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Abstract Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. 

Abstract Monolayer (ML) molybdenum disulfide (MoS₂) is a novel 2-dimensional (2D) semiconductor whose properties have many applications in devices. Despite its potential, ML MoS₂ is limited in its use due to its degradation under exposure to ambient air. Therefore, studies of possible degradation prevention methods are important. It is well established that air humidity plays a major role in the degradation. In this paper, we investigate the effects of substrate hydrophobicity on the degradation of chemical vapor deposition (CVD) grown ML MoS 2 . We use optical microscopy, atomic force microscopy (AFM), and Raman mapping to investigate the degradation of ML MoS 2 grown on SiO 2 and Si 3 N 4 that are hydrophilic and hydrophobic substrates, respectively. Our results show that the degradation of ML MoS₂ on Si 3 N 4 is significantly less than the degradation on SiO 2 . These results show that using hydrophobic substrates to grow 2D transition metal dichalcogenide ML materials may diminish ambient degradation and enable improved protocols for device manufacturing.