Aerosols and their impacts on future Arctic climate change under different emission projections in the GISS-E2.1 Earth system model
Bibliographic record
Abstract
In order to study the future aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model. Different future anthrpogenic emission projections have been used from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Results showed that Arctic BC, OC and SO42- burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m-2, of which -0.24±0.01 W m-2 were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m-2 for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m-2), of which the anthropogenic aerosols accounted for -0.26 W m-2. The 2030-2050 mean surface air temperatures are projected to increase by 2.1 °C and 2.4 °C compared to the 1990-2010 mean temperature according to the Eclipse CLE and MFR ensembles, respectively, while the CMIP6 simulation calculated an increase of 1.9 °C (SSP1-2.6) to 2.2 °C (SSP3-7.0). Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios with no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".