Bibliographic record
Abstract
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/
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".