Arctic sea ice seasonal transition metrics from coupled climate model simulations, 1979-2013
Bibliographic record
Abstract
This dataset includes annual, gridded Arctic sea ice seasonal transition metrics (dates and periods) for fifteen Coupled Model Intercomparison Project version 6 (CMIP6) models and the Community Earth System Model version 1.1 (CESM1.1) Large Ensemble (CESM LE) (Kay, et al., 2015). Seasonal transition dates include melt onset, opening, break-up, freeze onset, freeze-up and closing. Seasonal transition periods include the melt period, the seasonal loss-of-ice period, the freeze period, the seasonal gain-of-ice period, the melt season, the open water period and the outer ice-free period. Data are provided for one ensemble member of the following models: Australian Community Climate and Earth System Simulator CM2 (ACCESS-CM2), Beijing Climate Center Climate System Model 2 MR (BCC-CSM2-MR), Beijing Climate Center Earth System Model 1 (BCC-ESM1), Community Earth System Model 2 (CESM2), Community Earth System Model 2 FV2 (CESM2-FV2), Community Earth System Model 2 Whole Atmosphere Community Climate Model (CESM2-WACCM), Community Earth System Model 2 Whole Atmosphere Community Climate Model FV2 (CESM2-WACCM-FV2), Centre National de Recherches Météorologiques ESM 2-1 (CNRM-ESM2-1), Centre National de Recherches Météorologiques CM 6-1 (CNRM-CM6-1), EC-Earth3, Meteorological Research Institute Earth System Model 2-0 (MRI-ESM2-0), Norwegian Earth System Model 2 LM (NorESM2-LM) and Norwegian Earth System Model 2 MM (NorESM2-MM). Data are provided for 40 members of the Community Earth System Model Large Ensemble (CESM LE), 35 members of Canadian Earth System Model 5 (CanESM5) and 30 members of Institut Pierre Simon Laplace CM6A LR (IPSL-CM6A-LR). The data is stored in netcdf format, and includes metadata in the netcdf files. The raw CMIP6 and CESM LE model output that these transition metrics are calculated from are publicly available at https://esgf-node.llnl.gov/projects/cmip6/ and https://www.earthsystemgrid.org/ respectively. This dataset was created to evaluate climate model projections of Arctic sea ice using seasonal transition metrics in the context of both observations and internal variability. It is used in the article Smith, Jahn, Wang (2020), Seasonal transition dates can reveal biases in Arctic sea ice simulations, The Cryosphere, in press. The discussion paper with a link to the final paper can be found at https://doi.org/10.5194/tc-2020-81. This work was conducted at the University of Colorado Boulder from 2019-2020.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".