Toward more reliable projections of an ice-free Arctic: Integrating machine learning and causal diagnostics in CMIP6 ensembles

The timing of the first ice-free Arctic summer is a key indicator of climate change, yet projections remain highly uncertain due to inter-model spread, internal variability, and systematic model biases. We develop a prototype framework that combines machine-learning-based methods with causal diagnostics to assess how different bias-correction and emulation approaches influence projections of the first year of ice-free Arctic conditions. Linear scaling is used as a statistical baseline to provide a transparent reference for evaluating more complex machine-learning-based approaches.

Building on recent analyses of the drivers of summer Arctic sea-ice extent at the interannual time scale, we analyse CMIP6 multi-model large ensembles to quantify relationships between September Arctic sea-ice extent and its dominant drivers, including preceding winter sea-ice volume, Arctic near-surface air temperature, and ocean heat transport. Machine-learning-based regression and emulation models are applied to refine model output, while causal diagnostics based on information flow are used to evaluate the physical consistency of inferred driver–response relationships.

We focus on two CMIP6 large ensembles with contrasting historical Arctic temperature biases over 1980–2014. Ensemble uncertainty is explored by partitioning ensemble members into bias-based subsets to assess the sensitivity of projected ice-free timing and inferred driver relationships. Results show that linear scaling shifts projected timing without altering causal structure, whereas machine-learning-based methods can modify ice-free year distributions and induce state-dependent changes in inferred causal relationships. These findings highlight the value of causal diagnostics for interpreting machine-learning-based climate projections and underscore the need for physically interpretable frameworks when applying data-driven methods to critical Arctic climate transitions.