Artificial Intelligence (AI) and Machine Learning (ML) methods are becoming increasingly important in both science and society. In climate science - where complex biophysical and societal processes interact across diverse temporal and spatial scales, and datasets are often large, heterogenous and incomplete - AI and ML methods offer new powerful solutions.
Join us for the third session of the IIASA-wide seminar series, AI for Climate Science!
We are pleased to announce the next talk in the series, featuring Dr. Vassili Kitsios from CSIRO (Commonwealth Scientific and Industrial Research Organisation), Australia.
Dr. Vassili Kitsios holds a PhD jointly from the University of Melbourne and the Université de Poitiers, focusing on fluid dynamical stability and model reduction. His career has spanned postdoctoral research at CSIRO and Monash University in atmospheric and oceanic turbulence modeling, and a stint in the finance sector developing macroeconomics-based trading algorithms.
Currently based at CSIRO, Dr. Kitsios leads research on data assimilation and climate forecasting, with a strong focus on applying machine learning to climate emulation and impact assessment. He co-chairs the Machine Learning for Climate and Weather Working Group under the Australian Earth System Simulator initiative, is associate editor for Theoretical and Computational Fluid Dynamics, and serves on several national scientific committees.
For online participation, a registration is necessary.
Please note that the seminar is going to be recorded.
Title:
Machine Learnt Climate Emulators Enabling Multi-Sector Risk Assessments for a Multitude of Emissions Pathways
Abstract:
With the world economy grappling with a future transition to net-zero emissions, there is a need to assess climate risk for a broad range of future economic, and hence emissions, scenarios. A small selection of the possible scenarios is simulated by numerous climate models as part of the international Coupled Model Inter-comparison Projects (CMIP). These numerical simulations provide estimates of the future temperature, rainfall and other properties required to assess physical climate risk. Over the last several years they have been developing a series of climate emulators which rapidly reconstructs gridded climate data for arbitrary emissions pathways using machine learning (see article) and more physics-constrained data-driven approaches (see article). These emulators not only enable the assessment of the physical climate response across a much broader range of carbon concentration pathways, but has also enabled a broader assessment of the downstream human impacts.
The monthly seminar AI for Climate Science at IIASA will feature global experts in the field of AI and ML who will showcase the newest methodological advancements and applications in the field. Through a series of invited talks, the seminar showcases cutting edge research with the aim of strengthening AI and ML expertise at IIASA and to foster external collaborations. Additionally, it serves as an institute-wide platform for discussions and knowledge exchange across programs and working groups to spark new ideas and innovations.
As an initiative from the ECE/ ICI Theme on Extreme Weather and Climate Dynamics, this seminar is designed for both experts already integrating AI and ML into their workflows and those eager to expand their knowledge in these fields.
Upcoming Events
Hybrid: online and at the Austrian Academy of Sciences
Public lecture: Digitalization and AI within planetary boundaries
Chulalongkorn University, Thailand