Seasonal Forecast Skill of Arctic Sea Ice in Two Versions of a Dynamical Forecasting System and Comparisons with Potential Predictability Estimates
Bibliographic record
Abstract
In this study, we assess pan-Arctic and regional seasonal sea ice forecast skill in versions 1 and 2 of the Canadian Seasonal to Inter-annual Prediction System (CanSIPSv1 and CanSIPSv2) dynamical seasonal prediction systems. Each version applies a multi-model ensemble approach using two coupled general circulation models. CanSIPSv2 features a new model formulation (where one of the underlying models, CanCM3, was replaced with GEM-NEMO) and improved sea ice initialization. We show that the modifications made in the development of CanSIPSv2 substantially enhance forecast skill. For example, the lead time for skillful forecasts of detrended pan-Arctic September sea ice area increases from three months in CanSIPSv1 to seven months in CanSIPSv2. We also show that forecasts of detrended winter sea ice area are improved, with CanSIPSv2 producing skillful forecasts for all considered lead times (up to 11 months) for December, January, and February. We find that improvements in pan-Arctic forecast skill are due primarily to improved initialization methods.Further, a potential predictability experiment is conducted for one of the two CANSIPSv2 models, CanCM4, in order to establish – in conjunction with similar studies – the potential to further increase forecast skill with improved models, observations and initialization methods.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".