Hear from Prof. Emily Shuckburgh OBE, Director of Cambridge Zero, as we explore the potential for machine learning to help us navigate an increasingly difficult transition to net-zero.
Time is fast running out to limit end of century warming to 1.5oC. With significant European energy insecurity in the wake of the Ukrainian war, and increasing diplomatic tension between the world’s largest emitters, it is increasingly uncertain whether global emissions will fall rapidly enough to secure a 1.5oC warming scenario. Without doubt, we’re at a crossroads, and tough decisions need to be made about what level of climate risk we’re willing to accept for future generations.
At the same time, we’re also on the cusp of a revolution in our capacity to understand and tackle climate change through data and analytics. With the proliferation of satellite and remote sensing technologies, comes the potential for a complete paradigm shift in climate modelling through AI and machine learning. This opens up many exciting opportunities for both mitigating and adapting to climate change.
We will discuss:
- How machine learning can greatly improve the power of climate models;
- Whether the 1.5 degrees warming limit is still alive;
- And how to better communicate the urgency and complexity of climate-related issues.
Links from today’s discussion:
Speaker’s Bio(s)
Prof. Emily Shuckburgh OBE, Director of Cambridge Zero
Emily is a climate scientist and mathematician, holding several research and leadership positions at the University of Cambridge, where she has worked for almost 22 years. A polar expert, she previously led a UK national research programme on the Southern Ocean and its role in climate. In 2016 she was awarded an OBE for services to science and the public communication of science.
As Director of Cambridge Zero, the University’s climate initiative, Emily leads on cutting edge research, education, and collaboration related to the transition to a climate-resilient, zero-carbon future. Emily is also a Professor of Environmental Data Science at the Department of Computer Science and Technology.
