Short-Lived Climate Forcers over the Arctic between 1995 and 2015 as simulated by the GISS modelE2.1
Bibliographic record
Abstract
The Arctic Monitoring and Assessment Programme (AMAP) is currently assessing the impacts of Short-Lived Climate Forcers (SLCF) on Arctic climate and air quality. In support of the assessment, we used the NASA Goddard Institute of Space Sciences (GISS) Earth System Model (modelE2.1), with prescribed sea surface temperature and sea-ice fraction, to simulate SLCF concentrations globally between 1995 and 2015. Two simulations were conducted, using the One-Moment Aerosol (OMA) and the Multiconfiguration Aerosol TRacker of mIXing state (MATRIX) aerosol modules. OMA is a mass-based scheme in which aerosols are assumed to remain externally mixed and have a prescribed and constant size distribution, while MATRIX is an aerosol microphysics scheme based on the quadrature method of moments, which is able to explicitly simulate the mixing state of aerosols. Anthropogenic emissions from the ECLIPSE v6b emissions database were used, along with emissions from aircrafts and open biomass burning from the Coupled Model Intercomparison Project Phase 6 (CMIP6), while the natural emissions of sea salt, DMS, isoprene and dust are calculated interactively. The simulated monthly surface concentrations of sulfate (SO4), black carbon (BC), organic carbon (OA), and ozone (O3) are compared with observations from a set of Arctic stations, extracted from the EBAS and IMPROVE databases, as well as a few additional locations. Simulated aerosol optical depths (AOD) are also compared with Advanced Very-High Resolution Radiometer (AVHRR). The study will present the evaluation of the modelE2.1 in simulating SLCF levels over the Arctic using different aerosol schemes, along with observed and simulated trends of SLCFs over the Arctic between 1995 and 2015.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".