Resolving circumarctic zero-curtain phenomena with AI-integrated earth observations

Abstract

Across the circumarctic, permafrost landscapes store approximately 1700 billion metric tons of organic carbon—nearly twice atmospheric levels—yet reservoir stability depends on the zero-curtain, a subsurface thermal plateau that emerges when latent heat maintains soil temperatures near 0 °C during freeze–thaw phase transitions. The zero-curtain sustains liquid water through cryosuction within the active layer, enabling microbial activity to persist well into the cold season and regulating permafrost thermal stability. However, zero-curtain intensity, duration, and spatial extent remain inadequately quantified during transitional seasons when isothermal buffering exhibits maximum variability, limiting our understanding of permafrost-climate feedbacks. Using GeoCryoAI—a hybridized physics-informed (PI) transfer learning framework that integrates 62.71 million in situ measurements and 3.3 billion remote sensing observations—we show that zero-curtain candidate phenomena exhibit pronounced seasonal asymmetry, with extended vernal intensification (1000–4000 h) relative to compressed winter occurrence (100–500 h), and significant longitudinal variation: moderate intensity patterns across the North American Arctic, enhanced vernal amplification in Siberia, reduced winter suppression in Fennoscandia, and a delayed vernal response in the Canadian Archipelago. The framework quantifies these dynamics at statistically downscaled 30 m gridded resolution, achieving 96.4% candidate detection accuracy. Ablation studies confirm that the full GeoCryoAI architecture achieves 11.8% improvement over baseline multilayer perceptron architectures (93.4% vs. 81.6% in component validation experiments), with PI constraints providing essential regularization for thermodynamic consistency. Mechanistic analysis reveals soil moisture-latent heat coupling is the dominant framework-identified predictor of 60–90% duration variability; within the sampled range, this dependence is 20–40% stronger under warmer, high moisture conditions. This framework establishes a NISAR-ready circumarctic monitoring protocol, enabling 3–6-month forecasts and spatially explicit boundary inputs for Earth system models simulating carbon-climate feedbacks in a warming Arctic.