This workshop aims to bring together domain experts in the fields of climate science and data science to discuss the key challenges, including reducing uncertainties in future climate change predictions. Climate observations are generally sparse, inhomogeneous datasets presenting difficulties (e.g., non-Gaussian distributions, the importance of extreme/rare events) not found in other ‘Big Data’ projects. New techniques are urgently required to gain maximum leverage and therefore we propose to bring together the world-leading Big Data and Physical Climate Science communities from across Cambridge to meet this goal.
This workshop is timely as we will soon have access to a new Petabyte-scale climate dataset, the Coupled Model Intercomparison phase 6 (CMIP6). CMIP6 will provide the key data to underpin the next IPCC assessment report (AR6) and is the biggest coordinated effort to model the climate/Earth system to date. It will produce more than 43 thousand years of model simulations, and include an unprecedented number of weather and climate variables for across the globe. The current suite of climate analysis tools have major shortcomings, and are out-of-step with other science and engineering disciplines which are harnessing the power of state-of-the-art Machine Learning technologies.
Dr Emily Shuckburgh (British Antarctic Survey)
Professor Rod Jones (Department of Chemistry, Cambridge)
Attendance is free, however we expect this event to be oversubscribed and so ask applicants to tell us in one or two sentences how they would benefit from attending. Registration will close on Sunday 22nd October 2017.
Local Organising Committee:
Dr Scott Hosking (British Antarctic Survey; Cambridge Centre for Climate Science)
Dr Alex Archibald (Department of Chemistry, Cambridge)
Mr Michael Simmons (Cambridge Big Data)