Examining Internal and External Contributors to Greenland Climate Variability Using CCSM3
Bibliographic record
Abstract
Abstract Greenland climate variability is connected to internal and external sources of global climate forcing in six millennium simulations using Community Climate System Model, version 3. The external forcings employed are consistent with the protocols of Paleoclimate Modelling Intercomparison Project Phase 3. Many simulated internal climate modes are characterized over the years 850–1850, including the Atlantic meridional overturning circulation (AMOC), the Atlantic multidecadal oscillation (AMO), the east Atlantic pattern (EA), the El Niño–Southern Oscillation, the North Atlantic Oscillation (NAO), the North Atlantic sea ice extent, and the Pacific decadal oscillation (PDO). Lagged correlation and multivariate regression methods connect Greenland temperatures and precipitation to these internal modes and external sources of climate variability. Greenland temperature and precipitation are found to relate most strongly to North Atlantic sea ice extent, the AMO, and the AMOC, that are themselves strongly interconnected. Furthermore, approximately half of the multidecadal variability in Greenland temperature and precipitation are captured through linear relationships with volcanic aerosol optical depth, solar insolation (including total solar irradiance and local orbital variability), the NAO, the EA, and the PDO. Relationships are robust with volcanic aerosol optical depth, solar insolation, and an index related to latitudinal shifts of the North Atlantic jet. Differences attributable to model resolution are also identified in the results, such as lower variability in the AMOC and Greenland temperature in the higher-resolution simulations. Finally, a regression model is applied to simulations of the industrial period to show that natural sources alone only explain the variability in simulated Greenland temperature and precipitation up to the 1950s and 1970s, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".