Published: November 21, 2024; Aithor: Julia Sonrisa
Address: 2276 12th Avenue, Room 206, New York, NY 10027, United States
Speaker: Veronika Eyring (DLR/Univ. of Bremen)
Format: Hybrid
Virtual: Zoom link provided upon registration
In-person: Columbia Innovation Hub, Second Floor, Room 202
*Please note that in-person space is limited.*
__________________________________________________________________________________
Abstract: Earth System Models (ESMs) are fundamental to understanding and projecting climate change. The models have continued to improve over the years, but systematic errors and large uncertainties in their projections remain. A large contribution to this uncertainty stems from the representation of processes such as clouds and convection that occur at scales smaller than the resolved model grid. This impacts the models’ ability to accurately project global and regional climate change, climate variability, and extremes. New approaches are required with breakthroughs expected in particular from the combination of high-resolution simulations that can resolve small-scale and fast processes, the wealth of Earth observations, and machine learning (ML) techniques. High-resolution, cloud-resolving models with horizontal grid resolution of a few kilometers alleviate many biases of coarse-resolution models for deep clouds and convection, wave propagation, and precipitation, but they cannot be run at climate timescales for multiple decades or longer due to high computational costs. Yet short simulations from high-resolution models can serve as information to develop ML-based parametrizations that are then incorporated into hybrid ESMs that promise to have significantly reduced systematic errors and enhanced projection capability compared to current ESMs. In contrast to km-scale climate models, ESMs incorporate important Earth system processes and feedback while still being fast enough to provide large ensembles important to simulate internal variability and extremes and to improve attribution and understanding. This combination can drive a paradigm shift in current Earth system modeling and analyses towards a new data-driven, yet still physics-aware, science. The key goal is a hybrid modeling approach that maintains physical consistency and realistically extrapolates to unseen climate regimes while reducing climate projection uncertainties and improving Earth system understanding. The talk presents progress in hybrid modeling work with the ICOsahedral Non-hydrostatic (ICON) atmospheric model from the European Research Council (ERC) Synergy Grant on “Understanding and Modelling the Earth System with Machine Learning (USMILE)” as well as key challenges and visions on AI-empowered next-generation multiscale climate modeling for mitigation and adaptation.
Bio: Veronika Eyring is Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. Veronika’s research focuses on improving climate models and projections with machine learning and spaceborne Earth observations for actionable climate science and technology assessments in aeronautics, space, transport, and energy research. She has authored many peer-reviewed journal articles and has contributed to the Intergovernmental Panel on Climate Change (IPCC) climate assessments for many years, including her role as Coordinating Lead Author for Chapter 3 “Human Influence on the Climate System” in the IPCC Sixth Assessment Report of Working Group I published in 2021. Veronika has been involved in the World Climate Research Programme (WCRP) for many years, for example through her roles as Chair of the Coupled Model Intercomparison Project (CMIP) Panel (2014-2020) and member of scientific steering committees including the Working Group on Coupled Modeling (WGCM) from 2008-2018. She was PI of the Earth System Model Evaluation Tool (ESMValTool) until 2020, has been a Fellow of the European Lab for Learning & Intelligent Systems (ELLIS) since 2019, and Member of the Scientific Advisory Board of the World. Veronika received the German Research Foundation (DFG) Gottfried Wilhelm Leibniz Prize in 2021 for her significant contributions to improving the understanding and accuracy of climate projections through process-oriented modeling and model evaluation (see film portrait here). In 2023, the Technical University of Munich (TUM) appointed her as TUM Distinguished Affiliated Professor. She maintains a strong collaboration with the National Center for Atmospheric Research (NCAR, Boulder, CO, USA) as an Affiliate Scientist, with the DLR Causal Inference Group in Jena that she founded in 2017, and with the team of the European Research Council (ERC) Synergy Grant on “Understanding and Modelling the Earth System with Machine Learning (USMILE).”
Time: 2:00-3:00 pm EST
Free!
Detailed information and discussion of the event.