Cryospheric Science and Emergence of Machine Learning

Cryosphere is the layer in a negative temperature state on earth, with continuous distribution and certain thickness. The earth's cryosphere can be divided into three types as continental, marine, and aerial cryosphere, which includes glacier/ice sheet, permafrost, snow cover, lake and river ice, sea ice, ice shelf, iceberg, and solid precipitation, etc. The cryosphere is one of the five major spheres of the climate system. It plays an important role in the earth system with its huge fresh water reserves, latent heat of phase transitions, carbon storage, and unique species habitats and cultural forms.

The presentation starts with an introduction of IPCC main conclusions on human induced climate change and its extremes since the Industrial Involution, especially recent decades. Cryosphere is a sensitive indicator of climate change. The impacts of rapid cryospheric changes have received increasing concerns since 21st century under the background of global warming, extending the research to the interactions between earth’s multi-spheres, including anthroposphere. As a result, cryospheric science has been rapidly developed into a new interdisciplinary, covering its formation, change processes and mechanism, its interactions with and among atmosphere/hydrosphere/biosphere/lithosphere, the influences and adaptations of cryosphere change impacts, the changing functions for serving regional and global economy and society. Cryospheric science is an inevitable scope of international research on the earth and environmental changes, as well as on human sustainable development.

The study on Chinese cryosphere has been developing rapidly following the scope of Cryospheric Science in the past 20 years, especially in the last decade. It has presented systematic achievements in terms of changes in the cryosphere and their impacts on ecology, hydrology, climate, environment, society and economy, and also obtain systematic understanding of the connotation and extension of the Cryospheric Science, made important contributions to the establishment and development of research framework and disciplinary system of the cryospheric science.

The presentation will also show some case studies on cryosphere using machine learning, such as data mining, permafrost mapping and soil organic carbon estimation, Arctic sea ice prediction, outlet glacier instability estimation of ice sheet, as well as paleoclimatic proxy reconstructions. Machine learning is a promising tool for studying both natural aspects and the socioeconomic aspects when studying cryospheric impacts such as services and hazards. There are complex linkages between cryospheric impacts and UN 2030s’ Sustainable Development Goals (SDGs) over the cryospheric influential regions, it is promising to use big data and machine learning to deepen our knowledge.

Key words: IPCC, cryospheric science, sustainable development, machine learning