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Researchers reveal long-term impacts of extreme melt on Greenland Ice Sheet

Date Time Researchers reveal long-term impacts of extreme melt on Greenland Ice Sheet Researchers have deciphered a trove of data that shows one season of extreme melt can reduce the Greenland Ice Sheet’s capacity to store future meltwater – and increase the likelihood of future melt raising sea levels. By Danielle Torrent Tucker Nearly a decade ago, global news outlets reported vast ice melt in the Arctic as sapphire lakes glimmered across the previously frozen Greenland Ice Sheet, one of the most important contributors to sea-level rise. Now researchers have revealed the long-term impact of that extreme melt. Using a new approach to ice-penetrating radar data, Stanford University scientists show that this melting left behind a contiguous layer of refrozen ice inside the snowpack, including near the middle of the ice sheet where surface melting is usually minimal. Most importantly, the formation of the melt layer

Can extreme melt destabilize ice sheets?

 E-Mail IMAGE: In 2012, an extreme melt season in Greenland created a refrozen ice layer in the compacting snow near the surface of the ice sheet. In some places, this melt layer. view more  Credit: Farrin Abbott Nearly a decade ago, global news outlets reported vast ice melt in the Arctic as sapphire lakes glimmered across the previously frozen Greenland Ice Sheet, one of the most important contributors to sea-level rise. Now researchers have revealed the long-term impact of that extreme melt. Using a new approach to ice-penetrating radar data, Stanford University scientists show that this melting left behind a contiguous layer of refrozen ice inside the snowpack, including near the middle of the ice sheet where surface melting is usually minimal. Most importantly, the formation of the melt layer changed the ice sheet s behavior by reducing its ability to store future meltwater. The research appears in

Stanford researchers use AI to empower environmental regulators

Brick kilns have proliferated across Bangladesh to supply the growing economy with construction materials, which makes it really hard for regulators to keep up with new kilns that are constructed, said co-lead author Nina Brooks, a postdoctoral associate at the University of Minnesota s Institute for Social Research and Data Innovation who did the research while a PhD student at Stanford. While previous research has shown the potential to use machine learning and satellite observations for environmental regulation, most studies have focused on wealthy countries with dependable data on industrial locations and activities. To explore the feasibility in developing countries, the Stanford-led research focused on Bangladesh, where government regulators struggle to locate highly pollutive informal brick kilns, let alone enforce rules.

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