was exceptionally warm. Crocodiles and Palm trees, which cannot withstand a few nights of subfreezing temperatures, could be found in the waters of Greenland and in the middle of present day Canada, where current winter temperatures can drop to -40C. State-of-the-art climate general circulation models cannot reproduce the exceptionally warm continental winter temperature during these periods even with very high atmospheric CO2 concentrations. One wonders whether these models are missing some significant feedback that may also affect their future global warming projections. We present two cloud feedbacks that may have contributed to such past warming, and that are found to be part of the atmospheric response to future warm climate projections, explaining the lapse-rate feedback in future Arctic climate change scenarios and the projected appearance of tropical-like deep convection during winter in the Arctic. Recent studies (Cronin and Tziperman 2015; Cronin, Li and Tziperman, 2017), using Lagrangian single column atmospheric models, have proposed that in warmer climates low clouds would form as maritime air masses advect into Northern Hemisphere high-latitude continental interiors during winter (DJF). The greenhouse effect due to these low clouds could reduce surface radiative cooling and suppress Arctic air formation events, explaining the warm winter high-latitude continental interiors during past warm climates, and the positive lapse-rate feedback in future Arctic climate change scenarios. A 3D atmospheric general circulation model (Hu, Cronin and Tziperman, 2018) confirms these finding by simulating different warming scenarios under prescribed CO2 and sea surface temperature (SST) conditions. Winter 2-meter temperatures on extreme cold days is found to increase about 50\% faster than the winter mean temperatures and the prescribed SST. Low cloud fraction and surface longwave (LW) cloud radiative forcing also increase in both the winter mean state and on extreme cold days, consistent with the Lagrangian air-mass studies. Air parcels experiencing extreme cold events in the present climate often arrive from Siberia and pass over the Arctic. An ice-free Arctic (during past of future warm climates) allows air parcels can accumulate moisture and therefore experience the formation of low clouds and thus the suppression of Arctic air formation. An ice free Arctic may be triggered due to the convective cloud feedback of (Abbot and Tziperman 2008, 2009; Abbot et al. 2009; Arnold et al. 2014) in which tropical-like deep atmospheric convection is triggered at high-latitudes during winter time. The radiative effects of the high tropospheric clouds associated with the atmospheric convection act to keep the surface warm, and this in turn maintains the convection active. Finally, it will be shown that the proposed cloud feedback is at work also in effectively all models run under the extended RCP 8.5 scenario, and that this may aid in the elimination of both summer and winter sea ice from the Arctic in these simulations, acting together with other related Arctic feedbacks (Hankel and Tziperman 2021, submitted). References: https://www.seas.harvard.edu/climate/eli/reprints/

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)0.000
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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

Explore more

Same topicAtmospheric and Environmental Gas Dynamics→French-language works237,207→