Advancing Physical Interpretability of Arctic Sea Ice Dynamics through Automatic Feature Selection
Bibliographic record
Abstract
Modelling Arctic sea ice dynamics has proven to be a successful application for machine learning, leveraging its ability to generate accurate and computationally efficient forecasts. Nevertheless, prevailing limitations lie in the need for physical interpretability and the inability to unveil the dynamics and interdependencies between relevant ice-related variables and their drivers. In this study, we provide a two-step framework designed to combine the high accuracy and computational efficiency characteristics of machine learning while ensuring high interpretability.The first step of our framework entails time series clustering to identify subregions that are homogeneous with respect to the spatiotemporal variability in the considered variable and obtain the barycentric time series of each cluster. We then use an advanced feature selection algorithm, the Wrapper for Quasi Equally Informative Subset Selection, that identifies neural predictors, specifically Extreme Learning Machines, to forecast the future evolution of sea ice. It then provides the most relevant set of inputs necessary for accurately describing the evolution for each cluster.Our investigation focuses on the monthly evolution of sea ice thickness and uses data from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS). Other PIOMAS variables (i.e., sea ice concentration, snow depth, sea surface temperature, and sea surface salinity) as well as observed discharge from five major Arctic rivers (i.e., Ob, Yenisey, and Lena in Asia, Mackenzie and Yukon in North America, provided by the Arctic Great Rivers Observatory discharge dataset) are considered as potential driving factors. Our results indicate the pivotal role of past sea ice thickness values, since the forthcoming state of sea ice seems to be influenced by both the current situation and historical trends and periodicity. Sea surface salinity in the open Arctic Ocean is highly persistent, and therefore is not used by the algorithm to explain the sea ice evolution. On the other hand, the Arctic rivers’ flows are more representative of the processes occurring in the clusters along the coast. Finally, the interaction between sea surface temperature and snow depth controls the interplay between ice formation and melting, and therefore plays a significant role in shaping the sea ice evolution in the short term.Our framework aims to advance our comprehension of the complex physical processes governing sea ice thickness evolution in the Arctic region. Moreover, its effectiveness in uncovering sea ice related processes is expected to further improve with the inclusion of additional input variables and, possibly, of a longer data record.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".