High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and efficient automatic method for melt pond region segmentation and boundary extraction from high-resolution aerial photographs. The proposed algorithm is based on a swin transformer U-Net in which we introduce novel cross-channel attention mechanisms into the decoder design. The framework operates with optical data and allows for classifying imagery into four classes: sea ice/snow, open water, melt pond, and submerged ice. We use aerial photographs collected during the Healy-Oden Trans Arctic Expedition (HO-TRAX) expedition over Arctic sea ice in the summer season of 2005 as test data. The experimental results show that the proposed method is suitable for precise automatic extraction of melt pond geometry and it can also be extended for other optical data sources that involve melt ponds. The approach has a promising potential to be used to analyze melt ponds' corresponding changes between years.

This work discusses the creation and use of infographics in an undergraduate mathematics course. Infographics are a visualization of information that combines data, formulas, and images. This article discusses how to form an infographic and uses infographics on topics within mathematics and climate as examples. It concludes with survey data from undergraduate students on both the general use of infographics and on the specific infographics designed by the authors.

Climate emulators are a powerful instrument for climate modeling, especially in terms of reducing the computational load for simulating spatiotemporal processes associated with climate systems. The most important type of emulators are statistical emulators trained on the output of an ensemble of simulations from various climate models. However, such emulators oftentimes fail to capture the “physics” of a system that can be detrimental for unveiling critical processes that lead to climate tipping points. Historically, statistical mechanics emerged as a tool to resolve the constraints on physics using statistics. We discuss how climate emulators rooted in statistical mechanics and machine learning can give rise to new climate models that are more reliable and require less observational and computational resources. Our goal is to stimulate discussion on how statistical climate emulators can further be improved with the help of statistical mechanics which, in turn, may reignite the interest of statistical community in statistical mechanics of complex systems.