New data helps better understand glaciers

On February 20, a team of expert researchers published a new high-resolution calving front dataset from 149 glaciers in Svalbard spanning 1985-2023. This innovative dataset earth system science dataprovides an important tool for a deeper understanding of the mechanisms behind glacier melting, and therefore iceberg collapse, and will help improve our understanding of the climatic drivers behind glacier loss in Svalbard and the Arctic. Masu.

Mass loss of glaciers has accelerated in the past few decades, contributing significantly to global sea level rise. However, many of the mechanisms behind glacier loss, especially the dynamics of ocean-terminated glacier collapse, are not well understood.

“This new study uses state-of-the-art deep learning models to generate a 38-year record of changes in the calving front of tidewater glaciers in Svalbard with unprecedented density using high-resolution satellite imagery. ” said Dr. Tian Li. researcher at the Bristol Glaciology Center and lead author of the study.

This dataset contains the signatures of approximately 125,000 individual calving fronts, and the results show that the majority of Svalbard’s glaciers exhibit a retreating trend. Using an extensive catalog of satellite data, the researchers were able to analyze seasonal and annual fluctuations as well as capture the timing of surge events in which glaciers move significantly over short periods of time. These findings will help better understand and predict future glacier decline in the Arctic.

According to Tian Li, “This dataset can be used to improve the mass balance assessment of tidewater glaciers in Svalbard. Additionally, it will also enable investigation of the factors and processes controlling glacier melting.” is critical to understanding melt dynamics, a key indicator of how glaciers respond to climate change. ”

“This dataset is part of the results from Arctic PASSION’s work to build an improved observation system of key climate variables for the Arctic cryospheric system, providing an operational end-to-end forecasting and monitoring system. It will also be integrated into the work of Arctic PASSION to build “for Arctic land ice,” says Tian Li. The researchers now plan to apply the method to all other tidal glaciers in the Arctic.

An online platform using the Svalbard dataset is available.