Arctic amplification as a rapid response to increased CO2
Bibliographic record
Abstract
Arctic amplification (AA), or enhanced surface warming of the Arctic, is ubiquitous in observations, and in model simulations subjected to increased greenhouse gas (GHG) forcing. Despite its importance, the mechanisms driving AA are not entirely understood. Here, we show that in CMIP5 (Coupled Model Intercomparison Project 5) general circulation models (GCMs), AA develops within a few months following an instantaneous quadrupling of atmospheric CO2. We find that this rapid AA response can be attributed to the lapse rate feedback, which acts to disproportionately warm the Arctic, even before any significant changes in Arctic sea ice occur. Only on longer timescales (beyond the first few months) does the decrease in sea ice become an important contributor to AA via the albedo feedback and increased ocean-to-atmosphere heat flux. An important limitation of our CMIP5 analysis is that internal climate variability is large on the short time scales considered. To overcome this limitation – and thus better isolate the GHG-forced response – we produced a large ensemble (100 members) of instantaneous CO2-quadrupling simulations using a single GCM, the NCAR Community Earth System Model (CESM1). In our new CESM1 ensemble we find the same rapid AA response seen in the CMIP5 models, confirming that AA ultimately owes its existence to fast atmospheric processes.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".