Projection of sea ice conditions in the Canadian Arctic Archipelago based on CMIP6 assessments
Bibliographic record
Abstract
The Canadian Arctic Archipelago (CAA) includes the critical region of the Northwest Passage (NWP) and is one of the areas with the most severe sea ice conditions in the Arctic. Currently, studies on sea ice projections focusing on the CAA are limited. Furthermore, the prediction results for the CAA from different models based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibit uncertainty due to the inter-model spread. This study evaluated the projected data for sea ice concentration (SIC) and thickness (SIT) within the CAA from 14 CMIP6 models for the period 2015–2022 under the SSP2-4.5 scenario based on the satellite and reanalysis data. Although most models can capture the major characteristics of spatiotemporal variations in SIC and SIT within the CAA, there are considerable numerical differences compared to observations and reanalysis. Additionally, there is a notable spread among 14 CMIP6 models. The assessment of SIC indicates that CESM2, GFDL-CM4, IPSL-CM6A-LR, and UKESM1-0-LL exhibit better performance, with a relatively low bias (less than 7%), a root mean square error (RMSE) below 22%, and a relatively high correlation coefficient (CC) exceeding 0.75. In the evaluation of SIT, the four best-performing models are GFDL-CM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and MRI-ESM2-0. The multi-model ensemble of the best performance group (MMM BPG ) projects a declining trend in both sea ice area (SIA) and SIT for the CAA from 2025 to 2100, with respective trends of −0.21 × 10 5 km 2 per decade and −0.06 m per decade under the SSP2-4.5 scenario, and −0.56 × 10 5 km 2 per decade and −0.11 m per decade under the SSP5-8.5 scenario. Under both scenarios, the MMM BPG predicts a more notable reduction of sea ice in the NWP compared to the multi-model ensemble of all 14 models. Navigable conditions for the northern and southern routes are defined by SIA and SIT respectively, with each route’s SIA being less than 5% of its total area and the mean SIT being below 0.15 m. Based on the SIA threshold, under the SSP2-4.5 and SSP5-8.5 scenarios, MMM BPG projects that continuous annual navigability in the NWP, with at least three navigable months per year, will be achieved starting in 2060 and 2054, respectively. According to the SIT threshold, the MMM BPG projects that it will reach continuous annual navigability with at least one navigable month per year under both scenarios, starting in 2038 and 2036, respectively. This study enhances the understanding of CMIP6 model performance in projecting sea ice within the CAA and provides insights into future sea ice conditions in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".