Breaking the Ice Between Machine Learning Experts and Cryosphere Scientists - The ML4Cryo Research Community

Recently, Machine Learning (ML) has emerged as a powerful tool within cryospheric sciences, offering innovative and effective solutions for observing, modelling, and understanding Earth's frozen regions. However, the ML and cryosphere communities have traditionally been poles apart, each shaped by distinct research motivations, publishing paradigms, and evaluation criteria. These research silos can lead to common pitfalls of interdisciplinary research, such as "helicopter science", insights getting lost in translation, or the continued use of outdated (ML) methods. To fully harness the compelling opportunities for impactful research at the intersection of these two fields, machine learning practitioners and domain scientists must join forces. 

To address this gap between machine learning and cryosphere research, we established ML4Cryo (Machine Learning for the Cryosphere, see https://ml4cryo.github.io/), a global research community that leverages collective expertise across diverse fields such as deep learning, physics-informed ML, remote sensing, and both terrestrial and marine cryospheric domains. Our goal is not only to advance scientific discovery but also to foster application-driven advances in machine learning research. ML4Cryo aims to empower researchers by initiating conversations and collaborations, enabling machine learning specialists to learn about the most pressing challenges within the cryosphere, while cryosphere researchers can learn about the state-of-the-art models developed by the ML community. Contributing to ML4Cryo’s mission, our platform serves as a community-driven hub to share and discover ideas, recent publications, tools, software, datasets, knowledge resources, funding opportunities, best practices, as well as relevant conferences and events. We invite you to join ML4Cryo, where the synergy between machine learning and cryospheric science paves the way for impactful and rewarding research.